Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry

Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry: Methods for Entity-Scale Inventory

Office of the Chief Economist Climate Change Program Office Technical Bulletin 1939 July 2014

United States Department of Agriculture

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QuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventoryMarlenEve,DianaPape,MarkFlugge,RachelSteele,DerinaMan,MarybethRiley‐GilbertandSarahBiggar,Editors.

USDATechnicalBulletin1939July2014Publishedby:U.S.DepartmentofAgricultureOfficeoftheChiefEconomistWashington,DC20250

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HowtoObtainCopies:YoumayelectronicallydownloadthisdocumentfromtheU.S.DepartmentofAgriculture’swebsiteat:http://www.usda.gov/oce/climate_change/estimation.htmSuggestedCitation

ReportCitation

Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,(Eds),2014.QuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.

ChapterCitations

Eve,M.,M.Flugge,D.Pape,2014.Chapter1:Introduction.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

Eve,M.,M.Flugge,D.Pape,2014.Chapter2:ConsiderationsWhenEstimatingAgricultureandForestryGHGEmissionsandRemovals.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939,OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

Ogle,S.M.,P.R.Adler,F.J.Breidt,S.DelGrosso,J.Derner,A.Franzluebbers,M.Liebig,B.Linquist,G.P.Robertson,M.Schoeneberger,J.Six,C.vanKessel,R.Venterea,T.West,2014.Chapter3:QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939,OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

Ogle,S.M.,P.Hunt,C.Trettin,2014.Chapter4:QuantifyingGreenhouseGasSourcesandSinksinManagedWetlandSystems.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939,OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

Powers,W.,B.Auvermann,N.A.Cole,C.Gooch,R.Grant,J.Hatfield,P.Hunt,K.Johnson,A.Leytem,W.Liao,J.M.Powell,2014.Chapter5:QuantifyingGreenhouseGasSourcesandSinksinAnimalProductionSystems.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939,OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

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Hoover,C.,R.Birdsey,B.Goines,P.Lahm,GreggMarland,D.Nowak,S.Prisley,E.Reinhardt,K.Skog,D.Skole,J.Smith,C.Trettin,C.Woodall,2014.Chapter6:QuantifyingGreenhouseGasSourcesandSinksinManagedForestSystems.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939,OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

Ogle,S.M.,2014.Chapter7:QuantifyingGreenhouseGasSourcesandSinksfromLandUseChange.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939,OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

Breidt,F.J.,Ogle,S.M.,Powers,W.,Hover,C.,2014.Chapter8:UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939,OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

Acknowledgements:TheDepartmentofAgriculturewouldliketoacknowledgethemanycontributorstothisreport,includingcontractors,universityresearchersandFederalGovernmentemployees.MarlenD.Eve,Ph.D.,EnvironmentalScientistintheClimateChangeProgramOffice,servedastheProjectManagerforthisreport.Heprovidedguidanceontheprocessfordevelopingthereport,insightsonthelevelofdetailprovidedforthemethods,andeditorialreviewofthedocument.WilliamHohenstein,DirectorfortheClimateChangeProgramOffice,providedoveralldirectionontheguidingprinciplesfordevelopingthemethodsandassociatedreport.Specifically,wewouldliketoacknowledgetheteamatICFInternationalthatplayedakeyroleincoordinatingtheworkandleadingthedevelopmentofeachchapterincludedinthereport.ThecoreteamatICFInternationalincludes:SarahBiggar,ICFInternationalMarkFlugge,ICFInternationalDerinaMan,ICFInternationalDianaPape,ICFInternationalMarybethRiley‐Gilbert,ICFInternationalRachelSteele,ICFInternational

Additionally,wewouldliketoacknowledgethetremendouseffortputintothereportbyourworkinggroupsandexpertauthorsforeachchapterofthereport,aswellasourothercontributors,subjectmatterexpertsandscientificreviewers.USDArecognizestheirsignificantinvestmentoftimeandexpertiseandappreciatesthecontributionofeachmember.

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WorkingGroups:

Croplands/GrazingLands:StephenM.Ogle,ColoradoStateUniversity(LeadAuthor)PaulR.Adler,USDAAgriculturalResearchServiceJayBreidt,ColoradoStateUniversityStephenDelGrosso,USDAAgriculturalResearchServiceJustinDerner,USDAAgriculturalResearchServiceAlanFranzluebbers,USDAAgriculturalResearchServiceMarkLiebig,USDAAgriculturalResearchServiceBruceLinquist,UniversityofCalifornia,DavisPhilRobertson,MichiganStateUniversityMicheleSchoeneberger,USDAForestServiceJohanSix,UniversityofCalifornia,Davis;SwissFederalInstituteofTechnology,ETH‐ZurichChrisvanKessel,UniversityofCalifornia,DavisRodVenterea,USDAAgriculturalResearchServiceTristramWest,PacificNorthwestNationalLaboratory

Wetlands:StephenM.Ogle,ColoradoStateUniversity(LeadAuthor)PatrickHunt,USDAAgriculturalResearchServiceCarlTrettin,USDAForestService

AnimalAgriculture:WendyPowers,MichiganStateUniversity(LeadAuthor)BrentAuvermann,TexasA&MUniversityN.AndyCole,USDAAgriculturalResearchServiceCurtGooch,CornellUniversityRichGrant,PurdueUniversityJerryHatfield,USDAAgriculturalResearchServicePatrickHunt,USDAAgriculturalResearchServiceKristenJohnson,WashingtonStateUniversityAprilLeytem,USDAAgriculturalResearchServiceWeiLiao,MichiganStateUniversityJ.MarkPowell,USDAAgriculturalResearchService

Forestry:CoeliHoover,USDAForestService(LeadAuthor)RichardBirdsey,USDAForestService(Co‐LeadAuthor)BruceGoines,USDAForestServicePeterLahm,USDAForestServiceGreggMarland,AppalachianStateUniversityDavidNowak,USDAForestServiceStephenPrisley,VirginiaPolytechnicInstituteandStateUniversity

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ElizabethReinhardt,USDAForestServiceKenSkog,USDAForestServiceDavidSkole,MichiganStateUniversityJamesSmith,USDAForestServiceCarlTrettin,USDAForestServiceChristopherWoodall,USDAForestService

AdditionalContributors

MarkEaster,ColoradoStateUniversityRobertGleason,U.S.GeologicalSurveyJ.BooneKauffman,OregonStateUniversityErnieMarx,ColoradoStateUniversityKeithPaustian,ColoradoStateUniversityTomWirth,U.S.EnvironmentalProtectionAgencyAndre‐DenisWright,UniversityofVermont

AgroupofexpertswereconvenedinFebruary2012toreviewthesoilN2Omethodsinthecroplands/grazinglandssectionoftheReport.

SoilN2OWorkshopOrganizationCommittee:StephenM.Ogle,ColoradoStateUniversity(Co‐Chair)PhilRobertson,MichiganStateUniversity(Co‐Chair)SteveDelGrosso,USDAAgriculturalResearchServiceJohanSix,UniversityofCalifornia,Davis;SwissFederalInstituteofTechnology,ETH‐ZurichRodVenterea,USDAAgriculturalResearchService

SoilN2OWorkshopParticipants:MartinBurger,UniversityofCalifornia,DavisRaymondDesjardins,AgricultureandAgri‐FoodCanadaRonGehl,NorthCarolinaStateUniversityPeterGrace,QueenslandUniversityofTechnologyPeterGroffman,CaryInstituteofEcosystemStudiesArdellHalvorson,USDAAgriculturalResearchServiceWilliamHorwath,UniversityofCalifornia,DavisCesarIzaurralde,JointGlobalChangeResearchInstitute;UniversityofMarylandChangshengLi,UniversityofNewHampshireNevilleMillar,MichiganStateUniversityKeithPaustian,ColoradoStateUniversityPhilippeRochette,AgricultureandAgri‐FoodCanadaWilliamSalas,AppliedGeosolutionsCliffSnyder,InternationalPlantNutritionInstitute

ExpertReviewers

USDAwouldliketoacknowledgethefollowingexpertreviewers,whoreviewedallorpartsofthedocumentduringtheMarch2013ExpertReview:

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BobAbt,NorthCarolinaStateUniversityLeonHartwellAllen,USDAAgriculturalResearchServiceBenBond‐Lamberty,JointGlobalChangeResearchInstituteSandraBrown,WinrockInternationalDavidClay,SouthDakotaStateUniversityStevenDeGryze,TerraGlobalCapitalPeteEpanchin,AAASFellow,U.S.EnvironmentalProtectionAgencyErinFitzgerald,InnovationCenterforU.S.DairyRonGehl,NorthCarolinaStateUniversityAmrithGunasekara,CaliforniaDepartmentofAgricultureNoelGurwick,SmithsonianEnvironmentalResearchCenterLindaHeath,USDAForestServiceWilliamHorwath,UniversityofCalifornia,DavisCesarIzaurralde,JointGlobalChangeResearchInstitute;UniversityofMarylandJenniferJenkins,U.S.EnvironmentalProtectionAgencyKurtJohnsen,USDAForestServiceErmiasKebreab,UniversityofCalifornia,DavisWilliamLazarus,UniversityofMinnesotaDeanneMeyer,UniversityofCalifornia,DavisTimParkin,USDAAgriculturalResearchServiceCharlesRice,KansasStateUniversityNeilSampson,TheSampsonGroupKaramatSistani,USDAAgriculturalResearchServiceCliffSnyder,InternationalPlantNutritionInstituteBrentSohngen,OhioStateUniversityMarthaStevenson,WorldWildlifeFundRichardTodd,USDAAgriculturalResearchServiceMicheleWander,UniversityofIllinoisTomWirth,U.S.EnvironmentalProtectionAgency

Photocreditsforcoverandeachchapter: ExecutiveSummary:Stripsandshelterbelts:USDANRCS;NRCSMT00001.tif

http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=matches&value=4851&site=PhotoGallery

Chapter1:Planting:USDANRCS;JeffVanuga;NRCSVA02001.tifhttp://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=matches&value=6529&site=PhotoGallery

Chapter3:Farmstead:USDANRCS;TimMcCabe;NRCSMD81005.tifhttp://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=matches&value=4511&site=PhotoGallery

Chapter4:Wetland;USDANRCS;LynnBetts; NRCSIA99470.tifhttp://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=matches&value=3629&site=PhotoGallery

Chapter5:Hogs:USDANRCS;LynnBetts;NRCSIA99210.tifhttp://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=matches&value=3115&site=PhotoGallery

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Chapter6: Forest:USDANRCS;NRCSNM02093http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=matches&value=5971&site=PhotoGallery

Chapter7:ForestHWP:USDANRCS;JeffVanuga;NRCSNM02093http://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=matches&value=5471&site=PhotoGallery

Chapter8: Compost:USDANRCS;NRCSCA06008.tifhttp://photogallery.nrcs.usda.gov/netpub/server.np?find&catalog=catalog&template=detail.np&field=itemid&op=matches&value=961&site=PhotoGallery

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TableofContents

ExecutiveSummary

1 Introduction...............................................................................................................................................1‐31.1 OverviewoftheReport...............................................................................................................................1‐41.2 ReportObjectives..........................................................................................................................................1‐51.3 ProcessfortheDevelopmentoftheMethods....................................................................................1‐61.4 ContentsoftheReport................................................................................................................................1‐91.5 UsesandLimitationsoftheReport......................................................................................................1‐10Chapter1References.............................................................................................................................................1‐14

2 ConsiderationsWhenEstimatingAgricultureandForestryGHGEmissionsandRemovals .........................................................................................................................................................................2‐3

2.1 Scope...................................................................................................................................................................2‐32.1.1 DefinitionofEntity........................................................................................................................2‐32.1.2 DefinitionofSystemBoundaries.............................................................................................2‐4

2.2 ReviewofRelevantCurrentToolsandMethods............................................................................2‐122.3 SelectionofMostAppropriateMethodandMitigationPracticestoInclude......................2‐132.4 OverviewofSectors....................................................................................................................................2‐14

2.4.1 CroplandsandGrazingLands.................................................................................................2‐162.4.2 Wetlands..........................................................................................................................................2‐172.4.3 AnimalProduction.......................................................................................................................2‐192.4.4 Forestry............................................................................................................................................2‐21

2.5 Land‐UseChange.........................................................................................................................................2‐222.6 Uncertainty....................................................................................................................................................2‐23Chapter2References.............................................................................................................................................2‐24

3 QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems..... .........................................................................................................................................................................3‐4

3.1 Overview...........................................................................................................................................................3‐53.1.1 OverviewofManagementPracticesandResultingGHGEmissions.........................3‐63.1.2 SystemBoundariesandTemporalScale............................................................................3‐103.1.3 SummaryofSelectedMethods/ModelsSourcesofData.............................................3‐103.1.4 OrganizationofChapter/Roadmap......................................................................................3‐11

3.2 CroplandManagement..............................................................................................................................3‐123.2.1 ManagementInfluencingGHGEmissionsinUplandSystems...................................3‐123.2.2 ManagementInfluencingGHGEmissionsinFloodedCroppingSystems.............3‐253.2.3 Land‐UseChangetoCropland................................................................................................3‐28

3.3 GrazingLandManagement......................................................................................................................3‐293.3.1 ManagementActivityInfluencingGHGEmissions.........................................................3‐303.3.2 Land‐UseChangetoGrazingLands......................................................................................3‐36

3.4 Agroforestry..................................................................................................................................................3‐37

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3.4.1 CarbonStocks................................................................................................................................3‐393.4.2 NitrousOxide.................................................................................................................................3‐413.4.3 Methane...........................................................................................................................................3‐413.4.4 ManagementInteractions.........................................................................................................3‐42

3.5 EstimationMethods...................................................................................................................................3‐423.5.1 BiomassCarbonStockChanges.............................................................................................3‐433.5.2 LitterCarbonStockChanges...................................................................................................3‐493.5.3 SoilCarbonStockChanges.......................................................................................................3‐493.5.4 SoilNitrousOxide........................................................................................................................3‐583.5.5 MethaneUptakebySoils...........................................................................................................3‐743.5.6 MethaneandNitrousOxidefromFloodedRiceCultivation.......................................3‐773.5.7 CO2fromLiming...........................................................................................................................3‐833.5.8 Non‐CO2EmissionsfromBiomassBurning......................................................................3‐863.5.9 CO2fromUreaFertilizerApplications.................................................................................3‐90

3.6 SummaryofResearchGapsforCropandGrazingLandManagement..................................3‐92Appendix3‐A:SoilN2OModelingFrameworkSpecifications...............................................................3‐97

3‐A.1DescriptionofProcess‐BasedModels.....................................................................3‐993‐A.2EmpiricalScalarsforBaseEmissionRates.........................................................3‐1063‐A.3Practice‐BasedScalingFactors................................................................................3‐108

Appendix3‐B:GuidanceforCropsNotIncludedintheDAYCENTModel....................................3‐113Chapter3References..........................................................................................................................................3‐116

4 QuantifyingGreenhouseGasSourcesandSinksinManagedWetlandSystems................4‐34.1 Overview...........................................................................................................................................................4‐3

4.1.1 OverviewofManagementPracticesandResultingGHGEmissions.........................4‐44.1.2 SystemBoundariesandTemporalScale..............................................................................4‐74.1.3 SummaryofSelectedMethods/ModelsandSourcesofData......................................4‐74.1.4 OrganizationofChapter/Roadmap........................................................................................4‐8

4.2 ManagementandRestorationofWetlands........................................................................................4‐84.2.1 DescriptionofWetlandManagementPractices................................................................4‐84.2.2 Land‐UseChangetoWetlands................................................................................................4‐13

4.3 EstimationMethods...................................................................................................................................4‐144.3.1 BiomassCarboninWetlands..................................................................................................4‐144.3.2 SoilC,N2O,andCH4inWetlands............................................................................................4‐17

4.4 ResearchGapsforWetlandManagement.........................................................................................4‐21Chapter4References.............................................................................................................................................4‐23

5 QuantifyingGreenhouseGasSourcesandSinksinAnimalProductionSystems..............5‐55.1 Overview...........................................................................................................................................................5‐5

5.1.1 OverviewofManagementPracticesandResultingGHGEmissions.........................5‐55.1.2 SystemBoundariesandTemporalScale............................................................................5‐125.1.3 SummaryofSelectedMethods/Models/SourcesofData............................................5‐12

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5.1.4 OrganizationofChapter/Roadmap......................................................................................5‐145.2 AnimalProductionSystems....................................................................................................................5‐18

5.2.1 DairyProductionSystems........................................................................................................5‐185.2.2 BeefProductionSystems..........................................................................................................5‐225.2.3 SheepProductionSystems.......................................................................................................5‐255.2.4 SwineProductionSystems.......................................................................................................5‐255.2.5 PoultryProductionSystems....................................................................................................5‐28

5.3 EmissionsfromEntericFermentationandHousing.....................................................................5‐305.3.1 EntericFermentationandHousingEmissionsfromDairyProductionSystems........ .............................................................................................................................................................5‐315.3.2 EntericFermentationandHousingEmissionsfromBeefProductionSystems.5‐445.3.3 EntericFermentationandHousingEmissionsfromSheep........................................5‐525.3.4 EntericFermentationandHousingEmissionsfromSwineProductionSystems....... .............................................................................................................................................................5‐535.3.5 HousingEmissionsfromPoultryProductionSystems.................................................5‐605.3.6 EntericFermentationandHousingEmissionsfromOtherAnimals.......................5‐645.3.7 FactorsAffectingEntericFermentationEmissions.......................................................5‐665.3.8 LimitationsandUncertaintyinEntericFermentationandHousingEmissions

Estimates.........................................................................................................................................5‐735.4 ManureManagement.................................................................................................................................5‐75

5.4.1 TemporaryStackandLong‐TermStockpile.....................................................................5‐775.4.2 Source:U.S.EPA(2011).Composting...................................................................................5‐815.4.3 AerobicLagoon.............................................................................................................................5‐855.4.4 AnaerobicLagoon,RunoffHoldingPond,StorageTanks............................................5‐865.4.5 AnaerobicDigesterwithBiogasUtilization......................................................................5‐915.4.6 CombinedAerobicTreatmentSystems..............................................................................5‐935.4.7 Sand‐ManureSeparation..........................................................................................................5‐945.4.8 NutrientRemoval.........................................................................................................................5‐945.4.9 Solid–LiquidSeparation............................................................................................................5‐955.4.10 ConstructedWetland..................................................................................................................5‐975.4.11 Thermo‐ChemicalConversion................................................................................................5‐985.4.12 LimitationsandUncertaintyinManureManagementEmissionsEstimates......5‐99

5.5 ResearchGaps............................................................................................................................................5‐1055.5.1 EntericFermentation..............................................................................................................5‐1055.5.2 ManureManagement...............................................................................................................5‐106

Appendix5‐A:EntericCH4fromFeedlotCattle–MethaneConversionFactor(Ym)..............5‐109Appendix5‐B:FeedstuffsCompositionTable...........................................................................................5‐113Appendix5‐C:EstimationMethodsforAmmoniaEmissionsfromManureManagement

Systems......................................................................................................................................5‐1235‐C.1 MethodforEstimatingAmmoniaEmissionsUsingEquationsfromIntegrated

FarmSystemModel................................................................................................................5‐1235‐C.1.1RationaleforSelectedMethod...............................................................................5‐1235‐C.1.2ActivityData..................................................................................................................5‐123

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5‐C.1.3AncillaryData...............................................................................................................5‐1245‐C.2 MethodforAmmoniaEmissionsfromTemporaryStack,Long‐TermStockpile,

AnaerobicLagoons/RunoffHoldingPonds/StorageTanks,andAerobicLagoons........................................................................................................................................................5‐124

5‐C.3 MethodforEstimatingAmmoniaEmissionsfromCompostingUsingIPCCTier2Equations....................................................................................................................................5‐128

5‐C.3.1RationaleforSelectedMethod...............................................................................5‐1285‐C.3.2ActivityData..................................................................................................................5‐1295‐C.3.3AncillaryData...............................................................................................................5‐129

5‐C.4 MethodforAmmoniaEmissionsfromComposting..................................................5‐1295‐C.5 UncertaintyinAmmoniaEmissionsEstimates...........................................................5‐129

Appendix5‐D:ManureManagementSystemsShapeFactors( )...................................................5‐131Appendix5‐E:ModelReview:ReviewofEntericFermentationModels.......................................5‐134Chapter5References..........................................................................................................................................5‐139

6 QuantifyingGreenhouseGasSourcesandSinksinManagedForestSystems....................6‐46.1 Overview.........................................................................................................................................................6‐5

6.1.1 OverviewofManagementPracticesandResultingGHGEmissions.........................6‐66.1.2 SystemBoundariesandTemporalScale..............................................................................6‐96.1.3 SummaryofSelectedMethods/Models..............................................................................6‐106.1.4 SourcesofData..............................................................................................................................6‐116.1.5 OrganizationofChapter/Roadmap......................................................................................6‐12

6.2 ForestCarbonAccounting......................................................................................................................6‐156.2.1 DescriptionofForestCarbonAccounting..........................................................................6‐156.2.2 DataCollectionforForestCarbonAccounting.................................................................6‐236.2.3 EstimationMethods....................................................................................................................6‐256.2.4 Limitations,Uncertainty,andResearchGaps...................................................................6‐28

6.3 Establishing,Re‐establishing,andClearingForests....................................................................6‐296.3.1 Description.....................................................................................................................................6‐296.3.2 ActivityDataCollection.............................................................................................................6‐336.3.3 EstimationMethods....................................................................................................................6‐346.3.4 SpecificProtocolforComputation........................................................................................6‐376.3.5 ActualGHGRemovalsandEmissionsbySourcesandSinksfromForestClearing... .............................................................................................................................................................6‐436.3.6 LimitationsandUncertainty....................................................................................................6‐44

6.4 ForestManagement..................................................................................................................................6‐456.4.1 Description.....................................................................................................................................6‐456.4.2 ActivityData...................................................................................................................................6‐536.4.3 ManagementIntensityCategories........................................................................................6‐576.4.4 EstimationMethods....................................................................................................................6‐646.4.5 LimitationsandUncertainty....................................................................................................6‐66

6.5 HarvestedWoodProducts.....................................................................................................................6‐666.5.1 GeneralAccountingIssues.......................................................................................................6‐66

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6.5.2 EstimationMethods....................................................................................................................6‐686.5.3 ActivityDataCollection.............................................................................................................6‐696.5.4 Limitations,Uncertainty,andResearchGaps...................................................................6‐70

6.6 UrbanForests..............................................................................................................................................6‐716.6.1 Description.....................................................................................................................................6‐716.6.2 ActivityDataCollection.............................................................................................................6‐736.6.3 EstimationMethods....................................................................................................................6‐746.6.4 LimitationsandUncertainty....................................................................................................6‐80

6.7 NaturalDisturbance–WildfireandPrescribedFire...................................................................6‐826.7.1 Description.....................................................................................................................................6‐826.7.2 ActivityDataCollection.............................................................................................................6‐826.7.3 EstimationMethods....................................................................................................................6‐826.7.4 LimitationsandUncertainty....................................................................................................6‐87

Appendix6‐A:HarvestedWoodProductsLookupTables.....................................................................6‐88Chapter6References..........................................................................................................................................6‐107

7 QuantifyingGreenhouseGasSourcesandSinksfromLand‐UseChange.............................7‐37.1 Overview...........................................................................................................................................................7‐37.2 DefinitionsofLandUse...............................................................................................................................7‐47.3 Caveats...............................................................................................................................................................7‐67.4 EstimatingGHGFluxfromLand‐UseChange.....................................................................................7‐6

7.4.1 CarbonPoolsinLiveBiomass,DeadBiomass,andSoilOrganicCarbon................7‐87.4.2 ChangesinSoilCarbon................................................................................................................7‐87.4.3 ChangesinotherGHGemissions...........................................................................................7‐13

Chapter7References.............................................................................................................................................7‐14

8 UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks...................8‐3 ComponentsandInputstoanEntity‐ScaleMonteCarloUncertaintyAssessment............8‐48.1.1 ParameterUncertainty................................................................................................................8‐58.1.2 SamplingMethodUncertainty..................................................................................................8‐68.1.3 LargeDatasetUncertainty..........................................................................................................8‐98.1.4 ModelUncertainty.......................................................................................................................8‐16

ResearchGaps...............................................................................................................................................8‐20Appendix8‐A:ExampleOutputFilefromFVSSamplingUncertaintyBootstrappingApplicationFVSBoot(asprovidedinGreggandHummel,2002)................................................................................8‐21Appendix8‐B:UncertaintyTables....................................................................................................................8‐22Chapter8References.............................................................................................................................................8‐55

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QUANTIFYINGGREENHOUSEGASFLUXESINAGRICULTURE ANDFORESTRY:METHODSFORENTITY‐SCALE INVENTORY

Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,(Eds),2014.QuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.

ExecutiveSummary

BackgroundProvisionsofSection2709oftheFood,Conservation,andEnergyActof2008directtheU.S.DepartmentofAgriculture(USDA)topreparetechnicalguidelinesandscience‐basedmethodstomeasureenvironmentalservicebenefitsfromconservationandlandmanagementactivities,initiallyfocusedoncarbon.Themethodscontainedinthisdocumentaddressgreenhousegas(GHG)emissionsandremovalsfromagriculturalandforestryactivities.

Throughthedevelopmentofthisreport,USDAhaspreparedtwoprimaryproducts:

1. AcomprehensivereviewoftechniquescurrentlyinuseforestimatingGHGemissionsandremovalsfromagriculturalandforestryactivities;and

2. Atechnicalreportoutliningthepreferredscience‐basedapproachandspecificmethodsforestimatingGHGemissionsatthefarmorforestscale(i.e.,thisdocument).

PurposeoftheReportTheobjectiveforthisreportistocreateastandardsetofGHGestimationmethodsforusebyUSDA,landowners,andotherstakeholderstoassisttheminevaluatingtheGHGimpactsoftheirmanagementdecisions.ThemethodspresentedinthereportaddressGHGemissionsandcarbonsequestrationfortheentireentityoroperationandalsoprovidetheopportunitytoassessindividualpracticesormanagementdecisions.Therefore,easeofuseiscritical.

Aco‐objectiveistodemonstratecapacitywithinUSDA,establishingastandardized,consensussetofmethodsthatbecomethescientificbasisforentity‐scaleestimationoftheGHGimpactsoflandownermanagementdecisions.Therefore,scientificrigorandtransparencyarealsocritical.

UsesoftheReportandMethods:

EstimatingincreasesanddecreasesinGHGemissionsandcarbonsequestrationresultingfromcurrentandfutureconservationprogramsandpractices;

ProvidingmethodssuitableforGHGinventoryeffortsattheentity,farm,orforestscale,withpossibleimplicationsforregionalandnationalscaleassessmentsaswell;and

EstimatingincreasesanddecreasesinGHGemissionsandcarbonsequestrationassociatedwithchangesinlandmanagement.

Executive Summary

ES-2

Becausethereportisintendedasameansofevaluatingmanagementpracticesacrossthefullscopeofthefarm,ranch,andforestmanagementsystem,themethodsinthereportneedtobeascomprehensiveaspossible.Researchanddatagapsexistthatresultinsomemanagementpracticesnotbeingaccountedfororarereflectedinhigherlevelsofestimateuncertainty.Completenessisimportant,though,andthereportattemptstoidentifythemostsignificantresearchgapsanddataneeds.

ThisreportwillbeusedwithinUSDAandbyfarmers,ranchers,andforestlandowners,andwillbemadepubliclyavailable.Thesemethodsaredesignedto:

1. Provideascientificbasisformethodsthatcanbeusedbylandownersandmanagers,USDA,andotherstakeholderstoestimatechangesinGHGemissionsandremovalsatthelocalentityscale;

2. CreateastandardsetofGHGquantificationguidelinesandmethodsforusebystakeholders;

3. Quantifyallsignificantemissionsandremovalsassociatedwithspecificsourcecategories;

4. Quantifyemissionsfromland‐usechangeandcarbonsequestrationfromlandmanagementpracticesandtechnologies;and

5. Supportthedevelopmentofentity‐,farm‐,orforest‐scaleGHGinventoriesthatwillfacilitatetheparticipationoflandownersinpublicandprivateenvironmentalmarketregistriesandreportingsystems.

ThereportalsoservesasinputintothedevelopmentofaUSDAGHGEstimationTool.ThereportandthemethodsarenotintendedasanadditiontoorreplacementofanycurrentFederalGHGreportingsystemsorrequirements.

ProcessfortheDevelopmentoftheReportThisreportwasdevelopedbythreeauthorteams(i.e.,workinggroups)underthedirectionofoneleadauthorforeachteam(plusoneco‐leadauthorfortheforestrychapter).TheleadauthorswerechosenbasedontheirexperiencewithGHGinventoriesandaccountingmethodologiesandtheirprofessionalresearchexperience.Withinputfromeachleadauthor,USDAchose8to12workinggroupmembersperteamtowritethereport.TheseworkinggroupmemberseachhaddifferentbackgroundsthatfitwiththeanticipatedcontentofthedocumentandalsohadexperiencewithGHGaccountingand/orfieldresearchthatwasuniqueandaddressedoneormoreofthenichemethodsthatwereessentialforensuringthecomprehensivenessofthemethodsforeachsector.Theauthorteamswereprovidedwithapreliminaryoutlineoftheirchaptersandwithtwobackgroundreportsdevelopedaspartoftheproject.Onebackgroundreportwasananalysisofthescientificliteraturerelatedtoratesofcarbonsequestrationoremissionsreductionresultingfromvariousmanagementpracticesandtechnologies(Denefetal.,2011).Theotherreportwasacompilationofalloftheavailabletools,protocols,andmodels,withbasicinformationoneachone(Denefetal.,2012).

Themethodsweredevelopedaccordingtoseveralcriteriainordertomaximizetheirusefulness.Inparticular,themethodsmust:

1. Standontheirown,independentofanyotheraccountingsystem,yetmaintainconsistencywithotheraccountingsystemstothemaximumextentpossible;

2. Bescalableforuseatentity‐scalesitesacrosstheUnitedStates,withapplicabilityatcountyand/orStatelevelsaswell;

3. FacilitateusebyUSDAinassessingtheperformanceofconservationprograms;

Executive Summary

ES-3

4. ProvideabroadframeworktoassessmanagementpracticestoevaluatetheGHGaspectofproductionsustainability;

5. Maintainmaximumapplicabilityforuseinenvironmentalmarkets,includingpossiblefutureFederal,State,orlocalGHGoffsetsinitiatives;

6. BescientificallyvettedthroughUSDA,U.S.Governmentandacademicexpertreview,andpubliccomment;

7. Providereliable,real,andverifiableestimatesofonsiteGHGemissions,carbonstorage,andcarbonsequestration(themethodswillbedesignedsothatovertimetheycanbeappliedtoquantifyonsiteGHGreductionsandincreasesincarbonstorageduetoconservationandlandmanagementactivities);and

8. Provideabasisforconsistencyinestimationandtransparencyinreporting.

Developmentofthereporthasbeeniterativeasvariousdraftsofthedocumenthavebeenputthroughseveralreviewstages,includingaUSDAintra‐agencytechnicalreview,aFederalinteragencytechnicalreview,ascientificexpertreview,andapubliccommentperiod.

OverviewofRecommendedGHGEstimationMethodsintheReportThissectionprovidesanoverviewofthecurrentestimationmethodsorapproachesanentitycouldusetoestimateGHGemissionsandsinksonhisorherproperty.Thisoverviewisfollowedbyasummaryofeachsector’sproposedmethodologiesforentityGHGestimations.

Thereareseveralapproachesthatafarmer,rancher,orforestlandownercanusetoestimateGHGemissionsatanentityscale,andeachapproachgivesvaryingaccuracyandprecision.Themostaccuratewayofestimatingemissionsisthroughdirectmeasurement,whichoftenrequiresexpensiveequipmentortechniquesthatarenotfeasibleforasinglelandownerormanager.Ontheotherhand,lookuptablesandestimationequationsaloneoftendonotadequatelyrepresentlocalvariabilityorlocalconditions.Thisreportattemptstodelineatemethodsthatbalanceuser‐friendliness,datarequirements,andscientificrigorinawaythatistransparentandjustified.

Thefollowingapproacheswereconsideredfortheseguidelines:

Basicestimationequations(cf.,IPCC[IntergovernmentalPanelonClimateChange]Tier1)—involvecombinationsofactivitydata1withparametersanddefaultemissionfactors.2Anydefaultparametersordefaultemissionfactors(e.g.,lookuptables)areprovidedinthetext,orifsubstantialinlength,inanaccompanyingcompendiumofdata.

Models(cf.,IPCCTier3)—usecombinationsofactivitydatawithparametersanddefaultemissionfactors.Theinputsforthesemodelscanbeancillarydata3(e.g.,temperature,precipitation,elevation,andsoilnutrientlevelsthatmaybepulledfromanunderlyingsource),biologicalvariables(e.g.,plantdiversity)orsite‐specificdata(e.g.,numberofacres,

1Activitydataisdefinedasdataonthemagnitudeofhumanactivityresultinginemissionsorremovalstakingplaceduringagivenperiodoftime(IPCC,1997).2Emissionfactorisdefinedasacoefficientthatquantifiestheemissionsorremovalsofagasperunitofactivity.Emissionfactorsareoftenbasedonasampleofmeasurementdata,averagedtodeveloparepresentativerateofemissionforagivenactivitylevelunderagivensetofoperatingconditions(IPCC,2006).3Ancillarydataisdefinedasadditionaldatanecessarytosupporttheselectionofactivitydataandemissionfactorsfortheestimationandcharacterizationofemissions.Dataonsoil,croporanimaltypes,treespecies,operatingconditions,andgeographicallocationareexamplesofancillarydata.

Executive Summary

ES-4

numberofanimals).Theaccuracyofthemodelsisdependentontherobustnessofthemodelandtheaccuracyoftheinputs.

Fieldmeasurements—actualmeasurementsthatafarmerorlandownerwouldneedtotaketomoreaccuratelyestimatethepropertiesofthesoil,forest,orfarmortoestimateactualemissions.Measuringactualemissionsonthelandrequiresspecialequipmentthatmonitorstheflowofgasesfromthesourceintotheatmosphere.Thisequipmentisnotreadilyavailabletomostentities,sofieldmeasurementsaremoreoftenincorporatedintoothermethodsdescribedinthissectiontocreateahybridapproach.Afieldmeasurementsuchasasamplemeantreediametercouldbeincorporatedintoothermodelsorequationstogiveamoreaccurateinput.

Inference(cf.,IPCCTier2)—usesState,regional,ornationalemissions/sequestrationfactorsthatapproximateemissions/sequestrationperunitoftheinput.Theinputdataisthenmultipliedbythisfactortodeterminethetotalonsiteemissions.Thisfactorcanhavevaryingdegreesofaccuracyandoftendoesnotcapturethemitigationpracticesonthefarmortheuniquesoilconditions,climate,livestockdiet,livestockgenetics,oranyfarm‐specificcharacteristics,althoughtheycanbedevelopedwithspecificsoiltypes,livestockcategories,orclimacticregions.

Hybridestimationapproach(cf.,IPCCTier2orIPPCTier3)—anapproachthatusesacombinationoftheapproachesdescribedabove.Theapproachoftenusesfieldmeasurementsormodelstogenerateinputsusedforaninference‐basedapproachtoimprovetheaccuracyoftheestimate.

Thetypesofapproachesthattheauthorsrecommendedinthisreportincludebasicestimationequationswithdefaultemissionfactors(cf.,IPCCTier1);geography‐,crop‐,livestock‐,technology‐,orpractice‐specificemissionfactors(cf.,IPCCTier2);andmodifiedIPCC/empiricaland/orprocess‐basedmodeling(cf.,IPCCTier2orIPCCTier3).4TableES‐1categorizesthesourcesofemissionswiththetypesofapproachesthatarerecommendedinthisreport.

TableES‐2summarizesthesourcesofagriculturalandforestryGHGemissionsandremovalsdiscussedinthisreport,therecommendedmethodforestimatingemissionsandremovalsforeachsourcecategory,andthereference(s)usedforthedevelopmentofthemethod.

4Atierrepresentsalevelofmethodologicalcomplexity.Usuallythreetiersareprovided.Tier1isthebasicmethod,Tier2intermediate,andTier3mostdemandingintermsofcomplexityanddatarequirements.Tiers2and3aresometimesreferredtoashighertiermethodsandaregenerallyconsideredtobemoreaccurate(IPCC,2006).

Executive Summary

ES-5

TableES‐1:SummaryoftheSourcesofEmissionsandTypesofApproachesinthisReport

SourceBasicEstimation

Equation(cf.,IPCCTier1)

Inference(cf.,IPCCTier2)

ModifiedIPCCorEmpiricalModel

(cf.,IPCCTier2orIPPCTier3)

Processed‐BasedModel

(cf.,IPPCTier3)

Croplands/GrazingLands

DirectN2OEmissionsfromDrainageofOrganicSoils

CH4EmissionsfromRiceCultivation

CO2fromUreaFertilizerApplication

SoilOrganicCarbonStocksforOrganicSoils

CO2fromLiming N2OEmissionsfromRiceCultivation

Non‐CO2EmissionsfromBiomassBurning

IndirectN2OEmissions

BiomassCarbonStockChanges

CH4UptakebySoils DirectN2OEmissionsfromMineralSoils

SoilOrganicCarbonStocksforMineralSoils

Wet‐

lands — — — BiomassCarbon

SoilC,N2O,andCH4

AnimalProduction5

EntericCH4fromSwine

EntericCH4fromOtherAnimals(Goats,AmericanBison)

CH4fromPoultryHousing

CH4 fromDairyCattle,BeefCattle,andSwineHousing

CH4andN2OfromAerobicLagoons

CH4andN2OfromTemporaryStackandLong‐TermStockpile

CH4andN2OfromComposting

Enteric CH4 fromDairyCattle,Sheep,BeefCow‐Calf,Bulls,Stockers,FeedlotCattle

CH4fromManurefromBarnFloors–DairyCattle

N2OfromDairyCattle,BeefCattle,Swine,andPoultryHousing

CH4andN2OfromAnaerobicLagoon,RunoffHoldingPond,StorageTanks

CH4andN2OfromCombinedAerobicTreatmentSystems

CH4fromAnaerobicDigester

—

Forestry — — Establishing,Re‐

establishing,andClearingForest

HarvestedWood

ForestCarbonAccounting

ForestManagement UrbanForests

5Ammonia(NH3),asanimportantprecursortoGHGs,isincludedintheanimalproductionsystemsdiscussionwherenecessary,butisnotofprimaryfocus.Ifreadersareinterestedinmoretechnicalinformation,methodsforestimatingNH3emissionscanbefoundinAppendix5‐C.

Executive Summary

ES-6

SourceBasicEstimation

Equation(cf.,IPCCTier1)

Inference(cf.,IPCCTier2)

ModifiedIPCCorEmpiricalModel

(cf.,IPCCTier2orIPPCTier3)

Processed‐BasedModel

(cf.,IPPCTier3)

Products NaturalDisturbance—WildfireandPrescribedFire

Land‐useChange AnnualChangein

CarbonStocksinDeadWoodandLitterDuetoLandConversion

ChangeinSoilOrganicCarbonStocksforMineralSoils

— — —

OrganizationoftheReportThereportislargelyorganizedbysector,witheachchapterprovidinganoverviewofmanagementpracticesandresultingGHGemissionsandremovals.Foreachsector,backgroundandinformationonmanagementpracticesarepresentedfirst,followedbythedetailedmethodsproposedforestimatingemissionsandremovalsforthosepractices.

Chapter1providesanoverviewofthereport,reportobjectives,contentsofthereport,andusesandlimitationsofthereport.

Chapter2describesthelinkagesandcross‐cuttingissuesrelatingtosector‐specificandentity‐scaleestimationofGHGemissionsandremovals.

Chapter3describestheGHGemissionsfromcropandgrazinglandsystems.ThechapterpresentsmethodsforestimatingtheinfluenceoflanduseandmanagementpracticesonGHGemissions(andremovals)incropandgrazinglandsystems.Methodsaredescribedforestimatingbiomassandsoilcarbonstockschanges,directandindirectsoilnitrousoxide(N2O)emissions,methane(CH4)andN2Oemissionsfromwetlandrice,CH4uptakeinsoils,carbondioxide(CO2)emissionsorremovalsfromliming,non‐CO2GHGemissionsfrombiomassburning,andCO2emissionsfromureafertilizerapplication.

Chapter4providesguidanceforestimationofcarbonstockchangesandCH4andN2Oemissionsfromactivelymanagedwetlands.

Chapter5describeson‐farmGHGemissionsfromtheproductionoflivestockandmanuremanagement.ThechapterpresentsGHGestimationmethodsappropriatetotheproductionofeachcommonlivestocksector(beef,dairy,sheep,swine,andpoultry),withmethodsrelatedtomanuremanagementcombinedforalllivestocktypes.

Chapter6providesguidanceonestimatingcarbonsequestrationandGHGemissionsfrommanagedforestsystems.Thechapterisorganizedtoprovideanoverviewoftheelementsofforestcarbonaccounting,includingdefinitionsofthekeycarbonpoolsandbasicmethodsfortheirestimation.

Executive Summary

ES-7

Chapter7providesguidanceonestimatingthenetGHGemissionsandremovalsresultingfromchangesbetweenlandtypes—i.e.,conversionsintoandoutofcropland,wetland,grazingland,orforestland—attheentityscale.

Chapter8presentstheapproachforaccountingfortheuncertaintyintheestimatednetemissionsbasedonthemethodspresentedinthisreport.AMonteCarloapproachwasselectedasthemethodforestimatingtheuncertaintyaroundtheoutputsfromthemethodologiesinthisreportasitiscurrentlythemostcomprehensive,soundmethodavailabletoassesstheuncertaintyattheentityscale.

SummaryIndevelopingthisreport,theauthorshavesoughttooutlinethemoststate‐of‐theartandsuitablescience‐basedapproachesandspecificmethodsforestimatingfarm‐orforest‐scaleGHGemissions(seeTableES‐2).Insomecases,theproposedmethodshavenotpreviouslybeenappliedinspecificallythewaythatisproposed.Forexample,theforestrysystemschapterdescribestheintegrationoftheForestVegetationSimulator(FVS)withinotherestimationtoolsforforestcarbonaccounting.ThisapplicationofFVS,whiletechnicallysound,willrequireadditionalefforttoimplement.Inothercases,theauthorshaveproposednewmethodsthatbuildonorenhancepreviouslyusedmethods.Forexample,anewhybridapproachisproposedforestimatingdirectsoilN2Oemissionsfrommineralsoilsoncroplandsandgrazinglands.Thehybridapproachusesmodelstoderiveexpectedemissionratesatthetypicalfertilizationrateforthemajorsoiltextures,weatherpatterns,andcroprotationsystemsineachUSDALandResourceRegionandusesameta‐analysisofempiricalstudiestodevelopemissionscalingfactorsforcroplandandgrazinglandsystems.Themethodalsoappliespractice‐basedscalingfactorsderivedfromameta‐analysisofthemostrecentdata.ThishybridapproachistheresultofaworkshopheldinFebruary2012thatconvenedexpertsonN2OemissionsfromcroplandsinordertodevelopestimationmethodsthatwereinclusiveandbestmettheobjectivesofUSDA.

Inadditiontoproposingscience‐basedmethods,theauthorsalsoacknowledgethatforcertainpracticesandtechnologies,adequatedatadonotcurrentlyexisttoaccuratelyestimateGHGemissionsand/orcarbonsequestration.Ineachsectorchapter,theauthorshaveincludedadiscussionofresearchgapsorpriorityareasforfuturedatacollectionthatareimportantinordertoimprovethecompletenessandaccuracyoftheestimationmethodsputforthinthisreport.EstimationofGHGemissionsfrommanagedwetlandsystemsisagoodexample.Whileamethodisputforwardthatreflectsthebestcurrentlyavailablescience,theauthorsstateinSection4.3thatthemethodsfortheselandsarenotaswelldevelopedasforothersectors.LaterinthatsamesectionthereistextdiscussingtheconsiderablelimitationstoestimatingGHGfluxesfromthesesystemsandthelargelevelsofuncertaintyaroundfluxestimates.InSection4.4,theauthorsoutlineasignificantlistofresearchanddataprioritiesthatwouldhelptorefineandstrengthentheestimationmethods.

Inthecontinualefforttoadvancethescienceandimprovetheunderstandingofthesecomplexanddynamicsystems,thisreportprovidesthefoundationforentity‐leveltoolstoquantifytheGHGbenefitsfromconservationandlandmanagementactivities.Thereportalsoidentifiesprioritiesforfutureeffortinordertobroadenthescopeofentity‐scaleGHGfluxestimationandreduceestimationuncertainties.

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TableES‐2:SummaryofSourceCategories,Recom

mendedMethods,andEmissionFactorsinthisReport

Source

MethodologyApproach

PotentialManagem

ent

Practices

SourceofEmissionFactors

ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

Croplands/GrazingLands

BiomassCarbon

StockChanges

Herbaceousbiom

assisestimated

withanempiricalm

ethodusing

entityspecificdataasinputinto

theIPCC

6 equationsdevelopedby

Lascoetal.(2006)andVerchotet

al.(2006).Woodyplantgrowth

andlossesinagroforestryor

perennialtreecropsareestimated

withasimulationmodel

(DAYCEN

T)usingentityinput.

Changesintheestimated

biom

asscarbonstockfor

croplandandgrazingland

ifthereisaland‐use

changeorachangeinthe

croporforagespecies.

U.S.‐specificdefaultvalues

(Westetal.,2010)areused

forestimatingbiom

ass

carbonforannualcropsand

grazinglands.TheIPCC

defaultisproposedfor

estimatingthecarbonfraction

value.Yieldinunitsofdry

mattercanbeestimatedby

theentity,oraveragevalues

from

USDA‐Natural

AgriculturalStatisticsService

statisticscanbeused.

Thismethodwaschosenbecauseit

capturestheinfluenceofland‐use

changeandchangesincropor

foragespeciesonbiom

asscarbon

stocksbyusingU.S.‐specificdefault

valueswhereentityspecificdataare

notavailable.

6 IPCC=Intergovernm

entalPanelonClimateChange

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Source

MethodologyApproach

PotentialManagem

ent

Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

SoilOrganic

Carbonstocks

forMineral

Soils

TheDAYCEN

Tmodelisusedto

estimatethesoilorganiccarbonat

thebeginningandendoftheyear

formineralsoils.Thestocksare

enteredintotheIPCCequations

developedbyLascoetal.(2006)

andVerchotetal.(2006)to

estimatecarbonstockchanges.

Additionofcarbonin

manureandotherorganic

amendm

ents;tillage

intensity;residue

managem

ent(retentionin

fieldwithout

incorporation;retentionin

thefieldwith

incorporation;and

removalwithharvest,

burning,orgrazing);

influenceofbareand

vegetatedfallows;

irrigationeffectson

decompositionincropland

andgrazinglandsystems;

setting‐asidecropland

from

production;influence

offireonoxidationofsoil

organicmatter;andwoody

plantencroachm

ent,

agroforestry,and

silvopastureeffectson

carboninputsandoutputs.

TheDAYCEN

Tmodel(Parton

etal.,1987).

DAYCEN

Tmodelhasbeen

demonstratedtorepresentthe

dynamicsofsoilorganiccarbonand

estimatesoilorganiccarbonstock

changeincroplandandgrasslands

(Partonetal.,1993).Therehave

beenuncertaintiesnotedinthe

modelinOgleetal.(2007).The

modelcapturessoilmoisture

dynamics,plantproduction,and

thermalcontrolsonnetprimary

productionanddecom

positionwith

atimestepofamonthorless.

SoilOrganic

CarbonStocks

forOrganic

Soils

CO2em

issionsfrom

drainageof

organicsoils(i.e.,histosols)are

estimatedwithaninference

method

(cf.,IPCCTier2)usingtheIPCC

equationdevelopedbyAaldeetal.

(2006)andregion‐specific

emissionfactorsfrom

Ogleetal.

(2003).

Croplanddrainage

Emissionfactorsarefrom

Ogleetal.(2003)andare

region‐specificbasedon

typicaldrainagepatternsand

climaticcontrols(e.g.,

temperature/precipitation)

ondecom

positionrates.

Usesentity‐specificannualdataas

inputintotheequationusedinthe

U.S.Inventory.

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0Source

MethodologyApproach

PotentialManagem

ent

Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

DirectN

2O

Emissionsfrom

MineralSoils

DirectN

2Omethodsareestimated

withahybridestimationmethod.

Formajorcom

moditycrops,(e.g.,

corn,cotton,alfalfa)a

combinationofexperimentaldata

andprocess‐basedmodelingusing

DAYCEN

T7andDEnitrification‐

decomposition(DNDC)

8 areused

toderiveexpectedbaseem

ission

ratesfordifferentsoiltexture

classesineachUSDALand

ResourceRegion.Forminor

commoditycrops(e.g.,barley,

oats,peanuts)andincaseswhere

thereareinsufficientempirical

datatoderiveabaseemission

rate,thebaseem

issionrateis

basedontheIPCCdefaultfactor

(i.e.,0.01)multipliedbythe

agronomicnitrogeninput(de

Kleinetal.,2006).Theseemission

ratesarescaledwithpractice‐

basedscalingfactorstoestimate

theinfluenceofm

anagem

ent

changessuchasapplicationof

nitrificationinhibitorsorslow

‐releasefertilizers.

Nitrogenapplicationto

crops.Inaddition,specific

managem

entpracticesare

includedasscalingfactors

thatinfluenceaportionor

theentirepoolofm

ineral

nitrogen.9 Managem

ent

practicesthatinfluencea

portionoftheem

ission

rateinclude:

Useofslowrelease

form

ulation

Nitrificationinhibitor

application

Manurenitrogen

directlydepositedon

pasture/range/paddock

Managem

entpracticesthat

influencetheentirepoolof

mineralnitrogeninclude:

Tillage

Thebaseem

issionfactorsare

adjustedbyscalingfactors

relatedtospecificcrop

managem

entpracticesthat

arederivedfrom

experimentaldata.

Themethodisbasedonusing

resultsfrom

process‐basedmodels

andmeasuredN2Oemissionsin

combinationwithscalingfactors

basedonU.S.‐specificempiricaldata

onaseasonaltimescale.10

7 TheversionofDAYCEN

Tcodedandparam

eterizedforthemostrecentU.S.nationalGHGinventory(U.S.EPA,2013)wasusedtoderiveexpectedbaseem

issionrates.

8 DNDC9.5com

piledonFeb25,2013wasusedtoderiveexpectedbaseem

issionrates.

9 Practice‐basedemissionscalingfactors(0to1)areusedtoadjusttheportionoftheemissionrateassociatedwithslow

releasefertilizers,nitrificationinhibitors,and

pasture/range/paddock(PRP)manurenitrogenadditions.Theslow‐releasefertilizer,nitrificationinhibitor,andPRPmanurescalingfactorsareweightedsothattheir

effectisonlyontheam

ountofnitrogeninfluencedbythesepracticesrelativetotheentirepoolofnitrogen(i.e.,theam

ountofslow‐releasefertilizer,fertilizerwith

nitrificationinhibitororPRPmanurenitrogenaddedtothesoil).Incontrast,scalingfactorsfortillageareusedtoscaletheentireemissionrateundertheassumption

thatthispracticeinfluencestheentirepoolofm

ineralnitrogen.

10AfulldescriptionofthemethodisincludedinChapter3anditsappendix.Supplem

entaldataoutputsfrom

themodelrunswillbeavailableonlinetodow

nload.

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-11

Source

MethodologyApproach

PotentialManagem

ent

Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

DirectN

2O

Emissionsfrom

Drainageof

OrganicSoils

DirectN

2Oemissionsfrom

drainageoforganicsoils,i.e.,

histosols,areestimatedwitha

basicestimationequation(cf.,

IPCCTier1)method(deKleinet

al.,2006).

Drainageoforganicsoils.

Emissionrateforcropped

histosolsbasedonanIPCC

Tier1em

issionfactorof

0.008tonnesN

2O‐nitrogenha

‐

1 year‐1 .

Usesentityspecificannualdataas

inputintotheequationusedinthe

USDAInventory(USDA,2011).

IndirectN

2O

Emissions

IndirectsoilN

2Oemissionsare

estimatedwithaninference

(cf.,IPCCTier2)basedonIPCC

methodology(deKleinetal.,

2006).

Irrigation.

IPCCdefaultsareusedfor

estimatingtheproportionof

nitrogenthatissubjectto

leaching,runoff,and

volatilization.Where

croppingsystemswith

leguminousandnon‐

leguminouswintercover

cropsaregrow

n,aU.S.‐

specificem

issionfactoris

provided.

Thismethodusesentity‐specific

seasonaldataonnitrogen

managem

entpractices.

Methane

Uptakeby

Soils

11

Methaneuptakebysoilis

estimatedwithanequationthat

usesaveragevaluesformethane

oxidationinnaturalvegetation—

whethergrassland,coniferous

forest,ordeciduousforest—

attenuatedbycurrentlanduse

practices.Thisapproachisan

empiricalm

odel(cf.,IPCCTier2

orIPPCTier3).

Landmanagem

ent

includingcultivationfor

cropproduction,grazingin

grasslands,forestharvest,

grassland,orforest

fertilization.

AnnualaverageCH4oxidation

emissionsandremovalsare

from

thedatasetusedbyDel

Grossoetal.(2000).

Thisnewlydevelopedmethodology

makesuseofrecentU.S.‐based

researchthatisnotaddressedby

IPCCortheU.S.Inventory.The

methodincorporatesentityspecific

annualdata.

11Methaneuptakebysoilsisanaturalprocessinundisturbedsoils.Processesforrestoringmethanotrophicactivityarenotwellunderstood,andrequiredecadesto

develop.Amethodisoutlinedinthisreport,butadditionaldataandunderstandingarerequiredpriortouseorimplem

entationinquantificationtools.

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2Source

MethodologyApproach

PotentialManagem

ent

Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

Methaneand

NitrousOxide

Emissionsfrom

RiceCultivation

Abasicestimationequation(cf.,

IPCCTier1)isusedtoestimate

CH4,andaninference(cf.,IPCC

Tier2)methodisusedforN2O

emissionsfrom

floodedrice

production(Akiyamaetal.,2005;

deKleinetal.,2006;Lascoetal.,

2006;USDA,2011).

CH4:scalingfactorsare

differentiatedby

hydrologicalcontext(e.g.,

irrigated,rainfed,upland

(i.e.,drysoil)—allrice

fieldsintheUnitedStates

areirrigated),cultivation

periodfloodingregime

(e.g.,continuous,m

ultiple

aeration),timesincelast

flooding(priorto

cultivation;e.g.,morethan

180days,lessthan30

days)andtypeoforganic

amendm

ent(e.g.,com

post,

farmyardmanure).

N₂O:additionsfrom

mineralfertilizers,organic

amendm

ents,andcrop

residues.

CH4:thebaselineem

ission

factorortypicaldailyrateat

whichCH4isproducedper

unitoflandarearepresents

fieldsthatarecontinuously

floodedduringthecultivation

period,notfloodedatall

duringthe180dayspriorto

cultivationandreceiveno

organicam

endm

ents.CH4

scalingfactorstoaccountfor

waterregimescom

efrom

Lascoetal.(2006).

N2O:emissionfactorsrelyon

Lascoetal.(2006)andthe

scalingfactortoaccountfor

drainageeffects;com

esfrom

Akiyamaetal.(2005;USDA,

2011).

TheN

2OmethodusestheIPCC

(2006)equationwiththeadditionof

ascalingfactorfordrainagefrom

Akiyamaetal.(2005).Themethod

formethaneemissionsusesentity‐

specificannualdataasinputintothe

equationandisconsistentw

ithU.S.

Inventorymethod.

CO2from

Liming

Aninference(cf.,IPCCTier2)

methodisusedtoestimateCO

2em

issionsfrom

applicationof

carbonatelim

es(deKleinetal.,

2006)withU.S.‐specificemissions

factors(adaptedfrom

Westand

McBride,2005).

Theamountoflime,

crushedlim

estone,or

dolomiteappliedtosoils.

U.S.‐specificemissionsfactors

(WestandMcBride,2005).

UsesU.S.‐specificemissionfactorsas

annualinputintotheIPCCequation,

whichisconsistentw

iththeU.S.

Inventory.

Non‐CO2

Emissionsfrom

Biomass

Burning

Non‐CO2GHGemissionsfrom

biom

assburningofgrazingland

vegetationorcropresiduesare

estimatedwithaninference(cf.,

IPCCTier2)method(Aaldeetal.,

2006).

Areaburned.


valuesintheIPCCguidelines

(Aaldeetal.,2006)andWest

etal.(2010)forthe

residue:yieldratios.


inputintotheIPCCequation.

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Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

CO2fromUrea

Fertilizer

Application

CO2emissionsfrom

applicationof

ureaorurea‐basedfertilizersto

soilsareestimatedwithabasic

estimationequation(cf.,IPCCTier

1)method(deKleinetal.,2006).

Theamountofurea

fertilizerappliedtosoils.


valuesintheIPCCguidelines

(deKleinetal.,2006).This

methodassumesthatthe

sourceofCO2usedto

manufactureureaisfossilfuel

CO2capturedduringNH3

manufacture.


inputintotheIPCCequation,which

isusedfortheU.S.Inventory.

Wetlands

Biomass

Carbonin

Wetlands

Methodsforestimating

forestvegetationandshrub

andgrasslandvegetation

biom

asscarbonstocksusea

combinationoftheForest

VegetationSimulator(FVS)

modelandlookuptablesfor

dominantshruband

grasslandvegetationtypes

foundintheCroplandand

GrazingLandChapter.If

thereisaland‐usechange,

methodsforcropland

herbaceousbiomassare

suggested.

ForestedWetlands:Sam

easthose

describedforuplandforestsin

Section6.2.3.

ShrubandGrasslandVegetation:

Sameasthosedescribedfortotal

biom

asscarbonstockchanges

presentedintheCroplandand

GrazingLandChapter,Section

3.5.1.

ForestWetlands:Regional

variantsareavailableforFVS

thatallowforregion‐specific

focusonspeciesandforest

vegetationcom

munities.The

driverforproductivityisthe

availabilityofsiteindex

curves,andtheregional

variantsincludemany

wetlandtreespecies.

How

ever,ifaspecies‐specific

curveisnotavailable,thena

defaultfunctionisusedto

estimatecarbonstock

changes.

ShrubandGrassland

Vegetation:Sam

easthe

CroplandsandGrazingLands

Chapter,Section3.5.1.

Usesentity‐specificseasonaldata.

NoIPCCmethodologiescurrently

existforthissource;hence,thisisa

newlydevelopedmethod.

SoilCarbon,

N2O,andCH4in

Wetlands

TheDeNitrification‐

DeCom

position(DNDC)

process‐based

biogeochem

icalmodelisthe

methodusedforestimating

soilcarbon,N

2O,andCH4

emissionsfrom

wetlands.

Vegetationmanagem

ent,water

managem

entregime,soil

managem

ent,fertilization

practices,andland‐usehistory.

Processbasedmodelisused;

hence,noem

issionsfactors

areusedinthismethod.

ThismethodleveragestheDNDC

modeltosimulatesoilcarbon,N

2O,

andCH

4emissionsfrom

wetlandson

aseasonaltimescale.

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PotentialManagem

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Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

AnimalProductionSystems

EntericFerm

entation

MatureDairy

Cows

Mits3equationdeveloped

byMillsetal.(2003)and

furtherutilizedby

DairyGEM

(Rotzetal.,

2011).Mits3equationis

basedprimarilyon

metabolizableenergy

intake.Drymatterintake

(DMI),starch,acid

detergentfiber,crude

protein,andtotaldigestible

nutrientsprovidetheinputs

fortheequation.

Dietarychanges:increasingDMI,

usingfibrousconcentraterather

thanstarchconcentrate,feeding

rapidlydegradedstarch(suchas

barley),andadditionofdietary

fat.

Activitychanges:confining

currentlygrazinganimals,fewer

workhoursperday,fewerdays

onfeedpriortoslaughter.

Emissionfactorscalculated

withapproachdevelopedby

Millsetal.(2003)andRotzet

al.(2011).

UseoftheDairyGEM

/Mits3equation

isrecom

mendedovertheIPCCTier

2equation(2006)becauseithas

proventobemoreaccurate,in

general,fordairycows.

BeefCow

‐Calf

andBulls

IPCCTier2approach

(2006).Thecalculation

considersweight,weight

gain,m

atureweight,

pregnancy,lactation,other

activity(grazing,confined,

dailywork),andtheenergy

contentoftheanimals'

diets.






fat.



workhoursperday.

Emissionfactorsare

determinedwiththeIPCC

Tier2equation(2006).

Methaneconversionfactor

(Ym)basedonanimal‐specific

guidanceinU.S.EPA(2013).

Theequationsutilizedarethesame

asexistinginventorymethods;

however,themethodsutilizefarm‐

specificfeedtypesandutilize

monthly,ratherthanannual,level

data(i.e.,accountforseasonal

variationinforagequality).

Stockers

IPCCTier2approach



gain,m

atureweight,





diets.






fat.





Emissionfactorsare


Tier2equation(2006)onan

entity‐by‐entitybasis.Ym

basedonanimal‐specific

guidanceinU.S.EPA(2013).

Theequationsutilizedarethesame

asexistinginventorymethods;

however,themethodsutilizefarm‐

specificfeedtypesandutilize

monthly,ratherthanannual,level

data(i.e.,accountforseasonal

variationinforagequality).

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ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

FeedlotCattle

IPCCTier2approach



gain,m

atureweight,





diets.






fat.





Emissionfactorsare


Tier2equation(2006).Ym

basedonguidancedeveloped

byHales(2012).

Thecalculationconsidersweight,

weightgain,matureweight,

pregnancy,lactation,otheractivity

(grazing,confined,dailywork),and

theenergycontentoftheanimals'

diets.

Sheep

How

denequation(How

den

etal.,1994),basedon

dietaryDMI.

Dietarychanges,butnowell‐

developedresearchdueto

difficultyofobtainingaccurate

feed‐intakeestimatesforgrazing

sheep.

Theequationfrom

How

denet

al.(1994)estimates

emissionsbasedsolelyon

DMI;hence,emissionfactors

notutilized.

Thismethodusesactualm

onthly

estimatesofDMI,ratherthanhead

count,asutilizedbytheIPCCTier1

equation(2006).

Swine

IPCCTier1approach

(2006).

None.

UtilizesIPCCTier1em

ission

factor(IPCC,2006).

None.

OtherAnimals

(Goats,

American

Bison)

IPCCTier1approachfor

Americanbison(basedon

buffalo,modifiedbyaverage

animalweight)andgoats

(IPCC,2006).

None.

UtilizesIPCCTier1em

ission

factors(IPCC,2006).

None.

Housing

Methane

Emissionsfrom

Manureon

BarnFloorsfor

DairyCattle

DairyGEM

(asubsetofthe

IntegratedFarmSystems

Model)isusedtoestimate

CH4emissions.

None.

Empiricalrelationshipas

providedinChianeseetal.

(Chianeseetal.,2009).

Utilizesclim

ateandentity

characteristics.

Methane

Emissionsfrom

DairyCattle,

BeefCattle,and

SwineHousing

IPCCTier2approach.

Typeanddurationofm

anure

storage.

Utilizesacom

binationofIPCC

andU.S.EPAInventory

emissionfactors.

None.

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Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

NitrousOxide

Emissionsfrom

DairyCattle,

BeefCattle,

Swine,and

Poultry

Housing

IPCCTier2approach,using

AmericanSocietyof

AgriculturalEngineers

(ASAE)equationsto

estimatenitrogenexcretion

anddefaultvaluesfor

ammonialossestoaccount

fornitrogenbalance.

Animaldietsandtypeofm

anure

storage.

UtilizesIPCCemissionfactors

(IPCC,2006)andammonia

lossesfrom

Koelshand

Stow

ell(2005).

Usesnitrogenbalanceapproachto

adjustnitrogeninhousingto

accountforammonialosses.

Methane

Emissionsfrom

Poultry

Housing

IPCCTier1approach.

None.

UtilizesIPCCemissionfactors

thatvarybytemperatureand

whethermanureismanaged

asdrymanureorasaliquid

(IPCC,2006).

Ofthemodelsevaluatedforpoultry,

anestimateofconfidenceforoutput

wasonlyavailablefortheIPCCTier

1approach.Specifictoestimatesof

poultry,onmanureCH

4emissions,

theuncertaintywaslessthan20%

(Littleetal.,2008).

ManureStorageandTreatment

SolidManureStorageandTreatment‐Tem

poraryStackandLong‐TermStockpile

Methane

Emissions

IPCCTier2approachusing

IPCCandU.S.EPAInventory

emissionfactors,utilizing

monthlydataonvolatile

solidsanddrymanure.

Animaldiets.

Utilizesacom

binationofIPCC

andU.S.EPAInventory

emissionfactors.

UsesU.S.‐specificemissionfactors

andtakesintoaccountdiet

characterization.

NitrousOxide

Emissions

IPCCTier2approachusing

U.S.EPAInventoryem

ission

factorsandmonthlydataon

totalnitrogen,anddry

manure.

Durationofmanurestorageand

animaldiets.

Utilizesemissionfactorsfrom

U.S.EPAInventory.

UsesU.S.‐specificemissionfactors

andtakesintoaccountdiet

characterization.

ManureStorageandTreatment‐Composting

Methane

Emissions

IPCCTier2approach

utilizingmonthlydataon

volatilesolidsanddry

manure.

Configurationofstorageunit(e.g.,

compostingin‐vessel,staticpile,

intensivewindrow

,passive

windrow

)andanimaldiets.


IPCC.

Takesintoaccountdietandclim

ate

characteristics.

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PotentialManagem

ent

Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

NitrousOxide

Emissions

IPCCTier2approach

utilizingdataontotalinitial

nitrogenanddrymanure.

Manurehandling(i.e.,nomixor

activemix)andanimaldiets.


IPCC.

Takesintoaccountdietandclim

ate

characteristics.

LiquidManureStorageandTreatment–AerobicLagoon

Methane

Emissions

Themethanecorrection

factorforaerobictreatment

isnegligibleandwas

designatedas0%

in

accordancewiththeIPCC.

Notapplicable.


IPCC.

Notestimated.

NitrousOxide

Emissions

IPCCTier2method.

Configurationofstorage(e.g.,

volumeoflagoon),naturalor

forcedaeration,andanimaldiets.


IPCC.

None.

LiquidManureStorageandTreatment–AnaerobicLagoon,RunoffHoldingPond,StorageTanks

Methane

Emissions

SommerModel(Sommeret

al.,2004)isusedwith

degradableandnon‐

degradablefractionsof

volatilesolidsfrom

Møller

etal.(2004).


coveredoruncoveredstorage,

presenceorabsenceofcrust)and

animaldiets.

Parametersforestimation

from

Som

meretal.(2004).

Takesintoaccountdietandstorage

temperaturecharacteristics.

NitrousOxide

Emissions

Emissionsareafunctionof

theexposedsurfacearea

andU.S.‐basedemission

factors.


surfaceareaofm

anure).


Rotzetal.(2011a).

UtilizesU.S.‐specificemission

factors.

LiquidManureStorageandTreatment–AnaerobicDigestionwithBiogasUtilization

Methane

Emissions

Leakagefrom

anaerobic

digestionsystem

is

estimatedusingIPCCTier2

approachandsystem‐

specificem

issionfactors.

Configurationofdigester(e.g.,

steelorlinedconcreteor

fiberglassdigesters)andanimal

diets.


CDM(CDM,2012).

Takesintoaccountsystemdesign

anddiets.

CombinedAerobicTreatmentSystems

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PotentialManagem

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Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

Methane

Emissions

Assum

edtobe10percentof

theem

issionsresulting

from

methodtoestimate

emissionsfrom

Liquid

ManureStorageand

Treatment–Anaerobic

Lagoon,RunoffHolding

Pond,StorageTanks.


coveredoruncoveredstorage,

presenceorabsenceofcrust)and

animaldiets.

Parametersforestimation

from

Som

meretal.(2004).

Takesintoaccountdietandstorage

temperaturecharacteristics.

NitrousOxide

Emissions

Assum

edtobe10percentof

theem

issionsresulting

from

methodtoestimate

emissionsfrom

Liquid

ManureStorageand

Treatment–Anaerobic

Lagoon,RunoffHolding

Pond,StorageTanks.


surfaceareaofm

anure).


Rotzetal.(2011a).

UsesU.S.‐specificemissionfactors.

LiquidManure

Storageand

Treatment–

Sand/M

anure

Separation

NomethodprovidedasGHG

emissionsarenegligible.

How

ever,resultingvolatile

solids,totalnitrogen,

organicnitrogen,and

manuretemperatureofthe

separatedliquidmanure

shouldbemeasuredand

usedastheinputsto

estimateem

issionsofGHGs

forsubsequentstorageand

treatmentoperations.

Notapplicable.

Notapplicable.

Notapplicable.

LiquidManure

Storageand

Treatment–

Nutrient

Rem

oval

Notestimatedduetolimited

quantitativeinformationon

GHGsfrom

nitrogen

removalprocesses.

Notapplicable.

Notapplicable.

Notapplicable.

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PotentialManagem

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Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

LiquidManure

Storageand

Treatment–

Solid/Liquid

Separation

NomethodprovidedasGHG

emissionsarenegligible.

Efficiencyfactorsfor

differentm

echanicalsolid‐

liquidseparationsystems

provided.How

ever,

resultingvolatilesolids,

totalnitrogen,organic

nitrogen,andmanure

temperatureofthe

separatedliquidmanure

shouldbemeasuredand

usedastheinputsto

estimateem

issionsofGHGs

forsubsequentstorageand

treatmentoperations.

Notapplicable.

Notapplicable.

Notapplicable.

LiquidManureStorageandTreatment–ConstructedWetlands

GHGRem

ovals

Currentlynomethodis

provided,althoughGHG

removalsarenotedtolikely

begreaterthanCH4andN

2O

emissions,whichare

considerednegligible.

Notapplicable.

Notapplicable.

Notapplicable.

SolidManure

Storageand

Treatment–

Thermo‐

Chem

ical

Conversion

NotestimatedasCH

4and

N2Oemissionsconsidered

negligible.

Notapplicable.

Notapplicable.

Notapplicable.

ManureApplication

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PotentialManagem

ent

Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

SolidManure

Application

System

s(manure

handlingprior

toland

application)

Notestimatedduetolimited

quantitativeinformationon

GHGsfrom

manuremixing

andremovalfrom

storage

system

sorduringtransport

tofieldswheremanureis

landapplied.

Notapplicable.

Notapplicable.

Notapplicable.

LiquidManure

Application

System

s(manure

handlingprior

toland

application)

Nomethodisprovidedas

CH4andN

2OGHGemissions

arenegligible;how

ever,CO2

emissionswouldresultfrom

theoperationofequipment.

Notapplicable.

Notapplicable.

Notapplicable.

Forestry

ForestCarbon

Methodsinclude:(1)FVS

modelwithFireandFuels

Extensionmodule(FVS‐

FFE)withJenkinsetal.

(2003)allometric

equations;and(2)default

lookuptables.

FVS‐FFEmodelshundredsof

managem

entpractices(thinning

from

below

/above/evenly

throughastand,thinningwith

speciespreference,conditional

thinning/planting/regeneration,

pilingofsurfacefuelsand

prescribedfires,salvage

operations,m

astication

treatments,insect/disease

managem

ent,etc.)

Allometricequationsarefrom

Jenkinsetal.(2003);default

lookuptablesfrom

Smithet

al.(2006).

Themethodallowslarge

landow

nerstoestimatebaseyear

carbonstocksfrom

fieldsurveysand

repeatthefieldsurveyat

recommendedintervals(e.g.,5‐year,

10‐year)dependingonthe

region/foresttypegroup.

Smalllandownersestimatecarbon

stocksfrom

lookuptablesbasedon

USDAForestInventoryandAnalysis

programdata,whichiscom

parable

tootherGHGmethodologies(e.g.,

Section1605(b)Guidance).

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PotentialManagem

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Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

Establishing,

Re‐establishing,

andClearing

Forest

IPCCequationsdeveloped

byAaldeetal.(2006);w

ith

Jenkinsetal.(2003)

allometricequations.

Plantingtreesonpreviously

unforestedlands;replantingtrees

onpreviouslyforestedlands;and

permanentlyclearingtreesfrom

forestedlands.

Allometricequationsarefrom

Jenkinsetal.(2003)

Thismethodallowslarge

landow

nerstoestimatebaseyear

carbonstocksfrom

fieldsurveysand

repeatthefieldsurveyat

recommendedintervals(e.g.,5‐year,

10‐year)dependingonthe

region/foresttypegroup.

TheNationalInventoryReport(NIR)

usesacarbonstockchangemethod,

whichexplicitlyincludesthe

establishm

ent,re‐establishm

ent,

andclearingofforests.

Forest

Managem

ent

Methodsinclude:(1)FVS‐

FFEwithJenkinsetal.

(2003)allometricequations

and(2)defaultlookup

tablesofm

anagem

ent

practicescenarios.

Standdensitymanagem

ent;site

preparationtechniques;

vegetationcontrol;planting;

naturalregeneration;

fertilization;selectionofrotation

length;harvestingandutilization

techniques;fireandfuelload

managem

ent;reducingriskof

emissionsfrom

pestsanddisease;

short‐rotationwoodycrops.

Defaultlookuptablesof

carbonstocksovertimeby

region,foresttypecategories,

includingspeciesgroup(e.g.,

hardwood,softwood,mixed);

regeneration(e.g.,planted,

naturallyregenerated);

managem

entintensity(e.g.,

low,m

oderate,high,very

high)andsiteproductivity

(e.g.,low,high),tobe

developedasasupporting

productusingFVS.

Thismethodprovidesaconsistent

andcomparablesetofcarbonstocks

foreachregion,foresttypegroup,

managem

entintensity,andsite

productivityovertime,under

managem

entscenarioscom

monto

theforesttypesandmanagem

ent

intensities.

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Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

Harvested

WoodProductsU.S.‐specificharvested

woodproductstables

developedbySkog(2008),

takingtheestimated

averageam

ountof

harvestedwoodproduct

carbonfrom

thecurrent

year’sharvestthatrem

ains

storedinendusesand

landfillsoverthenext100

years.

Theapproachmodelsvarious

managem

entpracticesincluding

thedispositionofeachprimary

product(e.g.,lum

ber,structural

panels)tomajorenduses(e.g.,

percentageofproductgoingto

residentialhousing,non‐

residentialhousing,

manufacturing(furniture)),and

percentagegoingtoexports;w

ith

decayfunctionsindicatinghow

quicklyproductsgooutofusefor

eachenduse;fractionofmaterial

goingoutofusethatgoesto

landfills;fractionofmaterialin

landfillsthatdoesnotdecay,and

thedecayrateformaterialin

landfillsthatdoesdecay.

WOODCARBIImodelusedto

estimateannualchangein

carbonstoredinproductsand

landfills(Skog,2008).

Providesamethodthatissuitableto

counttheaverageamountofcarbon

storedinproductsinuseandin

landfills,andtheunderlyingmodel

isthesameusedfortheNational

InventoryReport(NIR)(i.e.,

TheNIRalsousesWOODCARBII

modeltoestimateannualchangein

carbonstoredinproductsand

landfills).Theharvestedwood

producttables(Skog,2008)provide

annualvaluesforzeroto10years

afterproductionand5‐year

intervalsfor10to100yearsafter

production.

UrbanForests

Methodsinclude:(1)Field

DataMethodusingi‐Tree

Eco(formerlyUFORE)

model;and(2)Aerial

Methodusingi‐TreeCanopy

modelwithaerialtreecover

estimatesandlookup

tables.

Maintenance(useofvehicles,

chainsaws,etc.)andaltering

buildingenergyuse(useoftrees

forshadingandwindbreaks);

quantitativemethodsfor

estimatingem

issionsfrom

these

managem

entpracticesare

includedforinformation

purposesonly.

i‐TreeEcomodel;i‐Tree

Canopymodel.

Thismethodprovidesarangeof

optionsdependentonthedata

availabilityoftheentities'urban

forestland.

TheNIRusesequationsbasedon

lookuptablesandaveragetree

canopyvalues.

Natural

Disturbance—

Wildfireand

PrescribedFire

Methodsinclude:(1)First

OrderFireEffects(FOFEM)

modelenteringmeasured

biom

ass;and(2)FOFEM

modelusingdefaultvalues

generatedbyvegetation

type.

Fireandfuelloadmanagem

ent.

FOFEM(Reinhardtetal.,

1997).

Thismethodprovidesarangeof

optionsdependentonthedata

availabilityoftheentities'disturbed

forestland.TheuseofaU.S.‐specific

fireandfuelloadmanagem

ent

modelisanimprovem

entcom

pared

totheNIR,whichusesequations

basedonIPCC(2006).

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PotentialManagem

ent

Practices


ImprovementsCom

paredto

OtherGreenhouseGas

Methodologies

Land‐useChange

AnnualChange

inCarbon

StocksinDead

Woodand

LitterDueto

Land

Conversion

Abasicestimationequation

(cf.,IPCCTier1)isusedto

estimatechangeincarbon

stocksindeadwoodand

litter(Aaldeetal.,2006).

Landconversion.

IPCC2006Guidelines(Aalde

etal.,2006).


inputintotheequationandis

consistentwithIPCC2006guidance.

ChangeinSoil

Organic

CarbonStocks

forMineral

Soils

Themethodologiesto

estimatesoilcarbonstock

changesfororganicsoils

andmineralsoilsare

adoptedfrom

IPCC(Aaldeet

al.,2006)andareabasic

estimationequation.

Landconversion.

IPCC2006Guidelines(Aalde

etal.,2006).


inputintotheequationandis

consistentwithIPCC2006guidance.

IPCC=Intergovernm

entalPanelonClimateChange

Executive Summary

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ReferencesAalde,H.,P.Gonzalez,M.Gytarski,T.Krug,etal.2006.Chapter2:Genericmethodologiesapplicable

tomultipleland‐usecategories.In2006IPCCGuidelinesforNationalGreenhouseGasInventories,S.Eggleston,L.Buendia,K.Miwa,T.NgaraandK.Tanabe(eds.).Japan:IGES.

Akiyama,H.,K.Yagi,andX.Yan.2005.DirectN2Oemissionsfromricepaddyfields:Summaryofavailabledata.GlobalBiogeochemicalCycles,19.

CDM.2012.Projectandleakageemissionsfromanaerobicdigesters.Ver.01.0.0:CleanDevelopmentMechanism,.

Chianese,D.S.,C.A.Rotz,andT.L.Richard.2009.Simulationofmethaneemissionsfromdairyfarmstoassessgreenhousegasreductionstrategies.Trans.ASABE,52:1313‐1323.

deKlein,C.,R.S.A.Novoa,S.Ogle,K.A.Smith,etal.2006.Chapter11:N2Oemissionsfrommanagedsoil,andCO2emissionsfromlimeandureaapplication.In2006IPCCguidelinesfornationalgreenhousegasinventories,Vol.4:Agriculture,forestryandotherlanduse,S.Eggleston,L.Buendia,K.Miwa,T.NgaraandK.Tanabe(eds.).Kanagawa,Japan:IGES.

DelGrosso,S.,W.Parton,A.Mosier,D.S.Ojima,etal.2000.GeneralCH4oxidationmodelandcomparisonsofCH4oxidationinnaturalandmanagedsystems.GlobalBiogeochemicalCycles,14:999‐1019.

Denef,K.,S.Archibeque,andK.Paustian.2011.GreenhouseGasEmissionsfromU.S.AgricultureandForestry:AReviewofEmissionSources,ControllingFactors,andMitigationPotential:InterimreporttoUSDAunderContract#GS‐23F‐8182H.http://www.usda.gov/oce/climate_change/techguide/Denef_et_al_2011_Review_of_reviews_v1.0.pdf.

Denef,K.,K.Paustian,S.Archibeque,S.Biggar,etal.2012.ReportofGreenhouseGasAccountingToolsforAgricultureandForestrySectors:InterimreporttoUSDAunderContract#GS‐23F‐8182H.http://www.usda.gov/oce/climate_change/techguide/Denef_et_al_2011_Review_of_reviews_v1.0.pdf.

Hales,K.E.,N.A.Cole,andJ.C.MacDonald.2012.Effectsofcornprocessingmethodanddietaryinclusionofwetdistillersgrainwithsolublesonenergymetabolismandentericmethaneemissionsoffinishingcattle.JournalofAnimalScience,90:3174‐3185.

Howden,S.M.,D.H.White,G.M.McKeon,J.C.Scanlan,etal.1994.Methodsforexploringmanagementoptionstoreducegreenhousegasemissionsfromtropicalgrazingsystems.ClimaticChange,27(1):49‐70.

IPCC.1997.Revised1996IPCCGuidelinesforNationalGreenhouseGasInventories,PreparedbytheNationalGreenhouseGasInventoriesProgramme.Bracknell,UK:IntergovernmentalPanelonClimateChange.http://www.ipcc‐nggip.iges.or.jp/public/gl/invs1.html.

IPCC.2006.2006IPCCGuidelinesforNationalGreenhouseGasInventories,PreparedbytheNationalGreenhouseGasInventoriesProgramme.EditedbyH.S.Eggleston,L.Buendia,K.Miwa,T.NgaraandK.Tanabe.Japan:IGES.http://www.ipcc‐nggip.iges.or.jp/public/2006gl/vol4.html.

Jenkins,J.C.,D.C.Chojnacky,L.S.Heath,andR.A.Birdsey.2003.National‐ScaleBiomassEstimatorsforUnitedStatesTreeSpecies.ForestScience,49(1):12‐35.

Lasco,R.D.,S.Ogle,J.Raison,L.Verchot,etal.2006.Chapter5:Cropland.In2006IPCCGuidelinesforNationalGreenhouseGasInventories,S.Eggleston,L.Buendia,K.Miwa,T.NgaraandK.Tanabe(eds.).Japan:IGES,IPCCNationalGreenhouseGasInventoriesProgram.

Little,S.,J.Linderman,K.MacLean,andH.Janzen.2008.HOLOS–atooltoestimateandreducegreenhousegasesfromfarms.Methodologyandalgorithmsforversions1.1x:Agricultureand

Executive Summary

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Agri‐FoodCanada.http://www4.agr.gc.ca/AAFC‐AAC/display‐afficher.do?id=1226606460726&lang=eng#s1.

Mills,J.A.N.,E.Kebreab,C.M.Yates,L.A.Crompton,etal.2003.Alternativeapproachestopredictingmethaneemissionsfromdairycows.JournalofAnimalScience,81(12):3141‐3150.

Møller,H.B.,S.G.Sommer,andB.K.Ahring.2004.Methaneproductivityofmanure,strawandsolidfractionsofmanure.BiomassandBioenergy,26(5):485‐495.

Ogle,S.M.,F.JayBreidt,M.D.Eve,andK.Paustian.2003.UncertaintyinestimatinglanduseandmanagementimpactsonsoilorganiccarbonstorageforUSagriculturallandsbetween1982and1997.GlobalChangeBiology,9(11):1521‐1542.

Ogle,S.M.,F.J.Breidt,M.Easter,S.Williams,etal.2007.Empiricallybaseduncertaintyassociatedwithmodelingcarbonsequestrationinsoils.EcologicalModelling,205:453‐463.

Parton,W.J.,D.S.Schimel,C.V.Cole,andD.S.Ojima.1987.AnalysisoffactorscontrollingsoilorganicmatterlevelsinGreatPlainsgrasslands.SoilScienceSocietyofAmericaJournal,51:1173‐1179.

Parton,W.J.,J.M.O.Scurlock,D.S.Ojima,T.G.Gilmanov,etal.1993.ObservationsandModelingofBiomassandSoilOrganicMatterDynamicsfortheGrasslandBiomeWorldwide.GlobalBiogeochemicalCycles,7(4):785‐809.

Reinhardt,E.D.,R.E.Keane,andJ.K.Brown.1997.FirstOrderFireEffectsModel:FOFEM4.0,User'sGuide.

Rotz,C.A.,M.S.Corson,D.S.Chianese,F.Montes,etal.2011.Integratedfarmsystemmodel:ReferenceManual.UniversityPark,PA:U.S.DepartmentofAgriculture,AgriculturalResearchService.http://ars.usda.gov/SP2UserFiles/Place/19020000/ifsmreference.pdf.

Skog,K.E.2008.SequestrationofcarboninharvestedwoodproductsfortheUnitedStates.ForestProductsJournal,58(6):56‐72.

Smith,J.E.,L.S.Heath,K.E.Skog,andR.A.Birdsey.2006.MethodsforcalculatingforestecosystemandharvestedcarbonwithstandardestimatesforforesttypesoftheUnitedStates.NewtownSquare,PA:USDepartmentofAgriculture,ForestService,NorthernResearchStation.

Sommer,S.G.,S.O.Petersen,andH.B.Møller.2004.Algorithmsforcalculatingmethaneandnitrousoxideemissionsfrommanuremanagement.NutrientCyclinginAgroecosystems,69:143‐154.

U.S.EPA.2013.InventoryofU.S.GreenhouseGasEmissionsandSinks:1990‐2011.Washington,DC:U.S.EnvironmentalProtectionAgency.http://epa.gov/climatechange/emissions/usinventoryreport.html.

USDA.2011.U.S.AgricultureandForestGreenhouseGasInventory:1990‐2008.Washington,DC:U.S.DepartmentofAgriculture.

Verchot,L.,T.Krug,R.D.Lasco,S.Ogle,etal.2006.Chapter5:Grassland.In2006IPCCGuidelinesforNationalGreenhouseGasInventories,S.Eggleston,L.Buendia,K.Miwa,T.NgaraandD.L.Tanaka(eds.).Japan:IGES.

West,T.O.,andA.C.McBride.2005.ThecontributionofagriculturallimetocarbondioxideemissionsintheUnitedStates:dissolution,transport,andnetemissions.Agriculture,Ecosystems&Environment,108(2):145‐154.

West,T.O.,C.C.Brandt,L.M.Baskaran,C.M.Hellwinckel,etal.2010.CroplandcarbonfluxesintheUnitedStates:increasinggeospatialresolutionofinventory‐basedcarbonaccounting.EcologicalApplications,20:1074‐1086.

Executive Summary

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Authors:

MarlenEve,U.S.DepartmentofAgriculture,OfficeoftheChiefEconomistMarkFlugge,ICFInternationalDianaPape,ICFInternational

Contents:

1 Introduction...............................................................................................................................................1‐31.1 OverviewoftheReport...............................................................................................................................1‐41.2 ReportObjectives..........................................................................................................................................1‐51.3 ProcessfortheDevelopmentoftheMethods....................................................................................1‐61.4 ContentsoftheReport................................................................................................................................1‐91.5 UsesandLimitationsoftheReport......................................................................................................1‐10Chapter1References.............................................................................................................................................1‐14

SuggestedChapterCitation:Eve,M.,M.Flugge,D.Pape,2014.Chapter1:Introduction.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.

USDAisanequalopportunityproviderandemployer.

Chapter 1

Introduction

Chapter 1: Introduction

1-2

Acronyms,ChemicalFormulae,andUnits

C CarbonCH4 MethaneCO2 CarbondioxideCO2‐eq CarbondioxideequivalentsEO ExecutiveOrderEPA U.S.EnvironmentalProtectionAgencyGHG GreenhousegasISO InternationalOrganizationforStandardizationLCA LifecycleassessmentLCI LifecycleinventoryN2O NitrousOxideUSDA U.S.DepartmentofAgriculture


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1 IntroductionIn2008,agriculturecontributed6.1percentofthetotalgreenhousegas(GHG)emissionsintheUnitedStates(USDA,2011).1ThedistributionofemissionsacrosstheagriculturesectorisillustratedinFigure1‐1.Inaddition,forestrysequesteredenoughcarbontooffsetabout13percentoftotalU.S.GHGemissions(USDA,2011).Sincethelate1990s,theU.S.DepartmentofAgriculture(USDA)hasanalyzedandreportedGHGemissionsandremovalsvianational‐scaleinventories,andfield‐scalemeasurementofthesefluxeshasbeendonefordecadesbyUSDAresearchers.USDAalsohasdonesignificantworkinthedevelopmentofGHGestimationmodelsandtoolswithintheagricultureandforestrysectors.

ThisreportprovidesmethodsandascientificbasisforestimatingGHGemissionsandsequestrationatthelandowner,land‐managerscale—entityscale.ThereportwasauthoredbyrecognizedexpertsfromacrossUSDA,otherU.S.Governmentagencies,andacademiaandreflectsestimationmethodsthatbalancescientificrigor,scale,practicality,andavailabilityofdata.

Thischapterprovidesanoverviewofthereportaswellastheobjectivessetoutfortheprojectandtheprocessusedindevelopingthereport.Theremainderofthechapterisorganizedasfollows:

OverviewoftheReport

ReportObjectives

ProcessfortheDevelopmentoftheMethods

ContentsoftheReport

UsesandLimitationsoftheReport

Chapter1References

1HeretheagriculturesectorincludesGHGemissionsandremovalsfromlivestock,grasslands,croplands,andenergyuseonfarms;itdoesnotincludeGHGemissionsandremovalsfromindustrialprocesses(e.g.,fertilizerproduction)orfromoff‐farmenergyuse(e.g.,transportationfuelsusedinexportingcommoditycrops).

a Croplandsoilsemissionsincludeemissionsfrommajorcrops;non‐majorcrops;histosolcultivation;andmanagedmanurethataccountsforthelossofmanurenitrogenduringtransport,treatment,andstorage,includingvolatilizationandleaching/runoff.Source:USDA(2011).

Figure1‐1:AgricultureSourcesofGreenhouseGasEmissionsin2008a

Total Emissions = 502 million metric tons CO2 eq


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1.1 OverviewoftheReport

UnderprovisionofSection2709oftheFood,Conservation,andEnergyActof2008,USDAhasbeendirectedto“establishtechnicalguidelinesthatoutlinescience‐basedmethodstomeasuretheenvironmentalservicebenefitsfromconservationandlandmanagementactivitiesinordertofacilitatetheparticipationoffarmers,ranchers,andforestlandownersinemergingenvironmentalservicesmarkets.”ThelegislationfurtherstatesthattheinitialemphasisofthemethodsdevelopmentshouldfocusonGHGemissions.Agreementonthatsetofmethodsistheprimaryscopeandpurposeforthisreport.Thefindingsinthisreportprovidethefoundationforentity‐leveltoolstomeasuretheGHGbenefitsfromconservationandlandmanagementactivities.

ThisreportandtheestimationmethodsarenotintendedasanadditiontoorreplacementofanycurrentFederalorStateGHGreportingsystemsorrequirements.ThisreporthasbeenpreparedtooutlinemethodstocalculatedirectGHGemissionsandcarbonsequestrationfromagricultureandforestryprocessesandbuildsuponexistinginventoryeffortssuchasU.S.EnvironmentalProtectionAgency(EPA)andUSDA’snationalinventoriesandtheU.S.DepartmentofEnergy’sVoluntaryGreenhouseGasReportingProgramSection1605(b)Guidelines,withanaimofprovidingsimple,transparent,androbustinventoryandreportingmethods.

ThereportprovidestechnicalmethodsforestimatingandreportingGHGsfromsignificantagricultureandforestrysourcesandsinks.Thesemethodsaredesignedtoquantifysignificantemissionsandsinksassociatedwithspecificsourcecategoriesaswellasannualreductionsinthoseemissionsorfluxesincarbonstorageresultingfromland‐usechangeandlandmanagementpracticesandtechnologies.Therefore,thereportwillsupportthedevelopmentofentity‐,farm‐,orforest‐scaleGHGestimatesandinventories.

Becausethereportisintendedasameansofevaluatingmanagementpracticesacrossthefullscopeofthefarm,ranch,andforestmanagementsystem,themethodsinthereportneedtobeascomprehensiveaspossible.Researchanddatagapsexistthatresultinsomemanagementpracticesnotbeingaccountedfororarereflectedinhigherlevelsofestimateuncertainty.Completenessisimportant,though,andthereportattemptstoidentifythemostsignificantresearchgapsanddataneeds.

Themethodsweredevelopedaccordingtoseveralcriteriainordertomaximizetheirusefulness.Inparticular,themethodsmust:

1. Standontheirown,independentofanyotheraccountingsystem,yetmaintainconsistencywithotheraccountingsystemstothemaximumextentpossible;

2. Bescalableforuseatentity‐scalesitesacrosstheUnitedStates,withapplicabilityatcountyand/orStatelevelsaswell;

3. FacilitateusebyUSDAinassessingtheperformanceofconservationprograms;

4. ProvideabroadframeworktoassessmanagementpracticestoevaluatetheGHGaspectofproductionsustainability;

5. Maintainmaximumapplicabilityforuseinenvironmentalmarkets,includingpossiblefutureFederal,State,orlocalGHGoffsetsinitiatives;

6. BescientificallyvettedthroughUSDA,U.S.government,andacademicexpertreviewandpubliccomment;

7. Providereliable,real,andverifiableestimatesofon‐siteGHGemissions,carbonstorage,andcarbonsequestration(methodswillbedesignedsothatovertimetheycanbeappliedto


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quantifyon‐siteGHGreductionsandincreasesincarbonstorageduetoconservationandlandmanagementactivities);and

8. Provideabasisforconsistencyinestimationandtransparencyinreporting.

1.2 ReportObjectives

TheobjectivesforthisreportaretocreateastandardsetofGHGestimationmethodsforusebyUSDA,landowners,andotherstakeholdersandtoserveasinputintothedevelopmentofUSDAestimationtools.ThemethodspresentedinthereportaddressGHGemissionsandcarbonremovalfortheentireentityoroperationandprovidetheopportunitytoassessindividualpracticesormanagementdecisions.

Aco‐objectiveistoestablishconsensusonastandardizedsetofmethodsfortheDepartment,whichbecomethescientificbasisforentity‐scaleestimationoftheGHGimpactsoflandownermanagementdecisions.Therefore,scientificrigorandtransparencyarealsocritical.

WhileUSDAhaslongbeeninvolvedindevelopmentofGHGinventoriesandestimationtools,thisreportbringstogetherestimationapproachesfromallagricultureandlandmanagementsectorsintooneplace.Thesemethodsarecombinedinsuchawaythatanintegratedestimatecanbederivedforallactivitieswithintheboundaryofthefarmandforestmanagementoperation.Figure1‐2showsthediversityofactivitiesandthecomplexityofestimatingGHGemissionsandcarbonsequestrationacrosstheentiremanagemententity.

Figure1‐2: ConceptualDiagramofActivitiesCoveredinThisReport

Combiningalandowners’ crop,livestockandforestmanagementactivitiesintoaseamlessgreenhousegasestimatefortheentity.

Source:Eve(2012).

GreenhouseGasMitigationOptionsandCostsforAgriculturalLandandAnimalProductionwithintheUnitedStatescoversmitigationpracticesincropproduction,animalproduction,andlandretirementsystemsintheUnitedStates.ThisreportreviewsavailablescientificmethodsforestimatingGHGsourcesandsinksatanentitylevelandrecommendsparticularestimationmethodsforeachlivestocktypeandagriculture/forestrypractice.Toestimatethecosts,USDAhasdevelopedanotherreportthatestimatestheimplementationcosts,GHGmitigationpotentialatthefarmlevel,andbreak‐evenprices(i.e.,GHGincentive)fordifferentmitigationpracticesonafarmlevel.

Thereportisavailablefordownloadontheprojectwebsiteat:http://www.usda.gov/oce/climate_change/mitigation_technologies/GHGMitigationProduction_Cost.htm.


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1.3 ProcessfortheDevelopmentoftheMethods

Thisreportwasdevelopedbythreeauthorteams(i.e.,workinggroups)underthedirectionofoneleadauthorforeachteam(plusoneco‐leadauthorfortheforestrychapter).TheleadauthorswerechosenbasedontheirexperienceswithGHGinventoriesandaccountingmethodologiesandtheirprofessionalresearchexperiences.Withinputfromeachleadauthor,USDAchose10to13workinggroupmembersperteamtowritethereport.Theseworkinggroupmemberseachhaddifferentbackgroundsthatfitwiththeanticipatedcontentofthedocument.MembersalsohadexperiencewithGHGaccountingand/orfieldresearchthatwasuniqueandaddressedoneormoreofthenichemethodsthatwereessentialforensuringthecomprehensivenessofthemethodsforeachsector.Theauthorteamswereprovidedwithapreliminaryoutlineofachapterandtwobackgroundreportsdevelopedaspartoftheproject.Onebackgroundreportwasananalysisofthescientificliteraturerelatedtoratesofcarbonsequestrationoremissionsreductionresultingfromvariousmanagementpracticesandtechnologies(Denefetal.,2011);theotherwasacompilationofalloftheavailabletools,protocols,andmodelsandbasicinformationoneachone(Denefetal.,2012).Bothreportsareavailablefordownloadontheprojectwebsiteat:http://usda.gov/oce/climate_change/estimation.htm.

ThereareseveralgeneralwaystoestimateGHGemissionsandsequestrationatanentityscale,andeachapproachgivesvaryingaccuracyandprecision.Typically,themostaccuratewaytoestimateGHGfluxesisthroughdirectmeasurement,whichoftenrequiresexpensiveequipmentortechniquesthatarenotfeasibleforasinglelandownerormanager.2

Lookuptablesandestimationequationscanbemuchsimplertoimplementanduse,butwhenusedalonemaynotadequatelyrepresentlocalvariabilityorlocalconditions.Thisreportattemptstodelineatemethodsthatbalanceuserfriendliness,datarequirements,andscientificrigorinawaythatistransparentandjustified.

Figure1‐3illustratesthescopeoftheGHGemissionsourcesandremovalsandprocessesinmanagedecosystemsthatthesemethodsestimate.

Theauthorteamsconsideredthefollowinggeneralapproachesinderivingthemethodsforthisreport:

Basicestimationequations–Involvecombinationsofactivitydata3withparametersanddefaultemissionfactors.4Anydefaultparametersordefaultemissionfactors(e.g.,lookuptables)areprovidedinthetext,orifsubstantialinlength,inanaccompanying(orreferenced)compendiumofdata.

2Examplesincludeintermittentmeasurementofsoilorganiccarbonandbiomassreserves.EstimatesoffluxfordynamicprocessmeasureslikeN2Oemissionsneedtobebasedonmultiplemeasurestakenatreasonablefrequency.Directmeasurementmayworkforcomparativeanalysisbutmustbeextendedtoestimatetotalemissionsusingassumptionsormodelingmethod.3Activitydataisdefinedasdataonthemagnitudeofhumanactivityresultinginemissionsorremovalstakingplaceduringagivenperiodoftime(IPCC,1997).4Emissionfactorisdefinedasacoefficientthatquantifiestheemissionsorremovalsofagasperunitofactivity.Emissionfactorsareoftenbasedonasampleofmeasurementdata,averagedtodeveloparepresentativerateofemissionforagivenactivitylevelunderagivensetofoperatingconditions(IPCC,2006).


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Figure1‐3:GreenhouseGasesEmissionSources/RemovalsandProcessesinManagedEcosystems

Source:Paustianetal.(2006).NMVOC=non‐methanevolatileorganiccompounds

Models–Usecombinationsofactivitydatawithparametersanddefaultemissionfactors.Theinputsforthesemodelscanbeancillarydata5(e.g.,temperature,precipitation,elevation,andsoilnutrientlevelsthatmaybepulledfromanunderlyingsource),biologicalvariables(e.g.,plantdiversity),orsite‐specificdata(e.g.,numberofacres,numberofanimals).Theaccuracyoftheprocessmodelisdependentontherobustnessofthemodelandtheaccuracyoftheinputs.

Fieldmeasurements–Actualmeasurementsthatafarmerorlandownerwouldneedtotaketomoreaccuratelyestimatethepropertiesofthesoil,forest,orfarmortoestimateactualemissions.Measuringactualemissionsonthelandrequiresspecialequipmentthatmonitorstheflowofgasesfromthesourceintotheatmosphere.Thisequipmentisnotreadilyavailabletomostentities,somoreoften,fieldmeasurementsareincorporatedintoothermethodsdescribedinthissectiontocreateahybridapproach.Afieldmeasurementsuchasasamplemeantreediametercouldbeincorporatedintoothermodelsorequationstogiveamoreaccurateinput.

Inference–UsesState,regional,ornationalemissions/sequestrationfactorsthatapproximateemissions/sequestrationperunitoftheinput.Theinputdataisthen

5Ancillarydataisdefinedasadditionaldatanecessarytosupporttheselectionofactivitydataandemissionfactorsfortheestimationandcharacterizationofemissions.Dataonsoil,croporanimaltypes,treespecies,operatingconditions,andgeographicallocationareexamplesofancillarydata.


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multipliedbythisfactortodeterminethetotalonsiteemissions.Thisfactorcanhavevaryingdegreesofaccuracyandmaynotcapturethemitigationpracticesonthefarmortheuniquesoilconditions,climate,livestockdiet,livestockgenetics,oranyfarm‐specificcharacteristics,unlesstheyaredevelopedforspecificsoiltypes,livestockcategories,orclimacticregions,etc.

Hybridestimationapproach–Anapproachthatusesacombinationoftheapproachesdescribedabove.Theapproachoftenusesfieldmeasurementsormodelstogenerateinputsusedforaninference‐basedapproachtoimprovetheaccuracyoftheestimate.

Withthisbackground,andevaluatingtheseandotherdataandresources,eachauthorteamdevelopedthetextforitschapter.Developmentofthetexthasbeeniterativeasvariousdraftsofthedocumenthavebeenputthroughseveralreviewstages.Thereviewprocessforthereportofmethodsconsistsof:

USDATechnicalReview.USDAperformedanintra‐agencyreview.Theresultofthisreviewwasaseriesofcommentsandquestionsfortheleadauthorsandtheirworkinggroups.Thesecommentswerereceivedby,discussedwithin,andrespondedtobytheworkinggroupsandleadauthors.Forexample,onespecificoutcomeofthisreviewprocesswasanitrousoxide(N2O)CroppingPracticesWorkshopconsistingof20expertsinthefieldofN2Oemissionsfromcroplandsandgrazinglands.TheworkshopwasconvenedtoreviewthemethodsthatwereoriginallyproposedbytheworkinggroupandtodetermineiftherewasamorescientificallyrigorousmethodtoquantifyingN2Oemissionsfromagriculturalsoils.

Inter‐agencyTechnicalReview.TheMay2012versionofthereportwascirculatedforreviewbyaninter‐agencygroupofGHGemissionsandinventoryexperts.Thereviewersincludedover50membersfromnineagenciesincludingUSDA,U.S.DepartmentofEnergy,U.S.DepartmentoftheInterior,EPA,U.S.DepartmentofState,andseveraloftheWhiteHouseOffices.Theresultofthisreviewwasaseriesofcommentsandquestionsfortheleadauthorsandtheirworkinggroups.Thesecommentswerereceivedby,discussedwithin,andrespondedtobytheworkinggroupsandleadauthors.

ScientificExpertReview.Followingtheinter‐agencyreview,thenextversionofthereportwasreviewedbyateamofscientificexperts.Thereviewerswerechosenbasedonrecognizedexpertise,experienceinexpertreviews,availability,andwillingnesstoparticipate.Eachreviewerwasaskedtoreviewthosechaptersand/orsectionsofthereportrelatingtohisorherexpertise.Asubsetofthegroupofexpertreviewerswasaskedtoreviewthereportinitsentiretyandprovidecommentsspecificallyregardingissuesofconsistency,completeness,andaccuracy.Again,theleadauthorsandauthorteamsrespondedtoeachofthecommentsposedbytheexpertpanelandeditedthedocumentasappropriate.

PublicCommentPeriod.Oncealloftheexpertcommentswereaddressedandappropriateeditsweremade,thereportwasmadeavailableforpubliccomment.ThiscoincidedwithafinalreviewbyUSDAandotherFederalagencyGHGexperts.Commentsfromthisreviewwereassessed,andthereportwaseditedasnecessarypriortofinalpublicationofthereport.


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1.4 ContentsoftheReport

Theremainderofthereportisorganizedbysector.Foreachsector,backgroundandinformationonmanagementpracticesarepresentedfirst,followedbythedetailedmethodsproposedforestimatingemissionsandsequestrationforthosepractices.Eachofthechaptersissummarizedasfollows:

Chapter2:ConsiderationsWhenEstimatingAgricultureandForestryGHGEmissionsandRemovals.Chapter2setsthecontextforthemethods,includinglinkagesandcross‐cuttingissuesthatspanthesectors.Thisincludes,forexample,definitionofentity,definitionofsystemboundaries,etc.

Chapter3:QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems.Chapter3describestheGHGemissionsfromcropandgrazinglandsystems.ThechapterpresentsmethodsforestimatingtheinfluenceoflanduseandmanagementpracticesonGHGemissions(andsinks)incropandgrazinglandsystems.Methodsaredescribedforestimatingbiomassandsoilcarbonstockschanges,directandindirectsoilN2Oemissions,methane(CH4)andN2Oemissionsfromwetlandrice,CH4uptakeinsoils,carbondioxide(CO2)emissionsorsinksfromliming,non‐CO2GHGemissionsfrombiomassburning,andCO2emissionsfromureafertilizerapplication.

Chapter4:QuantifyingGreenhouseGasSourcesandSinksinManagedWetlandSystems.Chapter4providesguidanceforestimationofcarbonstockchanges,CH4,andN2Oemissionsfromactivelymanagedwetlands.

Chapter5:QuantifyingGreenhouseGasSourcesandSinksinAnimalProductionSystems.Chapter5describeson‐farmGHGemissionsfromtheproductionoflivestockandmanuremanagement.ThechapterpresentsGHGestimationmethodsappropriatetotheproductionofeachcommonlivestocksector(i.e.,beef,dairy,sheep,swine,andpoultry),withmethodsrelatedtomanuremanagementcombinedforalllivestocktypes.

Chapter6:QuantifyingGreenhouseGasSourcesandSinksinManagedForestSystems.Chapter6providesguidanceonestimatingcarbonsequestrationandGHGemissionsfortheforestrysector.Thechapterisorganizedtoprovideanoverviewoftheelementsofforestcarbonaccounting,includingdefinitionsofthekeycarbonpoolsandbasicmethodsfortheirestimation.

HowtoUsetheReport

Inordertoaccomplishtheobjectivesnotedabove,thereportislaidoutbybroadland‐usesector(i.e.,croplandsandgrazinglands,wetlands,animalproduction,andforestry).Eachsectorchapterisfurtherdelineatedintotwomainparts:firstthecurrentscientificunderstandingandavailabledataforestimatingGHGfluxeswithinthesector;second,themethodsthatdemonstratethecurrentbestapproachtoestimatingGHGfluxes,balancingtheavailablescienceanddatawiththecriteriaandconsiderationsmentionedpreviously.Thereportisintendedtobeconsideredinitsentiretywithcontextualinformationprovidedinthefirstandsecondchaptersasbackgroundtothecontentpresentedinthefollowingchapters.Theauthorsrealizethatmanyusersmayfindspecificchaptersorsectionsespeciallyvaluableoruseful;therefore,summarizedcontextualinformationisalsoincludedatthebeginningofeachchapter.Thebeginningtothecroplandsandgrazinglands,wetlands,animalproduction,andforestrychaptersincludetablesthatsummarizethemethodsforeachsourceorremovalofGHGemissions.Thesubsequentsectionsinthereportareorganizedaccordingtothesourcesmentionedinthesummarytable.


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Chapter7:QuantifyingGreenhouseGasSourcesandSinksfromLand‐UseChange.Chapter7providesguidanceonestimatingthenetGHGfluxresultingfromchangesbetweenlandtypes—i.e.,conversionsintoandoutofcropland,wetland,grazingland,orforestland—attheentityscale.

Chapter8:UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks.Chapter8providesaframeworkforaMonteCarloassessmentofestimationuncertainty.

Thereportalsodescribesmethodsforuncertaintyassessmentforeachsourceaswellasfortheestimateintotal.Theauthorsrecognizethatforsomesources,currentdataarenotcompleteenoughtoallowforareliablestatisticalestimateofuncertainty.Insomecases,expertjudgmentwasusedtodelineateestimateduncertaintybounds.Inothercases,thereportsimplynotesthatmoredataarerequiredtoreliablyestimateuncertainty.Eachsectorchapterofthereportcontainsasectiononuncertaintyandlimitations.

Theauthorsacknowledgethatformanypracticesandtechnologies,adequatedatadonotcurrentlyexisttoaccuratelyestimateGHGemissionsand/orcarbonsequestration.Foreachsector,theauthorshaveincludedadiscussionofresearchgapsorpriorityareasforfuturedatacollectionthatareimportantinordertoimprovethecompletenessoraccuracyoftheestimationmethodsputforthinthisreport.

1.5 UsesandLimitationsoftheReport

Specificpotentialusesofthemethodsincludeaiding:

1. LandownersandotherstakeholdersinquantifyingincreasesanddecreasesinGHGemissionsandcarbonsequestrationassociatedwithchangesinlandmanagement;

2. USDAinunderstandingGHGandcarbonsequestrationincreasesanddecreasesresultingfromcurrentandfutureconservationprogramsandpractices;and

3. USDAandothersinevaluatingandimprovingnationalandregionalGHGinventoryefforts.

Thereportwillprovideadditionalcobenefits.Forexample,thereportmayprovideimprovedmethodsforvoluntaryGHGregistries,helptofacilitateregionalGHGmarkets,orinformexistingand/orfutureGHGreportingprograms(e.g.,sequestration/emissionsfromlanduseandagricultureunderExecutiveOrder[EO]13514).6

Thesemethodsaredesignedtoprovidethemostappropriate,single,accountingmethodforquantifyingGHGemissions/sequestrationforeachparticularsourcecategory(e.g.,CH4fromricecultivation)determinedfromtheactivitydata,publishedemissionfactors,andaccountingmethodsandtoolstypicallyavailablefortheentityscale.Thesemethodsarenotdesignedtoprovidearangeofemission/sequestrationaccountingoptions,orarangeofsimilaroptions,atvaryinglevelsofcomplexity(i.e.,tiers)foreachparticularsourcecategory.Thatsaid,theremaybespecificinstances(e.g.,forestcarbonstocks)wheredifferentindividualoptionsmightbespecifiedforentitieswithin

6ItshouldbenotedthatunderEO13514,agency‐levelreportingofemissionsandsequestrationasaresultoflandmanagementpracticesisnotrequiredatthistime.Inaddition,reportingofemissionsfromwildfiremanagement,prescribedburning,landuse,andland‐usechangesisnotrequired.AgencieschoosingtoreportactivitiesundertakentodateincalculatingsuchemissionswouldaddresstheminthequalitativeportionoftheirGHGinventory.EmissionsresultingfrommanuremanagementandentericfermentationwhentheanimalsareownedbytheFederalagencywouldbereportedvoluntarilyinscope1atthistime.IftheactivitiestakeplaceonFederalland,butareoperatedbyothers,theseemissionsmaybevoluntarilyreportedasscope3.


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sourcecategorieswheretherearesignificantlydifferentoperationalscales(e.g.,commercialforestplantationsversussmallwoodlots).

ThisreportisdesignedtoprovideGHGaccountingmethodstodetermineactualGHGemissionsattheentityscale(i.e.,anemissionsinventory)and/ortoquantifytheemission(oremissionreductions)associatedwithanexistingorfuturemitigationpractice/technology.Atthetimeofthiswriting,theUnitedStatesdoesnothaveanationalpolicyguidingGHGemissionsreduction,monitoring,orcreditingintheagricultureandforestrysectors.PresentedaretherecommendedmethodsforquantifyingGHGemissionsandemissionreductions.Thereportisnotintendedasanaccountingframeworkforemissionreductioncreditingortrading—i.e.,themethodsdonotconstituteanoffsetprotocol.Asaresult,thisreportdoesnotprovidespecificguidanceoncriticalpolicyfeaturesofsuchoffsetprotocolsincludingadditionality,permanence,andleakage.Anynationalpolicywouldprovideprecisedefinitionsoftheseterms,andthenthemethodsdescribedinthisreportwouldbeadaptedtoconformtopolicystandardsandrequirements.

Asstatedabove,thisreportdoesnotaddresspolicyissuesrelatedtocreditingreductionssuchaspermanence,additionality,orleakage.Theintendedpurposeissimplytoprovideaquantitativeestimateofwhatisoccurringunderagivensetofpracticesandactivities,orwhatcouldbeexpectedtooccurgivenachangeinmanagement.Whilethereportisnotaddressingpolicyissues,itmayaddresspracticalconcernsaroundGHGestimation,suchastheriskofreversalifmanagementpracticesrevertbackintheforeseeablefuture.Forexample,alandmanagermustunderstandthatachangeinmanagementthatresultsinsoilcarbonsequestration,ifreversed,willleadtotheextrastoredcarbonlikelybeingrereleasedtotheatmosphere.Forthecontextofthisreport,wearemostconcernedwith“whattheatmospheresees”orwhatthelong‐termneteffectistoGHGlevelsintheatmosphere.

Thesourcecategoriescoveredinthereportarespecifictotheagricultureandforestrysectors(e.g.,croplands,grazinglands,managedwetlands,animalagriculture,andforestry).Thereportdoesnotapproachemissionsfromthesesourcesfromalife‐cycleperspective.Inotherwords,thereportdoesnotincludesourcecategoriesthatareassociatedwithmanagementactivitiesrelatedtocertainagricultureandforestryactivities(e.g.,transportation,fueluse,heatingfueluse),upstreamproduction(e.g.,animalfeedproduction,fertilizermanufacture),ordownstream(e.g.,wastewatertreatment,pulpandpapermanufacture,orlandfills).Asaresult,thereportdoesnotprovideGHGaccountingmethodsforsectorsincluding:energyandindustrialprocesses(e.g.,fertilizerproduction).

Thereportalsodoesnotincludeemissionsfromstationarysourcecombustion(e.g.,burningheatingoilornaturalgastoheatanimalhousing)ormobilesourcecombustion(e.g.,fueluseinvehicles)atthistime.However,wherethereareobviouschangesinthelevelofcombustionduetoachangeinpractices,thatchangeisqualitativelydiscussed.Forexample,ashiftfromconventionaltillagetonotillcanresultinalargereductioninfuelconsumptionbecauseoffewertripsacrossthefield.Theserelationshipsarenotedqualitativelyinthereport,butquantitativemethodsarenotproposed.MethodsforquantifyingemissionsfromstationaryormobilecombustionsareavailablefromotherFederalagencies.

Thescopeofthisreportisassessingtheimpactofspecificdecisionsmadebythefarmorforestmanagerwithintheconfinesofthefarmorforestgate.Alife‐cycleperspective,whilevaluable,isoutsidethescopeofthisreport.Alife‐cycleassessment(LCA)isausefultoolforquantificationofenvironmentalimpactsandbenefitsonabasisthatallowsforanalysisofenvironmentalburdenshiftingandtrade‐offsbetweendifferentoptions.LCAsincludetheenvironmentalimpactofmanagementdecisionsduringproductmanufacturingandprocessingofrawinputsto,aswellasproductsoutputfrom,thefarmorforestsystem,continuingthroughitsusebytheendconsumer.


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ThemethodologiespresentedinthisreportdonotconstituteanLCA,butsupportseveralcomponentsofLCAs.Forexample,thisreportcoversemissions(e.g.,fromcroplands)thatcouldbeusedaspartofanattributionLCAforacommoditycropproduct,orusedaspartofaconsequentialLCAstudyingtheimpactsofagriculturalpolicydecisionsonGHGmitigationpotential.

ThetextboxbelowprovidesfurtherinformationonLCAsastheyrelatetoquantifyingGHGsourcesandsinksinagricultureandforestrysystems,includingbackgroundinformationonthepurposeofLCAs,theLCAprocess,theinterpretationofLCAresults,andcurrentLCAeffortsbyUSDAandotherorganizationsrelatedtoagricultureandforestry.

LifeCycleAssessment

AnLCAisatoolforaddressingtheenvironmentalaspects(e.g.,useofresources)andpotentialenvironmentalimpacts(e.g.,globalwarmingpotential)throughoutthelife‐cycleofaproductormaterial.Whenappliedtoagricultureandforestryproducts,thescopeofanLCAwouldlikelyincludeupstreamimpactsfromextractionandproductionofmaterialinputs(e.g.,fuels,fertilizers);theenvironmentalimpactsofmanagementdecisionsduringcrop,livestock,ortreegrowthonsite;andtheoutputsfromthefarmorforestsystem,includingthedownstreamimpactsfromuseanddisposalbytheendconsumer.TheaccountingboundaryofGHGemissionsourcesandsinksquantifiedinanLCAforanagriculturalorforestconsumerproductwouldextendbeyondtheaccountingboundaryofthemethodologiespresentedinthisreport.Forexample,anLCAforagrainproductwouldnotonlyincludeN2Oemissionsfromfertilizerapplication,butalsootherupstreaminputssuchasemissionsfromsyntheticfertilizerproduction,anddownstreamimpactssuchasemissionsfromgraintransportationandstorage,processing,use,anddisposal.

TheInternationalOrganizationforStandardization(ISO)hasestablishedseveralinternationalstandardsaddressingLCA,includingISO14040(ISO,2006a)describingtheprinciplesandframeworkforLCAs,ISO14044(ISO,2006b)addressingLCArequirementsandguidelines,andISO14048(ISO,2002)presentingastandardizedLCAdatadocumentationformat.aAsdefinedinISO14040(ISO,2006a),theLCAdevelopmentprocessincludesthefollowingprimarysteps:definingthegoalandscope;conductingalife‐cycleinventory(LCI)analysisbygatheringdataandquantifyingallrelevantinputsandoutputsoftheproductsystem,asdefinedinthescopeofthestudy;conductingalife‐cycleimpactassessmentthroughevaluationofthesignificanceoftheenvironmentalimpactsdefinedinthescopeofthestudyanddeterminedduringtheLCIprocess;andinterpretingtheresults(ISO,2002;2006a;2006b).USDAhasseveralinitiativesapplyingLCAstoagricultureandforestry.

USDA’sNationalAgriculturalLibraryhasdevelopedtheLCADigitalCommonsProject,adatabaseandtoolintendedtoprovideLCIdataforuseinLCAsoffood,biofuels,andotherbio‐products.Thedatabasecurrentlyincludesdataoninputs(e.g.,fertilizers)andoutputs(e.g.,airemissions,residues)perunitoffieldcropproductionfrom1996–2009forcorn,cotton,oats,peanuts,rice,soybeans,andwheat(durum,spring,andwinter)inStatescoveredbytheUSDAEconomicResearchServiceannualAgriculturalResourceManagementSurvey.Futurephasesofthisworkwillincludetheadditionofdatarepresentingirrigation,manuremanagement,farmequipmentoperation,cropstorage,transport,andproductionofmineralandorganicfertilizers,herbicides,insecticides,andfungicides.

(continued)


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LifeCycleAssessment (continued)

USDAalsorecentlyworkedwiththeNationalCattlemen’sBeefAssociationandthechemicalcompanyBASFinthedevelopmentofaneco‐efficiencyassessmentfortheU.S.beefindustrybyquantifyinglife‐cycleinputsandoutputsforbeefproductionovertime.Theprocessinvolvedmeasuringthelife‐cycleenvironmentalimpactsandlife‐cyclecostsfordifferentbeefproductionprocessesatadefinedlevelofoutput.TheUSDAAgricultureResearchService’sIntegratedFarmSystemModelwasusedtoestimateenvironmentalimpacts(e.g.,airemissions,wateruse,abioticdepletionpotential,toxicity,etc.)basedondatafromtheUSDA’sRomaL.HruskaMeatAnimalResearchCenter(Battaglieseetal.,2013).

BeyondUSDA,otherLCAsandstudiesrelatedtoquantifyingenvironmentalimpactsfromagricultureandforestryproductshavebeenpublished.Belowisalistofrecentstudies,projects,orresourcesthatuseLCAsorcouldbeusedinthedevelopmentofLCAstoevaluateclimateimpactsfromagricultureandforestry.

TheInnovationCenterforU.S.Dairyanalyzedfluidmilk,cheese,anddairyprocessingandpackaging.ThesedatahaverecentlybeenmadepubliclyavailablethroughtheUSDA’sLCADigitalCommonsdatabase.b

TheInnovationCenterforU.S.DairydevelopedtheFarmSmarttoolthatcomparesenergyuse,GHGemissions,andwateruseagainstregionalandnationalaverages.Thetooltakesapproximately20minutestocompleteandwillhaveenhanceddecisionsupportfeaturesaddedin2014.c

TheNationalPorkBoardfundedastudyofporkproductsconductedbyresearchersattheUniversityofArkansas.d

TheUnitedKingdom’sCarbonTrustdevelopeda“carbonfootprinting”methodologythathasbeenusedbythegrocerychainTescotodeterminethelife‐cycleGHGimpactsofmanyoftheirproducts.e

TheUnitedKingdomFoodClimateResearchNetworkmaintainsacompendiumoffoodLCAs.f

KumarVenkatofCleanMetricsCorp.compared12organicandconventionalfarmingsystemsfromalife‐cycleGHGemissionsperspectiveusingagriculturalproductiondatafromtheUniversityofCalifornia‐Davis.g

FieldtoMarketpreparedareportpresentingenvironmentalandsocioeconomicindicatorsformeasuringoutcomesfromon‐farmagriculturalproductionintheUnitedStates.h

Acoalitionoffoodindustrycompanies,academicorganizations,andnon‐governmentalorganizationscreatedTheCoolFarmTool,aGHGcalculatordesignedtohelpfarmersreduceemissions.i

(continued)aSeehttp://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_tc_browse.htm?commid=54854.bSeehttp://www.usdairy.com/sustainability/Greenhouse%20Gas%20Projects/Pages/ProcessingandPackagingLCA.aspxandhttp://www.lcacommons.gov/?q=node/16.cSeehttp://www.usdairy.com/FarmSmart/Pages/Home.aspx.dSeehttp://www.pork.org/filelibrary/NPB%20Scan%20Final%20‐%20May%202011.pdf.eSeehttp://www.carbontrust.com/our‐clients/t/tesco/.fSeehttp://www.fcrn.org.uk/research‐library/lca.gVenkat,K.2012.ComparisonofTwelveOrganicandConventionalFarmingSystems:ALifeCycleGreenhouseGasEmissionsPerspective.JournalofSustainableAgriculture36(6):620‐649.hSeehttp://www.fieldtomarket.org/report/national‐2/PNT_SummaryReport_A11.pdf.iSeehttp://www.coolfarmtool.org/Home.


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Finally,themethodsinthisreportarenotintendedasasustainabilityassessment.Otherenvironmentalservicesandcobenefitsarenotaddressedbythesemethods.Norarepotentialtradeoffsordetrimentstootherenvironmentalconcernsaddressedhere.ThemethodsarespecifictoGHGemissionsonly,andsustainablefarm,ranch,orforestmanagementshouldconsidertheGHGimplicationsofmanagementintandemwithotherenvironmentalconcernssuchaswaterquality,soilhealth,andecosystemhealth.

Chapter1References

Battagliese,T.,J.Andrade,I.Schulze,B.Uhlman,etal.2013.Phase1MoreSustainableBeefOptimizationProject.http://www.beefresearch.org/CMDocs/BeefResearch/Sustainability%20Completed%20Project%20Summaries/NCBA%20Phase%201%20Final%20Report_Amended%20with%20NSF%20Verified%20Report_for_posting_draft%20WM_KS.pdf.

LifeCycleAssessment (continued)

TheNationalPorkBoarddevelopedapredictivemodelthatprovidesestimatesontheGHGemissions,waterconsumption,andassociatedcostsinvolvedinsowandgrow‐finishproduction.ThePigProductionEnvironmentalFootprintCalculatorrequiresfundamentalinputsonly(herdsize,feedcomposition,manurehandlingsystem,farmlocation,barnsize,characteristicsofheating,ventilation,andairconditioningsystem)andgeneratesanannual“cradletogate”estimate.j

TheTechnicalWorkingGrouponAgriculturalGreenhouseGaseshaspublishedthreeeditionsofasynthesisofliteraturerelatedtotheGHGmitigationpotentialofagriculturallandmanagementintheUnitedStates.k

TheEPAdevelopedandmaintainstheWasteReductionModel,aninteractivetoolthatcalculatesandtotalsGHGemissionsofbaselineandalternativewastemanagementpracticesfor46commonmaterialtypes,includingfoodwaste,yardwaste,dimensionallumber,andotherorganicmaterials.EPAiscurrentlyintheprocessofdevelopingdetailedfoodwasteenergyandemissionfactorstoquantifythelife‐cycleimpactsofproductionanddisposaloffivecommonfoodtypes—grains,fruitsandvegetables,beef,chicken,anddairy.l

TherearemanypotentialapplicationsforLCAresults.Whenconductedforseveralcomparableagriculturalorforestproducts,LCAscanallowforanalysisofthetradeoffsbetweenyieldandenvironmentalimpactsbetweendifferentproductionprocessesorinputs.Forexample,comparingLCAresultsforgrainproductsusingdifferentproductioninputscouldshowfewerlife‐cycleGHGemissionsandsimilaryieldsbyswitchingtoadifferentfertilizer.However,therearelimitationstohowLCAresultscanbeapplied,includinguseofGHGemissionsresultsinannualreportingoremissioninventories.SinceLCAsareintendedtoquantifytheenvironmentalimpactsacrosstheentireproductlifecycle,theGHGemissionsandsinksfrequentlyoccuracrossseveralyears(andseveralsourcecategories)andarethereforenotappropriateforuseinapplicationsthatrequireannualemissionsdata.

jSeehttp://www.pork.org/Resources/1220/CarbonFootprintCalculatorHomepage.aspx#.Us7mGbSwWSo.kSeehttp://nicholasinstitute.duke.edu/ecosystem/land/TAGGDLitRev#.Usbx9tJDuSp.lSeehttp://www.epa.gov/warm.


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Denef,K.,S.Archibeque,andK.Paustian.2011.GreenhouseGasEmissionsfromU.S.AgricultureandForestry:AReviewofEmissionSources,ControllingFactors,andMitigationPotential:InterimreporttoUSDAunderContract#GS‐23F‐8182H.http://www.usda.gov/oce/climate_change/techguide/Denef_et_al_2011_Review_of_reviews_v1.0.pdf.

Denef,K.,K.Paustian,S.Archibeque,S.Biggar,etal.2012.ReportofGreenhouseGasAccountingToolsforAgricultureandForestrySectors:InterimreporttoUSDAunderContract#GS‐23F‐8182H.http://www.usda.gov/oce/climate_change/techguide/Denef_et_al_2011_Review_of_reviews_v1.0.pdf.

Eve,M.2012.PresentationattheN2OCroppingPracticesWorkshop.February7‐9,2012,FortCollins,CO.



ISO.2002.ISO14048:Environmentalmanagement‐Lifecycleassessment‐Datadocumentationformat:InternationalOrganizationforStandardization.

ISO.2006a.ISO14040:Environmentalmanagement‐Lifecycleassessment‐Principlesandframework:InternationalOrganizationforStandardization.

ISO.2006b.ISO14044:Environmentalmanagement‐Lifecycleassessment‐Requirementsandguidelines:InternationalOrganizationforStandardization.

Paustian,K.,N.H.Ravindranath,A.Amstel,andM.Gytarsky.2006.Chapter1‐Introduction.In2006IPCCGuidelinesforNationalGreenhouseGasInventories,PreparedbytheNationalGreenhouseGasInventoriesProgramme,H.S.Eggleston,L.Buendia,K.Miwa,T.NgaraandK.Tanabe(eds.).Japan:IGES.



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Authors:

MarlenEve,U.S.DepartmentofAgriculture,OfficeoftheChiefEconomistMarkFlugge,ICFInternationalDianaPape,ICFInternational

Contents:

2 ConsiderationsWhenEstimatingAgricultureandForestryGHGEmissionsandRemovals .........................................................................................................................................................................2‐3

2.1 Scope...................................................................................................................................................................2‐32.1.1 DefinitionofEntity..........................................................................................................2‐32.1.2 DefinitionofSystemBoundaries...............................................................................2‐4

2.2 ReviewofRelevantCurrentToolsandMethods............................................................................2‐122.3 SelectionofMostAppropriateMethodandMitigationPracticestoInclude......................2‐132.4 OverviewofSectors....................................................................................................................................2‐14

2.4.1 CroplandsandGrazingLands...................................................................................2‐162.4.2 Wetlands............................................................................................................................2‐172.4.3 AnimalProduction.........................................................................................................2‐192.4.4 Forestry..............................................................................................................................2‐21

2.5 Land‐UseChange.........................................................................................................................................2‐222.6 Uncertainty....................................................................................................................................................2‐23Chapter2References.............................................................................................................................................2‐24

SuggestedChapterCitation:Eve,M.,M.Flugge,D.Pape,2014.Chapter2:ConsiderationswhenEstimatingAgricultureandForestryGHGEmissionsandRemovals.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Editors.


Chapter 2Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals

Chapter 2: Considerations When Estimating Agriculture and Forestry GHG Emissions and Removals

2-2


CH4 MethaneCO CarbonmonoxideCO2 CarbondioxideCO2‐eq CarbondioxideequivalentsGHG GreenhousegasGWP Globalwarmingpotentialha HectaresHWP HarvestedwoodproductsIPCC IntergovernmentalPanelonClimateChangeN2O NitrousoxideNH3 AmmoniaNO NitricoxideNOx Mono‐nitrogenoxideNO2 NitriteNO3 NitratePDF ProbabilitydensityfunctionUSDA U.S.DepartmentofAgriculture


2-3

2 ConsiderationsWhenEstimatingAgricultureandForestryGHGEmissionsandRemovals

Thischapterdescribesthelinkagesandcross‐cuttingissuesrelatingtosector‐specificandentity‐scaleestimationofgreenhousegas(GHG)sourcesandsinks.Inparticular,thischapterdescribesthecommonelementsthatmustbeconsideredbothwithinanemissionssectororsourcecategoryaswellasacrosssectorsorsourcecategoriesinorderforanentitytoreportaccurateGHGinventoryestimates.

Chapter2isorganizedasfollows:

Scope

ReviewofRelevantCurrentToolsandMethods

SelectionofMostAppropriateMethodandMitigationPracticestoInclude

OverviewofSectors

− CroplandsandGrazingLands

− Wetlands

− AnimalProduction

− Forestry

− Uncertainty

2.1 Scope

InorderforanentitytoaccuratelyinventoryitsdirectGHGemissionsto(andremovalsfrom)theatmosphereandcompareemissionsandremovalsbetweenyears,practices,orentities,itisimportantthatestimationelements—e.g.,definitionsofentityandsystemboundaries—arecommontoallemissionsectorsandsourcecategories.Thesecommonelementsaredescribedinmoredetailinthesectionsthatfollowandinclude: DefinitionofEntity

DefinitionofSystemBoundaries:

− PhysicalBoundaries

− TemporalBoundaries

− ActivityBoundaries

− MaterialBoundaries

2.1.1 DefinitionofEntity

Thedefinitionofanentitywill,toalargedegree,determinethe(spatial)boundsoftheestimationmethodologies.Thiswillprimarilybedrivenbywhatdataalandownerchoosestoinput—i.e.,thedefinitionwillbeuser‐specificandprimarilydependontheuser’sdefinition.1However,itisanticipatedthatthescience‐basedmethodswillbesuitabletoquantifyGHGsourcesandsinksataprocessorpracticescale.Themethodsinthisreportprovideanintegratedassessmentofthenet

1Itshouldbenotedthatthedefinitionofanentityusedinthisreportisnotapolicyorregulatorydefinition,andisonlyprovidedtohelpthelandmanagerdeterminewhatpracticesshouldbeincludedintheestimation.


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GHGemissionsforanentity,alllandsforwhichthelandownerhasmanagementresponsibility.TheyalsoprovidethebasisforanintegratedtooltobeusedbytheU.S.DepartmentofAgriculture(USDA)aswellasbyindividualfarmers,ranchers,forestowners,andotherstakeholderstoevaluatethenetGHGemissionsonparcelsoflandundertheirmanagement.Sowhiletheentitywouldbedefinedasalloftheactivitiesoccurringonalltractsoflandunderthemanagementcontrolofthelandowner,thereportdescribespractice‐levelmethodologiesthatcanbesummedcollectivelytoarriveatanestimatefortheentity.Thedefinitionofentityappliedhereisintentionallybroad,understandingthatanypolicy,registry,ormarketwillprovideitsownnarrowerdefinition.

2.1.2 DefinitionofSystemBoundaries

ThesystemboundariesshouldincludetheGHGemissionsandcarbonsequestrationoccurring(orestablished)onsiteforthesourcecategoryandmanagementpracticeinquestion.Forexample,thisreportdoesnotaddressindirectland‐usechangesoccurringoffsiteorbiogenicGHGfluxrelatedtosubsequentuseofagriculturalorforestryoutputs(e.g.,foodprocessing,pulpandpapermanufacture,biomasscombustion).However,certainoffsitecarbonstorageconsiderations(e.g.,flowofharvestedwoodintoharvestedwoodproducts[HWPs])havebeenconsideredinthereporttomaintainconsistencywithnationalinventoryefforts.

Fourtypesofsystemboundariesareimportantforconsideration:

PhysicalBoundaries

TemporalBoundaries

ActivityBoundaries

MaterialBoundaries

2.1.2.1 PhysicalBoundaries

Physicalboundaries(e.g.,spatial,sectoral)addresstheareaandthemanagementtobeconsideredinthereporting.Settingtheboundariesforwhichemissionsandsequestrationwillbeestimatedismoredifficultthanitfirstseems.Althoughtheremaybemultiplealternatives,clarityandconsistencyareimportant.Therearemanyfacetstoconsider.Onefactoriswhatconstitutesanentityorafarm/ranch/forestoperation;anotheriswhatoperationsareassociatedwiththatentity.Forexample,doestheuseoffertilizeronafarmincludetheprocessesofmanufacturinganddeliveringthatfertilizer?Anotherconsiderationishowtosubdividethatlargerentityintotherelevantsectorsaspresentedintheindividualchaptersinthisreport.Forexample,istheentityentirelygrazinglandorissomeofitinforestmanagement?Finally,theremaybequestionsofhowtoassociatemanagementpracticestothemostrelevantcategoriesforuseoftheaccountingguidelinesprovided,includinganyguidanceonsizelimits,whatconstitutesmanagement,andhowtoaddresschanginglanduses.Definitionsareanimportantpartofsettingboundariesandwillbeprovidedhereaswell.Examplesofmanagementpractices(e.g.,irrigation,tillage,orresiduemanagementforcroplands)areincludedwithinthevarioussectordescriptionsbelow(i.e.,croplandsandgrazinglands,wetlands,animalproduction,andforestry);whenconsideringwhatconstitutesamanagementpractice,anentityshouldnotethatinthecontextoftheseguidelines,amanagementpracticereferstochangesinthemanagementofagriculture,animal,orforestproductionthatimpactGHGemissionsandremovals.

TheobjectiveofthesemethodsistoprovideacompleteestimationofGHGemissionsandcarbonsequestrationwithintheboundariesofanentity.Thisisnotintendedasalifecycleanalysis,aswillbefurtherexplainedbelowinthediscussionofmaterialboundaries.Themethodsaredesignedtobeappliedatthelocalscale,butneedtobeflexibleenoughtobevalidforverylargeentities.The


2-5

methodsaredesignedtoestimatefluxesfortheentiretyofanentity,butmustalsobecapableofevaluatingasinglepractice(e.g.,project)implementedwithinasingleentityoraggregatedacrossmultipleentities.

AsnotedinChapter1,thedefinitionofanentitycanbecomplicated.Forthepurposesofthisreport,usersshouldsimplydelineatethespatialextentofitsentityasthelandareathatisundertheirownershipand/ormanagementcontrolfortheforeseeablefuture.Thisisageneralizedapplicationofthetermentity,andtheusershouldrecognizethatanypolicy,program,orcontractualagreementmaydefinetheuser’sentitydifferentlyandresultinadifferentboundaryoftheentity.Withintheentityboundary,therewillbeavarietyoflandusesthatwillrelyonmethodsfromvariouschaptersinthisreport.Anentityshouldbesubdividedifitincludesdifferentcategoriesoflanduse,suchasgrazinglandandcropland,buttheentireentityshouldfallintosomeland‐usecategory.Norigidlowerboundisspecifiedhereforthearealextentofaland‐usecategorization,but,ingeneral,areasofanacreormoremeritidentification.

Withintheboundariesoftheoverallentity,areasofcroplandwillneedtobeidentified.Beyondjustareasproducingroworclose‐growncropsorhay,croplandalsoincludeslandthatisfallowandareasofhayandpasturethataremanagedinarotationwithothercrops.Wetlands(includingdrainedwetlandsandhydricsoils)andlandunderagroforestrypracticeswherethepredominantproductionactivityiscroppingshouldalsobeconsideredascroplandforthepurposesofthisreport.Finally,areasofcroplandthataresetaside,suchaslandsintheConservativeReserveProgram,areincludedinthismanagementtype.ThemethodsfortheselandsareincludedinChapter3ofthisreport.Thecroplandareasshouldbedelineatedasfieldsorgroupsoffieldsforwhichthebasicrotationsandmanagementpracticesareallsimilar.

Thenextlandmanagementtypetobeidentifiedisgrazingland.Thisislandthatisusedprimarilyforgrazinganimalsandnotaspartofarotationwithothercrops.Thisportionoftheentitywillprimarilybecomprisedofpastureland(whichismoreintensivelymanaged),andrangeland(whichistypicallylessintensivelymanagedandusuallyhasahigherproportionofnativespecies).Wetlands(includingdrainedwetlandsandhydricsoils)andlandmanagedasagroforestryshouldbeincludedinthiscategoryiftheprimaryuseofthetractoflandisforgrazinglivestock.Therewillbeobviousoverlapbetweengrazinglandandforestlandmethodswherethelandmatchesthedefinitionofbothuses.Forexample,ifanyactivemanagementisfocusedonenhancingtreegrowthandtimberproduction,theusershouldidentifytheseareasasforestlandandthemethodswillneedtobeintegratedtoaccountfortheimpactofgrazingmanagementontheforestland.Grazinglandsshouldbedelineatedascontiguousareasthatareunderasimilarstockingrateandsetofmanagementpractices,andthemethodsforgrazinglandsaspresentedinChapter3shouldbefollowed.Inaddition,theGHGestimationmethodsassociatedwiththegrazinganimalsaspresentedinChapter5shouldbefollowed.Developmentofanintegratedtoolthatfollowsthesemethodswillneedtoaccountforthesemanagementinteractions.

Cropland:Aland‐usecategorythatincludesareasusedfortheproductionofadaptedcropsforharvest,includingbothcultivatedandnon‐cultivatedlands.Cultivatedcropsincluderowcropsorclose‐growncropsandalsohayorpastureinrotationwithcultivatedcrops.Non‐cultivatedcroplandincludescontinuoushay,perennialcrops(e.g.,orchards),andhorticulturalcropland.Croplandalsoincludeslandwithalleycroppingandwindbreaks,aswellaslandsintemporaryfalloworenrolledinconservationreserveprograms(i.e.,set‐asides).Roadsthroughcropland,includinginterstatehighways,Statehighways,otherpavedroads,gravelroads,dirtroads,andrailroadsareexcludedfromcroplandareaestimatesandare,instead,classifiedassettlements.


2-6

Forestlandshouldbedelineatedaslandthatisusedprimarilyforwoodybiomassproduction,whetherforsawwood,pulp,biofuels,orotherforestorwoodlandrelatedindustry,orlandthatistreecoveredandmanagedforrecreationalorconservationpurposes.Thiswillincludeareasofagroforestryandsilvopasturewheretheprimarymanagementobjectiveonthelandscapeisforest‐relatedproduction.Anintegratedtoolwouldneedtobeflexibleenoughtoalsocapturetheimpactoftheadditionalcroppingorgrazingactivitiesoccurringontheparcel.Similarly,wetlandareasthatarewoodedorforestedandmanagedprimarilyasforestsandwoodlandswillbeconsideredinthiscategory.Also,becauseharvestingisoneofthemajormanagementpracticesinforestlandandbecauseharvestedwoodmovestoseverallong‐termcarbonpoolsthatundergodifferingratesofdecay,itisimportantthatthemethodsaccountforemissionsfromHWPs,eventhoughtheymaybemovedoutsideoftheboundaryofthefarm/ranch/forestoperation.

TheforestlandmethodsarepresentedinChapter6ofthisreport.Tractsofforestshouldbedelineatedsuchthatanygiventractismadeupoftreesofasimilarstandageandspeciesmix,andthattheentiretractisunderoneuniformsetofmanagementpractices.Onagivenentity,theremaybetreesthatexistoutsideofclearlydefinedforests,suchasorchardsandvineyards,farmsteadshelterbeltsandfieldwindbreaks,andagroforestrypractices.Eventhoughtheselandsmaynotmeetthedefinitionofaforest,thecarbonstorageinthetreesislikelysignificant.Insomecasesitmaybeusefultoevaluateindividualtreesorsmallstandsoftrees(usingmethodspresentedinChapter6).Inothercases,theestimationmayrequireablendingofmethodssuchascroplandmethodsfromChapter3withforestmethodsfromChapter6.

GrazingLand:Aland‐usecategoryonwhichtheplantcoveriscomposedprincipallyofgrasses,grass‐likeplants,forbs,orshrubssuitableforgrazingandbrowsing,andincludesbothpasturesandnativerangelands.Thisincludesareaswherepracticessuchasclearing,burning,chaining,and/orchemicalsareappliedtomaintainthegrassvegetation.Savannas,somewetlandsanddeserts,andtundraareconsideredgrazingland.Woodyplantcommunitiesoflowforbsandshrubs,suchasmesquite,chaparral,mountainshrub,andpinyon‐juniper,arealsoclassifiedasgrazinglandiftheydonotmeetthecriteriaforforestland.Grazinglandincludeslandmanagedwithagroforestrypracticessuchassilvopastureandwindbreaks,assumingthestandorwoodlotdoesnotmeetthecriteriaforforestland.Roadsthroughgrazingland,includinginterstatehighways,Statehighways,otherpavedroads,gravelroads,dirtroads,andrailroadsareexcludedfromgrazinglandareaestimatesandare,instead,classifiedassettlements.

Forestland:Aland‐usecategorythatincludesareasatleast120ft(36.6m)wideand1acre(0.4ha)insizewithatleast10percentcover(orequivalentstocking)bylivetreesofanysize,includinglandthatformerlyhadsuchtreecoverandthatwillbenaturallyorartificiallyregenerated.Forestlandincludestransitionzones,suchasareasbetweenforestandnon‐forestlandsthathaveatleast10percentcover(orequivalentstocking)withlivetreesandforestareasadjacenttourbanandbuilt‐uplands.Roadside,streamside,andshelterbeltstripsoftreesmusthaveacrownwidthofatleast120ft(36.6m)andcontinuouslengthofatleast363ft(110.6m)toqualifyasforestland.Unimprovedroadsandtrails,streams,andclearingsinforestareasareclassifiedasforestiftheyarelessthan120ft(36.6m)wideor1acre(0.4ha)insize;otherwisetheyareexcludedfromforestlandandclassifiedassettlements.Tree‐coveredareasinagriculturalproductionsettings,suchasfruitorchards,ortree‐coveredareasinurbansettings,suchascityparks,arenotconsideredforestland(Smithetal.,2009).


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Wetlandareaswillfallintooneoftwocategories:managedwetlandsornatural,unmanagedwetlands.Manywetlandareasmayhavealreadybeendelineatedinoneoftheabovecategories,andtheirmanagementwillbecapturedthroughestimationforthatcategory.If,however,therearewetlandareasthathavenotalreadybeenincludedinthecropland,grazingland,orforestlanddelineationsabove,thoseshouldbeidentifiedhere.AnaturallyoccurringwetlandthatdoesnothaveactivemanagementbeingappliedinordertoincreaseproductivityorprovideotherenvironmentalserviceswillnotbeincludedintheestimationofGHGfluxes.Thesenatural,unmanagedwetlandsshouldsimplybeincludedinthecategoryof“otherland”asdefinedbelow.Anywetlandareasthatareoutsidetheboundariesofthedefinedareasmentionedaboveandwherethelandmanagerisactivelyapplyingmanagementdecisionsinordertoenhanceproductivityorprovideenvironmentalservicesshouldbedelineatedasamanagedwetlandandincluded.ThisreportprovidesestimationmethodsinChapter4foremissionsfrompalustrinewetlands,2influencedbyavarietyofmanagementoptionssuchaswatertablemanagement,timberorotherplantbiomassharvest,andwetlandsthataremanagedwithfertilizerapplications.Currently,thereareinsufficientdataandtherefore,theGHGfluxeswilllikelynotbeincludedinanentity’sGHGestimationuntiladequatedataexisttoprovidethatestimationwithareasonableandmeasurablelevelofuncertainty.

Settlementswillfallintotwobroadcategories:(1)landwheretheentitymanagerimposesmanagementdecisions;and(2)landwherethemanagerdoesnotregularlyimposemanagementdecisionsthatimpactcarbonbalances.Examplesofsettlementlandthatmaybesignificantfromacarbonmanagementperspectivewouldbedevelopedlivestockfeedyards,dairybarns,poultryhouses,manurepiles,andmanureorrunofflagoons.Examplesofdevelopedlandwheremanagementisnotofconcerntocarbonbalancesishomes,yards,driveways,workshops,roads,andparkingareas.ForpurposesoftheGHGfluxestimation,onlytheareaswithcarbonmanagementimplications(e.g.,animalhousing,manurewastetreatmentareas)needtobeidentifiedwithinthespatialboundarydelineation.TheselivestockandmanuremanagementmethodsarepresentedinChapter5ofthisreport.Theremainingsettlementlandswithoutcarbonmanagementimplications(e.g.,roadsandrailroads)cansimplybeexcludedfromthespatialboundariesanentitychoosestoaccountforwithinthesettlementland‐usecategory.

2Palustrinewetlandsarenontidalwetlandsthatareprimarilycomposedoftrees,shrubs,persistentemergent,emergentmossesorlichens,andallwetlandsthatoccurintidalareaswheresalinityduetoocean‐derivedsaltsisbelow0.5percent.Palustrinewetlandsmusthaveanarealessthan20acres,nothaveactivewave‐formedorbedrockshoreline,haveamaximumwaterdepthoflessthan2m[6.6ft],andhaveasalinitylessthan0.5percent(USGS,2006).

Wetland:Aland‐usecategorythatincludeslandwithhydricsoils,nativeoradaptedhydrophyticvegetation,andahydrologicregimewerethesoilissaturatedduringthegrowingseasoninmostyears.Wetlandvegetationtypesmayincludemarshes,grasslandsorforests.Wetlandsmayhavewaterlevelsthatareartificiallychanged,orwherethevegetationcompositionorproductivityismanipulated.Theselandsincludeundrainedforestedwetlands,grazedwoodlandsandgrasslands,impoundmentsmanagedforwildlife,andlandsthatarebeingrestoredfollowingconversiontoanon‐wetlandcondition(typicallyasaresultofagriculturaldrainage).Provisionsforengineeredwetlandsincludingstormwaterdetentionponds,constructedwetlandsforwatertreatment,andfarmpondsorreservoirsarenotincluded.Naturallakesandstreamsarealsonotincluded.


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Anylandthatisactivelymanagedinsuchawayastoimpactbiomassgrowthorotherwiseimpactproduction‐relatedGHGemissionsshouldhavebeencapturedwithinthespatialboundariesdefinedfortheland‐usecategorieslistedabove.AnyremaininglandshouldbecategorizedasotherlandsorunmanagedlandandwillnotbeconsideredintheestimationofGHGfluxes.Thisincludesthewetlandanddevelopedareasthatwerepreviouslynotedasnothavingactivemanagement—i.e.,unmanagedwetlandsandunmanagedsettlements.Italsoincludesanyotherareaswithintheentityboundarythatrepresentbarren,mined,abandoned,orotherwiseunmanagedland—i.e.,otherland.

Land‐coverchangeissimplyavariationfromyeartoyearinwhatisgrowingonaparcelofland,suchasrotatingcornandsoybeancrops,andisnotconsideredland‐usechange.Incontrast,land‐usechangeisafundamentalshiftinpurposeorproductionofaparcel,suchasashiftfromcroppingtoforestproductionorviceversa.Land‐usechangeneedstobeaccountedforintheannualGHGflux,astheimpact(eitherpositiveornegative)onbiomassandsoilcarboncanbesignificant.Theseland‐usechangemethodsarepresentedinChapter7ofthisreport.

Animalproductionisnotnecessarilyaspatiallydefinedactivitywithintheentity,buthastobeconsideredaspartofthephysicalboundaryofthemanager’soperation.TherearethreemainareasthatneedtobeconsideredasimportanttoestimatingGHGemissionsfromananimalproductionsystem:methaneemissionsfromtheanimals,methaneandnitrousoxideemissionsfrommanagementofmanure,andanyemissionsimpactsrelatedtoanimalhousing.Animalproductioninthechapterisdiscussedbyanimalsystemtype,includingbeef,dairy,sheep,swine,andpoultry.Thecollectivenounforagroupofanimalstypicallyvariesbyspecies,butforthepurposesofthisreport,wewillrefertoanygroupofanimalsofthesameanimaltypethatarekepttogetherunderacommonsetofproductionmanagementpracticesasaherd.Followingthisdefinition,theentity’smanagermayhaveseveraldistinctlydifferentherdsthatmakeuptheentity.GHGemissionsfromanimalproductionwillvarygreatlydependinguponspecies(digestiveprocesses),growthstage,diet,andmanurestorageandmanagement.Timingisalsoachallengeinestimatingemissionsfromtheanimalproductionsector,asemissionsperanimalchangedramaticallyasayounganimalgrowsandmatures,asfeedlotcattlearefinished,orasdairycowscyclebetweengestatingandlactating.Insomecases,itwilllikelybenecessaryfortheusertoestimateemissionsforaherdusingaverageweight,averageage,andotherrepresentativecharacteristicstorepresenttheherdpopulation.Inothercases,itwillbenecessarytogeneralizebyseasons—manuremanagementmaybedifferentinwinterthansummer,animalfeedmixturemayvarybyseasonorbyanimalgrowthstage.Averaging

Settlements:Aland‐usecategoryrepresentingdevelopedareasconsistingofunitsof0.25acres(0.1ha)ormorethatincludesresidential,industrial,commercial,andinstitutionalland;constructionsites;publicadministrativesites;railroadyards;cemeteries;airports;golfcourses;sanitarylandfills;sewagetreatmentplants;watercontrolstructuresandspillways;parkswithinurbanandbuilt‐upareas;andhighways,railroads,andothertransportationfacilities.Alsoincludedaretractsoflessthan10acres(4.05ha)thatmaymeetthedefinitionsforforestland,cropland,grassland,orotherlandbutarecompletelysurroundedbyurbanorbuilt‐upland,andsoareincludedinthesettlementcategory.Ruraltransportationcorridorslocatedwithinotherlanduses(e.g.,forestland,cropland,andgrassland)arealsoincludedinsettlements.

OtherLand:Aland‐usecategorythatincludesbaresoil,rock,ice,andalllandareasthatdonotfallintoanyoftheotherfiveland‐usecategories,whichallowsthetotalofidentifiedlandareastomatchtheidentifiedlandbase.


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andgeneralizinginthiswayshouldbeadequateincapturingtheinformationneededtoprovideareasonableestimateofGHGemissionsaslongasthemanagerappliesassumptionsconsistentlyacrosstheherdsandthroughoutthetimeunderconsideration.Forexample,assuminganaveragefinishweightforfeederanimalsintheherdshouldprovideareasonableGHGestimateaslongastheassumedweightdoesnotchangefromyeartoyear,unlessaspecificmanagementdecision(suchasachangeinanimaldiet)resultsinanactualchangeinfinishingweight,inwhichcasethechangeinaverageswouldbeappropriate.SpecificmethodsforanimalproductionsystemsarepresentedinChapter5ofthereport.Insomecases,suchasmanureappliedtocropland,methodsfromChapter3willbeutilizedaswell.

Occasionally,physicalboundarieswillchangeovertime.Whetheraportionofacroplandfieldisconvertedtoananimalfeedlot,shelterbeltorripariantreesareplantedontoformercropland,orabandonedlandrevertstograzinglandorforestland,thesechangescouldresultintheneedforanewdelineationofparcelboundariesoradissectionofoneparcelintoseveralparcelswithmorethanonemanagementstrategy.Fortheportionoftheparcelwherethischangehasoccurred,theland‐usechangemethods(Chapter7)willbeusedtoestimateGHGfluxes.

Figure2‐1canbeusedtohelplandownersdeterminethelandusecategoryfortheirlandarea,accordingtothedefinitionsabove.

2.1.2.2 TemporalBoundaries

Temporalissuesincludesuchconsiderationsasthefrequencyofthereportedestimates,thetreatmentofactivitiesthatoccurwithinanaccountingperiodbuthavelong‐termimplicationsforcarbonbalances(e.g.,changesinsoilcarbonfollowingachangeintillagepractices),andhowtoaccountforshort‐termmanagementorshort‐termadjustmentstolong‐termmanagementdecisions.Alsosignificantishowtoaddressmovementofspatialboundariesovertimeandwithland‐usechange.ThissectionwillattempttoresolvesomeofthesetemporalissuesaroundGHGemissionestimationandreporting.

ThemethodsreportedhereareintendedtoprovideameansofannualaccountingandreportingofGHGfluxes.Annualchangesinsomeemissionsareeasilyquantified,butforothersitismuchmoredifficult.Carbonstoredintrees,forexample,mayneedtobeestimatedoveralongerperiod,withthechangethenconvertedtoanannualizedestimate.

Thereportmethodologiesassumeanaccountingperiodofonecalendaryear(e.g.,365days)whenestimatingannualizedemissionsinaparticularsectororsourcecategory.

Managementdecisionsalsoaresignificanttotheaccountingtimehorizon.Forexample,aforestmanagementplanmightcallfortimberharvestorthinning.Intheyearofharvest,theannualaccountingwillreflectalossofstandingliveand/orstandingdeadcarbonstocks,yetinthelongertermmanagementstrategy,thenetresultcouldbeanincreaseintotalcarbonstocks.Ifalandmanagerhasamanagementplanthatprescribesforestthinning,butthenharvestsmoreaggressivelythantheplan,considerationshouldbegivenastowhetherthisconstitutesachangeinforestmanagement,whichwouldbediscussedintheforestmanagementmethods(seeChapter6).

Therearealsotimeswhenmanagementhastotakecorrectiveactionortemporarilydeviatefromalong‐termmanagementplan.Thiscouldbethecasewhereacroplandmanagerhasadoptedano‐tillmanagementstrategy,butafterseveralyearshastousetillageoneyearbecauseofweather,pests,orotherextenuatingcircumstances.Inthiscase,themethodswillideallybesensitiveenoughtocapturetheGHGimpactofthedeviationfromthemanagementplan.


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Figure2‐1:DecisionTreeforDeterminingLand‐UseCategoryforLandAreas


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2.1.2.3 ActivityBoundaries

Itisimportanttodistinguishwhichactivitieswithinanentityaresubjecttoaccounting.Thisaccountingsystemisfocusedonland‐basedactivitiessuchastillageandharvesting,andnotonemissionsofGHGsthatarerelatedtofossilfueluse.Thus,emissionsfromtractorfuelorfuelusedforcropdryingarenotcounted,noraretheenergyinputsrequiredtomanufacturefertilizerorfarmtools,ortoheatfarmbuildings—i.e.,indirectGHGemissions(seeChapter1).However,asmentionedinChapter1,wherethereareobviouschangesinthelevelofcombustionduetoachangeinpractices,thatchangeisqualitativelydiscussed.Forexample,ashiftfromconventionaltillagetonotillcanresultinalargereductioninfuelconsumptionbecauseoffewertripsacrossthefield.Theserelationshipsarenotedqualitativelyinthereport,butquantitativemethodsarenotproposed.MethodsforquantifyingemissionsfromstationaryormobilecombustionsareavailablefromotherFederalagencies.

Aspreviouslymentioned,themethodsinthisreportdonotconstitutealife‐cycleassessmentfortwoprimaryreasons.Firsttheactivityboundariesdonotincludeemissionsfromfossil‐fueluse.Second,thetemporalboundariesarefocusedonannualreportinganddonotencompasstherangeofactivitiessuchascapitalinvestment,materialsupplies,anddisposal.

2.1.2.4 MaterialBoundaries

MaterialboundariesincludetheGHGsthataretobeconsideredintheestimationandshouldalsodelineatewhatsourcesofthosegasesareincludedandwhatareexcluded.Alsoincludedinthissectionisadiscussionoftheglobalwarmingpotentials(GWPs)usedthroughoutthereport.Itisimportanttodetermineupfrontwhichgasesareincludedandwhicharenot.Itisalsoimportanttodeterminehowmuchfreedomtheuserhasinwhatisestimatedandwheretheseboundarieslieinordertoensurethatachangeinmanagementthatreducesemissionsinonesectordoesnotinadvertentlycauseemissionstoriseoutsideoftheboundariesbeingreported.

ThereportincludesestimationmethodologiescoveringtheGHGemissionsfromthecroplandsandgrazinglands,wetlands,animalproduction,forestry,andland‐usechangesectors.Withinthesesectorsandsourcecategories,emissionsandremovalsofthemainGHGs—carbondioxide(CO2),methane(CH4),andnitrousoxide(N2O)—areaccountedfor.Itshouldbenotedthatcarbonsequestration(i.e.,increasesincarbonstocks)isestimatedintermsofcarbondioxideequivalents(CO2‐eq).Itshouldalsobenotedthattheanimalproductionchapterincludesdiscussionofammonia(NH3),asthisisanimportantprecursortoN2Oemissionsfrommanuremanagement.EstimatingNH3emissionsisbeyondthescopeofthisreport—NH3isnotconsideredaGHG—butsinceNH3issignificantasaprecursortoN2O,understandingchangesinNH3emissionsresultingfromchangesinmanagementisimportant.

Emissionsandsequestrationvaluesarepresentedinthisreportintermsofthemass(notvolume)ofeachgas,usingmetricunits(e.g.,metrictonsofmethane).Intheintegratedtool,themassesofeachgaswillbeconvertedintoCO₂equivalentunitsusingtheGWPsforeachgasintheInternationalPanelonClimateChange(IPCC)SecondAssessmentReport.

AGWPisanindexusedtocomparetherelativeradiativeforcingofdifferentgaseswithoutdirectlycalculatingthechangesinatmosphericconditions.GWPsarecalculatedastheratiooftheradiativeforcingthatwouldresultfromtheemissionsofonekilogramofaGHGtothatfromtheemissionsofonekilogramofCO₂overadefinedperiodoftime,suchas100years.EmissionsintermsofCO₂equivalents(CO2‐eq)areestimatedbymultiplyingthemassofaparticularGHG(e.g.,CH4,N2O)bytherespectiveGWPforthatparticularGHG.TheGWPsusedinthisreportareshowninTable2‐1below.


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Themethodsinthisreportfocusprimarilyonthedirectemissionsresultingfrommanagementdecisionsmadewithintheboundariesoftheentity—e.g.,withinthefarmandforestgate.Theindirectemissionsrelatedtoinputsintotheentityarenotconsidered.Thereasonforthisisthatthoseemissionswouldlikelybereportedbythemanufacturerproducingtheinputs.Ifonewereconductingafulllife‐cycleassessment,theseemissionswouldneedtobeincluded,butforpurposesoftheemissionsbeingestimatedherewefocusprimarilyontheemissionsresultingwithinthespatialboundaryoftheentity.Theonenotableexceptionthatisaccountedforiswhenmanagementdecisionsontheoperationhaveaspecificrelatedinfluenceonemissionsleavingtheentity’sboundary.AnexampleofthisisindirectemissionssuchasnitrogenthatisappliedwithintheoperationbutthencarriedoffsiteviaerosionorleachingandcontributestoN₂Oemissionsoffsite.Anotherexampletoconsiderisharvestedcommodities.Inthecaseofgrainsorotheragriculturalcommodities,theproductisassumedtobeconsumedwithinarelativelyshortamountoftime,resultinginnonetgainorlossrelatedtoGHGaccounting.HWPsaresomewhatdifferent,asmuchofthatharvestwillendupinlong‐termcarbonpoolseitherasstructures,furniture,orotherwoodproducts,orinlandfills.ThisreportdoesprovideadiscussionofN₂OlossesthatresultfromerosionandleachingoffertilizernitrogenandthecarbonpoolsrelatedtothefateofHWPs.

2.2 ReviewofRelevantCurrentToolsandMethods

ThissectionprovidesanoverviewofthecurrentestimationmethodsorapproachesanentitycouldusetoestimateGHGemissionsandsinksontheirproperty.Thisoverviewisfollowedbyasummaryofeachsector’sproposedmethodologiesforentityGHGestimations.

ThereareseveralapproachesthatafarmerorlandownercanusetoestimateGHGemissionsatanentityscale,andeachapproachgivesvaryingaccuracyandprecision.Themostaccuratewayofestimatingemissionsisthroughdirectmeasurement,whichoftenrequiresexpensiveequipmentortechniquesthatarenotfeasibleforasinglelandownerormanager.Ontheotherhand,lookuptablesandestimationequationsaloneoftendonotadequatelyrepresentlocalvariabilityorlocalconditions.Thisreportattemptstodelineatemethodsthatbalanceuser‐friendliness,datarequirements,andscientificrigorinawaythatistransparentandjustified.

Thefollowingapproacheswereconsideredfortheseguidelines:

Basicestimationequations–Involvecombinationsofactivitydata3withparametersanddefaultemissionfactors.4Anydefaultparametersordefaultemissionfactors(e.g.,lookuptables)areprovidedinthetext,orifsubstantialinlength,inanaccompanyingcompendiumofdata.

3Activitydataaredataonthemagnitudeofhumanactivityresultinginemissionsorremovalstakingplaceduringagivenperiodoftime(IPCC,1997).

4Emissionfactorisdefinedasacoefficientthatquantifiestheemissionsorremovalsofagasperunitactivity.Emissionfactorsareoftenbasedonasampleofmeasurementdata,averagedtodeveloparepresentativerateofemissionforagivenactivitylevelunderagivensetofoperatingconditions(IPCC,2006).

Table2‐1:GlobalWarmingPotentialsUsedintheReport

SpeciesChemicalFormula

Lifetime(years) GWPa

Carbondioxide CO2 Variable 1Methane CH4 12±3 21Nitrousoxide N2O 120 310a GWPsusedare100‐yeartimehorizon,inaccordancewiththeIPCCSecondAssessmentReport(IPCC,2007).


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Models–Usecombinationsofactivitydatawithparametersanddefaultemissionfactors.Theinputsforthesemodelscanbeancillarydata5(e.g.,temperature,precipitation,elevation,andsoilnutrientlevelsthatmaybepulledfromanunderlyingsource),biologicalvariables(e.g.,plantdiversity),orsite‐specificdata(e.g.,numberofacres,numberofanimals).Theaccuracyofthemodelsisdependentontherobustnessofthemodelandtheaccuracyoftheinputs.

Fieldmeasurements–Actualmeasurementsthatafarmerorlandownerwouldneedtotaketomoreaccuratelyestimatethepropertiesofthesoil,forest,orfarmtoestimateactualemissions.Measuringactualemissionsonthelandrequiresspecialequipmentthatmonitorstheflowofgasesfromthesourceintotheatmosphere.Thisequipmentisnotreadilyavailabletomostentities,somoreoftenfieldmeasurementsareincorporatedintoothermethodsdescribedinthissectiontocreateahybridapproach.Afieldmeasurementsuchasasamplemeantreediametercouldbeincorporatedintoothermodelsorequationstogiveamoreaccurateinput.

Inference–UsesState,regional,ornationalemissions/sequestrationfactorsthatapproximateemissions/sequestrationperunitoftheinput.Theinputdataisthenmultipliedbythisfactortodeterminethetotalonsiteemissions.Thisfactorcanhavevaryingdegreesofaccuracyandoftendoesnotcapturethemitigationpracticesonthefarmortheuniquesoilconditions,climate,livestockdiet,livestockgenetics,oranyfarm‐specificcharacteristics,althoughtheycanbedevelopedwithspecificsoiltypes,livestockcategories,orclimacticregions.

Hybridestimationapproaches–Anapproachthatusesacombinationoftheapproachesdescribedabove.Theapproachoftenusesfieldmeasurementsormodelstogenerateinputsusedforaninference‐basedapproachtoimprovetheaccuracyoftheestimate.

2.3 SelectionofMostAppropriateMethodandMitigationPracticestoInclude

Indraftingthereport,anumberofselectioncriteriawereconsidered(e.g.,transparency,consistency,comparability,completeness,accuracy,costeffectiveness,easeofuse).Adescriptionofeachappearsbelow:

Transparency–Theassumptionsandmethodologiesusedforaninventoryshouldbeclearlyexplainedtofacilitatereplicationandassessmentoftheinventorybyusersofthereportedinformation.Thetransparencyofinventoriesisfundamentaltothesuccessoftheprocessforthecommunicationandconsiderationofinformation.

Consistency–Themethodsusedtogenerateinventoryestimatesshouldbeinternallyconsistentinallitselementsandtheestimatesshouldbeconsistentwithotheryears.Aninventoryisconsistentifthesamemethodologiesareusedforthebaseandallsubsequentyearsandifconsistentdatasetsareusedtoestimateemissionsorremovalsfromsourcesorsinks.Consistencyisanimportantconsiderationinmergingdifferingestimationtechniquesfromdiversetechnologiesandmanagementpractices.

Comparability–Fortheguidelinestobecomparable,theestimatesofemissionsandsequestrationbeingreportedbyoneentityarecomparabletotheestimatesbeingreportedbyothers.Forthispurpose,entitiesshouldusecommonmethodologiesandformatsfor

5Ancillarydataareadditionaldatanecessarytosupporttheselectionofactivitydataandemissionfactorsfortheestimationandcharacterizationofemissions.Dataonsoil,croporanimaltypes,treespecies,operatingconditions,andgeographicallocationareexamplesofancillarydata.


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estimatingandreportinginventories.Consequently,ingeneral,themethodsspecifyonemethodforanytechnologyormanagementpractice(i.e.,methodssuggestedinthisreportdonotallowuserstoselectfromamenuofmethods).

Completeness–Themethodsmustaccountforallsourcesandsinks,aswellasallGHGstothegreatestextentpossible.Completenessalsomeansfullcoverageofsourcesandsinksunderthecontroloftheentity.Completenessisanimportantconsiderationtobebalancedwitheaseofuseinreportingappropriatelyforanentitythatmayhaveaminoractivityoranactivitywithseverelylimiteddataavailability.

Accuracy–Arelativemeasureoftheexactnessofanemissionorremovalestimate.Estimatesshouldbeaccurateinthesensethattheyaresystematicallyneitherovernorundertrueemissionsorremovals,asfarascanbejudged,andthatuncertaintiesarereducedasfaraspracticable.

Costeffectiveness–Ameasureoftherelativecostsandbenefitsofadditionaleffortstoimproveinventoryestimatesorreduceuncertainty.Forexamplethereisabalancebetweentherelativecostsandbenefitsofadditionaleffortstoreduceuncertainty.

Easeofuse–Ameasureofthecomplexityoftheuserinterfaceandunderlyingdatarequirements.

Theworkinggroupsdevelopedthefollowingselectioncriteriaforthemitigationpracticesthatcouldbeincludedinthemethods:

1. Thesciencereflectsamechanisticunderstandingofthepractice'sinfluenceonanemissionsource.

2. Publishedresearchsupportsareasonablelevelofrepeatability/consistency(canuseinternationalstudiesifsimilarmanagement,climate,andsoilsasU.S.conditions).

3. Thereisgeneralagreementthatatleastthesignandrangeofresponsesarereasonablywellunderstood.

4. Thereisconsensusoftheauthorsthatthepracticecanbeadequatelyincluded.Toreachconsensus,theauthorsdiscussedissuessuchas:Wouldleavingamitigationpracticeoutmakethereportincomplete?Istherestrongenoughevidencethatthemethodwillholdupforthispracticeforatleastthenextfiveyears?

Thereweremitigationpracticesthatdidnotfulfillthesecriteria,andthosepracticeswerecitedasareasthatrequiremoreresearchinordertofullyunderstandtheeffectofchangesinthepracticetoGHGemissions.TheseresearchgapsareintendedtobecomeareasthatUSDA,non‐governmentalorganizations,universities,andotherresearchinstitutionswillconsiderasimportantareastofocusagricultureandforestryclimate‐changeresearchpriorities.Othertopics,suchasalbedoeffects,werenotconsidered.Currently,withtheexceptionofurbanareas,albedoeffectsarehighlyvariableandaredifficulttoreliablyquantify.

2.4 OverviewofSectors

Thisreportcoversemissionssourcesandsinksfromcroplands/grazinglands,managedwetlands,animalproductionsystems,andforestry,alongwithchangesinlanduse.Figure2‐2canbeusedtohelplandownersdeterminewhichchaptercanbeusedtoestimatetheirGHGsourcesandsinksfromtheirland.


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Figure2‐2:DecisionTreeforDeterminingWhichMethodstoFollowinThisReport


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Figure2‐2:DecisionTreeforDeterminingWhichMethodstoFollowinThisReport(continued)

Thefollowingsectionsprovideanoverviewofthesectorscoveredinthisreport.Foreachsector,theemissionsourcesandsinksareintroducedaswellasthemanagementpracticesimpactingGHGemissions.

2.4.1 CroplandsandGrazingLands

Croplandsincludeallsystemsusedtoproducefood,feed,andfibercommodities,inadditiontofeedstocksforbioenergyproduction.MostU.S.croplandsaredrylands(irrigatedorunirrigated);riceandafewothercropsaregrowninwetlands.SomecroplandsaresetasideintheConservationReserveProgram.Croplandsalsoincludeagroforestrysystemsthatareamixtureofcropsandtrees,suchasalleycropping,shelterbelts,andriparianwoodlots.Grazinglandsaresystemsthatareusedforlivestockproductionandoccurprimarilyongrasslands.Grasslandsarecomposedprincipallyofgrasses,grass‐likeplants,forbs,orshrubssuitableforgrazingandbrowsing;theyincludebothpasturesandnativerangelands(EPA,2011).Savannas,somewetlandsanddeserts,andtundracanbeconsideredgrazinglandsifusedforlivestockproduction.Grazinglandsystemsincludemanagedpasturesthatmayrequireperiodicmanagementtomaintainthegrassvegetationandnativerangelandsthattypicallyrequirelimitedmanagementtomaintain.


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CroplandandgrazinglandsaresignificantsourcesofCO2,N2O,andCH4emissionsandcanalsobeasinkforCO2andCH4(U.S.EPA,2011).N2Oemissionsfromsoilsareinfluencedbylanduseandmanagementactivity,particularlynitrogenapplication.Landuseandmanagementalsoinfluencecarbonstocksinbiomass,deadbiomass,andsoilpools.CropandgrazinglandsystemscanbeeitherasourceorsinkforCO2,dependingonthenetchangesinthesecarbonpools.ThemaininfluencesonnitrogenuseefficiencyandN2Oemissionsarefertilizerrate,timing,placement,andnitrogensource.Tillageintensity,croppingintensity,andtheuseofcroprotationcanhavesignificanteffectsonsoilcarbonstocks.

OthermanagementactivitiesalsoaffectGHGemissionsfromsoils.IrrigationcanimpactCH4andN2Oemissionsaswellascarbonstocks.Burningdecreasesbiomasscarbonstocksandalsosoilorganiccarbonstocksduetodecreasedcarboninputtothesoilsystem.BurningwillalsoleadtoemissionsofCH4andN2Oandothergases(CO,NOx)thatareGHGprecursors.CH4canberemovedfromtheatmospherethroughtheprocessofmethanotrophyinsoils,whichoccursunderaerobicconditionsandgenerallyinundisturbedsoils.CH4isproducedinsoilsthroughtheprocessofmethanogenesis,whichoccursunderanaerobicconditions(e.g.,wetlandsoilsusedforproductionofrice).Bothprocessesaredrivenbytheactivityofmicro‐organismsinsoils,buttherateofactivityisinfluencedbylanduseandmanagement.

TheinfluenceofcropandgrazinglandmanagementonGHGemissionsisnottypicallythesimplesumofeachpractice’seffect.Theinfluenceofonepracticecandependonanotherpractice.Forexample,theinfluenceoftillageonsoilcarbonwilldependonresiduemanagement.Theinfluenceofnitrogenfertilizationratescandependonfertilizerplacementandtiming.Becauseoftheseinterconnections,estimatingGHGemissionsfromcropandgrazinglandsystemswilldependonacompletedescriptionofthepracticesusedintheoperation,aswellasancillaryvariablessuchassoilcharacteristicsandweatherorclimateconditions.ItisalsoimportanttonotethattrendsinGHGemissionsassociatedwithachangeincropandgrazinglandmanagementcanbereversedifthelandownerrevertstotheoriginalpractice.Forexample,afarmermightswitchfromconventionaltillagetono‐tillfor10yearsandseeanincreaseinsoilcarbonsequestration;if,however,thefarmerthenrevertstoconventionaltillage,thegainsinsoilcarbonwillbequicklylostasthestoredsoilcarbonisreleasedbackintotheatmosphereasCO2,negatingtheGHGmitigationoftheprevious10years.However,reversalswillnotnegatetheGHGmitigationforCH4orN2Othatoccurredpriortothereversion.IfemissionsarereducedforCH4orN2O,theemissionreductionispermanentandcannotbechangedbysubsequentmanagementdecisions.

Thetextbox,ManagementPracticesImpactingGHGEmissionsfromCroplandsandGrazingLands,liststhemostsignificantmitigationpracticesdiscussedinChapter3.Additionalmitigationpracticesarediscussedinthechapter,buttheseoftenhavesparseorconflictingevidenceinsupportoftheirmitigationeffects.Therefore,thetextboxliststhemorerobustlysupportedpractices.

2.4.2 Wetlands

WetlandsoccuracrosstheUnitedStatesonmanylandforms,particularlyinfloodplainsandriparianzones,inlandlacustrine,glaciatedoutwash,andcoastalplains.TheNationalWetlandsInventorybroadlyclassifieswetlandsintofive

ManagementPracticesImpactingGHGEmissionsfromCroplandsandGrazingLands

NutrientManagement(SyntheticandOrganic)

TillagePractices CropRotationsandCroppingIntensity Irrigation ResidueManagement Set‐Aside/ReserveCropland WetlandRiceCultivation LivestockGrazingPractices ForageOptions Silvopasture


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majorsystems,including(1)marine,(2)estuarine,(3)riverine,(4)lacustrine,and(5)palustrine(Cowardinetal.,1979).Thesesystemsarefurtherclassifiedbymajorvegetativelifeform.Forexample,forestedwetlandsareoftenclassifiedaspalustrine‐forested.Similarly,mostgrasslandwetlandsareclassifiedaspalustrinewetlandswithemergentvegetation(e.g.,grassesandsedges).Wetlandsalsovarygreatlywithrespecttogroundwaterandsurfacewaterinteractionsthatdirectlyinfluencehydroperiod,waterchemistry,andsoils(Cowardinetal.,1979;Winteretal.,1998).Allthesefactorsalongwithclimateandland‐usedriversinfluenceoverallcarbonbalanceandGHGflux.

GrasslandandforestedwetlandsaresubjecttoawiderangeoflanduseandmanagementpracticesthatinfluencethecarbonbalanceandGHGflux(Faulkneretal.,2011;Gleasonetal.,2011).Forexample,forestedwetlandsmaybesubjecttosilviculturalprescriptionsandintensityofmanagement,andhence,thecarbonbalanceandGHGemissionsshouldbeevaluatedonarotationbasis.Incontrast,grasslandwetlandsmaybegrazed,hayed,ordirectlycultivatedtoproduceaharvestablecommodity.AllthesemanipulationsinfluencetheoverallGHGflux.Thisreportwillfocusprimarilyonrestorationandmanagementpracticesassociatedwithriverineandpalustrinesystemsinforested,grassland,andriparianecosystems;althoughothermajorwetlandssystemsaresignificantintheglobalcarboncycle(e.g.,estuarine),thesewetlandssystemshavereceivedthemostattentionintermsofimplementationofrestorationandmanagementpracticestoconservewetlandshabitatsandsustainecosystemsservices(BrinsonandEckles,2011).Wetlandsthathavebeendrainedforacommodityproduction,suchasannualcrops,arenotconsideredwetlandsinthisguidance.Therefore,managementofdrainedwetlandsisaddressedinothersectionsoftheguidance,suchasinChapter3.

Wetlandemissionsarelargelycontrolledbythedegreeofwatersaturationaswellasclimateandnutrientavailability.Inaerobicconditions,commoninmostuplandwetlandecosystems,decompositionreleasesofCO2,andCH4emissionsaremoreprevalentinanaerobicconditions.Typically,wetlandsareasourceofCH4,withestimatedglobalemissionsof55to150millionmetrictonsCH4peryear(Blainetal.,2006).N2Oemissionsfromwetlandsaretypicallylow,unlessanoutsidesourceofnitrogenisenteringthewetland.Ifwetlandsaredrained,N2Oemissionsarelargelycontrolledbythefertilityofthesoil.WetlanddrainageresultsinlowerCH4emissionsandanincreaseinCO2emissionsduetooxidationofsoilorganicmatterandanincreaseinN2Oemissionsinnutrientrichsoil.Ontheotherhand,thecreationofwetlandsgenerateshigherlevelsofCH4andlowerlevelsofCO2(Blainetal.,2006).

Biomasscarboncanchangesignificantlywithmanagementofwetlands,particularlyinpeatlands,forestedwetlands,orchangesfromforesttowetlandsdominatedbygrassesandshrubsoropenwater.Peatlandscoverapproximately400millionhectaresorthreepercentofthegloballandsurface,accountingfor450billionmetrictonsofstoredcarbon(Couwenbert,2009).Emissionsfrompeatlanddegradationandfiresareestimatedat2billionmetrictonsofCO2‐eqperyear(IPCC,

ManagementPracticesImpactingGHGEmissionsfromWetlands

SilviculturalWaterTableManagement ForestHarvestingSystems ForestRegenerationSystems Fertilization ConversiontoOpenWetland ForestTypeChange WaterQualityManagement WetlandManagementforWaterfowl ConstructedWetlandsforWastewater

Treatment Land‐UseChangetoWetlands ActivelyRestoringWetlands ActivelyRestoringScrub‐GrassWetlands ConstructingWetlands PassiveRestorationofWetlands


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2011).Inforestedwetlands,therecanalsobesignificantcarbonindeadwood,coarsewoodydebris,andfinelitter.Harvestingpracticeswillalsoinfluencethecarbonstocksinwetlandstotheextentthatthewoodiscollectedforproducts,fuel,orotherpurposes.WetlandsarealsoasourceofN2Oemissions,primarilybecauseofnitrogenrunoffandleachingintogroundwaterfromagriculturalfieldsand/orlivestockfacilities.N2OemissionsfromwetlandsduetonitrogeninputsfromsurroundingfieldsorlivestockfacilitiesareconsideredanindirectemissionofN2O(deKleinetal.,2006).DirectN2Oemissionscanalsooccurifmanagementpracticesincludenitrogenfertilizationofthewetlands.

Thetextbox,ManagementPracticesImpactingGHGEmissionsfromWetlands,liststhemanagementpracticesinwetlandsthathaveaninfluenceonGHGemissions(CH4orN2O)orcarbonstockchanges,andwillbecoveredinmoredetaillaterinthereport.Individualsectionswilldealwithdifferenttypesofwetlandsincludingforested,grassland,andconstructedwetlandsthatcouldoccurinagriculturalandforestryoperations.Themethodsarerestrictedtoestimationofemissionsonpalustrinewetlandsthatareinfluencedbyavarietyofmanagementoptionssuchaswatertablemanagement,timberorotherplantbiomassharvest,andwetlandsthataremanagedwithfertilizerapplications.

2.4.3 AnimalProduction

GHGemissionsfromanimalproductionsystemsconsistofthreemaincategories:entericfermentation,housing,andmanuremanagement.Thethreecategoriesaredescribedinthesectionsthatfollow.Discussionaboutentericfermentationandhousingareaddressedtogetherinthisreport.

2.4.3.1 EntericFermentationandHousing

Entericfermentationreferstothemethaneemissionsresultingfromanimaldigestiveprocesses,whilehousingemissionsrefertoGHGemissionsfrommanurethatisstoredwithinthehousingstructure(i.e.,manurestoredunderabarnfloor).GHGemissionsarisingfrommanurestoredinhousinghavesimilaremissionstomanurethatismanagedinstockpiles.MorediscussiononhousingmanureemissionscanbefoundinSection2.4.3.2andChapter5.

Forentericfermentation,CH4‐producingmicro‐organisms,calledmethanogens,existinthegastrointestinaltractofmanyanimals.Ruminantanimals(hoofedmammals)thathavethreeorfourchamberedstomachs(andchewcudasapartofthedigestiveprocess),producemuchmoreCH4thandootheranimalsbecauseofthepresenceandfermentativecapacityoftherumen(thefirststomachinaruminantanimal).

Intherumen,CH4formationisadisposalmechanismbywhichexcesshydrogenfromtheanaerobicfermentationofdietarycarbohydratecanbereleased.Controlofhydrogenionsthroughmethanogenesisassistsinmaintenanceofanefficientmicrobialfermentationbyreducingthepartialpressureofhydrogentolevelsthatallownormalfunctioningofmicrobialenergytransferenzymes(Martinetal.,2010).CH4canalsoarisefromhindgutfermentation,butthelevelsassociatedwithhindgutfermentationaremuchlowerthanthoseofforegutfermentation.AlthoughanimalsproduceCO2through

ManagementPracticesImpactingGHGEmissionsfromEntericFermentationand

Housing

DietaryFat GrainSource,GrainProcessing,Starch

Availability FeedingCo‐ProductIngredients RoughageConcentrationandForm LevelofIntake FeedAdditivesandGrowthPromoters NovelMicroorganismsandTheirProducts Genetics


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respiration,theonlygasofconcerninentericfermentationprocessesisCH4.Infieldstudies,respirationchambersequippedwithN2OandNH3analyzershaveconfirmedthatentericfermentationdoesnotresultintheproductionofN2OorNH3(Reynoldsetal.,2010).

Thetextbox,ManagementPracticesImpactingGHGEmissionsfromEntericFermentationandHousing,listsseveralofthepracticesthatcanmodifyentericfermentationemissions.Mostofthepracticesrelatetodietcomposition.ThesepracticesarecoveredingreaterdetailinChapter5.

2.4.3.2 ManureManagement

Storageofanimalmanure(dungandurine)isapopularmanagementpracticebecauseitreducestheneedtobuycommercialfertilizer,allowsformorecontrolovermanureapplication,andhaslowerdemandsonfarmlabor.ThetreatmentandstorageofmanureinmanagementsystemscontributestotheGHGemissionsoftheagriculturalsector.Anaerobicconditions,asfoundinmanylong‐termstoragesystems,produceCH4throughanaerobicdecomposition.N2Oisproducedeitherdirectly,aspartofthenitrogencyclethroughnitrificationanddenitrification,orindirectly,asaresultofvolatilizationofnitrogenasNH3andnitrogenoxides(NO,NO2,orNO3)andrunoffduringhandling.

Animalmanurecanbeclassifiedas:

Slurry,wherethedrymatterisgreaterthan10percent;

Solid,wherethedrymatterisgreaterthan15percent;or

Liquid,wherethedrymatterislowerthan10percent.Thefoursolidmanurestorage/treatmentpracticesare:(1)temporarystack;(2)long‐termstockpile;(3)composting;and(4)thermo‐chemicalconversion.Theeightmainliquidmanurestorage/treatmentpracticesare:(1)anaerobicdigestion;(2)nutrientremoval;(3)anaerobiclagoon/runoffholdingpond/storagetanks;(4)aerobiclagoon;(5)constructedwetland;(6)sand‐manureseparation;(7)combinedaerobictreatmentsystem;and(8)solid‐liquidseparation.GreateranalysisofeachofthesesystemsisprovidedinChapter5.

ThemagnitudeofCH4andN2Oemissionsthatresultfromanimalmanureisdependentlargelyontheenvironmentalconditionsthatthemanureissubjectedto.CH4isemittedwhenoxygenisnotavailableforbacteriatodecomposemanure.Storageofmanureinponds,tanks,orpits,asistypicalwithliquid/slurryflushingsystems,promoteanaerobicconditionsandtheformationofCH4.Storageofsolidmanureinstacksordrylotsordepositionofmanureonpasture,range,orpaddocklandstendtoresultinmoreoxygen‐availableconditions,andlittleornoCH4willbeformed.OtherfactorsthatinfluenceCH4generationincludetheambienttemperature,moisturecontent,residencytime,andmanurecomposition(whichisdependentonthedietofthelivestock,growthrate,andtypeofdigestivesystem)(U.S.EPA,2011).

TheproductionofN2Ofrommanagedlivestockmanuredependsonthecompositionofthemanureandurine,thetypeofbacteriainvolved,theoxygenandliquidcontentofthesystem,andtheenvironmentforthemanureafterexcretion(U.S.EPA,2011).N2OoccurswhenthemanureisfirstsubjectedtoaerobicconditionswhereNH3andorganicnitrogenareconvertedtonitratesandnitrites(nitrification),andifconditionsbecomesufficientlyanaerobic,thenitratesandnitritescan

ManagementPracticesImpactingGHGEmissionsfromManureManagement

Thermo‐ChemicalConversion AnaerobicDigestion LiquidManureStorageandTreatment‐

Sand‐ManureSeparation LiquidManureStorageandTreatment–

Solid‐LiquidSeparation


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bedenitrified(reducedtonitrogenoxidesandnitrogengas)(Groffmanetal.,2000).N2Oisanintermediateproductofbothnitrificationanddenitrificationandcanbedirectlyemittedfromsoilasaresultofeitheroftheseprocesses.Drywastehandlingsystemsaregenerallyoxygenatedbuthavepocketsofanaerobicconditionsfromdecomposition;thesesystemshaveconditionsthataremostconducivetotheproductionofN2O(USDA,2011).

SomemanuremanagementsystemscaneffectivelymitigatethereleaseofGHGemissionsfromlivestockmanure.Thetextbox,ManagementPracticesImpactingGHGEmissionsfromManureManagement,listsseveralofthepracticesthatcanmodifymanuremanagementemissions.

2.4.4 Forestry

ForestsystemsrepresentasignificantopportunitytomitigateGHGsthroughthesequestrationandtemporarystorageofforestcarbonstocks.ForestsremoveCO2fromtheatmospherethroughphotosynthesisandstorecarboninforestbiomass(e.g.,stems,root,bark,leaves).RespirationreleasesCO2totheatmosphere.Netforestcarbonstocksincreaseovertimewhencarbonsequestrationduringphotosynthesisexceedscarbonreleasedduringrespiration.OtherGHGsarealsoexchangedbyforestecosystems—e.g.,CH4frommicrobialcommunitiesinforestsoilandN2Ofromfertilizeruse.

Harvestingforestsreleasessomesequesteredcarbontotheatmosphere,whiletheremainingcarbonpassesinHWPs,thefateofwhich(e.g.,combustionforenergy,manufactureofdurablewoodproducts,disposalinlandfills)determinestherateatwhichthecarbonisreturnedtotheatmosphere.

Therearemanyforestryactivities(i.e.,managementpractices)relevanttoreducingGHGemissionsand/orincreasingcarbonstocksintheforestrysectorincludingestablishingand/orre‐establishingforest,avoidedforestclearing,andforestmanagement.Moreinformationoneachisincludedbelow.

TheChapter6describesmethodsforthevarioussourcecategoriescontributingtotheGHGfluxfromforests.Thesesourcecategoriesincludeforestcarbonaccounting—e.g.,livetrees,understory,standingdead,downdeadwood,forestfloororlitter,forestsoilorganiccarbon—establishing,re‐establishing,andclearingforest,forestmanagement,HWPs,urbanforestry,andnaturaldisturbances(e.g.,forestfires).Thissubsectionbrieflydescribesthesesourcecategories.DescriptionsofthecurrenttoolsandmethodsusedtoestimateGHGfluxfromthesesourcecategoriesisdiscussedlaterinChapter6.

ForestCarbon.Accountingforforestcarbon(i.e.,forestbiomass)typicallydividestheforestintoforestcarbonpools—e.g.,livetrees,understory,standingdead,downdeadwood,forestfloororlitter,forestsoilorganiccarbon—thedefinitionsforwhicharedevelopedaroundacommonsetinusebyanumberofpublications,whicharefurtheroutlinedinChapter6.Themethodsforestimatingthekeyforestcarbonpoolsarewelldevelopedandfairlystandard.

Establishing,Re‐Establishing,andClearingForest.Inadditiontoforestlandremainingforestland,therearethreedistinctprocessesthatcansignificantlyalterforestcarbonstocks,andaretermed:

ManagementPracticesImpactingNetGHGEmissionsfromForestry

EstablishingandReestablishingForest AvoidingClearingForest StandDensityManagement SitePreparationTechniques VegetationControl Planting NaturalRegeneration Fertilization SelectionofRotationLength HarvestingandUtilizationTechniques FireandFuelLoadManagement ReducingtheRiskofEmissionsfromNatural

Disturbances ShortRotationWoodyCrops


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forestestablishment(i.e.,afforestation),forestre‐establishment(i.e.,reforestation),andforestclearing(i.e.,deforestation).Eachoftheseprocessesaltersstocksofcarboninabovegroundandbelowgroundcarbonpools.Establishmentinvolvestheintentionalplanting(orallowingthenaturalprocessofsecondarysuccession)onlandthatwasnotpreviouslyforest.Reestablishmentisreturninglandthatwasrecentlyforestbackintoforest.Ineithercase,establishingforestwillgenerallyincreasethecarbonstocksinabovegroundandbelowgroundcarbonpoolsovertime.Forestclearingistheremovaland/orconversionofaforestsystemintoanotherlandcover(cropland,grazingland,etc.)andisthemostsignificantsourceofGHGemissionsfromforests.

ForestManagement.Forestmanagementdescribestherangeofpracticesemployedbylandownerstomeettheirobjectives(e.g.,timberproduction)whilesatisfyingbiological,economic,andsocialconstraints.Anumberofthepracticesusedbyforestmanagerstoachievetheirobjectivesimpactthecarbondynamicsinforestseitherbyenhancingforestgrowthoracceleratingthelossofforestcarbon.Themanagementpracticesinclude:standdensitymanagement(e.g.,underplanting,pre‐commercialandcommercialthinning);sitepreparationtechniques(e.g.,mechanicalmethods,chemicalapplication,prescribedburning);vegetationcontrol;planting(e.g.,plantingdensity,speciesselection,geneticimprovement);naturalregeneration;fertilization(e.g.,nitrogenandphosphorousfertilizerapplication);selectionofrotationlengths;harvestingandutilizationtechniques;fireandfuelloadmanagement;reducingtheriskofemissionsfrompestsanddisease;andestablishingbiomassplantations(i.e.,shortrotationwoodycrops).

HarvestedWoodProducts.Aproportionofthewoodcarbonharvestedfromforestsendsupinsolidwood,paper,orotherproducts,whicharecollectivelyknownasHWPs.Thecarboncontainedintheseproductscanremainstoredforyearsordecadesdependingontheenduse,andmayeventuallybecombusted,decay,orbedivertedtolandfills.

UrbanForestry.Urban(orurbancommunity)forestdescribesthepopulationoftreeswithinanurbanarea.Urbantreesdirectlystoreatmosphericcarbonaswoodybiomassandalsoaffectlocalclimate(e.g.,secondaryeffects).ThemaintenanceofurbantreesalsoaffectsGHGemissionsinurbanareas(i.e.,indirecteffects).

NaturalDisturbances.Naturaldisturbancesinforestsystems(e.g.,forestfires,pestsanddisease,storms)cansignificantlyimpactforestcarbonstockseitherdirectlyinthecaseofcombustionfromforestfiresorindirectlybyconvertinglivebiomasstodeadorconvertingstandingtreestodowneddeadwoodandacceleratingdecomposition.

Thetextbox,ManagementPracticesImpactingNetGHGEmissionsfromForestry,liststhemanagementpracticesrelevanttoreducingGHGemissionsand/orincreasingcarbonstocksintheforestrysectorincludingestablishingand/orreestablishingforest,avoidingforestclearing,andimprovingforestmanagement.

2.5 Land‐UseChange

Convertinglandparcelsfromoneland‐usecategorytoanothercanhaveasignificanteffectonaparcel’scarbonstocks.Forexample,carbonstockgainscanberealizedbyconvertingcroplandsoilstowetlandsorforestland,whilecarbonstocklossesoftenresultfromaconversionfromforestlandstograzinglands.Aland‐usecategorizationsystemthatisconsistentandcomplete(bothtemporallyandspatially)isneededinordertoassesslanduseandland‐usechangestatuswithinanentity’sboundaries.Allofthelandwithinanentity’sboundaryshouldbeclassifiedaccordingtothefollowingland‐usetypes:cropland,grazingland,forestland,wetland,settlements(e.g.,residentialandcommercialbuildings),andotherland(e.g.,baresoil,rock);seedefinitionsprovidedabove.Individualparcelareasshouldsumtothetotallandareabeforeandafterland‐usechange.


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Inmanycases,themethodsproposedtoestimatecontributionstotheGHGfluxresultingfromland‐usechangearethesameasthoseusedtoestimatecarbonstockchangesintheindividualcroplandandgrazingland,wetland,andforestrychapters;although,inspecificcases,guidanceisalsoprovidedonreconcilingcarbon‐stockestimatesbetweendiscretedatasetsandestimationmethods(e.g.,reconcilingforestsoilcarbonestimatesandcroplandsoilcarbonestimatesforland‐usechangefromforestlandtocropland).ThemethodsforquantifyingGHGfluxfromland‐usechangeareintendedforuseattheentityscaleonlandsmanagedtoenhancetheproductionoffood,feed,fiber,andrenewableenergy.Methodsarecurrentlynotprovidedforestimatingemissionsfromenergyusedwhenconvertinglandusefromonecategorytoanother.Neitheraremethodsprovidedforland‐usechangefromsettlementsorthe“otherland”categorytocropland,grazingland,wetlandorforestland.ThemethodshavebeendevelopedforU.S.conditionsandareconsideredapplicabletoagriculturalandforestryproductionsystemsintheUnitedStates.Thissubsectionbrieflydescribesthesourcecategoriescovered.FurtherdescriptionsofthecurrenttoolsandmethodsusedtoestimateGHGfluxfromthesesourcecategoriesarediscussedlaterinChapter7.

AnnualChangeinCarbonStocksinDeadWoodandLitterDuetoLandConversion.Liveanddeadbiomasscarbonstocksandsoilorganiccarbonconstituteasignificantcarbonsinkinmanyforestandagriculturallands.Followingland‐useconversion,theestimationofdeadbiomasscarbonstockchangesduringtransitionperiodsrequiresthattheareasubjecttoland‐usechangeontheentity’soperationbetrackedforthedurationofa20‐yeartransitionperiod.

ChangeinSoilOrganicCarbonStocksforMineralSoils.Soilorganiccarbonstocksareinfluencedbyland‐usechange(Aaldeetal.,2006)duetochangesinproductivitythatinfluencecarboninputsandtochangesinsoilmanagementthatinfluencecarbonoutputs(DavidsonandAckerman,1993;Ogleetal.,2005;PostandKwon,2000).Themostsignificantchangesinsoilorganiccarbonoccurwithland‐usechange,particularlyconversionstocroplands,duetochangesinthedisturbanceregimesandassociatedeffectsonsoilaggregatedynamics(Sixetal.,2000).

SpecificmitigationpracticesarenotexplicitlydescribedinChapter7;however,avoidingland‐useconversionsthatresultinsignificantcarbonlossescouldmitigatenetGHGemissions(e.g.,avoidingtheconversionofforestlandstograzinglands).

2.6 Uncertainty

QuantifyingtheuncertaintyofGHGemissionsandreductionsfromagricultureandforestrypracticesisanimportantaspectofdecisionmakingforfarmersandlandownersastheuncertaintyrangeforeachGHGestimatecommunicatesourlevelofconfidencethattheestimatereflectstheactualbalanceofGHGexchangebetweenthebiosphereandtheatmosphere.Inparticular,afarm,ranch,orforestlandownermaybemoreinclinedtoinvestinmanagementpracticesthatreducenetGHGemissionsiftheuncertaintyrangeforanestimateislow,meaningthathigherconfidenceintheestimatesexists.Thisreportpresentstheapproachforaccountingfortheuncertaintyintheestimatednetemissionsbasedonthemethodspresentedinthisreport.6AMonteCarloapproach

6TheIPCCGoodPracticeGuidance(IPCC,2000)recommendstwoapproaches—Tier1andTier2—fordevelopingquantitativeestimatesofuncertaintyforemissionsestimatesforsourcecategories.TheTier1methoduseserrorpropagationequations.Theseequationscombinetheuncertaintyassociatedwiththeactivitydataandtheuncertaintyassociatedwiththeemission(orother)factors.Thisapproachisappropriatewhereemissions(orremovals)areestimatedastheproductofactivitydataandanemissionfactororasthesumofindividualsub‐sourcecategoryvalues.TheTier2methodutilizestheMonteCarloStochasticSimulationtechnique.Usingthistechnique,anestimateofemission(orremoval)foraparticularsourcecategoryisgeneratedmanytimesviaanuncertaintymodel,resultinginanapproximatePDFfortheestimate.


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wasselectedasthemethodforestimatingtheuncertaintyaroundtheoutputsfromthemethodologiesinthisreport,asitiscurrentlythemostcomprehensive,soundmethodavailabletoassesstheuncertaintyattheentityscale.Limitationsanddatagapsexist;however,asnewdatabecomeavailable,themethodcanbeimprovedovertime.ImplementationofaMonteCarloanalysisiscomplicatedandrequirestheuseofastatisticaltooltoproduceaprobabilitydensityfunction(PDF)7aroundtheGHGemissionsestimate.8Fromtheprobabilitydensityfunction,theuncertaintyestimatecanbederivedandreported.

Chapter2References

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Wheresufficientandreliableuncertaintydatafortheinputvariablesareavailable,theTier2methodisthepreferredoption.

7TheintegralofaPDFoveragivenintervalofvaluesistheprobabilityforarandomvariabletotakeonsomevalueintheinterval.Thatis,thePDFisafunctiongivingprobability“densities”anditsintegralgivesprobabilities.AnarrowerPDFforanestimateindicatessmallervariancearoundthecentral/mostlikelyvalue,i.e.,ahigherprobabilityofthevaluetobeclosertothecentral/mostlikelyvalue.Theuncertaintyforsuchanestimateislower.

8GiventhecomplexityofMonteCarloanalysisandthenecessityforatool,theapproachpresentedhereisnotintendedfordevelopmentbyalandowner,ratheritisintendedforuseindevelopingatoolthatalandownerwouldusetoassessuncertaintyestimates.


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Reynolds,C.K.,J.A.N.Mills,L.A.Crompton,D.I.Givens,etal.2010.Ruminantnutritionregimestoreducegreenhousegasemissionsindairycows.InEnergyandProteinmetabolismandnutrition,G.M.Crovetto(ed.).

Six,J.,E.T.Elliot,andK.Paustian.2000.Soilmacroaggregateturnoverandmicroaggregateformation:amechanismforCsequestrationunderno‐tillageagriculture.SoilBiol.Biochem.,32:2099‐2103.

Smith,W.B.,P.D.Miles,C.H.Perry,andS.A.Pugh.2009.ForestResourcesoftheUnitedStates,2007.Washington,DC:U.S.DepartmentofAgricultureForestService.

U.S.EPA.2011.InventoryofU.S.GreenhouseGasEmissionsandSinks:1990‐2009.Washington,DC:U.S.EnvironmentalProtectionAgency.


Winter,T.C.,J.W.Harvey,andO.L.Franke.1998.Groundwaterandsurfacewater:asingleresource:U.S.GeologicalSurvey.


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Authors:StephenM.Ogle,ColoradoStateUniversity(LeadAuthor)PaulR.Adler,USDAAgriculturalResearchServiceJayBreidt,ColoradoStateUniversityStephenDelGrosso,USDAAgriculturalResearchServiceJustinDerner,USDAAgriculturalResearchServiceAlanFranzluebbers,USDAAgriculturalResearchServiceMarkLiebig,USDAAgriculturalResearchServiceBruceLinquist,UniversityofCalifornia,DavisPhilRobertson,MichiganStateUniversityMicheleSchoeneberger,USDAForestServiceJohanSix,UniversityofCalifornia,Davis;SwissFederalInstituteofTechnology,ETH‐ZurichChrisvanKessel,UniversityofCalifornia,DavisRodVenterea,USDAAgriculturalResearchServiceTristramWest,PacificNorthwestNationalLaboratory

Contents:

3 QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems..... .........................................................................................................................................................................3‐4

3.1 Overview...........................................................................................................................................................3‐53.1.1 OverviewofManagementPracticesandResultingGHGEmissions...........3‐63.1.2 SystemBoundariesandTemporalScale..............................................................3‐103.1.3 SummaryofSelectedMethods/ModelsSourcesofData...............................3‐103.1.4 OrganizationofChapter/Roadmap........................................................................3‐11

3.2 CroplandManagement..............................................................................................................................3‐123.2.1 ManagementInfluencingGHGEmissionsinUplandSystems.....................3‐123.2.2 ManagementInfluencingGHGEmissionsinFloodedCroppingSystems........

...............................................................................................................................................3‐253.2.3 Land‐UseChangetoCropland..................................................................................3‐28

3.3 GrazingLandManagement......................................................................................................................3‐293.3.1 ManagementActivityInfluencingGHGEmissions...........................................3‐303.3.2 Land‐UseChangetoGrazingLands........................................................................3‐36

3.4 Agroforestry..................................................................................................................................................3‐373.4.1 CarbonStocks..................................................................................................................3‐393.4.2 NitrousOxide...................................................................................................................3‐413.4.3 Methane.............................................................................................................................3‐413.4.4 ManagementInteractions...........................................................................................3‐42

Chapter 3

Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems

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3.5 EstimationMethods...................................................................................................................................3‐423.5.1 BiomassCarbonStockChanges...............................................................................3‐433.5.2 LitterCarbonStockChanges.....................................................................................3‐493.5.3 SoilCarbonStockChanges.........................................................................................3‐493.5.4 SoilNitrousOxide..........................................................................................................3‐583.5.5 MethaneUptakebySoils.............................................................................................3‐743.5.6 MethaneandNitrousOxidefromFloodedRiceCultivation.........................3‐773.5.7 CO2fromLiming.............................................................................................................3‐833.5.8 Non‐CO2EmissionsfromBiomassBurning........................................................3‐863.5.9 CO2fromUreaFertilizerApplications...................................................................3‐90

3.6 SummaryofResearchGapsforCropandGrazingLandManagement..................................3‐92Appendix3‐A:SoilN2OModelingFrameworkSpecifications...............................................................3‐97

3‐A.1DescriptionofProcess‐BasedModels.....................................................................3‐993‐A.2EmpiricalScalarsforBaseEmissionRates.........................................................3‐1063‐A.3Practice‐BasedScalingFactors................................................................................3‐108

Appendix3‐B:GuidanceforCropsNotIncludedintheDAYCENTModel....................................3‐113Chapter3References..........................................................................................................................................3‐116

Ogle,S.M.,P.R.Adler,F.J.Breidt,S.DelGrosso,J.Derner,A.Franzluebbers,M.Liebig,B.Linquist,G.P.Robertson,M.Schoeneberger,J.Six,C.vanKessel,R.Venterea,T.West,2014.Chapter3:QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.



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C CarbonCH4 MethaneCO2 CarbondioxideCO2‐eq CarbondioxideequivalentsCRP ConservationReserveProgramEPA U.S.EnvironmentalProtectionAgencyGHG GreenhousegasH2CO3 Carbonicacidha HectareIPCC IntergovernmentalPanelonClimateChangeK PotassiumLRR LandResourceRegionm MeterMg MegagramsN NitrogenN2 NitrogengasN2O NitrousOxideNH4+ AmmoniumNO NitricoxideNO3‐ NitrateNOx Mono‐nitrousoxidesNRCS NaturalResourcesConservationServiceNUE NitrogenuseefficiencyO2 OxygenPg PetagramPRISM Parameter‐ElevationRegressionsonIndependentSlopesModelSOC SoilorganiccarbonSOM SoilorganicmatterSSURGO SoilSurveyGeographicDatabaseTg TeragramsUSDA U.S.DepartmentofAgriculture


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3 QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems

Thischapterprovidesmethodologiesandguidanceforreportinggreenhousegas(GHG)emissionsandsinksattheentityscaleforcroplandandgrazinglandsystems.Morespecifically,itfocusesonmethodsforlandusedfortheproductionofcropsandlivestock(i.e.,grazinglands).Section3.1providesanoverviewofcroplandandgrazinglandsystemsmanagementpracticesandresultingGHGemissions,systemboundariesandtemporalscale,asummaryoftheselectedmethods/models,sourcesofdata,andaroadmapforthechapter.Section3.2presentsthevariousmanagementpracticesthatinfluenceGHGemissionsinuplandandwetlandcroppingsystemsandland‐usechangetocropland.Section3.3providesasimilardiscussionforgrazinglandsystemsandland‐usechangetograzingsystems.Section3.4discussesagroforestry,andSection3.5providestheestimationmethods.Finally,Section3.6includesasummaryofresearchgapswithadditionalinformationonthenitrousoxide(N2O)methodologyandsupplementalmethodologyguidanceintheAppendices.

3.1 Overview

Croplandandgrazinglandsystemsaremanagedinavarietyofways,whichresultsinvaryingdegreesofGHGemissionsorsinks.Table3‐1providesadescriptionofthesourcesofemissionsorsinksandthesectioninwhichmethodologiesareprovidedalongwiththecorrespondingGHGs.

Table3‐1:OverviewofCroplandandGrazingLandSystemsSourcesandAssociatedGreenhouseGases

SourceMethodforGHGEstimation Description

CO2 N2O CH4

Biomassandlittercarbonstockchanges

Estimating herbaceousbiomasscarbon stockduringchangesinlanduseisnecessarytoaccountfortheinfluenceofherbaceousplantsoncarbondioxide(CO2)uptakefromtheatmosphereandstorageintheterrestrialbiosphereforatleastaportionoftheyearrelativetothebiomasscarbonandassociatedCO2uptakeinthepreviouslandusesystem.Agroforestrysystemsalsohavealongertermgainorlossofcarbonbasedonthemanagementoftreesinthesesystems.

Soilorganiccarbonstocksformineralsoils

Soilorganiccarbon stocksareinfluencedbylanduseandmanagementincroplandandgrazinglandsystems,aswellasconversionfromotherlandusesintothesesystems(Aaldeetal.,2006).Soilorganiccarbonpoolscanbemodifiedduetochangesincarboninputsandoutputs(Paustianetal.,1997).

Soilorganiccarbonstocksfororganicsoils

Emissionsoccurinorganicsoilsfollowingdrainageduetotheconversionofananaerobicenvironmentwithahighwatertabletoaerobicconditions(ArmentanoandMenges,1986),resultinginasignificantlossofcarbontotheatmosphere(Ogleetal.,2003).

DirectandindirectN2Oemissionsfrommineralsoils

N2Oisemittedfromcroplandbothdirectlyandindirectly.Directemissionsarefluxesfromcroplandorgrazinglandswheretherearenitrogenadditionsornitrogenmineralizedfromsoilorganicmatter.Indirectemissionsoccurwhenreactivenitrogenisvolatilizedasammonia(NH3)ornitrogenoxide(NOx),ortransportedviasurfacerunofforleachinginsolubleformsfromcroplandorgrazinglands,leadingtoN2Oemissionsinanotherlocation.


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CO2 N2O CH4

DirectN2Oemissionsfromdrainageoforganicsoils

Organicsoils (i.e.,histosols) areaspecialcaseinwhichdrainageleadstohighratesofnitrogenmineralizationandincreasedN2Oemissions.Themethodassumesthatorganicsoilshaveasignificantorganichorizoninthesoil,andtherefore,themaininputsofnitrogenarefromoxidationoforganicmatter.

Methaneuptakebysoils

Agronomicactivityuniversallyreducesmethanotrophyinarablesoilsby70%ormore(Mosieretal.,1991;Robertsonetal.,2000;Smithetal.,2000).Recoveryofmethane(CH4)oxidationuponabandonmentfromagricultureisslow,taking50to100yearsforthedevelopmentofeven50%offormer(original)rates(Levineetal.,2011).

MethaneandN2Oemissionsfromricecultivation

ThereareanumberofmanagementpracticesthataffectCH4 andN2Oemissionsfromricesystems.Themethodaddresseskeypracticesincludingtheinfluenceofwatermanagement,residuemanagementandorganicamendmentsonCH4emissionsfromrice(Lascoetal.,2006;Yanetal.,2005)andassociatedimpactsonN2Oemissions.

CO2fromliming

AdditionoflimetosoilsistypicallythoughttogenerateCO2emissionstotheatmosphere(deKleinetal.,2006).However,prevailingconditionsinU.S.agriculturallandsleadtoCO2uptakebecausethemajorityoflimeisdissolvedinthepresenceofcarbonicacid(H2CO3).Therefore,theadditionoflimewillleadtoacarbonsinkinthemajorityofU.S.croplandandgrazinglandsystems.

Non‐CO2emissionsfrombiomassburning

BiomassburningleadstoemissionsofCO2aswellasotherGHGsorprecursorstoGHGsthatareformedlaterthroughadditionalchemicalreactions.Note:CO2emissionsarenotaddressedforcropresiduesorgrasslandburning,becausethecarbonisre‐absorbedfromtheatmosphereinnewgrowthofcropsorgrasseswithinanannualcycle.

CO2fromureafertilizerapplication

UreafertilizerapplicationtosoilscontributesCO2emissions totheatmosphere.TheCO2emittedisincorporatedintotheureaduringthemanufacturingprocess.IntheUnitedStates,thesourceoftheCO2isfossilfuelusedforNH3production.TheCO2capturedduringNH3productionisincludedinthemanufacturer’sreportingsoitsreleaseviaureafertilizationisanadditionalCO2emissiontotheatmosphereandisincludedinthefarm‐scaleentityreporting.

3.1.1 OverviewofManagementPracticesandResultingGHGEmissions

GuidanceisprovidedinthissectionforreportingofGHGemissionsassociatedwithentity‐levelfluxesfromfarmand/orlivestockoperations.TheguidancefocusesonmethodsforestimatingtheinfluenceoflanduseandmanagementpracticesonGHGemissions(andsinks)incropandgrazinglandsystems.Methodsaredescribedforestimatingbiomassandsoilcarbonstockchanges,soilN2Oemissions,CH4emissionsfromfloodedrice,CH4sinksfrommethanotrophicactivity,CO2emissionsorsinksfromliming,biomassburningnon‐CO2GHGemissions,andCO2emissionsfromureafertilizerapplication(seeTable3‐2).


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Table3‐2:OverviewofCroplandandGrazingLandSystemsSources,MethodandSection

Section Source Method

3.5.1‐3.5.2

Biomasscarbonstockchanges

HerbaceousbiomassisestimatedwithanIPCCTier2methodusingentityspecificdataasinputintotheIPCCequationsdevelopedbyLascoetal.(2006)andVerchotetal.(2006).WoodyplantgrowthandlossesinagroforestryorperennialtreecropsareestimatedwithanIPCCTier3method,usingasimulationmodelapproachwithentityinput.

3.5.3Soilorganiccarbonstocksformineralsoils

AnIPCCTier3methodisusedtoestimatetheSOCatthebeginningandendoftheyearformineralsoilswiththeDAYCENTprocess‐basedmodel.ThestocksareenteredintotheIPCCequationsdevelopedbyLascoetal.(2006),Verchotetal.(2006)toestimatecarbonstockchanges.

3.5.3Soilorganiccarbonstocksfororganicsoils

CO2emissionsfromdrainageoforganicsoils(i.e.,Histosols)areestimatedwithanIPCCTier2methodusingtheIPCCequationdevelopedbyAaldeetal.(2006)andregionspecificemissionfactorsfromOgleetal.(2003).

3.5.4

DirectN2Oemissionsfrommineralsoils

ThedirectN2OmethodsareestimatedwithanIPCCTier3method.Formajorcommoditycrops,acombinationofexperimentaldataandprocess‐basedmodelingusingtheDAYCENT1modelandDNDC2(denitrification‐decomposition)areusedtoderiveexpectedbaseemissionratesfordifferentsoiltextureclassesineachU.S.DepartmentofAgricultureLandResourceRegion.Forminorcommoditycropsandincaseswherethereareinsufficientempiricaldatatoderiveabaseemissionrate,thebaseemissionrateisbasedontheIPCCdefaultfactormultipliedbythenitrogeninput(deKleinetal.,2006).Theseemissionratesarescaledwithpractice‐basedscalingfactorstoestimatetheinfluenceofmanagementchangessuchasapplicationofnitrificationinhibitorsorslow‐releasefertilizers.

DirectN2Oemissionsfromdrainageoforganicsoils

DirectN2Oemissionsfromdrainageoforganicsoils,i.e.,Histosols,areestimatedwiththeIPCCTier1method(deKleinetal.,2006).

IndirectN2Oemissions

IndirectsoilN2OemissionsareestimatedwiththeIPCCTier1method(deKleinetal.,2006).

3.5.5Methaneuptakebysoils

Methaneuptakebysoilisestimatedwithanequationthatusesaveragevaluesformethaneoxidationinnaturalvegetation—whethergrassland,coniferousforest,ordeciduousforest—attenuatedbycurrentlandusepractices.ThisapproachisanIPCCTier3method.

3.5.6

MethaneandN2Oemissionsfromfloodedricecultivation

IPCCTier1methodsareusedtoestimateCH4andN2Oemissionsfromfloodedriceproduction(deKleinetal.,2006;Lascoetal.,2006).

1TheversionofDAYCENTcodedandparameterizedforthemostrecentU.S.nationalGHGinventory(U.S.EPA,2013)wasusedtoderiveexpectedbaseemissionrates.2DNDC9.5compiledonFeb25,2013wasusedtoderiveexpectedbaseemissionrates.


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3.5.7 CO2fromlimingAnIPCCTier2methodisusedtoestimateCO2emissionsfromapplicationofcarbonatelimes(deKleinetal.,2006)withU.S.‐specificemissionsfactors(adaptedfromWestandMcBride,2005).

3.5.8Non‐CO2emissionsfrombiomassburning

Non‐CO2GHGemissionsfrombiomassburningofgrazinglandvegetationorcropresiduesareestimatedwiththeIPCCTier2method(Aaldeetal.,2006).

3.5.9CO2fromureafertilizerapplication

CO2emissionsfromapplicationofureaorurea‐basedfertilizerstosoilsareestimatedwiththeIPCCTier1method(deKleinetal.,2006).

3.1.1.1 DescriptionofSector

Croplandsincludeallsystemsusedtoproducefood,feed,andfibercommodities,inadditiontofeedstocksforbioenergyproduction.Croplandsareusedfortheproductionofadaptedcropsforharvestandincludebothcultivatedandnon‐cultivatedcrops(U.S.EPA,2013).Cultivatedcropsaretypicallycategorizedasroworclose‐growncrops,suchascorn,soybeans,andwheat.Non‐cultivatedcrops(orthoseoccasionallycultivatedtoreplenishthecrop)includehay,perennialcrops(e.g.,orchardsandvineyards),andhorticulturalcrops.ThemajorityofU.S.croplandisinuplandsystemsoutsideofwetlandsasdefinedinSection4.1.1,Wetlands,anduplandcroppingsystems(i.e.,dryland)mayormaynotbeirrigated.Ricecanbegrownonnaturalorconstructedwetlands,butwewillrefertothesesystemsasfloodedricetoavoidconfusionwithChapter4.Inaddition,wetlandscanalsobedrainedforcropproduction,whichagainisconsideredacroplandbecausetheprincipaluseiscropproduction.Somecroplandsaresetasideinreserve,suchaslandsenrolledintheConservationReserveProgram(CRP).Croplandsalsoincludeagroforestrysystemsthatareamixtureofcropsandtrees,suchasalleycropping,shelterbelts,andriparianbuffers.

Grazinglandsaresystemsthatareusedforlivestockproduction,andoccurprimarilyongrasslands.Grasslandsarecomposedprincipallyofgrasses,grass‐likeplants,forbs,orshrubssuitableforgrazingandbrowsing,andincludebothpasturesandnativerangelands(U.S.EPA,2013).Furthermore,savannas,somewetlandsanddeserts,andtundracanbeconsideredgrazinglandsintheUnitedStatesifusedforlivestockproduction.Grazinglandsystemsinclude:(1)managedpasturesthatmayrequireperiodicclearing,burning,chaining,and/orchemicalstomaintainthegrassvegetation;and(2)nativerangelandsthattypicallyrequirelimitedmanagementtomaintainbutmaybedegradedifoverstockedorotherwiseoverused.

CropandgrazinglandmanagementinfluencesGHGemissions(Smithetal.,2008b),whichcanbereducedbyadoptingconservationpractices(CAST,2004;2011).Operatorsofcroplandsystemsuseavarietyofpracticesthathaveimplicationsforemissions,suchasnutrientadditions,irrigation,limingapplications,tillagepractices,residuemanagement,fallowingfields,forageandcropselection,set‐asidesoflandsinreserveprograms,erosioncontrolpractices,watertablemanagementinwetlands,anddrainageofwetlands.OperatorsofgrazingsystemsalsohaveavarietyofmanagementoptionsthatinfluenceGHGemissions,suchasstockingrate,forageselection,useofprescribedfires,nutrientapplications,wetlanddrainage,irrigation,limingapplications,andsilvopastoralpractices.

3.1.1.2 ResultingGHGEmissions

CroplandandgrazinglandsaresourcesofN2OandCH4emissionsandhavealargepotentialtosequestercarbonwithchangesinmanagement(Smithetal.,2008b).Infact,N2Oemissionsfrom


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managementofagriculturalsoilsareakeysourceofGHGemissionsintheUnitedStates(U.S.EPA,2013).N2Oemissionsresultfromtheprocessesofnitrificationanddenitrification,whichareinfluencedbylanduseandmanagementactivity.Landuseandmanagementcanalsoinfluencecarbonstocksinbiomass,deadbiomass,andsoilpools.Carbonstockscanbeenhancedorreduceddependingonlanduseandmanagementpractices(CAST,2004;IPCC,2000;Smithetal.,2008b).Consequently,cropandgrazinglandsystemscanbeeitherasourceorsinkforCO2,dependingonthenetchangesinbiomass,deadbiomass,andsoilcarbon.Burningbiomassisapracticethatcaninitiallyreducebiomasscarbonstockbutcanprovidesufficientstimulustoenhanceensuingecosystemcarbonstorage.Ingeneralthough,burningcausesadeclineinsoilorganiccarbonstocksduetolossofcarboninputfromplantlitterandroots.Burningwillalsoleadtonon‐CO2GHGemissions—CH4,N2O,andotheraerosolgases(CO,NOx)—thatcanbelaterconvertedtoGHGsintheatmosphereoroncedepositedontosoil.

SoilsincropandgrazinglandsystemscanalsobeasourceorsinkforCH4dependingontheconditionsandmanagementofsoil.CH4canberemovedfromtheatmospherethroughtheprocessofmethanotrophyinsoils.Methanotrophyoccursunderaerobicconditionsandiscommoninmostsoilsthatdonothavestandingwater.Incontrast,CH4isproducedinsoilsthroughtheprocessofmethanogenesis,whichoccursunderanaerobicconditions(e.g.,soilswithstandingwatersuchassoilsusedforfloodedriceproduction).Bothoftheseprocessesaredrivenbytheactivityofmicroorganismsinsoils,andtheirrateofactivityisinfluencedbylanduseandmanagement.

3.1.1.3 Managementinteractions

TheinfluenceofcropandgrazinglandmanagementonGHGemissionsisnottypicallythesimplesumofeachpractice’seffect.Theinfluenceofonepracticecandependonanotherpractice.Forexample,theinfluenceoftillageonsoilcarbonwilldependonresiduemanagement.Theinfluenceofnitrogenfertilizationratescandependontheapplicationofnitrificationinhibitors.AvarietyofexamplesisgiveninSection3.2andSection3.3.Becauseofthesesynergies,estimatingGHGemissionsfromcropandgrazinglandsystemswilldependonacompletedescriptionofthepracticesusedintheoperation,includingpastmanagementtocapturelegacyeffectsonGHGemissions,aswellasancillaryvariablessuchassoilcharacteristicsandweatherorclimateconditions.

3.1.1.4 RiskofReversals

AnytrendinGHGemissionsassociatedwithachangeincropandgrazinglandmanagementcanbereversediftheoperatorrevertstotheoriginalpractice.ReversalswillnotnegatetheGHGmitigationforCH4orN2Othatoccurredpriortothereversion.IfemissionsarereducedforCH4orN2O,theemissionreductionispermanentandcannotbechangedbysubsequentmanagementdecisions.

Reversalscanoccurwithcarbonsequestrationinbiomassandsoils.CO2canberemovedfromtheatmospherethroughcropandforageproductionandsequesteredinbiomassorsoilsfollowingtheadoptionofaconservationpractice,suchasno‐till(CAST,2004;USDA,2011).Ifcarbonisincreasinginthebiomassorsoils,thenthepracticeeffectivelyreducestheamountofCO2intheatmosphere.However,netCO2canbereturnedtotheatmosphereifthereisareversioninmanagementtothepreviouspracticethatcausesadeclineinthebiomassorsoilcarbonstocks.Forexample,enrollmentoflandintheCRPhasincreasedtheamountofcarboninsoils(i.e.,increaseinsoilcarbonstock),andthusmitigatesCO2emissionstotheatmosphereassociatedwithotheremissionssources,suchasfossilfuelcombustion(USDA,2011).However,tillingformerCRPlandswillleadtoadeclineinsoilcarbonstocks,therebyreversingthetrendforCO2uptakefromtheatmosphereandleadingtoCO2emissiontotheatmosphere.Ingeneral,GHGemissionsinvolving


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carbonstocksinbiomass,deadbiomass,orsoilscanbeconsideredreversible,dependingonfuturemanagementdecisions.Consequently,reversalsinvolvingcarbonstocksnotonlyaffectfutureemissiontrends,butalsohaveconsequencesonpastmitigationeffortsbyreturningpreviouslysequesteredCO2totheatmosphere.

3.1.2 SystemBoundariesandTemporalScale

Systemboundariesaredefinedbythecoverage,extent,andresolutionoftheestimationmethods.ThecoverageofmethodsinthisguidancecanbeusedtoestimateGHGemissionsourcesthatoccuronfarmandranchoperations,includingemissionsassociatedwithbiomasscarbon,littercarbon,andsoilscarbonstockchanges;CH4andN2Ofluxesfromsoils;emissionsfromburningofbiomass;andCO2fluxesassociatedwithureafertilizationandadditionofcarbonatelimes.GHGemissionsalsooccurwithproductionofmanagementinputs,suchassyntheticfertilizersandpesticides,andtheprocessingoffood,feed,fiber,andbioenergyfeedstockproductsfollowingharvest;butmethodsarenotprovidedtoestimatetheseemissions.Moreover,emissionsfromenergyuse,includingthoseoccurringontheentity’soperation,arenotaddressedinthemethods.

Themethodsprovidedforcropandgrazinglandsystemshavearesolutionofanindividualparceloflandorfieldandincludethespatialextentofallfieldsintheentity’soperation.Fieldsareareasusedtoproduceasinglecroporrotationofcrops,ortoraiselivestock(i.e.,pasture,rangeland).Fieldsareoften,butnotalways,dividedbyfences.Emissionsareestimatedforeachindividualfieldthatisusedforcroplandandgrazinglandontheoperation,andthentheemissionsareaddedtogethertoestimatethetotalemissionsfromthecropandgrazinglandsystemsintheentity’soperation.Thetotalsarethencombinedwithemissionsfromforestandlivestocktodeterminetheoverallemissionsfromtheoperationbasedonthemethodsprovidedinthisguidance.EmissionsareestimatedonanannualbasisforasmanyyearsasneededforGHGemissionsreporting.

3.1.3 SummaryofSelectedMethods/ModelsSourcesofData

TheIntergovernmentalPanelonClimateChange(IPCC)(IPCC,2006)hasdevelopedasystemofmethodologicaltiersrelatedtothecomplexityofdifferentapproachesforestimatingGHGemissions.Tier1representsthesimplestmethods,usingdefaultequationsandemissionfactorsprovidedintheIPCCguidance.Tier2usesdefaultmethods,butemissionfactorsthatarespecifictodifferentregions.Tier3usescountry‐specificestimationmethods,suchasaprocess‐basedmodel.ThemethodsprovidedinthisreportrangefromthesimpleTier1approachestothemostcomplexTier3approaches.Higher‐tiermethodsareexpectedtoreduceuncertaintiesintheemissionestimates,ifsufficientactivitydataandtestingareavailable.

Tier1methodsareusedforestimatingCO2emissionsfromureafertilization,CH4emissionsfromfloodedrice,indirectsoilN2Oemissions,anddirectsoilN2Oemissionsfromdrainedorganicsoils.Thesemethodsarethemostgeneralizedglobally,andlackabilitytocapturespecificconditionsatlocalsites,andconsequentlyhavemoreuncertaintyforestimatingemissionsfromanentity’soperation.SoilN2Oemissions,CO2emissionsorsinksfromliming,biomasscarbonstockchanges,soilcarbonstockchangesfordrainedorganicsoils,andbiomassburningnon‐CO2GHGemissionsallhaveelementsofTier2methods,butmayrelypartlyonemissionfactorsprovidedbytheIPCC(2006).ThesemethodsincorporatesomeinformationaboutconditionsspecifictoU.S.agriculturalsystemsandtheinfluenceonemissionrates,butagainlackspecificityforlocalsiteconditionsinmanycases.SoilcarbonstockchangesformineralsoilsareestimatedusingaTier3methodwithaprocess‐basedsimulationmodel(i.e.,DAYCENT).CH4sinksfrommethanotrophicactivityarealsoestimatedwithaTier3method,duetotheabsenceofIPCCguidanceforestimatinglanduseandmanagementeffectsonCH4uptakeinsoils.TheTier3methodassociatedwithsoilcarbonstockchangesinmineralsoilshasthegreatestpotentialforestimatingtheinfluenceoflocalconditionson


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GHGemissions.Theapplicationhasageneralsetofparametersthathavebeencalibratedacrossanationalsetofexperiments.However,themodeldoesincorporatedriversassociatedwithlocalconditions,includingspecificmanagementpractices,soilcharacteristics,andweatherpatterns,providingestimatesofGHGemissionsthataremorespecifictotheentity’soperation.FutureresearchandrefinementsofthecroplandandgrazinglandmethodswilllikelyincorporatemoreTier3methodsinthefuture,andthusprovideamoreaccurateestimationofGHGemissionsforentityreporting.

Allmethodsincludearangeofdatasourcesfromvaryinglevelsofspecificityonoperation‐specificdatatonationaldatasets.Operation‐specificdatawillneedtobecollectedbytheentity,andgenerallyareactivitydatarelatedtothefarmandlivestockmanagementpractices(e.g.,tillagepractices,grazingpractices,fertilizerusage).Nationaldatasetsarerecommendedforancillarydatarequirementsthatareusedinmethods,suchasclimatedataandsoilcharacteristics.However,theentitydoeshavetheoptiontouseoperation‐specificdataforclimate(i.e.,weatherdata)andsoils.

3.1.4 OrganizationofChapter/Roadmap

Thecroplands/grazinglandsportionofthisreportisorganizedintofourprimarysections.Sections3.2and3.3provideadescriptionofmanagementimpactsonGHGemissionsincropandgrazinglandsystems.Section3.2isfurthersubdividedintosectionsfocusedonuplandagriculture,floodedmanagementforcropproduction,andtheinfluenceofland‐usechange.Section3.3issubdividedintoageneraldescriptionofmanagementpracticesandtheinfluenceofland‐usechange.ThefirsttwosectionsprovidethescientificbasisforhowmanagementpracticesinfluenceGHGemissions.Thesetwosectionsalsodiscussmanagementoptionsthatrequirefurtherstudy.Section3.4providesanoverviewofagroforestrysystems.AgeneraldescriptionofthevariousGHGemissionsandsinksthatresultfrommanagementpracticesandpotentialmanagementinteractionsisprovidedinthissection.

Section3.5describesthemethods.Eachmethodincludesageneraldescription(includingequationsandfactorsifappropriate),activitydatarequirements,ancillarydatarequirements,limitationsofthemethod,anduncertaintiesassociatedwiththeestimation.AsinglemethodisprovidedforeachoftheGHGemissionsources(andsinks),basedonthebestavailablemethodforapplicationinanoperationalsystemforentity‐scalereporting.Asinglemethodwasselectedtoensureconsistencyinemissionestimationbyallreportingentities.Moreadvancedapproachesmaybeadoptedinthefutureasthemethodsmature.

Section3.6providesasummaryofresearchgaps.Thegapshighlightkeyresearchareasthatrequirefurtherstudyforoneoftworeasons.ThefirstreasonisthatapracticelackssufficientevidenceoraclearimpactonGHGemissionsbasedonexistingresearch.Thisgapismostoftenrelatedtoalackofmechanisticunderstandingoftheprocessesinfluencedbythepractice.Thesepracticesmaybeincludedinfuturerevisionstothemethodsiffurtherstudyleadstoaconsensusthatthepracticehasanimpactonemissions.Thesecondreasonforidentifyingtheneedforfurtherstudyisthatthepracticeisincludedinestimationmethods,butthereisneedforfurtherresearchtoreduceuncertainty.Thissecondgapmayinvolvefurthermechanisticstudy,butcouldalsorequirefurthermethodsofdevelopmentorrefinement.

Finally,Appendix3‐AprovidesamorecomprehensivedescriptionofthesoilN2Omodelingframeworkspecifications.Thisappendixincludesadiscussionoftheprocess‐basedmodelsusedinthemethodology;theempiricalscalarsforthebaseemissionrates;andthepractice‐basedscalingfactors.Appendix3‐BprovidesalternativemethodologiesincaseswhereanentityismanagingcropsnotincludedintheDAYCENTmodel.


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3.2 CroplandManagement

HowcroplandismanagedcanhaveasignificanteffectonGHGemissionsandremovals.ThissectionprovidesasummaryofthecurrentstateofthescienceanddescribeshowmanagementpracticesdriveGHGemissionsorsinksinuplandcroplandsystems.

3.2.1 ManagementInfluencingGHGEmissionsinUplandSystems

ThecroplandmanagementpracticespresentedbelowfocusprimarilyonmitigationpotentialforsoilN2O,CH4emissions,andcarbonsequestration.EachsubsectiondescribesthepracticeandtheunderlyingGHGphenomenonthatinfluencemitigationpotential.

3.2.1.1 NutrientManagement(ManufacturedandOrganic)

Nutrientmanagementreferstotheadditionandmanagementofsyntheticandorganicfertilizerstocroplandsoils,primarilytoaugmentthesupplyofnutrientstothecrop.Nitrogenisgenerallythemostimportantnutrientfromanagronomicstandpoint,becauseitisusuallytheprimarynutrientlimitingcropyieldsandoftenmustbeaddedmorefrequentlyandingreateramountsthanothernutrientssuchasphosphorusandpotassium(ERS,2011;RobertsonandVitousek,2009).NitrogenisalsotheprimarynutrientofconcernwithregardtoGHGemissions,becauseoncefertilizernitrogenentersthesoilitcanbedirectlyconvertedtoN2Obysoilbiologicalprocessesand,insomecases,chemicalreactions(FirestoneandDavidson,1989;Kooletal.,2011;Venterea,2007).WhilerelativelylittleofthefertilizernitrogenappliedisconvertedtoN2O,theseemissionsaregenerallyalargecomponentofthetotalGHGbudgetofcroplands(e.g.,Mosieretal.,2005;Robertsonetal.,2000)becauseN2Ohas310timestheglobalwarmingpotentialofCO2(IPCC,2007).Otherformsofnitrogenoriginatingfromfertilizersmayalsobelosttotheenvironment,includingNH3,nitricoxide(NO),andnitrate(NO3‐).Oncetransportedtodownwindordownstreamecosystems,theseothernitrogenspeciescanbeconvertedtoN2O;suchemissionsarereferredtoas“indirect”N2Oemissions(Beaulieuetal.,2011;deKleinetal.,2006).

NutrientmanagementcanalsoaffectGHGemissionsotherthanN2O,mostnotablythesequestrationofcarbonuponmanureadditionandcropresidueretentionoraddition.Theadditionoforganiccarbonamendments,suchasmanureorresidues,canincreasesoilcarbonwithintheboundariesofthelandparcelreceivingtheamendment(Ogleetal.,2005).However,soilcarbonlossesmayoccurfromthesourcefield(Schlesinger,2000)dependingonthemanagement(Izaurraldeetal.,2001).Manufacturednitrogenadditionscanalsoleadtocarbonsequestration(Ladhaetal.,2011)whereadditionsleadtoincreasedresiduereturntosoil.

Fertilizerrate,timing,placement,andformulationstronglyaffectN2Ofluxes.Ingeneral,anypracticethatincreasescropnitrogenuseefficiency(NUE)wouldbeexpectedtoreduceN2Oemissions,becauseappliednitrogenthatistakenupbycropsorcovercropsisnotavailabletothesoilprocessesthatgenerateN2O,atleastintheshortterm;thisalsomaypreventnitrogenleaching.Thus,strategiestoreduceN2OemissionscanalsoreducethelossofNO3‐andotherformsofreactivenitrogenfromcroppingsystems.

However,practicesthatimproveNUEwillnotalwaysreduceN2Oemissions.Differentfertilizerformulations,forexample,canresultindifferentN2OemissionsirrespectiveofNUEeffects(e.g.,GagnonandZiadi,2010;Gagnonetal.,2011).Likewise,bandedfertilizerplacementcanincreaseNUE(e.g.,Yadvinder‐Singhetal.,1994)butalsocanincreaseratherthandecreaseN2Oemissions(e.g.,Engeletal.,2010),andtillagemanagementcanalsoincreaseNUEwithoutreducingN2Oemissions(Grandyetal.,2006).Thus,NUEisgenerallyimportantbutnotbyitselfsufficienttopredictormanageN2Oemissions.Fertilizerrate,timing,placement,andformulationcanaffectNUEandN2Oemissionsindependently.


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FertilizerRate:Morethananyotherfactor,theamountofnitrogenfertilizerappliedtosoilaffectstheamountofN2Oemitted;inmanycasesothernitrogen‐usestrategies(timing,placement,andformulation)providetheirbenefitbyeffectivelyreducingfertilizernitrogenavailableinthesoil.Inthissense,fertilizerrateintegratestheeffectsofmultiplepracticesandisthebasisfortheIPCCTier1N2Oaccountingmethod(deKleinetal.,2006),wherebyN2Oemissionsareassumedtobeasimplefractionofnitrogeninputs.

Irrespectiveofotherpractices,however,fertilizerrateitselfcanberefinedtoreduceN2Oemissionssolongasratesarenotreducedtothepointthatyieldsdecline.Otherwisemarketleakage—theneedtomakeupyieldselsewherewithmoreintensivefertilizeruseandconcomitantN2Oloss—maylimitthebenefitofreducinglocalfertilizerrates.Thequestionthenbecomeswhethernitrogenfertilizerratescanbereducedwithoutreducingyieldsinaparticularfield.Atleastforcorn,recentchangesinrecommendedfertilizerratesformanyMidwestStatessuggestthatthereislatitudeforreducingfertilizernitrogenratesforsomefarmers.Sincethe1970s,mostfertilizernitrogenrecommendationshavebeenbasedonyieldgoals,whichuseexpectedmaximumyieldmultipliedbynitrogenyieldfactorstocalculatefertilizerrecommendations(Stanford,1973).Precedinglegumecrops,manureinputs,andsoilnitrogentestsarethenusedtofurtherrefineorreducerecommendednitrogenapplicationrates(AndraskiandBundy,2002).

Analternativetotheyield‐goalapproachistheMaximumReturntoNitrogenapproach(Sawyeretal.,2006),wherebytherateofnitrogenfertilizerappliedisbasedonthemaximumfertilizerratethatgeneratessufficientadditionalyieldtojustifythefertilizercost.Theratesaredeterminedfromcropnitrogenresponsecurves.Typically(butnotalways)thisrateissignificantlylessthanthatrecommendedbytheyieldgoalapproach.MaximumReturntoNitrogencalculatorsforcornhavebeenadoptedinatleastsevenStatesintheMidwest.Thiscalculatorandsimilardecisionsupporttoolshavethepotentialforreducingtheamountoffertilizernitrogenappliedtocropsandmorepreciselymatchcropnitrogenrequirements,withoutaffectingthenetreturns(Archeretal.,2008),andwithconcomitantdecreasesinN2Oemissions(Millaretal.,2010).

Hundredsoffertilizeradditionexperimentsworldwidehaveshownthattypically0.5to3percentofnitrogenaddedtosoilisemittedasN2O(Bouwmanetal.,2002;Linquistetal.,2011;StehfestandBouwman,2006).Site‐to‐sitevariationiswellrecognizedandistobeexpectedbasedonsoils,climate,andfertilizerpractices—includingrate.Recentevidencesuggeststhatemissionratesmaybeevenhigheratnitrogeninputlevelsthatexceedcropdemand(Hobenetal.,2011;Maetal.,2010;McSwineyandRobertson,2005;VanGroenigenetal.,2010).

FertilizerTiming:Amajorchallengeinmanagingnitrogenfertilizerforcropproductionissynchronizingnitrogenavailabilityinthesoilwiththecrop’sdemandfornitrogen.Ingeneral,cropdemandfornitrogenisminimalearlyinthegrowingseasonandincreasesseveralweeksafterplanting.

Inmanycases,itmaybemostconvenientand/orcost‐effectivefortheproducertoapplynitrogenfertilizerpriortoplantingorsoonafterplantemergence.InmanypartsoftheU.S.CornBelt,however,applicationofnitrogenfertilizercommonlyoccursinthefallpriortothegrowingseason(Biermanetal.,2011;Ribaudoetal.,2011).Intheabsenceofanactiveandwell‐developedrootsystemtoutilizethefertilizernitrogen,thesepracticesincreasethepotentialforsoilmicrobialandchemicalprocessestotransformtheappliednitrogenintoN2OandothermobileformssuchasNO3,whichcancontributetoindirectN2Oemissions.

Improvingthesynchronybetweensoilnitrogenavailabilityandcropnitrogendemandcanbeachievedbyswitchingfromfalltospringnitrogenapplication;applyingnitrogenseveralweeksafterplantingwith“sidedress”fertilizerapplicationsthataretimedtocoincidewithplantgrowthstages;andusingmultiple“split”applicationsdistributedintimeoverthegrowingseason.Eachof


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thesestrategieshasthepotentialtoreduceN2Oemissions,butthisisnotalwaysthecase.Switchingfromfalltospringnitrogenfertilizer,forexample,hasbeenshowntoreduceN2Oemissionsinsomecases(Burtonetal.,2008a;Haoetal.,2001)butnotalways(Burtonetal.,2008a).Similarly,switchingfrompre‐planttopost‐plantapplicationshasbeenshowntoreduceN2Oemissionsinsomestudies(Matsonetal.,1998),butonlypartofthetimeornotatallinotherstudies(Burtonetal.,2008b;Phillipsetal.,2009;Zebarthetal.,2008b).Somestudieshavefoundreducednitrateleaching,whichimpliesreducedindirectN2Oemissions,withfertilizerapplicationlaterintheseason(e.g.,Errebhietal.,1998).

FertilizerPlacement:ThemannerinwhichnitrogenfertilizerisappliedtosoilcanaffectitsavailabilityforcropuptakeandthereforeitssusceptibilitytosoiltransformationandN2Oproduction.ThreeaspectsoffertilizerplacementaresignificanttoN2Oemissions:(1)broadcastapplicationversusbandingwithinthecroprow;(2)thesoildepthtowhichnitrogenisapplied;and(3)addingfertilizeruniformlyacrossafieldversusapplyingataspatiallyvariablerate.

ThereissomeevidencethatapplyingnitrogenfertilizerinnarrowbandscanimprovecropNUE(MalhiandNyborg,1985).However,bandingalsocreateszonesofhighlyconcentratedsoilnitrogen,whichcanincreaseN2Oproductioncomparedwithbroadcastapplications(Engeletal.,2010).OtherstudieshavefoundnodifferencesinN2Oemissionsinbroadcastversusbandedapplications(Burtonetal.,2008a;Sehyetal.,2003).DirectcomparisonsofapplicationdeptheffectsonN2Oemissionshavealsoshowninconsistentresults(e.g.,BreitenbeckandBremner,1986b;Druryetal.,2006;Fujinumaetal.,2011;Hosenetal.,2002;Liuetal.,2006).However,variablerateapplicationusesdifferentnitrogenratesfordifferentareasoffield,basedonexpectedvariationsincropnitrogendemand.Thisisanewtechniquethatappearspromisingbasedonitsabilitytosubstantiallyimprovefertilizeruseefficiencyatthefieldscale(Mamoetal.,2003;Scharfetal.,2005),andatleastoneearlystudyhasshownreducedN2Oemissionswhennitrogenratewasvariedtomatchcropyieldpotential(Sehyetal.,2003).

FertilizerFormulationandAdditives:ThemostcommonlyusedformsofsyntheticnitrogenfertilizerintheUnitedStatesincludeanhydrousammonia(35percentoftotaluse),urea(24percent),andliquidsolutions,includingureaammoniumnitrate(29percent)(ERS,2011).AvailableevidencesuggeststhatN2Oemissionsfollowingapplicationsofanhydrousammoniaaregreaterthanemissionsfollowingbroadcasturea,althoughinsomestudiesthismaybepartlyduetofertilizerplacement.Infivestudies,anhydrousammoniaresultedin40to200percentgreaterN2Oemissionscomparedwithbroadcasturea(BreitenbeckandBremner,1986a;Fujinumaetal.,2011;Thorntonetal.,1996;Ventereaetal.,2005).Onestudy(Burtonetal.,2008a)foundnodifferenceinN2Oemissionsbetweenanhydrousammoniaandbroadcastureawhenbothwereappliedatalowerrate(80kgNha‐1year‐1)comparedwiththeotherstudies(≥120kgNha‐1).Consequently,theremaybeathresholdintheapplicationratebeforethereisasignificanteffectonemissions.

Thechemicalformofnitrogenfertilizerinfluenceslossesofnitrogenfromthreemajorpathways:surfacevolatilization,soilmicrobialprocesses,andNO3‐leaching.Allfertilizersaresusceptibletodenitrificationoncenitrifiedto(orappliedas)NO3‐.Ammonium‐basedfertilizers,includinganhydrousammonia,urea,andorganicsourcessuchasmanure,arealsosusceptibletoN2Olossduringnitrification.Urea,anhydrousammonia,andmanureareadditionallysusceptibletosurfacevolatilizationasNH3undersomeconditions.VolatilizedNH3andleachedNO3‐contributetoindirectN2Oloss.

Chemicaladditiveshavebeendevelopedtoreleasefertilizernitrogenintothesoilmoregraduallyandtodelaythenitrificationofnitrogenfromammonium(NH4+)toNO3‐inordertoimprovethesynchronybetweencropnitrogendemandandsoilnitrogenavailability.Polymer‐coatedureaslowlyreleasesnitrogenwithincreasingsoiltemperatureandwater,andisintendedtomake


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nitrogensupplymoresynchronouswithplantnitrogendemandandreducenitrogenlosses.EffectsonN2Oproduction,however,appearmixed,withsomestudiesshowingreducedN2Oforpolymer‐coatedurea(e.g.,Hyattetal.,2010)andothersshowingnoimpactorevenhigheremissions(Ventereaetal.,2011a).Arecentmeta‐analysisof13studiesofmostlyvolcanicandwetland‐derivedsoilsfoundthatpolymer‐coatedureareducedN2Oemissionsby35percentonaveragecomparedwithconventionalfertilizers,butresultsaredifficulttogeneralizebecausemostofthesoilsincludedintheanalysiswerenottypicalforU.S.croppingsystems(Akiyamaetal.,2010).

Fertilizersformulatedwithnitrificationinhibitorscanpotentiallyreduceemissionsfromnitrificationanddenitrification,aswellasNO3‐leaching.SomeU.S.fieldstudiesshowsubstantialreductionsinN2Oemissionswhenfertilizerswithnitrificationinhibitorsareaddedcomparedwithconventionalfertilizers(e.g.,Halvorsonetal.,2010a),whileothersshowlittleornoimpact(e.g.,ParkinandHatfield,2010a).Ameta‐analysisofsome28studiesworldwidereportedanaveragereductionof38percent(Akiyamaetal.,2010),butagainresultsaredifficulttogeneralizeduetothesmallsamplesizeandsoilsthatarenottypicalofU.S.croppingsystems.

Onereasontheimpactsoffertilizersdesignedtoreduceemissionsareinconsistentisthattheformofnitrogenappliedinteractswithotherfactorstocontrolnitrogenlosses.Amongthesefactorsisweather,whichdirectlyaffectstheprocessesthatleadtogaseousnitrogenlossesandNO3‐leaching,andindirectlyaffectstheseprocessesbycontrollingplantnitrogenuptake.Soilpropertiessuchastextureandhydraulicstatusarealsoimportant.Ingeneral,nitrificationisimportantinwell‐aeratedsoils,whiledenitrificationismoreimportantinpoorlydrainedsoils.Thenitrogensourcealsointeractswithothermanagementpractices.Forexample,polymer‐coatedureasubstantiallyreducedN2Oemissionsunderno‐tillbutnotfulltillcultivationforirrigatedcorninColorado(Halvorsonetal.,2010a).

OrganicFertilizerEffectsonN2OEmissions:LandapplicationofanimalmanurehasbeenrelatedtoN2Oemissions.Mosieretal.(1998)andPetersen(1999)measuredincreasesinN2Oemissionswithmanureapplication.KaiserandRuser(2000)measuredannualemissionsoftheaddednitrogeninslurryrangingfrom0.74to2.86percent,andDeKleinetal.(2001)foundthatannualN2O‐Nlossesrangedfromzerotofivepercentoftheorganicnitrogenappliedtosoils.Others(e.g.,BartonandSchipper,2001)foundN2OemissionsfollowingtheadditionofmanureslurriesexceededemissionsfromanequivalentamountofmanufacturedN,likelyduetotheslurry’screatingenhancedconditionsfordenitrification.However,GHGemissionsalsooccurifmanureismanagedinpits,lagoons,orsolidstorage.

Injectionofmanureisacommonpracticetoavoidsurfacerunoffandreduceobjectionableodorsfrommanureapplication.BothFlessaandBesse(2000)andWulfetal.(2002)suggestedthatinjectionofswinemanurewouldcreatemorefavorableconditionsforN2OandCH4formationbecauseofthereducedaerationwithinthesoil.However,Dendoovenetal.(1998)didnotfinddifferencesineitherN2OorCH4emissionsfrominjectedorsurface‐appliedswineslurryontoaloamysoil.Thesefindingssuggestthattherate,timing,placement,andformulationofmanureisimportanttoN2Oproduction,similartomanufacturednitrogenfertilizer,butthereisaneedforadditionalresearch.

CO2EmissionsGeneratedfromUreaFertilizerApplications:Unlikeothernitrogenfertilizers,urearesultsinthedirectproductionofCO2inadditiontowhateverN2Omightbesubsequentlyproducedbymicrobes(deKleinetal.,2006).Sinceureais20percentC,everymetrictonofureaappliedtosoilresultsinthedirectemissionof20kgCO2‐C;alternatively,everykilogramofnitrogenappliedasurearesultsinthedirectemissionsof0.43kgCO2‐C.UreaismanufacturedbyreactingNH3andCO2toformammoniumcarbamate,whichisthendehydratedtoformureaprills.IntheUnited


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StatestheCO2inureaiscapturedfromthefossilfuelusedtomanufactureNH3,sothesoilCO2producedrepresentsafossilfuelemission.

ManagementSystemInteractions:NitrogenmanagementpracticescaninteractwithothercroplandmanagementcomponentsinregulatingGHGemissions.Asemphasizedabove,anyfactorthataffectscropNUEhasthepotentialtoaffectN2Oemissions.Therefore,optimizingotherpractices—includingtillageandthemanagementofsoilpH,pests,irrigation,drainage,andotherfactors—willtendtoincreasenitrogenfertilizeruptakebythecropandthereforereduceN2Oemissions.Forthisreason,nutrientmanagementeffectsonGHGemissionsshouldbeconsideredinthecontextoftheentiresetofcroplandmanagementpractices.Forexample,thereisevidencethatfertilizerplacementcaninteractwithtillagemanagementincontrollingN2Oemissions(Ventereaetal.,2005),andthatinadequatemanagementofothernutrients(e.g.,phosphorusandpotassium)canreduceNUE(Snyderetal.,2009).EffortstominimizeorremediatewaterqualityimpactsofnitratetransportfromfarmtoaquaticsystemsmayalsoreduceindirectN2Oemissions.Forexample,theuseofsubsurfacebioreactorstoremovenitratefromdrainagewaterhasbeneficialimpactsonindirectN2O.However,todatethesebioreactorshavenotbeenimplementedatlarge(field)scalesandtherearealsoquestionsaboutreleaseofN2OandCH4duringthetreatmentprocessthatneedtobeansweredbeforetheirneteffectonGHGscanbeassessed(Elgoodetal.,2010).Also,environmentalandclimatefactors,whicharegenerallynotundermanagementcontrol,mayaffectN2Oemissions;forexample,nitrogenfertilizerappliedjustbeforelargerainfalleventscanstimulateincreasedemissions(Lietal.,1992).

3.2.1.2 TillagePractices

Differenttillagepracticesaregenerallyclassifiedintooneofthreecategories:fulltillage,reducedtillage,ornotillage.Tillageintensityisbasedonimplements,numberofpasses,andthepercentageofsurfaceanddepthoftillagedisturbance.Toolsareavailabletodeterminetillageintensity(e.g.,theSTIRModel;seeUSDANRCS,2008).No‐tillagepracticesarecharacterizedbytheuseofseeddrillsandfertilizerorpesticideapplicatorswithnoadditionaltillageeventsorimplements.Surfaceresiduesarenotincorporatedintothesoilwhenfollowingno‐tillagepractices,andthereislimiteddisturbancetothesoilprofile;consequentlyno‐tillagemanagementincreasessoilcoverandimprovesaggregatestability(Sixetal.,2000).Incontrast,examplesoffulltillage(oftenreferredtoasconventionaltillage)includeoneormorepasseswiththefollowingtillageimplements:moldboardplow,diskplow,diskchisel,twistedpointchiselplow,heavydutyoffsetdisk,subsoilchiselplow,andbedderordiskripper.Systemsarealsoclassifiedasfulltillageiftherearetwoormorepasseswithoneofthefollowingimplements:chiselplow,singledisk,tandemdisk,offsetdisk‐lightduty,one‐waydisk,heavy‐dutycultivator,ridgetill,orrototiller.Systemswithothertillagepractices,suchasasinglepasswitharidgetillimplement,mulchtill,orchiselplow,leadtointermediatedisturbanceofthesoilandareclassifiedasreducedtillage.

Changesintillagepracticescaninfluenceverticaldistributionofcarboninthesoilprofileandtotalsoilcarbonstocks(Paustianetal.,1997).Historically,fulltillagehasresultedinthereductionofsoilcarbonstocks(Laletal.,2004).Asynthesisofpreviousanalysesestimatedthatlong‐termfulltillagecandecreasesoilcarbonstocksby30percent(Ogleetal.,2005;Westetal.,2004).ChangingfromfulltillagetonotillagecanreversehistoriclossesofsoilC.No‐tillagepracticescanleadtoaccumulationofsoilcarbonintheuppersoilprofile(0to30cm),withlittletonochangeinthelowersoilprofile(30to60cm)(Syswerdaetal.,2011).Theopposite,adecreaseintheuppersoilhorizonwithanincreaseinthelowersoilhorizon,cansometimesoccurwithachangefromnotillagetofulltillage(Bakeretal.,2007).However,changesinthelowersoilprofiletendtobemorevariable,therebyrequiringalargersamplesizetodetectsignificantdifferences(KravchenkoandRobertson,2011).Areductionincarboninputassociatedwiththeinfluenceofno‐tillmanagementoncropproductionmayalsoleadtolossesofsoilcarbon,particularlyincoolerandwetterclimates


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(Ogleetal.,2012).However,whiledifferencesintheresponseofsoilcarbonstockstotillageoccuramongfieldexperiments,comprehensiveanalysesofavailablefielddataindicatethat,onaverage,soilcarbonstocksincreasewithachangefromfulltillagetoreducedtillageornotillage,especiallywithlong‐termadoptionofnotillage(Ogleetal.,2005;Sixetal.,2004;WestandMarland,2002).

Decreasedtillageintensityincreasessoilcarbonbecauseofreduceddisturbancetosoilaggregates,reducedexposureofsoilorganicmattertoweatheringprocesses,anddecreaseddecompositionrates(Paustianetal.,2000).Theextenttowhichsoilcarbonaccumulationoccursafterareductionintillageintensityisdeterminedbythehistoryoflandmanagement,soilattributes,regionalclimate,andcurrentcarbonstocks(WestandSix,2007).Ingeneral,greatersoilcarbonaccumulationwillbeobservedinC‐poorsoils(i.e.,duetolong‐termcultivation)withaclayeytextureunderhighbiomasscroppingsystemsintemperatehumidandwarmclimates(FranzluebbersandSteiner,2002;Planteetal.,2006;Sixetal.,2004).Insomecases,intermittenttillage,duringlong‐termreducedornotillage,isneededtoreducesoilcompaction,forweedcontrol,ortoreducepestsorpathogens.Whileintermittenttillagecancauseadecreaseinsoilstocks,upto80percentofsoilgainsfromno‐tillagepracticescanbemaintainedwhenimplementingnotillagewithintermittenttillage(Conantetal.,2007;Ventereaetal.,2006).

TheeffectoftillagemanagementchangesonsoilN2Oemissionsisvariableandnotfullyunderstood.Increases(Rochette,2008),decreases(Mosieretal.,2006),andnochanges(Grandyetal.,2006;Lemkeetal.,1998)insoilN2Oemissionshavebeenobserved.However,thosedifferencesarenottotallyrandomandpastmeta‐analyseshaveconcludedthatclimateregime,durationofpractice,andnitrogenfertilizerplacementhaveinfluencedtillageeffectsonN2Oemissions(Sixetal.,2004;vanKesseletal.,2012).Othervariablessuchassoiltexturemayalsobeimportant.

RegionalclimatehasalsobeenidentifiedasamajordriverforthechangeinN2Oemissionswithadoptionofno‐tillagepractices,withemissionsincreasinginhumidclimatesanddecreasingindryclimates(Sixetal.,2004).However,timesinceadoptionofnotillagemightalsoplayarolewithhigheremissionsinitiallyafteradoptionofnotillageinbothhumidanddryclimates,butovertimeemissionsfromno‐tillagesystemsmaydeclineinhumidclimatesrelativetopreviousemissionsfromfulltillagesystems.Nevertheless,variousfieldstudieshaveshownmixedresults,bothsupportingandcontradictingthefinding.StudiesindrierclimatesoftheGreatPlainshaveshownadecreaseinemissionsevenwhenno‐tillagepracticeshadbeenadoptedforlessthan10years(Kessavalouetal.,1998;Mosieretal.,2006).Long‐termnotillageinmoistclimatesofMinnesotaandCanadaledtobothhigherandloweremissionsofN2O(Druryetal.,2006;Ventereaetal.,2005).

AnotherimportantfactorinfluencingN2Oemissionsundernotillage,andonethatfarmerscanactivelymanage,isfertilizerplacement(vanKesseletal.,2012).Ventereaetal.(2005)foundthatwhennitrogenfertilizerwasplacedonthesurface,N2Oemissionsweregreaterundernotillagethanfulltillage,butthereversewasfoundwhennitrogenfertilizerwasplacedbelow10centimeters.FertilizerplacementingeneralhasbeenfoundtohavedifferingresultsonN2Oemissions,asdiscussedinSection3.2.1.1.However,thefindingsofVentereaetal.(2005)aswellasotherstudies(e.g.,Groffman,1985;VentereaandStanenas,2008)indicatethatdeepernitrogenplacementtendstodecreaseN2Oemissionswhenaccompanyingno‐tillorreduced‐tillagepractices,atleastrelativetofulltillagecroppingsystemsatthesamelocation.TheconflictingresultsassociatedwithN2Oemissionsfromfertilizerapplicationsmaybepartlyexplainedbythetillagepractice.

Inaddition,Lemkeetal.(1998)determinedthatsoilclaycontentexplained92percentofthevariationinN2OemissionsbetweenfulltillageandnotillageacrossmultiplesitesinAlberta.Similarly,Burfordetal.(1981)foundthatemissionsfromno‐tillagepracticesweregreaterthan


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fromfulltillageonsoilswithhigherclaycontentsatastudysiteintheUnitedKingdom.ItisarguedthatsoilswithhigherclaycontentshavehighermoisturecontentandthereforehaveagreaterpotentialforincreasedN2Oemissionsundernotillage.Indeed,Rochette(2008)attributedhigherratesofN2Ofluxfromminimumversusstandardtillagetogreatersoilcompaction,poorsoildrainage,reducedgasdiffusivity,andair‐filledporosityinhighclaysoils.

3.2.1.3 CropRotations,CoverCrops,andCroppingIntensity

Croprotationreferstothesequenceofcropsplantedinafield,withinoracrossyears.Croprotationsvarybylocationandgrowingregion,andmaybepracticedforavarietyofreasonssuchasimprovedeconomicreturns,pestmanagement,diseasecontrol,nutrientmanagementandwateravailability.Asimplerotationmaybeasequenceofcornandsoybeansthatisrepeatedovertime,whilemorecomplexrotationsmightincludeperennialcropssuchasalfalfawithcornandsunflowerrotationoverfiveyears,withthreeyearsofalfalfaandoneyeareachofcornandsunflower.Theactualrotationscanalsovaryfromastrictordertothesequence,particularlyinresponsetomarketdemand,i.e.,opportunisticrotations.Rotationswithhighbiomass‐yieldingcropsorperennialhaycropsorgrasscovercanincreasesoilcarbonstocks(Ogleetal.,2005).

Croppingintensitycanvaryacrossyears,duetovariationsinfallowfrequencyanduseofmultiplegrowingseasonswithmorethanonecropplantedandharvestedinasingleyear.Forexample,insemi‐aridenvironments,croprotationsoftenincludeayear‐longfallowperiodinordertoincreasetheamountofwaterstoredinthesoilprofileforthesubsequentcrop.Thislimitstheamountoforganicmatterinputtothesoil,andwiththeseverewaterlimitation,thesecroppingsystemsproducesmallamountsofbiomass,leadingtoareductioninsoilcarbonstocks(Doranetal.,1998).Consequently,intensifyingcropproductionbyreducingfallowfrequency,whichwillgenerallyinvolveadoptionofno‐tillagepractices,willincreasecarboninputacrossthewholerotationandpossiblytheamountofsoilorganiccarbon(Sherrodetal.,2003;2005).

Wintercovercropscanalsobeusedtoprovideplantcoveroutsideofthenormalgrowingseason.Priortoplantingthefollowingsummercrop,thecovercropiseitherlefttodecomposeasagreencoverorharvestedforforage.Ingeneral,theinclusionofacovercropinacroprotationwillleadtoanincreaseinsoilcarbonduetotheincreasedcarboninputderivedfromthecovercrop(Kongetal.,2005),especiallycovercroproots(KongandSix,2010).Covercropscanalsobeusedeffectivelyfornitrogenmanagement.InthefallandspringtheycancapturesoilnitrogenthatwouldotherwisebetransformeddirectlytoN2ObysoilmicrobesorleachtogroundwaterandcontributetoindirectN2Oemissions(i.e.,offsiteemissionsduetonitrogenlossesfromthesite).Additionally,whenkilledpriortoplantingthemaincrop,theirdecompositioncanprovidenitrogenthatwilldisplacesomeportionofcropfertilizationrequirements(whethermanufacturedororganic).Therefore,covercropscanreduceindirectN2Oemissionsandpossiblyoffsetfertilizationrates.However,therearenostudiesdemonstratingthataddingnitrogentosoilsincovercropsratherthanthroughfertilizationwillreducedirectN2Oemissions.Inthefuture,covercropbiomassmayalsobeharvestedforcellulosicethanolfeedstock,leavingrootstoenhancesoilcarbonstockssimilartoperennialplantsgrowninrotation(Ogleetal.,2005).

Theeffectsofcroprotationandintensityonsoilorganiccarboncanalsointeractwithothermanagementpractices,suchasresiduemanagement,tillage,andirrigation(Eghballetal.,1994).Consequently,managementinteractionsamongpracticesincludingtillageandirrigationwillbeimportantindeterminingtheinfluenceofcroprotationsonGHGemissions.Additionally,cropselectionasacomponentofcroprotationcanhaveamajoreffectonN2Oemissions(CavigelliandParkin,2012)insofarascropscanvaryintheirnitrogenuseefficienciesandnitrogenfertilizerneeds.Thisisparticularlythecasewhenlong‐livedperennialcropsaresubstitutedforannualcropsinforageorcellulosicbiofuelcroppingsystems(Robertsonetal.,2011).


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3.2.1.4 Irrigation

Typesofirrigationsystemsincludesurfaceorfloodirrigation,(micro‐)sprinklerirrigation,subsurfacedripirrigation,andsubirrigation.Ingeneral,irrigationincreasessoilwatercontent,evapotranspirationrates,andrelativehumidity;decreasessoilandairtemperatures;andcanleadtoincreasedregionalprecipitation(LobellandBonfils,2008;Pielkeetal.,2007).ThesechangesaffectimportantprocessessuchasplantgrowthratesandsoilmicrobialactivitythatcontrolnetGHGfluxes.

Assoilwatercontentapproachessaturation,oxygen(O2)diffusionisinhibited,resultinginanaerobicconditionsthatcanenhanceCH4emissions(ChanandParkin,2001;Delgadoetal.,1996),oratleastreducetheCH4sinkstrengthofotherwiseaerobicsoils(Livesleyetal.,2010).SaturatedconditionsalsoenhancedenitrificationratesandpotentiallyN2Oemissions(Delgadoetal.,1996;Jambertetal.,1997;Livesleyetal.,2010),butnotethatpeakN2OemissionsfromdenitrificationoftenoccuratwatercontentslowerthansaturationbecausewhenO2isextremelylimiting,N2OislikelytobefurtherreducedtoN2beforediffusingfromthesoilsurfacetotheatmosphere(Davidson,1991;Dunfieldetal.,1995).Furthermore,nitrificationratespeakatapproximately50percentofsaturation,andwatercontentsclosetofieldcapacity(60to70percentofsaturation)areexpectedtosupportmaximumtotalN2Oemissionrates(Davidson,1991).Inaddition,irrigationcanincreaseindirectN2OemissionsbyenhancingNO3‐leachingandrunoffifmorewaterisaddedthanisevaporated(Gehletal.,2005;Spaldingetal.,2001).

WettingofdrysoilstypicallyincreasesCO2emissions(FiererandSchimel,2002).However,irrigationalsoincreasesplantgrowthratesand,therefore,soilorganiccarbonlevelstypicallyincreaseafteruplandcroppingisconvertedtoirrigatedcropping,althoughlossofsoilcarbonfromerosioncanalsoincreaseunderirrigation(Follett,2001;Laletal.,1998).Furthermore,irrigationcanaffectinorganiccarbonlevels,butcurrentavailabledatashowcontrastingresults(Blanco‐Canquietal.,2010;Denefetal.,2008;Entryetal.,2004).

FloodandSurfaceIrrigation:Floodirrigationinvolvesfloodingtheentirefieldwithwater.Undercontinuouslyfloodedconditions,soilsarehighlyanoxic,thusfacilitatinghighmethanogenesisanddenitrificationrates(Mosieretal.,2004).However,highdenitrificationratesdonotnecessarilyimplyhighN2OemissionsbecausetheextremelyanoxicconditionsfacilitatefurtherreductionofN2OtoN2beforeitisemittedfromthesoil(Mahmoodetal.,2008).ThisissupportedbyobservationsshowinghigherN2Oemissionsfromintermittentcomparedtocontinuouslyfloodedricesystems(Katayanagietal.,2012;Xuetal.,2012),althoughitremainsdifficulttopredicttherelativeportionofdenitrifiednitrogenthatisemittedasN2OrelativetoN2.

Surfaceirrigationalsoinvolvessupplyinglargeamountsofwatertothesurfaceofsoils,butinthiscasethewaterisaddedthroughfurrowsadjacenttocropbeds.Thesesystemsareoftennotveryefficient,becausewaterlossesfromevaporationandseepagecanbelarge.TheimpactoffurrowirrigationonGHGemissionsdependsonhowoftenandtheextenttowhichfurrowsarefilledwithwater.WettinganddryingcyclesarelikelytoemitlargepulsesofNOandN2O(Davidson,1992),aswellasCO2(FiererandSchimel,2002).Spatialvariabilitycanalsobehigh,suchasthehigherN2Oemissionsfromfurrowscomparedwithbedsthathavebeenobservedforirrigatedcottoncropping(Graceetal.,2010).Inaddition,microtolandscapescaleheterogeneityinenvironmentalconditions,duetotopographyandotherfactors,contributetomultiscalevariabilityinN2Oemissions(Hénaultetal.,2012;Yatesetal.,2006).ThisspatialandtemporalheterogeneityinenvironmentalconditionsandfluxratesmakesitverydifficulttoquantifyGHGfluxesfromthesetypesofsystemswithhighlevelsofaccuracyandprecision.

SprinklerSystems:Sprinklersystemsdeliverwatertovegetationandthesoilfromabovethesurfaceusingoverheadsprinklersorguns.Thisisusuallymoreefficientthansurfaceirrigation,but


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evaporativelossesfromwaterinterceptedbyvegetation,litter,andthesoilsurfacecanstillbesubstantial.Duringandshortlyafterirrigationevents,soilmaybecomesaturatedandemitpulsesofN2O,butbecausethesoilisnotcontinuouslysaturated,N2Oemissionsareexpectedtobelowercomparedwithsurfaceirrigation(NelsonandTerry,1996).BothN2Oemissionsandsoilcarbonlevelsareexpectedtoincreasewithsprinklerirrigationcomparedwithuplandcropping.

SurfaceandSubsurfaceDripIrrigation:Surfacedripirrigationsupplieswaterfromdriplinesplacedadjacenttocroprows.Evaporativelossesshouldbelesscomparedwithabove‐surfacesprinklersystems,becauselesswaterisinterceptedbygrowingvegetation.However,evaporativelossescanstilloccurtotheextentthatsurfacelitterandsoillayersabsorbwaterfromthedripsprinkler.TheimpactsofsurfacedripirrigationonGHGfluxesareexpectedtobesimilartothoseofsprinklersystems,althoughthereisearlyevidencethatbothsurfaceandsubsurfacedripirrigationleadstolessemissionsofCH4andN2O(Kallenbachetal.,2010;Kennedyetal.,2013).

Subsurfacedripirrigationtargetswaterdeliverytotherootzoneusingburiedpipesandtubing.Thesesystemscanbeveryefficient,becausewaterisconcentratedintherootzoneataslow,steadyrate,henceminimizingoreliminatingevaporationlossesandavoidingsaturationofthewholesoilprofile.Consequently,thesesystemsarenotexpectedtobelargeCH4sources(DelGrossoetal.,2000a).Soilwatercontenthaslesstemporalvariationwithsubsurfacedripirrigationcomparedwithsprinklerandsurfacesystems,sopulsesofN2OandCO2emissionsarealsoexpectedtobeofsmallermagnitude(Kallenbachetal.,2010).Similarly,subsurfacedripirrigation/fertigationofhighvaluescrops,suchastomatoes,hasbeenshowntoreduceN2Oemissionscomparedwithfurrowirrigation(Kennedyetal.,2013).

Subirrigation:Subirrigationisusedinareaswithrelativelyhighwatertablesandinvolvesartificiallyraisingthewatertabletoallowthesoiltobemoistenedfrombelowtherootzone.Becausewaterissuppliedtorootsfrombelow,evaporationlossesarenotenhancedastheywouldbewithsurfaceirrigationsystems.ThissystemcandecreaseNO3‐leaching(Elmietal.,2003)butmayincreaseN2Olossesfromdenitrification(Munozetal.,2005).

ManagementInteractions:Irrigationsystemsinteractwithothercropmanagementstrategiessuchaschangesincroprotation,croppingintensity,tillage,andfertilizeramounttocontrolnetGHGfluxes.IrrigationtendstoamplifytheeffectsofthesefactorsonN2OandCH4emissionsatthesametimeasthepracticesincreasecropyieldsandsoilcarbonstocks.However,theresponseofsoilcarbontoirrigationiscomplexanddrivenbyinteractingfactors.Whenwaterandnutrientstressarereducedthroughirrigationandfertilization,theportionoftotalplantproductionallocatedbelowgroundcandecrease,butabsolutebelowgroundproductionandsoilorganiccarboncanincrease(Bhatetal.,2007).Howevernotallexperimentsshowincreasedsoilcarbonwithirrigation(Denefetal.,2008).Consequently,theirrigationbenefitsofincreasedyieldsandpotentialcarbonstoragemaybecounter‐balancedwiththeincreasedN2OandCH4fluxes.

However,therearealsooptionsforlimitingemissions,particularlywithfertilization.Fertigationaddsnutrientstotheirrigationsystemtodeliverwateralongwithsolublenutrientstotherootzone.Thesesystemshavethepotentialtobeveryefficientfrombothnutrientandwateruseperspectives(Spaldingetal.,2001),becausetheslowandtimedsupplyofnutrientsandwaterismoresynchronouswithplantdemandandtheyareconcentratedintherootzone.Consequently,N2Oandothernitrogenlossesareminimizedwhileplantgrowth,carboninputs,andcarbonsequestrationcanbemaximized.Similarly,CH4emissionsareminimizedbecausesoilsaturationisavoided.


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3.2.1.5 ErosionControl

Soilerosionprocessesincludesoildetachment,transport,anddeposition.Soilerosioncanpotentiallyreducesoilcarbonstocksandincreasenetcarbonfluxtotheatmospherethroughdecreasedplantproductivityandsubsequentdecreasedorganicmatterinputtosoilandincreaseddecompositionoftheerodedsoilfraction(Lal,2003).However,soilerosioncanalsopotentiallyincreasenetsoilcarbonstocksanddecreasenetcarbonfluxtotheatmospherethroughdynamicreplacementofsoilcarbononerodedlandscapesanddecreaseddecompositionratesinzonesofsoildeposition(Hardenetal.,1999;Stallard,1998).

Lal(2003)estimatedthat20percentofcarboninerodedsoilisemittedtotheatmosphere,duetooxidationofsoilorganiccarbonfollowingthedisruptionofsoilaggregatescausedbydetachmentandtransport.However,inananalysisof1,400soilprofiles,VanOostetal.(2007)foundnegligiblecarbonlossasadirectresultofsoildetachmentandtransport.Atsiteswherethetransportedsoilwasdeposited,therewasaslight(~onepercent)decreaseinsoilcarbondecompositionrates,resultinginslightlyhighersoilcarbonaccumulation.Moreimportantly,itwasfoundthatonaverage,25percentoferodedcarbonwasreplacedontheerodedsitesovera50‐yearperiod(Hardenetal.,2008).Thecombinationofthesefindingssupportsanapproximate26percentsinkcapacityoferodedsoil(VanOostetal.,2007).

Theaccumulationofsoilcarbononerodedlocationswithinlandscapesisreferredtoasdynamicreplacement(Hardenetal.,1999).Dynamicreplacementoccursasaresultofsoilcarbonbuildingtowardasteadystateofsoilcarboncontent,constrainedbysoiltypeandclimate(WestandSix,2007).Steadystateoccurswhensoilcarbonaccumulationequalssoilcarbonlosses.BothVanOostetal.(2007)andLalandPimentel(2008)notethatthedynamicreplacementratemaybelowinareaswithlowercroplandproductioninputs.Forexample,dynamicreplacementmaybelowincropsystemswithlowresidueproduction,suchascottonandtobaccointheUnitedStates,whichhavelowercarbonaccumulationratesthanhighresidueinputscrops(Ogleetal.,2005).

Notethatwhilewatererosioncangenerateasmallcarbonsink,thebenefitofacarbonsinkisoffsetbyothernegativeimpactsfromsoilerosion.Forexample,soilerosioncanresultinwaterpollutionduetosedimentloading,airpollutionfromairborneparticulatematter(PM10),anddecreasedsoilfertilityresultinginsubsequentyielddeclines.

3.2.1.6 ManagementofDrainedWetlands

Drainageofwetlandseffectivelycreatesanuplandcroppingsystembyloweringwatertableswithtilesorditchestoproduceannualcrops.Themostobviouseffectofwetlanddrainageisincreasedoxidationandtillageofsoils.Forexample,conversionofnativewetlandsandgrasslandsintocroplandhasbeenshowntodepletenativesoilcarbonstocksby20tomorethan50percent(BlankandFosberg,1989;Eulissetal.,2006;Mann,1986).Inturn,CO2emissionsincreasewithhigherdecompositionrates,particularlyinorganicsoils,i.e.,Histosols(Allen,2012;ArmentanoandMenges,1986).LossoftheorganiclayerhascausedtremendoussubsidenceinU.S.croplands(Stephensetal.,1984)suchastheFloridaEverglades(Shihetal.1998)andtheCaliforniaDeltaregion(Broadbent,1960;Weir,1950),whereratesvaryfrom0.46to2.3cmyear‐1(DeverelandRojstaczer,1996;Devereletal.,1998;RojstaczerandDeverel,1995).SimilarsubsidencerateshavealsooccurredinotherregionssuchastheFloridaEverglades.

Manipulationofwaterlevelscanhavemultipleeffectsonnutrientcyclinginwetlands.Drainagealsomayresultinmoreoptimalsoilmoistureconditions(e.g.,40to60%water‐filledporespace)thatenhanceformationofN2Oasabyproductofnitrificationanddenitrificationreactions(Davidsonetal.,2000).DrainageincreasesnitrogenmineralizationrateswithconversionfromanaerobictoaerobicconditionsandenhancesN2Oemissions(Duxburyetal.,1982;Kasimir‐


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Klemedtssonetal.,1997).Incontrast,drainagedecreasesCH4emissionsbyreducingthefrequencyanddurationofsoilsaturationrequiredforCH4productionaswellasenhancingfrequencyofmethanotrophicactivity(Dorretal.,1993;Gleasonetal.,2009;PhillipsandBeeri,2008).However,insituationswherewetlandsareinacropproduction,butnotdirectlydrained,CH4productioncanactuallybeenhancedduetoincreasedrunofffromadjacentcroplandsorconsolidationdrainage,whichincreaseswaterdepthandhydroperiods(Gleasonetal.,2009).

ManagingthewatertablebyraisingthedepthofdrainagetotheextentpossiblehasbeenaneffectivemeasuretoreducelossofCO2andotherGHGsfromdrainedorganicsoils(Jongedyketal.,1950;Shihetal.,1998).RecentresearchsuggeststhatevenperiodicfloodingoforganicsoilsthataredrainedforcropproductionmaybeeffectiveinreducingCO2emissions(Morrisetal.,2004).Thereislimitedinformationontheeffectofdrainageinmineralsoilswithahighwatertable(i.e.,hydricsoils),buttheinfluenceonGHGemissionsislikelylesssignificantthanindrainedorganicsoils.Itisimportanttonotethatwetlandsareaffordedsomeprotectionbylaws(e.g.,CleanWaterAct)andconservationprogramsthatrecognizetheimportanceofwetlands,suchasforwildlifehabitat,andprovideagriculturalproducersincentivestoavoiddrainingwetlands(e.g.,the“Swampbuster”provisionoftheFoodSecurityAct).

3.2.1.7 LimeAmendments

Agriculturallimeconsistsprimarilyofcrushedlimestone(CaCO3)anddolomite(CaMg(CO3)2)invaryingproportions.Agriculturallime,hereinafterreferredtoaslime,isappliedtosoilstodecreasesoilacidity.Limeiscommonlyappliedtoagriculturallandswherenitrogenousfertilizersarecontinuouslyusedandwhereprecipitationexceedsevapotranspiration.

TheapplicationoflimetosoilscancreateasinkorsourceofCO2totheatmosphere(Hamiltonetal.,2007),dependingonthestrengthoftheweatheringagent.Weatheringoflimebycarbonicacid(H2CO3),formedwhenCO2isdissolvedinwater,resultsintheuptakeofonemoleofCO2foreverymoleoflime‐derivedcarbondissolved(Eq.1).Carbonicacidweatheringproducesbicarbonate(HCO3‐)thatcontributestoalkalinityingroundwater,streams,andrivers(OhandRaymond,2006;Raymondetal.,2008).Alternatively,whenlimereactswiththestrongernitricacid(HNO3),whichisproducedwhennitrifyingbacteriaconvertNH4+basedfertilizerandothersourcesofNH4+tonitrate(NO3‐),carboninlimeisdissolvedandreleaseddirectlytotheatmosphere(Eq.2).

CaCO3+H2O+CO2=Ca2++2HCO3‐ Eq.1

CaCO3+2HNO3=Ca2++2NO3‐+H2O+CO2 Eq.2

Fieldmeasurementsandmodelinganalysesindicatethatmorelimeisdissolvedbycarbonicacidthanbynitricacid.Forexample,WestandMcBride(2005)estimatedthat62percentoflimewasdissolvedbycarbonicacidweathering,Hamiltonetal.(2007)estimated75to88percent,andOhandRaymond(2006)estimated66percent.Biasietal.(2008)usedchamberfluxmeasurementstoestimate15percentlossoflime‐derivedcarbonbydissolutionwithstrongacidsandinferredthat85percentisdissolvedbycarbonicacid.

WestandMcBride(2005)alsoestimatedtheprecipitationofHCO3‐backtoCaCO3onceHCO3‐reachestheocean,therebyreleasingCO2totheatmosphere.However,thelongtimeperiod(manydecadestocenturies)overwhichprecipitationwouldoccurintheocean(Hamiltonetal.,2007)effectivelyresultsincarbonsequestrationforannualaccountingpurposes.

Currentconsensusofleacheddrainagesamples,streamgaugedata,andmassbalancemodelingindicatesthatabout66percentofcarboninappliedlimeisessentiallytransferredfromonelong‐livedpool(CaCO3ingeologicformations)toanother(HCO3‐inoceans),andisthereforenotcountedasnewsequestration.However,theatmosphericCO2newlycapturedbythisprocessdoes


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representsequestrationwhencorrectedforthe33percentreleasedtotheatmosphereasCO2;thisresultsinanet33percentsinkstrengthpercarboninlime.ThisestimateissimilartothatofOhandRaymond(2006)andWestandMcBride(2005),andiswithintherangeofHamiltonetal.(2007).Whilelimecanincreasesoilcarbonviaeffectsonsoilmicrobialactivity(Fornaraetal.,2011),inmostsoilsliminghasnodirectcarboneffect(Pageetal.,2009).

3.2.1.8 ResidueManagement

Cropresiduesaretheresidualremainingafterharvestoftheeconomicpartofthecrop.Theamountofcropresiduevarieswiththecropandtheharvestoperationmethod.Forexample,cottonharvestcontributesverylittleabovegroundresiduetothesoilduetotheplant’slowleafareaindexandsmallamountofplantmaterialafterleafdrop.SoybeanandotherlegumecropsalsohavesmallamountsofabovegroundresiduethatrapidlydecomposebecauseoflowC:Nratios.Incontrast,cropslikecorncanleavesubstantialamountsofresidueonthesoilsurfaceunlessthewholeplantisharvestedforsilageortheresidueiscollectedforbeddingorotherpurposes.

Abovegroundresiduemanagemententailsfivepotentialstrategies:(1)leavetheresidueonthesoilsurfacetodecayandbeincorporatedintothesoil(requiresno‐tillmanagement);(2)incorporatetheresidueintothesoilviatillage;(3)removetheresiduethroughaharvestingoperation(i.e.,silageorcellulosicbiomassharvest);(4)allowlivestocktograzeontheresidue;or(5)burntheresidue.EachofthesemanagementpracticeshasthepotentialtoaffectGHGemissions.LeavingcropresidueonthesurfaceandincorporatingitintothesoilafterdecaybymicroorganismsaffectsCO2releasefromthesoilduetotheenhancedbiologicalactivity,andpotentiallyincreasesN2Oemissionsthroughanalterationofthenitrogenbalanceinthesoil.Asimilarprocessoccurswhenresidueisincorporatedintothesoilviatillage.Notethattillagealsocausesreductionsinsoilcarbonstocks,andadditionalCO2isreleasedthroughburningfueltoruntillageequipment.HarvestingtheresiduereleasesCO2fromburningfuelintheengineslinkedwiththeharvestingprocess,althoughresidueharvestedforbiofuelproductionmaycreatenetfossilfueloffsetcredits.BurningcropresiduesinthefieldreleasesCO2,CH4,andN2O(aswellasCOandNOx)emissionstotheatmosphere.Ingeneral,butnotalways,residueremovalreducessoilcarbonstocks(GreggandIzaurralde,2010;Wilhelmetal.,2007).

ManagementinteractionsarealsoimportantwhenconsideringtheinfluenceofresiduemanagementonGHGemissions.Forexample,theinfluenceofresiduemanagementonsoilorganiccarbonwillbeaffectedbythetillagepractices(Malhietal.,2006).

3.2.1.9 Set‐Aside/ReserveCropland

The1985FarmBillestablishedtheConservationReserveProgram(CRP)topayproducerstoconverthighlyerodiblecroplandorotherenvironmentallysensitiveagriculturalareasintovegetativecover.Theseareascouldbeconvertedintograssland,nativebunchgrasses,pollinatorhabitat,shelterbelts,filterorbufferstrips,orriparianbuffers.Areasareremovedfromproductionandseededwithannualandperennialspeciestoformacoverthatwouldbeundisturbedforaminimumof10years.Inreturn,producersorlandownersreceivedapaymentforenrollingtheselandareasintotheCRP.ThroughouttheagriculturalhistoryoftheUnitedStates,therehavebeentimesinwhichagriculturallandsweresetasidetoreduceagriculturalsurpluses;however,thetimeperiodofremovalwastypicallyshort‐term(onetotwoyears)andmaintainedinaweed‐freestate.

TheprimaryaimsofCRParetodecreaseerosion,restorewildlifehabitat,andsafeguardgroundandsurfacewaterquality.Animportantancillaryaimiscarboncapture:CRPlandssequestercarboninsoilandlong‐livedplants,andthusrepresentavaluablemitigationopportunity.Inameta‐analysisofpairedsoils,Ogleetal.(2005)foundthat20yearsofset‐asideresultedintemperateregionsoils’accumulating82to93percentofthecarbonlevelsunderoriginalnative


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vegetation,onaverage.PostandKwon(2000)concludedfromaglobalmeta‐analysisthat,onaverage,soilcarbonsequestrationratesonlandconvertedfromagriculturalproductiontograsslandis33gCm‐2year‐1.At39pairedCRP‐cropsitesinWisconsin,Kucharik(2007)foundsequestrationratesof50gCm‐2year‐1onMollisolsand44gCm‐2y‐1onAlfisols.Follettetal.(2009)estimatethatCRPsoilssequester~50gCm‐2year‐1onaverage.TheCouncilforAgriculturalScienceandTechnology(2011)estimatesthatCRPlandsarecurrentlyresponsiblefor6.3Tgofsoilcarbonsequestrationperyear.Gebhartetal.(1994)reportedamean18.8percentincreaseonfiveCRPsitesduringasix‐yearperiod.However,therearestudiesshowinglittleornoincreaseinC,leadingtouncertaintyintheeffectofset‐asidelandinareserveprogram(JelinskiandKucharik,2009;Karlenetal.,1999;Reederetal.,1998).Forexample,Karlenetal.(1999)comparedCRPlandwithperennialgrassestocroplandacrossfiveStatesandfoundthatonlyonesiteofthefiveshowedasignificantdifferenceintotalorganiccarboncontentinthesoilafterbeinginCRP.

IncreasesinsoilcarbonresultingfromsettingasidecroplandinCRPcanbereversedbyconvertingtheselandsbackintoproduction.Gilleyetal.(1997)foundthatthepositivechangesinCRPlanddisappearedimmediatelywhenthesoilsweretilleduponconversionbackintocropproduction.However,manystudiesindicatethatiflandunderCRPisreturnedtocultivation,someorallofthesoilcarboncanpotentiallyberetainedifthelandiscultivatedwithno‐tillpractices(BowmanandAnderson,2002;Daoetal.,2002;Olsonetal.,2005).Inadditiontochangesinsoilcarbonstocks,changeswillalsooccurinN2Oemissionsdependingonthenutrientmanagementpractices.Gelfandetal.(2011)measuredanetcarboncostof10.6MgCO2‐eqha‐1(289gC‐eqm‐2)forthefirstyearofno‐tillsoybeansfollowing20yearsofCRPgrassland,andasignificantportionofthenetemissionwasduetoN2Oproducedintheconversionyear.

3.2.1.10 Biochar

Biocharisasoilamendmentthatispromotedforitsabilitytoimprovecropproductionandsequestercarboninsoils(Atkinsonetal.,2010;Lehmann,2007a;2007b).Biocharischarcoalproducedwhenwoodorotherplantbiomassisburnedunderlow‐oxygenconditions,knownaspyrolysis.Whenappliedtosoils,biocharcanpersistforlongperiodsoftime;itschemicalstructuremakesitresistanttomicrobialattackundermostsoilconditions.However,itspersistencecanvarygreatlyforreasonsnotyetcompletelyunderstood.BiocharisacommoncomponentofmostU.S.agriculturalsoils(Skjemstadetal.,2002),leftfromfiresthatoccurredpriortoconversionoftheoriginalforestorprairie.Addingbiochartosoilshasbeenproposedasawaytosequestercarbon(Lehmann,2007a)becauseofthispotentialtopersistforcenturies(KimetuandLehmann,2010;Nguyenetal.,2008).Butbiochar’slongevityinsoildependsonanumberoffactorsincludingpyrolysisconditions(e.g.,pyrolysistemperature)andthechemicalcompositionofthebiocharfeedstock(Spokas,2010).Climateandsoilfactorssuchasmineralogyandpre‐existingorganicmattercontentalsoaffectbiochar’spersistenceinsoil.

Anadditionalbenefitofbiocharisitspositiveeffectsonagriculturalsoilfertility(Atkinsonetal.,2010;Lairdetal.,2010),largelybyprovidingadvantagessimilartootherformsofsoilorganicmatter:improvedsoilstructure,waterholdingcapacity,andcation‐exchangecapacity.BiocharhasalsobeenshowntoreducesoilN2Oemissionsinsomelaboratorystudies,butthesmallnumberoffieldtrialssofarreportedhavedocumentednosignificanteffectsunderfieldconditions(e.g.,Scheeretal.,2011).

Itistooearlytoknowifpromisingresultsfromlaboratoryandshort‐termfieldexperimentscanbegeneralizedtolong‐termfieldconditions.Biocharsoiladditionsmaybeafuturesourceofcarboncreditsforpyrolysiswasteiflong‐termfieldexperimentsconfirmresultsfromshortertermstudies.Theclimateadvantageofaddingbiochartosoilislessclear,however,relativetootherpotentialusesofplantbiomass.Lifecycleanalyses(e.g.,Robertsetal.,2010)suggestthatbiocharmay


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increaseordecreasenetemissionsdependingonalternativeusesoftheoriginalbiomassandlifecyclesystemboundaries.Furthermore,ifthebiomass(orbiochar)wasburneddirectlyforenergythenthesourceofdisplacedenergymustalsobeconsidered(Robertsetal.,2010).Nevertheless,boththesequestrationandN2Osuppressionpotentialofbiocharmeritfurtherstudy.

3.2.2 ManagementInfluencingGHGEmissionsinFloodedCroppingSystems

ThereareavarietyoffloodedcroppingsystemsintheUnitedStates,includingsystemsforrice,wildrice,cranberries,andtaro.Apartfromrice,thesesystemsarerelativelyminor(specialtycrops)andthereislittletonoresearchorinformationontheirGHGemissions.RicesystemsemitbothCH4andN2O;however,manyreportsshowaninverserelationshipbetweenCH4andN2Oduringthericecroppingseason,withCH4occurringunderanaerobicconditionsandN2Oemissionsoccurringunderaerobicconditions(Zouetal.,2005).Therefore,toaccuratelydetermineamitigationstrategyoneneedstoconsiderthenetcumulativeeffectofGHGemissionsbyevaluatingbothCH4andN2O.WaterandresiduemanagementhavereceivedthemostattentionintermsofofferingpossibilitiesformitigatingCH4emissions.Othermitigationoptionshavealsobeenexaminedandshowpromise(e.g.,Fengetal.,2013;Linquistetal.,2012;Majumdar,2003;WassmannandPathak,2007;Yagietal.,1997)andfurtherresearchisrequiredinmanyareasbeforetheseoptionscanbescaledup.TheintenthereisnottoprovideareviewoftheliteraturebuttoprovideabriefoverviewofsomefactorsaffectingGHGemissionsfromfloodedricesystems.

3.2.2.1 WaterManagementinFloodedRice

IntheUnitedStates,riceisplantedinoneoftwoways:(1)waterseeded,whereseedsaresownbyairplaneinfloodedfields;or(2)dry‐seeded,whereseedsaredrilledorbroadcast(thenincorporated)intodryfields.WaterseedingisthepredominantpracticeinCaliforniaandpartsofLouisiana,whiledryseedingispredominantinmuchofthesouthernUnitedStates(e.g.,Arkansas,Mississippi,Missouri,andTexas).Watermanagementvariesbetweenthesetwoestablishedpractices.Inwater‐seededrice,thefieldsaretypicallyfloodedfortheentireseason.However,inLouisiana,thefieldmaybedrainedwithapinpointfloodsystem(threetofivedays)orwithadelayedflood(upto20days)afterseeding.Indry‐seededrice,rainfallorflushirrigationeventsarerelieduponduringthefirstthreetofiveweeksofestablishmentandthenfloodedfortherestoftheseason.Inallcases,fieldsaretypicallydrainedafewweeksbeforeharvesttoallowthesoiltodryoutenoughtosupportharvestequipment.FurtherdetailsofU.S.riceproductionsystemscanbefoundinSnyderandSlaton(2001)andStreetandBollich(2003).

MidseasondrainorintermittentirrigationisastrategytomitigateCH4emissions.Thispracticeresultsinaerobicconditionsthatareunfavorableformethanogens.However,suchconditionsarefavorableforN2Oemissions(e.g.,Zouetal.,2005).MoststudiesreportthatmidseasondrainssignificantlydecreaseCH4emissionsbutincreaseN2Oemissionsrelativetocontinuousflooding.Regardless,netGHGemissionsinricesystemsareusuallydecreasedwithmidseasondraindespitetheincreaseinN2O.Wassmanetal.(2000)reportedthatCH4emissionreductionsrangedfromsevenpercentto80percent.ThereductioninCH4emissionsdependsonthenumberofdrainageeventsduringthecroppingseasonandonothermanagementfactorsandsoilproperties.Yanetal.(2005)reportedthatCH4fluxesfromricefieldswithsingleandmultipledrainageeventswerereducedby60percentand52percentcomparedtocontinuouslyfloodedricefields.ThispracticehasnotbeenwidelyevaluatedintheUnitedStates,anditmaybedifficulttodrainandre‐floodthelargerelativelyflatparcelsoflandthatarecommonlyusedforriceproductionintheUnitedStates.Furthermore,suchpracticescanleadtoincreasedweedanddiseasepressurealongwithloweryieldsandgrainquality.


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Soilcarbonstocksarealsoinfluencedbywatermanagement.Forexample,carbonstocksinChinesericesystemsarehigherthaninuplandcrops,presumablyduetotheaccumulationofcarbonunderthefloodedconditions(Panetal.,2010;Wu,2011).ItremainsunknownifeffortstomitigateCH4emissionsintheUnitedStatesusingintermittentfloodingwillleadtoareductioninsoilcarbonstocks.

TheuseofmidseasondrainagehasbeenshowntodelayharvestinCalifornia.Therefore,inclimateswithashortgrowingseason,theuseofamidseasondrainwillincreaseriskofcropfailure,andthereforewillbealessappealingalternativetogrowers.

3.2.2.2 ResidueManagement

StrawmanagementhasalargeimpactonCH4production.Strawadditions,particularlythosewithahighcarbontonitrogenratio,increaseCH4emissionsbuthavethepotentialtoreduceN2Oemissions(e.g.,Zouetal.,2005).ThisreductioninN2OmaybeduetoincreasednitrogenimmobilizationormoreeffectiveconversiontoN2.LowcarbontonitrogenorganicmaterialstendtoincreaseN2Oemissions(Kaewpraditetal.,2008).Yanetal.(2005)reportedthatthetimingofstrawapplicationisalsoanimportantfactor.Forexample,applyingricestrawbeforetransplantingincreasedCH4emissionsby2.1times,whileapplyingricestrawinthepreviousseasonincreasedCH4emissionsby0.8times.SeveralstudieshavedemonstratedthatcompostingricestrawpriortoincorporationreducesCH4emissions(Wassmannetal.,2000);however,thisrequiresadditionalenergytocollectthestrawandthenspreaditbackonthefieldaftercomposting.

IncontrasttothepotentialforreducingCH4emissionswithremovalofricestraw,thereisalsothepotentialtoreducesoilcarbonstocksduetolesscarboninputtosoils.Othernutrients(particularlyK)areremovedinlargeamountswithresidues,andtheseneedtobereplacedtomaintaintheproductivityofthesystem.

3.2.2.3 OrganicAmendments

Variousorganicamendmentscanbeappliedtoricefields,includingfarmyardmanurespecialtymixesoforganicfertilizers,andgreenmanures(e.g.,covercrops).Basedonameta‐analysisbyLinquistetal.(2012),livestockmanureincreasesCH4emissionsby26percentandgreenmanuresincreasedCH4by192percent.NeithermanuresourcehadasignificanteffectonN2Oemissions.FewstudieshaveevaluatedtheinfluenceofdifferentmanurestorageandprocessingtechniquesonCH4emissions.OneexampleisastudybyWassmanetal.(2000),whofoundthatfermentationoffarmyardmanurepriortoapplicationcanreduceCH4emissions.FarmyardmanurewillalsoinfluencesoilcarbonstockandsoilN2Oemissions.

3.2.2.4 Varieties,RatoonCropping,andFallowManagement

SeasonalCH4(Lindauetal.,1995)andN2O(Chen‐Ching,1996)emissionsareaffectedbyricevariety.Thecauseofvarietaldifferencesvarybutmaybeduetogastransportthrougharenchymacells,differentrootingstructures,ordifferencesamongvarietiesintermsofrootexudates(WassmannandAulakh,2000).IdentifyingthemechanismsforvarietaldifferencesmayenablebreedingprogramstoselectvarietiesthathavelowerCH4emissions.

InsomeStates,theclimateallowsre‐sproutingofasecond,orratooncrop,thatgrowsfromthestubbleofthefirstcropafterharvesting.Ratooncropyieldsaresmallerthanthefirstcrop,butcanaddsubstantiallytotheoverallannualyield,therebyreducingcostsofproductionperunit.Inaddition,ittakesfewerresourcesandlesstimetogrowaratooncropthantogrowthefirstcrop.However,ratooninghashigherCH4emissionrates(abouttwotothreetimeshigher)thanthefirstcrop,becausethestrawfromthefirstcropremainsinthefieldunderanaerobicconditionsduringtheratoonperiodratherthanthefieldbeingdrainedsothatthestubblecandecayaerobically


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(Lindauetal.,1995).Therefore,theamountofCH4producingorganicmaterial(i.e.,materialavailableforanaerobicdecomposition)isconsiderablyhigherthanwiththeprimarycrop.

ManagementofricefieldsduringthewinterhasasignificanteffectonannualGHGemissions.Forexample,inCalifornia,legislationinthe1990shaslimitedtheburningofricestrawtoamaximumof25percentofanarea,althoughinrealityonlyabout10percentofriceproductionfieldsareburned.Currently,ricestrawisincorporatedafterharvestonabout85percentofthericeproductionfieldsinCalifornia,andinthesefieldsabouthalfareintentionallyfloodedtofacilitatestrawdecomposition,althoughthisvaluecanvarywidelyfromyeartoyear.WinterfloodinghasincreasedannualCH4emissions(Devitoetal.,2000),butithasalsoincreasedthequalityofhabitatforoverwinteringwaterfowlinthePacificFlyway.Ricestrawisbaledandremovedonaboutfivepercentofthearea.

3.2.2.5 NitrificationandUreaseInhibitorsinFloodedRice

NitrificationinhibitorspreventorslowtheconversionofNH4+toNO3‐andthusreduceN2Oemissionsfromnitrificationandsubsequentdenitrification.Inameta‐analysisoftheseproducts,Akiyamaetal.(2010)foundthatinricesystemstheuseofnitrificationinhibitorsonaveragereducedN2Oemissionsby30percent,althoughsomeproductsweremoreeffectivethanothers.Certainnitrificationinhibitors(i.e.,dicyandiamide,thiosulfate,andencapsulatedcalciumcarbide)canmitigatebothCH4andN2Oemissions.ReducedCH4emissionsusingdicyandiamidewasattributedtoahigherredoxpotential,lowerpH,lowerFe2+,andlowerreadilymineralizablecarboncontent(Bharatietal.,2000).

Ureaseinhibitors,suchashydroquinone,slowthemicrobialconversionofureatoNH4+,thusreducingtheamountofnitrogenavailablefornitrificationanddentrification.BothCH4andN2Oemissionswerereducedwiththeuseofhydroquinone(Boeckxetal.,2005).ItissuggestedthatureaseinhibitorsmitigateCH4emissionbyinhibitingthemethanogenicfermentationofacetate(Wangetal.,1991).Furthermore,acombinationofaureaseinhibitor(hydroquinone)andanitrificationinhibitor(dicyandiamide)wasshowntoresultinlowerGHGemissionscomparedwithusingonlyoneoftheproducts(Boeckxetal.,2005).SeeSection3.2.1.1formoreinformationonnitrificationandureaseinhibitors.

3.2.2.6 FertilizerPlacementinFloodedRice

Incorporating/injectingorplacingfertilizerdeepintothesoilhasbeenshowninsomestudiestoreducebothCH4(Wassmannetal.,2000)andN2O(Keerthisingheetal.,1995)emissions.Whilemuchofafloodedricefield’ssoilisanaerobic,thefloodwaterandtopfewcentimetersofsoiltypicallyremainaerobicwhilesoilbelowfivecentimetersexistsinananaerobic,reducedstate(KeeneyandSahrawat,1986).Thusmineralnitrogeninthetopfewcentimetersofsoilmayundergonitrificationanddenitrification,whichcanleadtoN2Oemissions;butmineralnitrogeninlowersoildepthswillremainasammonium.Incontrast,nitrogenfertilizerthatisappliedtothesoilsurface(eitherpreseasonormidseason)tendsbemoresusceptibletolosseseitherfromammoniavolatilizationormorerapidnitrification‐denitrificationprocesses(Griggsetal.,2007).Byplacingnitrogenintoanaerobicsoillayers,itisbetterprotectedfromlossesandremainsavailableforcropnitrogenuptake(Linquistetal.,2009).TheeffectofdeepfertilizerplacementonCH4reductionremainsuncertain.SeeSection3.2.1.1formoreinformationonfertilizerplacement.

3.2.2.7 SulfurProducts

Sulfur‐containingfertilizers(i.e.,ammoniumsulfate,calciumsulfate,phosphogypsum,andsinglesuperphosphate)reduceCH4emissions(Lindauetal.,1998).ThemagnitudeofCH4reductionisdependentonfertilizationratewithaveragesbetween208and992kgSha‐1,reducingCH4


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emissionsby28percentand53percent,respectively(Linquistetal.,2012).Atlowlevelsofsulfurfertilization,whicharecommoninrecommendedrates,theeffectonCH4emissionswillbelimited(Linquistetal.,2012).SulfurmitigatesCH4emissionsintwoways.First,SO4additionstosoiladdelectronacceptors,thusslowingsoilreduction(Majumdar,2003).Second,theproductofSO4reduction(H2S)mayinhibitmethanogenicbacteriaandthusdepressCH4production.Unfortunately,moststudieshavenotexaminedtheeffectonN2Oemissions.

3.2.3 Land‐UseChangetoCropland

Conversionfromoneland‐usecategory(e.g.,forestland,wetlands)tocroplandcanhavesignificanteffectsontheGHGemissionsandremovalsassociatedwiththelandunderconversion.Whenlandisconvertedtocropland,thereisoftenalossofcarbon,anincreaseinN2OandCH4emissions,areductioninCH4oxidation,andifbiomassisburned,anincreaseinnon‐CO2GHGemissions.Anumberofvariablesinfluencethedirectionandmagnitudeoftheemissionsandsinksincludingpriorlanduse,climate,andmanagement.Theinfluenceofland‐usechangeoncarbon,nitrogen,methane,andnon‐CO2GHGsarediscussedbelow.

3.2.3.1 InfluenceonCarbonStocks

Land‐useconversiontocroplandcanhavesignificanteffectsonbiomass,litter,andsoilcarbon(IPCC,2000).Houghtonetal.(1999)estimatedthatlandclearanceintheUnitedStateshasledtoalossof27PgCtotheatmospheresincethe1700s,althoughrecentlysomecarbonhasbeenrestoredwithconversionofcroplandbacktootherusesandalsoimprovedsoilmanagement(U.S.EPA,2010).ClearingforestleadstoalargelossofabovegroundandbelowgroundbiomassandlitterC;grasslandconversioncanalsoreducetheamountofcarboninthesepools,buttoalesserextentthanforestconversionbecausegrasslandshavelessbiomass.Soilcarbonlossescanbesignificantwithconversiontocultivatedcropmanagement(DavidsonandAckerman,1993),withrelativelossesintemperateregionsfrom20to30percentonaverage(Ogleetal.,2005).

Ultimately,thenetinfluenceoflandconversionwilldependonthepreviouslanduse,vegetationcomposition,andmanagement,andtheresultingcroplandsystemanditsassociatedvegetationcompositionandmanagement.Forexample,conversionofgrasslandtotreecrops,suchasorchards,mayleadtogainsincarbonrelativetothegrasslandduetoaccumulationofcarboninwoodybiomass.

3.2.3.2 InfluenceonSoilNitrousOxide

Theconversionoflandtocroplandgenerallyacceleratesnitrogencycling,withsubsequenteffectsonN2OandCH4fluxes.SoilnitrogenavailabilityisthefactorthatmostoftenlimitssoilN2Oemissions(seeSection3.2.1.1),soanypracticethatincreasestheconcentrationofinorganicnitrogeninsoilislikelytoalsoaccelerateN2Oemissions.Asnotedabove,land‐usechangetypicallyresultsinfastersoilorganicmatterturnoverandassociatednitrogenmineralization,whichmeansthatevenintheabsenceofnitrogenfertilizer,soilN2Ofluxeswillbehigheronconvertedland.Additionalnitrogenfromfertilizers,whethersyntheticororganic,orfromplantedlegumeswillfurtherenhanceN2Ofluxes,aswilltillage—insofarastillagestimulatesnitrogenmineralization.

TheconversionofunmanagedlandtocellulosicbiofuelproductionmayavoidadditionalGHGloadingifcareistakentoavoidsoilcarbonoxidationandexcesssoilnitrogenavailability(Robertsonetal.,2011).Thismightoccur,forexample,ifexistingperennialvegetationwereharvestedforfeedstockorwhennewperennialgrassesweredirect‐seededintoanotherwiseundisturbedsoilprofile,andwhennoorminimalnitrogeninputsareused.Althoughthecurrentmarketforcellulosicbiomassisnascentatbest,asitdevelopsinresponsetolegislativemandatesandenergydemandtherewillbepressuretoconvertlandsnowunmanagedintobiofuelcropping


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systems.MinimizingtheGHGimpactoftheseconversionswillbecrucialforavoidinglong‐termcarbondebtthatwillotherwiseleadtocarbonsourcesratherthancarbonsinks,irrespectiveoftheircapacitytogeneratefossilfueloffsetcredits(Fargioneetal.,2008;Gelfandetal.,2011;Pineiroetal.,2009).

3.2.3.3 InfluenceonMethanotrophicActivity

Methanotrophicbacteriacapableofconsuming(oxidizing)atmosphericCH4arefoundinmostaerobicsoils.CH4uptakeinsoilsisgloballyimportant;thesizeofthesoilsinkisthesamemagnitudeastheatmosphericincreaseinCH4(IPCC,2001),suggestingthatsignificantchangesinthestrengthofthesoilsinkcouldsignificantlyaffectatmosphericCH4concentrationsifuptakedeclinesduetolanduseandmanagement.Inunmanageduplandecosystems,CH4uptakeiscontrolledlargelybytherateatwhichitdiffusestothesoilmicrositesinhabitedbyactivemethanotrophs.Diffusionisregulatedbyphysicalfactors—principallymoisturebutalsotemperature,soilstructure,andtheconcentrationofCH4inthesoil.

AgriculturalmanagementtypicallydiminishessoilCH4oxidationby70percentormore(Mosieretal.,1991;Robertsonetal.,2000;Smithetal.,2000)foratleastaslongasthesoilisfarmed.Themechanismforthissuppressionisnotwellunderstood;likelyitisrelatedtonitrogenavailabilityasaffectedbyenhancednitrogenmineralization,fertilizer,andothernitrogeninputs(Steudleretal.,1989;SuwanwareeandRobertson,2005).NH4+isknowntocompetitivelyinhibitmethanemonooxygenase,theprincipalenzymeresponsibleforoxidationatatmosphericconcentrations.Microbialdiversityalsoseemstoplayanimportantrole(Levineetal.,2011).

TherearenoknownagronomicpracticesthatpromotesoilCH4oxidation;althoughabetterunderstandingofthemechanismsresponsibleforitssuppressionmayeventuallysuggestmitigationopportunities.Todate,recoveryofsignificantCH4oxidationcapacityfollowingagriculturalmanagementhasonlybeendocumenteddecadesafterconversiontoforestorgrassland;completerecoveryappearstotakeacenturyorlonger(Robertsonetal.,2000;Smithetal.,2000).

3.2.3.4 Non‐CO2GHGEmissionsfromBurning

Burningcanbeconductedonlandsinpreparationforcultivationtofacilitateaccessforequipment,removestandingaccumulatedbiomass,andprovideorganicmaterial(ash)forincorporationintosoils.Burningofthebiomasscanbeanimportantsourceofnon‐CO2GHGs(N2O,CH4)aswellasprecursorstoGHGformation(CO,NOx)followingadditionalchemicalreactionsintheatmosphereorsoils.MoreinformationonburningofgrazinglandsvegetationcanbefoundinSection3.3.1.5,andburningoftheremainingbiomasswithclearingofforestcanbefoundinSection6.4.1.9.

3.3 GrazingLandManagement

Rangelandsaredefinedaslandonwhichtheclimaxorpotentialplantcoveriscomposedprincipallyofnativegrasses,grass‐likeplants,forbsorshrubssuitableforgrazingandbrowsing,andintroducedforagespeciesmanagedforgrazingandbrowsing.Conversely,pasturelandsrepresentlandmanagedprimarilyfortheproductionofintroducedforageplantsforlivestockgrazing,withmanagementconsistingoffertilization,weedcontrol,irrigation,reseedingorrenovation,andcontrolofgrazing(USDA,2009).HowgrazinglandsaremanagedinfluencesthepotentialforcarbonsequestrationorGHGemissions.TheparagraphsbelowhighlightsomeofthekeymanagementpracticesandtheirassociatedGHGemissionsandremovalssummarizingthecurrentstateofthescience.


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3.3.1 ManagementActivityInfluencingGHGEmissions

Soilorganiccarbondominatestheterrestrialcarbonpoolingrazinglands.Abovegroundcarbonis<fivepercentofthetotalecosystemcarbonpoolinmostnon‐woodyplantdominatedecosystems,butupto25percentingrassland‐shrublandecosystems.Grazinglandscanbecarbonsinks,withratesofsoilorganiccarbonsequestrationupto0.5MgCha‐1year‐1forrangelands(DernerandSchuman,2007;Liebigetal.,2010)and1.4MgCha‐1year‐1forpastures(Franzluebbers,2005;2010a).Actualratesareoftenlessthantheseapparentmaximalratesofsoilorganiccarbonsequestrationduetomanagement,climate,weather,andotherenvironmentalconstraints.Potentiallyhighratesofsoilorganiccarbonaccumulationarepredictedinnewlyestablishedpasturesandrestorationofdegradedrangelands,whileimpropermanagementanddroughtcanresultinsignificantcarbonreleases.Duetothelargelandarea,themovementofcarbonintoandoutofthesoilreservoiringrazinglandcanbeanimportantcomponentoftheglobalcarboncycle.InadditiontosoilorganicC,alargepoolofsoilinorganiccarbonoccursascarbonatesinsemi‐aridandaridrangelandsoilsthatcanleadtoeithersequestrationorreleaseofCO2(Emmerich,2003).However,thedirectionandmagnitudeofsoilinorganiccarbonstocksarecurrentlypoorlyunderstood(Follettetal.,2001;Liebigetal.,2006;Svejcaretal.,2008).

Twoimportantmanagementfactorsthatcontrolthefateofsoilorganiccarboningrazinglandsarelong‐termchangesinproductionandqualityofabovegroundandbelowgroundbiomassthatcanalterthequantityofnitrogenavailableandtheC‐to‐Nratioofsoilorganicmatter(Pineiroetal.,2010),andgrazing‐inducedeffectsonvegetationcomposition,whichcanbeasimportantasthedirectimpactofgrazing(e.g.,grazingintensity)onsoilorganiccarbonsequestration(DernerandSchuman,2007).Therateofsoilorganiccarbonsequestrationcanbelinearfordecades(Franzluebbersetal.,2012),buteventuallydiminishestoasteady‐statelevelwithnofurtherchangeinthestockfollowingseveraldecadesofamanagementpractice(DernerandSchuman,2007).Additionalpositivechangesinmanagementorinputsareoftenneededtosequesteradditionalsoilorganiccarbon(Conantetal.,2001),butnegativechangesinmanagementcausinglossofsoilstructureandsurfacelittercovercanleadtoerosionandlossofproductivityresultinginadeclineinsoilorganiccarbon(Pineiroetal.,2010).

MethanefluxfromgrazinglandsiscontrolledbythebalanceofentericandmanureemissionsfromruminantanimalsanduptakeofCH4bysoil.(EmissionsandmethodsforestimatingCH4emissionsfromruminantsarediscussedfurtherinSection5.3).InthewesternUnitedStates,grasslandshavegreaterCH4uptakebysoilthandoneighboringcroplands(Liebigetal.,2005),probablyduetogreatersurfacesoilorganicmatterthatpromotesthegrowthofmethanotrophicbacteria.InanassessmentofGHGemissionsfromthreegrazinglandsystemsinNorthDakota,entericemissionsofCH4fromgrazingcattlewerethreetoninetimesgreater(onaCO2equivalentbasis)thanCH4uptakebysoil(Liebigetal.,2010).WithCH4emissionsdirectlytiedtonumberofcattle,fertilizedgrasslandsareoftenanetcarbonsourceduetoenhancedCH4emissionfromcattleandpotentiallygreaterN2Oemissions,whileunfertilizedgrasslandsareoftenanetcarbonsink(Luoetal.,2010;Tunneyetal.,2010).

3.3.1.1 LivestockGrazingPractices

Livestockgrazingpractices(i.e.,stockingrateandgrazingmethod)aresummarizedbelowalongwithdataontheinfluencethesepracticeshaveonGHGemissionsandremovals.

StockingRate:Stockingrateisthenumberofanimalspermanagementunitutilizedoveraspecifiedtimeperiod,e.g.,numberofsteersperacrepermonth.Basedonpublishedstudies,responsesofsoilorganiccarbontostockingrateandgrazingintensityhavebeenvariable,despitegrazingeithercausinganincreaseorhavinglittleeffectonthemorecommonlymeasuredpropertyofsoilbulkdensity(GreenwoodandMcKenzie,2001;Schumanetal.,1999).Innorthernmixed‐grassprairie,


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soilorganiccarbonhasincreasedingrazedcomparedwithungrazedareas,partlyresultingfromincreasingdominanceofshallow‐rooted,grazing‐resistantspecies,suchasbluegrama(Boutelouagracilis),whichincorporatesalargeramountofrootmassintheuppersoilprofilethanthemid‐grassspeciesthatitreplacesduringgrazing(Derneretal.,2006).Furtherresearchisneededtodeterminetheextentofdifferentrootdistributionsontotalcarbonstorageinanentiresoilprofile.Increasingstockingratebeyondanoptimumforachievingmaximumlivestockproductionperunitlandarea(Bement,1969;Dunnetal.,2010)wouldbeexpectedtoresultinalossofsoilorganiccarbonduetoreducedplantvigorandrootdistributioninthesoilprofile.Withsuboptimalstockingrate,vigorofpastureforagesmaydeclineasplantresiduesdevelopathicklitterlayeratthesoilsurface.However,insemi‐aridregions,thehighUVlightintensitymaysignificantlyreducelitteronthesoilsurfacethroughphotochemicaldecompositionprocesses,regardlessofgrazingintensity(Brandtetal.,2010).Vegetationcompositionshiftsthatchangethequantityandqualityofplantmaterialproducedcaninfluencetheamountofcarboninputstosoils.Inmanagedpastures,ithasbeenshownthatsoilorganiccarboncanbeoptimizedwithamoderatestockingratecomparedwithnograzingorheavy,continuousgrazing(Franzluebbers,2010b).Anoptimizedstockingrateforaparticularregion(climaticconditions),vegetationcomposition,andsoiltypeisthoughttomaximizetheamountofsoilorganiccarbonsequestered.

Limitedevidenceshowsthatgrazingatmoderatelevelscanfurtherincreaseenvironmentalbenefitsoverthoseofgrasslandestablishmentalone,inadditiontoprovidinganimportanteconomicreturntoproducers.Ifsoilorganiccarbonweretodeclinewithovergrazing,therewouldalsobeadeclineinanimalproductivityduetolackofforage.Therefore,anegativerelationshipbetweensoilorganiccarbonstorageandanimalproductivityislikelywhengrazingintensityexceedsamoderatelevel.ThisresponseislikelymodifiedundermoderategrazingpressureduetothefactthatgreateranimalproductperheadcanbeachievedwithlowerGHGemissions.Limitingtheeffectofhighstockingrateonsoilorganiccarbonlevelsmaybeachievablewithhighnitrogenfertilizerinputs,anoutcomewithanuncertaincarbonfootprintrelativetoGHGintensity.StockingrateandfertilizernitrogeninputinteractionsneedtobequantifiedtoaccuratelyassesstotalGHGintensity.SomeevidenceinthehumidUnitedStatessuggeststhatovergrazingcanleadtoincreasedsoilerosionandareductioninsoilquality.Literaturefromotherregionshasalsoshownincreasingsoilerosionanddecliningsoilqualitywithexcessivestockingrates.Whileevidenceislacking,anassumptionisthatsoilorganiccarbonfollowsthissamepositiveresponsetomoderategrazingandnegativeresponsetoovergrazing.

EmissionsofN2Ofromgrazinglandsareaffectedbygrazing,butnetfluxcanbeincreasedordecreased,dependingonstockingrate,grazingsystem,andseason(Allardetal.,2007).StockingratehadlittleinfluenceonN2Oemissionsfrommixed‐grassprairieinNorthDakota(Liebigetal.,2010).WhileelevatedN2Oemissionsmaybeexpectedunderincreasedstockingrate,Wolfetal.(2010)suggestedthatgrazingcancounteractpotentialN‐inducedemissionsonrangelandsbyreducingsurfacebiomass,resultinginmoreextremesoiltemperatures,lowersoilmoisture,andcorrespondinginhibitionofmicrobialactivityresponsibleforN2Oemissions.Ifgrazingintensityonpastureswereviewedasafertilizereffectwithincreasinganimalmanuredeposition,thenN2Ofluxfromagrazingeffectdoesnotbehaveinthesamemannerasmanufacturednitrogenfertilizerinputs.Interactionsbetweenstockingrateandnitrogenfertilizerinputshavenotbeenquantified,despitesuchdiversityinmanagementlikelyoccursamongproducers.StockingrateandmanureandfertilizernitrogeninputsareareasrequiringfurtherresearchtobetterunderstandthecomplexsetofcontrollingfactorsinadditiontosoiltextureandenvironmentalconditionsonN2Oemissionsingrazinglands.Onrangelands,theabundanceofN‐fixinglegumesintheplantcommunitybecomesmorecriticalforincreasingSOC,particularlysincefertilizeradditionsandmanurearenotassignificantforreturningnitrogentothesoilcomparedtopasturesystems.ThisisanarearequiringfurtherresearchtobetterunderstandthecontrollingfactorsonN2Oemissions.


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GrazingMethod:Grazingmethodsvarybasedonproducergoalsandthetypeofforageavailable(Scheafferetal.,2009).Twodistinctgrazingmethods,continuousandrotationalgrazing,representtheprevalentmethodsemployedongrazinglandsintheUnitedStatestomanagethelivestock.Continuousgrazingallowsanimalstofreelymoveandhavefullaccesstoagrazingarea,whereasrotationalgrazingismorecontrolled,involvingmovementofanimalsbasedonmonitoringforagecondition,suchasplantheight,betweentwoormorepaddockssubdividedfromalargergrazingarea.Rotationalgrazingterminologyhasbeenconfusedwithtermssuchasholisticgrazing,plannedgrazing,prescribedgrazing,andmanagement‐intensivegrazing,whichcontinuetobeusedwithmultipleandambiguousmeaningsdespiteattemptstostandardizedefinitions(SRM,1998).Termstodefineintentionsofrotationalgrazingsystemsincluderest‐rotation,deferred‐rotation,high‐intensity‐short‐duration,andseason‐longgrazing(Briskeetal.,2008;Briskeetal.,2011).Herewedefinerotationalgrazingasthemovementoflivestockbetweentwoormoresubunitsofgrazinglandsuchthatalternatingperiodsofgrazingandnograzing(‘rest’)occurwithinasinglegrowingseason(HeitschmidtandTaylor,1991).

Rotationalgrazinglimitsplantsfromreachingreproductivestagesinwhichforagequalityrapidlydeclines.Thiscontrastswithcontinuousgrazinginwhichthereismoreselectivegrazingofthehighestqualityforages.Assuch,foragequalitymaybemaintainedatahighlevellongerintothegrowingseason.Therefore,rotationalstockinginthehumidUnitedStatescouldprovidemoreuniformforageconsumptionacrosspasturesandallowsufficientresttoforagespeciesbetweengrazingeventstopromotegreaterproduction.Pastureswithgreaterplantproductionviaanimprovedstockingmethodwouldbeexpectedtohavelowersoilerosionandgreatersoilorganiccarbonstorage.Althoughtheseexpectationsseemintuitive,therearelimiteddatainthescientificliteraturetosupportthem.Twostudieshavesuggestedanincreaseinsoilorganiccarbonwithrotationalgrazingcomparedwithcontinualseason‐longgrazing(Conantetal.,2003;Teagueetal.,2010),andanotherstudyfoundnodifferencebetweensystems(Manleyetal.,1995).Sincerotationalgrazingdataaremostlyavailableforrangelandandfewstudiesconductedonpastures,thereisnotenoughevidencetoevaluatehowrotationalgrazingmightaffectsoilorganiccarboninpastures.Giventhatthepreponderanceofevidencesuggeststhatrotationalgrazingdoesnotinfluencevegetationproductioninrangelands(Briskeetal.,2008),changesinsoilorganiccarbonwithrotationalgrazingwouldbeexpectedonlyifsubstantialvegetationchangeoccurredindependentlyfromstockingrate.Rangelandstypicallyhaveamuchhigherdiversityandmultiplegrowthpatternsofforbs,cool‐seasonandwarm‐seasongrasses,whichwouldresultinasmallerinfluenceofstockingmethodonvegetationphenology(i.e.,keepingforageinavegetativeratherthanareproductivestate)thanwouldoccurinmonocultureorsimplemixturesofforagesinpastures.Muchmoreresearchongrazingmethodisneeded,duetothehighadoptionrateandpromotionofthebenefitsofimprovedgrazingmethodsforsoilorganiccarbonsequestrationbyproducersandagriculturaladvisors(BeetzandRhinehart,2010).

3.3.1.2 ForageOptions

Cool‐andwarm‐seasonforageshavegrowthactivityatdifferenttimesoftheyear,therebyaffectingwhenrootandlittercarboninputsaresuppliedtosoil.Dependingonenvironmentalgrowingconditions(i.e.,relativelyshort,cool,andwetsummerwithlong,coldwinterversuslong,hot,anddrysummerwithmild,wetwinter),theperformanceofcool‐versuswarm‐seasonforageswillvaryacrossregions.InthesoutheasternUnitedStates,perennialcool‐seasonforages(e.g.,tallfescue)haveproducedgreatersoilorganiccarbonthanwarm‐seasonforage(e.g.,bermudagrass)ingrazinglandsystems,despitethemorevigorousgrowinghabitofbermudagrass(Franzluebbersetal.,2000).Thisresultislikelyduetotheopportunitiesofforagesforgrowthandthebalanceofwaterinsoilthatremainsformicrobialdecompositionoforganicmatter.


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Timingofforagegrazingcanaffectplantproductivity,wildlifehabitat,andcompactionofsoil.Eachoftheseeffectscan,inturn,affectsoilorganiccarbonsequestrationandGHGemissions.Thecapacityofsoiltowithstandcompactionforcesofanimaltreading,resultinginsignificantdeformation,destabilization,lossofinfiltrationcapacity,andsoilorganiccarbonsequestration,canbeexceeded—especiallyunderwetconditions(Bilottaetal.,2007).Soilsaturationduringwinterandspringleadtosevereeffectsfromanimaltrampling.InnorthernlatitudesandrangelandsofthewesternUnitedStatessubjecttofreeze‐thawcycles,sandyandloamysoilsarelesslikelytobeaffectedbythenegativeimpactsofcompaction.Intuitively,deferringgrazingtoperiodsoflimitedactiveforagegrowth(e.g.,winterandspring)mightcontributetoincreasedsoilcompaction.However,allowingforagetoaccumulatetofullcanopypriortograzingmightbebeneficialtocontrollingerosionbyprovidingalongerperiodofforageandresiduecover.Grazingofwintercovercropsmayalsobeaneffectivefarm‐diversitystrategy,buttheeffectsonsoilerosioncontrolandsoilconditionneedtobequantified.Wildlifemanagementguidelinesonrangelandsuggestlonger‐term(>oneyear)resttoaccumulatevegetationstructureforcertainbirdsneedinghabitat.Timingofgrazingcouldbeacriticalfactorincontrollingcompaction,susceptibilitytoerosion,andsoilorganiccarbonsequestration,sothesequenceofwhenpasturesaregrazedshouldberotatedamongyearstoensurethatplantcommunitiesarenotalwaysgrazedatthesametimetoensuregreatercommunitysustainability.

Organicmatter‐richsurfacesoilabsorbscompactiveforcesofgrazingmuchlikeasponge,inwhichsoiloftenreboundsinvolumeonceforcesareremoved.However,effectsofwintergrazingofdeferredgrowthmaybedifferentincolderthaninwarmerregions:frozensoilmayavoidcompaction,butnutrientrunoffmaybecomemoreimportant(Clarketal.,2004).InthesouthernUnitedStates,perennialcool‐seasongrassesareoftengrazedduringlatewinterandthroughoutspringduringtypicallywetconditions,butduetoactiveforagegrowth,soilcanalsodryquicklyandtramplingmaynotalwayscausedamage.InGeorgia,soilorganiccarbonwasgreaterunderlong‐termstandsofcool‐seasontallfescue(typicallygrazedinspringandautumn)thanunderwarm‐seasonbermudagrass(typicallygrazedinsummer)(Franzluebbersetal.,2000).

InthesoutheasternUnitedStates,annualcool‐seasonforagesareoftenplantedasacovercropfollowingsummercropsorsod‐seededintoperennialgrasspastures.ThispracticecanenhanceforageproductionandshouldincreasesoilorganicC,althoughlimiteddataareavailabletosupportthisconclusion.Inanintegratedcrop/livestocksysteminthesoutheasternUnitedStates,therewasalimitedeffectofgrazingannualcovercropsonsoilorganicC,eitherinthesummerorwintercomparedwithungrazedcovercrops(FranzluebbersandStuedemann,2009).

3.3.1.3 Irrigation

WaterisalimitingfactorintheabilityofplantstofixcarbonandsubsequentlyproducethecarboninputnecessarytoaccumulatesoilorganicC.ItisalsoafactorlimitingdecompositionofsoilorganicC.Whiletheextentofirrigationingrazinglandsislimited,whereitoccursthereareconsequencesforsoilorganiccarbonstorage.Forexample,someproductivemeadowsinthewesternUnitedStatesareirrigated.Howirrigationaffectssoilorganiccarbonwilldependonthequantity,frequency,andtimingofirrigationevents.Irrigationonlyatpeakplantgrowthstageswilllikelycauseamuchgreaterpositiveimpactonforagecarbonfixationthananegativeimpactonsoilorganiccarbondecomposition.Inthesamemanner,irrigationquantity,frequency,andtimingwilllikelyaffectN2OandCH4emissions,althoughpulsedresponsesoftheseGHGscouldlikelybemuchmoredramatic.Unfortunately,thereareonlylimitedstudiesonthesepotentialimpacts.SeeSection3.2.1.4formoreinformationonirrigationmethods.

Inacomparisonofagriculturalsystemswithsurroundingaridandsemi‐aridnaturalvegetation,Entryetal.(2002)foundthatsoilorganiccarbonwasgreaterinirrigatedagriculturalsystemsdue


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toenhancedproductivity.EmissionofN2Ofromirrigatedsystemsoccursfollowingcloselytimedirrigationandnitrogenfertilizerapplicationsincroplandconditions,andthiswouldbeexpectedundergrazinglandsaswell,buttherearefewdataavailable(Liebigetal.,2006;Liebigetal.,2012).

3.3.1.4 NutrientManagement(SyntheticandOrganic)

Fertilizersareoftenappliedtopastures,duetothehighyieldresponsewithadequateprecipitation,butarelesscommoninwesternrangelandsduetoinconsistentyieldresponseandriskycost‐effectivenesswithlimitedandvariableprecipitation.NitrogenavailabilityinsoildeterminestoalargeextenttheemissionsofN2O.Grazinglandstypicallyhavelowernitrogenavailabilityinsoilthancroplands,andthereforehavelowerN2Oemissions(Liebigetal.,2005).However,applicationoffertilizernitrogentorangelandhasbeenfoundtoconsistentlystimulateN2Oemissions(Flechardetal.,2007).Liebigetal.(2010)observedtwo‐foldgreaterN2Oemissionsfromfertilizedcrestedwheatgrasscomparedwithunfertilizedmixed‐grassprairie.AdditionoffertilizernitrogentopastureinMichiganhadanegligibleeffectonN2Oemissions(AmbusandRobertson,2006),whereasapplicationofpoultrymanureonabermudagrasspastureinArkansasincreasedN2Oemissionsby45percentcomparedwithpasturewithoutmanure;N2Ofluxandsoilnitratedynamicswerepositivelyassociated(Saueretal.,2009).Astrategytoreducesoilnitratebyinterseedingannualryegrassonmanure‐amendedsoildecreasedN2Oemissionsby50percent.Similartocropland,reducingsoilnitratetolowlevelsduringperiodsoflowrootactivityandhighlevelsduringperiodsofhighrootactivitywillgenerallyenhanceplantnitrogenuptakeandreduceN2Oemissions.ApplicationofcompostedgreenwastecouldsequesterC,butthisresearchtopichasnotbeenfullyevaluated.AsignificantincreaseinsoilorganiccarbonhasonlybeendemonstratedatoneoftwositesinCalifornia(Ryalsetal.,2014).Frommodelsimulations,compostapplicationhasbeenshowntoreducetheoverallGHGemissiononCO2equivalentbasis,bysequesteringcarbonandreducingN2Oemissions,whilemanureslurryandinorganicfertilizerapplicationsledtonetGHGemissionsonCO2equivalentbasis(DeLongeetal.,2013).Formoreinformationonmanagementoptionsassociatedwithfertilizationpractices,seeSection3.2.1.1.

3.3.1.5 PrescribedFires

Burninghasthepotentialtoaltersoilorganiccarbonthrougheffectsonphotosynthesis,soil,andcanopyrespiration,andthroughspecieschanges,inadditiontostabilizingorincreasinglivestockgains,improvinghabitatdiversity,andreducingfuelloads(Bouttonetal.,2009;Toombsetal.,2010).Althoughcarbonlossfromburninggrazinglandsisaminorcomponentoftheannualcarbonemissions,burningrangelandswithasignificantwoodyabovegroundplantbiomasscanresultinsubstantialimmediateecosystemcarbonloss(BremerandHam,2010;Rauetal.,2010).However,prescribedburningofgrazinglandscouldalsoaffectlong‐livedcharthataccumulatesinsoil,andthereforewouldinfluencesoilcarbonstocks.Burningalsoleadstonon‐CO2GHGemissions,whichcanbesignificantduetothehigherglobalwarmingpotentialofthesegasescomparedwithCO2(IPCC,2006).Formoreinformationonnon‐CO2GHGemissionsfromburning,seeSection3.2.3.4.

3.3.1.6 ErosionControl

Riparianbufferscanbeasignificantsinkforexcessnutrientsrunningoffneighboringgrazinglands.Thefateofnutrientsisdependentontheflowcharacteristicsandtypeofvegetation.ExcessnitrateinsaturatedsoilofriparianareascanleadtosignificantN2Oemissions—althoughtheseemissionsaretypicallytreatedasindirect,withtheemissionsassociatedwiththefieldorlivestockfacilitythatiscontributingtheexcessnutrients(SeeSection3.2.1.1).TransportofsolublecarbonintoriparianareascouldalsoenhanceCH4emissionsfromsaturatedsoil.


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3.3.1.7 ManagementofDrainedWetlands

DrainageofwetlandorhydricsoilsthatareusedforgrazinghasimplicationsforsoilorganiccarbonandGHGemissions,similartodrainageforcropproduction.Thewaterregimeandplantcommunitiesaresignificantlyalteredandsoilsaremodifiedfromanaerobictoaerobicconditions.Increasingoxygeninsoilwillcauseorganicmattertodecomposemorerapidlythanundersaturatedconditions,resultinginreleaseofCO2(Eagleetal.,2010;FranzluebbersandSteiner,2002;IPCC,2006;Liebigetal.,2012).LargeemissionsofCO2resultfromdrainageofwetlands(Allen,2007;2012),anddrainagecanalsoincreasenitrogenmineralizationandenhanceN2Oemissionsdirectly(IPCC,2006).EmissionsofCH4arereducedconsiderablywithdrainage,butthisimpactisoftennotconsideredinestimationofGHGemissions(IPCC,2006).Alargeproportionofgrasslandwetlandshavebeendirectlydrainedormodifiedtoenhanceagriculturalproduction(DahlandJohnson,1991),andmanyotherwetlandsareindirectlyaffectedbysubsurfacetiledrainsandagriculturalpracticesinsurroundingcatchments.SeeSection3.2.1.6formoreinformationaboutmanagementofdrainedsoils.

3.3.1.8 LimeAmendments

LimeamendmentsareneededwhensoilpHislow(e.g.,pH<5)toenhanceproductivityandsupportbalancednutrientlevelsingrazinglandsoils.Typicallimingmaterialsingrazinglandsarecalciticlimestone(CaCO3),dolomiticlimestone(CaMg(CO3)2),andconfinedlivestockmanure,particularlypoultrylitter,whichhaslimingactivityfromlimeadditivetothefeedration.Whencarbonatelimeisappliedtosoilitdissolvesinsolutionovertime,withthecationandcarbonatedissociating.ThereispotentialforreleasingCO2totheatmospheredependingonwhetherthelimereactswithcarbonicornitricacidinthesoilsolution.Theenhancedplantnutrientofferedbylimingcanhaveanetpositiveeffectonthecarbonbalanceforanextendedperiodoftime.SeeSection3.2.1.7formoreinformationonlimeandtheconsequencesforGHGemissions.

3.3.1.9 WoodyPlantEncroachment

Woodyplantencroachment3leadstocarbonaccumulationinabove‐groundandrootbiomassandmayincreaseoverallecosystemcarbonstorage,butcandegradeagriculturalproductivityofgrazingland(McClaranetal.,2008).Overthepastcenturyinwesternrangelands,soilorganiccarbonhasincreasedinnear‐surfacesoilswithwoodyplantencroachment(Bouttonetal.,2009;Creameretal.,2011;Liaoetal.,2006;Liebigetal.,2012).Removalofwoodyplantsbyfireorothermechanismsdepletestheseshallow,relativelysusceptiblesoilorganiccarbonstoresassociatedwithencroachment(Neffetal.,2009;Rauetal.,2010);butdoesnothaveaneffectonSOCortotalnitrogenstocksatdepthsof>20cm(Daietal.,2006).Regardless,removalofthewoodyplantswillcauseadeclineinabovegroundbiomasscarbonstocks(Rauetal.,2010).

InasummaryofresearchonCH4emissionsfromgrazinglands,Liebigetal.(2012)reportedCH4uptakeundermesquite,butnetCH4productionundergrasslandanddeadmesquitestumps.Methaneuptakeundermesquitewasassociatedwithreducedsoilbulkdensityandincreasedsoilmoisture(McLainandMartens,2006),aswellasgreaternitrogenaccrual/accumulationassociatedintheareaaroundmesquiteplants(10meters)(BouttonandLiao,2010;Liaoetal.,2006;Liuetal.,2010).Methaneuptakeundermesquitewasalsoassociatedwithalteredsoilmicrobialcommunities(Hollisteretal.,2010;LiaoandBoutton,2008),whichcanaffectNOxandN2Orates,whileCH4productionfromgrasslandandwoodydetrituswaslikelycausedbytermiteactivity.The

3Woodyencroachmentwilleventuallyleadtoatransitionfromgrazinglandtoaforest.SeeChapter7:LandUseChangefordefinitionofforestlandtodeterminewhenwoodyencroachmentwillleadtoatransitiontoforestland.


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roleofmesquitetofixN,therebyalteringnitrogendynamics,resultedinN2Oemissionsundermesquitecanopyfour‐foldgreaterthanundergrassesorwoodydetritus(McLainetal.,2008).

3.3.2 Land‐UseChangetoGrazingLands

Land‐useconversiontograzinglandsinfluencesthecarbonstocksandGHGemissionsofaparcel.Priorlanduse,climate,soiltype,andmanagementpracticesarejustafewofthefactorsinfluencingthemagnitudeanddirectionofGHGemissionsandremovalsresultingfromaland‐useconversiontograzinglands.Theparagraphsbelowsummarizethecurrentstateofthescienceontheinfluenceofaland‐useconversiononcarbonstocks,soilN2O,CH4,andnon‐CO2GHGsresultingfrombiomassburning.

3.3.2.1 InfluenceonCarbonStocks

Establishmentofpasturesonpreviouscroplandhelpsreducesoilerosionandimprovessoilquality(Singeretal.,2009).Thereissubstantialevidencethatestablishmentofpasturesleadstosignificantsoilorganiccarbonsequestration.Therateofaccumulationacrossanumberofstudiesaveraged0.84MgCha‐1year‐1(Franzluebbers,2010a).Literatureisinadequatetodeterminewhetherforagecompositionorsoiltypehaveadiscernibleinfluenceonsoilorganiccarbonstock(seeSection3.3.1.2).Thequantityofforageproducedandthequantityofresiduesfromsurfacelitterandrootbiomassarelikelykeydeterminantsofsoilorganiccarbonaccumulation.Thesequantitiescanbeinfluencedbyfactorssuchasforagemixture,climaticconditions,soiltype,inherentsoilfertility,fertilizerapplication,andliming.

3.3.2.2 InfluenceonSoilNitrousOxide

Dependinguponpreviouslanduse,grasslandestablishmentmayormaynotaffectnetN2Oemissionsduringland‐usechange.Ingeneral,emissionsofN2Oarecontrolledbysoilnitrogenavailabilitywithadditionalinfluenceofsoiloxygenandsolublecarbonavailability.Ifthepreviouslandusewasforexample,anutrient‐limitedforest,convertedsubsequentlytohigh‐fertilitypasture,thenN2Oemissionswouldlikelyincrease.Ifthepreviouslandusewasnutrient‐richcroplandconvertedtopasture,thenN2OemissionswouldlikelydeclineduetogreateropportunityforperennialforagespeciestoassimilateavailablesoilnitrogenandthusreduceopportunitiesforsoilnitrogentransformationstoN2O.Thisisanarearequiringfurtherresearchtoobtainquantitativeresponses,however.

3.3.2.3 InfluenceonMethanotrophicActivity

Land‐usechangetograzingland,particularlyfromforestland,mayinvolvefertilizationtoenhanceforageproduction.NitrogenfertilizationcausesareductionofmethanotrophicactivityinsoilsandthereforereducestheuptakeofCH4fromtheatmosphere(AmbusandRobertson,2006).SeeSection3.2.3.3formoreinformationontheimpactofland‐usechangeonmethanotrophicactivity.

3.3.2.4 Non‐CO2GHGEmissionsfromBurning

BiomassburningingrazinglandcanbeanimportantsourceofGHGs(CO2,N2O,CH4)(Aaldeetal.,2006;AndreaeandMerlet,2001;Badarinathetal.,2009;IPCC,2006).Whileconversionofcroplandtograzinglandrarelyinvolvesburning,conversionofforesttograzinglandcaninvolveburningofthewoodand/orslashleftfromlandclearing.TheeffectonGHGemissionsfrombiomassburningisdiscussedfurtherinthecroplandsection(Section3.2.3.4)andintheforestlandsection(Section6.4.1.9).


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3.4 Agroforestry

AgroforestryrepresentsauniquecasewithinGHGaccounting,encompassingbothforestandagriculturalcomponents,alongwithmanycombinationsoftheirrespectivemanagementactivities(Table3‐1andTable3‐2).AgroforestryisdefinedwithintheUnitedStatesasan“intensiveland‐usemanagementthatoptimizesthebenefits(physical,biological,ecological,economic,andsocial)frombiophysicalinteractionscreatedwhentreesand/orshrubsaredeliberatelycombinedwithcropsand/orlivestock”(GoldandGarrett,2009).Anotherwayoflookingatagroforestryisasasetoftree‐based4conservation/productionpracticescombinedintobiggeragriculturaloperations,providingforest‐derivedfunctionsandinteractingwithagriculture‐derivedfunctionsinsupportofagriculturallanduse.Whileprovidingmanyotherservices(seeTable3‐3),agroforestrycancontributetocarbonsequestration,GHGmitigation,andadaptationtoshiftingclimate(CAST,2011;IPCC,2000;Morganetal.,2010;Verchotetal.,2007).

Table3‐3:SixCategoriesofAgroforestryPracticesPracticedintheUnitedStates

Practice Descriptiona Benefitsb

Alleycropping

Treesorshrubsplantedinsetsofsingleormultiplerowswithagronomic,horticulturalcrops,orforagesproducedinthealleysbetweenthesetsofwoodyplantsthatproduceadditionalproducts

Produceannualandhigher‐valuebutlonger‐termcropsfordiversificationofincome

Enhancemicroclimateconditionstoimprovecroporforagequalityandquantity

Reducesurfacewaterrunoffanderosion Improvesoilqualitybyincreasingutilizationandcyclingofnutrients

Altersubsurfacewaterquantityorwatertabledepths

Enhancewildlifeandbeneficialinsecthabitat Decreaseoffsitemovementofnutrientsorchemicals Increasecarbonstorageinplantbiomassandsoils Improveairquality

Forestfarming(alsocalledmulti‐storycropping)

Existingorplantedstandsoftreesorshrubsthataremanagedasanoverstorywithanunderstoryofwoodyand/ornon‐woodyplantsthataregrownforavarietyforproducts

Improvecropdiversitybygrowingmixedbutcompatiblecropshavingdifferentheightsonthesamearea

Improvesoilqualitybyincreasingutilizationandcyclingofnutrientandmaintainingorincreasingsoilorganicmatter

Increasenetcarbonstorageinplantbiomassandsoil

Riparianforestbuffersc(combinesNaturalResourcesConservationServicePracticeStandards:RiparianForestBufferandFilterStrip)

Acombinationoftrees,shrubs,andgrassesestablishedonthebanksofstreams,rivers,wetlands,andlakes

Decreaseoffsitemovementofnutrientsorchemicals Stabilizestreambanks Enhanceaquaticandterrestrialhabitats Provideeconomicdiversificationeitherthroughplantproductionorrecreationalfees

Increasecarbonstorageinplantbiomassandsoils

4Alsoreferredtoastrees‐outside‐forests,theterm“tree”hereincludesbothtreeandshrubs(Bellefontaineetal.,2002).


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Practice Descriptiona Benefitsb

SilvopastureTreescombinedwithpastureandlivestockproduction

Providediversificationofcropsintimeandspace Produceannualandhigher‐valuebutlonger‐termcrops

Decreaseoffsitemovementofnutrientsorchemicals

Windbreaks(alsoreferredtoasshelterbelts)

Linearplantingsoftreesandshrubstoformbarrierstoreducewindspeed(maybespecificallyreferredtoascroporfieldwindbreak,livestockwindbreak,livingsnowfence,orfarmsteadwindbreak,dependingontheprimaryuse)

Controlwinderosion Protectwind‐sensitivecrops Enhancecropyields Reduceanimalstressandmortality Serveasabarriertodust,odor,andpesticidedrift Conserveenergy Providesnowmanagementbenefitstokeeproadsopenorharvestmoisture

Specialapplications

Useofagroforestrytechnologiestohelpsolvespecialconcerns,suchasdisposalofanimalwastesorfilteringirrigationtailwater,whileproducingashort‐orlong‐rotationwoodycrop

Treatmunicipalandagriculturalwastes Treatstormwater Useincenterpivotcornerplantings Producebiofeedstock Reduceimpactsofflooding Decreaseoffsitemovementofnutrientsorchemicals

Source:USDANaturalResourcesConservationService(2012).aDescriptionsfollowUSDANaturalResourcesConservationServiceConservationPracticesStandards.bAllagroforestryplantingsaddincreaseddiversitywithintheagriculturallandscape.Assuch,theywillimprovewildlifehabitatandgenerallyaredesignedormanagedwiththisasasecondarybenefit.cRiparianforestbufferreferstotheplantedpractice.Thiscategorydoesnotincludenaturallyestablishedriparianforests.

IntheUnitedStates,fivemaincategoriesofagroforestrypracticesarerecognized:alleycropping,forestfarming,riparianforestbuffers,silvopasture,andwindbreaks.Thereisanemergingsixthcategoryofspecialapplicationsoradaptationsofthesepractices(Table3‐3).Thesepracticesaretreatedwithinthecroplandandgrazinglandsystemsectionwiththeexceptionofforestfarming.Forestfarming(alsoreferredtoasmulti‐storycroppingwithinUSDANaturalResourcesConservationServicePracticeStandards)involvesthemanipulationofexistingforestcanopycoverinordertoproducehigh‐valuenon‐timber(i.e.,food,floral,medicinal,andcraft)productsintheunderstory,thusmaintaininglanduseasforest.Assuch,GHGaccountinginforestfarmingpracticeswillneedtobetreatedwithinthemethodsandapproachespresentedinSection6.2andSection6.4.

Themanyservicesderivedfromagroforestrypracticescanextendwellbeyondthesmallparceloramountoflandtheyphysicallyoccupywithintheagriculturallandscape(Bellefontaineetal.,2002;Garrett,2009).Theuseofagroforestrytechnologiesareimportantcomponentsattherural/communityinterface,aswellaswithinurbansettingstoaddressemergingneedssuchasstormwatertreatment,recreationorgreenspace,andfeedstockproduction(Schoenebergeretal.,2001).Althoughagroforestryiscategorizedintothesepractices,eachagroforestryplanting,evenwithinapractice,potentiallyrepresentsauniquecaseofspeciesselection,arrangement,placementwithinotherpracticesandthelargerlandscape,anduseofmanagementactivities,dependingonlandownerobjectives.Agroforestryplantingsarethereforemoreofa“designerlandscapefeature”thanastandardizedandeasilydescribedpractice(Mizeetal.,2008)withinGHGaccountingactivities.

Silvopastureprovidesagoodillustrationofthiscomplexityinagroforestrysystems.Silvopastureisthedeliberatecombinationofthreecomponents—trees,forage,andlivestock—alongwiththerangeoftheirrespectivemanagementactivities.Studiesdemonstrateahighercarbonsequestrationpotentialinsilvopasturecomparedwithforestorpasturealone(Haileetal.,2010;Nairetal.,2007;


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SharrowandIsmail,2004).Muchofthisnewcarbonisinthewoodybiomass,butsoilcarbonalsohasthepotentialtoincreaseasaconsequenceofcarboninputsfromthetrees,whichovertimeextendfurtherintotheforagecomponent(Peichletal.,2006),aswellasmanagementoftheforageandofthelivestock(seeFranzluebbersandStuedemann,2009;Karkietal.,2009).Managementactivitieswithinasilvopasturemayincludefertilization,liming,cultivation,andharvestingoftheforagecrop(insomeyears);periodicharvestingofpineneedlesforpinestraw;incorporationofprunedwoodymaterialintotheforagecomponent;anddifferentgrazingintensitiesandrotations.Thefrequencyandintensityofmanagementactivitiesandinputsfromallthreecomponentscanvarysignificantlyfromyeartoyear,whichmakesaccountingforthesequesteredcarboninasilvopastureoperationchallenging.

RatesandamountsofGHGemissionswithineachagroforestryplantingwillvarydependingonpriorlandmanagementandcurrentconditions(i.e.,site,climate),aswellasbystanddevelopment.Theseratesandamountswillalsobedependentonlandowners’decisionsthatdetermineplantingdesign,aswellasmanagementactivities—agricultural,forestry,andgrazing—usedoverthelifetimeofanagroforestrysystem(Table3‐4).

Table3‐4:ManagementActivities5andOtherFactorsWithinAgroforestryPracticesThatMayAlterCarbonSequestrationandGHGEmissionAmounts

Practice ManagementActivities

Windbreaks

Establishmentdisturbancetosoilduringsitepreparation Depositionofwind‐andwater‐transportedsediments,nutrients,andotheragriculturalchemicalsintotheplanting Windbreakrenovation(removalofdeadanddyingtreesovertime)

Riparianforestbuffers

Establishmentdisturbancetosoilduringsitepreparation Depositionofwind‐andwater‐transportedsediments,nutrients,andotheragriculturalchemicalsintotheplanting HarvestingofherbaceousmaterialsplantedinZone3(zoneclosesttocrop/grazingsystem)andofwoodymaterialsplantedinZone2(middlezone)

Alleycropping

Establishmentdisturbancetosoilduringsitepreparation Weedcontrol(mechanicalorchemical) Pruning,thinning,andharvestingofwoodymaterial(amountandfrequencyvarygreatlydependingonshort‐andlong‐termobjectiveofpractice) Fertilizationforalleycropandoccasionallyneededfortreesinrows Tillageinalleys(frequencyandintensity) Cropspeciesusedinalleyproduction Complexharvestingschedulesstratifiedinspaceandtime

Silvopasture

Establishmentdisturbancetosoilduringsitepreparation Weedcontrol(mechanicalorchemical) Pruning,thinning,andharvestingofwoodymaterial(amountandfrequencyvarygreatlydependingonshort‐andlong‐termobjectiveofpractice) Fertilizationofforagecomponent Tillageinforagecomponent(frequencyandintensity) Cropspeciesusedinforagecomponent Grazingmanagement(timing,intensity,frequency) Complexharvestingschedulesstratifiedinspaceandtime

3.4.1 CarbonStocks

Agroforestry’spotentialforsequesteringlargeamountsofcarbonperunitareaiswellrecognized(Dixonetal.,1994;KumarandNair,2011;Nairetal.,2010),withsequestrationratesbeinggreater

5ForestFarmingisnotincludedintheseconsiderations.


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thanmanyoftheotheragriculturaloptions(IPCC,2000).Carbonissequestereddirectlyintothewoodybiomassandsoil.Indirectly,agroforestrypracticescanaltercarboncyclingbyenhancingcropandforageproduction(upto15H—heightoftrees—distancefromthewindbreak)andtrappingwind‐blownandrunofferosion(Brandleetal.,2009).Lackofdatalimitsaccountingoftheseothercarbonfluxesimpactedbytheadditionoftreesandisbeyondthescopeofthiseffort.

WoodyBiomass:Themajorityofnewcarboncontributedtoasitebyagroforestrywillbefromtheproductionofwoodybiomass,withthelargercontributionbeingfromtheabovegroundwoodybiomass,asgenerallyobservedinforestestablishmentplantings(NuiandDuiker,2006).Themoreopenenvironmentcreatedinagroforestryplantingsresultsinthetreeshavingdifferentgrowthformsthanencounteredunderforestconditions—e.g.,greaterbranchproduction(Zhou,1999)andspecificgravity(Zhouetal.,2011)—whichwillneedtobetakenintoaccountwhenestimatingtheabovegroundwoodybiomass.

Thebelowgroundbiomasspoolinagroforestryplantingswillalsobeasignificantportionofnewcarbonaddedtothesite.However,measuring,estimating,and/orverifyingthiscomponentisverydifficultandexpensive.Thecontributionsfromrootbiomasscanbeestimatedusingvariousapproachesthatrelyonknowingtheabovegroundportion.

ForestProductsandOtherRemovedMaterials:Windbreaksandriparianforestbuffersareplantedforpurposesthatrequirethetreestobeinplaceforthetargetedfunction(s)(i.e.,alterationofmicroclimate;interceptionofsediments,nutrients,andchemicals).Windbreakrenovation(removalofdeadtreesandreplanting)isrecommendedtomaintainmicroclimatebenefits(Brandleetal.,2009).Periodicharvestingofplantmaterialsintheherbaceouszone(adjacenttocropfield)andmiddlewoodyzoneisalsorecommendedinriparianforestbufferstomaintainhigherratesofnutrientuptakeandthereforewaterqualityservices(Dosskeyetal.,2010).Moreinnovativeanddiversifiedplantingdesignsthatincorporatebioenergyfeedstocksarebeingconsideredforbothofthesepractices,whichwouldincreaselevelsofharvestingwithinthesesystems.Inthecaseofriparianforestbuffers,harvestingoftheherbaceousandwoodymiddlezoneforbioenergyfeedstockswouldservetoreplenishahighernutrientuptakerateandthuswaterqualityservices,aswellasprovideanadditionalincomestream(Schoenebergeretal.,2008).Manyalleycroppingandsilvopasturesystemsaremanagedforhigh‐valueveneerandsaw‐timber.Thesetrees,alongwithsomespecialapplicationsofagroforestrytechnologies,arealsobeinginvestigatedfortheiruseinproducingbioenergyfeedstocks.Fortheseplantings,removalorharvestingofabovegroundwoodymaterialcanoccurasearlyasthreeyearsto75yearsormore,dependingontheproduct.Harvestedmaterialscanalsoincludestem‐pruning,generallyupto15feetoverseveralyearstoattainacleanbole,toperiodicthinninginordertomaintainacanopycoverthatisoptimalforthegrowthofthetreeaswellasthecropbeinggrowninthealleys.Thematerialmaybeleftonsitetocreatewildlifehabitat,choppedandincorporatedintothesoil,ortakenoff‐siteandburned.

Soil:StudieshavedocumentedthatU.S.agroforestrypracticesgenerallyhavegreatersoilcarbonstocks(underthewholepractice,whichmayvaryfromjustunderawindbreaktounderthewholetree/cropsystem,suchasalleycropping)whencomparedwiththatinconventionalagriculturalandgrazingpractices(Nairetal.,2010).However,estimatingchangeorfluxinsoilcarbonstocksinagroforestryplantingsischallengingduetoitsinherentlyhighspatialandtemporalvariability.Forinstance,SharrowandIsmail(2004)foundvariabilityofsoilcarbontobetwotothreetimesgreaterinanon‐grazedsilvopasturesystemthanintheadjacentforestorpasturealone.

Soilcarboncanincreaseinagroforestrysystemsduetoaddedcarboninputsfromthetrees,theeliminationofcarbonlossduetoannualcroppingactivities(i.e.,conservationtillage),andpotentiallytheadditionofcarbonthroughotheragriculturalmanagementactivities,suchasincorporationofdifferentcrops,covercrops,residuemanagement,andfertilizationregimes.


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ChangesinsoilcarbonstockshavebeenestimatedinanumberofforestestablishmentplotsfromtheMidwest,andwerefoundtovaryfrom‐0.07to0.58MgCha‐1year‐1and‐0.85to0.56MgCha‐1year‐1indeciduousandconiferousplots,respectively.Pauletal.(2003)attributedthevariationtotheimpactandvariablerecoveryfromtreeplanting,butalsomentionedthepossibilitythatvariationmaybeduetotheuseofpresent‐daycroppingfieldsasthecarbonbaselineforcomparison.Manyagroforestrystudiesarereportingcomparableratesofsoilsequestration(seeNairetal.,2010).Resultsfromtemperateagroforestrystudiesindicate,especiallyforalleysreceivinghighleveloforganicmatterinputfromthetrees,thatitmaybeseveralyearsbeforesignificantlymeasurablecarbondifferencesaredetectablebetweentheagroforestryplantingandtraditionalsolecroppingsystem(Peichletal.,2006;Udawattaetal.,2009).Theamountanddurationofsoilorganicmatteraccumulationinagriculturalsoilswithagroforestrymanagementwilldependonthedegreetowhichpriorsoilcarbonstocksaredepleted.Inaddition,itwilldependonthesoilsingeneral,climate,placementwithinalandscape,typeofvegetation,andmostimportantly,bytheadditionalmanagementactivitiesemployedinthemixedtree/agriculturalsystem(Table3‐4).

NotethatcarbonincreasesfromnitrogeninputsmaybeoffsetthroughenhancedN2Oemissions,dependingonanumberoffactors(seeSection6.4.1.6).Manyagroforestryplantings,suchaswindbreaksandriparianforestbuffers,arepurposefullydesignedtointerceptsoilinwinderosionandsurfacerunoff,whichisanotheradditionofcarbontothispool(Saueretal.,2007).DepositionofsedimentwillinfluencecyclingofbothelementsandthereforenetGHGvalues(McCartyandRitchie,2002;SudmeyerandScott,2002).Wecurrentlylacktheunderstandinganddataneededforadequatelymodelingandthereforepredictingtheseintra‐andinter‐soilcarbontransfersfromerosionanddeposition.

3.4.2 NitrousOxide

DataondirectN2Oemissionsinagroforestryplantingsaresparse.Thefewstudiesto‐datefoundreducedN2Oemissionsinafforestedplotsthatwereolderthanfiveyears(Allenetal.,2009),underwindbreaks(RyskowskiandKedziora,2007)andriparianforestbuffers(Kim,2008).AlleycroppingsystemsreducedN2Oemissionsby0.7kgha‐1year‐1comparedwiththeannualcroppingsystemswithnotreecover(ThevathasanandGordon,2004).Thesestudiessuggestthetreescanactasa“nitrogen‐safetynet”inthesystem,takingupthe“extra”nitrogenthatmightotherwiseresultinN2Oemissions.Inaddition,reducednitrogenleachinghasbeendocumentedwithinagroforestryplantingscomparedwiththeannualcroppingsystemwithnotreecover(Allenetal.,2004;Lopez‐Diazetal.,2011;Nairetal.,2007).ThereducedleachingimpliesthatlessnitrogenisavailableforindirectsoilN2Oemissions,whichcouldbebeneficialinthoseagroforestryplantingsrequiringfertilization(i.e.,alleycroppingandsilvopasturesystems)orthatreceivelargeinputsofnitrogenthroughsurfaceandsubsurfacerunoff(i.e.,riparianforestbuffers).Asmanyagroforestryplantingsarepurposefullydesignedandplantedtoprovidetighternutrientcyclingcapabilitiesasameanstoprotectwaterquality(Olsonetal.,2000),thecapabilityandcapacityofthesesystemstoreduceN2OemissionsinagriculturalsystemswarrantsfurtherstudytodeterminewhetherandhowitshouldbeaccountedforinGHGaccountingmethods.

3.4.3 Methane

VerylittleresearchhasbeendonetodeterminewhethertheestablishmentofagroforestryplantingscanleadtoanychangeinCH4sinksorsourcesinsoilsduetochangesinmethanotrophyormethanogenesis,respectively.Kimetal.(2010)didnotfindanyevidenceinestablishedriparianforestbuffersinIowa(sevento17yearsold)thatCH4fluxdifferedfromneighboringcropfields.RiparianforestbufferscouldpotentiallyserveasaCH4emittergiventheperiodicfloodingthatmayoccurwithintheseplantings.However,riparianforestbuffersestablishedonagriculturallandsmay


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notbesignificantemittersofCH4becausethehydrologicalconnectionswithintheselandscapeshavebeendecoupled.Thisindicatesuseofriparianforest(naturallyoccurring)deriveddatamayresultinoverestimatingsink/sourcecapacityofriparianforestbuffers.Ingeneral,thereisinsufficientdatatomodelandpredictmethanefluxesinagroforestryatthistime.

3.4.4 ManagementInteractions

Agroforestrypracticescanindirectlyaltercarboncyclingbyenhancingcropandforageproductionandtrappingwindblownandsurfacerunoffsediments.ExaminingthecarbonpotentialofwindbreaksintheGreatPlains,Brandleetal.(1992)estimatedindirectcarbonbenefitscouldpotentiallybedoubletheamountofthecarbonsequesteredinthewood.Althoughprojectstoexamineindirectcarbonbenefitsfromseveraloftheagroforestrypracticesareongoing,wecurrentlylacktheabilitytomodelorpredicttheseimpacts.

3.5 EstimationMethods

ThissectionprovidesmethodsforestimatingGHGemissionsfromcroplandandgrazinglandsystemsonanentity’sland.Themethodsareappliedforbothlandremainingincroplandorgrazinglands,aswellasland‐usechangetocroplandorgrazinglands.Themethodsprovidedareforestimatingtheemissionlevelsforagivenyearonaparcelofland.Aparcelisafieldintheentity’soperationwithuniformmanagement.Ifmanagementvariesacrossthefield,thenthefieldshouldbesubdividedintoseparateparcelsforestimatingtheemissions.

Trendsacrossyearsorcomparisonstobaselinescanbemadeusingtheannualemissionestimates.Guidanceisnotgivenhereonhowtodevelopbaselinesorsubsequenttrendsforemissionestimation.Thelevelofemissionsforcarbonstocksisbasedonestimatingthechangeinstockfromthebeginningandendoftheyear,whilethelevelofemissionsforN2OandCH4arebasedonestimatingthetotalannualemissions.Methodsarealsoprovidedforestimatingtotalemissionsofprecursorgasesemittedduringbiomassburning,aswellasnitrogencompoundsthatarevolatilizedorsubjecttoleachingandrunofffromanentity’scroplandorgrazinglandthatarelaterconvertedintoGHGs.

Themethodsrangeincomplexityforthedifferentemissionsourcecategoriesaccordingtothestateofthescienceandpriormethoddevelopment.Simplemethodsareselectedforseveraloftheemissionorcarbonstockchangesourcecategories;becausethemorecomplexmethodsarenotfullydevelopedforoperationalaccountingofemissionsorthesimplemethodsprovideareasonablyaccurateandpreciseresult.Althoughsimplicitymaybepreferredfortransparencyinestimation,someofthemethodsusemorecomplexapproaches,suchasprocess‐basedsimulationmodels,becausethesemethodsgreatlyimprovetheaccuracyand/orprecisionoftheresult.


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3.5.1 BiomassCarbonStockChanges

3.5.1.1 RationaleforSelectedMethod

BothIPCC(2006)andtheU.S.EnvironmentalProtectionAgency(2011)considerherbaceousbiomasscarbonstockstobeephemeral,andrecognizethattherearenonetemissionstotheatmospherefollowingcropgrowthandsenescenceduringoneannualcropcycle(Westetal.,2011).However,withrespecttochangesinlanduse(e.g.,foresttocropland),theIPCC(Lascoetal.,2006)recommendsthatcroplandbiomassbecountedintheyearthatlandconversionoccurs,andthesameassumptionalsoappliesforgrassland(Verchotetal.,2006).AccordingtotheIPCC,accountingfortheherbaceousbiomasscarbonstockduringchangesinlanduseisnecessarytoaccountfortheinfluenceofherbaceousplantsonCO2uptakefromtheatmosphereandstorageintheterrestrialbiosphere.However,thismethoddoesnotrecognizechangesinherbaceousbiomassthatoccurwithchangesincroprotations,nordoesitrecognizelong‐termincreasesinannualcropyields.ThemethodisaconsideredaTier2methodasdefinedbytheIPCCbecauseitincorporatesfactorsthatarebasedonU.S.specificdata.

Agroforestry,alongwithotherwoodyvegetationincroplands,suchasorchardsandvineyards,sequestersignificantamountsofnewcarbonwithinlong‐livedbiomassovertimewithtreegrowth.MethodsforestimatingtheabovegroundwoodyandwholetreebiomassfortreesgrowingunderforestconditionsaredescribedintheForestrySectionofthisreport.However,thesemethods,developedfromforest‐derived(i.e.,greatercanopyclosure)conditions,donotaccuratelyreflectconditionsencounteredinagroforestryorwoodycrops.Treesgrowingunderwindbreakandotherlinear‐typeplantingshavebeendocumentedtodifferfromforest‐growntreesintermsofarchitectureandproperties,suchascrown:trunkallocation(Zhou,1999),specificgravity(Zhouetal.,2011),andtaper(Zhouetal.,inreview).Moreover,theForestInventoryandAnalysisprogramoftheUSDAForestServiceandNationalResourceInventoryoftheUSDANaturalResourcesConservationServicedonotcollectagroforestryorwoodycropdatathroughtheirsurveys(Perryetal.,2005).Therefore,aTier3methodusingprocess‐basedmodelsisaviablealternativeforestimatingthecarbonstockchangesassociatedwithagroforestryandwoodycropswithoutdirectmeasurementthroughasurvey.Specifically,theDAYCENTmodelhasbeenparameterizedtosimulatetreegrowthandhasbeenadoptedforestimatingwoodybiomasscarbonforagroforestryandwoodycrops.

MethodforEstimatingBiomassCarbonStockChanges

AmodifiedversionofthemethodologydevelopedbytheIPCC(Lascoetal.,2006;Verchotetal.,2006)hasbeenadoptedforentity‐scaleestimationofherbaceousandwoodybiomassstockchangesassociatedwithlanduse.

TheDAYCENTprocess‐basedsimulationmodelorthetraditionalforestinventoryapproachesareusedtoestimatecarbonforabovegroundbiomassforagroforestry.

U.S.specificdefaultvalues(Westetal.,2010)areusedforestimatingbiomasscarbonforannualcropsandgrazinglands.TheIPCCdefaultisusedforestimatingthecarbonfractionvalue.YieldinunitsofdrymattercanbeestimatedbytheentityoraveragevaluesfromUSDA‐NationalAgriculturalStatisticsServicestatisticscanbeused.

Thismethodwaschosenbecauseitcapturestheinfluenceofland‐usechangeoncroporforagespeciesonbiomasscarbonstocksbyusingU.S.specificdefaultvalueswhereentityspecificactivitydataarenotavailableandaprocess‐basedsimulationmodelforagroforestrysystems.


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3.5.1.2 DescriptionofMethod

AmodifiedversionofthemethodologydevelopedbytheIPCC(Lascoetal.,2006;Verchotetal.,2006)hasbeenadoptedforentity‐scalereportingintheUnitedStatesofherbaceousandwoodybiomassstockchangesassociatedwithlandusechange.Themethodconsistsofestimatingthemeanannualbiomassstockforacroplandorgrazinglandsfollowingalandusechange,whichcanbeaveragedacrossyearsforacroporrotation.Thismethodonlyaddressesachangeintheherbaceousbiomasscarbonstocksintheyearfollowingaland‐usechange,consistentwiththeIPCCmethods(Lascoetal.,2006;Verchotetal.,2006).Incontrast,carbonstockchangeinwoodybiomassisestimatedeveryyear.

UseEquation3‐1toestimatethetotalbiomasscarbonstockchangeforalandparceloverayear:

HerbaceousBiomass:Estimatethemeanannualherbaceousbiomassstockinalandparcelforcroplandorgrazinglandfollowingalandusechangewiththefollowingequation:

Themeanannualbiomassstockisintendedtorepresentthetimeperiodfollowingharvestwherenocropexistsandbothlitterandrootsaredecomposingquickly(Gilletal.,2002),andthetimeperiodduringthegrowingseasonwherebiomasscontinuestogrowuntilitreachespeakannualbiomass.Theaverageofzerobiomassandpeakbiomass(e.g.,peakbiomassdividedbytwo)isconsideredrepresentativeofthemeanannualcarbonstock(i.e.,Yf=0.5).

Equation3‐3isusedtoestimatethepeakabovegroundbiomassinalandparcelfromharvestyielddataincroplandsorpeakforageyieldsingrazinglands.

Equation3‐1:TotalBiomassCarbon StockChange

ΔCBiomass=(Ht+Wt)–(Ht‐1+Wt‐1)

Where:

ΔCBiomass=Totalchangeinbiomasscarbonstock(metrictonsCO2‐eqyear‐1)

H =Meanannualherbaceousbiomass(metrictonsCO2‐eqyear‐1)

W =Meanannualwoodybiomass(metrictonsCO2‐eqyear‐1)

t =Currentyearstocks

t‐1 =Previousyear’sstocks

Equation3‐2:MeanAnnualHerbaceousBiomassCarbon Stock

H=[Hpeak+(Hpeak×R:S)]×A×CO2MW/Yf

Where:

H =Meanannualherbaceousbiomasscarbonstock(metrictonsCO2‐eqyear‐1)

Hpeak =Annualpeakabovegroundbiomass(metrictonsCha‐1year‐1)

R:S =Root‐shootratio(unitless)

A =Areaoflandparcel(ha)

CO2MW=RatioofmolecularweightofCO2tocarbon=44/12(metrictonsCO2(metrictonsC)‐1)

Yf =Approximatefractionofcalendaryearrepresentingthegrowingseason(unitless)


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Thismethodcapturestheinfluenceofland‐usechangeandchangesincroporforagespeciesonbiomasscarbonstocks.Therefore,cropharvestorpeakforageyieldsshouldbeaveragedacrossyearsaslongasthesameforagespecies,croporrotationofcropsaregrown.Theharvestindexissettooneforgrazinglands.

Peakforageestimatesforgrazinglandscanbeestimatedusingthebiomassclippingmethod.6Thismethodisdestructivewiththeremovalofforagesamplesfromthefield.Non‐destructivemethodscanalsobeusedincludingthecomparativeyieldmethodforrangelands7,ortherobelpolemethodonrangelandsorpastures(Harmoneyetal.,1997;Vermeireetal.,2002).Anysamplingthatisdone,whetherdestructiveornon‐destructive,shouldoccuratlocationsthatarerepresentativeofthelandparcel.Ifsamplingtheforageisnotfeasible,defaultforageproductionvaluesareprovidedbytheNaturalResourcesConservationServiceinEcologicalSiteDescriptions(ESDs).8AfteridentifyingtheappropriateESD,theentitywouldselecttheplantcommunitythatisrepresentativeoftheparcel.ThesevaluesrepresenttotalproductionforthesitesoYfinEquation3‐2wouldbesetto1iftheabovegroundforageproductionisobtainedfromanESD.

WoodyBiomass:Thelargestamountofcarboncapturedbyagroforestrysystemsisinwoodybiomass,withthemajorityoccurringintheabovegroundbiomass.Woodycropsalsogaincarbonastheygrow.Thismethodalsoaddressescarbonremovalsthroughharvestorothereventsthatremovetreebiomass.

Themethodstoestimatebiomasscarboninalandparcelforthemore‐opengrowthofagroforestrysystemsandwoodycrops(WtandWt‐1inEquation3‐1)arebasedonDAYCENTmodelsimulationsandgrowthfunctionsforagroforestry.AgroforestrypracticesarebasedontheNaturalResourcesConservationServiceagroforestrypracticestandards,whichareprovidedinapicklist.Forwoodycrops,theDAYCENTmodelsimulatestheinfluenceofcommonmanagementpracticesonbiomassstocks,includingirrigation,fertilization,organicmatteramendments,groundcovermanagement,

6Seesection15,“StandingBiomass”http://www.nrisurvey.org/nrcs/Grazingland/2011/instructions/instruction.htm7Seesection13,“DryWeightRank”http://www.nrisurvey.org/nrcs/Grazingland/2011/instructions/instruction.htm8SeeESDshttps://esis.sc.egov.usda.gov/

Equation3‐3:AbovegroundHerbaceousBiomassCarbonStock

Hpeak=(Ydm/HI)×C

Where:

Hpeak=Annualpeakabovegroundherbaceousbiomasscarbonstock (metrictonsCha‐1year‐1)

Ydm =Cropharvestorforageyield,correctedfordrymattercontent (metrictonsbiomassha‐1year‐1)

=YxDM

Y =Cropharvestorforageyield(metrictonsbiomassha‐1year‐1)

DM =Drymattercontentofharvestedcropbiomassorforage(dimensionless)

HI =HarvestIndex(dimensionless)

C =Carbonfractionofabovegroundbiomass(dimensionless)


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pruningofbranches,thinningofyoungfruit,andharvestandremovalofmaturefruit.Giventhepractice,DAYCENTsimulateschangesinwoodybiomasscarbonstocksforthereportingperiod.

Foragroforestrysystemswheretheentityhasmeasuredtreeparameters,anempiricalmodelisprovidedtomorepreciselyestimatewoodybiomasscarbongrowthincrementfortheyear(MerwinandTownsend,2007;Merwinetal.,2009).TheempiricalmodelusesanindividualtreegrowthequationsbasedonLessard(2000)andLessardetal.(2001).Carbonpoolsarethenderivedfromdiameter‐basedallometricequationsthatpredicttotalabovegroundbiomasscomponentsfor10broadspeciesgroupsintheUnitedStates.(Jenkinsetal.,2003;2004).BothpublishedandunpublisheddatafortheU.S.ForestServiceForestInventoryandAnalysisprogramwereusedtodevelopthegrowthincrementmodel.

Inaddition,harvestedwoodyproductsassociatedwithagroforestryareestimatedusingtheapproachesdescribedintheForestryChapter(Section6.5).Woodyproductsmaybeharvestedfromsilvopasture,alleycropping,andotheragroforestrypractices,providingavarietyofproductssuchasveneer,sawtimber,andbioenergyfeedstocks.

3.5.1.3 ActivityData

Activityandrelateddataneededtoestimatebiomasscarbonforannualcropsandgrazinglands(asapplicable)include:

Croptype,croplandarea,andharvestindices; Typeofforage,grazingarea,andpeakforageyielddata; Totalabovegroundyieldofcroporpeakforageyieldforgrazinglands(metrictonsbiomass

perha); Root:shootratios; Carbonfractions;and Drymattercontentofforageandharvestedcropbiomasstoestimatedrymattercontent.

Iftheentitydoesnotprovidevalues,defaultvaluesformoisturecontent,residue‐yieldratios,androot:shootratiosareprovidedinTable3‐5.Ageneraldefaultvalueforcropcarbonfractionis0.45.Insomeyears,theentitymaynotharvestthecropduetodrought,pestoutbreaksorotherreasonsforcropfailure.Inthosecases,theentityshouldprovidetheaverageyieldthattheyhaveharvestedinthepast,andanapproximatepercentageofaveragecropgrowththatoccurredintheyear.Theyieldisestimatedbasedonmultiplyingtheaveragecropyieldbythepercentageofcropgrowthobtainedpriortocroploss.Peakforageyieldswillvaryfromyeartoyear,butcanbebasedonafive‐yearaverage.

Table3‐5:RepresentativeDryMatterContentofHarvestedCropBiomass,HarvestIndex,andRoot:ShootRatiosforVariousCrops.a

CropDryMatterContent

HarvestIndexRoot:ShootRatio

FoodcropsBarley 0.865(3.8%) 0.46(18.7%) 0.11(90.7%)Beans 0.84(3.3%) 0.46(18.7%) 0.08(89.7%)Corngrain 0.86(1.9%) 0.53(15.0%) 0.18(97.3%)Cornsilage 0.74(1.9%) 0.95(3.3%) 0.18(97.1%)Cotton 0.92(1.4%) 0.40(20.0%) 0.17(44.0%)Millet 0.90(1.9%) 0.46(17.6%) 0.25(91.1%)Oats 0.865(1.9%) 0.52(18.7%) 0.40(90.9%)Peanuts 0.91(1.9%) 0.40(16.6%) 0.07(12.4%)Potatoes 0.20(9.3%) 0.50(20.0%) 0.07(44.1%)


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CropDryMatterContent

HarvestIndexRoot:ShootRatio

Rice 0.91(1.6%) 0.42(28.1%) 0.22(13.2%)Rye 0.90(1.9%) 0.50(18.7%) 0.14(90.1%)Sorghumgrain 0.86(1.9%) 0.44(14.8%) 0.18(97.2%)Sorghumsilage 0.74(1.9%) 0.95(3.3%) 0.18(97.2%)Soybean 0.875(1.7%) 0.42(16.7%) 0.19(89.8%)Sugarbeets 0.15(12.4%) 0.40(24.1%) 0.43(43.9%)Sugarcane 0.258(11.6%) 0.75(6.4%) 0.18(37.4%)Sunflower 0.91(1.9%) 0.27(11.1%) 0.06(44.0%)Tobacco 0.80(1.9%) 0.60(3.3%) 0.80(44.0%)Wheat 0.865(3.8%) 0.39(17.7%) 0.20(86.2%)

ForageandFoddercrops

Alfalfahay 0.87(1.8%) 0.95(3.3%) 0.87(21.8%)Non‐legumehay 0.87(1.8%) 0.95(3.3%) 0.87(21.8%)Nitrogen‐fixingforages 0.35(3.3%) 0.95(3.3%) 1.1(21.2%)

Non‐nitrogen‐fixingforages 0.35(3.3%) 0.95(3.3%) 1.5(21.2%)

Perennialgrasses 0.35(3.3%) 0.95(3.3%) 1.5(21.2%)Grass‐clovermixtures 0.35(3.3%) 0.95(3.3%) 1.5(21.2%)

Source:RevisedfromWestetal.(2010).aUncertaintyisexpressedonapercentagebasisashalfofthe95%confidenceinterval.

Activitydataforestimatingcarboninabovegroundbiomassforagroforestrywillentailthecollectionofsomelevelofinventoryoftreesassociatedwiththeagroforestrypractice.SimplifiedinventoryapproachesrequiringaminimumofworkbythelandownerhavebeendevelopedbytheUSDANaturalResourcesConservationServiceandtheColoradoStateUniversityNaturalResourceEcologicalLaboratory(USDA,2012),whicharelargelybasedonmethodsdescribedintheNaturalResourcesConservationServiceNationalForestHandbook(USDANRCS,2004).Thespecificactivitydatarequirementsinclude:

Speciesoftreesandnumberbyageofdiameterclassforeachagroforestrypractice;and Diameteratbreastheightforasubsampleoftreesusingoneofthreesamplingmethodsthat

capturethespacingarrangementsanddensitieswithinthedifferentpractices(i.e.,rowtypeplantings,woodlot‐likeplantings,andriparianforestbuffers).

3.5.1.4 AncillaryData

Noancillarydataareneededforthismethod.

3.5.1.5 ModelOutput

Modeloutputisgeneratedforthechangeinbiomasscarbonstocks.Thischangeisdeterminedbasedonsubtractingthetotalbiomasscarbonstockinthepreviousyearfromthetotalstockinthecurrentyear,whichwillincludebothherbaceousandwoodybiomass.Theherbaceousstockswillrepresentmeanestimatesoveryearsifthesameforages,crop,orrotationofcropsaregrown,andisonlyestimatedforalandusechange.TheapproachforestimatingbiomasscarbonforwetlandsandforestlandsaredescribedinSections4.3.1and6.2.1,respectively.

Emissionsintensityisalsoestimatedbasedontheamountofemissionsperunitofyieldforcropsincroplandsystems,orofanimalproductsingrazingsystems.Notethatthebiomasschangeisbasedsolelyonwoodyplantgrowthexceptinayearfollowingaland‐usechange.


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Theemissionsintensityisestimatedwiththefollowingequation:

3.5.1.6 LimitationsandUncertainty

Uncertaintyinherbaceouscarbonstockchangeswillresultfromlackofprecisionincroporforageyields,residue‐yieldratios,root‐shootratios,andcarbonfractions,aswellastheuncertaintiesassociatedwithestimatingthebiomasscarbonstocksfortheotherlanduses.Emissionsintensitywillalsoincludeuncertaintyinthetotalyieldforthecrop,meat,ormilkproduct.Thisherbaceousbiomassmethodisbasedontheassumptionthathalfofthecropharvestyieldsorpeakforageamountsprovideanaccurateestimateofthemeanannualcarbonstockincroplandorgrazinglands.Thisassumptionwarrantsfurtherstudy,andthemethodmayneedtoberefinedinthefuture.

UncertaintiesinmodelparametersarecombinedusingaMonteCarlosimulationapproach.Uncertaintyisassumedtobeminorforthemanagementactivitydataprovidedbytheentity.Table3‐6providestherelativeuncertaintyfortheDAYCENTmodelandthecarbonfractionofbiomass.

Table3‐6:AvailableUncertaintyDataforBiomassCarbonStockChanges

Parameter Mean UnitsRelativeUncertainty

Distribution DataSourceLow(%) High(%)

DAYCENT(empiricaluncertainty)

NS Various NS NS NormalOgleetal.(2007);EPA(2013)

Carbonfractionofabovegroundbiomass

0.45 Fraction 11 11 Normal IPCC(1997)

NS=NotShown.Dataarenotshownforparametersthathave100’sto1000’sofvalues(denotedasNS).

Theuncertaintydifferswhetheritisherbaceousbiomassortrees.Uncertaintyassociatedwithestimatingcarboninlivetreesisinfluencedbyanumberoffactors,includingsamplingandmeasurementerroranderrorassociatedwithregressionmodels(seeMelsonetal.2011;furtherdiscussioninForestrySection).Estimatingcarboninagroforestrytrees,especiallyforyoungseedlingsandsaplings(upto10yearsorsodependingonspeciesandgrowingconditions)remainshighlyuncertainparticularlysincetraditionalforestry‐derivedequationshavebeenshowntounderestimatewhole‐treebiomassinagroforestrysystemsandrequiresadditionalfieldworktofurtherdocumentbiomasscarbonallocationdifferences.Melsonetal.(2011)notedintheirforest‐basedworkthatestimationoflive‐treecarbonwassensitivetomodelselection(withmodel‐selectionerrorofpotentially20to40percent),andthatmodelselectioncouldbeimprovedbymatchingtreeformtoexistingequationsforuseinthemodels.On‐goingworkcomparingagroforestry‐derivedequationswithavarietyofforest‐derivedequationsintheGreatPlainsregionindicateuncertaintycouldbereducedthroughuseofacorrectionfactor.Currentlybelowgroundbiomass/Cestimatesarecalculatedusingtwoapproaches:root:shootratios(seeBirdsey,1992),

Equation3‐4:EmissionsIntensityofBiomassCarbon StockChange

EIBiomassC=ΔCBiomass/Y

Where:

EIBiomassC =Emissionsintensity(metrictonsCO2permetrictondrymattercropyield,metrictonsCO2perkgcarcassyield,ormetrictonsCO2perkgfluidmilkyield)

ΔCBiomass =ChangeinbiomassstockinCO2equivalents(metrictonsCO2‐eqyear‐1)

Y =Totalyieldofcrop(metrictonsdrymattercropyield),meat(kgcarcassyield)ormilkproduction(kgfluidmilkyield)


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andabovegrounddensityallometry(Cairnsetal.,1997),bothwithlargeuncertaintiesduetolackofdata.Thefullsetofprobabilitydistributionshavenotbeendevelopedfortheagroforestrymethod,andsowillrequirefurtherresearchbeforeuncertaintycanbeestimated.SeeChapter6,Forestry,forfurtherdiscussionofuncertaintyoftreevolumeandbiomassequations.

3.5.2 LitterCarbonStockChanges

Litterinherbaceousbiomassdecomposesmostlyoveraone‐yearperiod.HowevertheinfluenceoflittercarbonstocksonatmosphericCO2isassumedtobeinsignificantafteraddressingthechangesinbiomassandsubsequentinfluenceonsoilcarbonstocks.Furthermethodsdevelopmentmaybepossibleinthefuture,giventhispotentiallimitationtothemethodsinthisreport.Forcroplandorgrazinglandsystemswithtrees,coarsewoodydebrisandlittercarbonshouldbeestimatedbasedonforestmethods(SeeSection6.2.2.4and6.2.2.5).ThelossoflitterandcoarsewoodydebriswithconversionfromforestlandtocroplandandgrazinglandisalsoaddressedinSection6.3.

3.5.3 SoilCarbonStockChanges


SOCstocksareinfluencedbylanduseandmanagementincroplandandgrazinglandsystems,aswellasconversionfromotherlandusesintothesesystems(Aaldeetal.,2006).SOCpoolscanbemodifiedduetochangesincarboninputsandoutputs(Paustianetal.,1997).Carboninputswillchangeovertimeduetointerannualvariabilityandlongertermtrendsinnetprimaryproduction,aswellasdifferencesincarbonremovalsfromharvestingandresiduemanagementpractices.ExternalcarboninputswillalsohaveaninfluenceontheSOCstocks,suchasmanure,compost,sewagesludge,woodchips,andbiocharamendments.Carbonoutputswillchangeduetointerannualvariabilityandlongertermtrendsinmicrobialdecompositionrates.Inaddition,erosionanddepositioncontributetochangesinSOCstocksassociatedwithcropandgrazinglandsoils.Recentstudies(Hardenetal.,2008;VanOostetal.,2007)provideevidencethatthemajorityofcarboninerodedsoilsisdynamicallyreplaced,compensatingforthelosses,andatleastsomeofthecarbontransportedfromthesiteisdepositedattheedgeoffields,downslope,orinrivers.Inallcases,SOCismovedfromonelocationtoanotherundertheassumptionthatonlyaportionofthe

MethodforEstimatingSoilCarbonStockChanges

Mineralsoils: TheDAYCENTprocess‐basedsimulationmodelestimatesthesoilorganiccarbon(SOC)at

thebeginningandendoftheyear.TheseinputsareenteredintotheIPCCequationtoestimatecarbonstockchangesinmineralsoilsdevelopedbyLascoetal.(2006),andVerchotetal.(2006).

ThismethodwaschosenbecausetheDAYCENTmodelhasbeendemonstratedtorepresentthedynamicsofsoilorganiccarbonandestimatesoilorganiccarbonstockchangeinU.S.croplandandgrasslands(Partonetal.,1993),anduncertaintieshavebeenquantified(Ogleetal.(2007).Themodelcapturessoilmoisturedynamics,plantproduction,andthermalcontrolsonnetprimaryproductionanddecompositionwithatimestepofamonthorless.

OrganicSoils: IPCCequationdevelopedbyAaldeetal.(2006;USDA,2011)usingregionspecific

emissionfactorsfromOgleetal.(2003). Thismethodwaschosenbecauseitistheonlyreadilyavailablemodelforestimatingsoil

carbonstockchangesfromorganicsoils.


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carbonintransportislosttotheatmosphere.Thisassumptionmayhavesignificantvariationduetothediversityofenvironmentalconditionsinwhicherodedcarbonistransportedandsubsequentlyresides.Otherenvironmentaldriverswillalsoinfluencecarbondynamicsinsoils,particularlyweatherandsoilcharacteristics.

Process‐basedmodels,whichareconsideredanIPCCTier3methodology,havebeendevelopedandsufficientlyevaluatedforapplicationinanoperationaltooltoestimateSOCstockchangesinmineralsoils.TheDAYCENTprocess‐basedmodel(Partonetal.,1987;Parton,1998)hasbeenselectedbecauseitiswell‐testedforestimatingsoilcarbondynamicsincroplandandgrazinglandsystems(Partonetal.,1993)andisalsousedintheU.S.nationalGHGinventory(Ogleetal.,2010;U.S.EPA,2011).DelGrossoetal.(2011)demonstratedthereductioninuncertaintyassociatedwiththemoreadvancedapproachusingtheDAYCENTmodelcomparedtothelowertiermethods.TheDAYCENTmodelsimulatesplantproductionbyrepresentinglong‐termeffectsoflanduseandmanagementonnetprimaryproduction(NPP),asinfluencedbyselectionofcropsandforagegrasses.TheinfluenceofmanagementpracticesonNPParealsosimulated,includingmineralfertilization,organicamendments,irrigationandfertigation,liming,greenmanuresandcovercrops,croppingintensity,hayorpastureinrotationwithannualcrops,grazingintensityandstockingrate,andbarefallow.Nutrientandmoisturedynamicsareinfluencedbysoilcharacteristics,suchassoiltexture.ThemethodaddressesinterannualvariabilityduetoannualchangesinmanagementandtheeffectofweatheronNPP.

IntheDAYCENTmodel,threesoilorganiccarbonpoolsareincludedrepresentingactive,slow,andpassivesoilorganicmatter,whichhavedifferentturnovertimes.Itisgenerallyconsideredthattheactivecarbonpoolismicrobialbiomassandassociatedmetaboliteshavingarapidturnover(monthstoyears),theslowcarbonpoolhasintermediatestabilityandturnovertimes(decades),andthepassivecarbonpoolrepresentshighlyprocessedandhumifieddecompositionproductswithlongerturnovertimes(centuries).However,thesepoolsarekineticallydefinedanddonotnecessarilyrepresentexplicitfractionsofsoilorganiccarbonthatcanbeisolated.Soiltexture,temperature,moistureavailability,aeration,burning,andotherfactorsarerepresentedinthesimulationsthatinfluencethedecompositionandlossofcarbonfromthesepools.

Themodelsimulatesmanagementpracticesinfluencingsoilorganiccarbonpools.Thesepracticesincludeadditionofcarboninmanureandotherorganicamendments,suchascompost,woodchips,andbiochar;tillageintensity;residuemanagement(retentionofresiduesinfieldwithoutincorporation,retentioninthefieldwithincorporation,andremovalwithharvest,burning,orgrazing).Theinfluenceofbareandvegetatedfallowsisrepresented,inadditiontoirrigationeffectsondecompositionincroplandandgrazinglandsystems.Themodelcanalsosimulatesetting‐asidecroplandfromproduction;theinfluenceoffireonoxidationofsoilorganicmatter;andwoodyplantencroachment,agroforestry,andsilvopastureeffectsoncarboninputsandoutputs.

Awater/soilmoisturesubmodel(e.g.,Partonetal.,1987)isusedtorepresenttheinfluenceofweather,irrigation,croptype,andmanagementonsoilmoisturedynamics.Thisimpactisparticularlyimportantbecausemoisturetendstobeamoreproximalfactorcontrollingsoilorganiccarbondynamics,which,inturn,isinfluencedbylanduseandmanagementactivity.Forexample,irrigationinfluencesplantproductionandcarboninputsbecauseofthemodificationtothemoistureregime.

ThemodeledestimatesfromDAYCENTarecombinedwithmeasurementdatafromamonitoringnetworktoformallyevaluateuncertainty.Thisapproachleveragesthescalabilityofthemodelwhileprovidinganunderlyingmeasurement‐basisforthemethod(Conantetal.,2011;Ogleetal.,2007).


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Erosionanddepositioninfluencesoilorganiccarbonstocks(Izaurraldeetal.,2007)andthereforearerepresentedinthemethod,althoughthereisuncertaintyintheneteffectonCO2exchangebetweenthebiosphereandatmosphere.Moreover,thereisalsosomeriskofdouble‐countingcarbonasitistransferredacrossownershipboundaries,intermsofwhoreceivescreditfortheerodedcarbonintheiraccounting.Regardless,erosionclearlyhasanimpactoncarbonstocksinafield,whichcanbeestimatedwithreasonableaccuracyusingerosioncalculators,suchastheRevisedUniversalSoilLossEquation,Version2(RUSLE2)forwatererosion(USDA,2003)andWindErosionPredictionSystem(WEPS)forwinderosion(USDA,2004).Therefore,thecurrentmethodwillincludeanestimateoferosion‐relatedcarbonlossfromafield,butneitherthefateoferodedC,northedepositionofcarbonfromotherareasontoalandparcel,willbeestimated.Asmorestudiesareconducted,carbontransportanddepositioncanbeincorporatedinfutureversionsofthemethod.

Drainageoforganicsoilsforcropproductionleadstonetannualemissionsduetoincreaseddecompositionoftheorganicmatterafterloweringthewatertableandcreatingaerobicconditionsintheupperlayersofthesoil(Allen,2012;ArmentanoandMenges,1986).Therehasbeenlessevaluationofprocess‐basedmodelsfororganicsoils,particularlythesimulationofwatertabledynamicsthroughouttheyear,whichwillinfluencetheemissionrate.Consequently,theapproachisbasedonmoresimplisticemissionfactorapproachdevelopedbytheIPCC(Aaldeetal.,2006).ThemethodincorporatesU.S.emissionratesassociatedwithregion‐specificdrainagepatterns(Ogleetal.,2003),soitisaTier2methodasdefinedbytheIPCC.


ThemethodrepresentingtheinfluenceoflanduseandmanagementonSOCandassociatedCO2fluxtotheatmosphereisestimatedwithacarbonstockchangeapproach(Aaldeetal.,2006).Formineralsoils,themethodwillrequireestimatesofcarbonstocksatthebeginningandendoftheyearinordertoestimatetheannualchangeusingtheequationbelow.Incontrast,carbonstockchangesinorganicsoils(i.e.,Histosols)willaddressonlytheemissionsoccurringwithdrainage,whichisthetypicalsituationincropland.Emissionsoccurinorganicsoilsfollowingdrainageduetotheconversionofananaerobicenvironmentwithahighwatertabletoaerobicconditions(ArmentanoandMenges,1986),resultinginasignificantlossofcarbontotheatmosphere(Ogleetal.,2003).Recentdataonsubsidencewereusedtoderivetheseestimates(e.g.,Shihetal.,1998).

MineralSoils:ThemodeltoestimatechangesinSOCstocksformineralsoilshasbeenadaptedfromthemethoddevelopedbyIPCC(Aaldeetal.,2006).Theannualchangeinstockstoa30centimeterdepthforalandparcelisestimatedusingthefollowingequation:

Equation3‐5:ChangeinSoilOrganicCarbon StocksforMineralSoils

ΔCMineral=[(SOCt‐SOCt‐1)/t]×A×CO2MW

Where:

ΔCMineral =Annualchangeinmineralsoilorganiccarbonstock(metrictonsCO2‐eqyear‐1)

SOCt =Soilorganiccarbonstockattheendoftheyear(metrictonsCha‐1)

SOCt‐1 =Soilorganiccarbonstockatthebeginningoftheyear(metrictonsCha‐1)

t =1year

A =Areaofparcel(ha)

CO2MW =RatioofmolecularweightofCO2tocarbon =44/12(metrictonsCO2(metrictonsC)‐1)


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TheDAYCENTmodelisusedtosimulatetheSOCstocksatthebeginningandendofeachyearforEquation3‐5basedonrecentmanagementpracticesforalandparcel.InitialvaluesforDAYCENTareneededfortheSOCt‐1andarebasedonasimulationofhistoricalmanagementtoprovideaccuratestocksanddistributionoforganiccarbonamongthepoolsrepresentedinthemodel(active,slow,andpassivesoilorganicmatterpools).Eachpoolhasadifferentturnoverrate(representingtheheterogeneousnatureofsoilorganicmatter),andtheamountofcarbonineachpoolatanypointintimeinfluencestheforwardtrajectoryofthetotalsoilorganiccarbonstorage(Partonetal.,1987).Bysimulatingthehistoricallanduse,thedistributionsofcarboninactive,slow,andpassivepoolsareestimatedinanunbiasedway.

Threestepsarerequiredtoestimatetheinitialvalues.Thefirststepinvolvesrunningthemodeltoasteady‐statecondition(e.g.,equilibrium)undernativevegetation,historicalclimatedata,andthesoilphysicalattributesforthelandparcel.Thesecondstepistosimulateperiodoftimefromthe1800’sto1980and1980to2000.Theentityisprovidedalistofoptionsforselectingthepracticesthatbestmatchthelandmanagementfortheparcel.From2000totheinitialyearforreporting,theentityentersmorespecificdataoncropsplanted,tillagepractices,fertilizationpractices,irrigation,andothermanagementactivity(SeeSection3.5.3.3formoreinformation).Thesimulatedcarbonstockattheendofthesimulationprovidestheinitialbaselinevalue(SOCt‐1).

Thestockattheendofayear(SOCt)isestimatedbytheDAYCENTmodelbasedonsimulatingmanagementactivityduringthespecificyear.Theentityprovidesthemanagementactivityforthelandparcel,includingcropsplanted,tillagepractices,fertilizationpractices,irrigationandothermanagementactivitydata(SeeSection3.5.3.3formoreinformation).ThechangeinSOCstocksareestimatedforadditionalyearsbyusingtheendingstockfromthepreviousyearastheinitialSOCstock(SOCt‐1)andthensimulatingthemanagementforanotheryeartoproducethestockattheendofthenextyear(SOCt).

ErodedcarbonisestimatedwiththeRUSLE2forwatererosion(USDA,2003)andWEPSforwinderosion(USDA,2004).NeitherthedepositionofcarbononthesitenorthefateoferodedcarbonisinthisversionoftheUSDAmethods.TheerodedcarbonestimateisreportedseparatelytoaccountforuncertaintyassociatedwiththepotentialeffectoferosiononSOCstocks,andmaybeusedasadiscountfortheSOCstockchangesestimatewithEquation3‐5.

TheDAYCENTmodelisnotabletoestimatesoilorganiccarbonstocksinmineralsoilsforallcrops.IninstanceswhereacropisnotestimatedbytheDAYCENTmodel,themethoddevelopedbytheIPCC(2006)(i.e.,aTier1methodology)maybeused(SeeAppendix3‐B).

OrganicSoils:ThemethodologyforestimatingsoilcarbonstockchangesindrainedorganicsoilshasbeenadoptedfromIPCC(Aaldeetal.,2006).ThemethodappliestoHistosolsandsoilsthathavehighorganicmattercontentanddevelopedundersaturated,anaerobicconditionsforatleastpartoftheyear,whichincludesHistels,Historthels,andHistoturbels.Thefollowingequationisusedtoestimateemissionsfromalandparcel:


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EmissionfactorshavebeenadoptedfromOgleetal.(2003)andareregion‐specific,basedontypicaldrainagepatternsandclimaticcontrolsondecompositionrates;theseratesarealsousedintheU.S.nationalGHGinventory(U.S.EPA,2011).Drainedcroplandsoilslosecarbonatarateof11±2.5metrictonsCha‐1year‐1incooltemperateregions,14±2.5metrictonsCha‐1year‐1inwarmtemperateregions,and14±3.3metrictonsCha‐1year‐1insubtropicalclimateregions.Organicsoilsingrazinglandsaretypicallynotdrainedtothedepthofcroplandsystems,andthereforetheemissionfactorsareonly25percentofthecroplandvalues(Ogleetal.,2003).


Theactivitydatarequirementsvarybetweenmineralsoilsandorganicsoils.Mineralsoilsrequirethefollowingactivitydataforcroplands:

Areaoflandparcel(i.e.,field); Cropselectionandrotationsequence; Plantingandharvestingdates; Residuemanagement,includingamountharvested,burned,grazed,orleftinthefield; Irrigationmethod,applicationrate,andtimingofwaterapplications; Mineralfertilizertype,applicationrate,andtimingofapplication(s); Limeamendmenttype,applicationrate,andtimingofapplication(s); Organicamendmenttype,applicationrate,andtimingofapplication(s); Tillageimplements,datesofoperation,andnumberofpassesineachoperation(whichcan

beusedtodeterminetillageintensitywiththeSTIRModel(USDANRCS,2008)); Useofdrainagepracticesanddepthofdrainage(commonlyinhydricsoils);and Covercroptypes,planting,andharvestingdates(ifapplicable).

Themethodforgrazinglandonmineralsoilsrequiresthefollowingmanagementactivitydata:

Areaoflandparcel(i.e.,field); Plantspeciescomposition; Periodsofgrazingduringtheyear; Animaltype,class,andsizeusedforgrazing; Stockingratesandmethods; Irrigationmethod,applicationrate,andtimingofwaterapplications; Mineralfertilizertype,applicationrate,andtimingofapplication(s); Limeamendmenttype,applicationrate,andtimingofapplication(s); Organicamendmenttype,applicationrate,andtimingofapplication(s); Pasture/Range/Paddock(PRP)Nexcreteddirectlyontolandbylivestock(i.e.,manurethat

isnotmanaged);

Equation3‐6:ChangeinSoilOrganicCarbon StocksforOrganicSoils

ΔCOrganic=A×EF×CO2MW

Where:

ΔCOrganic=AnnualCO2emissionsfromdrainedorganicsoilsincropandgrazinglands (metrictonsCO2‐eqyear‐1)

A =Areaofdrainedorganicsoils(ha)

EF =Emissionfactor(metrictonsCha‐1year‐1)

CO2MW=RatioofmolecularweightofCO2toC(=44/12)(metrictonsCO2(metrictonsC)‐1)


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Useofdrainagepracticesanddepthofdrainage(commonlyinhydricsoils); Levelofwoodyplantencroachment;and Totalyieldofcrop(metrictonsdrymattercropyieldyear‐1),meat(kgcarcassyieldyear‐1)

ormilk(kgfluidmilkyear‐1).

Longer‐termhistoryofsitemanagementwillbeusedtosimulateinitialsoilorganiccarbonstocksforthecroporgrazingsystem.Inordertoestimatetheinitialvalues,theentitywillneedtoprovidemanagementactivitydataforthepastthreedecades.Alistofmanagementsystemswillbeprovided.Theentitywillalsoprovidethepreviouslanduseandyearofconversionifaland‐usechangeoccurredduringthepastthreedecades.Historicaldataforactivityfrommorethanthreedecadesinthepastwillberepresentedbasedonnationalagriculturalstatisticsusingenterprisebudgetsandcensusdataforvariousregionsinthecountry.However,anentitycanprovidethelongertermhistoryifitisknown.Dataonthecarbonandnitrogencontentoforganicamendmentswillalsobeneededfromtheentity,althoughdefaultsareprovidedbelowiftheentitydoesnothavethisinformation.Pasture/Range/Paddock(PRP)manureNinputistheNexcreteddirectlyontolandbylivestock,andthemanureisnotcollectedormanaged(deKleinetal.,2006).TheamountofPRPmanureNisestimatedwiththelivestockmethods(SeeChapter5,Section5.3.2EntericFermentationandHousingEmissionsfromBeefProductionSystems)andassumedtobesplitwith50%oftheNinurineandtheother50%oftheNinsolids.

Table3‐7:NitrogenandCarbonFractionsofCommonOrganicFertilizers–MidpointandRange(PercentbyWeight)

OrganicFertilizer %Na %CPoultrymanure 2.25%(1.5‐3) 8.75%(7‐10.5)b

Pig,horse,cowmanure 0.45%(0.3‐0.6) 5.1%(3.4–6.8)c

Greenmanure 3.25%(1.5‐5) 42%d

Compost 1.25%(0.5‐2) 16%(12‐20)e

Seaweedmeal 2.5%(2‐3) 27%f

Sewagesludge 3%(1‐5) 11.7%(3.9‐19.5)b

Fishwaste 7%(4‐10) 24.3%(14.6‐34)g

Blood 11%(10‐12) 35.2%(32‐38.4)h

Humanurine/nightsoil 1.25%(1‐1.5) 9.5%(9‐10)iaHue,N.V.OrganicFertilizersinSustainableAgricultureRetrievedfromhttp://www.ctahr.hawaii.edu/huen/hue_organic.htm.bUSDA.1992.AgriculturalWasteCharacteristics.Chapter4.InAnimalWasteManagementFieldHandbook:NaturalResourcesConservationService,UnitedStatesDepartmentofAgriculture.cEPA,2013.InventoryofU.S.GreenhouseGasEmissionsandSinks:1990‐2011.WeightedU.S.averagecarbon:nitrogenratioformanureavailableforapplication.dAssumesdrymatteris42%carbon.eA1Organics.CompostClassification,SpecificationandResourceManual.http://www.a1organics.com/CLSP/CLASS%20MANUAL%20‐%20COLORADO.pdffhttp://www.naorganics.com/en/science_analysis.asp.NorthAtlanticOrganics.gHartz,T.K.andP.R.Johnstone.2006.Nitrogenavailablefromhigh‐nitrogen‐containingorganicfertilizers.HortTechnology16:39‐42.hSonon,D,etal.2012.Mineralizationofhigh‐Norganicfertilizers.ClemsonUniversity.iPolprasert,C.2007.OrganicWasteRecycling:TechnologyandManagement.IWAPublishing.

Themethodfororganicsoilsrequiresthefollowingactivitydataforcroplandsandgrazinglands:

Areaoflandparcel(i.e.,field);and Totalyieldofcrop(metrictonsdrymattercropyieldyear‐1),meat(kgcarcassyieldyear‐1)

ormilk(kgfluidmilkyear‐1).


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Ancillarydataforthemineralsoilmethodincludehistoricalweatherpatternsandsoilcharacteristics.WeatherdatamaybebasedonnationaldatasetssuchastheParameter‐ElevationRegressionsonIndependentSlopesModel(PRISM)data(Dalyetal.,2008).SoilcharacteristicsmayalsobebasedonnationaldatasetssuchastheSoilSurveyGeographicDatabase(SSURGO)(SoilSurveyStaff,2011).However,therewillalsobeanoptionforentitiestosubstitutesoilsdatacollectedfromthespecificfield.Theerosionmodelwillalsorequireancillarydataontopography(i.e.,slope),lengthoffieldandroworientation,cropcanopyheight,diversions,surfaceresiduecover,andsoiltexture.

Noancillarydataareneededforthemethodtoestimateemissionsfromdrainageoforganicsoils.

3.5.3.5 ModelOutput

Modeloutputisgeneratedforthequantityofemissionsandemissionsintensity.Thechangeinmineralsoilorganiccarbonstocksisestimatedbasedonstockchangesoverfive‐yeartimeperiodsinordertomanageuncertainty.Uncertaintiesinthemodel‐basedestimatesareaboutthreetimeslargerforannualestimatesinchangeratecomparedwithfive‐yearblocks(CompareU.S.EnvironmentalProtectionAgency(2009)and(2010)).Uncertaintiesarelargeratthefinertimescalebecausethereislargevariabilityinmeasurementsofsoilcarbonstockchangesatannualtimescales,andthisvariabilityisincorporatedintothemodeluncertaintyusingtheempiricallybasedmethod(Ogleetal.,2007).Inaddition,trendsinsoilorganiccarbonwillbeestimatedforthe30previousyearsofhistoryandthereportingperiod.

Emissionsintensityisbasedontheamountofemissionsperunitofyieldforcropsincroplandsystemsoranimalproductsingrazingsystems.Theemissionsintensityisestimatedwiththefollowingequation:


Uncertaintiesinthemineralsoilmethodsincludeimprecisionandbiasintheprocess‐basedmodelparametersandalgorithms,inadditiontouncertaintiesintheactivityandancillarydata.Uncertaintyintheparameterizationandalgorithmswillbequantifiedwithanempiricallybasedapproach,asusedintheU.S.nationalGHGinventory(Ogleetal.,2007;U.S.EPA,2011).ThemethodcombinesmodelingandmeasurementstoprovideanestimateofSOCstockchangesforentityscalereporting(Conantetal.,2011).Measurementsofcarbonstockchangesareexpectedtobebasedon

Equation3‐7:EmissionsIntensityofSoilOrganicCarbon StockChange

EISoilC=(ΔCMineral+ΔCOrganic)/Y

Where:

EISoilC =Emissionsintensity(metrictonsCO2permetrictondrymattercropyield,metrictonsCO2perkgcarcassyield,metrictonsCO2perkgfluidmilkyield)

ΔCMineral =AnnualCO2equivalentemissionsfromsoilorganiccarbonchangeinmineralsoils(metrictonsCO2‐eqyear‐1)

ΔCOrganic =AnnualCO2equivalentemissionsfromsoilorganiccarbonchangeinorganicsoils,Histosols(metrictonsCO2‐eqyear‐1)

Y =Totalyieldofcrop(metrictonsdrymattercropyieldyear‐1),meat(kgcarcassyieldyear‐1)ormilkproduction(kgfluidmilkyieldyear‐1)


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anationalsoilmonitoringnetwork(Spenceretal.,2011).Thenetworkshouldincludesamplesfromdifferentregionsofthecountryandsoiltypesthatareusedforcropproductionorgrazing,andarandomsamplingofthemanagementsystemsineachoftheregions.ThesamplingplotswillneedtobedesignedforresamplingovertimeinordertoevaluatethemodeledchangesinSOCstocks(Conantetal.,2003).Uncertaintiesinnationaldatasetsforweatherwillbebasedoninformationincludedwiththedataset,whileuncertaintiesintheSSURGOshouldbequantifiedusingtheunderlyingfielddatathatformthebasisforthemappingexercise,oranindependentaccuracyassessmentofthemapproduct.Otherinputdataisassumedtobeknownbytheentity,suchasthecropplants,yields,tillage,andresiduemanagementpractices.

Thelimitationsofthemineralsoilcarbonmethodincludenoassessmentoftheeffectoflanduseandmanagementinsub‐surfacelayersofthesoilprofile(below30centimeters),noassessmentofthelocationoftransportanddepositionoferodedC,andlimiteddatatoassessuncertaintyintheparametersandalgorithmsusingtheempiricallybasedmethod.Foragroforestry,theDAYCENTmodelhasbeenusedintheCOMET‐Farmvoluntarycarbonreportingtooltosimulatesoilorganiccarbonstockchanges.However,thereareseveralunknownswiththeuseoftheDAYCENTmodelforestimatingsoilorganiccarbonstockchangesinagroforestry,includingwhetherthemodelisabletotakeintoaccounttheinteractionsoccurringbetweenwoodyandherbaceousvegetationandrespectivemanagementactivities.OelbermannandVoroney(2011)evaluatedtheuseoftheCenturymodel,themonthlytime‐stepversionoftheDAYCENTmodel,topredictsoilorganiccarbonintemperateandtropicalalleycroppingsystemsthatwere13and19yearsold,respectively.Theyfoundthatthemodelunderestimatedthelevelsofsoilorganiccarboncomparedwithmeasuredvalues.Withmoretesting,themethodsmayberevisedinthefuturetousetheDAYCENTmodelforthepurposesofestimatingsoilorganiccarbonstockchangesinagroforestrysystems.

Biocharresearchhasbeenanareaofrapiddevelopmentoverthepastfewyears,buttherearestilluncertainties.Biocharisaproductofcombustedbiomassthathasavarietyofchemicalstructuresdependingonthebiomassandpyrolysismethod,andthevariationhasimplicationsforthestabilityofthecarboninthesoil(Spokas,2010).BiocharcanhaveconcomitantimpactsonemissionsofotherGHGssuchasCH4andN2O(Cayuelaetal.,2010;Malghanietal.,2013;Yuetal.,2013),althoughsomestudieshaveshownnoeffect(Caseetal.,2013;Cloughetal.,2010).SoilamendmentswithbiocharmayalsoprimethedecompositionofthenativesoilorganicmatteralthoughtheCO2emissionsfromprimingappeartobeconsiderablysmallerthanthecarbonaddedinthebiochar(Stewartetal.,2013;WoolfandLehmann,2012).Otherresearchsuggeststhattheremayevenbe“negative”primingleadingtoareductioninheterotrophicrespiration(Caseetal.,2013).Furthermore,thetemporaldurationoftheGHGmitigationpotentialofbiocharisalsouncertainbutappearstobeofashorttermnature(Spokas,2013).TheinfluenceofbiocharonemissionsandprimingneedsmoreresearchbeforethefulleffectofbiocharoncarbonsequestrationandGHGemissionscanbeincorporatedintomodelsandGHGreportingframeworks.Microbialdegradationofbiocharcanoccurovertimescalesrangingfromaslittleasafewdecadesto1000sofyears(Spokas,2010).Inthetechnicalmethods,biocharistreatedasahighcarbontolownitrogenamendmentintheDAYCENTmodelframework,butwithaconservativeresidencetimeofthecarbonfromdecadestoacentury.Thesemethodscanbefurtherrefinedinthefutureasthedifferenttypesandresidencetimesofbiochararefurtherresolved.

Themethodfororganicsoilsalsohaslimitations,particularlytheinabilitytoestimatetheeffectofmitigationmeasuressuchaswatertablemanagementbecauseemissionfactorsaresetforeachclimateregion(i.e.,currentlyscalingfactorsarenotavailabletorevisetheemissionfactorsforwatertablemanagement).Onlycompleterestorationofthewetlandwithnofurtherdrainagecanbeaddressedwiththemethod(i.e.,assumesnofurtheremissionsofCO2).However,ifcrop


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productionismaintainedonthelandparcel,themostpracticalmethodforreducingemissionsistoraisethewatertabletoneartherootingdepthofthecropduringthegrowingseasonandthennotdrainingthesoilduringthenon‐growingseason(Jongedyketal.,1950;Shihetal.,1998),orpossiblymanagingthesystemwithperiodicflooding(Morrisetal.,2004).

Forallsystemsthereisadditionaluncertaintyassociatedwithclimatechange.Modeledoutputforanygivenlocationassumestemperatureandprecipitationsimilartothatofthepast30years,theperiodforwhichhistoricalweatherisusedtosimulatesoilorganiccarbondynamics.Expectedchangesintemperature,precipitation,andextremeeventssuchasdroughts,floods,andheatwaveswilladdfurtheruncertaintytoestimatesofsoilorganiccarbonstockchange.

WhilethereisconsiderableevidenceandmechanisticunderstandingabouttheinfluenceoflanduseandmanagementonSOC,thereislessknownabouttheeffectonsoilinorganicC.Consequently,thereisuncertaintyassociatedwithlanduseandmanagementimpactsonsoilinorganiccarbonstocks,whichcannotbequantified.CurrentmethodsdonotincludeimpactsoninorganicC,butthismaybeaddedinthefutureasmorestudiesareconductedandmethodsaredeveloped.

UncertaintiesinmodelparametersandstructurearecombinedusingaMonteCarlosimulationapproach.Uncertaintyisassumedtobeminorforthemanagementactivitydataprovidedbytheentity.Table3‐8providestheprobabilitydistributionfunctionsassociatedwiththemineralandorganicsoilsmethods.

Table3‐8:AvailableUncertaintyDataforSoilOrganicCarbonStockChange

Parameter Mean Units

RelativeUncertainty

Distribution DataSourceLow(%)

High(%)

DAYCENT(empiricaluncertainty) NS Various NS NS Normal

Ogleetal.(2007);EPA(2013)

Emissionfactorforcroplandincooltemperateregions 11

metrictonsCha‐1year‐1 45 45 Normal

Ogleetal.(2003)

Emissionfactorforcroplandinwarmtemperateregions 14


Ogleetal.(2003)

Emissionfactorforcroplandinsubtropicalregions 14


Ogleetal.(2003)

Emissionfactorforgrazinglandincooltemperateregions

2.8metrictonsCha‐1year‐1

45 45 NormalOgleetal.(2003)

Emissionfactorforgrazinglandinwarmtemperateregions



Emissionfactorforgrazinglandinsubtropicalregions



NS=NotShown.Dataarenotshownforparametersthathave100’sto1000’sofvalues(denotedasNS).


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3.5.4 SoilNitrousOxide


N2Ofluxesarenotoriouslydifficulttomeasurebecauseofthelaborrequiredtosampleemissions,combinedwithhighspatialandtemporalvariability.AgronomicpracticesthataffectN2Ofluxesinonesoil,climate,orsite‐yearmayhavelittleornomeasurableeffectinothers.Consequently,considerablecareisrequiredtoensurethatmethodstoestimatechangesinemissionsforaparticularcroppingpracticeareaccurateandrobustforthegeographicregionforwhichtheyareproposed,oraresufficientlygeneralizabletobeaccurateinaggregate.

DeKleinetal.(2006)providethreeestimationstrategiesfordirectN2Oemissionsfromcropland.Twoarebasedonemissionfactors,theproportionofnitrogenaddedtoacropthatbecomesN2O.Tier1isbasedonanear‐universalemissionfactor,applicablegloballywithoutregardtogeography,croppingpractice,orfertilizerplacement,timing,orformulation.Tier2methodsutilize

MethodforEstimatingSoilDirectN2OEmissions

MineralSoils

Themethodisbasedonusingresultsfromprocess‐basedmodelsandmeasuredN2OemissionsincombinationwithscalingfactorsbasedonU.S.specificempiricaldataonaseasonaltimescale.

Process‐basedmodeling(anensembleapproachusingDAYCENTandDNDC)combinedwithfielddataanalysisareusedtoderivebaseemissionratesforthemajorcroppingsystemsanddominantsoiltextureclassesineachUSDALandResourceRegion.Incaseswherethereareinsufficientempiricaldatatoderiveabaseemissionrate,thebaseemissionrateisbasedontheIPCCdefaultfactor.Thebaseemissionfactorsareadjustedbyscalingfactorsrelatedtospecificcropmanagementpracticesthatarederivedfromexperimentaldata.

OrganicSoils

DirectN2OemissionsfromdrainageoforganicsoilsusestheIPCCequationsdevelopedindeKleinetal.,(2006).Themethodfororganicsoilsassumesthatthereisstillasignificantorganichorizoninthesoil,andtherefore,therearesubstantialinputsofnitrogenfromoxidationoforganicmatter.

TheemissionratefordrainedorganicsoilsisbasedonIPCCTier1emissionfactor(0.008metrictonsN2O‐Nha‐1year‐1).

Thismethodreliesonentityspecificactivitydataasinputintotheequations.

MethodforEstimatingSoilIndirectN2OEmissions

ThismethodusestheIPCCequationforindirectsoilN2O(deKleinetal.,2006). IPCCdefaultsareusedforestimatingtheproportionofnitrogenthatissubjectto

leaching,runoff,andvolatilization.Inlandparcelswheretheprecipitationplusirrigationwaterinputislessthan80percentofthepotentialevapotranspiration,nitrogenleachingandrunoffareconsiderednegligibleandnoindirectN2Oemissionsareestimatedfromleachingandrunoff.

Thismethodusesentityspecificseasonaldataonnitrogenmanagementpractices.


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geographic,crop,orpractice‐specificemissionfactorswherefieldtestsshowthatafactordifferentfromtheonepercentTier1factoriswarranted.AtpresentthereisonlyoneTier2exampleintheprimaryliteraturethatisspecifictoconditionsintheUnitedStates,anditisforcornintheNorthCentralregion(Millaretal.,2010).ThismethodhasbeenincorporatedintoseveralN2Oreductionprotocols(VerifiedCarbonStandard,AmericanCarbonRegistry,andClimateActionReserve).ThethirdoptionforestimatingdirectN2Oemissions,orTier3,isameasurementorprocess‐basedmodelingapproach.Inthiscase,emissionsaremonitoredspecificallyfortheentity’sfieldbydeployinginstrumentsinameasurementsystemorbygatheringtheinformationspecifictothefieldconditionstosimulateN2Oemissionswithaprocess‐basedmodel.Thisthirdoptionisthemostprecise,butrequiresmoreresourcesandsufficienttestingpriortoimplementation.

InSection3.2.1.1,severalpracticesarediscussedthathavebeenshowntoreduceN2Oemissionsinfieldexperiments.However,manyoftheexperimentshavebeenconductedforalimitednumberofspecificcroppingsystemsandregions.Consequently,therearenomitigationpracticesforwhichemissionreductionshavebeenquantifiedunderallconditionsintheUnitedStates.Nevertheless,formanypracticesthereissufficientknowledgeatthecroppingsystemandregionallevelstoestablishthatadoptionwillreducesoilN2Oemissions.

Process‐basedsimulationmodelsuseknowledgeofC,N,andwaterprocesses(amongothers)topredictecosystemresponsestoclimateandotherenvironmentalfactors,includingcropandgrazinglandmanagement(seesoilcarbonmethodologyinSection3.5.3).N2Ofluxescanbepredictedusingsimulationmodels(Chenetal.,2008;DelGrossoetal.,2010).Akeyadvantageofsimulationmodelsisthattheyaregeneralizabletoawidevarietyofsoils,climates,andcroppingsystems,allowingfactorstointeractincomplexwaysthatmaybedifficulttopredictwithlesssophisticatedapproaches.However,adisadvantageisthatcomplexitycanlimittheirtransparency,andatpresenttherearestillsubstantialdatagapsthatlimitourabilitytofullytestavailablemodelsfortheirsensitivitytodifferentmanagementpracticesacrossvariousregionsandcropsintheUnitedStates.

Toovercomethesechallenges,ahybridapproachthatutilizesprocess‐basedsimulationmodelsandfielddatawasdevelopedtoestimateN2Oemissions.Themethodusesabaseemissionrateassociatedwiththetypicalamountofnitrogenapplied,andthenadjustmentsareappliedviascalingfactorstoaccountformanagementpracticesthataffectN2Oemissions.ThisapproachisaTier3methodasdefinedbytheIPCC.

Baseemissionratesareestimatedforeachdominantcropandthreesoiltextureclasses(coarse,medium,fine)withinaclimaticregionusingprocess‐basedsimulationmodeling.ThefactorsaredevelopedatthescaleofUSDALandResourceRegions(LRR).FielddataindicatethatN2Oemissionsgenerallyincreaseastheamountofappliednitrogenincreases,especiallywhennitrogenapplicationratesexceedcropuptakerates(Hobenetal.,2011;Kimetal.,2013;McSwineyandRobertson,2005;Shcherbaketal.,inpress)Researchdatafromfieldexperimentswerecompiledandusedtoadjusttheemissionratesfornitrogenfertilizerapplicationratesthatexceededthetypicalnitrogenapplicationrateforthecropinalandresourceregion.Forcropswheresufficientdataarenotavailabletosimulatethebaseemissionratewithaprocess‐basedmodel,thestandardIPCCTier1emissionfactorisapplied.Inaddition,forlandparcelsthathaveamixofcropswhereonlysomecanbesimulated,thestandardIPCCTier1approachshouldalsobeapplied.

Emissionsareaffectedbyspecificfarmmanagementpracticessuchasreducingtillageintensity;addingnitrificationinhibitors,orchanginghow,whenandwherenitrogenfertilizersareapplied.ToaccountfortheeffectofmanagementpracticesonN2Oemission,scalingfactorsweredevelopedtoadjustthebaseemissionrates.Thescalingfactorswereestimatedfromavailableresearchdata(SeeAppendix3‐Aformoreinformation).Managementpracticesotherthanthoseincludedinthe


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equationmayalsomitigateN2Oemissions,buttherearenotcurrentlysufficientdatatocreategeneralizedscalingfactors.Additionaldatamayleadtotheirinclusioninfutureupdatestothemethod.

ThismethodincorporatesmoreinformationthanamethodbasedsolelyontheIPCCmodel.Itprovidesatransparentandscience‐basedmeansofestimatingannualizedN2Oemissionsfromcropandgrazinglands,anditfacilitatestheestimationofuncertainty.ForN2Oemissionsfromcropandgrazinglands,anIPCCTier1approachisonlysensitivetonitrogenapplicationrate,andthereforedoesnotreflectthefullsuiteoffactorsthatareknowntoinfluenceN2Oemissionsincludingclimate,soils,crops,andmanagementpracticesthatrangefromtillagetocovercropstofertilizertiming,placement,formulation,andadditives.DynamicprocessmodelsasembodiedintheIPCCTier3approachcan,inconcept,accountformostofthesefactorsbuttodatehavenotbeensufficientlyevaluatedformanyU.S.locations,crops,andmanagementpractices.Thisreporttakesahybridapproachthatrepresentsthebestavailablescienceatthetimeofpublication:dynamicprocessmodelstoestimatebaselineN2Oemissionsforthosecropsandlocationssufficientlyevaluated,thenscaledbymanagementpracticestotheextentsupportedbyavailableresearchresults.InitialtestingindicatesthatthismethodismoresensitivetoU.S.nutrientmanagementpracticesthantheIPCCTier1approach.Theauthorsanticipatepublicationofanaddendumthatwillprovidetestresultsandsuggestfurthertuningofthemethod.Overtime,asdynamicprocessmodelsarefurtherdevelopedandtested.ThemethodwilllikelymigratetowardsanexclusiveTier3approachtobetteraccountformanagementeffectsgiventhelocalvariablesandconditions.Intheinterim,inadditiontoprovidingbest‐availableandreliableestimatesofN2Oemissionsfromcropandgrazinglands,themethodoutlinedhereisexpectedtosetaresearchagendathatprovidesforbroaderevaluationofenvironmentalconditionsandmanagementpracticesinfluencingN2Oemissionsaswellasfurtherdevelopmentofmodelstomoreaccuratelyestimateemissions.

OffsiteorindirectN2Oemissions,whichoccurwhenreactivenitrogenescapestodownwindordownstreamecosystemswherefavorableconditionsforN2Oproductionexist,areevenmoredifficulttoestimatethandirectemissionsbecausethereisuncertaintyinboththeamountofreactivenitrogenthatescapesandtheportionofthisnitrogenthatisconvertedtoN2O.Ideally,fluxesofvolatileandsolublereactivenitrogenleavingtheentity’sparceloflandwouldbecombinedwithatmospherictransportandhydrologicmodelstosimulatethefateofreactiveN.Atpresenttherearenolinkedmodelingapproachessufficientlytestedtobeusedinanoperationalframework.Consequently,theindirectN2OemissionsarebasedontheIPCCTier1method(deKleinetal.,2006).

Similarly,directN2OemissionsfromdrainageoforganicsoilsarebasedontheIPCCTier1methods(deKleinetal.,2006).AlthoughresearchisongoingtoprovideimprovedemissionfactorsandmethodsforestimatingN2Oemissionsfromdrainageoforganicsoils(Allen,2012),moretestingwillbeneededbeforeincorporatingthemintoanoperationalmethod.Futurerevisionstothesemethodswillneedtoconsideradvancementsandrevisethemethodsaccordingly.


N2Oisemittedfromcroplandbothdirectlyandindirectly.Directemissionsarefluxesfromcroplandorgrazinglandswheretherearenitrogenadditionsornitrogenmineralizedfromsoilorganicmatter.IndirectemissionsoccurwhenreactivenitrogenisvolatilizedasNH3orNOxortransportedviasurfacerunofforleachinginsolubleformsfromcroplandorgrazinglands,leadingtoN2Oemissionsinanotherlocation.


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DirectN2OEmissions

MineralSoils:TotaldirectN2OemissionsfrommineralsoilsareestimatedforalandparcelusingEquation3‐8.

Thepractice‐scaledemissionratefortheparcelofland(ERp)isestimatedusingEquation3‐9.

Equation3‐8:DirectSoilN2OEmissionsfromMineralSoils

N2ODirect=ERp×A×N2OMW×N2OGWP

Where:

N2ODirect=TotaldirectsoilN2Oemissionforparcelofland(metrictonsCO2‐eqyear‐1)

ERp =Practice‐scaledemissionrateforlandparcel(metrictonsN2O‐Nha‐1year‐1)

A =Areaofparcelofland(ha)

N2OMW =RatioofmolecularweightsofN2OtoN2O‐N =44/28(metrictonsN2O(metrictonsN2O‐N)‐1)

N2OGWP =GlobalwarmingpotentialforN2O(metrictonsCO2‐eq(metrictonsN2O)‐1)


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aAdifferencearisesintheERbestimationofPRPmanureNinputandtheactualPRPmanureNinputbecauseatypicalrateofNinputwasassumedintheDAYCENTandDNDCsimulationsfortheERbcalculation(SeeTextbox3‐1andAppendix3‐A).bEmissionfactorsfromdeKleinetal.(2006).

Inthisequation,thebaseemissionrate(ERb)variesbytheamountofnitrogeninputtothesoil.TheratemayalsovaryfordifferentcropandgrazinglandsystemsbyLRRtocapturevariationinclimate,andbytextureclassinordertorepresenttheinfluenceofsoilheterogeneityonN2Oemissions.MoreinformationaboutbaseemissionratesisgiveninTextbox3‐1.

Practice‐basedemissionscalingfactors(0to1)areusedtoadjusttheportionoftheemissionrateassociatedwithslowreleasefertilizers(Ssr),nitrificationinhibitors(Sinh),andpasture/range/paddock(PRP)manurenitrogenadditions(Sprp,cps).Theslow‐releasefertilizer,

Equation3‐9:Practice‐Scaled SoilN2OEmissionRateforMineralSoils

ERp=[ERb+(ΔNprp*EFprp)]x{1+[Ssrx(Nsr/Ni)]}x{1+[Sinhx(Ninb/Ni)]}x(1+Still)x{1–[Nresidr/(Ni+Nresidr)]}

Where:

ERp =Practice‐scaledemissionrateforlandparcel(metrictonsN2O‐Nha‐1year‐1)

ERb =Baseemissionrateforcroporgrazinglandthatvariesbasedonnitrogeninputratefrommineralfertilizer,organicamendments,residues,andadditionalmineralizationwithland‐usechangeortillagechange

(metrictonsN2O‐Nha‐1year‐1)

ΔNprp =DifferenceinPRPmanureNexcretionabetweenthePRPmanureNexcretionbasedonentityactivitydata(NPRPe)andPRPmanureNexcretionforthebaseemissionrate(NPRPb)(metrictonsN)

=NPRPe‐NPRPb

EFprp =EmissionfactorforPRPmanureNinputtosoils,0.02metrictonsN2O‐Nha‐1year‐1(metrictonsN)‐1forcattle,poultryandswine,and0.01metrictonsN2O‐N(metrictonsN)‐1forotherlivestockb

Ni =Nitrogeninputs,includingmineralfertilizer,organicamendments,PRPmanureN,residues,andSOMmineralization(SeeEquation3‐11)

(metrictonsNha‐1year‐1)Ssr =Scalingfactorforslow‐releasefertilizers,0wherenoeffect(dimensionless)

Nsr =Nitrogeninslow‐releasenitrogenfertilizerappliedtotheparcelofland (metrictonsNha‐1year‐1)

Sinh =Scalingfactorfornitrificationinhibitors,0wherenoeffect(dimensionless)

Ninh =Nitrogeninnitrogenfertilizerwithinhibitorappliedtotheparcelofland (metrictonsNha‐1year‐1)

Still =Scalingfactorforno‐tillage,0exceptforNT(dimensionless)

Nresidr =Nremovedthroughcollection,grazing,harvestingorburningofabovegroundresidues(metrictonsNha‐1year‐1).EstimateusingEquation3‐10forresultsgeneratedwithDAYCENTandDNDCmodelswiththeexceptionofhaycrops.NocalculationisneededforresultsgeneratedbytheIPCCmethodorforresultsassociatedwithhaycropsgeneratedbyDAYCENTandDNDC(setvalueto0).


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nitrificationinhibitorandPRPmanurescalingfactorsareweightedsothattheireffectisonlyontheamountofnitrogeninfluencedbythesepracticesrelativetotheentirepoolofnitrogen(i.e.,theamountofslow‐releasefertilizer,fertilizerwithnitrificationinhibitororPRPmanurenitrogenaddedtothesoil).Incontrast,scalingfactorsfortillage(Still)areusedtoscaletheentireemissionrateundertheassumptionthatthispracticeinfluencestheentirepoolofmineralnitrogeninputs(i.e.,Ni).

Table3‐9:ScalingFactorsforNitrogenManagementPractices

ManagementPracticeNitrogen Management

FactorFactor(ProportionalChangeinEmissions) Source

Slow‐releasefertilizeruse Ssr ‐0.21(‐0.12to‐0.30) SeeAppendix3‐AManurenitrogendirectlydepositedonpasture/range/paddock

Sprp,cps +0.5(0.33to0.67) IPCC(2006)

Nitrificationinhibitoruse Sinh–semiarid/aridclimate ‐0.38(‐0.21to‐0.51) SeeAppendix3‐ANitrificationinhibitoruse Sinh–mesic/wetclimate ‐0.40(‐0.24to‐0.52) SeeAppendix3‐A

TillageStill–semiarid/aridclimate(<10yearsfollowingno‐till

adoption)0.38(0.04to0.72)

vanKesseletal.(2012),Sixetal.(2004)

TillageStill–semiarid/aridclimate(≥10yearsfollowingno‐till

adoption)‐0.33(‐0.16to‐0.5)


Equation3‐10:AbovegroundResidueN Removal

ForCrops:

Nresidr=[((Ydm/HI)–Ydm)xRr)xNa]

ForGrazingForage:

Nresidr=[Ydmx(Fr+Rr)xNa]

Where:

Nresidr=Nremovedthroughcollection,grazing,harvestingorburningofabovegroundresidues(metrictonsNha‐1year‐1)

Ydm =Cropharvestorforageyield,correctedformoisturecontent (metrictonsbiomassha‐1year‐1) =YxDM

Y =Cropharvestortotalforageyield(metrictonsbiomassha‐1year‐1)

DM =Drymattercontentofharvestedbiomass(dimensionless)


Fr =Proportionofliveforageremovedbygrazinganimals(dimensionless)

Rr =Proportionofcrop/forageresidueremovedduetoharvest,burningorgrazing(dimensionless)

Na =Nitrogenfractionofabovegroundresiduebiomassforthecroporforage (metrictonsN(metrictonsbiomass)‐1)


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ManagementPracticeNitrogen Management

FactorFactor(ProportionalChangeinEmissions)

Source

TillageStill–mesic/wetclimate

(<10yearsfollowingno‐tilladoption)

‐0.015(‐0.16to0.16)vanKesseletal.(2012),

Sixetal.(2004)

TillageStill–mesic/wetclimate

(≥10yearsfollowingno‐tilladoption)

‐0.09(‐0.19to0.01) vanKesseletal.(2012),Sixetal.(2004)

Note:SeeAppendix3‐AforfurtherexplanationonthepracticesincludedinthesoilN2Omethodandthesourcesofdatathatwereusedtoderivethebaseemissionratesandscalingfactorsforthemanagementpractices.


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Textbox3‐1:BaseEmissionRateforDirectSoilN2OEmissionsfromMineralSoils

Thebaseemissionrateisacroporgrazinglandspecificestimatethatvariesbasedonthetotalmineralnitrogeninputtothesoil.Therearetwomethodsusedtoestimatethebaseemissionrate.Thefirstmethodusesacombinationofprocess‐basedmodelingandmeasurementdatatoestimatesN2Obaseemissionratesbylandresourceregion,majorcroptype,andsoiltextureclass.ThesecondmethodusesthedefaultIPCCemissionfactorofonepercent(deKleinetal.,2006),multiplyingthisvaluebythetotalnitrogeninput(SeeEquation3‐11)toestimatethebaseemissionrate.Thesecondapproachisusedforcropsthatarenotincludedintheprocess‐basedmodelinganalysis.

Theremainderofthisboxdescribesthefirstmethod.Theequationforthefirstmethod,combiningthemodelingandmeasurementdata,isgivenbelow:

ERb=ER0+(EFtypical+(SEF×ΔNf))×Nf

ERb =Baseemissionrate(metrictonsN2O‐Nha‐1year‐1)

ER0 =Emissionratemodeledat0levelofnitrogeninput(Nt=0) (metrictonsN2O‐Nha‐1year‐1)

EFtypical =Emissionfactorforthetypicalfertilizationrate(metrictonsN2O‐N(metrictonsN)‐1) =(ERtypical–ER0)/Ntf

ERtypical=Emissionrateforthetypicalcasemodeled(metrictonsN2O‐Nha‐1year‐1)

SEF =BaseEFscalar; forΔNf>zero:SEF=0.0274forallnon‐grasslandcrops,

SEF=0.117forgrasslands; forΔNf<zero(lessthanorthesameastypicalfertilizerrates):SEF=0; ((metrictonsN2O‐N(metrictonsN)‐2)hayear)

ΔNf =Nf‐Ntf(metrictonsNha‐1year‐1)

Nf =Actualnitrogenfertilizerrate,includingsyntheticandorganic(metrictonsNha‐1year‐1)

Ntf =Typicalnitrogenfertilizerrate(metrictonsNha‐1year‐1)

Process‐basedmodelswereusedtosimulateN2OemissionsatthetypicalnitrogenfertilizationrateformajorcommoditycropsaccordingtotheUSDAAgriculturalResourceManagementSurveydata(ERtypical),inadditiontoazerorateapplication(ER0).TheN2Oemissionatthetypicalrateoffertilizationformajorcommoditycropsareproducedforcoarse,medium,andfinetexturedsoilsineachlandresourceregion.Theemissionfactor(EFtypical)forfertilizationratesgreaterthanthetypicalrateforthecroporgrassarescaledaccordingtothetrendinmeasuredsoilN2Odataacrossarangeoffertilizationratesbasedonexperimentaldata.ThechangeintheemissionfactorbetweenthetypicalnitrogenfertilizationrateandahigherratewasaveragedtoderiveanemissionfactorscalarorrateofchangeperunitofadditionalN.Thescalarismultipliedbytheadditionalnitrogentoderiveanadjustmenttotheemissionfactor(SEF×ΔNf)thatisthenaddedtotheemissionfactorderivedforthetypicalfertilizerrate(EFtypical).NoscalingisdoneforthecasewhereΔNf≤zero,i.e.,wherethefertilizationrateisequaltoorlessthanthetypicalrateofnitrogenapplication.InthiscaseSEF=0suchthatSEF×ΔNf=0.Theresultingemissionfactorismultipliedbytheactualfertilizerrate(Nf)andaddedtotheemissionrateatthe0levelofnitrogenfertilization(ER0)toderivethebaseemissionrate(ERb).


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Nitrogeninputsareestimatedwiththefollowingequation:

aTheapproachforestimatingnitrogenmineralizationinputsisconsistentwiththeU.S.NationalInventory(U.S.EPA,2013).bPasture/Range/Paddock(PRP)manureNisatermutilizedbytheIPCC(deKleinetal.,2006)fortheNexcreteddirectlyontolandbylivestock,andthemanureisnotcollectedormanaged.ThetotalPRPmanureNisestimatedwithmethodsfromChapter5,andassumedtobesplitwith50%oftheNinurineand50%oftheNinsolids.

ThetotalNmineralizationisestimatedfromtheDAYCENTmineralsoilCmethodinaggregateformanureamendments(Nman),compost(Ncom),residues(Nres),soilorganicmatter(Nsmin)andsolidsassociatedwithPRPmanure,andisusedtoapproximatetheseNinputsinEquation3‐11.ThisapproachcreatesalinkagebetweenthemineralsoilCmethod(SeeSection3.5.3.2)andtheN2Omethod,ensuringconsistencyintreatmentofN.IninstanceswherecropscannotbeestimatedbytheDAYCENTmineralsoilCmethod,themethodfromtheIPCCguidelines(Aaldeetal.,2006)canbeusedtoestimatetheNinputsfrommineralizationwiththeexceptionofNsmin,whichissetto0(SeeAppendix3‐B).

OrganicSoils:ThemethodfororganicsoilsincludesHistosolsandsoilsthathavehighorganicmattercontentanddevelopedundersaturated,anaerobicconditionsforatleastpartoftheyear,whichincludesHistels,Historthels,Histoturbels.Themethodassumesthatthereisasignificantorganichorizoninthesoil,andtherefore,majorinputsofnitrogenarefromoxidationoforganicmatterratherthanexternalinputsfromsyntheticandorganicfertilizers.Iftheseassumptionsarenottrue,thentheentityshouldusethemineralsoilmethodtoestimatetheN2Oemissions.TotaldirectN2Oemissionsfromdrainedorganicsoilsareestimatedforindividualparcelsofland(i.e.,fields)withthefollowingequation:

Equation3‐11:NitrogenInputsa

Ni=Nsfert+Nman+Ncomp+Nresid+Nsmin+Nprp

Where:

Ni =Nitrogeninputs,includingmineralfertilizer,organicamendments,PRPmanureN,residues,andSOMmineralization

(metrictonsNha‐1year‐1)

Nsfert =Nitrogeninsyntheticfertilizerappliedtoaparcelofland (metrictonsNha‐1year‐1)

Nman =Nitrogenmineralizationfrommanureamendments(orsewagesludge)appliedtoaparcelofland(metrictonsNha‐1year‐1)

Ncomp =Nitrogenmineralizationfromcompostappliedtoaparcelofland (metrictonsNha‐1year‐1)

Nresid =Nitrogenmineralizationfromcropandcovercropresiduesaboveandbelowgroundthatareleftontheparceloflandfollowingsenescence(i.e.,notcollected,grazed,orburned)(metrictonsNha‐1year‐1)

Nsmin=NitrogeninputsfromsoilorganicmattermineralizationasestimatedbytheDAYCENTmineralsoilCmethod(SeeSection3.5.3.2)(metrictonsNha‐1year‐1).Valuesetto0forcropsthatarenotestimatedwiththeDAYCENTmineralsoilCmethod.

Nprp =Nitrogeninurineandmineralizationfromsolidsassociatedwithmanureinpasture/range/paddock(PRP)(metrictonsNha‐1year‐1)b


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IndirectN2OEmissions:ThemethodtoestimateindirectN2OemissionsformineralsoilshasbeenadoptedfromtheapproachdevelopedbyIPCC(deKleinetal.,2006).ThefollowingequationisusedtoestimatethetotalindirectN2Oemissionsassociatedwithnitrogenvolatilizationandnitrogenleachingandrunofffromthelandparcel:

Thefollowingequationisusedtoestimatetheindirectemissionsassociatedwithnitrogenvolatilizationfromthelandparcel:

Equation3‐12:DirectN2OEmissionsfromDrainageofOrganicSoils(Histosols)

N2OORGANIC=AOS×EROS

Where:

N2OORGANIC =DirectsoilN2Oemissionfromdrainageoforganicsoils (metrictonsN2O‐Nyear‐1)

Aos =Areaoforganicsoilsdrainedonaparcelofland(ha)

EROS =EmissionrateforcroppedHistosols, IPCCTier1EROS=0.008metrictonsN2O‐Nha‐1year‐1

Equation3‐13:TotalIndirectSoilN2OEmissionsfromMineralSoils

N2OIndirect=(N2OVol+N2OLeach)×N2OMW×N2OGWP

Where:

N2OIndirect=IndirectsoilN2Oemission(metrictonsCO2‐eqyear‐1)

N2OVol =N2Oemittedbyecosystemreceivingvolatilizednitrogen (metrictonsN2O‐Nyear‐1)

N2OLeach =N2Oemittedbyecosystemreceivingleachedandrunoffnitrogen (metrictonsN2O‐Nyear‐1)

N2OMW =RatioofmolecularweightsofN2OtoN2O‐N=44/28 (metrictonsN2O(metrictonsN2O‐N)‐1)

N2OGWP =GlobalwarmingpotentialforN2O(metrictonsCO2‐eq(metrictonsN2O)‐1)


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TheIPCCdefaultsareusedforFRSNandFRON.

ThefollowingequationisusedtoestimatetheindirectN2Oemissionsassociatedwithleachingoroverlandflowofreactivenitrogenthatistransportedfromthelandparcel(i.e.,field):

Thefractionofnitrogenthatisleachedfromaprofilewillvarydependingonthelevelofprecipitationandirrigationwaterappliedinthefield.Inlandparcels(i.e.,fields)wheretheprecipitationplusirrigationwaterinputislessthan80percentofthepotentialevapotranspiration,nitrogenleachingandrunoffareconsiderednegligibleandnoindirectN2Oemissionsareestimated(U.S.EPA,2011).IPCCdefaultfractionsareusedforEFleachandFRleachwherenocovercropsarepresent.Wherewintercovercropsprecedethecashcrop,FRleachisfurtheradjustedtoaccountforcovercropeffectsonnitrateleaching.Inameta‐analysisof36geographicallydistributedfield

Equation3‐14:IndirectSoilN2OEmissionsfromMineralSoils—Volatilization

N2OVol=[(FSN×FRSN)+(FON×FRON)]×EFVOL

Where:

N2OVol =IndirectsoilN2Oemittedbyecosystemreceivingvolatilizednitrogen (metrictonsN2O‐Nyear‐1)

FSN =Syntheticnitrogenfertilizerapplied(metrictonsNyear‐1)

FRSN =FractionofNSNthatvolatilizesasNH3andNOx.IPCCdefaultTier1=0.10 (metrictonsN(metrictonNsfert)‐1)

FON =Nitrogenfertilizerappliedoforganicoriginincludingmanure,sewagesludge,compostandotherorganicamendments(metrictonsNyear‐1)

FRON =FractionorproportionofFONthatvolatilizesasNH3andNOx.IPCCdefaultTier1=0.2(metrictonsN(metrictonNON)‐1)

EFVOL =EmissionfactorforvolatilizednitrogenorproportionofnitrogenvolatilizedasNH3andNOxthatistransformedtoN2Oinreceivingecosystem;IPCCTier1EF=0.01(metrictonsN2O‐N(metrictonN)‐1)

Equation3‐15:IndirectSoilN2OEmissionsfromMineralSoils—LeachingandRunoff

N2Oleach=(Ni×FRleach)×EFleach

Where:

N2Oleach =IndirectsoilN2Oemittedbyecosystemreceivingleachedandrunoffnitrogen(metrictonsN2O‐Nyear‐1)

Ni =Nitrogeninputs,includingmineralfertilizer,organicamendments,PRPmanureN,residues,andSOMmineralization(metrictonsNha‐1year‐1)(SeeEquation3‐11)

FRleach =FractionorproportionofNithatleachesorrunsoff.IPCCdefaultTier1=0.30excepta)whereirrigation+precipitationislessthan80%ofpotentialevapotranspiration(metrictonsN(metrictonN)‐1)FRleach=0;andb)croppingsystemswithleguminousornon‐leguminouswintercovercrops,forleguminouscovercrops,FRleach=0.18,andfornon‐leguminouscovercrops,FRleach=0.09.

EFleach =EmissionfactorforleachedandrunoffnitrogenorproportionofleachedandrunoffnitrogenthatistransformedtoN2Oinreceivingecosystem;IPCCTier1EF

=0.0075(metrictonsN2O‐N(metrictonN)‐1)


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studies,Tonittoetal.(2006)founda40percentand70percentreductioninnitrateleachingwiththeuseoflegumeandnon‐legumecovercrops,respectively.Accordingly,FRleach,isreducedto0.18forlegumecovercrops(0.3×(1‐0.4);or18%oftotalnitrogeninputs)and0.09fornon‐legumecovercrops(0.3×(1‐0.7);orninepercentoftotalnitrogeninputs).


Calculatingemissionsrequiresthefollowingactivitydataforcroplands:

Areaoflandparcel(i.e.,field); Prior‐yearcroptype,drymatteryields,andresidue‐yieldratiostocalculatecropresidue

nitrogeninput,includingcovercrop(ifpresent); Residuemanagement,includingamountharvested,burned,grazed,orleftinthefield; Syntheticfertilizertype(chemicalformulation)andcoatings(ifpresent); Syntheticandorganicfertilizerapplicationrate,applicationmethod(broadcast,banded,or

injected,includingdepthofinjection),timingofapplication(s); Typeofnitrificationinhibitorapplications(ifused); Tillageimplements,datesofoperation,andnumberofpassesineachoperation(whichcan

beusedtodeterminetillageintensitywiththeSTIRModel),(USDANRCS,2008); Irrigationmethod,applicationrateandtimingofapplications; Totaldrymatteryieldofcrop(metrictonsdrymatteryear‐1),drymattercontentofyield,

andharvestindex;and Covercroptypes,planting,andharvestingdates(ifapplicable).

Themethodforgrazinglandrequiresthefollowingmanagementactivitydata:

Areaoflandparcel(i.e.,field); Prior‐yeargrasstypeanddrymatterproductiontocalculategrassnitrogeninput; Syntheticfertilizertype(chemicalformulation)andcoatings(ifpresent); Organicamendmenttypesandtiming; Syntheticandorganicamendmentapplicationrate,applicationmethod(broadcast,banded,

orinjected,includingdepthofinjection),timingofapplication(s); Pasture/range/paddock(PRP)Nexcreteddirectlyontolandbylivestock(i.e.,manurethat

isnotmanaged); Typeofnitrificationinhibitorapplications(ifused); Tillageimplements,datesofoperation,andnumberofpassesineachoperationwhichcan

beusedtodeterminetillageintensitywiththeSTIRModel,(USDANRCS,2008); Irrigationmethod,applicationrate,andtimingofapplications; Periodsofgrazingduringtheyear; Animaltype,class,andsizeusedforgrazing; Stockingratesandmethods;and Totalyieldofmeat(kgcarcassyieldyear‐1)ormilk(kgfluidmilkyear‐1).

Cropyieldsareprovidedbytheentityforthecropsystem,orpeakforageamountsforgrazingsystems.Insomeyears,theentitymaynotharvestthecropduetodrought,pestoutbreaks,orotherreasonsforcropfailure.Inthosecases,theentityshouldprovidetheaverageyieldthattheyhaveharvestedinthepastfiveyears,andanapproximatepercentageofcropgrowththatoccurredpriortocropfailure.Theyieldisestimatedbasedonmultiplyingtheaveragecropyieldbythepercentageofcropgrowthobtainedpriortofailure.

Tocalculatetheamountofsyntheticfertilizernitrogenappliedtosoils,thetypeoffertilizerappliedanditsnitrogencontentarerequired.Table3‐10providesnitrogencontentinformationforcommontypesofsyntheticfertilizers.


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Pasture/range/paddock(PRP)manureNinputistheNexcreteddirectlyontolandbylivestock,andthemanureisnotcollectedormanaged(deKleinetal.,2006).TheamountofPRPmanureNisestimatedwiththelivestockmethods(SeeChapter5),andassumedtobesplitwith50%oftheNinurineandtheother50%oftheNinsolids.


AncillarydataforestimatingdirectsoilN2Oemissionsfrommineralsoilsincludelandresourceregion,soiltexture,andclimatevariables.Landresourceregioncanbeidentifiedbasedonthegeographiccoordinatesofthefield.SoildataareavailablefromnationaldatasetssuchasSSURGO(SoilSurveyStaff,2011),andaveragegrowingseasonprecipitationandevapotranspirationdataareavailablefromnationalweatherdatasetssuchasPRISM(Dalyetal.,2008).Thesedataareusedbythemodelstodeterminebaseemissionrates.

3.5.4.5 ModelOutput

N2Oemissionsareexpressedbothasthequantityofemissionsandasemissionsintensity—emissionsperunityield,e.g.,gN2OperMggrainoranimalproduct.Reducingtheemissionsintensitycanbeassumedtoavoidemissionsfromindirectland‐usechange.Incontrast,iftheemissionsintensityincreasesduetoalossofyield,thenthereispotentialforadditionallandtobeconvertedintoagriculturetomakeupforayieldloss.


TheprimarylimitationofN2Oestimationmodelsisthattheydependonsurrogatemeasuresthatwillnotallowfluxesforaparticularlocationortimetobepredictedprecisely.Nevertheless,whileitmaybedecades,ifever,beforeannualratesofN2Oemissionsfromaspecificfieldcanbemeasuredwithgreatcertaintyandforlowcost,averageestimatesforsimilarcroppingsystemsandlandscapeswillconvergeasestimatesaggregatetolargerareas.

Table3‐10:NitrogenFractionofCommonSyntheticFertilizers(percentbyweight)

SyntheticFertilizer %NAmmoniumnitrate(NH4NO3) 33.5%Ammoniumnitratelimestone 20.5%Ammoniumsulfate 20.75%Anhydrousammonia 82%Aquaammonia 22.5%Calciumcyanamide(CaCN2) 21%Calciumammonianitrate 27.0%Diammoniumphosphate 18%Monoammoniumphosphate 11%Potassiumnitrate(KNO3) 13%Sodiumnitrate(NaNO3) 16%UreaCO(NH2)2 45%Source:Fertilizer101(2011).

Equation3‐16:SoilN2OEmissionsIntensity

EIN2O=(N2ODirect+N2OIndirect)/Y

Where:

EIN2O =N2Oemissionsintensity (metrictonsCO2‐eqpermetrictondrymattercropyieldorkgcarcassorkgfluid

milk)

N2ODirect =TotaldirectsoilN2Oemission(metrictonsCO2‐eqyear‐1)(SeeEquation3‐8)

N2OIndirect=TotalindirectsoilN2Oemission(metrictonsCO2‐eqyear‐1)(SeeEquation3‐13)

Y =Totalyieldofcrop(metrictonsdrymattercropyieldyear‐1),meat(kgcarcassyieldyear‐1),ormilkproduction(kgfluidmilkyieldyear‐1)


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Limitationsinthemethodalsooccurdueto:

Lackofknowledgeofhowdifferentpracticesaffectfluxesinsomeregionsandcroppingsystems.

LackofknowledgeabouthowsomeofthemanagementpracticesinteractwitheachotherandwithsoilandclimatefactorstoaffectthefundamentalprocessesdrivingN2Oemissions—e.g.,nitrification,denitrification,gasdiffusion,etc.—andincorporationoftheseeffectsintoprocessmodels.

Limitednumberofdatasetscurrentlyavailabletotesttheefficacyofpracticestomitigatefluxesandtoevaluateprocess‐basedmodels.

Limitednumberofdatasetswithmorethantwofertilizerratestoestimatethescalarsforemissionfactorsassociatedwiththebaseemissionrates,particularlythepossibilityfornon‐linearscalars.

ThemineralsoilsmethodassumesaonepercentemissionfactorforindirectN2Oemissionsfromvolatilizednitrogenand0.75percentemissionfactorforleachedNO3‐.However,thereisevidencethattheEFforNO3‐leachingvariesfrom0.75%,dependingonthetypeofwaterway(Beaulieuetal.,2011)anditisalsolikelythatthesoilN2Oemissionsfromatmosphericdepositionofnitrogenwillvarydependingonthenitrogenstatusofthereceivingecosystem.

Thefractionofnitrogenthatisvolatilized(assumedtobe10percentforinorganicnitrogensourcesand20percentfororganicnitrogensourcesinEquation3‐15)isveryuncertain.Likewise,thefractionofnitrogenthatisleachedfromaprofileorrunsoffishighlyuncertain(assumedtobe30percentofallnitrogensourcesexceptwhereprecipitationplusirrigationislessthan80percentofpotentialevapotranspiration;U.S.EnvironmentalProtectionAgency,2011).Experimentssuggestthatgrossgeneralizationsarenotvalidandthatmanypracticescanreducebothvolatilizednitrogenandthenitrogenthatislostbyleachingandrunoff.9

ClimatechangewillaffectmodeloutputinsofarasbaselineN2Oestimatesaresimulatedforanygivenlocationusingtemperatureandprecipitationdistributionsforthepast30years.Expectedchangesintemperature,precipitation,andextremeeventssuchasdroughts,floods,andheatwaveswilladdfurtheruncertaintytoestimatesofallN2Oemissionsandpotentiallyinteractwithscalingfactors.Cropnitrogenmanagementmayfurtherchangewithclimatechange(Robertson,2013).

UncertaintiesinmodelparametersarecombinedusingaMonteCarlosimulationapproach.UncertaintyisassumedtobeminorforthemanagementactivitydataprovidedbytheentityTable3‐11providestheprobabilitydistributionfunctionstoestimateuncertaintyinthedirectandindirectsoilN2Oemissions.DataarenotshownforDNDCandDAYCENToutputthataredelineatedbyLRR,soiltype,andclimate.

9TheIPCCfactorsassumethatthemaximumabovegroundnitrogenrecoverybycropsis50to60percent.However,ratesofnitrogenrecoverycanbesignificantlyhigherwithbestpractices.


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Table3‐11:AvailableUncertaintyDataforDirectandIndirectN2OEmissions

ParameterEstimatedValue Units

EffectiveLowerLimit

EffectiveUpperLimit

Distribution DataSource

TypicaldirectN2Oemissionrateand0‐levelinputratefromprocess‐basedmodel

NS Various NS NSMultiple

distributionsDAYCENT,DNDC

Scalingfactorforslow‐releasefertilizers

‐0.21ProportionalChangeinEmissions

‐0.30 ‐0.12 Normal Appendix3‐A

ScalingfactorforPRPmanureN

+0.5ProportionalChangeinEmissions

0.33 0.67 NormalAppendix3‐

A

Scalingfactornitrificationinhibitors–semi‐arid/aridclimate


‐0.51 ‐0.21 Normal Appendix3‐A

Scalingfactornitrificationinhibitors–mesicclimate


‐0.52 ‐0.24 NormalAppendix3‐

A

Scalingfactorforno‐till,semi‐arid/aridclimate,<10years

0.38ProportionalChangeinEmissions

0.04 0.72 Normal


Scalingfactorforno‐till,semi‐arid/aridclimate,≥10years


‐0.5 ‐0.16 Normal


Scalingfactorforno‐till,mesic/wetclimate,<10years

‐0.015

ProportionalChangeinEmissions

‐0.16 0.16 Normal


Scalingfactorforno‐till,mesic/wetclimate,≥10years


‐0.19 0.01 Normal


BaseEFscalar–croplandfornon‐grasslandcrops

0.0274

(metrictonsN2O‐N(metrictonsN)‐2)ha

year

NormalAppendix3‐

A

BaseEFscalar–forgrasslands 0.117

(metrictonsN2O‐N(metrictonsN)‐2)ha

year

NormalAppendix3‐

A

EmissionrateforcroppedHistosols 0.008

metrictonsN2O‐Nha‐1year‐1

0.002 0.024 Uniform IPCC(2006)

Fractionofsyntheticnitrogen(NSN)thatvolatilizesasNH3andNOx

0.1metrictonsN(metrictonNsfert)‐1

0.03 0.3 Uniform IPCC(2006)


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ParameterEstimatedValue Units

EffectiveLowerLimit

EffectiveUpperLimit


Fractionofnitrogeninorganicamendments(FON)thatvolatilizesasNH3andNOx

0.2metrictonsN(metrictonNON)‐1

0.05 0.5 Uniform IPCC(2006)

EmissionfactorforvolatilizednitrogenasNH3andNOxthatistransformedtoN2O.

0.01metrictonsN2O‐N(metric

tonN)‐10.002 0.05 Uniform IPCC(2006)

FractionofNtthatleachesorrunsoffexceptinsystemswithcovercrops

0.3metrictonsN(metrictonN)‐1 0.1 0.8 Uniform IPCC(2006)

FractionofNtthatleachesorrunsoffwithaleguminouscovercrop

0.18 metrictonsN(metrictonN)‐1

0.14 0.26 Log‐Normal Tonittoetal.(2006)

FractionofNtthatleachesorrunsoffwithnon‐leguminouscovercrop

0.09metrictonsN(metrictonN)‐1

0.06 0.15 Log‐NormalTonittoetal.(2006)

EmissionfactorforleachedandrunoffnitrogenthatistransformedtoN2O

0.0075metrictonsN(metrictonN)‐1 0.0005 0.025 Uniform IPCC(2006)

NS=NotShown.Dataarenotshownforparametersthathave100’sto1000’sofvalues(denotedasNS).Dataareprovidedinsupplementarymaterialavailableonline.


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3.5.5 MethaneUptakebySoils


TherearenoagronomicpracticesknowntoenhanceCH4uptake(oxidation)incroplands,otherthaninwetlandsconvertedtofloodedrice(discussedinSection3.2.2).Agronomicactivityuniversallyreducesmethanotrophyinarablesoilsby70percentormore(Mosieretal.,1991;Robertsonetal.,2000;Smithetal.,2000).RecoveryofCH4oxidationuponabandonmentfromagricultureisslow,probablytaking50to100yearsforthedevelopmentofeven50percentofformer(original)rates(Levineetal.,2011).NorecoveryhasbeendocumentedforCRPgrasslandsorperennialbiofuelcropstodate.TherearecurrentlynomodelsforquantifyingCH4oxidationrecoveryotherthanrateofreversiontonaturalvegetation,sothisisaTier3methodasdefinedbytheIPCC.


Themodelisbasedonaveragevaluesformethaneoxidationinnaturalvegetation—whethergrassland,coniferousforest,ordeciduousforest—attenuatedbycurrentlandusepractices.AveragevaluesarefromthedatasetusedbyDelGrossoetal.(2000a),whoreportedaveragefluxes(±standarddeviation)fortemperateandtropicalgrasslandsoilsof3.2±1.9kgCH4ha‐1year‐1;forconiferousforestsoils,2.8±1.4kgCH4ha‐1year‐1;andfordeciduousforestsoils,11.8±5kgCH4ha‐1year‐1.Managementreducespotential(historic)oxidationto30percentoforiginalratesbasedonavailabledata(DelGrossoetal.,2000a;Mosieretal.,1991;Robertsonetal.,2000;Smithetal.,2000)asnotedinSections3.2.3.3and3.3.2.3.Recoveryofoxidationisassumedtooccurovertheperiodrequiredforecologicalsuccessiontorestoreoriginalvegetation(DelGrossoetal.,2000a;Mosieretal.,1991;Robertsonetal.,2000;Smithetal.,2000),whichisapproximatedat100yearsafterabandonmentfromagricultureorforestharvest.Recoveryisassumedtooccuratalinearrate(Smithetal.,2000)suchthatsuccessionalforestsandgrasslandswillconsumeCH4ataratethatisbetween30and100percentoftheoriginaloxidationcapacitybetweentheinitialyearofabandonmentuntilyear100.Thefollowingequationisusedtoestimatemethaneoxidationforalandparcel:

MethodforEstimatingMethaneUptakebySoil

Methaneuptakebysoilusesanequationbasedonaveragevaluesformethaneoxidationinnaturalvegetation—whethergrassland,coniferousforest,ordeciduousforest—attenuatedbycurrentlandusepractices.

AnnualaverageCH4oxidationfluxesarefromthedatasetusedbyDelGrossoetal.(2000a)whoreviewedaveragefluxesfromgrasslandandagriculturalsoils,coniferousforestsoils,anddeciduousforestsoils.Managementreducespotential(historic)oxidationto30percentoforiginalratesbasedonavailabledata(DelGrossoetal.,2000a;Mosieretal.,1991;Robertsonetal.,2000;Smithetal.,2000).KuchlerpotentialvegetationmapscanbeusedtodeterminethenaturalvegetationacrosstheUnitedStatesiftheentitydoesnothaveinformationforlandparcelsinoperation.

ThisnewlydevelopedmethodologymakesuseofrecentU.S.‐basedresearchthatisnotaddressedbyIPCCortheU.S.Inventory.Themethodincorporatesentityspecificannualdatasuchascurrentmanagementofthelandparcel,cultivationforcropproduction,grazingactivity,recentlyharvestedforests,orfertilizedgrasslandsorforests.


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Thismethodrequireslanduseandtypeofvegetationforthepast80years.KuchlerpotentialvegetationmapscanbeusedtodeterminethenaturalvegetationacrosstheUnitedStates(grassland,coniferousforest,ordeciduousforest)iftheentitydoesnothavethisinformationforlandparcelsintheoperation.Theentitywillneedtoidentifyifthecurrentmanagementofthelandparcelincludescultivationforcropproduction,grazingingrasslands,recentlyharvestedforests,orfertilizedgrasslandsorforests.Assumingtheparceloflandisnotundercultivation,fertilized,grazedgrasslands,orrecentlyharvestedforest,theentitywillneedtoprovidethetimesincethelandhasbeenmanagedwithoneofthesepractices.


Noancillarydataarerequiredforthismethod.

3.5.5.5 ModelOutput

ThemodelprovidesavaluefordiminishedCH4oxidationcapacity.ThechangeinCH4oxidationcapacitywillbenegative,andsothereisnopotentialforincreasedCH4oxidationwiththismethod.Unlikeothermethodsinthissection,theemissionsintensityisnotrelevantforthismethod.


Lackofprecisioninknowledgeofpriorlanduse. UncertaintiesassociatedwithestimatingCH4oxidationratespriortoconversion(PCH4in

Equation3‐17).Inareviewofavailabledata,DelGrossoetal.(2000a)notedannualCH4

oxidationratesof<1.8kgCH4ha‐1year‐1forgrasslandandagriculturalsoils,1.4to4.1kgCH4ha‐1year‐1forconiferousandtropicalforestsoils,and5.3to12kgCH4ha‐1year‐1fordeciduousforestsoils.

Equation3‐17:Methane(CH4)Oxidation

CH4SoilOxidation=(PCH4×AF)×SF×A×CH4GWP

Where:

CH4SoilOxidation=CH4oxidationinsoils(metrictonsCO2‐eqyear‐1)

PCH4 =PotentialCH4oxidationbasedonhistoricnaturalvegetation;grasslands=3.2;coniferousforests=2.8,deciduousforests=11.8(kgCH4ha‐1year‐1)

AF =CH4oxidationattenuationfactor;croplandincludingset‐aside(CRP)grassland,grazingland,andfertilizedorrecentlyharvestedforests=0.30;naturalvegetation,0‐100yearsafterabandonmentofagriculturalproductionortimberharvest=0.3+(0.007×yearssinceabandonment);>100yearspost‐managementorneverusedforagriculturalmanagementortimberharvest=1.0

SF =Scalingfactor,1/1000(metrictonskg‐1)

A =Area(ha)

CH4GWP =GlobalwarmingpotentialofCH4(metrictonsCO2‐eq(metrictonsCH4)‐1)


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Uncertaintyassociatedwiththeattenuationfactor.Inareviewoftemperateregioncomparisonsofpairedsitesinnaturalvegetationvs.agriculturalmanagement,Smithetal.(2000)foundthatagriculturalconversiontocroplandorpasturereducedoxidationby71percentonaverage.

UncertaintiesinmodelparametersarecombinedusingaMonteCarlosimulationapproach.Uncertaintyisassumedtobeminorforthemanagementactivitydataprovidedbytheentity,althoughthismaynotbethecaseifthereislimitedknowledgeaboutland‐usechange.Table3‐12providestheprobabilitydistributionfunctionsassociatedwithestimatinguncertaintyinmethaneoxidation.

Table3‐12AvailableUncertaintyDataforMethaneOxidation

ParameterEstimatedValue

EffectiveLowerLimit

EffectiveUpperLimit


CH4oxidationratespriortoconversion(PCH4)grasslands(kgCH4ha‐1year‐1)

3.2 0 6.9 NormalDelGrossoetal.(2000a)

CH4oxidationratespriortoconversion(PCH4)coniferousforests(kgCH4ha‐1year‐1)

2.8 0.1 5.5 Normal DelGrossoetal.(2000a)

CH4oxidationratespriortoconversion(PCH4)deciduousForests(kgCH4ha‐1year‐1)

11.8 1.9 21.6 Normal DelGrossoetal.(2000a)

CH4oxidationattenuationfactor:croplandincludingset‐aside(CRP)grassland,grazingland,andfertilizedorrecentlyharvestedforests

0.30 0.07 1 Log‐NormalSmithetal.(2000)

CH4oxidationattenuationfactor:naturalvegetation,0‐100yearsafterabandonmentofagriculturalproductionortimberharvest

0.3+(0.007×yearssince

abandonment)

0.07+(0.007×yearssince

abandonment)

1 Log‐NormalSmithetal.(2000)

CH4oxidationattenuationfactor:>100yearspost‐managementorneverusedforagriculturalmanagementortimberharvest

1 0.07 1 Log‐Normal Smithetal.(2000)


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3.5.6 MethaneandNitrousOxidefromFloodedRiceCultivation


ThereareanumberofpossibilitiesforestimatingGHGemissionsfromfloodedricesystems.ProcessbasedmodelsarebeingdevelopedtoquantifyGHGemissions,suchastheDNDC(e.g.,Zhangetal.,2011)andDAYCENTmodels(Chengetal.,2013).While,thesemodelshavebeenevaluatedforvariousregionsandcountriesinAsia,theyhavenotbeensufficientlyevaluatedforU.S.ricesystems,whicharesignificantlydifferentfromthosefoundinAsia(establishmentpractices,residuemanagement,watermanagement,andvarieties).Therefore,theselectedmethodisbasedontheIPCCTier1methodology.WhiletheIPCCmethodologyhasalsobeenlargelydevelopedfromAsianricestudies,itismoretransparentanduncertaintiescanbederivedintheemissionsestimates.Itisanticipatedthattheprocess‐basedmodelsmaybefurthertestedandcalibratedinthenearfutureforU.S.conditionsandpossiblyusedinafutureversionofthesemethods.

SeveralmanagementpracticeshavethepotentialtoinfluenceCH4andN2Oemissionsfromfloodedricesystems.However,therearecurrentlynotenoughdataavailabletoquantitativelyaccountfor(orestablishscalingfactorsfor)theeffectsofallofthesemanagementpractices.Thereissufficientinformationtoaccountfortheinfluenceofwatermanagement,residuemanagement,andorganicamendmentsonCH4emissionsfromfloodedrice(Lascoetal.,2006;Yanetal.,2005).


Methane:Themethodologyassumesabaselineemissionfactoror“typical”dailyrateatwhichCH4isproducedperunitoflandarea.Thisbaselinefactorrepresentsfieldsthatarecontinuouslyfloodedduringthecultivationperiod,notfloodedatallduringthe180dayspriortocultivation,andreceivenoorganicamendments.Differencesbetweenthebaselinescenarioandotherscenariosareaccountedforbytheuseofscalingfactorsthatareusedtoadjustthebaselineemissionfactorfor

MethodforEstimatingMethaneandN2OEmissionsfromRiceCultivation

IPCCequationsdevelopedbyLascoetal.(2006)forCH4anddeKleinetal.(2006)forN2O.‐ ThebaselineemissionfactorortypicaldailyrateatwhichCH4isproducedper

unitoflandarearepresentsfieldsthatarecontinuouslyfloodedduringthecultivationperiod,notfloodedatallduringthe180dayspriortocultivation,andreceivenoorganicamendments.Differencesbetweenthebaselinecontinuouslyfloodedfieldswithoutorganicamendmentsareaccountedforbyscalingfactors(e.g.,waterregimeadjustments(pre‐andduringthecultivationperiod),ororganicamendments).CH4scalingfactorstoaccountforwaterregimesandorganicamendmentscomefromLascoetal.(2006).

‐ N2OemissionfactorsrelyonLascoetal.(2006),andthescalingfactortoaccountfordrainageeffectscomesfromAkiyamaetal.(2005;USDA,2011).

ThismethodusestheIPCC(2006)equationswiththeadditionofascalingfactorforestimatingN2Oemissionsfromdrainage(Akiyamaetal.,2005;U.S.EPA,2011).ThemethodformethaneemissionsusesentityspecificseasonalparceldataasinputintotheIPCCequation.

Thismethodwaschosentominimizeuncertainty.Processmodelswereconsidered,butnotchosenforthismethodduetoaneedforfurtherresearchonU.S.ricecultivationconditionsandpractices.


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theeffectsofwatermanagement(occurringbothbeforeandduringthecultivationperiod)andtheamountoforganicamendments.TherateatwhichCH4isemitteddependsonwaterflooding/drainageregimesandonratesandtypesoforganicamendmentsappliedtothesoil.Assuch,scalingfactorsforabroadrangeofscenariosareprovidedwiththismethodology.Thefactorsaredifferentiatedbyhydrologicalcontext(e.g.,irrigated,rainfed,upland—allricefieldsintheUnitedStatesareirrigated),cultivationperiodfloodingregime(e.g.,continuous,multipleaeration),timesincelastflooding(priortocultivation;e.g.,over180days,under30days)andtypeoforganicamendment(e.g.,compost,farmyardmanure).

ThefollowingequationhasbeenadoptedfromthemethodologydevelopedbytheIPCCtoestimateCH4emissionsfromalandparcel(Lascoetal.,2006):

Thedailyemissionfactorisestimatedbasedontheconditions(i,j,k,etc.)thatinfluenceCH4emissionsforfloodedriceproduction,includingtheecosystemtype,waterregime,andorganicamendmentrate.Asmoredatabecomeavailable,additionalconditionsthatinfluenceCH4emissionsmaybeadded.The“i"intheequationsbelowrepresentsthespecificscenarioor“otherconditions”thatcansignificantlyinfluenceCH4emissionsonaparcel.Inthefuture,additionalscenarioswithfactorsthataffectCH4emissionsmaybeincludedastherelationshipbetweentheseconditionsbecomesclear.Thefollowingequationisusedtoestimatethedailyemissionfactorforalandparcel:

Equation3‐18:Flooded RiceMethaneEmissions

CH4Rice=CH4GWP×Σijk(EFijkxtijkxAijkx10‐3)

Where:

CH4Rice =Annualmethaneemissionsfromricecultivation(metrictonsCO2‐eqyear‐1)

EFijk =Adailyemissionfactorfori,j,andkconditions(kgCH4ha‐1day‐1)

tijk =Cultivationperiodofricefori,j,andkconditions(days)

Aijk =Annualharvestedareaofricefori,j,andkconditions(hayear‐1)

CH4GWP =GlobalwarmingpotentialforCH4(metrictonsCO2‐eq(metrictonsCH4)‐1)

i,j,andk =Representdifferentecosystems,waterregimes,typeandamountoforganicamendments,soiltype,ricecultivar,sulfatecontainingamendments,andotherconditionsunderwhichCH4emissionsfromricemayvary.


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Thescalingfactorfororganicamendmentstoalandparcelisestimatedusingthefollowingequation:

ThescalingfactorsforEquation3‐19andEquation3‐20arefromLascoetal.(2006)andshownbelow.

Table3‐13:RiceWaterRegimeEmissionScalingFactors(DuringCultivationPeriod)

WaterRegimeDuringtheCultivationPeriod(assumesirrigated) SFwContinuouslyflooded 1Intermittentlyflooded–singleaeration 0.6Intermittentlyflooded–multipleaeration 0.52Source:Lascoetal.(2006),Table5.12.

Table3‐14:RiceWaterRegimeEmissionScalingFactors(BeforeCultivationPeriod)

WaterRegimeBeforetheCultivationPeriod SFpNonfloodedpre‐season<180days 1Nonfloodedpre‐season>180days 0.68Floodedpre‐season>30days 1.9Source:Lascoetal.(2006),Table5.13.

Equation3‐19:Flooded RiceMethaneEmissionFactor

EFi=EFcxSFwxSFpxSFoxSFs,r

Where:

EFi=adjusteddailyemissionfactorforaparticularharvestedarea(kgCH4ha‐1day‐1)

EFc=baselineemissionfactorforcontinuouslyfloodedfieldswithoutorganicamendments(kgCH4ha‐1day‐1)

SFw=scalingfactortoaccountforthedifferencesinwaterregimeduringthecultivationperiod(fromLascoetal.2006,Table5.12)(unitless)

SFp=scalingfactortoaccountforthedifferencesinwaterregimeinthepre‐seasonbeforethecultivationperiod(fromLascoetal.2006,Equation5.3andTable5.14)(unitless)

SFo=scalingfactorshouldvaryforbothtypeandamountoforganicamendmentapplied(Equation3‐20)(unitless)

SFs,r=scalingfactorforsoiltype,ricecultivar,etc.,ifavailable

Equation3‐20:OrganicAmendmentsScalingFactor

SFo=(1+(ROAixCFOAi))0.59

Where:

SFo =scalingfactorforbothtypeandamountoforganicamendment

ROAi=rateofapplicationoforganicamendment(s)(metrictonsha‐1)

CFOAi =conversionfactorfororganicamendments(fromLascoetal.2006,Table5.14)(unitless)


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Table3‐15:RiceOrganicAmendmentEmissionScalingFactors;adaptedfromLascoetal.(2006)

OrganicAmendments CFOAStrawincorporatedshortly(<30days)beforecultivation 1Strawincorporatedlong(>30days)beforecultivation 0.29Compost 0.05Farmyardmanure 0.14Greenmanure 0.50Source:Lascoetal.(2006),Table5.14.

SoilN2O:TheIPCCmethodology(deKleinetal.,2006)hasbeenadaptedtoestimatedirectN2Oemissionsfromricefields.Theemissionfactorforricesoilsaccountsfornitrogenadditionsfrommineralfertilizers,organicamendments,andcropresidues.Notethataneffectofnitrogenmineralizedfrommineralsoilasaresultoflossofsoilcarbonisnotincludedinthisequation.Floodedricecultivationleadstominimallossesofsoilcarbonduetoperiodicflooding,whichisthedefaultassumptionwiththeIPCCmethod(Lascoetal.,2006),andthereforeitisnotnecessarytoincludetheeffectofenhancednitrogenmineralizationfromlossofsoilC.

ThefollowingequationisusedtoestimatethesoilN2Oemissionsfromaparcelofland:

TheemissionfactorandSFDfactorsarebasedonresearchconductedbyAkiyamaetal.(2005).TheIPCC(2006)doesnotaccountfordifferencesinwatermanagement,andusesanemissionfactorof0.3,butAkiyamaetal.(2005)providefurtherdisaggregationoftheemissionfactorsbasedonwatermanagement.Therefore,theselectedemissionfactorvalueis0.0022basedonAkiyamaetal.(2005),andthescalingfactorsare0forcontinuouslyfloodedriceand0.59foraeratedsystems(i.e.,drainageeventsduringthegrowingseason).

IndirectN2OEmissions:ForindirectN2Oemissionsfromfloodedrice,thesamemethodisusedasdescribedinSection3.5.4.2,byapplyingEquation3‐13,TotalIndirectSoilN2OEmissionsfromMineralSoils;Equation3‐14,IndirectSoilN2OEmissionsfromMineralSoils—Volatilization;andEquation3‐15,IndirectSoilN2OEmissionsfromMineralSoils—LeachingandRunoff.Inthelatter

Equation3‐21:DirectSoilN2OEmissionsfromfloodedRice

N2ORice=Nt×EF×(1+SFD)×N2OMW×N2OGWP

Where:

N2ORice=DirectemissionsofN2Ofromsoilsinfloodedriceproductionsystems(metrictonsCO2‐eqyear‐1)

Nt =Totalnitrogeninputsfromallagronomicsources:mineralfertilizer,organicamendments,residues,andadditionalmineralizationfromland‐usechangeortillagechange(metrictonsNyear‐1)

EF =EmissionfactororproportionofNttransformedtoN2O(kgN2O‐N(kgN)‐1)

SFD =Scalingfactortoaccountfordrainageeffects;0forcontinuouslyflooded(dimensionless)

N2OMW=RatioofmolecularweightsofN2OtoN2O‐N=44/28(metrictonsN2O(metrictonsN2O‐N)‐1)

N2OGWP=GlobalwarmingpotentialforN2O(metrictonsCO2‐eq(metrictonsN2O)‐1)


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twoequations,usetheIPCCdefaultfractionsforFRSN,FRON,andFRleach,whichareprovidedintheequationboxes.


Theactivityandrelateddatarequirementsforthismethodinclude:

Harvestedarea(ha); Cultivationperiodindays; Watermanagementpracticesthroughouttheyear(e.g.,aerationornot); Organicmatteramendment(includingresidue)rate; OrganicfertilizerN; Fertilizernitrogenmanagement(rate); Typeoffertilizer(s)applied(qualitative); CropresidueN;and Cropyield,metrictonsdrymattercropyieldyear‐1.


Noancillarydataareneededforthismethod.

3.5.6.5 ModelOutput

ModeloutputisthecombinedemissionsofCH4andN2OinCO2equivalents,expressedonanareabasis.TheintensityofCH4emissionsandnitrousoxide(i.e.,emissionsperunitoflandareacultivated)isrelatedtothequantityofcropsgrownandcanbeestimatedwiththefollowingequation:


Thismethodhasseverallimitationsthatwillpotentiallycreatebiasorimprecisionintheresults.Currently,scalingfactorsaccountonlyforwaterandorganicmattermanagementanddonotaccountforothermitigationoptions.Asindicatedearlierthereareothermanagementopportunitiesthatmayreduceemissions,butfurtherresearchisrequiredintheseareas.Baselineemissionsarehighlyvariable,butthismethodologyprovidesonlyonefactorvaluerepresentingthebaselineemissions.Inaddition,themethodologyassumesaperiodofdrainage;however,drainevents(eventhoseofsimilarduration)canvarymarkedlybasedonsoilandclimaticconditions,fromdryandcrackingonthesurfacetosaturatedattheendofadrainageevent.Theinfluenceofdrainageonthesoilsaturationisnotaddressedwiththecurrentmethod.Inaddition,thereiscurrentlyinsufficientinformationtodevelopamethodfortheuseofsulfurproductsasamendments;futureguidancemaybeupdatedwithamethodforthispractice.

Equation3‐22:FloodedRiceCombinedMethaneandNitrousOxideEmissionsIntensity

EI=(CH4Rice+N2ORice)/Y

Where:

EI =Emissionsintensity(metrictonsCO2‐eqpermetrictonsdrymattercropyield)

CH4Rice=Annualmethaneemissionsfromricecultivation(metrictonsCO2‐eqyear‐1)

N2ORice=DirectemissionsofN2Ofromsoilsinfloodedriceproductionsystems(metrictonsCO2‐eq‐year‐1)

Y =Totalyieldofcrop(metrictonsdrymattercropyieldyear‐1)


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CH4emissionsaretheresultofanumberofinteractingbiologicalprocesses,whichbynaturevaryspatiallyandtemporally.Thegreatestamountofuncertaintyisthebaselineemissionfactor.Whenusingthismethodology,theemissionfactorisanaverageemissionfactorforcontinuouslyfloodedricesystemsthathavenotbeenfloodedthe180dayspriortocultivationandhavenotreceivedorganicamendments.InthecaseofCH4emissionsfromricecultivation,theuncertaintyrangesofTier1values(emissionandscalingfactors)areadopteddirectlyfromLascoetal.(2006).Rangesaredefinedasthestandarddeviationaboutthemean,indicatingtheuncertaintyassociatedwithagivendefaultvalueforthissourcecategory.

UncertaintiesinmodelparametersarecombinedusingaMonteCarlosimulationapproach.Uncertaintyisassumedtobeminorforthemanagementactivitydataprovidedbytheentity.Table3‐16providestheprobabilitydistributionfunctionsassociatedwithestimatinguncertaintyinmethaneandN2Oemissionsfromricecultivation.

Table3‐16:AvailableUncertaintyDataforMethane,DirectandIndirectN2OEmissions

MethanefromFloodedRiceCultivation

ParameterAbbreviation/

SymbolEstimatedValue

EffectiveLowerLimit

EffectiveUpperLimit

DistributionDataSource

Baselineemissionfactorforcontinuouslyfloodedfieldswithoutorganicamendments

EFc 1.3 0.8 2.2 UniformIPCC(2006)

Waterregimeduringthecultivationperiod–Scalingfactor

SFwforcontinuouslyflooded

1 0.79 1.26 UniformIPCC(2006)


SFwforsingleaeration

0.6 0.46 0.8 UniformIPCC(2006)


SFwformultipleaerations

0.52 0.41 0.66 UniformIPCC(2006)

Waterregimebeforethecultivationperiod–Scalingfactor

SFpfornon‐floodedpre‐season<180

days1 0.88 1.14 Uniform IPCC

(2006)


SFpfornon‐floodedpre‐season>180

days0.68 0.58 0.8 Uniform IPCC

(2006)


SFpforfloodedpre‐season>30days

1.9 1.65 2.18 Uniform IPCC(2006)

Organicamendmentconversionfactor

CFOAiforstrawincorporationlessthan30daysbefore

cultivation

1 0.97 1.04 Uniform IPCC(2006)


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MethanefromFloodedRiceCultivation (continued)


SymbolEstimatedValue

EffectiveLowerLimit

EffectiveUpperLimit Distribution

DataSource


CFOAiforstraw

incorporationmorethan30daysbeforecultivation

0.29 0.2 0.4 Uniform IPCC(2006)


CFOAiforcompost

0.05 0.01 0.08 UniformIPCC(2006)


CFOAiforfarmyardmanure 0.14 0.07 0.2 Uniform

IPCC(2006)


CFOAiforgreenmanure

0.5 0.3 0.6 Uniform IPCC(2006)

N2OfromFloodedRice


SymbolMean

RelativeUncertaintyLow(%)

RelativeUncertaintyHigh(%)

DistributionDataSource

EmissionfactororproportionofNttransformedtoN2O

EF 0.0022 0.24% 0.24% Normal Akiyamaetal.(2005)

Scalingfactortoaccountfordrainageeffects

SFDforaeratedsystems 0.59 0.35% 0.35% Normal

Akiyamaetal.(2005)

3.5.7 CO2fromLiming


AdditionoflimetosoilsistypicallythoughttogenerateCO2emissionstotheatmosphere(deKleinetal.,2006).However,prevailingconditionsinU.S.agriculturallandsleadtoCO2uptakebecausethemajorityoflimeisdissolvedinthepresenceofcarbonicacid(H2CO3).Therefore,theadditionoflimeleadstoacarbonsinkinthemajorityofU.S.croplandandgrazinglandsystems.Whetherlimingcontributestoasinkorsourcedependsonthepathwaysofdissolutionandratesofbicarbonateleaching.Theemissionsfactorprovidedinthisguidancehasbeenestimatedfroma

MethodforEstimatingCO2 EmissionsfromLiming

ThismethodusestheIPCCequation(deKleinetal.,2006)withU.S.specificemissionsfactors.

EntityspecificannualparceldataasinputintotheIPCCequation(e.g.,theamountoflime,crushedlimestone,ordolomiteappliedtosoils).

ThismethodwasselectedasitwastheonlyreadilyavailablemodelforestimatingCO2

emissionsfromliming.


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reviewofexistingmodelsandmassbalanceanalysesconductedfortheapplicationoflimeintheUnitedStatesandisaTier2methodasdefinedbytheIPCC.

Sincecrushedlimestone(CaCO3)contains12percentC,anapplicationof1,000kgCaCO3places120kgConthesoilsurface.Itisassumedthattwo‐thirdsofthis(80kg)isacidifiedtoHCO3‐andleachedtotheoceanwhereitwillbesequesteredfordecadestocenturies(OhandRaymond,2006).Becausethistransferrepresentsamovementfromonelong‐termpool(geologicformations)toanother(ocean),thiscarbontransferdoesnotrepresentanetuptakeofCO2fromtheatmosphere.However,withthistransfer,thereis80kgCofatmosphericCO2uptakeintosoils.TheuptakeofCO2fromtheatmosphere,aftersubtractingtheone‐thirdofcarboninthelimethatisacidifieddirectlytoCO2(40kgC),yieldsatotalnetCO2uptakeof40kgCper1,000kgCaCO3applied.Thisresultsinacarboncoefficientoremissionfactorof40/1000=‐0.04kgCperkgCaCO3.Thisequatestoacarbonsink(40kgCsequestered/120kgC×100).DolomitecontainsonlyslightlymorecarbonthandoesCaCO3(13percentvs.12percent)sothefactorsareessentiallythesame.

Theemissionfactoriscountry‐specificbasedonarevisionoftheestimatesproposedinWestandMcBride(2005),whicharecurrentlyusedintheU.S.NationalGHGInventory(U.S.EPA,2011).TheunderlyingdifferencewiththeearlieremissionfactorfromWestandMcBride(2005)isthattherevisedvalueassumesthattheamountofbicarbonatecarriedintorivershasalongturnovertimeandisessentiallynotreturnedtotheatmosphereoverdecadaltocenturytimescales.


ThemodeltoestimateCO2emissionsfromliminghasbeenadaptedfrommethodsdevelopedbytheIPCC(deKleinetal.,2006),withrefinementintheemissionfactorsbasedonconditionsinU.S.agriculturallands.Thefollowingequationisusedtoestimateemissionsfromcarbonatelimeadditionstoalandparcel:


Themethodrequiresdataontheamountoflime(crushedlimestoneordolomite)appliedtosoils.


Noancillarydataareneededinordertoapplythemethod.

Equation3‐23:ChangeinSoilCarbon StocksfromLimeApplication

ΔCLime=M×EF×CO2MW

Where:

ΔCLime =Annualchangeinsoilcarbonstocksfromlimeapplication(metrictonsCO2‐eq)

M =Annualapplicationoflimeascrushedlimestoneordolomite

(metrictonsofcrushedlimestoneordolomiteyear‐1)

EF =MetrictonCO2emissionspermetrictonoflime‐0.04

(metrictoncarbon(metrictonlime)‐1)

CO2MW =RatioofmolecularweightofCO2tocarbon(44/12)(metrictonsCO2(metrictonsC)‐1)


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3.5.7.5 ModelOutput

Modeloutputisgeneratedonbothanabsolutequantityofemissionsandemissionsintensity.Thelatterisbasedontheamountofemissionsperunitofyieldforcropsincroplandsystemsorgrazingsystems.Theemissionsintensityisestimatedwiththefollowingequation:

Yieldsarebasedonthetotalamountofproductfromthelandmanagedwithlimeapplication.


LimitationsincludevariationinsoilcarbonemissionsduetosoilpHandrateofnitrogenfertilizerapplication,whichinfluencethechemicalpathwayoflimedissolution(Hamiltonetal.,2007;WestandMcBride,2005).Morespecifically,theEFwillnotaccuratelycapturetheresultoflimedissolutioninthepresenceofstrongernitricacid(HNO3),whichisproducedwhennitrifyingbacteriaconvertammonium(NH4+)basedfertilizerandothersourcesofNH4+tonitrate(NO3‐).

Uncertaintiesinthelimeemissionsmethodsincludeimprecisionatthefarmscale,becausethemethodofestimationisbasedonstream‐gaugedatathatarecollectedatthewatershedscale.UncertaintiesinmodelparametersarecombinedusingaMonteCarlosimulationapproach.Uncertaintyisassumedtobeminorforthemanagementactivitydataprovidedbytheentity.Table3‐17providestheprobabilitydistributionfunctionsassociatedwithCO2emissionspermetrictonoflimeapplied.

Table3‐17:AvailableUncertaintyDataforCO2fromLiming

Parameter MeanRelative

UncertaintyLow(%)



Emissionsfactor(metrictonCO2emissionspermetrictonoflime)

‐0.04 46% 46% NormalAdaptedfromWestand

McBride(2005)

Equation3‐24:EmissionsIntensityfromLimeApplication

EI=ΔCLime/Y

Where:

EI =Emissionsintensity(metrictonsCO2permetrictondrymattercropyield)

ΔCLime =Annualchangeinsoilcarbonstocksfromlimeapplication(metrictonsCO2)



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3.5.8 Non‐CO2EmissionsfromBiomassBurning


Non‐CO2GHGemissionsfrombiomassburningincludeCH4andN2O.COandNOxarealsoemittedandareprecursorsthatarelaterconvertedintoGHGsfollowingadditionalreactions(i.e.,releaseofthesegasesleadstoGHGformation).CO2isalsoemittedbutnotaddressedforcropresiduesorgrasslandburningbecausethecarbonisreabsorbedfromtheatmosphereinnewgrowthofcropsorgrasseswithinanannualcycle.

Therehasbeenlimiteddevelopmentandtestingofprocess‐basedapproachesforestimatingnon‐CO2GHGemissionfrombiomassburning.Moreover,country‐specificdataarelimitedontheamountofnon‐CO2GHGemissions.Therefore,thisguidancehasadoptedtheIPCCTier1methodasdescribedbyAaldeetal.(2006).


Themodeltoestimatenon‐CO2GHGemissionsandprecursorshasbeenadaptedfrommethodsdevelopedbyIPCC(Aaldeetal.,2006).Thefollowingequationisusedtoestimateemissionsduetoburningbiomassonaparcelofland:

Combustionefficiency,asdefinedinIPCC(2006)combinestheproportionofbiomassthatisactuallyburnedinafirewiththeamountofcarbonreleasedasaproportionofthetotalcarbonintheburnedbiomass.Themassofthefuelcombustedincludesliveanddeadbiomass(i.e.,dead

MethodforEstimatingNon‐CO2 EmissionsfromBiomassBurning

ThemethodusestheIPCCequationandemissionfactorsdevelopedbyAaldeetal.(2006).

Entityspecificannualparceldata(e.g.,areaburnedforcroplandsandgrazingland;croptypeandharvestyielddata;residue‐yieldratios(Westetal.,2010);typeofforage,grazingarea,andamountofbiomassbeforethefireingrazinglandsthatareburned;andcombustionefficiency)areinputstotheIPCCequation.

Thismethodwasselectedasitwastheonlyreadilyavailablemodelforestimatingnon‐CO2emissionsfrombiomassburning.

Equation3‐25:GHGEmissionsfromBiomassBurning

GHGBiomassBurning=A×M×C×EF×10‐3×GHGGWP

Where:

GHGBiomassBurning=AnnualemissionsofGHGorprecursorduetobiomassburning(metrictonsofCO2‐eqyear‐1)

A =Areaburned(ha)

M =Massoffuelavailableforcombustion(metrictonsdrymatterha‐1year‐1)

C =Combustionefficiency,dimensionless

EF =Emissionfactor(gGHG(kgofburnedbiomass)‐1)

GHGGWP =GlobalwarmingpotentialforeachGHG(metrictonsCO2‐eq(metrictonsGHG)‐1)


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biomassincludesplantresiduesingrazingandcroplandsystems)andisapproximatedforalandparcelwiththefollowingequation:

PeakabovegroundbiomassisestimatedwithEquation3‐3forcropsandgrassvegetation.Forcroplandsthatareburnedfollowingharvest,theresiduemassisestimatedbysubtractingtheharvestindex(HI)fromoneandconvertingtoapercentage,whichistheresidualbiomassleftinthefield.DefaultharvestindicesaregiveninTable3‐5.TheestimatedmassoffuelforgrazingsystemsbasedonEquation3‐3doesnotincludethedeadbiomass.Ifthereissignificantresiduallitteringrazingsystems,thenmultiplythemassoffuelbytwoasaconservativeestimateofthetotalliveanddeadbiomassonthelandparcel.Alternatively,entitiesmayenteranestimatefortheproportionofresiduallittermassrelativetothelivebiomass,insteadofusingtwo,whichdoublesthemassoffuel.AsummaryofemissionfactorsbylandusecategoryisprovidedinTable3‐18.


Thefollowingactivityandrelateddataareneededtoapplythemethod:

Areaburnedforcroplandsandgrazingland; Croptypeandharvestyielddataforcropsgrown

infieldswithresidueburningmanagement; Residue:yieldratios(optional); Typeofforage,grazingarea,andamountof

biomassbeforethefireingrazinglandsthatareburned;and

Combustionefficiency(optional).

Alistofdefaultcombustionefficienciesisprovidedforresiduesandforages(Table3‐19andTable3‐20),buttheentitycanprovidevaluespecifictotheiroperation.Defaultdrymattercontentsandresidue‐yieldratiosareprovidedinTable3‐5,butcanalsobeenteredbytheentityiftheinformationisavailable.

Table3‐18:EmissionFactorsforBiomassBurning

Land‐UseCategoryCO CH4 N2O NOx

(gkg‐1)

Grasslandburning 65 2.3 0.21 3.9

Croplandresidue 92 2.7 0.07 2.5

Forestbiomass(withconversiontocroplandorgrazinglands)

107 4.7 0.26 3.0

Source:Aaldeetal.(2006).

Table3‐19:DefaultCombustionEfficienciesforSelectedCrops

Crop CombustionEfficiency(C)

Corn 0.88x0.93=0.82Cotton 0.88x0.93=0.82Lentils 0.88x0.93=0.82Rice 0.88x0.93=0.82Soybeans 0.88x0.93=0.82Sugarcane 0.68x0.81=0.55Wheat 0.88x0.93=0.82Source:EPA(2013),Table6‐25.

Equation3‐26:MassofFuel

M=(Hpeak/C)×(D/100)

Where:

M =Massoffuelavailableforcombustion(metrictonsdrymatterha‐1year‐1)

Hpeak =Annualpeakabovegroundherbaceousbiomasscarbonstock(metrictonsCha‐1year‐1)

C =Carbonfractionofabovegroundbiomass(dimensionless)

D =Percentageofbiomasspresentatthestageofburningrelativetopeak(%)


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Insomeyears,theentitymaynotharvestthecropduetodrought,pestoutbreaks,orotherreasonsforcropfailure.Inthosecases,theentityshouldprovidetheaverageyieldthatithasharvestedinthepast,andanapproximatepercentageofaveragecropgrowththatoccurredpriortoburning.Theyieldisestimatedbasedonmultiplyingtheaveragecropyieldbythepercentageofcropgrowthobtainedpriortoburning.



3.5.8.3 ModelOutput

Modeloutputisgeneratedonbothanabsolutequantityofemissionsandemissionsintensity.Thelatterisbasedontheamountofemissionsperunitofyieldforcropsincroplandsystemsoranimalproductsingrazingsystems.Theemissionsintensityisestimatedwiththefollowingequation:

Yieldsarebasedonthetotalamountofproductfromthelandmanagedwithburning.

Table3‐20:DefaultCombustionEfficienciesforSelectVegetationTypes

VegetationTypeCombustionEfficiency(C)

BorealForest(all) 0.34Wildfire 0.40

Crownfire 0.43Surfacefire 0.15

Postloggingslashburn 0.33Landclearingfire 0.59

TemperateForest(all) 0.45Postloggingslashburn 0.62

Felledandburned(land‐clearingfire) 0.51Shrublands(all) 0.72

Shrubland(general) 0.95Calluna health 0.71

Fynbos 0.61Savannawoodlands(earlydryseasonburns)(all)

0.40

Savannawoodland(early) 0.22Savannaparkland(early) 0.73

Savannawoodlands(mid/latedryseasonburns)(all)

0.74

Savannawoodland(mid/late) 0.72Savannaparkland(mid/late) 0.82

Tropicalsavanna 0.73Othersavannawoodlands 0.68

Savannagrasslands(earlydryseasonburns)(all)

0.74

Tropical/sub‐tropicalgrassland 0.74SavannaGrasslands/Pastures(mid/latedryseasonburns)(all) 0.77

Tropical/sub‐tropicalgrassland 0.92Tropicalpasture 0.35

Savanna 0.86Source:Aaldeetal.(2006),Table2.4(C×M)andTable2.6(C)

Equation3‐27:BiomassBurningEmissionsIntensity

EI=GHGBiomassBurning/Y

Where:

EI =Emissionsintensity(metrictonsCO2permetrictondrymattercropyield,metrictonsCO2perkgcarcassyield,metrictonsCO2perkgfluidmilkyield)

GHGBiomassBurning=AnnualCO2equivalentemissionsfromburning(metrictonsCO2‐eqyear‐1)



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Uncertaintyintheemissionestimatesisattributedtoimprecisionincarbonfractions,drymattercontents,harvestindices,combustionefficiencies,andtheemissionfactors.UncertaintiesinmodelparametersarecombinedusingaMonteCarlosimulationapproach.Uncertaintyisassumedtobeminorforthecropyields,peakforage,andrelativeamountofcroporforagegrowthcomparedtothepeakproduction.However,thesevaluesarelikelytohavesomelevelofuncertainty,andmethodswillneedtoberefinedinthefuturetobetteraddresstheseuncertainties,particularlythemassoffuelingrazinglands.Table3‐21providestheprobabilitydistributionfunctionsforestimatinguncertaintyinnon‐CO2emissionsfrombiomassburning.

Table3‐21:AvailableUncertaintyDataforNon‐CO2EmissionsfromBiomassBurning


UncertaintyLow(%)



CH4EFforgrassland(gCH4kg‐1)

2.3 8% 8% Normal IPCC(2006)

CH4EFforcropresidue(gCH4kg‐1)

2.7 50% 50% Normal IPCC(2006)

N2OEFforgrassland(gN20kg‐1)

0.21 93% 93% Normal IPCC(2006)

N2OEFforcropresidue(gN20kg‐1)

0.07 50% 50% Normal IPCC(2006)

Combustionefficiencyforshrublands

0.72 68% 68% Normal IPCC(2006)

Combustionefficiencyforgrasslandswithearlyseasonburns

0.74 50% 50% Normal IPCC(2006)

Combustionefficiencyforgrasslandswithmidtolateseasonburns

0.77 66% 66% Normal IPCC(2006)

Combustionefficiencyforsmallgrains

0.9 50% 50% NormalExpert

Assessmentbyauthors

Combustionefficiencyforlargegrainandothercropresidues

0.8 50% 50% NormalExpert

Assessmentbyauthors

CombustionefficiencyBorealforest(all) 0.34 102% 102% Normal IPCC(2006)

Wildfire 0.40 340% 340% Normal IPCC(2006)Crownfire 0.43 104% 104% Normal IPCC(2006)Surfacefire 0.15 96% 96% Normal IPCC(2006)

Postloggingslashburn 0.33 130% 130% Normal IPCC(2006)CombustionefficiencyTemperateforest(all) 0.45 51% 51% Normal IPCC(2006)

Postloggingslashburn 0.62 264% 264% Normal IPCC(2006)CombustionefficiencyShrublands(all)

0.72 147% 147% Normal IPCC(2006)

Callunahealth 0.71 121% 121% Normal IPCC(2006)Fynbos 0.61 195% 195% Normal IPCC(2006)

CombustionefficiencySavannawoodlands(earlydryseasonburns)(all)

0.40 93% 93% Normal IPCC(2006)


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UncertaintyLow(%)



CombustionefficiencySavannawoodlands(mid/latedryseasonburns)(all)

0.74 99% 99% Normal IPCC(2006)

Savannawoodland(mid/late)

0.72 270% 270% Normal IPCC(2006)

Tropicalsavanna 0.73 598% 598% Normal IPCC(2006)Othersavannawoodlands 0.68 931% 931% Normal IPCC(2006)CombustionefficiencySavannagrasslands(earlydryseasonburns)(all)

0.74 183% 183% Normal IPCC(2006)

Tropical/sub‐tropicalgrassland

0.74270% 270% Normal IPCC(2006)

Tropical/sub‐tropicalgrassland

0.92151% 151% Normal IPCC(2006)

Tropicalpasture 0.35 427% 427% Normal IPCC(2006)Savanna 0.86 85% 85% Normal IPCC(2006)

3.5.9 CO2fromUreaFertilizerApplications


UreafertilizerapplicationtosoilscontributesCO2emissionstotheatmosphere.ThesourceoftheCO2thatisincorporatedintotheureaduringthefertilizerproductionprocessisfromfossilfuelsourcesintheU.S.fertilizerplants.TheCO2capturedduringtheproductionprocessisconsideredanemissionsremovalinthemanufacturer’sreportingsoitsreleasefollowingureafertilizationonsoilsisincludedinthefarm‐scaleentityreporting.IfmanufacturersdonotestimateCO2captureduringureaproductionandincludetherecapturedCO2asanemission,thereisnoneedforafarm‐scaleentitytoreportrelease.

TheTier1methodhasbeenadoptedfromtheIPCC(deKleinetal.,2006).Noothermethodshavebeendevelopedortestedsufficientlyforanoperationalsystem.


ThemodeltoestimateCO2emissionsfromureaapplicationhasbeenadoptedfromthemethodologydevelopedbytheIPCCandusestheIPCCdefaultemissionfactor(deKleinetal.,2006).ThefollowingequationisusedtoestimatetheCO2emissionfromalandparcelwhereurea‐basedfertilizershavebeenapplied:

MethodforEstimatingCO2 EmissionsfromUreaFertilizerApplication

ThismethodusesIPCCequationandemissionfactorsdevelopedbydeKleinetal.(2006). ThismethodusesentityspecificannualparceldataasinputintotheIPCCequation(e.g.,

theamountofureafertilizerappliedtosoils). ThismethodassumesthatthesourceofCO2usedtomanufactureureaisfossilfuelCO2

capturedduringNH3manufacture.


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Thismethodrequiresdataontheamountofureafertilizerappliedtosoils.



3.5.9.5 ModelOutput

Modeloutputisgeneratedonbothanabsolutequantityofemissionsandemissionsintensity.Thelatterisbasedontheamountofemissionsperunitofyieldforcropsincroplandsystemsoranimalproductsingrazingsystems.Theemissionsintensityisestimatedwiththefollowingequation:

Yieldsarebasedonthetotalamountofproductfromthelandmanagedwithureaapplication.


Urea(CO(NH2)2)isconvertedintoammoniumandCO2inthepresenceofwaterandtheenzymeurease.TheCO2willdissolveinwatertoformcarbonate,bicarbonate,andcarbonicacidasafunctionofsoilpHandtemperature.Someofthebicarbonatemaybetransferredtogroundwater,waterways,andeventuallytheocean,andthereforereducetheCO2emissionstotheatmosphere(deKleinetal.,2006;Hamiltonetal.,2007)).However,thereisinsufficientinformationavailabletoincludethispossibilityintheureamethod,soitisassumedthatanyincreaseinbicarbonatewillleadtoproductionofCO2.

Equation3‐28:CO2 EmissionsfromUreaFertilization

CUrea=M×EF×CO2MW

Where:

CUrea =Annualreleaseofcarbonfromureaaddedtosoil(metrictonsCO2‐eqyear‐1)

M =Annualamountofureafertilization(metrictonsureayear‐1)

EF =Emissionfactororproportionofcarboninurea,0.20 (metrictonC(metrictonurea)‐1)

CO2MW=RatioofmolecularweightofCO2tocarbon(44/12) (metrictonsCO2(metrictonsC)‐1)

Equation3‐29:EmissionsIntensityfromUreaFertilization

EIUrea=CUrea/Y

Where:

EIUrea=Emissionsintensity(metrictonsCO2permetrictondrymattercropyield,metrictonsCO2perkgcarcassyield,metrictonsCO2perkgfluidmilkyield)

CUrea =Annualchangeinsoilcarbonstocksduetoureaapplication(metrictonsCO2year‐1)



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Uncertaintyisassumedtobeminorforthemanagementactivitydataprovidedbytheentity,althoughthismaynotbethecaseifthereislimitedknowledgeaboutlandusehistoryforindividualparcels.Uncertaintymayalsoexistintheemissionfactor,assumingthatsomeofthebicarbonateisnotconvertedtoCO2.However,themethodassumesallCO2isemittedbecauseuncertaintyestimatesarenotavailableforthisemissionfactor.Therefore,nouncertaintyisestimatedforthissourceofGHGemissionsbasedonthisconservativeassumptionthatallCO2isemitted.

3.6 SummaryofResearchGapsforCropandGrazingLandManagement

ThissectiondiscussesresearchgapsassociatedwithcroplandandgrazinglandmanagementimpactsonsoilcarbonstockchangesandGHGemissions.Thelistisnotnecessarilyexhaustive,buthighlightssomekeygapsthatwillneedfurtherresearchbeforethereissufficientevidenceforadditionalcriteriatobeincludedinthemethodology.Ingeneral,themajorityofpriorexperimentaleffortshavefocusedoncomponentsofGHGs,butfewstudieshavebeenconductedontotalGHGbudgetstoincludeCO2,N2O,andCH4incombination,whichisneededtoquantifyinteractingeffectsonthenetemissionsofthesegases(Liebigetal.,2010).Inaddition,limitedresearchhasbeenconductedtoaddresstheinfluenceofcatastrophicweathereventsonGHGemissions,suchasmajorfloods,tornadoes,andhurricanes.

CarbonStocks:10Thefollowingprocessesandpracticesrequirefurtherstudytoimprovethefundamentalunderstandingorfilldatagapsinthecarboninventorymethods.Inparticular,deficienciesinunderstandingcontinuetounderminethedevelopmentofrobustestimatesofnetGHGemissionsinrangelandsandpastures.Suchdeficienciesstemfromalackofmeasurementsacrossthemajorgrasslandecoregions,aswellaslimitationsassociatedwithbasicunderstandingofmechanisticprocessesrelatedtoGHGfluxes.Therearealsomajorgapswithrespecttoagroforestry,woodyplantencroachment,andperennialwoodycropsystems.

Moredataonallometricrelationshipsforagroforestry,woodyplantencroachment,andperennialwoodycropsystems,suchasorchards.

Improvedabilitytoquantifytheinfluenceofagroforestry,woodyplantencroachment,andperennialwoodycropsonsoilorganiccarbonstocks,includingoptimaldensityoftrees,thetypeoftrees,andthelandscapepositionofsilvopasturesystems.

Improvedmechanisticunderstandingandabilitytoquantifythefateofcarbonwithtransportandsedimentationfollowingerosionevents.

Fieldestimatesoftheamountofcarbonaddedtosoilsthroughdynamicreplacementonerodiblelands.

Improvedmechanisticunderstandingofcarbondynamicsinthesubsoilhorizons. Furtherstudyontheeffectofirrigationonplantproductionanddecompositiontoquantify

theneteffectonsoilorganiccarbonstocks. Furtherresearchonthevariationintypesandresidencetimesofbiocharamendments,in

additiontobiocharimpactonotherGHGemissions,primingofsoilorganicmatterdecomposition,andtheoverallphysicalbreakdownanddisintegrationofbiocharovertime(Jafféetal.,2013).

Dataonlong‐termresponsesofsoilorganiccarbontovariationinstockingrate,grazingmethod(i.e.,continuous,rotational,short‐durationrotational,andultra‐highstockingdensity),andvegetationcomposition(i.e.,forbandgrassmixtures,cool‐andwarm‐seasongrassmixtures,grassandlegumemixtures,grassandwoodymixtures,andplantarchitecturetypes),andwhethertheseresponsesaremediatedbydifferentsoilstypes,climaticconditions,botanicalcomposition,grazingmethodused,fertilizerregime,etc.

10Exceptagroforestrycarbonstockchanges,whicharecoveredlaterinthissection.


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FurtherstudytoaddressmitigationofGHGsinaridrangelands,particularlyinshrublands,includinginteractionsbetweenmanagementandenvironmentalconditions(Ingrametal.,2008).Additionaldatacollectionandmodelimprovementarealsoneededinaridrangelands,asuncertaintyisextremelylargeforthesoilcarbonsequestrationestimatesassociatedwithreducedstockingratesandseedingoflegumes(Brownetal.,2010;Brown,2010).OurbasicknowledgeofcarbonsequestrationandGHGmitigationinaridandsemiaridenvironmentsislimited,andtheeffectofmanagementisrelativelyunderstudied.

Needforlife‐cycleassessmentofgrazingsystemswithparticularattentiontobalanceofsoilorganiccarbon,N2Oemissionsfromsoil,andCH4emissionsfromruminantsandsoil,dependingonstockingrate,stockingmethod,foragetypeassociatedwithqualityofintake,andenvironmentalconditionsofgrazingsystem.

DatafromadaptivemanagementapproachestoinformunderstandingofsoilorganiccarbonsequestrationandGHGemissionsunderdifferentgrazingmanagementstrategies.Thisapproachcouldhelpstrengthenconservation‐orientedprogramstoobtaingreaterimpactforreducingGHGemissionsandsequesteringsoilorganicC.

Additionalfieldexperimentsanddataonsoilcarbonemissionsresultingfromthecombinedapplicationoflimeandnitrogenfertilizers.

SoilNitrousOxideEmissions:Thefollowingpracticeshave,insomestudies,significantlyaffectedN2Oemissions,butrequireadditionalresearchinside‐by‐sidecomparisonstudiesacrossdifferentsoiltypesandclimate,especiallyforextensivelygrownrowcropsthatreceivehighlevelsofnitrogenfertilizers(cornandwheatinparticular):

EffectsofsplitordelayednitrogenapplicationsonloweringN2OfluxesandonincreasingNUEtoprovideequivalentyieldsatlowertotalnitrogeninput.

Capacityofspatiallyprecisefertilizerapplicationtechnology(variablerateapplicators)tolowerN2Ofluxes(bothdirectandindirect)andincreaseNUE.

Effectsofbandednitrogenfertilizerapplications,showninsomestudiestoincreaseNUEandinotherstoincreaseN2Oemissions.

ThegeneralizabilityofhigherN2OEFsandnitratelossatnitrogenfertilizerratesgreaterthancropneeds(i.e.,atratesgreaterthanthoserecommendedbyMaximumReturntoNitrogenapproaches).

ThegeneralizabilityofdifferentfertilizerformulationsonN2Oemissions,inparticularforureavs.anhydrousammoniavs.injectedsolutions.

Thegeneralizabilityofcoatedfertilizerssuchaspolymercoatedurea,ureaseinhibitors,biocharadditions,andnitrificationinhibitorsforloweringN2Oemissionsandnitrateloss.

MoreresearchontheresponsesofsoilN2Oemissionstovariationsinstockingrates,grazingmethods(continuous,rotational,short‐durationrotational,andultra‐highstockingdensity),andvegetationcomposition(forbandgrassmixtures,cool‐andwarm‐seasongrassmixtures,grassandlegumemixtures,grassandwoodymixtures,andplantarchitecturetypes),bothindividuallyandincombinations.

ThepotentialformobilewaterandsheltersourcesinpasturestoreduceN2Oemissionsbyallowingforamoreevendistributionofmanure.

InfluenceofcropresidueharvestingonN2Oemissions,aswellassoilorganiccarbonstocks,giventheinterestinusingcropresiduesasafeedstockforbioenergyproduction.

InfluenceofcovercropsonN2Oemissions,includingeffectsofplanttype(e.g.,legumevs.nonlegume)andresiduemanagement(e.g.,harvestedvs.incorporated).

InfluenceofmanureandcompostonN2Oemissionsinsofaraseffectsmaydifferfromsyntheticnitrogeninputswithrespecttorate,timing,placement,andformoforganicnitrogenadded(e.g.,liquidvs.drymanurevs.compostwithdifferentC:Nratios).


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ImprovedquantificationofspatialandtemporalvariationofN2Oemissionsindifferentcroppingsystemsandlandscapestoprovideamoreaccurateassessmentofseasonalandannualemissionsacrosswholefields.

Improvedestimatesofindirectemissions,andinparticularthepercentageofnitrogenthatislostfromafieldthroughvolatilizationorleaching/runoff,andlaterconvertedtoN2Oindownstreamanddownwindecosystems.AdditionalstudyonpracticesthatcanreducenitratelossesaswellaspracticesthatcanreduceNH3andNOxlosses.

ResearchisalsoneededtoimprovemodelingandempiricalquantificationofsoilN2OemissionsinordertoprovideestimatesofN2Ofluxesthatintegrateacrossmultiplemanagementpracticessimultaneously:

FurtherdevelopmentandvalidationofquantitativesimulationmodelscapableofaccuratelypredictingN2Ofluxesinresponsetodifferingmanagementpractices,withparticularrespecttorate,timing,placement,andformulationofaddedfertilizers,bothsyntheticandorganic;tillagetypeandintensity;andresiduemanagement.

MoredataregardingseasonalandannualN2Oemissions,includingemissionsduringthenon‐growingseasonandinparticularwinterandfreeze‐thawperiods.

BetterknowledgeoffluxesacrossallLandResourceRegions(LRRs)concentratedespeciallyinthoseareasandcroppingandgrazedsystemsexpectedtocontributemosttolocalandregionalN2Ofluxes,withside‐by‐sidecomparisonsofdifferentmanagementpractices.

DevelopmentofstandardizedmethodologiesandcreationofnewtechnologiesforrapidassessmentofN2Ofluxesinthefield.

AnimprovedunderstandingofthesourcesofN2Oincroppedsoils(e.g.,nitrificationvs.denitrification)andconsequencesforfeedbacksamongadaptivemanagement,soilphysicalandbiologicalattributes,andSOCdynamics.

DevelopmentofasetofgeographicallystratifiedtestsitesatwhichfactorsknowntoaffectagronomicN2Oemissionscouldbetestedinthecontextofdifferentmanagementsystems.ThiswouldprovidearobustempiricaldatasetforestablishingTier2and3models.

FloodedRiceProductionEmissions:TheprimaryresearchgapisthelimitedamountofresearchconductedintheUnitedStatesonGHGfromricesystems.Therefore,mostofthecurrentconclusionsaboutmanagementinfluencesonriceCH4emissionsarebasedonAsianstudieswherericeistransplantedasopposedtodirectseeded.ThismaybeproblematicbecausewaterismanageddifferentlyinAsiantransplantedfloodedricesystemsduringtheestablishmentperiodthaninU.S.systems.Untilrecently,nostudiesevaluatedseasonalorannualN2OemissionsfromricesystemsintheUnitedStates(Adviento‐Borbeetal.,2007;Pittelkowetal.,2013).IntheUnitedStates,muchoftheresearchonGHGemissionscomesfromLouisiana,Texas,andCalifornia.Lindau’slabconductedonstationresearchinLouisianatoevaluateCH4emissions(e.g.,Lindauetal.,1995;Lindauetal.,1998).Sass’sgroupalsoevaluatedCH4emissionsonexperimentalstationsinTexas(e.g.,Huangetal.,1997;Sassetal.,1994).InCalifornia,variousresearchergroups(e.g.,Bossioetal.,1999;Fitzgeraldetal.,2000)havebeenconductingresearchbothonstationandoffstationandhaverecentlyalsoincludedN2Omeasurements(Adviento‐Borbeetal.,2007;Pittelkowetal.,2013).

ThefollowingpracticeshaveinsomestudiessignificantlyaffectedCH4orN2Oemissionsbutrequirefurtherside‐by‐sidecomparisonswithexperimentaldesignsacrossdifferentsoiltypesandclimateswithintheUnitedStates.

Watermanagementpractices(inparticularmidseasondrainsorintermittentirrigation)areoftensuggestedasviableoptionstomitigateCH4emissions.Whiledatasupportthisconclusion,thesemanagementpracticeshavenotbeenwidelytestedintheUnitedStates.In


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studieswherethesoilhasbeendrainedduringtheseason,investigatorshavereporteddelayedcropmaturation(aproblemintemperateclimateswithrelativelyshortgrowingseasons),reducedyieldsandgrainquality,andincreasedweedanddiseasepressure.Therefore,althoughmidseasondrainageismentionedasamitigationoption,moreresearchisrequiredbeforeitisrecommendedforuseinU.S.ricesystems.

ReturningricestrawtosoiloftenresultsinincreasedCH4emissions,buttheremovalofstrawrequiresenergyandtime.Furthercompoundingtheproblemisthattherearerelativelyfewusesforricestraw.Theremovalofricestrawalsoremovesnutrientswhichwouldneedtobereplaced.Ofparticularconcernispotassium,asricestrawcontainsanaverageof1.4percentofpotassium.Therefore,itispossibletoremovemorethan100kg/haofpotassiumthroughremovalofricestraw,whichwillneedtobereplacedinordertomaintainasustainablecroppingsystem.

InCalifornia,farmerstypicallyincorporatericestrawandfloodtofacilitatestrawdecompositionduringthewinter.ThispracticeincreasesCH4emissionsfromricefieldsduringthewinterandthefollowinggrowingseason.However,ithasalsosignificantlyimprovedhabitatforoverwinteringwaterfowlinthePacificFlyway.Fitzgeraldetal.(2000)reportedthatuptohalfoftheannualCH4emissionsoccurredduringthewinterfallowperiodwhenstrawwasincorporatedandflooded.Recentstudiessuggestthat50percentmaybeahighestimateandthatfurtherresearchisneeded(Adviento‐Borbeetal.,2007;Pittelkowetal.,2013).

WhilemanystudieshaveshownvarietaldifferencesinhowmuchCH4isemitted,thesestudiesareallrelativelyoldandmanyofthevarietiesarenolongerwidelyused.Furtherresearchoncurrentvarietiesneedstobeconducted.

LimiteddataonnitrogenplacementsuggeststhatdeepplacementoffertilizerreducesCH4emissions,butmoreresearchisneededtoconfirmthefindings.

Side‐by‐sidecomparisonswithexperimentaldesignsareneededofwet‐anddry‐seededricetoevaluatetheirinfluenceonCH4andN2Oemissions.ThesearethetwomostcommonriceestablishmentpracticesintheUnitedStates.

SomestudiesfromChinasuggestthatmorecarbonissequesteredinricesystemsthaninupland(aerobic)systems,butthishasnotbeenevaluatedintheUnitedStates.

Agroforestry:Asufficientdatabasefordevelopingthemethodstoreadilymeasureand/ormodelthevariousGHGimpactsofagroforestryiscurrentlylacking.FullGHGmonitoringandaccountinginagroforestrywillrequireamixofmethodologiesfromamongtheGHGaccountingframeworksbecauseofthediversityinusesassociatedwithagroforestrysystems.Thefollowingresearchgapsarehighlighted.

Assessmentofapproachesforestimatingwoodybiomassinagroforestryplantings,whichincludescomparisonofexistingequationsandlookuptableswithagroforestry‐generatedvolumeandbiomassequationstodeterminebestapproachforestimatingcarboninthewoodybiomassofagroforestryplantings.

Developmentofeffectivestrategiesformeasuring/monitoringcarbonsequestrationandGHGemissionsinsoilandwoodycomponents.

EffectofdifferentspeciesmixturesandcombinationsofmanagementactivitiesonsoilcarbonsequestrationandminimizingtotalGHGemissions.

ImpactofmanagementoptionsandenvironmentinteractionsoncarbonsequestrationandtotalGHGemissionswithinagroforestrysystems.

Developmentoftoolsrelevanttotheinventory/measurement/estimationofthese“treesoutsideofforests.”Inaddition,testingthevalidityofcurrentcarbonaccountingtools(e.g.,DAYCENT,HOLOS)inprovidingaccurateestimatesofcarbonsequesteredinthewoodybiomassofagroforestryplantings.


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Understandingsoilcarbondynamicsinagroforestrysystems,alongwiththeimpactofsoilerosion,transportanddepositiononcarbonstocks.

Developinginventorymethodologies(suchastheuseofLightDetectionandRanging)toestablishacost‐effectivenationalagroforestryinventorycompatibleforinclusionwithcurrentinventoriescontributingtoregional/nationalGHGassessments.

Developingstandardizedexperimentalprocedures,measurement,andmonitoringprotocols,suchasthosebeingdevelopedthroughtheGreenhouseGasReductionthroughAgriculturalCarbonEnhancementnetwork(GRACEnet)11toagroforestrypracticeswiththestandardizedmeasurementandmonitoringforagriculturalN2Oemissions.

MethaneOxidationinSoils:SoilCH4oxidationisknowntodecreaseby~70percentuponconversionoflongstandingnaturalvegetationtocropandpastureland(seeSection3.5.5).CH4oxidationratesforsoilsundernaturalvegetationarenotwellknownforallclimatesandsoils,soadditionalmeasurementswouldbeuseful.AswithN2O,thefurtherdevelopmentandvalidationofquantitativesimulationmodelscapableofaccuratelypredictingCH4fluxeswouldalsobehelpfulforbettergeneralizingeffectsandforfutureinclusionoffactorsthatmaybediscoveredtorestoreoxidationincroppedsoils.ThereisalsolimitedresearchontheeffectofgrazinglandmanagementonCH4oxidationalthoughvariationinstockingrates,grazingmethods,andassociatedpracticesmayhaveaninfluenceonthisprocess.

InorganicSoilCarbon:TheeffectofmanagementonsoilinorganiccarbondynamicsandexchangeofCO2withtheatmosphereisalsoinneedoffurtherresearch.ThefollowinglistisabriefsummaryofsomeofthekeygapsidentifiedforquantificationofGHGemissions:

Wheninorganiccarbonisaddedtosoilasagriculturallimeorasabreakdownproductofurea,partoftheinorganiccarbonbecomesbicarbonate.ImprovedunderstandingofthefateofthisbicarbonateindifferentsoilsandlandscapeswouldhelptobettercharacterizethepresenceandstrengthoftheresultingbicarbonateCO2sink.

ImprovedquantificationofemissionsoruptakeofatmosphericCO2withadditionofcarbonatelimestosoilswillrequiremethodstodeterminethedominanceofweatheringduetocarbonicacid(H2CO3)vs.thestrongernitricacid(HNO3)incroplandandgrazinglandsoils.

Improvedmechanisticunderstandingandquantificationofinorganiccarbondynamicsareneededinirrigatedsystems,aswellasinnonirrigatedsystems—particularlyinaridandsemiaridregions.

11GRACEnetisaresearchprograminitiatedbyUSDAAgriculturalResearchServiceto“identifyandfurtherdevelopagriculturalpracticesthatwillenhancecarbonsequestrationinsoils,promotesustainability,andprovideasoundscientificbasisforcarboncreditsandtradingprograms”(USDAARS,2013).


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Appendix3‐A:SoilN2OModelingFrameworkSpecifications

SoilN2Oemissionsareestimatedusingacombinationofprocess‐basedmodeling,empiricalscalarsbasedonexperimentaldata,andscalingfactorsforpracticesinfluencingtheN2Oemissionsasrepresentedinthebaseemissionrates(Section3.5.4.1,Equations3‐8and3‐9,andTextbox3‐1).Thisappendixprovidesmoreinformationabouttheprocess‐basedmodels,inadditiontothederivationofempiricalscalarsandthepractice‐basedscalingfactors.

DAYCENTandDNDCmodelswereusedtoestimateN2Oemissionsforthetypicalfertilizerrateanda0‐levelnitrogenfertilizationrateassociatedwithmajorcropsineachUSDALRR.CropssimulatedarelistedinTable3‐A.1;baseemissionratesforothercrops(e.g.,sugarcane,millet,rye)wereestimatedusingtheTier1emissionfactor(onepercentofnitrogeninputs).Toestimateemissionfactorsfromthemodeloutput,theN2Oemissionsatthe0‐leveladditionwassubtractedfromtheN2Oemissionforthetypicalfertilizationrate.Thedifferencewasthendividedbythesyntheticagronomicnitrogeninputtoestimatetheemissionfactoratthetypicalrateoffertilization.ScalarswereusedtoscaletheN2Oemissionsforfertilizationratesthatweregreaterthanthetypicalrate.Thescalarswerederivedfromempiricaldatabasedonthechangeinemissionfactorsacrossarangeoffertilizationrates.SeeTextbox3‐1formoreinformationabouthowtheresultingemissionfactorswereusedtoestimatebaseemissionratesforthedirectsoilN2Omethod.

Meta‐analyseswereusedtoderivepractice‐basedscalingfactorsfromexperimentaldata.ThescalingfactorswereusedtoadjustthebaseemissionratesforspecificpracticesthatinfluencesoilN2Oemissions.Thescalingfactorsincludedtheeffectofnitrificationinhibitors(Sinh),slow‐releasefertilizers(SSR),pasture/range/paddockmanure(SPRP),andtillage(Still).TheresultingscalingfactorsareusedinEquation3‐9toscalethebaseemissionratesforlandparcelsmanagedwiththesepractices.

Figure3‐A.1providesanoverviewofthedecisionsandstepsinvolvedinestimatingN2Oemissionsfrommineralsoils.


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Figure3‐A.1:DecisionTreeforEstimatingN2OEmissionsfromMineralSoils


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3‐A.1DescriptionofProcess‐BasedModels

DAYCENT12isageneralterrestrialbiogeochemicalmodelthatsimulatescarbonandnitrogentransformationsinvolvedinprimaryproductivity,decompositionandnutrientdynamics(DelGrossoetal.,2000b;Partonetal.,2001).Themodelalsosimulatesheatandwaterfluxesverticallythroughthesoilprofile(one‐dimensional).Lateralflowofwaterisnotsimulatedexceptthatoverlandrunoffoccurswhenrainfalleventsofsufficientmagnitudeoccurgiventhepermeabilityofthesurfacesoillayer.Keysubmodelsincludeplantgrowthwithdynamiccarbonallocationamongplantcomponents,soilorganicmatterdecompositionandnutrientmineralization,andN2Oemissionsfromnitrificationanddenitrification.Plantgrowthiscontrolledbynutrientavailability,soilwaterandtemperature,andvegetationtypespecificparameterscontrollingmaximumplantgrowthrates,maximum/minimumC:Nratiosofbiomasscomponents,andphenology.Decompositionofsenescedplantmaterialandsoilorganicmatteriscontrolledbythequalityandquantityoflitterinputs,soiltexture,water,andtemperature.N2OemissionsarecontrolledbysoilNH4andNO3,watercontent,temperature,gasdiffusivity,andlabilecarbonavailability.Landmanagement/disturbanceeventssuchascultivation,waterandnutrientadditions,fire,andgrazing,canbereadilyimplementedinthemodel.ThemodelhasbeenappliedtosimulatesoilGHGfluxesatscalesrangingfromplotstoregionstotheglobe(DelGrossoetal.,2010;DelGrossoetal.,2005;DelGrossoetal.,2009).TheabilityofDAYCENTtosimulatecropyields,SOM,N2Oemissions,andNO3leachinghasbeentestedagainstavarietyoffieldexperimentsincroplandandgrasslandintheUnitedStates(DelGrossoetal.,2005;DelGrossoetal.,2008a;DelGrossoetal.,2008b).

DNDC13isaprocess‐basedbiogeochemicalmodelthatisusedtopredictplantgrowthandproduction,carbonandnitrogenbalance,andgenerationandemissionofsoil‐bornetracegasesby

12TheversionofDAYCENTcodedandparameterizedfortheU.S.NationalGHGinventory(U.S.EPA,2013)wasusedtoderiveexpectedbaseemissionrates.13DNDC9.5compiledonFeb.25,2013,wasusedtoderiveexpectedbaseemissionrates.


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meansofsimulatingcarbonandnitrogendynamicsinnaturalandagriculturalecosystems(Lietal.,2000;Miehleetal.,2006;Stangetal.,2000)andforestedwetlands(Zhangetal.,2002).Themodelintegratesdecomposition,nitrification‐denitrification,photosynthesisandhydrothermalbalancewiththeecosystem.Thesecomponentsaremainlydrivenbyenvironmentalfactors,includingclimate,soil,vegetation,andmanagementpractices.ThemodelhasbeentestedandusedforestimatingGHGemissionsfromforestedecosystemsinawiderangeofclimaticregions,includingboreal,temperate,subtropical,andtropical(Kesiketal.,2006;Kieseetal.,2005;Kurbatovaetal.,2008;Lietal.,2004;Stangetal.,2000;Zhangetal.,2002),andsimilarlyforgrasslandsandcultivatedwetlands(Giltrapetal.,2010;Rafiqueetal.,2011).

Modelinputs,forbothmodels,includetheweatherdata,14soilcharacteristics,andmanagementdataforthesesimulations.Atotalof1,200samplesweredrawnforcroplandsitesimulationsandanother1,200samplesforgrasslandsitesimulations.Thesamplenumberwasoriginallydeterminedfromaplantoselectthreesoiltypesfrom20countiesdominatedbyagricultureineachof20LRRs(3x20x20=1,200).Theemissionratesthatwereproducedbybothmodelswillbeavailableonlineinsupplementarymaterialfiles.Anexampleoftheratesforcorn,winterwheat,andgrassaregiveninFigure3‐A.2.

Figure3‐A.2:ExampleofMedianBaseEmissionRatesforCorn,WinterWheat,andGrassProductioninLandResourceRegionswithCoarse,Medium,andFineTexturedSoils

Table3‐A.1providesthe2.5,50,and97.5percentilebaseemissionratesforeachcrop,LRR,andsoiltexturecombination.EmissionratesarekgN2O‐Nperhawhencropsarefertilizedattypicalnitrogenrates.

14ThemodelsusedDAYMETweatherforthecentroidofgrassland/croplandineachcounty.


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Table3‐A.1BaseEmissionRate(kgN2O‐Nha‐1)PercentilesbyLandResourceRegion(LRR),Crop,andSoilTextureatTypicalNitrogenFertilizerRates

LRR Crop SoilGroupEmissionRate(25thPercentile)

EmissionRate(50thPercentile)

EmissionRate(97.5thPercentile)

A Grass Coarse 0.02 0.56 5.28A Grass Medium 0.41 1.20 3.86A Grass Fine 0.49 1.34 5.30A Tomato Coarse 0.04 1.08 4.83A Tomato Medium 0.28 1.69 8.31A Tomato Fine 0.49 2.09 15.73A Wheat,Spring Coarse 0.03 0.61 3.53A Wheat,Spring Medium 0.16 1.00 2.87A Wheat,Spring Fine 0.40 1.32 3.50A Wheat,Winter Coarse 0.05 0.55 4.00A Wheat,Winter Medium 0.19 0.91 2.99A Wheat,Winter Fine 0.35 1.21 2.77B Grass Coarse 0.01 0.40 5.25B Grass Medium 0.02 0.45 5.41B Grass Fine 0.05 0.74 8.20B Pea Coarse 0.00 0.36 2.43B Pea Medium 0.00 0.61 3.80B Pea Fine 0.02 0.53 3.02B Wheat,Spring Coarse 0.00 0.49 2.71B Wheat,Spring Medium 0.01 0.80 4.43B Wheat,Spring Fine 0.04 0.87 3.56B Wheat,Winter Coarse 0.00 0.40 2.05B Wheat,Winter Medium 0.01 0.54 3.58B Wheat,Winter Fine 0.04 0.75 3.72C Alfalfa Coarse 0.01 0.58 0.99C Alfalfa Medium 0.01 0.66 1.60C Alfalfa Fine 0.00 0.86 2.25C Corn Coarse 0.21 0.78 3.00C Corn Medium 0.27 0.93 8.23C Corn Fine 0.60 1.60 12.96C Grass Coarse 0.05 0.32 1.17C Grass Medium 0.08 0.36 1.37C Grass Fine 0.07 0.42 1.16C Rice Coarse 0.04 0.63 1.34C Rice Medium 0.03 0.70 2.19C Rice Fine 0.02 0.95 7.50C Safflower Coarse 0.17 0.89 2.86C Safflower Medium 0.38 1.15 7.46C Safflower Fine 0.56 2.09 12.92C Sunflower Coarse 0.07 0.58 2.13C Sunflower Medium 0.15 0.73 6.45C Sunflower Fine 0.29 1.37 9.16C Tomato Coarse 0.48 1.15 2.90C Tomato Medium 0.57 1.21 8.01C Tomato Fine 0.79 2.25 18.94C Wheat,Winter Coarse 0.05 0.86 1.81C Wheat,Winter Medium 0.06 0.96 3.30C Wheat,Winter Fine 0.15 1.47 5.08


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D Alfalfa Coarse 0.01 0.55 1.47D Alfalfa Medium 0.01 0.49 2.91D Alfalfa Fine 0.01 0.67 4.79D Corn Coarse 0.20 0.85 2.03D Corn Medium 0.26 0.87 3.28D Corn Fine 0.30 1.32 5.99D Cotton Coarse 0.01 1.04 2.53D Cotton Medium 0.02 0.97 3.37D Cotton Fine 0.09 1.63 5.68D Grass Coarse 0.02 0.39 3.14D Grass Medium 0.02 0.46 6.27D Grass Fine 0.05 0.55 6.91D Wheat,Winter Coarse 0.00 0.35 1.27D Wheat,Winter Medium 0.00 0.36 2.21D Wheat,Winter Fine 0.04 0.56 5.10E Grass Coarse 0.01 0.46 7.35E Grass Medium 0.02 0.63 8.00E Grass Fine 0.12 0.66 5.52E Wheat,Spring Coarse 0.02 0.59 2.46E Wheat,Spring Medium 0.05 0.70 4.67E Wheat,Spring Fine 0.07 0.87 2.92E Wheat,Winter Coarse 0.02 0.39 1.97E Wheat,Winter Medium 0.06 0.53 4.80E Wheat,Winter Fine 0.10 0.63 2.89F Corn Coarse 0.28 0.76 1.57F Corn Medium 0.36 0.92 2.92F Corn Fine 0.45 1.29 4.92F Grass Coarse 0.12 0.57 2.80F Grass Medium 0.15 0.66 2.69F Grass Fine 0.16 0.80 3.52F Soybean Coarse 0.20 0.95 3.26F Soybean Medium 0.26 1.05 3.23F Soybean Fine 0.29 1.48 4.40F Wheat,Spring Coarse 0.10 0.69 1.85F Wheat,Spring Medium 0.11 0.93 2.92F Wheat,Spring Fine 0.12 1.19 4.90F Wheat,Winter Coarse 0.14 0.85 3.17F Wheat,Winter Medium 0.19 1.03 6.43F Wheat,Winter Fine 0.18 1.41 11.05G Corn Coarse 0.11 0.69 1.88G Corn Medium 0.16 0.90 3.41G Corn Fine 0.23 1.62 6.59G Grass Coarse 0.09 0.55 1.85G Grass Medium 0.09 0.54 1.92G Grass Fine 0.18 0.91 3.67G Wheat,Winter Coarse 0.08 0.49 1.64G Wheat,Winter Medium 0.09 0.64 2.05G Wheat,Winter Fine 0.10 0.91 4.43H Corn Coarse 0.31 0.92 5.62H Corn Medium 0.62 1.49 11.03H Corn Fine 0.81 2.67 20.40


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H Cotton Coarse 0.14 0.70 2.28H Cotton Medium 0.18 1.17 4.38H Cotton Fine 0.41 1.55 8.88H Grass Coarse 0.30 0.88 2.53H Grass Medium 0.29 0.95 3.53H Grass Fine 0.57 1.64 4.34H Wheat,Winter Coarse 0.15 0.65 2.29H Wheat,Winter Medium 0.21 0.99 3.81H Wheat,Winter Fine 0.32 1.30 9.16I Cotton Coarse 0.25 0.63 4.38I Cotton Medium 0.23 0.63 8.15I Cotton Fine 0.34 1.27 8.70I Grass Coarse 0.36 1.02 4.24I Grass Medium 0.42 1.09 5.49I Grass Fine 0.56 1.90 5.27I Sorghum Coarse 0.34 0.78 5.69I Sorghum Medium 0.31 0.79 8.75I Sorghum Fine 0.43 1.60 9.35I Wheat,Spring Coarse 0.38 0.78 6.87I Wheat,Spring Medium 0.41 0.82 12.28I Wheat,Spring Fine 0.60 1.60 15.24I Wheat,Winter Coarse 0.19 0.43 4.66I Wheat,Winter Medium 0.20 0.58 6.57I Wheat,Winter Fine 0.22 1.06 7.75J Corn Coarse 0.48 1.10 4.33J Corn Medium 0.61 1.54 7.48J Corn Fine 0.71 2.63 17.71J Grass Coarse 0.48 1.41 3.95J Grass Medium 0.61 1.86 5.13J Grass Fine 0.69 2.41 5.77J Sorghum Coarse 0.35 0.90 3.81J Sorghum Medium 0.47 1.31 6.67J Sorghum Fine 0.52 1.96 14.66J Wheat,Spring Coarse 0.37 0.89 3.65J Wheat,Spring Medium 0.48 1.30 5.93J Wheat,Spring Fine 0.72 2.31 13.76J Wheat,Winter Coarse 0.24 0.80 3.30J Wheat,Winter Medium 0.33 1.02 5.63J Wheat,Winter Fine 0.32 1.13 11.65K Alfalfa Coarse 0.16 0.90 2.35K Alfalfa Medium 0.28 1.39 2.95K Alfalfa Fine 0.16 1.25 2.96K Corn Coarse 0.40 1.14 2.41K Corn Medium 0.72 1.75 4.57K Corn Fine 0.45 1.81 5.27K Grass Coarse 0.35 1.07 3.77K Grass Medium 0.56 1.45 4.17K Grass Fine 0.35 1.54 5.64K Soybean Coarse 0.26 0.94 2.07K Soybean Medium 0.57 1.37 2.80K Soybean Fine 0.37 1.43 3.35


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K Wheat,Spring Coarse 0.35 1.04 2.33K Wheat,Spring Medium 0.77 1.65 4.58K Wheat,Spring Fine 0.46 1.79 5.19L Corn Coarse 0.41 1.42 3.31L Corn Medium 0.63 1.97 5.92L Corn Fine 1.36 3.09 15.09L Grass Coarse 0.47 1.39 6.01L Grass Medium 0.56 1.82 7.02L Grass Fine 0.63 2.08 6.61L Soybean Coarse 0.31 1.29 2.45L Soybean Medium 0.45 1.66 3.10L Soybean Fine 0.95 2.31 6.22L Wheat,Winter Coarse 0.44 1.65 3.14L Wheat,Winter Medium 0.54 1.97 3.34L Wheat,Winter Fine 1.06 2.75 8.73M Corn Coarse 0.55 1.51 4.33M Corn Medium 0.87 2.28 11.87M Corn Fine 0.99 2.76 15.46M Grass Coarse 0.49 1.31 4.06M Grass Medium 0.68 1.91 4.97M Grass Fine 0.65 1.94 5.19M Soybean Coarse 0.41 1.29 2.66M Soybean Medium 0.71 1.86 5.03M Soybean Fine 0.78 2.08 7.52M Wheat,Winter Coarse 0.55 1.62 2.91M Wheat,Winter Medium 0.85 2.16 5.17M Wheat,Winter Fine 0.84 2.45 7.72N Corn Coarse 0.60 1.48 12.11N Corn Medium 0.76 2.11 19.17N Corn Fine 1.14 2.80 32.82N Grass Coarse 0.42 1.64 3.94N Grass Medium 0.57 2.08 5.03N Grass Fine 0.91 2.61 5.95N Soybean Coarse 0.58 1.31 4.04N Soybean Medium 0.73 1.80 5.24N Soybean Fine 1.00 2.07 11.18O Corn Coarse 0.60 1.55 4.52O Corn Medium 0.67 2.14 9.63O Corn Fine 1.07 3.08 24.03O Cotton Coarse 0.51 1.19 4.95O Cotton Medium 0.61 1.84 14.76O Cotton Fine 0.99 3.24 25.42O Grass Coarse 0.39 1.70 3.92O Grass Medium 0.44 2.24 7.03O Grass Fine 0.76 2.81 7.97O Rice Coarse 0.52 1.11 5.15O Rice Medium 0.73 1.29 9.18O Rice Fine 1.00 2.45 11.14O Soybean Coarse 0.53 1.22 3.73O Soybean Medium 0.55 1.66 6.67O Soybean Fine 0.86 2.18 14.83


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P Corn Coarse 0.43 0.93 4.56P Corn Medium 0.60 1.85 12.27P Corn Fine 0.76 2.23 27.80P Cotton Coarse 0.37 0.81 4.04P Cotton Medium 0.63 1.68 10.68P Cotton Fine 0.73 2.18 20.32P Grass Coarse 0.29 1.26 4.30P Grass Medium 0.41 1.95 5.44P Grass Fine 0.50 2.79 7.47P Soybean Coarse 0.36 0.80 2.98P Soybean Medium 0.56 1.65 5.62P Soybean Fine 0.67 1.72 12.55R Alfalfa Coarse 0.09 1.35 3.01R Alfalfa Medium 0.26 1.63 3.10R Alfalfa Fine 0.25 1.85 3.61R Corn Coarse 0.25 1.35 2.84R Corn Medium 0.51 1.81 4.92R Corn Fine 0.53 2.25 4.97R Grass Coarse 0.30 1.77 7.53R Grass Medium 0.49 1.96 7.25R Grass Fine 0.56 2.82 9.59R Soybean Coarse 0.20 1.24 2.69R Soybean Medium 0.45 1.62 3.06R Soybean Fine 0.41 1.95 3.80S Alfalfa Coarse 0.16 1.03 2.23S Alfalfa Medium 0.36 1.54 2.99S Alfalfa Fine 0.44 1.53 3.44S Corn Coarse 0.44 1.14 2.84S Corn Medium 0.86 1.81 6.89S Corn Fine 0.97 2.20 12.36S Grass Coarse 0.60 1.37 3.02S Grass Medium 0.77 1.85 4.99S Grass Fine 0.93 2.35 6.43S Soybean Coarse 0.39 1.04 1.66S Soybean Medium 0.77 1.59 3.48S Soybean Fine 0.89 1.78 4.72T Corn Coarse 0.45 0.92 5.78T Corn Medium 0.48 1.15 11.08T Corn Fine 0.63 2.76 24.52T Grass Coarse 0.33 1.05 4.89T Grass Medium 0.41 1.23 8.49T Grass Fine 0.50 2.32 9.65T Soybean Coarse 0.40 0.81 4.06T Soybean Medium 0.48 0.98 8.03T Soybean Fine 0.50 1.79 17.49T Wheat,Winter Coarse 0.33 0.81 4.89T Wheat,Winter Medium 0.36 1.10 8.05T Wheat,Winter Fine 0.46 2.72 17.87U Corn Coarse 0.36 0.64 2.64U Corn Medium 0.34 0.66 4.67U Corn Fine 0.47 1.18 14.76


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U Grass Coarse 0.33 0.99 4.74U Grass Medium 0.35 0.79 4.09U Grass Fine 0.39 1.72 5.90U Potato Coarse 0.57 0.82 2.53U Potato Medium 0.63 1.05 13.93U Potato Fine 0.79 1.53 13.88U Wheat,Spring Coarse 0.23 0.55 2.08U Wheat,Spring Medium 0.30 0.54 5.11U Wheat,Spring Fine 0.32 0.84 10.58

3‐A.2EmpiricalScalarsforBaseEmissionRates

AsdescribedinTextbox3‐1,thebaseemissionratemodeledbyDAYCENTandDNDCisusedtocalculateanemissionfactorforthetypicalfertilizercasethatisthenscaledtoreflecttheincreaseinemissionfactorwithincreasingnitrogeninputs(SEFinTextbox3‐1).TocalculateSEFameta‐analysiswasperformedusingdatafromallfieldstudiesintheliteraturewhereatleastthreedifferentlevelsofnitrogeninput,includingazeronitrogenrate,wereappliedtothesamecropatthesamesiteduringthesamegrowingseason.EmissionfactorswerecalculatedasthedifferencebetweentheN2Ofluxesat0NandatxNdividedbytheN2Ofluxat0N.Thenullhypothesiswasthatemissionfactorswillbeconstantacrossdifferentnitrogenrates.

Atotalof44datasetsthatmeetthebasecriteriawereidentified.Fromeachdataset,slopesforeachfertilizeradditionintervalwerecalculatedandcomparedtotheslopeofthefirstinterval(0Ntothefirstnitrogenadditionlevel).Thevalueoftheslopeisameasureofhowmuchtheemissionfactorchangesperadditionalunitofnitrogenfertilizerinput(kgNha‐1)foragivenstudysiteyear.Thus,theslopemeasuresthedegreeofnonlinearityoftheemissionfactor.Theslopeiszeroiftheemissionfactorisconstant,asassumedbytheIPCCTier1method.Apositiveslopeindicatesthatthetotalemissionfunctionisconvexwithrespecttototalnitrogeninput,i.e.,thattheunitoffluxincrease(theemissionfactor)isgreaterwitheachsuccessiveunitofnitrogeninput.Uncertaintywasquantifiedwithaconfidenceintervalobtainedbyperformingabootstrapanalysis(n=100,000)ontheoriginalslopes.

Thereweresufficientdatatoanalyzefivedifferentsub‐categories:corn,grassland,othercrops,clay‐texturedsoils,andother‐texturedsoils.Themeanslopewassignificantlygreaterthanzeroforallanalyzedcategoriesbutonlythegrasslandcategorywassignificantlydifferentfromtheothers.ThusintheERbequationinTextbox3‐1therearetwovaluesforSEF,oneforgrasslandsandanotherforallothercrops.

Thestudiesusedinthemeta‐analysisareprovidedbelow.

Breitenbeck,G.A.,andJ.M.Bremner.1986.Effectsofrateanddepthoffertilizerapplicationonemissionofnitrousoxidefromsoilfertilizedwithanhydrousammonia.Biologyandfertilityofsoils,2(4):201‐204.

Cardenas,L.M.,R.Thorman,N.Ashlee,M.Butler,etal.2010.QuantifyingannualN2Oemissionfluxesfromgrazedgrasslandunderarangeofinorganicfertilisernitrogeninputs.Agriculture,EcosystemsandEnvironment,136:218‐226.

Chang,C.,C.M.Cho,andD.H.Janzen.1998.Nitrousoxideemissionfromlong‐termmanuredsoils.SoilScienceSocietyAmericaJournal,62:677‐682.

Ding,W.,Y.Cai,X.Cai,K.Yagi,etal.2007.Nitrousoxideemissionsfromanintensivelycultivatedmaize‐wheatrotationinsoilintheNorthChinaPlain.ScienceandtheTotalEnvironment,373.


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Halvorson,A.D.,S.J.DelGrosso,andC.A.Reule.2008.Nitrogen,Tillage,andCropRotationEffectsonNitrousOxideEmissionsfromIrrigatedCroppingSystems.JournalofEnvironmentalQuality,37(4):1337‐1344.

Hoben,J.P.,R.J.Gehl,N.Millar,P.R.Grace,etal.2011.Nonlinearnitrousoxide(N2O)responsetonitrogenfertilizerinon‐farmcorncropsoftheUSMidwest.GlobalChangeBiology,17(2):1140‐1152.

Kaiser,E.A.,K.Kohrs,M.Kücke,E.Schnug,etal.1998.Nitrousoxidereleasefromarablesoil:importanceofN‐fertilization,cropsandtemporalvariation.SoilBiologyandBiochemistry,30:1553‐1563.

Kammann,C.,L.Grünhage,C.Müller,S.Jacobi,andH.‐J.Jäger.1998.SeasonalvariabilityandmitigationoptionsforN2Oemissionsfromdifferentlymanagedgrasslands.EnvironmentalPollution,102(S1):179‐186.

Kim,D.‐G.,G.Hernandez‐Ramirez,andD.Giltrap.2013.Linearandnonlineardependencyofdirectnitrousoxideemissionsonfertilizernitrogeninput:ameta‐analysis.Agriculture,EcosystemsandEnvironment,168:53‐65.

Letica,S.A.,C.A.M.deKlein,C.J.Hoogendoorn,R.W.Tillman,etal.2010.Short‐termmeasurementofN2Oemissionsfromsheep‐grazedpasturereceivingincreasingratesoffertilisernitrogeninOtago,NewZealand.AnimalProductionScience,50:17‐24.

Lin,S.,J.Iqbal,R.Hu,J.Wu,etal.2011.Nitrousoxideemissionsfromrapefieldasaffectedbynitrogenfertilizermanagement:acasestudyincentralChina.AtmosphericEnvironment,45:1775‐1779.

Ma,B.L.,T.Y.Wu,N.Tremblay,W.Deen,etal.2010.Nitrousoxidefluxesfromcornfields:on‐farmassessmentoftheamountandtimingofnitrogenfertilizer.GlobalChangeBiology,16(1):156‐170.

McSwiney,C.P.,andG.P.Robertson.2005.NonlinearresponseofN2Ofluxtoincrementalfertilizeradditioninacontinuousmaize(ZeamaysL.)croppingsystem.GlobalChangeBiology,11(10):1712‐1719.

Mosier,A.R.,A.D.Halvorson,C.A.Reule,andX.J.Liu.2006.NetglobalwarmingpotentialandgreenhousegasintensityinirrigatedcroppingsystemsinnortheasternColorado.JournalofEnvironmentalQuality,35(4):1584‐1598.

Signor,D.,C.E.P.Cerri,andR.Conant.2013.N2OemissionsduetonitrogenfertilizerapplicationsintworegionsofsugarcanecultivationinBrazil.EnvironmentalResearchLetters,8(1):015013.

Song,C.,andJ.Zhang.2009.Effectsofsoilmoisture,temperature,andnitrogenfertilizationonsoilrespirationandnitrousoxideemissionduringmaizegrowthperiodinnortheastChina.ActaAgriculturaeScandinavia,59:97‐106.

vanGroenigen,J.W.,G.J.Kasper,G.L.Velthof,A.vandenPol‐vanDasselar,etal.2004.Nitrousoxideemissionsfromsilagemaizefieldsunderdifferentmineralnitrogenfertilizerandslurryapplications.PlantandSoil,263.

Velthof,G.L.,O.Oenema,R.Postma,andM.L.VanBeusichem.1997.Effectsoftypeandamountofappliednitrogenfertilizeronnitrousoxidefluxesfromintensivelymanagedgrassland.NutrientCyclinginAgroecosystems,46:257‐267.

Zebarth,B.J.,P.Rochette,andD.L.Burton.2008.N2Oemissionsfromspringbarleyproductionasinfluencedbyfertilizernitrogenrate.CanadianJournalofSoilScience,88:197‐205.

Zhang,J.,andX.Han.2008.N2Oemissionfromthesemi‐aridecosystemundermineralfertilizer(ureaandsuperphosphate)andincreasedprecipitationinnorthernChina.AtmosphericEnvironment,42:291‐302.


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3‐A.3Practice‐BasedScalingFactors

Datawereanalyzedtoderivescalingfactorsforthefollowingpractices:nitrogenfertilizerplacement,nitrificationinhibitors,no‐tillmanagement,andslow‐releasefertilizers.PracticeswereincludediftherewassufficientevidencefromfieldexperimentstosuggestthatthepracticeinfluencedN2Oemissions,orforwhichapreviousmeta‐analysishadbeenconductedandshownthatthepracticehadaneffectonN2Oemissions(i.e.,no‐tillmanagement;vanKesseletal.,2012).AllpracticeswerefoundtohaveasignificanteffectonN2Oemissionwiththeexceptionofnitrogenplacement.ThescalingfactorsareprovidedinTable3‐9.

Documentationfortheno‐tillscalingfactorcanbefoundinvanKesseletal.Scalingfactorsfornitrificationinhibitorswerederivedusingalinearmixed‐effectmodelingapproach(PinheiroandBates,2000),similartothemethodusedbyOgleetal.(2005)toderivefactorsthatwereusedinthe2006IPCCGuidelines(IPCC,2006).Variancesassociatedwithindividualexperimentalresultswerenottakenintoconsiderationinthemeta‐analysesbecausemanystudiesdidnotprovidethisinformation.AgoalforfutureanalysessupportingtheUSDAmethodswillbetoincludevariances,undertheassumptionthatstudieswillreportthisinformationinfuturepublications.Covariateswereincludedintheanalysistodetermineifthepracticehadadifferenteffectdependingonthelanduse,climate,soiltype,watermanagement,tillagepractice,orcroptype.Covariateswereretainedinthemodelifthevariablewassignificantatanalphalevelof0.05.Forotherscalingfactors,therewereinsufficientdatatousethelinearmixed‐effectmodelingapproach,andsoaveragedifferencesbetweenthecontrolandtreatmentswereestimatedfromthestudiestoestimateascalingfactor.Theresultingestimateswereevaluatedforstatisticalsignificantfromavalueof0(ornoeffect)usinganalphalevelof0.05.A95percentconfidenceintervalwasderivedforeachscalingfactorandprovidedinTable3‐6asanupperandlowerboundontheestimatedfactor.

Thestudiesusedineachmeta‐analysisareprovidedbelow.

NitrogenFertilizerPlacement:Burton,D.L.,X.Li,andC.A.Grant.2008.Influenceoffertilizernitrogensourceandmanagement

practiceonN2OemissionsfromtwoBlackChernozemicsoils.CanadianJournalofSoilScience,88:219‐227.

Drury,C.F.,W.D.Reynolds,C.S.Tan,T.W.Welacky,etal.2006.EmissionsofNitrousOxideandCarbonDioxide.SoilScienceSocietyofAmericaJournal,70(2):570‐581.

Engel,R.,D.L.Liang,R.Wallander,andA.Bembenek.2010.InfluenceofUreaFertilizerPlacementonNitrousOxideProductionfromaSiltLoamSoil.JournalofEnvironmentalQuality,39(1):115‐125.

Halvorson,A.D.,andS.J.DelGrosso.2013.Nitrogenplacementandsourceeffectsonnitrousoxideemissionsandyieldsofirrigatedcorn.JournalofEnvironmentalQuality,42(Inpress).

Hou,A.X.,andH.Tsuruta.2003.NitrousoxideandnitricoxidefluxesfromanuplandfieldinJapan:effectofureatype,placement,andcropresidues.NutrientCyclinginAgroecosystems,65:191‐200.

Hultgreen,G.,andP.Leduc.2003.Theeffectofnitrogenfertilizerplacement,formulation,timing,andrateongreenhousegasemissionsandagronomicperformance:AgricultureAgri‐FoodCanada,PrairieAgriculturalMachineryInstitute.

Liu,X.J.,A.R.Mosier,A.D.Halvorson,andF.S.Zhang.2005.Tillageandnitrogenapplicationeffectsonnitrousandnitricoxideemissionsfromirrigatedcornfields.PlantandSoil,276:235‐249.

Maharjan,B.,andR.T.Venterea.Inreview.Nitritedynamicsexplainfertilizermanagementeffectsonnitrousoxideemissionsinmaize.SubmittedtoSoilBiologyandBiochemistry.

Zebarth,B.J.,P.Rochette,D.L.Burton,andM.Price.2008.EffectoffertilizernitrogenmanagementonN2Oemissionsincommercialcornfields.CanadianJournalofSoilScience,88:189‐195.


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NitrificationInhibitors:Akiyama,H.,H.Tsuruta,andT.Watanabe.2000.N2OandNoEmissionsfromSoilsafterthe

ApplicationofDifferentChemicalFertilizers.Chemosphere‐GlobalChangeScience,2(3):313‐320.

Ball,B.C.,K.C.Cameron,H.J.Di,andS.Moore.2012.EffectsofTramplingofaWetDairyPastureSoilonSoilPorosityandonMitigationofNitrousOxideEmissionsbyaNitrificationInhibitor,Dicyandiamide.SoilUseandManagement,28(2):194‐201.

Bremner,J.M.,G.A.Breitenbeck,andA.M.Blackmer.1981.EffectofNitrapyrinonEmissionofNitrousOxidefromSoilFertilizedwithAnhydrousAmmonia.GeophysicalResearchLetters,8(4):353‐356.

Bronson,K.F.,A.R.Mosier,andS.R.Bishnoi.1992.NitrousOxideEmissionsinIrrigatedCornasAffectedbyNitrificationInhibitors.SoilScienceSocietyofAmericaJournal,56(1):161‐165.

Cui,M.,X.C.Sun,C.X.Hu,H.J.Di,etal.2011.EffectiveMitigationofNitrateLeachingandNitrousOxideEmissionsinIntensiveVegetableProductionSystemsUsingaNitrificationInhibitor,Dicyandiamide.JournalofSoilsandSediments,11(5):722‐730.

deKlein,C.A.M.,K.C.Cameron,H.J.Di,G.Rys,etal.2011.RepeatedAnnualUseoftheNitrificationInhibitorDicyandiamide(Dcd)DoesNotAlterItsEffectivenessinReducingN2OEmissionsfromCowUrine.AnimalFeedScienceandTechnology,166‐167:480‐491.

Delgado,J.A.,andA.R.Mosier.1996.MitigationAlternativestoDecreaseNitrousOxidesEmissionsandUrea‐NitrogenLossandTheirEffectonMethaneFlux.JournalofEnvironmentalQuality,25(5):1105‐1111.

Dittert,K.,R.Bol,R.King,D.Chadwick,etal.2001.UseofanovelnitrificationinhibitortoreducenitrousoxideemissionfromN15‐labelleddairyslurryinjectedintosoil.RapidCommunicationsinMassSpectrometry,15:1291‐1296.

Dobbie,K.E.,andK.A.Smith.2003.ImpactofDifferentFormsofNFertilizeronN2OEmissionsfromIntensiveGrassland.NutrientCyclinginAgroecosystems,67(1):37‐46.

Ghosh,S.,D.Majumdar,andM.C.Jain.2003.MethaneandNitrousOxideEmissionsfromanIrrigatedRiceofNorthIndia.Chemosphere,51(3):181‐195.

Hadi,A.,O.Jumadi,K.Inubushi,andK.Yagi.2008.MitigationOptionsforN2OEmissionfromaCornFieldinKalimantan,Indonesia.Soilscienceandplantnutrition,54(4):644‐649.


Halvorson,A.D.,S.J.DelGrosso,andF.Alluvione.2010.TillageandInorganicNitrogenSourceEffectsonNitrousOxideEmissionsfromIrrigatedCroppingSystems.SoilScienceSocietyofAmericaJournal,74(2):436‐445.

Halvorson,A.D.,S.J.DelGrosso,andC.P.Jantalia.2011.NitrogenSourceEffectsonSoilNitrousOxideEmissionsfromStrip‐TillCorn.JournalofEnvironmentalQuality,40(6):1775‐1786.

Halvorson,A.D.,andS.J.D.Grosso.2012.NitrogenSourceandPlacementEffectsonSoilNitrousOxideEmissionsfromNo‐TillCorn.JournalofEnvironmentalQuality,41(5):1349‐1360.

Halvorson,A.D.,C.S.Snyder,A.D.Blaylock,andS.J.DelGrosso.Inreview.EnhancedEfficiencyNitrogenFertilizers:PotentialRoleinNitrousOxideEmissionMitigation.AgronomyJournal.

Jumadi,O.,Y.Hala,A.Muis,A.Ali,M.Palennari,K.Yagi,andK.Inubushi.2008.InfluencesofChemicalFertilizersandaNitrificationInhibitoronGreenhouseGasFluxesinaCorn(ZeaMaysL.)FieldinIndonesia.Microbesandenvironments,23(1):29‐34.

Kelly,K.B.,F.A.Phillips,andR.Baigent.2008.ImpactofdicyandiamideapplicationonnitrousoxideemissionsfromurinepatchesinnorthernVictoria,Australia.AustralianJournalofExperimentalAgriculture,48:156‐159.


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Kumar,U.,M.C.Jain,H.Pathak,S.Kumar,etal.2000.NitrousOxideEmissionfromDifferentFertilizersandItsMitigationbyNitrificationInhibitorsinIrrigatedRice.Biologyandfertilityofsoils,32(6):474‐478.

Linzmeier,W.,R.Gutser,andU.Schmidhalter.2001.NitrousOxideEmissionfromSoilandfromaNitrogen‐15‐LabelledFertilizerwiththeNewNitrificationInhibitor3,4‐DimethylpyrazolePhosphate(Dmpp).Biologyandfertilityofsoils,34(2):103‐108.

Macadam,X.M.B.,A.Prado,P.Merino,J.M.Estavillo,etal.2003.Dicyandiamideand3,4‐DimethylPyrazolePhosphateDecreaseN2OEmissionsfromGrasslandbutDicyandiamideProducesDeleteriousEffectsinClover.Journalofplantphysiology,160(12):1517‐1523.

Magalhaes,A.M.T.,P.M.Chalk,andW.M.Strong.1984.EffectofNitrapyrinonNitrousOxideEmissionfromFallowSoilsFertilizedwithAnhydrousAmmonia.NutrientCyclinginAgroecosystems,5(4):411‐421.

Majumdar,D.,S.Kumar,H.Pathak,M.C.Jain,etal.2000.ReducingNitrousOxideEmissionfromanIrrigatedRiceFieldofNorthIndiawithNitrificationInhibitors.Agriculture,ecosystems&environment,81(3):163‐169.

Majumdar,D.,H.Pathak,S.Kumar,andM.C.Jain.2002.NitrousOxideEmissionfromaSandyLoamInceptisolunderIrrigatedWheatinIndiaasInfluencedbyDifferentNitrificationInhibitors.Agriculture,ecosystems&environment,91(1):283‐293.

Malla,G.,A.Bhatia,H.Pathak,S.Prasad,etal.2005.MitigatingNitrousOxideandMethaneEmissionsfromSoilinRice‐WheatSystemoftheIndo‐GangeticPlainwithNitrificationandUreaseInhibitors.Chemosphere,58(2):141‐147.

McTaggart,I.P.,H.Clayton,J.Parker,L.Swan,etal.1997.NitrousOxideEmissionsfromGrasslandandSpringBarley,FollowingNFertiliserApplicationwithandwithoutNitrificationInhibitors.Biologyandfertilityofsoils,25(3):261‐268.

Menendez,S.,P.Merino,M.Pinto,C.González‐Murua,etal.2006.3,4‐DimethylpyrazolPhosphateEffectonNitrousOxide,NitricOxide,Ammonia,andCarbonDioxideEmissionsfromGrasslands.JournalofEnvironmentalQuality,35(4):973‐981.

Merino,P.,J.M.Estavillo,L.A.Graciolli,M.Pinto,etal.2002.MitigationofN2OEmissionsfromGrasslandbyNitrificationInhibitorandActilithF2AppliedwithFertilizerandCattleSlurry.SoilUseandManagement,18(2):135‐141.

Parkin,T.B.,andJ.L.Hatfield.2010.InfluenceofNitrapyrinonN2OLossesfromSoilReceivingFall‐AppliedAnhydrousAmmonia.Agriculture,ecosystems&environment,136(1):81‐86.

Pathak,H.,A.Bhatia,S.Prasad,S.Singh,etal.2002.EmissionofNitrousOxidefromRice‐WheatSystemsofIndo‐GangeticPlainsofIndia.Environmentalmonitoringandassessment,77(2):163‐178.

Sanz‐Cobena,A.,L.Sánchez‐Martín,L.García‐Torres,andA.Vallejo.2012.GaseousemissionsofN2OandNOandNO3‐leachingfromureaappliedwithureaseandnitrificationinhibitorstoamaize(Zeamays)crop.Agriculture,ecosystems&environment,149:64‐73.

Shoji,S.,J.Delgado,A.Mosier,andY.Miura.2001.UseofControlledReleaseFertilizersandNitrificationInhibitorstoIncreaseNitrogenUseEfficiencyandtoConserveAirAndwaterQuality.CommunicationsinSoilScienceandPlantAnalysis,32(7‐8):1051‐1070.

Smith,L.C.,C.A.M.deKlein,andW.D.Catto.2008.EffectofDicyandiamideAppliedinaGranularFormonNitrousOxideEmissionsfromaGrazedDairyPastureinSouthland,NewZealand.NewZealandJournalofAgriculturalResearch,51(4):387‐396.

Vallejo,A.,L.Garcia‐Torres,J.A.Diez,A.Arce,etal.2005.ComparisonofNlosses(NO3‐,N2O,NO)fromsurfaceapplied,injectedoramended(DCD)pigslurryofanirrigatedsoilinaMediterraneanclimate.PlantandSoil,272:313‐325.

Vallejo,A.,U.M.Skiba,L.Garcia‐Torres,A.Arce,etal.2006.Nitrogenoxidesemissionfromsoilsbearingapotatocropasinfluencedbyfertilizationwithtreatedpigslurriesandcomposts.SoilBiologyandBiochemistry,38:2782‐2793.


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Venterea,R.T.,B.Maharjan,andM.S.Dolan.2011.Fertilizersourceandtillageeffectsonyield‐scaledN2Oemissionsinacorncroppingsystem.JournalofEnvironmentalQuality.

Weiske,A.,G.Benckiser,andJ.C.G.Ottow.2001.EffectoftheNewNitrificationInhibitorDmppinComparisontoDcdonNitrousOxide(N2O)EmissionsandMethane(CH4)OxidationDuring3YearsofRepeatedApplicationsinFieldExperiments.NutrientCyclinginAgroecosystems,60(1):57‐64.

Zaman,M.,M.L.Nguyen,J.D.Blennerhassett,andB.F.Quin.2008.ReducingNH3,N2Oand‐NLossesfromaPastureSoilwithUreaseorNitrificationInhibitorsandElementalS‐AmendedNitrogenousFertilizers.Biologyandfertilityofsoils,44(5):693‐705.

Slow‐releaseFertilizers:Akiyama,H.,H.Tsuruta,andT.Watanabe.2000.N2OandNOEmissionsfromSoilsafterthe

ApplicationofDifferentChemicalFertilizers.Chemosphere‐GlobalChangeScience,2(3):313‐320.

Akiyama,H.,andH.Tsuruta.2002.EffectofchemicalfertilizerformonN2O,NOandNO2fluxesfromAndisolfield.NutrientCyclinginAgroecosystems,63:219‐230.


Burton,D.L.,X.Li,andC.A.Grant.2008.InfluenceoffertilizernitrogensourceandmanagementpracticeonN2OemissionsfromtwoBlackChernozemicsoils.CanadianJournalofSoilScience,88:219‐227.

Cheng,W.,Y.Nakajima,S.Sudo,H.Akiyama,etal.2002.N2OandNOemissionsfromafieldofChinesecabbageasinfluencedbybandapplicationofureaorcontrolled‐releaseureafertilizers.NutrientCyclinginAgroecosystems,63:231‐238.

Chu,H.Y.,Y.Hosen,andK.Yagi.2007.NO,N2O,CH4andfluxesinwinterbarleyfieldofJapaneseAndisolasaffectedbyNfertilizermanagement.SoilBiology&Biochemistry,39:330‐339.




Halvorson,A.D.,S.J.DelGrosso,andF.Alluvione.2010a.NitrogenSourceEffectsonNitrousOxideEmissionsfromIrrigatedNo‐TillCorn.JournalofEnvironmentalQuality,39(5):1554‐1562.

Halvorson,A.D.,S.J.DelGrosso,andF.Alluvione.2010b.TillageandInorganicNitrogenSourceEffectsonNitrousOxideEmissionsfromIrrigatedCroppingSystems.SoilScienceSocietyofAmericaJournal,74(2):436‐445.






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Hyatt,C.R.,R.T.Venterea,C.J.Rosen,M.McNearney,etal.2010.Polymer‐CoatedUreaMaintainsPotatoYieldsandReducesNitrousOxideEmissionsinaMinnesotaLoamySand.SoilScienceSocietyofAmericaJournal,74(2):419‐428.

Ji,Y.,G.Liu,J.Ma,H.Xu,etal.2012.Effectofcontrolled‐releasefertilizeronnitrousoxideemissionfromawinterwheatfield.NutrientCyclinginAgroecosystems,94:111‐122.

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Appendix3‐B:GuidanceforCropsNotIncludedintheDAYCENTModel

TheDAYCENTmodelisrecommendedforuseinestimatingSoilCarbonStockChanges(Section3.5.3),andwasused(alongwiththeDNDCmodel)togeneratebaseemissionratesforEquation3‐9(SeeAppendix3‐AforadiscussionofhowmodelswereusedtoestimateN2Oemissionsfrommineralsoils).Inaddition,nitrogenmineralizedfromsoilorganicmatter(Nmin);additionalnitrogeninputsfromachangeinsoilorganicmattermineralizationduetoaland‐usechangeortillagechange(Ndmin);nitrogenmineralizationfromorganicamendments(e.g.,manure,sewagesludge,compost);andnitrogenmineralizationfromcrop,grass,andcovercropresidues(Nresid)aregeneratedbytheDAYCENTmodel.

TheDAYCENTmodelisnotusedtogenerateestimatesforallcropsgrownintheUnitedStates.TheDAYCENTmodeliscurrentlyusedtoestimateSOCstocksforthefollowingcropsandsectors:agroforestry,almond,alfalfa,windbreak,woodlot,sorghum,springwheat,winterwheat,woodlot—softwoods,woodlot—hardwoods,clover,cotton,drylandbeans,corn,oats,millet,grass‐cloverpasture,grass,peas,potato,sugarbeets,sunflower,soybean,sugarcane,peanut,tobacco,uplandrice,windbreakthree‐row,andwalnut.Thesecropsrepresent90percentofthecropsgrownintheUnitedStates,andmorecropsaretestedandaddedtotheDAYCENTmodel‐basedassessmentonaregularbasis.

However,ifanentityismanagingacropthatisnotincludedintheDAYCENTlistofcrops,the2006IPCCGuidelinesmaybeusedtoestimateemissionsorsinksforthesourceslistedabove.ThisapproachisconsistentwiththeU.S.EnvironmentalProtectionAgencyNationalInventoryReport(U.S.EnvironmentalProtectionAgency,2013),andacompletediscussionofthisalternativemethodologyinprovidedinAnnex3(Section3.12)oftheNationalInventoryReport.15Specifically,theNationalInventoryReportusesacombinationofTier1,2,and3approachestoestimatedirectandindirectN2Oemissionsandsoilchangesinagriculturalsoils.ThisreportfollowsthesameapproachforthecropsnotincludedintheDAYCENTmodelwhenestimatingsoilcarbonstockchangesanddirectN2Oemissions(SeeTable3‐B‐1).

Table3‐B‐1AlternativeMethodologiesforCropsNotIncludedintheDAYCENTModel

Source Tier1 Tier2

SoilcarbonstockchangesIPCC2006Guidelines(SeeChapter5,Section5.2.3.3)

DirectN2OemissionsfrommineralsoilsforthecropsNOTestimatedbytheDAYCENTmodel

IPCC2006Guidelineswithmanagementbasedscalingfactors(SeeSection3.5.4)

Nsmin, Notestimated

Nitrogeninputsfromorganicamendments(NmanandNcomp)

IPCC2006Guidelines(SeeChapter11Section11.2.1.1)

Nresid Equation3‐B‐1Residuenitrogen(Seebelow)

15SeeU.S.EnvironmentalProtectionAgency,NationalGHGInventoryAnnex3:http://www.epa.gov/climatechange/Downloads/ghgemissions/US‐GHG‐Inventory‐2013‐Annex‐3‐Additional‐Source‐or‐Sink‐Categories.pdf


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Defaultvaluesfordrymattercontent,root:shootratioandharvestindexareprovidedinTable3‐5inSection3.5.1.2.DefaultvaluesfromtheIPCCguidelinesvaluesareprovidedinTable3‐B‐2forthenitrogencontentofabovegroundandbelowgroundresiduesinmajorcroptypesandindividualcrops.

Equation3‐B‐1:ResidueN

ForCrops:

Nresid=[((Ydm/HI)–Ydm)x(1–Rr)xNa]+[(Ydm/HI)xR:SxNb]

ForGrazingForage:

Nresid=[Ydmx(1–Fr–Rr)xNa]+[YdmxR:SxNb]

Where:

Nresid =Nitrogeninresiduesaboveandbelowgroundontheparcelofland (metrictonsNyear‐1ha‐1)

Ydm =Cropharvestorforageyield,correctedformoisturecontent (metrictonsbiomassha‐1) =YxDM

Y =Cropharvestortotalforageyield(metrictonsbiomassha‐1)

DM =Drymattercontentofharvestedbiomass(dimensionless)


Fr =Proportionofliveforageremovedbygrazinganimals(dimensionless)

Rr =Proportionofcrop/forageresidueremovedduetoharvest,burningorgrazing(dimensionless)

Na =Nitrogenfractionofabovegroundresiduebiomassforthecroporforage(dimensionless)

Nb =Nitrogenfractionofbelowgroundresiduebiomassforthecroporforage(dimensionless)

R:S =Root‐shootratio(unitless)


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Table3‐B‐2:NitrogenContentofAbovegroundandBelowgroundResiduesofMajorandIndividualCrops

CropNitrogenContentof

AbovegroundResidues(kgN(kgdm)‐1)

NitrogenContentofBelowgroundResidues

(kgN(kgdm)‐1)

Majorcroptypes

Grains 0.006 0.009Beansandpulses 0.008 0.008Grass‐clovermixtures 0.025 0.016Nitrogen‐fixingforages 0.027 0.022Non‐nitrogen‐fixingforages 0.015 0.012Perennialgrasses 0.015 0.012Rootcrops,other 0.016 0.014Tubers 0.019 0.014IndividualcropsAlfalfa 0.027 0.019Barley 0.007 0.014Drybean 0.01 0.01Maize 0.006 0.007Millet 0.007 NANon‐legumehay 0.015 0.012Oats 0.007 0.008Peanut(w/pod) 0.016 NAPotato 0.019 0.014Rice 0.007 NARye 0.005 0.011Sorghum 0.007 0.006Soybean 0.008 0.008Springwheat 0.006 0.009Wheat 0.006 0.009Winterwheat 0.006 0.009Source:deKlein(2006).


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Authors:

StephenM.Ogle,ColoradoStateUniversity(LeadAuthor)PatrickHunt,USDAAgriculturalResearchServiceCarlTrettin,USDAForestService

Contents:

4 QuantifyingGreenhouseGasSourcesandSinksinManagedWetlandSystems................4‐34.1 Overview...........................................................................................................................................................4‐3

4.1.1 OverviewofManagementPracticesandResultingGHGEmissions...........4‐44.1.2 SystemBoundariesandTemporalScale................................................................4‐74.1.3 SummaryofSelectedMethods/ModelsandSourcesofData........................4‐74.1.4 OrganizationofChapter/Roadmap..........................................................................4‐8

4.2 ManagementandRestorationofWetlands........................................................................................4‐84.2.1 DescriptionofWetlandManagementPractices..................................................4‐84.2.2 Land‐UseChangetoWetlands..................................................................................4‐13

4.3 EstimationMethods...................................................................................................................................4‐144.3.1 BiomassCarboninWetlands....................................................................................4‐144.3.2 SoilC,N2O,andCH4inWetlands..............................................................................4‐17

4.4 ResearchGapsforWetlandManagement.........................................................................................4‐21Chapter4References.............................................................................................................................................4‐23

SuggestedChapterCitation:Ogle,S.M.,P.Hunt,C.Trettin,2014.Chapter4:QuantifyingGreenhouseGasSourcesandSinksinManagedWetlandSystems.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.


Chapter 4

Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems

Chapter 4: Quantifying Greenhouse Gas Sources and Sinks in Managed Wetland Systems

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C CarbonCH4 MethaneCO2 CarbondioxideCO2‐eq CarbondioxideequivalentsDNDC Denitrification‐DecompositionEPA EnvironmentalProtectionAgencyFVS ForestVegetationSimulatorGHG Greenhousegasha HectareIPCC IntergovernmentalPanelonClimateChangeN NitrogenN2O NitrousoxideNOx Mono‐nitrogenoxidesNRCS USDANaturalResourcesConservationServiceP PhosphorousSOC SoilorganiccarbonTg TeragramsUSDA U.S.DepartmentofAgricultureUSDA‐ARS U.S.DepartmentofAgriculture, AgriculturalResearchService


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4 QuantifyingGreenhouseGasSourcesandSinksinManagedWetlandSystems

Thischapterprovidesmethodologiesandguidanceforreportinggreenhousegas(GHG)emissionsandsinksattheentityscaleformanagedwetlandsystems.Morespecifically,itfocusesonmethodsformanagedpalustrinewetlands.1Section4.1providesanoverviewofwetlandsystemsandresultingGHGemissions,systemboundariesandtemporalscale,asummaryoftheselectedmethods/models,sourcesofdata,andaroadmapforthechapter.Section4.2presentsthevariousmanagementpracticesthatinfluenceGHGemissionsinwetlandsystemsandland‐usechangetowetlands.Section4.3providestheestimationmethodsforbiomasscarboninwetlandsandforsoilcarbon,N2O,andCH4emissionsandsinks.Finally,Section4.4includesadiscussionofresearchgapsinwetlandmanagement.

4.1 OverviewWetlandsoccuracrossmostlandforms,existingasnaturalunmanagedandmanagedlands,restoredlandsfollowingconversionfromanotheruse(typicallyagriculture),andasconstructedsystemsforwatertreatment,suchasanaerobiclagoons.AllwetlandssequestercarbonandareasourceofGHGs.Table4‐1providesadescriptionofthesourcesofemissionsorsinksandthegasesestimatedinthemethodology.

Table4‐1:OverviewofWetlandSystemsSourcesandAssociatedGreenhouseGases


CO2 N2O CH4

Biomasscarbon

Provisionsforestimatingabovegroundbiomassforwetlandforestsandaboveandbelowgroundbiomassandcarbonareincludedforshrubandgrasswetlandsinthischapter.Abovegroundbiomassforforestedwetlandsandshrubandgrasswetlandsincludeslivevegetation,trees,shrubs,andgrasses,standingdeadwood(deadbiomass),anddowndeadorganicmatter—litterlayer(deadbiomass).

SoilC,N2O,andCH4inwetlands

Theproductionandconsumptionofcarbon inwetland‐dominatedlandscapesareimportantforestimatingthecontributionofGHGs,includingCO2,CH4,andN2Oemittedfromthoseareastotheatmosphere.ThegenerationandemissionofGHGsfromwetland‐dominatedlandscapesarecloselyrelatedtoinherentbiogeochemicalprocesses,whichalsoregulatethecarbonbalance(RoseandCrumpton,2006).However,thoseprocessesarehighlyinfluencedbythelanduse,vegetation,soilorganisms,chemicalandphysicalsoilproperties,geomorphology,andclimate(SmemoandYavitt,2006).

1Palustrinewetlandsincludenon‐tidalandtidalwetlandsthatareprimarilycomposedoftrees,shrubs,persistentemergent,emergentmosses,orlichens,wheresalinityduetoocean‐derivedsaltsisbelow0.5‰(partsperthousand).Palustrinewetlandsalsoincludethosewetlandslackingvegetationthathavethefollowingfourcharacteristics:(1)arelessthan20acres;(2)donothaveactivewave‐formedorbedrockshorelines;(3)haveamaximumwaterdepthoflessthan6.5ft.atlowwater;and(4)haveasalinityduetoocean‐derivedsaltslessthan0.5%(StedmanandDahl,2008).


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ThischapterprovidesmethodsforestimatingcarbonstockchangesandCH4andN2Oemissionsfromnaturallyoccurringwetlands2andrestoredwetlandsonpreviouslyconvertedwetlandsites.Constructedwetlandsforwatertreatment,includingdetentionponds,areengineeredsystemsthatarebeyondthescopeconsideredherebecausetheyhavespecificdesigncriteriaforinfluentandeffluentloads.Inaddition,themethodsarerestrictedtoestimationofemissionsonpalustrinewetlandsthatareinfluencedbyavarietyofmanagementoptionssuchaswatertablemanagement,timber,orotherplantbiomassharvest,andwetlandsthataremanagedwithfertilizerapplications.ThemethodsarebasedonestablishedprinciplesandrepresentthebestavailablescienceforestimatingchangesincarbonstocksandGHGfluxesassociatedwithwetlandmanagementactivities.However,giventhewidediversityofwetlandstypesandthevarietyofmanagementregimes,thebasisforthemethodsprovidedinthissectionarenotaswell‐developedasothersectionsinthisguidance(i.e.,CroplandandGrazingLands,AnimalProduction,andForestryMethods).Table4‐2providesasummaryofthemethodsandtheircorrespondingsectionforthesourcesofemissionsestimatedinthisreport.

Table4‐2:OverviewofWetlandSystemsSources,Method,andSection


4.3.1Biomasscarbon

MethodsforestimatingforestvegetationandshrubandgrasslandvegetationbiomasscarbonstocksuseacombinationoftheForestVegetationSimulator(FVS)modelandlookuptablesfordominantshrubandgrasslandvegetationtypesfoundinChapter3,Cropland,andGrazingLand.Ifthereisaland‐usechangetoagriculturaluse,methodsforcroplandherbaceousbiomassareprovidedinChapter3.

4.3.2SoilC,N2O,andCH4inwetlands

TheDenitrification‐Decomposition (DNDC) process‐basedbiogeochemicalmodelisthemethodusedforestimatingsoilC,N2O,andCH4emissionsfromwetlands.DNDCsimulatesthesoilcarbonandnitrogenbalanceandgeneratesemissionsofsoil‐bornetracegasesbysimulatingcarbonandnitrogendynamicsinnaturalandagriculturalecosystems(Lietal.,2000;Miehleetal.,2006;Stangetal.,2000)andforestedwetlands(Daietal.,2011;Zhangetal.,2002),usingplantgrowthestimatedasdescribedinSection4.3.1.


TheNationalWetlandsInventorybroadlyclassifieswetlandsintofivemajorsystems:(1)marine,(2)estuarine,(3)riverine,(4)lacustrine,and(5)palustrine(Cowardinetal.,1979).Fourofthosesystems(marine,estuarine,riverine,andlacustrine)areopen‐waterbodiesandnotconsideredwithinthemethodsdescribedinthisguidance.Palustrinewetlandsencompassthewetlandtypesoccurringonthelandandarefurtherclassifiedbymajorvegetativelifeformandwetnessorfloodingregime.CommonpalustrinewetlandsareillustratedinFigure4‐1.Forexample,forestedwetlandsareoftenclassifiedaspalustrine—forested.Similarly,mostgrasswetlandsareclassifiedaspalustrine—emergent,reflectingemergentvegetation(e.g.,grassesandsedges).Wetlandsalsovarygreatlywithrespecttogroundwaterandsurfacewaterinteractionsthatdirectlyinfluence

2WetlandsaredefinedinChapter7,LandUseChange.Wetlandsthatareconvertedtoanon‐wetlandstatusshouldbeconsideredintheappropriatechapter(e.g.,CroplandandGrazingLands,AnimalProduction,andForestryMethods).


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hydroperiod(i.e.,thelengthoftimeandportionoftheyearthewetlandholdswater),waterchemistry,andsoils(Cowardinetal.,1979;Winteretal.,1998).AllthesefactorsalongwithclimateandlandusedriversinfluencetheoverallcarbonbalanceandGHGfluxes.

Figure4‐1:PalustrineWetlandClassesBasedonVegetationandFloodingRegime

Source:Cowardinetal.(1979).

GrasslandandforestedwetlandsaresubjecttoawiderangeoflanduseandmanagementpracticesthatinfluencethecarbonbalanceandGHGflux(Faulkneretal.,2011;Gleasonetal.,2011).Forexample,forestedwetlandsmaybesubjecttosilviculturalprescriptionswithvaryingintensitiesofmanagementthroughthestandrotation;hence,thecarbonbalanceandGHGemissionsshouldbeevaluatedonarotationbasis,whichcouldrangefrom20tomorethan50years.Incontrast,grasswetlandsmaybegrazed,hayed,ordirectlycultivatedtoproduceaharvestablecommodityannually.WhileeachmanagementpracticemayinfluencecarbonsequestrationandGHGfluxes,theeffectisdependentonvegetation,soil,hydrology,climatologicalconditions,andthemanagementprescriptions.Thissectionfocusesonrestorationandmanagementpracticesassociatedwithpalustrinewetlandsthataretypicallyforestedorgrassland.


GHGemissionsfromwetlandsarelargelycontrolledbywatertabledepthanddurationaswellasclimateandnutrientavailability.Underaerobicsoilconditions,whicharecommoninmostuplandecosystems,organicmatterdecompositionreleasesCO2,andatmosphericCH4canbeoxidizedinthesurfacesoillayer(Trettinetal.,2006).Incontrast,theanaerobicsoilsthatcharacterizewetlandscanproduceCH4(dependingonthewatertableposition)inadditiontoemittingCO2.Accordingly,wetlandsareaninherentsourceofCH4,withgloballyestimatedemissionsof55to150teragrams(Tg)ofCH4peryear(Blainetal.,2006).

Toaccommodateentity‐scalereportingintheUnitedStatesforagriculturalandforestryoperations,Tier2and3methodsaddresspalustrinewetlandscontainingbothorganicandmineralhydricsoils.Thesewetlandsmaybeinfluencedbyagriculturalandforestrymanagement,andmethodsarecurrentlyavailableforbothtypesofmanagement.Thischapterprovidesmethodologiesforthefollowingwetlandsourcecategories:


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1. Biomasscarboninforested,shrub,andgrasswetlands;2. Soilcarbonsinksinwetlands;and3. N2OandCH4emissionsinwetlands.

Biomasscarboncanchangesignificantlywithmanagementofwetlands,particularlyinforestedwetlands,changesfromforesttowetlandsdominatedbygrassesandshrubs,oropenwater.Inforestedwetlands,therecanalsobesignificantcarbonindeadwood,coarsewoodydebris,andfinelitter.Harvestingpracticeswillalsoinfluencethecarbonstocksinwetlandstotheextentthewoodiscollectedforproducts,fuel,orotherpurposes.

WetlandsarealsoasourceofsoilN2Oemissions,primarilybecauseofnitrogenrunofffromadjoininguplandsandleachingintogroundwaterfromagriculturalfieldsand/oranimalproductionfacilities.N2OemissionsfromwetlandsduetonitrogeninputsfromsurroundingfieldsoranimalproductionareconsideredindirectemissionsofN2O(deKleinetal.,2006).MethodologiesforestimatingindirectN2Oareprovidedintherespectivesourcechapter(i.e.,Chapter3,CroplandandGrazingLands,orChapter5,AnimalProduction).However,directN2Oemissionsoccurinwetlandsifmanagementpracticesincludenitrogenfertilization,hence,guidanceisprovidedforthissourceofemissions.


Wetlandsinherentlyaccumulatecarboninthesoilsduetoanaerobicconditions,andtheyarenaturalsourcesofCO2andCH4totheatmosphere.Managementmayalterconditionsthataffectboththepoolsandfluxes.Forexample,accumulatedsoilcarboncanbereturnedtotheatmosphereifthewetlandisdrained(ArmentanoandMenges,1986).Incontrast,silviculturalwatermanagementinwetlandscanleadtohigherbiomassproduction,whichmaypartiallyoffsetincreasedsoilorganicmatteroxidation.Conversely,thesoilcarbonpoolinconvertedwetlandsistypicallylowerthantheunmanagedsoil,andrestoringwetlandconditionsmayincreasecarbonstorageovertimeifinherenthydricsoilconditionsaremaintainedwithconsistentorganicmatterinputs.

Reversalsofemissiontrendscanoccurifamanagerrevertstoapriorconditionoranearlierpractice.Forexample,anentitymaydecidetoreturnawetlandthathadbeendrainedandcroppedbacktoaforestedwetlandcondition.Anothercommonexamplewouldbeifarestoredforestedwetlandisrevertedbacktoagriculture.ThesereversalsdonotnegatethemitigationofCH4orN2Oemissionstotheatmospherethathadoccurredpreviously,totheextentthatwetlandrestorationorchangeinmanagementcanreduceorchangetheseemissions.Correspondingly,thestartingpointfromthereversionwilldeterminetheeffectoncarbonsequestrationandGHGflux.Forexample,inarestoredforestedwetland,reversionofthesitetocropproductionwouldreturncarbonsequesteredduringtherestorationperiodtotheatmosphereovertime.

Thereisatrade‐offinCH4andN2Oemissionswithmanagementofthewatertableposition.WetlandswithanaerobicsoilconditionsthatarepersistentnearthesurfaceforalongerperiodduringtheyearwilltendtohavehigherCH4emissionsandloweremissionsofN2O.N2Oemissionsaregreatlyreducedifsoilsaresaturatedbecausethereislittleinherentnitrification,anddenitrificationwillleadtoN2production(Davidsonetal.,2000).Forexample,restorationofwetlandswillnormallyleadtoahigherwatertableforalongerperiodoftheyear,andthuscontributetohigheremissionsofCH4butloweremissionsofN2O.Thesetrendscanbereversedifthewatertableisloweredthroughmanagementordrought,whichwilltendtoenhanceN2Oemissionsifthereisasourceofnitrate,whilereducingemissionsofCH4.Figure4‐2providesanillustrationofthecarboncycletypicallyfoundinwetlandforestandgrasslandwetlandsandrepresentsthescopeofthemethodspresentedinthisguidance.


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Figure4‐2:CarbonCycleforForestandGrasslandWetlands

Source:TrettinandJurgensen(2003).


Systemboundariesaredefinedbythecoverage,extent,andresolutionoftheestimationmethods.ThelocationofthewetlandsmaybeapproximatedbyuseoftheNationalWetlandsInventory,3thelocationofhydricsoilsasconveyedbytheNRCSsoilsmap,orthroughdirectdelineationofwetlands.Thecoverageofthemethodscanbeusedtoestimateavarietyofemissionsources,includingemissionsassociatedwithbiomassC,litterC,andsoilscarbonstockchangesandCO2,CH4,andN2Ofluxesfromsoils.Systemboundariesarealsodefinedbytheextentandresolutionoftheestimationmethod.Themethodsprovidedforwetlandshaveaspatialextentthatwouldincludeallwetlandsintheentity’soperation,withestimationoccurringattheresolutionofanindividualwetland.EmissionsareestimatedonanannualbasisforasmanyyearsasneededforGHGemissionsreporting.

4.1.3 SummaryofSelectedMethods/ModelsandSourcesofData

TheIPCC(2006)hasdevelopedasystemofmethodologicaltiersforestimatingGHGemissions.Tier1representsthesimplestmethodsusingdefaultequationsandfactorsprovidedintheIPCCguidance.Tier2usesdefaultmethodsbutemissionfactorsthatarespecifictodifferentregions.Tier3utilizesaregion‐specificestimationmethod,suchasaprocess‐basedmodel.Highertiermethodsareexpectedtoreduceuncertaintiesintheemissionestimatesifthereissufficientinformationandtestingtodevelopthesemethods.Inthisguidance,biomass,litter,andsoilcarbonstockchanges,inadditiontosoilN2OandCH4emissions,areestimatedusingTier2and3methods.

Thedatarequiredtoapplythesemethodsrangefrombasicinformationonsoils,vegetation,weather,landuse,andmanagementhistorytodataonfertilizationratesordrainageconditions.Whilesomeofthesedataareoperation‐specificandmustbeprovidedbytheentity,otherdatacanbeobtainedfromnationaldatabases,suchasweatherdataandsoilcharacteristics.

3SeeNationalWetlandsInventoryhttp://www.fws.gov/wetlands/.


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Thewetlandssectionofthisreportisorganizedintothreeprimarysections.Section4.2providesadescriptionofwetlandmanagementeffectsonGHGemissions,elaboratingonthescientificbasisforhowvariouspracticesinfluenceGHGemissions.Section4.3providesarationalefortheselectedmethod,adescriptionofthemethod,includingageneraldescription(withequationsandfactors),activitydatarequirements,ancillarydatarequirements,limitationsofthemethod,anduncertaintiesassociatedwiththeestimation.Asinglemethodisprovidedforeachsourcepresentedinthischapter(i.e.,biomasscarboninforested,shrub,andgrasswetlands;soilcarbonandCH4inwetlands;anddirectN2Oemissionsinwetlands).Asinglemethodwasselectedtoensureconsistencyinemissionestimationbyallreportingentities,andtheselectedmethodisconsideredthebestoptionamongpossibilitiesforentity‐scalereporting.Methodsmayberefinedinthefutureastheyarefurtherdeveloped.Thelastsectionprovidesasummaryofselectedresearchgaps.

4.2 ManagementandRestorationofWetlandsHowwetlandsaremanagedcanhaveasignificanteffectonGHGemissionsandsinks,whichareprimarilyinfluencedbythedegreeofwatersaturation,climate,andnutrientavailability.Inamajorityofwetlands,90percentofcarboningrossprimaryproductionisreturnedtotheatmospherethroughdecay,andtheremaining10percentaccumulatesinthebottomofthewaterbodyaccumulatingonpreviouslydepositedmaterials(Blainetal.,2006).ManagementofthewatertablewithinawetlandwillresultinbothlowerCH4emissionsduetodecreasedproductionandoxidationofCH4producedinthesubsoilandanincreaseinCO2emissionsduetoincreasedoxidationofsoilorganicmatter.N2Oemissionsfromwetlandsaretypicallylow,unlessananthropogenicsourceofnitrogenentersthewetland.Indrainedwetlands,N2Oemissionsarelargelycontrolledbythefertilityofthesoilandwatermanagementregime.Incontrast,restoredandconstructedwetlandsgeneratehigherlevelsofCH4andlowerlevelsofCO2becauseofthechangeinawatertabledepth(Blainetal.,2006).

4.2.1 DescriptionofWetlandManagementPractices

ThissectionprovidesadescriptionofmanagementpracticesinwetlandsthatinfluenceGHGemissions(CH4orN2O)orcarbonstocks.Individualsectionsdealwithforestedandgrasswetlandsthatcouldoccurinagriculturalandforestryoperations.Itisimportanttonotethatdrainageofwetlandsforcommodityproduction,suchasannualcrops,orforotherpurposesarenotconsideredwetlandsintheseguidelines.MethodsfordrainedwetlandscanbefoundinChapter3,CroplandsandGrazingLands,orChapter6,ForestLands,dependingonthelanduseafterdrainageofthewetland.

4.2.1.1 SilviculturalWaterTableManagement

Silviculturalwatermanagementsystemsareprincipallyusedtoregulatethewatertabledepthinordertoreducesoildisturbanceassociatedwithharvestingoperationsandalleviatestressfromsaturatedsoilconditionsonartificiallyregeneratedplantations.Thesilviculturalwatermanagementsystemshouldnoteliminatethewetlandconditionsofthesite.

SilviculturalwatermanagementsystemsaffectthecarbonbalanceandGHGemissionsfromthesite(Bridghametal.,2006).Typicallyorganicmatterdecompositionisenhancedwiththeimpositionofadrainagesystem,CH4emissionsarereduced,andN2Oemissionsmayincrease(Lietal.,2004).Carbonsequestrationinbiomassmaybeenhancedonsiteswithsilviculturaldrainagesystemsduetoincreasedtreeproductivity(MinkkinenandLaine,1998).


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4.2.1.2 ForestHarvestingSystems

Therearetwogeneraltypesofsystemsusedtoharvesttreesfromforestedwetlands:partialcuttingandclearcutting.Apartialcutinvolvestheremovalofselectedtreesfromthestand.Thenumberoftreesremovedortheresidualdensityofthestandwilldependonthestandtype,species,intendedproduct(s),andstandage.Theamountoftreebiomassremovedduringthepartialcutmayalsovary;topsmaybeleftonsiteifonlylogsareremoved,ortheymaybeconcentratedinalandingifwhole‐treeharvestingisused.Withthelattersystem,thetopsmayalsobeutilizedandremovedfromthesite.Partialcuttingistypicallyusedinriparianzonesandsitesthataremanagedforsolidwoodproducts.Clearcuttingresultsintheremovalofalloverstorytreesfromthesite.ClearcuttingistypicallyusedonnaturalstandsoccurringinfloodplainsofthesoutheasterncoastalplainandlacustrineandoutwashplainsoftheupperMidwest.Clearcuttingisalsothetypicalsystememployedtoharvestconiferandhardwoodplantations.

Partialcuttingaffectsthecarbonbalanceofthesitebydirectremovalofbiomass;increasedbiomassontheforestfloor,whichisthensubjecttodecayprocesses;andincreasedgrowthoftheremainingtreesforseveralyears.Decompositionofdeadbiomasswithinthestandmaybeacceleratedtemporarilyduetothechangesinambientconditionsandtheaddedresiduefromtheharvest.

Clearcuttingaffectscarbonstocksofthesitebydirectlyremovingthebiomass;increasingamountsofbiomassaddedtotheforestfloor;alteringthecarbonsequestrationforseveralyears,dependingonthetypeofregeneration;andalteringtherateoforganicmatterdecompositionintheforestfloorandsoil(Lockabyetal.,1999).Clearcuttingaffectstheambientconditionsofthesitebecauseoftheremovaloftheoverstoryvegetation.Italsoaltersthewaterbalanceofthewetlandduetothereductioninevapotranspirationfollowingharvesting.Typically,asaresultoflowerevapotranspiration,thewatertablerises,andthesitewillexhibitlongerperiodsofsaturation.ThischangeinthewatertablepositionhasdirecteffectsontheproductionofCH4andN2Oandsubsequentfluxestotheatmosphere(Lietal.,2004).

4.2.1.3 ForestRegenerationSystems

Therearetwobasicforestregenerationsystems,characterizedas(a)naturalregeneration,and(b)artificialregeneration.Naturalregeneration,asthenameimplies,reliesuponregenerationofthetreesfromseedorsproutsthatareleftbyharvestedtrees.Naturalregenerationisusedinbothpartial‐cutandclear‐cutharvestsystems.Naturalregenerationwillleadtoeven‐agedstandsofshade‐intolerantorearlysuccessionalcommunities,typicallyinfloodplainsinthesoutheasternUnitedStatesandtheconiferousplainsoftheupperMidwest.

Artificialregenerationresultsfromplantingseedlingsonapreparedsite.Thesitepreparationpracticesmayinvolveremovaloftheharvestresiduebiomass,mechanicalscarificationand/ortheapplicationofherbicidetotemporarilyreduceweedcompetitionwithseedlings,andthecreationofplantingbeds.

TheeffectoftheforestregenerationsystemoncarbonstocksandtraceGHGemissionsdependsonthetypeofharvestingsystemthatwasused(Lockabyetal.,1999;Trettinetal.,1995).Thecombinationofpartialcuttingandnaturalregenerationhaslittleadditiveeffectbecausetheextentofregenerationistypicallyquitelowfollowingapartialcutthatremoveslessthanhalfofthebasalarea.Carbonstocksfollowingclear‐cutharvestingwithnaturalregenerationisaffectedbytherateofgrowthoftheregeneration,changesinambientconditions,andchangesinthesoilwaterregime.Thosefactorsalsoaffectartificialregenerationsystems;additionally,thetypeandextentofsitepreparationalsoaffectsthecarbonstocks.


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4.2.1.4 Fertilization

Fertilizationisusedprimarilyinforestedwetlands,suchastreeplantations,toenhancegrowth(Albaughetal.,2004).Grasswetlandsalsoreceivefertilizerasaresultofadjacentagriculturalactivities,andwhendryconditionspermit,aredirectlytilled,planted,andfertilized.Nitrogenisthemostcommonlyappliedfertilizer,andincreasednitrogeninputsareknowntoincreaseemissionsofN2O(Bedard‐Haughnetal.,2006;Davidsonetal.,2000;Gleasonetal.,2009;Merbachetal.,2002;PhillipsandBeeri,2008;ThorntonandValente,1996).NitrogenfertilizerswillalsoenhanceN2Oemissionsbothdirectlyonthesiteandindirectlyifnitrogenislostfromthesiteasnitrateingroundwaterorrunoff,aswellasvolatilizationofnitrogenasammoniaorNOx.TheindirectlosseswillcontributetoN2Oemissionsatothersites.

Theeffectoffertilizationoncarbonstocksisprincipallyrealizedthroughchangesintreegrowthrates.Theeffectwouldresultfromnitrogenfertilizers,butphosphorusmayalsobeappliedinthesoutheasternUnitedStates.

4.2.1.5 ConversiontoOpen‐WaterWetland

Theconversionofwetlandtoopenwateroccursprimarilyasaresultofbeaverimpoundmentsandtoalesserdegreeimproperlyinstalledroadsorotherartificialembankmentsthroughawetlandthatimpedesnaturaldrainage.Theconversiontoopenwatersignificantlyreducescarbonsequestrationthroughplantgrowth,becauseuptakeislimitedtosubmergedaquaticvegetation.ThehigherwatertableforalongerperiodoftheyearwillalsotendtoincreaseCH4flux.

4.2.1.6 ForestTypeChange

Changingamanagedforesttoacharacteristicnativeconditionisalsoconsideredaformofrestoration.TheeffectoftherestorationactivitiesonthecarbonstocksandCH4emissionsdependsontheextentofthehydrologicmodificationsthatwereemployedintheprevioussilviculturalsystem.Thetwomostcommonsituationsareasitethathasbeenmanagedforaparticularspeciesorproductwithouthydrologicmodification;theothercommonsituationiswherethesitehasbeenmanagedforplantationforestryandthehydrologyandvegetationhavebeenextensivelymodified.

4.2.1.7 WaterQualityManagement

Riparianzonesalongstreams,rivers,andlakesmaybemanagedtoprotectwaterqualitybymitigatingnonpointsourcepollution(Balestrinietal.,2011;Chaubeyetal.,2010).4Pollutantsareremovedbyphysicalfiltration,chemicaladsorption,plantuptake,andmicrobialtransformations(Abu‐Zreigetal.,2003;Borinetal.,2005).5However,riparianbuffersarelimitedintheiradsorptioncapacitiesforsomeconstituents,whichmaythenflowintowaterways.Thebufferzonesizeandconfigurationvariesaccordingtorunoffpatternsofthesite,phosphorus/nitrogeninputs,hydrologicconnectivity,organiccarbon,mineralcontent,andoxidative/reductivestate(Abu‐Zreigetal.,2003;Hoffmannetal.,2009;Novaketal.,2002;YoungandBriggs,2008).

Riparianbufferzonesarecomprisedofnativeandnon‐nativevegetationormayalsocontaincultivatedplantsinsomecases.Managementactivitiesofthenativevegetationbufferzonesaretypicallyconstrainedorlimitedtosmallremovals.Inthecaseofforestriparianbuffers,aselective‐4Additionalreferencesinclude(Choetal.,2010;Fliteetal.,2001;Hoffmannetal.,2009;Huntetal.,2004;Leeetal.,2004;Lowranceetal.,2007;Montreuiletal.,2010;PeterjohnandCorrell,1984;RanalliandMacalady,2010;Schoonoveretal.,2005;Tabacchietal.,1998;YoungandBriggs,2008).5Additionalreferencesinclude(Dillahaetal.,1989;Dillahaetal.,1988;Hoffmannetal.,2009;Jordanetal.,2003;Kellyetal.,2007;Novaketal.,2002;Vellidisetal.,2003;YoungandBriggs,2008).


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harvestregimewouldbeusedthatinfluencesbothcarbonstocksandGHGemissions.Inmixedbuffers(i.e.,grassstripsfollowedbyforest),themanagementofthecultivatedbufferwouldlargelydeterminetheeffectofthepractice,whichwillbeanalogoustohaycultivation.Riparianzonesmaycontainamosaicofhydric(wetland)andnon‐hydricsoils;accordingly,thedistributionofsoiltypesisimportantforassessingtheeffectofthemanagementactivity.

Whereasriparianbuffersoccupylowlandscapepositionsandaretypicallywet,theyareoftenveryeffectiveinremovingnitrogenviadenitrification(Ambus,1991;Davisetal.,2008;Dodlaetal.,2008;Hilletal.,2000;Huntetal.,2007;Jordanetal.,1998;Roobroecketal.,2010;Smithetal.,2006;Stoneetal.,1998;Woodwardetal.,2009),whichleadstoindirectN2Oemissions(Jetten,2008).Denitrificationinriparianbuffersisoftenspatiallyunevenbecauseriparianbuffersvaryconsiderablyintheirsizeandlandscapepositionsaswellastheirsoil,vegetative,andhydrologicalconditions(Bowdenetal.,1992;BrulandandMacKenzie,2010;Fliteetal.,2001;Hilletal.,2000).StudieshavesuggestedthatN2Oemissionsinriparianzoneswerenotasignificant“pollution‐swappingphenomenon”(Dhondtetal.,2004;Kimetal.,2009a;Kimetal.,2009b).Significantemissionsarelikelytobelimitedtospatialandtemporalhotspots(Groffmanetal.,2000;Huntetal.,2007;Kimetal.,2009b).Moreover,someriparianwetlandsystemscanserveassinksfornitrogen(Roobroecketal.,2010).WhilemanyfactorsaffectthemicrobialproductionofN2O,oneofthemostdominatingfactorsisthecarbontonitrogenratio;largerratiosgenerallyhavelowN2Oemissionsbecausenitrogenisimmobilizedinthesoilorganicmatter(Huntetal.,2007;Klemedtssonetal.,2005).However,itisimportanttonotethatindirectN2Oemissionsareattributedtothesourceofthenitrogen,whichcanbeaneighboringfieldorlivestockfacility;sothemethodstoestimateindirectN2Oemissionsareprovidedinothersectionsofthisreport(i.e.,Chapter3,CroplandandGrazingLands,orChapter5,AnimalProduction).

RiparianbufferscanserveasbothsourcesandsinksofCH4(Hopfenspergeretal.,2009;Soosaaretal.,2011).TheirhydrologyandbiogeochemicalcharacteristicsexhibitsignificantinfluenceonthenetCH4emission.Thesecharacteristicsincludewatertableposition,temperature,oxidative/reductivepotential,andplantcommunitycompositions(Pennocketal.,2010;Whalen,2005).Moreover,N2Oemissionsfromdenitrificationcanbesignificantlyinfluencedbymethanotrophs(Costaetal.,2000;Knowles,2005;Modinetal.,2007;Osakaetal.,2008).

Similarbuffersexistforgrasswetlands,eitheraspartofaconservationprogramorasanaturallyoccurringareaaroundawetlandwheremoist‐soilconditionspreventtillage.Grassbuffersreducerunoffandinterceptsedimentsthatwouldaffectwaterqualitybyincreasingturbidityandinputsoffertilizersandagrichemicals.Moreover,plantingtheentirecatchmentwithgrasscanreduceCH4emissionsbydecreasingtheartificiallyhighwaterlevelsandextendedhydroperiodsthatoftenareassociatedwithcroplandsites(EulissJrandMushet,1996;Gleasonetal.,2009;vanderKampetal.,2003).

4.2.1.8 WetlandManagementforWaterfowl

Wetlandsmaybemanagedforwaterfowlhabitat.ActivitiesthatarespecifictowetlandwaterfowlmanagementhavedirectinfluencesoncarbonstocksandGHGemissions,includingregulationofthewaterregime,specificallydepthanddurationofinundation,aswellasplantingandcultivationofcropsforfoodandhabitat.Waterregimesimposedforwaterfowlmanagementmaybedifferentthanthenaturalwatertablecycleofthesite.Accordingly,changingthewatertablealterstheperiodsofsoilaerationandsaturationinfluencingratesofCH4andN2O,aswellascarbonstockchangesintimberstandsandotherwetlandvegetation.CultivatingcropsinwetlandsmanagedforwaterfowlwillalsoinfluencecarbonstocksandN2Oemissionsbasedonselectionofcropsand/orrotationpractice,tillage,liming,andnutrientmanagement.


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4.2.1.9 ConstructedWetlandsforWastewaterTreatment,SedimentCapture,andDrainageWaterAbatement

Constructedwetlandsareengineeredsystemsforwastewatertreatment,captureofsediments,anddrainagewaterabatementinagriculturalandforestryoperations(Chenetal.,2011;Elgoodetal.,2010).Surface‐flowandsubsurfaceflowsystemsarethetwoprincipaltypesofconstructedwetlands(KadlecandKnight,1996).Theprincipaldifferencebetweenthesetwotypesofconstructedwetlandsisthewaterflowpath.Inthecaseofthesubsurfaceflowwetlands,allthewaterflowsarebeneaththesoilsurface;thesurface‐flowsystemshaveflowbothaboveandwithinthesoil.

Thesubsurfacewetlandstypicallyconsistofwetlandplantsgrowinginabedofhighlyporousmediasuchasgravelorwoodchipsthathaveawatertablefromonetotwometersabovethesoilsurfacewitharectangularshape.ThereislackofagreementabouttherelativeimpactofmicrobialandplantprocessesinthefunctionofsubsurfacewetlandsincludingGHGemissions.However,plantsandmicrobesaretypicallyinterdependentlyinvolvedintheprocessesthatcontributetoemissions(Faubertetal.,2010;Luetal.,2010;Piceketal.,2007;TannerandHeadley,2011;Wangetal.,2008;Zhuetal.,2007).WhilethemicrobialcommunitydrivesthebiogeochemicalprocessesthatspecificallyemitGHGs(Dodlaetal.,2008;Faulwetteretal.,2009;Huntetal.,2003;Tanneretal.,1997;Zhuetal.,2010),theplantcommunitymodifiestheenvironmentalconditionscontributingtoemissionrates,includingtransportingoxygenintothedepthofthewetlands,providingrootsurfacesforrhizospherereactions,andventinggasestotheatmosphere.Theplantprocessesaresignificantlyimpactedbyplantcommunitycompositionandweatherconditions(Steinetal.,2006;SteinandHook,2005;Tayloretal.,2010;Towleretal.,2004;Wangetal.,2008;Zhuetal.,2007).

SurfaceflowwetlandshaveamuchmoredirectexchangeofoxygenandGHGswiththeatmosphere.Theycanbevariableinshapeandaregenerallylessthan0.5metersindepth.Surfacewetlandsminimizecloggingproblems,buttheycanhaveasignificantlossoftreatmentasaresultofchannelflow.Theyaretypicallydesignedforeithercarbonornitrogenremoval(Steinetal.,2006;Steinetal.,2007;Stoneetal.,2002;Stoneetal.,2004),includingthepreventionofexcessiveammoniaemissions(Poachetal.,2004;Poachetal.,2002).

Constructedwetlandsaretypicallycreatedinuplandsettings(e.g.,non‐wetland);accordingly,thesiteassumesthesamebiogeochemicalprocessesthatareinherenttonaturalwetlands.CarbonstocksandGHGemissionsareaffectedbythetypeandquantityofeffluentbeingtreated,thetypeofvegetationinthewetlandcells,andmanagementofthehydrologicregimeswithinthecells.ThemanagementofCH4andN2OfromconstructedwetlandsissomewhatsimilartomanagingGHGemissionsfromwetlandricesystems(Feyetal.,1999;Freemanetal.,1997;Johanssonetal.,2003;Maltais‐Landryetal.,2009;Manderetal.,2005a;Manderetal.,2005b;Piceketal.,2007;Tanneretal.,1997;TeiterandMander,2005;Wuetal.,2009).Ofparticularimportanceisthemaintenanceofwetlandoxidative/reductivepotentialsthataresufficientlypositivetoavoidCH4production(InsamandWett,2008;SeoandDeLaune,2010;Tanneretal.,1997).Thisrequireshigherlevelsofoxygenandlowerlevelsofavailablecarbon.ThemanagementofN2Oemissionsiscomplicatedbythefactthatnitratesareoftenpresentinthewastewatersordrainagewaters,andsoGHGemissionscanbereducedintheconstructedwetlandsifN2gasisemittedinsteadofN2O.CompletedenitrificationtoN2gasrequireshighercarbon/nitrogenratios(Huntetal.,2007;Hwangetal.,2006;Klemedtssonetal.,2005).Thus,thereisanimportantbalancebetweensufficientcarbonforcompletedenitrificationandcopiouscarbonthatdriveswetlandsintothelowredoxconditionsassociatedwithCH4production.


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Thissectionisincludedforcompleteness,butnomethodforconstructedwetlandsisprovidedinthissection.Section5.4.10inChapter5,AnimalAgriculture,providesaqualitativediscussionofestimatingemissionsfromliquidmanurestorageandtreatment‐constructedwetlands.However,Chapter5doesnotprovidemethodstoestimategreenhousegasemissionsfromconstructedwetlands.

4.2.2 Land‐UseChangetoWetlands

Conversionoflandtowetlandsmayinvolverestoringagriculturallandintoafunctioningwetland.However,wetlandscanberestoredfrompreviouslydrainedforestorgrasslands,andthechangetendstovaryfordifferentregionsoftheUnitedStates.Wetlandscanalsobeconstructedinanylocationforwastewatertreatment.Theoriginalconversionofwetlandstoanotherusetypicallyinvolvesanalterationofthenaturalwetlandhydrology.Chapter7,LandUseChange,addressesthistypeofconversion.Restorationofwetlandsentailsreestablishmentoftherequisitehydrologytosupportforest,scrub‐shrub,sedge,oremergentwetlandplantcommunitiesandoccursinfloodplains,riparianzones,depressions,andslopesandvalleys.

4.2.2.1 ActivelyRestoringWetlands

TheeffectofrestoringbothforestedandgrasswetlandswillleadtocarbonsequestrationandCH4emissionsthatwouldbecharacteristicforthatwetlandtype.However,theextenttowhichcarbonsequestration,organicmatterturnover,andgasfluxesreturntoratestypicalforthewetlandtypedependsonmanyfactors,particularlythedegreeofalteration,timesincerestoration,hydrology,anddevelopmentofthevegetation.Ingeneral,restoredsiteswillbecarbonsinksduetosequestrationinthedevelopingbiomass(e.g.,foreststand)andsoils(EulissJretal.,2008).Soilcarbonisexpectedtoincreaseslowlyinforestedsettingsandsomewhatmorerapidlyingrasslandsites(Gleasonetal.,2009);however,theextentandratesofchangeareuncertain.ReestablishmentofthewetlandhydrologywillalsoaltertheCH4fluxfromtherestoredsitesincehydrologicmodificationsforotherlanduseswilltypicallyinvolvedrainageordiversions.RaisingthewatertableandincreasingtheperiodoftimethatthesoilsurfaceiscoveredwithwaterwillincreaseCH4production.However,manyrestoredgrasslandsitesarenotdirectlydrained,andreestablishmentofgrassesinthecatchmentcanshortenthehydroperiod(VanDerKampetal.,1999;Voldsethetal.,2007),thusreducingCH4production.

Conversionofscrub‐shrubwetlandstypicallyinvolvesdrainagetoanon‐wetlandstate,andtheimpositionofcultivationorotherpracticesdependingonthelanduse.Accordingly,therestorationofprior‐convertedscrub‐shrubwetlandstypicallyinvolvesreestablishmentofthenaturalwetlandhydrologyandselectiveplantingtoestablishnativevegetation.ThedevelopmentofthecharacteristicwetlandhydrologyistheprincipalfactoraffectingthecarbonstocksandGHGemissionsfromthesitefollowingconversion,butthetypeofvegetationandtimesinceestablishmentwillalsohavesomeinfluence.

4.2.2.2 CreatedWetlands

Createdwetlandsareengineeredintonon‐wetlandoruplandsites.Typicalexamplesincludemitigationsites,anaerobiclagoons(SeeSection5.4.10inChapter5,AnimalAgriculture)onlivestockoperations,andstormwaterdetentionbasins.TheprincipalactivityaffectingthecarbonstocksandGHGemissionsistheimpositionofahydrologicregimethatinduceshydricsoilpropertiesandsupportshydrophyticplants,inadditiontoclearingofthepreviousvegetationthatmayleadtoachangeinbiomasscarbonstocks.


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4.2.2.3 PassiveRestorationofWetlands

Allowinganareatoregeneratethroughnaturalsuccessionisalsoconsideredaformofrestoration.TheeffectoftherestorationactivitiesonthecarbonstocksandCH4emissionsdependsonwhethertherewashydrologicremediationandthedegreeofvegetationchangeovertime.

4.3 EstimationMethodsSection4.3.1providesmethodsforestimatingliveanddeadbiomassinforested,shrub,andgrasslandwetlands.Section4.3.2providesmethodsforestimatingsoilC,N2O,andCH4emissionsfrommanagednaturallyoccurringwetlands.

4.3.1 BiomassCarboninWetlands


Variousapproachesareusedforestimatingtreebiomasscarbon,butultimatelyeachreliesonallometricrelationshipsdevelopedfromacharacteristicsubsetoftrees.TheForestVegetationSimulator(FVS)hasbeenselectedasthemethodtoestimatetreebiomass.FVSismodel‐basedapproachthatisspecifictoU.S.conditionsandaTier3methodasdefinedbytheIPCC.ThesimulatoristhemostcompletemodelintheUnitedStatestoestimatetreebiomass.RegionalversionsofFVShavebeenrefinedbasedonlargedatabasesdevelopedfrommanyyearsofdatacollectiononforeststandsthroughouttheUnitedStates,therebyprovidingimprovedestimateswhilerequiringfewinputparametersfromtheuser.

BothIPCC(2006)andEPA(2011)considerherbaceousbiomasscarbonstockstobeephemeral,andrecognizethattherearenonetemissionstotheatmospherefollowinggrowthandsenescence.However,withrespecttochangesinlanduse(e.g.,foresttocropland),theIPCC(Lascoetal.,2006)recommendsthatgrazinglandbiomassbecountedintheyearthatlandconversionoccurs(Verchotetal.,2006).AccordingtotheIPCC,accountingfortheherbaceousbiomasscarbonstockduringchangesinlanduseisnecessarytoaccountfortheinfluenceofherbaceousplantsonCO2uptakefromtheatmosphereandstorageintheterrestrialbiosphere.ThemethodisconsideredaTier2methodasdefinedbytheIPCCbecauseitincorporatesfactorsthatarebasedonU.S.specificdata.

Themethodspresentedinthissectionarebasedonthefollowingdefinitions.

Livevegetationbiomass:Livevegetationincludestrees,shrubs,andgrasses.Thetreecarbonpoolincludesabovegroundandbelowgroundcarbonmassoflivetrees,asdefinedinSection6.2.3.1,andtheabovegroundbiomassoftheforestunderstoryisdefinedinSection6.2.3.2.Themethodstoestimatefull‐treeandabovegroundbiomassfortreesgreaterthanoneinchindiameteratbreastheightarebasedonthemodelsprovidedintheforestsection.

MethodforEstimatingLiveandDeadBiomassCarboninWetlands

MethodsforestimatingforestvegetationandshrubandgrasslandvegetationbiomasscarbonstocksuseacombinationoftheForestVegetationSimulatormodelandthebiomasscarbonstockchangesmethodinSection3.5.1ofChapter3,CroplandandGrazingLand.Ifthereisaland‐usechangetoagriculturaluse,methodsforcroplandherbaceousbiomassareprovidedinChapter3.

Thesemethodswerechosenbecausetheyofferthemostconsistentapproachwithinthecontextofthisreport.


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Theforestunderstoryvegetationincludesallbiomassofundergrowthplantsinaforest,includingwoodyshrubsandtreeslessthanoneinchindiameteratbreastheight.

Standingdeadwood(deadbiomass):ThecarbonpoolofstandingdeadwoodinaforestedwetlandisdefinedandestimatedaccordingtothemethodsinSection6.2.3.3ofChapter6,Forestry.

Downdeadorganicmatter—litterlayer(deadbiomass):Downdeadorganicmatterincludesthelitterlayercomposedofsmallpiecesofdeadwood,branches,leaves,androotsinvariousstagesofdecay.Thislayeristypicallydesignatedastheorganiclayerofthesoil.Thispoolalsoincludeslogsinvariousstagesofdecaythatlieonthesoilsurface(e.g.,Section6.2.3.4,down‐deadwood,andSection6.2.3.5,forestfloororlitter).


Provisionsforestimatingabovegroundbiomassforwetlandforestsandaboveandbelowgroundbiomassandcarbonareincludedforshrubandgrasswetlandsinthissection.Sincethevegetativecoveronwetlandsmayvaryfromnaturalcommunitiestoagriculturalcrops,cross‐referencesaremadetoensurecongruitywithSection3.5.1ofChapter3,Croplands,andGrazingLands,andSection6.2.3ofChapter6,Forestry.

Forestvegetation:BiomasscarbonstocksareestimatedforforestsinwetlandsusingthemethodsdescribedinSection6.2.3ofChapter6,Forestry.TheapproachusestheFVS,whichisasystemofgrowthandyieldmodelsthatestimategrowthandyieldforU.S.forests.FVSisanindividualtreemodelandcanestimatebiomasscarbonstockchangefornearlyanytypeofforeststand.TheFireandFuelsExtensiontoFVScanbeusedtogeneratereportsofallliveanddeadbiomasscarbonpoolsinadditiontoharvestedwoodproducts.RegionalvariantsareavailableforFVSthatallowforregion‐specificfocusonspeciesandforestvegetationcommunities.Thedriverforproductivityistheavailabilityofsiteindexcurves,6andtheregionalvariantsincludemanywetlandtreespecies.RegionalvariantsofFVSmayalsoprovideprovisionsforrefiningthebasisforestimatingproductivitybyclassifyingtheareaofinterestintoecologicalunits,habitattype,orplantassociations.However,ifaspecies‐specificcurveisnotavailable,thenadefaultfunctionisusedtoestimatecarbonstockchanges.

Grasslandvegetation:Thechangeincarbonstockforgrasswetlandsisgenerallysmallunlesstherearedroughtconditionsortheareaisactivelymanaged.Incaseswherereportingisrequired,biomasscarbonstockchangescanbeestimatedfollowingalandusechangeusingthemethodinSection3.5.1ofChapter3,CroplandsandGrazingLands.Therearenomethodscurrentlyavailabletoestimatetheshrubcover.


Forestedwetlands:ThedataandrequirementsforestimatingthechangesincarbonstocksinwetlandforestsarethesameasthosedescribedforuplandforestsinSection6.2.3.

Grasslandvegetation:ThedataandrequirementsforestimatingthechangesincarbonstocksingrasslandvegetationarethesameasthosedescribedfortotalbiomasscarbonstockchangespresentedintheCroplands/GrazingLandsSections3.5.1.

6Siteindexisthemeasureofaforest’spotentialproductivity.Theheightofthedominantorco‐dominanttreesataspecifiedageinastandarecalculatedinanequationthatusesthetree’sheightandage.Siteindexequationsdifferbytreespeciesandregion.Siteindexcurvesareconstructedbyusingthetreeheightsatabaseageandanequationisderivedfromthecurvestoestimatethesiteindexwhenanindividualtree’sageisnotthesameasthebaseage(Hansonetal.,2002).


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4.3.1.4 ModelOutput

Changeinabovegroundcarbonpoolsassociatedwithwetlandforestsareprovidedforlivevegetation,standingdeadbiomass,anddowndeadbiomass.Changeinlivebiomasscarbonisalsoprovidedforbelowgroundbiomass.Theunitsofreportingaremetrictonnesha‐1CO2‐eq.


Estimatesoftheforestbiomasscarbonpoolsinwetlandsareconstrainedbylimiteddataonproductivityresponsetomanagementandaresensitivetothewidearrayofcharacteristicvegetativecommunitiesandsoiltypes.AlthoughFVSisthemostinclusivemodelavailable,manyresultsforwetlandswillstillbebasedondefaultmodelfunctions,becausethereislimiteddataonthegrowthofspecificwetlandspeciesunderparticularmanagementregimes.Accordingly,theresultswillprovidearelativebasisfortrackingchangesovertimeinbiomasscarbon.Table4‐3summarizesadditionallimitationsinthecurrentapproach.

Table4‐3:KeyLimitationstoEstimatingBiomassCarbonPoolsinForestWetlandVegetation

Consideration Limitation

Ratioforbelowgroundbiomass

Aratioisused toestimatebelowgroundbiomassinuplandandwetlandforestsbasedonabovegroundbiomass.Whileacommonratiowillprovideabasisforestimatingrelativechange,itwilllikelyoverorunderestimateactualstocksinmanywetlands.

Responsetomanagementorclimaticconditions

Wetlandvegetationisknowntorespondtomanagementpractices,soil, andclimaticconditions.ThoserelationshipsarenotnecessarilyreflectedinFVSbecausethereisinsufficientbasisforgeneralizedassessmentpurposes.Forexample,inresponsetodynamicwater‐levelfluctuationsduringwetanddrycycles,wetlandsoftenexhibitmajorintraandinterannualshiftsinvegetativestructure,rangingfromopenwatertoemergentherbaceousvegetation.Correspondingly,thealteredsiteconditionsunderthemanagementregimeandthegeneticqualityoftheplantedtreesmayexhibitresponsesthatarenotcapturedbytheexistingallometricrelationshipsinFVS.

ThisshrubandgrasslandmethodisbasedontheassumptionsfoundinChapter3,CroplandandGrazingLand.Essentially,themethodassumesthathalfofthecropbiomassatharvestorpeakforage/shrubbiomassprovidesanaccurateestimateofthemeanannualcarbonstock.Thisassumptionwarrantsfurtherstudy,andthemethodmayneedtoberefinedinthefuture.

Majorsourcesofuncertaintyincludebelowgroundbiomass,vegetationresponsetomanagement,andhydrologicregime(e.g.,seasonalhydroperiod).Uncertaintyinherbaceouscarbonstockchangeswillresultfromlackofprecisionincroporforageyields,residue‐yieldratios,root‐shootratios,andcarbonandcarbonfractions,aswellastheuncertaintiesassociatedwithestimatingthebiomasscarbonstocksfortheotherlanduses.

Measurement,sampling,andregression/modelingerrorsareallpartoftheestimationprocessinFVS.Somesimilarmeasureoftherepresentativenessofselectedforestinventoryandanalysisplotstotheentities’forestsisneeded.Uncertaintiesaboutcarbonconversionfactorsarealsosignificantinsomecases.


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4.3.2 SoilC,N2O,andCH4inWetlands

4.3.2.1 RationaleofMethod

Theproductionandconsumptionofcarboninwetland‐dominatedlandscapesareimportantforestimatingthecontributionofGHGs,includingCO2,CH4,andN2Oemittedfromthoseareastotheatmosphere.ThegenerationandemissionofGHGsfromwetland‐dominatedlandscapesarecloselyrelatedtoinherentbiogeochemicalprocessesthatalsoregulatethecarbonbalance(RoseandCrumpton,2006).However,thoseprocessesarehighlyinfluencedbythelanduse,vegetation,soilorganisms,chemicalandphysicalsoilproperties,geomorphology,andclimate(SmemoandYavitt,2006).

Giventhiscomplexity,aprocess‐basedmodelingapproachisdesirablebecausetheseapproachestypicallyaccountformoreofthevariabilitythansimpleremissionfactormethods(IPCC,2006).However,fewprocess‐basedmodelshavebeentestedsufficientlytobeusedforoperationalreportingofGHGemissions.OneofthemorewidelytestedmodelsforestimatingGHGfluxesfromwetlandsistheDNDCmodel.DNDCisaprocess‐basedbiogeochemicalmodelthatisusedtopredictplantgrowthandproduction,carbonandnitrogenbalance,andgenerationandemissionofsoil‐bornetracegasesbymeansofsimulatingcarbonandnitrogendynamicsinnaturalandagriculturalecosystems(Lietal.,2000;Miehleetal.,2006;Stangetal.,2000)andforestedwetlands(Zhangetal.,2002).Themodelisdesignedtoexplicitlyconsideranaerobicbiogeochemicalprocesses,whicharefundamentaltoaddressingsoilcarbondynamicsandtraceGHGdynamicsinwetlands(Trettinetal.,2001).Itintegratesdecomposition,nitrification–denitrification,photosynthesis,andhydro‐thermalbalancewithintheecosystem.Thesecomponentsaremainlydrivenbyenvironmentalfactors,includingclimate,soil,vegetation,andmanagementpractices.

DNDChasbeentestedandusedforestimatingGHGemissionsfromforestedecosystemsinawiderangeofclimaticregions,includingboreal,temperate,subtropical,andtropical(Kesiketal.,2006;Kieseetal.,2005;Kurbatovaetal.,2008;Lietal.,2004;Stangetal.,2000;Zhangetal.,2002),andsimilarlyforgrasslandsandcultivatedwetlands(Giltrapetal.,2010;Rafiqueetal.,2011).


Themethodconsistsofusingtheprocess‐basedmodel—DNDC—toestimatethechangesinsoilorganiccarbon(SOC)stocks,CH4,andN2Oemissions,basedonthestandingbiomassandplantgrowththatareprovidedbythevegetationmethodoutlinedabove(Section4.3.1),wetlandcharacteristics,andtheplannedmanagementactivities.ThemodelsimulatesSOCstocks,CH4,andN2Oemissionsatthebeginningofthereportingperiodbasedonanassessmentofinitialconditionsatthesite;thenthemodelsimulatesthereportingperiodbasedonthecurrent/recentmanagementactivityandanychangesinthewetlandconditions.Thisinformationcharacterizesthephysicalandchemicalsoilpropertiesthatinturninteractwiththeclimaticregime,managementpractices,and

MethodforEstimatingSoilC,N2OandCH4 inWetlands

TheDNDCprocess‐basedbiogeochemicalmodelisthemethodusedforestimatingsoilC,N2O,andCH4emissionsfromwetlands.

DNDCpredictssoilcarbonandnitrogenbalanceandgenerationandemissionofsoil‐bornetracegasesbysimulatingcarbonandnitrogendynamicsinnaturalandagriculturalecosystems(Lietal.,2000;Miehleetal.,2006;Stangetal.,2000)andforestedwetlands(Daietal.,2011;Zhangetal.,2002),usingplantgrowthestimatedasdescribedinSection4.3.1.


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thevegetationresponse.Thereportedemissionsforthelandparcelmustreflectthetotalfortheentirelandarea.Accordingly,theper‐unitareaemissionratesfromDNDCareexpandedbasedonthetotalwetlandareaforthelandparceltoestimatetotalemissions.

Equation4‐1isusedtoestimateSOCstockchangesfromaparceloflandinawetland:

Equation4‐2isusedtoestimateCH4emissionsfromaparceloflandinawetland:

N2OemissionsareestimatedforalandparcelinawetlandusingEquation4‐3:

Equation4‐1:ChangeinSoilOrganicCarbon StocksforWetlands

ΔCSoil=(SOCt‐SOCt‐1)xAxCO2MW

Where:

ΔCSoil =Annualchangeinmineralsoilorganiccarbonstock(metrictonsCO2‐eqyear‐1)

SOCt =Soilorganiccarbonstockattheendoftheyear(metrictonsCha‐1)

SOCt‐1 =Soilorganiccarbonstockatthebeginningoftheyear(metrictonsCha‐1)

A =Areaofparcel(ha)

CO2MW =RatioofmolecularweightofCO2toC=44/12(metrictonsCO2(metrictonsC)‐1)

Equation4‐2:MethaneEmissionsfromWetlands

CH4=ERxAxCH4MWxCH4GWP

Where:

CH4 =TotalCH4emissionsfromthelandparcel(metrictonsCO2‐eqyear‐1)

ER =Emissionrateonaperunitwetlandarea(metrictonsCH4ha‐1year‐1)

A =Area(ha)

CO2MW =RatioofmolecularweightofCH4toC=16/12(metrictonsCH4(metrictonsC)‐1)

CH4GWP =GlobalwarmingpotentialofCH4

Equation4‐3:NitrousOxideEmissionsfromWetlands

N2O=ERxAxCO2MWxCH4GWP

Where:

N2O =TotalN2Oemissionsfromthelandparcel(metrictonsCO2‐eqyear‐1)

ER =Emissionrateonaperunitlandarea(metrictonsN2Oha‐1year‐1)

A =Area(ha)

CO2MW =RatioofmolecularweightofN2OtoN=44/28

(metrictonsN2O(metrictonsN2O‐N)‐1)

CH4GWP =GlobalwarmingpotentialofN2O


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ToestimatetheSOCstockchanges,CH4,andN2Oemissions,DNDCrequiresaconsiderableamountofinformationtocharacterizetheplantproduction(Section4.3.1),wetlandcharacteristics,andmanagementactivities.TheinitialstepinapplyingthemethodistoparameterizeDNDCusingthebaselinesoilconditions,alongwiththecorrespondingforestorgrasslandconditions.Forexample,ifaforestplantationistobeharvestedandregeneratedduringthereportingperiod,theinitialconditionsshouldreflectthepre‐harvestconditions.Basedontheinitialconditions,themodelsimulatesbaselinefluxesandtheSOCstockpriortothereportingperiodfortheentity.Subsequently,theentityspecifiesthetypeofmanagementactivity(s)changesthatoccurredduringthereportingperiod(ifanyoccurred).Provisionsareavailabletohavemultiplemanagementactivitiesonasingletractifthereweremixedactivities.Climaticfactors,especiallyprecipitation,canaffectcarbonturnoverandwetlandconditions.Consequently,weatherdataareakeyinputtoDNDC,andwillbeprovidedfromaclimatologicaldataset.

ThesimulationoutputattheendofeachyearisusedtoestimatechangeinSOCstocksandthetotalamountofCH4andN2Oemissionsfortheyear.AnnualchangesinSOCcanbeestimatedbasedonthedifferencebetweenyears,andthetotalchangeinemissionscanbeestimatedbycombiningthechangesinSOCpoolswiththeannualCH4andN2Oflux.


ActivitydatafortheapplicationofDNDCaresummarizedinTable4‐4.Vegetationmanagementinformationaffectstheamountoforganicmatterthatisavailablefordecompositionprocesses.Watermanagementinformationconveyshowthedrainagesystemaffectsthesoilwatertabledynamicascomparedtoanundrainedcondition.Thesoiltillageinformationisusedtoconveywhenthesurfacesoilisdisturbedoritselevationchangedbecauseoftheassociatedeffectsondecomposition.ThefertilizationinformationisneededbecausetheadditionofnitrogengreatlyaffectsdecompositionandN2Oproduction.Inaddition,landusehistoryinfluencestheamountofsoilorganiccarbon.Ifanentityiscomposedofdifferentwetlandtypes,itisrecommendedthatseparateestimatesbepreparedbecausethecarbonturnoverrateandGHGemissionscanvarywidelydependingonhydricsoilpropertiesandthetypeofvegetation.

Table4‐4:ActivityDataforApplicationofDNDC

Category ManagementPractice Data

Vegetationmanagement

Grazingormanagementeventsshouldbeincludedtocapturetheinfluenceoncarboninputtosoilsandsubsequenteffectsonthesoilcarbonstocks.

Harvesting:date,harvest,orcutfraction Understorythinningorchopping:date,

choppedfraction Prescribedfire:date,proportionofforest

floor,andunderstoryconsumed Treeplanting:date,species,density

Watermanagementregime

Watertableresponsetothedrainagesystem,dailydata.

Drainagesystem:date,controlledwatertableelevation

Soilmanagement

Applicationofsoilamendmentsorsitepreparationpracticesfortreeplanting. Typeofsitepreparation

Fertilizationpractices

ApplicationsofmineralororganicnitrogenfertilizerswillbeneededtosimulatetheeffectonN2Oemissions.

Fertilizationfrequency,date,applicationrate(N,Pkgha‐1)

Landusehistory

Summaryoflandusepracticesoverthepast5years.Forassessingifprioruseaffectsparameterization.Thetimesinceachangeinlandmanagementpracticeforassessingeffectsondecomposition.

Fertilizationregimes,drainageregimes,cropping,orforestmanagementhistory


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TheDNDCmodelrequiresrelativelydetailedinformationaboutthesite(Table4‐5).Whiledefaultvaluesareavailableformostparameters,someentity‐specificdataareneededtoproducereasonableestimates.Mostoftherequiredsoilsinputdataareavailablefromthenationalsoilsdatabase.7Similarly,climatedataareavailablefromtheNationalClimateDataCenter.8

Table4‐5:InputInformationNeededfortheApplicationofDNDC

Category Data

ClimateDailymaximumandminimumtemperature,dailyrainfall; nitrogendepositioninrainfall,orusedefaultvalue.

Vegetation StandingbiomassandbiomassanddetritalinputsprovidedinSection4.3.1;belowgroundbiomassestimatedbasedonabovegroundbiomass.

Soil

Hydraulicparametersandphysicalandchemicalcomponents, includingthickness;layers;hydraulicconductivity;porosity;fieldcapacity;wiltingpoint;carboncontent;pH;organicmatterfractions;contentofstone,sand,silt,andclay;andbulkdensityformajorsoillayers.

Hydrology WatertablebelowsurfaceasdailyinputorstartingpositionandDNDCcanestimateGHGemissionsandsinksusingempiricalfunctions.

4.3.2.5 ModelOutput

ModeloutputincludesannualestimatesofCH4,N2Oemissions,andchangesinsoilorganiccarbonstocks.TheunitsofreportingaremetrictonsCO2‐eqha‐1.


Themodelstoestimatecarbonsequestrationinvegetationarerobustwithrespecttospeciesandcommunitycomposition.However,uncertaintiesmaybehigherthanforuplandsbecauseoflimitedbackgroundinformation.ThemeritoftherecommendedapproachisthatitensuresconsistencyforestimatingchangesinthevegetativecarbonpoolamonglandtypesandusesbyusingcommonmethodsasdescribedinSection4.3.1.However,thisapproachcomplicatestheapplicationofDNDCforestimatingchangesinsoilcarbonpoolsandfluxesbecauseitcontainsprovisionsforsequesteringcarbonincrops,grasslands,andforestvegetation.Accordingly,DNDCwouldhavetoundergosubstantialrevisionstoaccommodatethevegetativecomponentasaninputvariablebecausethevegetationgrowthfunctionsareintegralwiththeconsiderationofhydrologicprocesses(especiallyevapotranspiration)andbiogeochemicalprocesses.TheDNDCmodelcouldbeusedasastand‐alonetoolforwetlands,butunfortunately,theproductionorcarbonsequestrationfunctionshavenotbeenvalidatedformanyofthewetlandplantcommunities.

Theavailabilityofwatertabledataisessentialtomodelingthecarboncycleinwetlandsoils.Sincethelackofsite‐specificwatertabledataforasufficientperiodislikelyaconstraintformostentities,anapproachincorporatingahydrologicmoduleorlook‐uptableisneeded.Hydrologicmodelsthatprovideinformationonwatertabledynamicsareinherentlycomplex,buttheycanbeeffective(Dai.etal.,2010).Accordingly,thedevelopmentofcharacteristicwatertableconditionsforarangeofclimatologicalandsoilsettingswouldbeaviableapproachthatcanalsoincorporatewatermanagementeffects(e.g.,Skaggsetal.,2011).

7SeeNationalCooperativeSoilSurveySoilCharacterizationdatahttp://soils.usda.gov/survey/nscd/.8SeeNOAANationalClimaticDataCenterhttp://www.ncdc.noaa.gov/.


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Tidalfreshwaterforestedwetlands,whichoccurtoalimitedextentalongtheAtlantic,Gulf,andPacificcoasts,areaspecialcase.Thetidalinfluenceonwatertabledynamicscanmakecharacterizingthewatertableregimeofsuchsitesmoredifficult.ForDNDCtosimulatethecarbondynamicswouldrequiredetaileddataondailywatertabledynamics,andsuchdetaileddataareunavailable.

WhiletheeffectsofthevariousmanagementregimesonsoilcarbonpoolsandGHGfluxeshavenotbeenwidelystudied,thisismoreofaconsiderationwithrespecttouncertaintiesintheestimatesasopposedtoalimitationtoitsapplication.TheDNDCframeworkisrobustbecauseitisaprocess‐basedmodelthathasbeenvalidatedinawidevarietyofwetlandtypesandsoils.However,ithasnotbeenextensivelytestedonHistosolsorpeatsoils,especiallywithrespecttochangesinsoilcarbonstocks.ThemodelwasvalidatedsuccessfullyforestimatingCH4frommicotopographicpositionsinapeatland(Zhangetal.,2002),butadditionalworkisneededtobetteraddressthewidearrayofmanagedHistosolsthatexistacrossthecountry.

Similarly,thismethodisnotapplicabletoconstructedwetlands,impoundments,orshallowreservoirsystemsthathaveextendedperiodsofponding;thosesiteswouldtendtohavedynamicsmoresimilartoalakeorpondasopposedtoaterrestrialecosystem.

Withrespecttotheforestmodel,accuracyoftheestimatesisdependentonapplicabilityoftheavailablesiteindexcurves.Whilethegeneralcurvesareavailableforallspecies,theymaynotaccuratelyrepresentthesiteortheentity’smanagementregime.ProvisionsareincludedwithinFVSforcustomizingthetreesiteindexcurves,whichcouldbeimportantforanentityespeciallyifgenetically‐improvedplantingstockandfertilizationregimesareemployed.

Detritalorganicmatteristhesourcefordecompositionprocesses.Theeffectofvegetationonwetlandcarbondynamicsispromulgatedthroughtheamountoforganicmatterandthewaterregime(e.g.,evapotranspiration).Accordingly,theaccuracyofthevegetationproductivityandturnoverwillaffecttheestimatesofthesoilcarbonpoolsandGHGflux.

WatertablepositionisthemostcriticalfactoraffectingCH4andN2Ofluxfromthewetlandsoil(Trettinetal.,2006).Accordingly,considerationstoimprovethatestimateasdiscussedinSection4.3.2willimprovetheestimatesofGHGemissionsfromthesoil.Thereareotheruncertaintiesintheactivityandancillarydata,aswellasmodelstructurethatcancreatebiasandimprecisionintheresultingestimates.Wetlandstypicallyexistinamosaicwithuplandforests,grasslands,andcultivatedlands.Accordingly,theaccuracyofpartitioningtheentityintoupland(agriculture,forest)andwetlandswillaffecttheaccuracyoftheestimates.

4.4 ResearchGapsforWetlandManagementWetlandmanagement,anditsinfluenceonGHGemissions,isnotaswellstudiedassomeoftheothermanagementpracticesinthisreport,suchastillageincroplandsorforestharvestingpracticesinuplands.ThereisthepotentialforimprovingtheestimationofGHGemissionsassociatedwithdifferentmanagementpracticesinthefutureiftherearemonitoringactivitiesandstudiestofillinformationgaps.Aselectnumberofinformationneedsandresearchgapsareidentifiedhere.

The2013Supplementtothe2006IntergovernmentalPanelonClimateChange(IPCC)Guidelinesprovidenewguidanceforestimatingemissionsfromdrainedinlandorganicsoils,rewettedorganicsoils,coastalwetlands,inlandwetlandmineralsoils,andconstructedwetlandsforwastewatertreatment(Blainetal.,2013).Thesenewlydevelopedguidelineswillbecomparedtothetechnicalmethodsprovidedinthisreport.


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Watertablepositionistheprincipalfactoraffectingcarbondynamicsinwetlands;unfortunatelythereisalackoflong‐termdata,whichisneededtocharacterizethewatertableresponsetoamanagementregimeandtoprovideabasisforvalidatingassessmenttools.EstablishmentofanetworkofwatertablemonitoringsiteswithinselectedUSDAForestServiceexperimentalforestsandrangesandUSDA‐AgriculturalResearchService(ARS)experimentstationscouldprovidethecontinuityinmeasurementsandlinkageswithcommonmanagementpracticestorepresentthemajorsoilandclimaticconditionintheUnitedStates.

Improvingmodelingcapabilitiesthatintegratesurroundingareaswiththewetlandsthatreceivesurfaceandsubsurfacedrainagewaterswillallowformodelingtheflowsofnutrientsandorganicmatterintowetlandsandsubsequentlossestootherwetlandsbeyondtheentity’soperation.Thistypeofassessmentframeworkisusedinseveralestablishedspatially‐explicithydrologicmodels;theneedistointegratethebiogeochemistry.Linkedmodelscanbeusedatpresent;butdevelopmentofafunctionally‐integratedsystemisneededtosupportbroad‐basedapplications.

Thereisaneed,generally,forimprovedinformationonbiomassproductionandallocationinmanagedwetlands.ThesedatacouldbeobtainedthroughacoordinatedmonitoringprogramemployingUSDA‐ForestServiceexperimentalforestsandranges,USDA‐ARSexperimentstations,andU.S.DepartmentoftheInteriorwildliferefugestomonitorproductionofkeyspeciesorvegetationtypesinassociationwithcommonmanagementprescriptions.Thereisalsoneedformoredetailedmechanisticresearchtoprovideinformationonenergy,water,andGHGdynamicsonselectedmanagedsites;thisinformationiscriticalforvalidatingprocess‐basedmodels.

Field‐basedstudiesareneededtodevelopmorecompletedatabasesthatprovideancillarydataforGHGestimation,particularityCH4emissionsforDNDCorsimilarprocess‐basedmodels,ratherthanrelyingonentityinput,whichwilllikelybechallenging.Akeyattributeofthisworkshouldbetheconsiderationoftheinherentspatialandtemporalvariabilitywithinasite.

FurtherquantificationofthecontrollingandthresholdparametersandassociateduncertaintywithinDNDCorsimilarprocess‐basedmodelstoestimatetracegasemissionsiswarranted.ThisworkcouldalsosuggestapathtowardsdevelopmentofanassessmenttoolthatwasnotreliantonawidearrayofparameterstoeffectivelysimulatetheGHGdynamicsofthesite.

AmorerobustandextensivedatabaseonGHGemissionsfromfreshwatertidal(salinity<0.5‰)palustrinewetlandsisneededtomorefullyunderstandthedriversofemissions,inadditiontoprovidingamorecompletedatasetforparameterizationandevaluationofprocess‐basedmodels.

Studiesonindividualsitesandmeta‐analysesofexistingdataareneededtofullyevaluatethenetGHGfluxforCH4,N2O,andsoilcarbon.MoststudiesonlyconsideroneoftheGHGsandmaymasksomeofthedifferencesinfluxesamongtheGHGsassociatedwithamanagementactivity.

ConstructedwetlandsarediscussedqualitativelyinSection5.4.10ofChapter5,AnimalProductionSystemsforLiquidManureStorageandTreatmentinConstructedWetlands.Moreresearchisneededinthisareatoaccuratelyestimateemissionsfromconstructedwetlands.

ThislistisnotexhaustivebutisintendedtoprovidesomedirectionforimprovingtheestimationmethodsforGHGemissionfromwetlands.


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Authors:WendyPowers,MichiganStateUniversity(LeadAuthor)BrentAuvermann,TexasA&MUniversityN.AndyCole,USDAAgriculturalResearchServiceCurtGooch,CornellUniversityRichGrant,PurdueUniversityJerryHatfield,USDAAgriculturalResearchServicePatrickHunt,USDAAgriculturalResearchServiceKristenJohnson,WashingtonStateUniversityAprilLeytem,USDAAgriculturalResearchServiceWeiLiao,MichiganStateUniversityJ.MarkPowell,USDAAgriculturalResearchService

Contents:5 QuantifyingGreenhouseGasSourcesandSinksinAnimalProductionSystems..............5‐5

5.1 Overview...........................................................................................................................................................5‐55.1.1 OverviewofManagementPracticesandResultingGHGEmissions.........................5‐55.1.2 SystemBoundariesandTemporalScale............................................................................5‐125.1.3 SummaryofSelectedMethods/Models/SourcesofData...........................................5‐125.1.4 OrganizationofChapter/Roadmap......................................................................................5‐14

5.2 AnimalProductionSystems....................................................................................................................5‐185.2.1 DairyProductionSystems........................................................................................................5‐185.2.2 BeefProductionSystems..........................................................................................................5‐225.2.3 SheepProductionSystems.......................................................................................................5‐255.2.4 SwineProductionSystems......................................................................................................5‐255.2.5 PoultryProductionSystems....................................................................................................5‐28

5.3 EmissionsfromEntericFermentationandHousing.....................................................................5‐305.3.1 EntericFermentationandHousingEmissionsfromDairyProductionSystems.......

.............................................................................................................................................................5‐315.3.2 EntericFermentationandHousingEmissionsfromBeefProductionSystems.5‐445.3.3 EntericFermentationandHousingEmissionsfromSheep.......................................5‐525.3.4 EntericFermentationandHousingEmissionsfromSwineProductionSystems......

.............................................................................................................................................................5‐535.3.5 HousingEmissionsfromPoultryProductionSystems................................................5‐605.3.6 EntericFermentationandHousingEmissionsfromOtherAnimals......................5‐645.3.7 FactorsAffectingEntericFermentationEmissions.......................................................5‐665.3.8 LimitationsandUncertaintyinEntericFermentationandHousingEmissionsEstimates.........................................................................................................................................................5‐73

5.4 ManureManagement.................................................................................................................................5‐75

Chapter 5Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems

Chapter 5: Quantifying Greenhouse Gas Sources and Sinks in Animal Production Systems

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5.4.1 TemporaryStackandLong‐TermStockpile.....................................................................5‐775.4.2 Source:U.S.EPA(2011).Composting..................................................................................5‐815.4.3 AerobicLagoon.............................................................................................................................5‐855.4.4 AnaerobicLagoon,RunoffHoldingPond,StorageTanks............................................5‐865.4.5 AnaerobicDigesterwithBiogasUtilization......................................................................5‐915.4.6 CombinedAerobicTreatmentSystems..............................................................................5‐935.4.7 Sand‐ManureSeparation..........................................................................................................5‐945.4.8 NutrientRemoval........................................................................................................................5‐945.4.9 Solid–LiquidSeparation............................................................................................................5‐955.4.10 ConstructedWetland.................................................................................................................5‐975.4.11 Thermo‐ChemicalConversion................................................................................................5‐985.4.12 LimitationsandUncertaintyinManureManagementEmissionsEstimates......5‐99

5.5 ResearchGaps............................................................................................................................................5‐1055.5.1 EntericFermentation..............................................................................................................5‐1055.5.2 ManureManagement..............................................................................................................5‐106

Appendix5‐A:EntericCH4fromFeedlotCattle–MethaneConversionFactor(Ym)..............5‐109Appendix5‐B:FeedstuffsCompositionTable...........................................................................................5‐113Appendix5‐C:EstimationMethodsforAmmoniaEmissionsfromManureManagementSystems.....................................................................................................................................................................5‐123

5‐C.1 MethodforEstimatingAmmoniaEmissionsUsingEquationsfromIntegratedFarmSystemModel...............................................................................................................................5‐123


5‐C.2 MethodforAmmoniaEmissionsfromTemporaryStack,Long‐TermStockpile,AnaerobicLagoons/RunoffHoldingPonds/StorageTanks,andAerobicLagoons.....5‐1245‐C.3 MethodforEstimatingAmmoniaEmissionsfromCompostingUsingIPCCTier2Equations....................................................................................................................................................5‐128


5‐C.4 MethodforAmmoniaEmissionsfromComposting..................................................5‐1295‐C.5 UncertaintyinAmmoniaEmissionsEstimates...........................................................5‐129

Appendix5‐D:ManureManagementSystemsShapeFactors( )...................................................5‐131Appendix5‐E:ModelReview:ReviewofEntericFermentationModels.......................................5‐134Chapter5References..........................................................................................................................................5‐139

SuggestedChapterCitation:Powers,W.,B.Auvermann,A.Cole,C.Gooch,R.Grant,J.Hatfield,P.Hunt,K.Johnson,A.Leytem,W.Liao,J.M.Powell,2014.Chapter5:QuantifyingGreenhouseGasSourcesandSinksinAnimalProductionSystems.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939,OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington.DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.



5-3

Acronyms,ChemicalFormulae,andUnitsAA AminoacidsAD AnaerobicdigestionADF AciddetergentfiberAGP AntibioticgrowthpromotersASABE AmericanSocietyofAgricultural andBiological EngineersB0 MaximummethaneproductioncapacitiesbLS backwardLagrangianstochasticBNR BiologicalnitrogenremovalBW BodyweightCH4 MethaneCNCPS CornellNetCarbohydrateandProteinSystemCO2‐eq CarbondioxideequivalentsCP CrudeproteinCSTR ContinuousstirredtankreactorDDGS DrieddistillersgrainswithsolublesDE DigestibleenergyDFM DirectfedmicrobialsDGS DistillersgrainswithsolublesDIP DietarycrudeproteinDMI DrymatterintakeDRC Dry‐rolledcornEF Emissionfactorg GramsGg GigagramsGEI GrossenergyintakeGHG GreenhousegasHCW HotcarcassweightHMC High‐moisturecornIFSM IntegratedFarmSystemModelkcal Kilocaloriekg Kilogramslb(s) Pound(s)LCA LifecycleanalysisLU Livestockunitm MetersMCF MethaneconversionfactorME Metabolizableenergymg MilligramMGA MelengestrolacetateMJ MillijoulesNE NetenergyNex NitrogenexcretedN NitrogenN2O NitrousoxideNDF NeutraldetergentfiberNFC Non‐fibercarbohydrateNH3 AmmoniaNPN Non‐proteinnitrogen


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NSP Non‐starchpolysaccharideO2 OxygenOM Organicmatterppb partsperbillionppm partspermillionRDP RuminaldegradableproteinRFI ResidualfeedintakeRMSPE ResidualmeansquarepredictionerrorSF6 SulfurhexafluorideSFC Steam‐flakedcornTAN TotalammoniacalnitrogenTDN TotaldigestiblenutrientsTKN TotalKjeldahlnitrogenTMR TotalmixedrationUASB UpflowanaerobicsludgeblanketUP UnprocessedU.S.EPA U.S.EnvironmentalProtectionAgencyVFA VolatilefattyacidsVS VolatilesolidsWDGS WetdistillersgrainswithsolublesYm Methaneconversionfactor,percentofgrossenergyinfeedconverted

tomethane


5-5

5 QuantifyingGreenhouseGasSourcesandSinksinAnimalProductionSystems

Thischapterprovidesguidanceforreportinggreenhousegas(GHG)emissionsassociatedwithentity‐levelfluxesfromanimalproductionsystems.Inparticular,itfocusesonmethodsforestimatingemissionsfrombeefcattle(cow‐calf,stocker,andfeedlotsystems),dairycattle,sheep,swine,andpoultry(layers,broilers,andturkey).Informationprovidedisbasedonavailabledataatthetimeofwriting.Inmanycasessystemsareoversimplifiedbecauseoflimiteddataavailability.Itisexpectedthatmoredatawillbecomeavailableovertime.Thischapterprovidesinsightintothecurrentstateofthescienceandservesasastartingpointforfutureassessments.

Section5.1summarizesanimalmanagementpracticesandtheresultingGHGemissions. Section5.2presentsanoverviewofeachproductionsystemandageneraldiscussionof

commonmanagementsystemsandpractices.

Section5.3describesthemethodsforestimatingGHGemissionsfromentericfermentationandhousing(entericfermentationbeingamuchmoresignificantemissionssourcethan

housing).

Section5.4describesmethodsforestimatingGHGsfrommanuremanagementsystems. Section5.5identifiesresearchgapsthatexistforquantifyingGHGsfromanimalproduction

systems.Theintentofidentifyingresearchgapsistohighlightwhereimprovementsin

knowledgecanbestimprovetheusefulnessofthisdocumentatfarm‐,regional‐,and

industry‐scales.

5.1 Overview

ThissectionsummarizesthekeypracticesinanimalmanagementandtheresultingGHGemissionsthatarediscussedindetailinthischapter.Theagriculturalpracticesdiscussedincludethoserequiredtobreedandhouselivestock,includingthemanagementofresultantlivestockwaste.Emissionsconsideredhereincludethosefromentericfermentation(resultingfromlivestockdigestiveprocesses),livestockwasteinhousingareas,andlivestockwastemanagedinsystems(suchasstockpiles,lagoons,digesters,solidseparation,andothers).OptionsformanagementchangesthatmayresultinchangesinGHGemissionsarealsodiscussed.


Animalproductionsystemsincludeagriculturalpracticesthatinvolvebreedingandrearinglivestockformeat,eggs,dairy,andotheranimalproductssuchasleather,wool,fur,andindustrial

AmmoniaEmissionsinAnimalProductionSystems

Ammonia(NH3),althoughnotaGHG,isemittedinlargequantitiesfromanimalhousingandmanuremanagementsystemsandisanindirectprecursortonitrousoxide(N2O)emissionsaswellasanenvironmentalconcern.Insidebarnsandhousingunits,NH3isconsideredanindoorairqualityconcernbecauseitcanhaveanegativeimpactonanimalhealthandproduction.Volatilizedammoniacanreactwithothercompoundsintheairtoformparticulatematterwithadiameterof2.5microns.Thisfineparticulatemattercanpenetrateintothelungs,causingrespiratoryandcardiovascularproblems,andcontributetotheformationofhaze.

InformationaboutammoniahasbeenincludedinthischapterandproposedquantificationmethodsarepresentedinAppendix5‐C.


5-6

productslikeglueoroils.Farmersandotherfacilityownersraiseanimalsineitherconfined,semi‐confinement,orunconfinedspaces;thepracticesusedtoraisethemaredependentonanimaltype,region,landavailability,andindividualpreferences(e.g.,conventionalor“organic”standards).Regardlessoftheconditionsinwhichanimalsareraisedandhoused,theyproduceGHGemissions.Themagnitudeofemissionsdependsprimarilyonthequalityofthediet,theanimals’requirementsandintake(e.g.,grazing,pregnant,lactating,performingwork),andthetypesofsystemsinplacetomanagemanure.Theprimarysourceofmethane(CH4)emissionsfromanimalproductionsystemsisentericfermentation,whichisaresultofbacterialfermentationduringdigestionoffeedinruminantanimals.Thesecondlargestsourceofemissionsfromanimalproductionsystemsisfromthemanagementoflivestockmanure.Methaneemissionsalsooccurfromthedigestiveprocessesinmonogastricanimals;however,thequantityissignificantlylessthantheseothertwosources.Forsimplicity,inthereport,thetermentericfermentationreferstoemissionsfromthedigestiveprocessofbothruminantandmonogastricanimals.

Manuremanagementisthecollection,storage,transfer,andtreatmentofanimalurineandfeces.Storageofanimalmanurehasbecomeincreasinglypopularasitallowssynchronizationoflandapplicationofmanurenutrientswithcropneeds,reducestheneedforpurchasedcommercialfertilizer,andreducespotentialforsoilcompactionduetopoortimingofmanureapplication.Dependingonthestorageandtreatmentpractices,manuremanagementhastheaddedbenefitofreducingairandwaterpollution.However,manurestoredinanaerobicconditionsresultsintheproductionandpotentialreleaseofGHGsandodors.Greenhousegasemissionsfromthreesolidmanurestorage/treatmentpractices(temporarystackandlong‐termstockpile,composting,andthermo‐chemicalconversion)andeightliquidmanurestorage/treatmentpractices(aerobiclagoon,anaerobiclagoon/runoffholdingpond/storagetanks,anaerobicdigestion,combinedaerobictreatmentsystem,sand‐manureseparation,nutrientremoval,solid‐liquidseparation,andconstructedwetland)areconsideredinthereport.

Figure5‐1providesanoverviewoftheconnectionsbetweenfeed,animals,manure,andGHGemissionsinananimalproductionsystem.Atthetopoftheconceptualmodel,livestockarefedavarietyofdiets.Ruminantanimalseatfeedstuffsand,throughfermentationbytheruminalmicrobes,CH4isproduced.Poultryandswine,althoughtheydonotreleaseasignificantamountofCH4throughentericfermentation,depositmanureintobedding,anduponmanuredecomposition,mayreleasenitrousoxide(N2O),CH4andammonia(NH3)intotheatmosphere.Methodologytoestimateemissionsfrombeddinganddrymanureinhousingissimilarto,andoftenparallelto,themethoddescribedfordrymanurehandlingandstoragesystems.Manurefromgrazinglivestockisleftonfieldsorpaddocks,andthemanuremaybecollectedtobetreatedandstored.Manurethathasbeencollectedandstoredcanbeappliedtocroplands.GHGemissionsfromgrazinglandsandcroplandsareaddressedinChapter3,QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems.


5-7

Figure5‐1:ConnectionsBetweenFeed,Animals,Manure,andGHGforAnimalAgriculture


5-8

5.1.1.1 ResultantGHGEmissions

Forthisreport,methodsarecategorizedaccordingtothosefromentericfermentation,housing,andmanuremanagementsystems.Thehousingdiscussionincludesemissionsfrommanuredepositedinthehousingunitandmanurethatismanagedinsidethoseareas(suchasinteriorstockpiles).Manuremanagementincludesemissionsfrommanaged,treated,andstoredmanure.1

EntericFermentationandHousingEmissionsMethane‐producingmicroorganisms,calledmethanogens,existinthegastrointestinaltractofmanyanimals.However,thevolumeofCH4emittedbyruminantsisvastlydifferentfromthatofotheranimalsbecauseofthepresenceandfermentativecapacityoftherumen.Intherumen,CH4formationisadisposalmechanismbywhichexcesshydrogenfromtheanaerobicfermentationofdietarycarbohydratecanbereleased.Controlofhydrogenionsthroughmethanogenesisassistsinmaintenanceofefficientmicrobialfermentationbyreducingthepartialpressureofhydrogentolevelsthatallownormalfunctioningofmicrobialenergytransferenzymes(Morgavietal.,2010).TheonlyGHGofconcernresultingfromentericfermentationisCH4.RespirationchambersequippedwithN2OanalyzersindicatethatentericfermentationdoesnotresultintheproductionofN2O(Reynoldsetal.,2010).Methanecanalsoarisefromhindgutfermentation,butthelevelsassociatedwithhindgutfermentationaremuchlowerthanthoseofforegutfermentation.

Becausethemagnitudeofentericemissionsissogreatand,therefore,asignificantcontributortomanycountries’GHGemissions,decadesofresearchhavegoneintocharacterizing,understanding,andattemptingtomitigateentericCH4emissions.Afundamentalchallengeinthistypeofresearchhasbeenthemeasurementoftheseemissions.

Methane,N2O,carbondioxide(CO2),andNH3areproducedfromlivestockfecesandurine,andsomegaseousformsareemittedsoonaftermanureexcretion.Indry‐lotsituations,fecesandurinearedepositedonthepensurfaceandaremixedviaanimalhoofaction.MicroorganismsinthefecesorunderlyingsoilmetabolizenutrientsinthemanuretoproduceGHGs.Infeedlots,wheremanureisnormallycleanedfrompensonceortwiceperyear,distinctive,hard‐packedlayersofmanureandsoilmaydevelopthatproducemicroenvironmentsfavorabletooxidativeandreductiveprocesses(Woodburyetal.,2001;Coleetal.,2009b).Periodsofrainfallordryconditionsmayalterthemicrobialandchemicalnatureofthepensurface.ProductionofCH4andN2Ooccurintheunderlyingmanure/soillayersandinwater‐saturatedareaswhereoxygenislimited,suchaswetareasofthepenaroundwatertroughsanddepressionsthatcollectrainwaterandsnowmelt.Incontrast,mostNH3producedinthepenprobablycomesfromfreshurinespotsonthepensurface.Todate,fewmeasurementsofGHGemissionsfromfeedlotordry‐lotpensurfaceshavebeenmade.

Runofffromdry‐lotandfeedlotpensisnormallycollectedinretentionponds(moretypicalinfeedlots),orlagoons(morecommonindairies).Insomecases,runoffmayundergopartialremovalofsuspendedsolidsinsettlingbasins(feedlotsanddairies)orinmechanicalseparators(dairiesonly)thatparallelstreatmentofmanurecollectedinthesesamesystems.LossesofGHGsandNH3

1EmissionsfrommanuredepositedongrazinglandsareaddressedinChapter3:CroplandsandGrazingLands.

Background:Ruminants

Ruminantsareanimalsthathavefour‐chamberedstomachs,whichallowforeasierdigestionofhigh‐fiber,hard‐to‐digestfeedstuffs.Theyinclude: Cattle Goats Sheep Deer AmericanBison


5-9

fromthesefacilitiesdependuponclimaticfactorsandtheoxidative‐reductivepotential,pH,andchemistryoftheeffluentinthepondorlagoon.AlimitednumberofstudieshavemeasuredGHGorNH3emissionsfromretentionpondsorlagoons.

ManureManagementManureismanagedinawidevarietyofsystems.TheresultingGHGemissionsdifferbyGHGandmagnitudeofemissionsperquantityofmanure.Table5‐1providesanoverviewoftheliquidandsolidmanuresystemsconsideredinthisreportandtheresultingGHGs.

Table5‐1:OverviewofManureManagementSystemsandAssociatedGreenhouseGases

StorageandTreatmentPractices

EstimationMethod Description

CH4 N2O NH3a

SolidManure

Temporaryandlong‐termstorage

Manuremaybestoredtemporarilyforafewweekstoavoidlandapplicationduringunfavorableweatheroritcanbestoredforseveralmonths.

Composting

Compostinginvolvesthecontrolledaerobicdecompositionoforganicmaterialandcanoccurindifferentforms.Estimationmethodsareprovidedforinvessel,staticpile,intensivewindrow,andpassivewindrowcomposting.

Thermo‐chemicalconversion

Thermo‐chemicalconversioninvolvesthecombustionofanimalwaste,convertingCH4toCO2.Pyrolysis/gasificationisonemethodthathasreceivedmuchinterest.NomethodisprovidedasGHGsareconsiderednegligible.

LiquidManure

Aerobiclagoon Aerobiclagoonsinvolvethebiologicaloxidationofmanureasaliquidwithnaturalorforcedaeration.

Anaerobiclagoon/runoffholdingponds/storagetanks

Anaerobiclagoonsareearthenbasinsthatprovideanenvironmentforanaerobicdigestionandstorageofanimalwaste.Lagoonsmaybecoveredoruncoveredandhaveacrustornocrustformation.Runoffandholdingpondsareconstructedtocaptureandstorerunofffromfeedlotsanddry‐lots.Insomecaseswashwaterfromdairyparlorsmaybestoredinholdingponds.Storagetankstypicallystoreslurryorwastewaterthatwasscrapedorpumpedfromhousingsystems.

Combinedaerobictreatmentsystem

Thisprocessinvolvesremovingsolidsusingflocculationandthencompostingthesolidstreamandaeratingtheliquidstreamofmanure.

Anaerobicdigester

Anaerobicdigestersaremanuretreatmentsystemsdesignedtomaximizeconversionoforganicwastesintobiogas.Thesecanrangefromcoveredanaerobiclagoonstohighlyengineeredsystems.MethanegasleakageisthemainsourceofGHGemissions;NH3andN2Oleakageisnegligible.

Sand–manureseparation

Manureisseparatedfromsandandbeddingbymechanicalandsedimentationseparation.Nomethodisprovidedasemissionsarenegligible.Separatedliquidsandsolidscouldbeinputsintootherstoragesystems.

Nutrientremoval

Therearefourmainnitrogenremovalapproaches:biologicalnitrogenremoval,Anammox(i.e.,anaerobicammoniumoxidation),NH3stripping,ionexchange,andstruvitecrystallization.NomethodisprovidedduetolimitedquantitativeinformationonGHGgenerationfromnutrientremovalsystems.


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StorageandTreatmentPractices

EstimationMethod Description

CH4 N2O NH3a

Solid–liquidseparation

Mechanicalseparationofliquidsandsolidsthroughscreens,centrifuges,pressing,filtration,ormicroscreening.Separatedliquidsandsolidscouldbeinputsintootherstoragesystems.

Constructedwetland

Typicallyconsistofwetlandplantsgrowinginabedofhighlyporousmedia.Nomethodisprovidedasemissionsarenegligible;GHGsinksarenotedtolikelybegreaterthanemissions.

aAlthoughNH3isconsideredinthischapterasanimportantprecursortoparticulateformulation(affectingradiationbalance)andGHGsandisakeyelementofdiscussion,NH3itselfisnotaGHG.Therefore,methodsforestimatingNH3emissionsareprovidedinAppendix5‐C.

AnentitycanreduceitsGHGemissionsfrommanurebyutilizingalternativetreatmentoptionsand/ormanagementsystems.AnaerobicdigestersdonotreducetheamountofCH4releasedbutofferanoptiontocaptureandconverttheCH4toCO2andenergythroughcombustion.DigestersofferbothCH4reductionsaswellasGHGavoidancebyreducinganentity’selectricitydemand.

5.1.1.2 ManagementInteractions

Table5‐2depictsthekeytypesofinformationdesiredforestimatingGHGemissionsfromananimalproductionfacility.Thistableillustratestheattributesofasystemthathavethegreatestinfluenceoveremissionswithineachcomponent.AnumberofexistingmodelscanbeusedtoestimateGHGemissionsthatutilizethekeyactivitydataindicatedinTable5‐2.

Table5‐2:DesiredActivityandAncillaryDataforEstimatingGHGEmissionsfromAnimalProductionSystems

GeneralCategory SpecificData

CattleSheep Swine Poultry Goats

Amer.BisonCow–

calfStockers Feedlot Dairy

Animal

Characteristics Bodyweight ● ● ● ● ● ● ● ● ●

Bodyconditionscore ● ● ● ●

Stageofproduction(dry,lactating,pregnant)

● ● ●

Dieta

ry

Facto Dietintake(orfactorsthatcanbeusedtopredictintake)

● ● ● ● ● ● ● ● ●

CombinedAerobicTreatmentComparedtoAnaerobicLagoons

Acombinedaerobictreatmentsysteminvolvesthetreatmentofamanurestreamwithflocculantstoremovethemajorityofsolidsfromthestream.Thesolidsportioniscompostedwhiletheremainingliquidistransferredtoastoragetankwhereitisaerated.MethaneisavoidedbyaerobicallytreatingthesolidsviacompostingwhileNH3inthewastewaterisavoidedvianitrification.TheGHGsresultingfromacombinedaerobictreatmentareonly10percentofwhatwouldbeemittedfromananaerobiclagoon,thuscombinedaerobictreatmentsrepresentapotentialmitigationoptionforentities.


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GeneralCategory SpecificData

CattleSheep Swine Poultry Goats

Amer.BisonCow–

calfStockers Feedlot Dairy

Typeofforage(conservedorgrazed,pasturecomposition,stageofplantgrowth)

● ● ● ● ● ●

Dietdrymatterintake,crudeprotein,neutraldetergentfiber,aciddetergentfiber,non‐structuralcarbohydrates,fiber,fat,energycontent

● ● ● ● ● ● ● ● ●

Dietdigestibilityand/orrateofpassage

● ● ● ● ● ●

Degradabilityofcarbohydratesandproteins

● ● ●

Supplementationpractices– type(e.g.,grains,protein,liquid,dryblocks,non‐proteinnitrogen)andquantity

● ● ● ● ●

Supplementalordietionophoreconcentration

● ● ● ●

Dietarybeta‐agonists ● ●

Nutrient

Excretion:

Quantity

Carbon,nitrogen,andvolatilesolids

● ● ● ● ● ● ● ● ●

Other

Animal

Factors

Growthpromotingimplants

● ●

ManureManagem

entFactors

Animalmanagementregimenusedtospreadmanureoverpasturetoreduceconcentrationnearwaterorfeedsources

● ● ● ● ● ●

Soiltype ● ● ● ● ● ● ● ● ● Practicestocontrolrunofffrompastures/lots/fields

● ● ● ● ● ● ● ● ●

Ifhoused,thelengthoftimetheyarehoused,animalconcentration,manurehandlingprocedures

● ● ● ● ● ● ● ● ●

Typeofmanurecollection/storagesystem

● ● ● ● ●

Frequencyofmanurecollectionsandcomposition

● ● ● ● ●

Bedding/litteruseandsource

● ● ● ● ●


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Systemboundariesaredefinedbythecoverage,extent,andresolutionoftheestimationmethods.ThemethodsinthisreportcanbeusedtoestimateGHGemissionsourcesthatoccurwithintheproductionareaofananimalproductionsystem,includingtheanimals,animalhousing,andmanurehandling,treatment,andstorage.Methaneemissionsfromentericfermentation,aswellastheCH4andN2Oemissionsfrommanuremanagementsystemsormanurestoredinhousing,areconsideredinthisreport.Ammonia,whilenotaGHG,isaprecursortoN2Oformationandis,therefore,included,primarilyinAppendix5‐C.Theactoftransportingmanuretothefieldforlandapplicationisincludedintheproductionareaboundary,butemissionsfromvehicletransportarenotincludedinthescopeofthisreportastherearemanyvariablesthatwoulddetermineemissionsfromvehicles(ageofvehicle,type,fuelefficiency,idletime),andtheyarenotdirectagriculturalemissionsandcouldinsteadbeconsideredpartofthetransportsector(off‐road).Additionally,thisreportdoesnotencompassafulllifecycleanalysis(LCA)ofGHGemissionsfromanimalproductionsystems.TheadjacenttextboxsummarizesseveralstudiesonLCAsforanimalproductionsystems;however,theyarenotutilizedinthisreport.EmissionsthatresultfollowingmanureapplicationareaddressedseparatelyinChapter3,QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems.

Foremissionsfromanimalproductionsystems,themethodsprovidedhavearesolutionofindividualherdswithinanentity’soperation.Aherdisdefinedasagroupofanimalsthatarethesamespecies,grazeonthesameparcelofland(samedietcomposition),andutilizethesamemanuremanagementsystems.Emissionsareestimatedforeachindividualherdwithinanoperationandthenaddedtogethertoestimatethetotalanimalproductionemissionsforanentity.Theanimalproductiontotalsarethencombinedwithemissionsfromcroplands,grazinglands,andforestrytodeterminetheoverallemissionsfromtheoperationbasedonthemethodsprovidedinthisdocument.Emissionsareestimatedonanannualbasis.

5.1.3 SummaryofSelectedMethods/Models/SourcesofData

TheIntergovernmentalPanelonClimateChange(IPCC,2006)hasdevelopedasystemofmethodologicaltiersrelatedtothecomplexityofdifferentapproachesforestimatingGHGemissions.Tier1representsthesimplestmethods,usingdefaultequationsandemissionfactorsprovidedintheIPCCguidance.Tier2usesdefaultmethods,butemissionfactorsthatarespecifictodifferentregions.Tier3usescountry‐specificestimationmethods,suchasaprocess‐basedmodel.ThemethodsprovidedinthisreportrangefromthesimpleTier1approachestothemostcomplexTier3approaches.Higher‐tiermethodsareexpectedtoreduceuncertaintiesintheemissionestimates,ifsufficientactivitydataandtestingareavailable.

EstimatingCH4emissionsfromentericfermentationinswine,goats,Americanbison,llamas,alpacas,andmanagedwildlifeuseTier1methods.EntericemissionsfromsheepareestimatedusingtheHowdenequation(Howdenetal.,1994),andemissionsfromdairyproductionsystemsareestimatedusingtheMitscherlich3(Mits3)equation(Millsetal.,2003)asprovidedintheDairyGasEmissionsModel(DairyGEM)(Rotzetal.,2011a).EmissionsfrombeefcowsareestimatedusingtheIPCCTier2approach.EmissionsfromfeedlotsareestimatedusingamodificationoftheIPCCTier2approach.

QualitativeDiscussiononManureSources

Estimationmethodsarenotavailableforsomesources.Qualitativediscussionisprovidedfor:

Sand‐ManureSeparation NutrientRemoval Solid‐LiquidSeparation ConstructedWetlands Thermo‐chemicalConversion


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Formanuremanagement,theIPCCTier2methodologyisusedforCH4emissionsfromtemporarystackandlong‐termstockpile,CH4andN2Oemissionsfromcomposting,andN2Oemissionsfromaerobiclagoons.TheSommermodelisusedtoestimateCH4emissionsfromanaerobiclagoons.

Allmethodsincludearangeofdatasourcesfromoperation‐specificdatatonationaldatasets.Operation‐specificdatawillneedtobecollectedbytheentityandgenerallyareactivitydatarelatedtothefarmandlivestockmanagementpractices(e.g.,dietaryinformation,volatilesolidscontentofmanure).Nationaldatasetsarerecommendedforancillarydatarequirements,suchasclimatedataandsoilcharacteristics.

AsummaryofproposedmethodsandmodelsforestimatingGHGemissionsfromanimalproductionsystemsisprovidedinTable5‐3.

Life Cycle Analysis of Cattle Production Systems

Petersetal.(2010)reportedthattheestimatedcarbonfootprintofcattleproductionsystemsaroundtheworldrangedfrom8.4kgofCO2‐eq(kgHCW)‐1(HCW=hotcarcassweight)inanAfricanpastoralsystemto25.5kgCO2‐eq(kgHCW)‐1inanintensiveJapanesegrainfeedingsystem.FiveNorthAmericanstudies(Vergeetal.(2008)andBeaucheminetal.(Sweeten,2004;2010)inCanada,Pelletieretal.(2010)andLupoetal.(2013)intheU.S.Midwest,andStackhouseetal.(2012)andStackhouse‐Lawsonetal.(2012)inCalifornia)estimatedthecarbonfootprintofvariousbeefcattleproductionsystems:Thecarbonfootprintforthetotalbeefproductionsystemsrangedfrom10.4to19.2kgCO2‐eq(kgfinalbodyweight)‐1(or16.7to32.5kgCO2‐eq(kgHCW)‐1).Sixtyfourto80percentofthetotalCO2‐eqwasproducedinthecow‐calfsectorofproduction;whereas8to20percentofCO2‐eqwasproducedinthestockerphase,andonly12to16percentwasproducedduringthefinishingphase.Themajority(55to63percent)ofthetotalCO2‐eqwasentericCH4,18to23percentwasmanureN2O,and14to24percentwasfromfossilenergyuseandsecondaryemissions.

Ingeneral,thedailycarbonfootprintwasgreaterduringthegrazing(stocker)phasethanduringthefeedlotfinishingphase.BothPelletieretal.(2010)andStackhouseetal.(2012)reportedthatthecarbonfootprintwasslightlylowerforcalvesthatwereweanedandwentdirectlytothefeedlot(21.1and23.0kgCO2‐eq(kgHCW)‐1or2,382and3,493kghead‐1,respectively)thanforcattlethatwentthroughastockergrazingphasebeforeenteringthefeedlot(22.6and26.1kgCO2‐eq(kgHCW)‐1or2,904and4,522kgCO2‐eqhead‐1,respectively).Pelletieretal.(2010)andLupoetal.(2013)bothreportedthatthecarbonfootprintofgrass‐finishedcattlewasgreaterthanforcalvesthatwereweanedandwentdirectlytothefeedlot.Thesedifferencesaredueinparttoslowerweightgainandlighterfinalbodyweightsandcarcassweightsofgrass‐fedcattlethancattlefinishedongrain‐andbyproduct‐baseddietsinthefeedlot.

MostLCAsassumethatcarbonsequestrationisminimalinestablished,unfertilizedpastures.Phetteplaceetal.(2001)andLiebigetal.(2010)suggestedtheremaybesomesmallnetcarbonsequestration,inestablishednativepastures.However,Liebigetal.(2010)notedthatfertilized,improvedpastureshadnetCO2‐eqemissions;primarilybecauseofincreasedlossesofN2Ofromfertilizernitrogen.Lupoetal.(2013)notedthattheassumedcarbonsequestrationofpastures(equilibriumvs.netsequestration)affectedthecarbonfootprintofgrass‐finishedcattle;however,regardlessofthecarbonsequestrationassumption,grass‐finishedcattlehadagreatercarbonfootprintthangrain‐finishedcattle.


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Table5‐3:SummaryofSourcesandProposedGHGEstimationMethodsforAnimalProductionSystems


AnimalProductionSystems,IncludingEntericFermentationandHousingEmissions5.3.1.2 DairyCattle Mits3equation; ASABEStandardD384.2andIPCCTier2(housing)5.3.2.2 BeefCattle ModifiedIPCCTier2 (entericandhousing);ASABEStandardD384.2

(housing)5.3.3.2 Sheep Howdenequationforgrazingsheep(Howden etal.,1994)andBlaxterand

Clapperton(1965)forfeedlotsheep5.3.4.2 Swine IPCCTier1 (entericmethane);ASABEStandardD384.2andIPCCTier2

(housing)5.3.5.2 Poultry IPCCTier1;ASABEStandardD384.2andIPCCTier2(housing)5.3.6.1 Goats IPCCTier15.3.6.2 AmericanBison,

Llamas,Alpacas,andManagedWildlife

IPCCTier1

ManureStorageandTreatmentTemporaryStack&Long‐TermStockpile5.4.1.2 Methane IPCCTier2usingU.S.EPAInventoryemissionfactors(EFs)anddiet

characterization5.4.1.4 NitrousOxide IPCCTier2usingU.S.‐basedEFsandmonthlydataComposting5.4.2.2 Methane IPCCTier2withmonthlydata5.4.2.4 NitrousOxide IPCCTier2AerobicLagoon5.4.3.2 Methane MethaneConversionFactor foraerobictreatmentisnegligibleandwas

designatedas0%inaccordancewithIPCC5.4.3.4 NitrousOxide IPCCTier2usingIPCCEFsAnaerobicLagoon,RunoffHoldingPond,StorageTanks5.4.4.2 Methane Sommermodelbasedon fractionsofvolatilesolids(Mølleretal.,2004)5.4.4.4 NitrousOxide FunctionoftheexposedsurfaceareaandU.S.‐basedemissionfactorsAnaerobicDigestion5.4.5.2 Methane IPCCTier2usingCleanDevelopmentMechanismEFsfordigestertypesto

estimateCH4leakagefromdigestersCombinedAerobicTreatmentSystems5.4.6.25.4.6.2

MethaneNitrousOxide

10%ofemissionsfromestimationofliquidmanurestorageandtreatment–anaerobiclagoon,runoffholdingpond,storagetanks

OtherTreatmentMethods5.4.7 Sand–Manure

SeparationNomethodprovidedbecause GHGemissionsarenegligible

5.4.8 NutrientRemoval Notestimatedduetolimitedquantitativeinformation5.4.9 SolidLiquid

SeparationNomethodprovidedbecause GHGemissionsarenegligible

5.4.10 ConstructedWetland Nomethodprovidedbecauseemissionsarenegligible;GHGsinksarenotedtolikelybegreaterthanemissions

5.4.11 Thermo‐chemicalConversion

NomethodprovidedasGHGemissionsarenegligible


Theremainderofthischapterisorganizedintofourprimarysections,asillustratedinFigure5‐2.Section5.2providesoverviewsofdairycattle,beefcattle,sheep,swine,andpoultryproduction


5-15

systemsandprovidesinformationondietandhousing.Section5.3providesthemethodsforestimatingGHGsfromhousing,primarilyfocusingonGHGsfromentericfermentation.MethodsarealsoprovidedforallthespeciesdescribedinSection5.2,plusadditionalanimaltypes(i.e.,goats,Americanbison,llamas,alpacas,andmanagedwildlife).Section5.4providesthemethodologyforestimatingemissionsfromdifferentmanuremanagementsystems.MethodologyisprovidedtoestimateCH4andN2Ofromtemporarystackandlong‐termstockpiles,composting,aerobiclagoons,anaerobiclagoons,andcombinedaerobictreatmentsystems.Section5.4alsoprovidesmethodsforestimatingCH4fromanaerobicdigestion.Aqualitativediscussionisprovidedforsand‐manureseparation,nutrientremoval,solid‐liquidseparation,constructedwetlands,orthermo‐chemicalconversion.Section5.5presentsresearchgapsforbothentericfermentationandmanuremanagement.

Therearefiveappendicestotheanimalproductionsystemschapterofthisreport.Appendix5‐AprovidesYmadjustmentfactorsforcalculatingentericCH4fromfeedlotcattle.Appendix5‐Bprovidesnutritionalinformationaboutanimalfeedstuffs(Ewan,1989;Preston,2013).Appendix5‐CdiscussesavailablemethodologiesforestimatingNH3emissionsfromanimalproductionsystems.Appendix5‐DdescribestheshapefactorsandrelatedequationsthatcanbeappliedinAppendix5‐Ctomoreaccuratelyestimateemissionsfrommanurestockpilesthatareshapeddifferently(assurfaceareapartiallydeterminesemissions).Appendix5‐Eprovidesadetailedreviewofmodelsevaluatedforsuitabilityforestimatingemissionsfromanimalproductionsystems.


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Figure5‐2:AnimalProductionSystemsRoadMap


5-17


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5.2 AnimalProductionSystems

Thissectionprovidesdiscussionontheproductionsystemsforbeefanddairycattle,sheep,swine,andpoultry.ThisprovidesthebackgroundnecessaryforunderstandingSection5.3,whichcoversGHGemissionsfromanimalproductionsystems.

5.2.1 DairyProductionSystems

5.2.1.1 OverviewofDairyProductionSystems

TheU.S.dairyproductionsystemiscomprisedofseveralkeyprocessesfordairycattle,theirmanure,andtheirendproducts(meat,dairy)asdepictedinFigure5‐3.Thisconceptualmodelprovidesanoverviewofthetypicaldairysystem,followingcattlefrombirthtoslaughterandfollowingmanurefromtheanimalthroughamanagementsystem.Manureisproducedduringeachstage,anddependingonthelocation,ismanageddifferently.ThemanagementoftheresultantmanurehasimplicationsonthequantityofGHGemissionsandsinks;thekeypracticesarediscussedindetailbelow.Theestimationmethodsinthischapterincludediscussionsforemissionsfromentericfermentation,housing,andmanuremanagementandarenotafullLCA.

TheU.S.dairyindustryiscomposedprimarilyoffourmajorsegmentsofproduction:1)calfrearing;2)replacementheifers;3)lactatingcows;and4)nonlactating(dry)cows.TheU.S.dairycattlepopulationin2012consistedofapproximately9.2millionmilkcowsandfirstcalfheifersandapproximately4.6millionreplacementheifers.ThemajorityofdairycattleintheUnitedStatesareHolstein(Holstein‐Friesian),followedbyJersey,withsmallernumbersofGuernsey,BrownSwiss,andAyrshire.Overthelast65yearstherehavebeendramaticincreasesinmilkproductionperanimal,duetochangesinherdmanagement,nutrition,composition,andbreedingprograms.Present‐daydairyherdsaredominatedbyHolsteincows(90percent)asopposedtoamixofthefivemostcommonbreeds(Jersey,Guernsey,Ayrshire,BrownSwiss,andHolstein)aswascommoninthe1940s.Withachangeinbreeddominanceandenhancedgenetics,thetypicalmilkproductionpercowhasincreasedfrom2,074to9,193kgofmilkperyear(Capperetal.,2009).

5.2.1.2 DietsforDairyCattle

Cowsinintensivedairyproductionsystemsarefeddietsthatreflectregionallyavailablefeedsandtypicallycontainbetween40and60percentconcentrates,suchasfeedgrains,proteinsupplements,andbyproductssuchasdistiller’sgrains.Typicaldietsincludecornsilage,alfalfaorgrasssilage,alfalfahay,groundorhigh‐moistureshelledcorn,soybeanmeal,fuzzywholecottonseed,andoftenbyproductfeeds(e.g.,corngluten,distiller’sgrains,soybeanhulls,citruspulp,beetpulp).Byproductfeedsmaymakeupalargeportionofthedietcomposition,providingkeynutrientsandameansofdisposalforotherwiselandfilledingredients.Proximitytocropprocessingplantsandindustriesmaydictatetheavailabilityofbyproductfeedsbyregion.

GrowingHeifersDietsforgrowingheifersareformulatedbasedongrowthrateandstageofrumendevelopment.Dietsrangefromliquiddiets(e.g.,milkormilkreplacer)innewborncalvestopelletedcompletefeedsinthegrowingcalf(e.g.,calfstarter)todietsthataresimilartothatofferedtolactatingcowsasthecowsgrowandrumensdevelop.Roughagecontentofthedietincreasesastherumendevelopswithhayorsilageoftenofferedinconjunctionwithacalfstarterduringatransitionperiod.Followingthattransition,typicalfeedsincludethoselistedabove.FeedsareoftenmixedtogetherinamixerandfedasaTotalMixedRation(TMR).Insomecases,feednotconsumedbythelactatingherdisfedtogrowingheiferswhentherumenisfullydeveloped(>9monthsofage).


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Figure5‐3ConceptualModelofDairySystemsintheUnitedStates


5-20

LactatingCowsDietsforlactatingcowsareformulatedbytargetmilkproductionorstageoflactation,whichreflectsthedifferencesinenergyandproteinrequiredfordifferentamountsofmilkproduced.Peaklactationoccursabout60daysaftercalving,andproductionslowlydeclinesoverthenextseveralmonths.FeedstuffsarecommonlyblendedtogetherinamixerandfedasaTMR.

DryCowsDrycowdietsareoftenformulatedintotwostages:far‐offdryandclose‐updry.Duringthefar‐offdryperiod,cowsarefeddietswithhighforagecontent(>60%)usingingredientssimilartothatfedtothelactatingherd.Asdrycowsapproachcalving,energycontentofthedietincreasesbydecreasingforagetoincludemoreconcentratefeedsandmineralformulationchangesinordertoavoidpre‐andpost‐partummetabolicdisordersthatoftencenteraroundcalciummobilizationasthecowbeginstolactate.FeedstuffsarecommonlyblendedtogetherinamixerandfedasaTMR

5.2.1.3 DairyHousingandManureHandling

TwogeneraldairyfarmtypescanbedistinguishedintheUnitedStates:confinementfeedingsystems(includingbarnsanddry‐lots)andpasture‐basedsystems(USDA,2004a).Typicalhousingsystemsforconfinementfeedingoperationsincludetiestallbarns,freestallbarns,freestallbarnswithdrylotaccess,anddrylots.Drylotsystemsconsistofhousinganimalsinpenssimilartobeefcattlefeedlots,butatalowerstockingdensity.Inpasture‐basedsystems,cattlegrazepastureforperiodsoftime,basedonfeedavailabilityandenvironmentalconditions,andarehousedinbarnsandfedstoredfeedwhenpastureisnotavailable.Thedairycattlelifecycleproductionphaseisgenerallydividedintothreesegments:growinganimals(calvesandreplacementheifers),lactatingmaturecows,anddrymaturecows.Nutrientneeds,andthereforediets,andintakeareverydifferentbetweenthedifferentlifecyclephases:growingcattle(calvesandheifers),lactatingcows,anddrycows.Housingandmanuremanagementsystemsvaryconsiderablythroughoutthecountryandcandifferinaregionandbythesizeoftheherd.Incaseswherehousingandmanuremanagementvariesbyanimalgroup(e.g.,heifers,dry,andlactatingcows),estimatesofGHGemissionsfromonegrouparenotapplicabletoothergroups.Whenhousingandmanuremanagementaresimilarbetweengroups(e.g.,allcattleondry‐lots),dietandintakeadjustmentfactorscanbeusedtocompareGHGemissionsforthedifferentgroups.

Withtheexceptionofcalves,replacementheifersanddrycowsmaybehousedandmanagedinsimilarwaysaslactatingcows.Whenthisisthecase,muchofthediscussionisrelevanttothethreegroups.Incaseswherethelactatingherdismanagedinconfinementbutreplacementanddryanimalsaremanagedonpastureorindry‐lots,emissionsfromlactatingcattlearenotapplicablenotonlyduetodifferencesindietandintakebutalsoduetohousingdifferences.Therearenoreadilyavailablestudiesthathavefocusedstrictlyonemissionsfromdairycalfmanagementandhousing.Summarizedbelowarekeycharacteristicsofdifferenceinhousingbylifecyclephaseofadairycow.

Growing(calvesandreplacementheifers).Followingbirth,calvesareusuallyremovedfromthecowwithinafewhoursandaretypicallyrearedonmilkormilkreplacerincalfhutchesorbarnsforthreetosevenweeksuntilweaning.Femalecalves(replacementheifers)aretypicallymovedtogrouphousing(e.g.,superhutches,transitionbarns,openhousing,orpasture)untiltheyreachappropriatebreedingweightatabout14to15monthsofage.Somereplacementsarecontract‐rearedbyheifergrowersorsold.Followingbreeding,heifersareoftenraisedinlots,pasture,orbarnsuntiltheyarereadytocalve.Manureingrouphousingmaybehandledasasolid(beddedpackorcompostbarn)orasaslurry,similartothatdescribedbelowforlactatingcowsinfreestallbarns.


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LactatingCows.Heiferstypicallyhavetheirfirstcalfatabout23to24monthsofage,afterwhichtheyjointheproductionherd.Acowtypicallyremainsintheherduntilaboutfiveyearsofage,althoughmanycowsarecapableofremainingproductiveintheherdfor12to15years.Eachperiodofproductionorlactationlastsfor11to14monthsorlongerandspansthetimeperiodfromcalvingtodry‐off,whichiswhenmilkingisterminatedabout40to60daysbeforethenextanticipatedcalving.Thus,cowsarebredwhiletheyareproducingmilk,usuallybeginningatabout60daysaftercalving,tomaintainayearlycalvingschedule.Followingthe35to60‐daydryperiod,thecowcalvesagain,andthelactationcyclebeginsanew.Cowsaverageabout2.8lactations,althoughmanyremainproductiveconsiderablylonger(Hareetal.,2006).

Lactatingcowsmaybehousedintiestall(stanchion)barns,whichlimitthecows’mobilitybecausethecowsaretethered,fed,andmilkedinthestalls.Agutterisusedtoremovethemanurebyabarncleaner,whichtypicallyplacesthemanuredirectlyintoamanurespreaderorinatemporarystoragepile.Freestallbarnsallowthecowstomovefreelyinandoutofstalls,andthecowsaremovedtoaseparatearea(milkingcenterorparlor)formilking.Manuretypicallyaccumulatesinalleywaysandisremovedviascraping,vacuuming,orflushingwitheithercleanorrecirculatedwater.Somefreestallbarnshaveslottedfloorswithlong‐termmanurestoragebelowthefloors.Manureisgenerallyworkednaturallythroughtheslotsbythecows’feetandwithassistanceviamechanicalscrapingequipment.Dairyfacilitiesmayalsousepasturesanddry‐lotstohouselactatingcows.Lotsarescrapedperiodically,asarepasturesoccasionally,andthesolidmanureiscollected.Althoughnotprevalent,somedairyfacilitiesmayhouselactatingcowsinbeddedpackorcompostbarns,againhandlingmanureasasolidmaterial.

DryCows.Muchlikegrowingcows,housingoptionsfordrycowsarethesameasdescribedaboveforlactatingcows.Thekeydeterminantismanagementpreferenceforthefarmownerand/orfacilityavailability.

Manureandsoiledbeddingfrombarnscanbehandledinanumberofways.Manurecanberemovedfromthebarnsmechanicallyanddirectlyloadedintomanurespreaders,althoughthisisnotcommononmediumandlargefarms.Manurecanalsobeprocessedinananaerobicdigesterwherebacteriacanbreakdownmanuretoproducebiogasthatcanbeflaredorcapturedforenergypurposespriortostorageofdigestereffluent.Whenmanurehasalowersolidscontent,itmaybestoredinatankorpitasaslurry,ortransportedtoasolid‐liquidseparationsystemwiththeliquidfractionconveyed(pumpedorbygravity)toalong‐termstoragepond,whilethesolidscanbedewaterednaturallyandreusedasbedding,composted,land‐applied,and/orsold.Indry‐lotsystems,themanureinthepensistypicallystackedandfollowingstorageiseitherland‐appliedorcomposted.Lotrunoffandmilkingparlorwashwaterispumpedtoastoragepond.Therearesomedry‐lotdairiesthatuseaflushsystemtocleanmanurefromalleywaysbehindthefeedbunks;thiswashwateriseventuallystoredinawastewaterpond.Openfreestalldairieshaveacombinationofbarnswithexerciseyardsbetweenthebarns,andthereforemanureishandledsimilarlyasinatraditionalfreestallbarnanddry‐lotproductionsystem.Wastewaterfrommilkingcenters(manure,clean‐in‐placewater,andfloorwashdownwater)istypicallycombinedwithbarnmanuredestinedforlong‐termstorage,andmaygothroughasolid‐liquidseparationprocessfirst.Inpasture‐basedsystems,manureisdepositeddirectlyontothepastureandthereforenotintensivelymanaged,butmayaccumulateinareaswhereanimalstendtocongregate(e.g.,wateringareas,shade).


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5.2.2 BeefProductionSystems

5.2.2.1 OverviewofBeefProductionSystems

TheU.S.beefproductionsystemiscomprisedofseveralkeycomponentsforbeefcattle,theirwaste,andtheirendproducts,asdepictedinFigure5‐4.Thisconceptualmodelprovidesanoverviewofthetypicalbeefprocessingsystems,followingthesegmentsofthebeefcattleindustry(i.e.,cow‐calf,stocker,feeder/finisher,andpacker)frombirthtoslaughterandfollowingwastefromtheanimalthroughamanagementsystem.Wasteisproducedduringeachstageofactivityoccurringinthesystem,anddependingonthelocation,ismanageddifferently.

Ofthe90millionbeefcattleintheUnitedStates,approximately50millionarematurecowsandtheircalvesoncow‐calfoperations(USDANASS,2012),whichrangeinsizefromafewcowstoseveralthousandcows.Theseoperationsarenormallybasedonforages,eitherimprovedpasturesornativerange,andvaryinsizefromafewacrestohundredsofsections.Typically,whencalvesare150to220daysofagetheyareweanedandmovedtopastureforperiodsof60to200days(thestockerphase),althoughsomemaymovedirectlytoafeedlot.Thepasturesmaybenativerange,improvedperennialpastures,orannualssuchaswheatpasture,forage‐sorghums,andcropresiduessuchascornstalks.Afterthestockerphase,calvesnormallymovetofeedlotswheretheyarefedgrain‐andbyproduct‐baseddietsfor110to160days,untiltheyarereadyforharvest.Inaddition,steersandcullheifersfromdairyoperationsarealsofed.Approximately23millioncattlearefedinfeedlotsannuallyintheUnitedStates.Feedlotsrangeinsizefromafewhundredheadtomorethan100,000headcapacity.

5.2.2.2 DietInformationforBeefCattle

Cow‐CalfandBullsGrazingpasturesmaybenativerange,improvedperennialpastures,orannualssuchaswheatpasture,forage‐sorghums,andcropresiduessuchascornstalks.Beefcowsandbullsaretypicallyfedsupplementalfeedsduringtimeswhenpastureorrangeforagedoesnotmeettheirnutritionalrequirements,usuallyinwinter.Arecentsurveyofthebeefcow‐calfindustryfoundthat74percentofoperationsfedaproteinsupplementand51percentfedanenergysupplement(USDA,2010).Overallproteinwassupplementedforanaverageof173days(SE=9.6)andenergyfor162days(SE=12.7),butthiswashighlyvariableacrossregionsofthecountry.Ninety‐sevenpercentofoperationsinthesurveysupplementedthecowherdwithroughageforanaverageof154days(SE=7.0).Theproteinsupplementswerereportedasplantproteinorurea‐based.Cornwasreportedastheprimaryenergysupplement.Theamountofsupplementfedperheadperdaywasnotincludedinthereport.

StockersStockersgrazeforage,includingwheatpasture,improvedpastures,range,andcropresidues.Stockercattlemayalsoreceivesupplementalproteinorenergyfeedstoincreaseperformanceand/orextendpastureforage.Supplementsmayormaynotcontainanionophore.Somestockercalvesmaybeimplantedwithagrowthpromotingimplant;othersarenot.


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Figure5‐4ConceptualModelofBeefProductionSystemsintheUnitedStates


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FeedlotCattletypicallyenterfeedyardsbetweentheagesof100and350daysweighing200to350kg,andgotoslaughterweighingbetween500to700kg.Theyarefedhigh‐concentrateorhigh‐byproductdietsfor100to200days.Ofthecattlefed,approximately55percentarebeefsteers,25to30percentarebeefheifers,and12to20percentaredairysteersandheifers.ThevastmajorityofcattlefedarebeefbreedsofBritishorContinentalbreeding.However,manycattlewithBrahmangeneticsarealsofed,mostlyinthesouthernplains.Inareaswithasignificantdairyindustry,steersandheifersofdairybreeding(mostlyHolstein)arealsofed.

Typicalfeedlotdietscontainhighconcentrationsofgrain(75percentormore)and/orbyproductssuchasdistillersgrainsandglutenfeed.Theyarenormallybalancedforprotein,energy,vitamins,andminerals(VasconcelosandGalyean,2007).Becausemanybyproductscontainhighconcentrationsofproteinandmineralssuchasphosphorusandsulfur,whenthesebyproductsarefed,dietaryconcentrationsofproteinandsomemineralsmayexceedanimalrequirements.FeedingofionophoressuchasmonensiniscommonintheUnitedStates,asistheuseofgrowth‐promotingimplants.Thedietsfedinfeedyardstendtodifferbetweenthenorthernandsouthernplains.Finishingdietsbasedondry‐rolledcorn(DRC)andhigh‐moisturecorn(HMC)dominateintheNorth,whereasdietsbasedonsteam‐flakedcorn(SFC)dominateintheSouth.Theuseofbioethanolco‐productssuchasdistiller’sgrainsandcorn‐millingco‐productssuchascornglutenfeedinfinishingdietsisgreaterinthenorthernplainsbecauseofthegreateravailabilityoftheseco‐products,buttheiruseisincreasinginthesouthernplains.

5.2.2.3 BeefCattleHousingandManureHandling

Cow‐CalfandBullsCowherdsandreplacementheifersaremostoftenhousedonpasture.Fecesandurinearedepositedonpasturesandrangelandandmaybeconcentratedinareasinwhichfeedingorwateringtakesplace.

StockersStockersareusuallyhousedonpastureandthusnomanurehandlingisusedandGHGemissionsareapartofthecroplandssection(seeChapter3,QuantifyingGreenhouseGasSourcesandSinksinCroplandandGrazingLandSystems).Calvestobeusedasstockerscanbehousedforshortperiodsoftimeindry‐lots.

FeedlotHousingandmanuremanagementatmostbeefcattlefeedingoperationsdiffergreatlyfromthoseusedinotherlivestockspecies,withthevastmajoritybeingfinishedindry‐lotpenswithsoilsurfaces.Manureisnormallydepositedonthepensurfaceandscrapedfromthepensaftereachgroupofcattlegoestomarket.Partofthemanuremaybestackedinthepentoprovidemoundsthatimprovependrainageandassurethatcattlehaveadryplacetolieafterrains.Manureremovedfromthepenmaybeimmediatelyappliedtofieldsnearthefeedlot,stockpiledforlateruse,orcompostedinwindrows.Manurescrapedfromthepensnormallyhasamoisturecontentof30to50percentandmaycontainsomesoilfromthepen.Becausethemanuremayremaininthepenorinstockpilesforseveralmonthsbeforeitisappliedtothefield,aportionofthenitrogenandcarbonmaybelostbeforethemanureiscollectedorappliedtoland.Runofffrompensisnormallycollectedinretentionponds.Settlingbasinsmaybeusedtolimitthequantityofmanuresolidsandsoilparticlesthatreachtheretentionpond.

IntheNorthernUnitedStates,andinareaswithhighrainfall,cattlemaybefedinnaturallyventilatedbarnswithslottedfloorsforcollectionofurineandfecesorindeep‐beddedbarnswithconcretefloorsinwhichthemanureandbedding(normallystraworstalks)areallowedto


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accumulateduringthefeedingperiod(Spiehsetal.,2011).Addingbeddingwillincreasethequantityofcarbon(andpossiblynitrogen)availabletobemetabolizedbymicrobesinthepen.Thesefacilitiesarecharacterizedbytheabsenceofrunoffcontrolsystems.

5.2.3 SheepProductionSystems

5.2.3.1 OverviewofSheepProductionSystems

Thereare81,000sheepandlamboperationsintheUnitedStates,withaninventoryof5.53millionsheepandlambsasofJanuary1,2011(USDANASS,2011).Mostbreedingflocksaresmallandconsistoflessthan100headofewes.Thelambfeedingindustryisalsodiverseinsize,withsmallfeedlotslocatedthroughoutthefarmflockareasandlargefeedingoperationslocatedincloseproximitytolocalgrainproductioncapacity(Shiflett,2011).

5.2.3.2 Diets,Housing,andManureHandlingforSheep

Lambingseasonmayoccuratvarioustimesduringtheyear,dependingonproductionobjectives,feedresources,environmentalconditions,andmarkettargets.Whenlambingoccurs,JanuarythroughMarch,ewesaregenerallyhousedinbeddedbarns.Beddingisremovedandspreadafteranimalsareturnedoutonpasture.EwesaregenerallybredonpastureinSeptemberthroughNovemberand,dependingonweather,willbemovedintobarnspriortolambing—orearlierasforageavailabilityandweatherdictate.Dietsconsistofpastureorgrazingcropresiduefromspringturnoutthroughearly‐andmid‐gestation.Whengrazedforageisnolongeravailable,ewesarehousedormovedtodry‐lotsandfedhayand/orhayandgraindietsasgestationrequirementsdictate.Theprimaryforagesourceisalfalfa,andcornisthepredominantgrain.Dietsrangefrom100percenthayto60:40percentforage:concentratewhilelactating.Mostlambsareweanedatapproximately90daysand41kgandsenttofeedlotsforfinishing.

Pasturelambingisanotherfarmflockproductionsystemthatisusedtomaximizenutrientsprovidedbygrazedforages.InthiscasetheeweisbredinNovemberorDecembertolambonpastureinAprilorMay.Lambsareweanedatapproximately120daysand32kgandmaybesenttothefeedlotorfinishedongrass.Ewesarenotfedgrain,andharvestedforageisprovidedonlywhengrowingseasonsandweatherdictate.Theseflockswillbehousedinbeddedbarnsinareasrequiringprotectionfromwinterweatherconditions.RangeproductionsystemsincludelambinginAprilorMay,wheremost(andinsomecasesall)dietsareprovidedbygrazedforages.Supplementationwithharvestedfeedsorgrainsisusuallyinresponsetounpredictableweatherandenvironmentalconditions.

Mostlambsarefinishedinfeedlotsandfeddietscontaining85to90percentgrain.Lengthoffeedingperiodswillrangefromweekstomonthsdependingonin‐weightsandtimerequiredtoreachfinalweight(industryaveragefinalweight=61kg).Sheepfeedlotsareprimarilydry‐lots,andmanureisscrapedfromthepenssimilarlytobeefcattlefeedlots.

5.2.4 SwineProductionSystems

5.2.4.1 OverviewofSwineProductionSystems

Theconceptualmodel(Figure5‐5)oftheU.S.swineproductionsystemprovidesanoverviewoftypicalproductionsystems,followinganimalsfrombirthtoharvestandfollowingmanurefromtheanimalthroughamanagementsystem.Manureisproducedduringeachstageofproductionoccurringinthesystem,anddependingonthelocation,ismanageddifferently.ThishasimplicationsonthequantityofGHGemissionsandsinks,someofwhicharediscussedindetailintheemissionsdiscussionsection(Section5.3.4).


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Figure5‐5:ConceptualModelofSwineProductionSystemsintheUnitedStates


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SwineproductionintheUnitedStatesremainsimportanttoboththenation’sdietandeconomy(Davies,2011),withsignificantlevelsofconsumption,imports,andexports.AccordingtotheU.S.DepartmentofAgriculture’sNationalAgriculturalStatisticsService,the2011populationwasnearly66millionhead(USDANASS,2012).

Swinearepredominantlygrownwithproductionofporkoccurringinatwo‐stageorthree‐stagesystem:

Stage1:Sowoperation,pigletsleaveatweaning. Stage2(optional):Nurseryoperation,weaning(10daysofage/17lbs)to42daysofage/45

lbs. Stage3:Severaloptions:

− Afinishingoperation(16‐weekproductionsitewherepigletsaredeliveredfromanurserysiteatapproximately42daysofage/45lbsandstayuntil154daysofage(22weeks)or

− Awean‐to‐finishoperation(24‐weekproductionsitewherepigsaredeliveredatweaningdirectlyfromasowoperation(10daysofage/17lbs)andstayuntil178daysofage(25.5weeks)).

Themanuremanagementsystemsassociatedwiththeseproductionoperationsallhavethebasicelementsofcollection,storage,treatment,transport,andutilization.Mostswinefacilitieshandlemanureasaslurryeitherwithinthebuilding(deeppitfinishingbarnsorshallowpitnursery,gestationorfinishingbarns)orinoutsidestorage(pull‐plugsystemsfornurseries,sows,orfinishingpigs).Collectionandstorageisgenerallyaccomplishedbystorageofthewasteunderthefacility,dischargetoaseparatestoragetank,orflushingtoananaerobiclagoon.Inthecaseofin‐housemanurestorage,littlewaterisaddedtothestoragestructure,andanaerobicconditionsprevailwithlittlebiologicalprocessingofmanuretakingplace.Outsidestoragestructuresthatcontainslurrywithlittledilutionwaterofferminimalbiologicaltreatmentaswell.However,lagoonsystemswheremanureisflushedfromhousingandadditionaldilutionwaterisaddedoffermoretreatment.Drysystemsordeep‐beddedsystemsexisttoamuchlesserextent,primarilyforsoworfinishingproduction.Inthesecasesbeddingmaterial,oftenstraw,isprovidedandmanureplusbeddingishandledassolidmaterial,sometimescomposted.

IntheMidwest,thesystemofmovingstoredswinewastetocropfieldsiswelldefinedandunderstood(HatfieldandPfeiffer,2005;Maloneetal.,2007;Jareckietal.,2008;Vanottietal.,2008;BrooksandMcLaughlin,2009;Jareckietal.,2009;Agnewetal.,2010;Cambardellaetal.,2010;Lovanhetal.,2010).Yetthesesystemscontinuetoevolvetoaddressbotholdandnewissues,suchasfrozenground,applicationtiming,andemissionsassociatedwithsoilapplicationvianewequipment.AllofthemanuremanagementsystemsresultinGHGemissions,buttheyvaryintermsofpointandnon‐pointsources.

5.2.4.2 DietInformationforSwine

Theswineindustryfeedsprimarilyacorn‐soybeanmealbaseddiet.Drieddistillersgrainswithsolubles(DDGS)areoftenfedtobothsowsandfinishingpigsand,asavailabilityofthisfeedincreases,theamountfedincreasestoasmuchas40percentofdietdrymatterintake(DMI).Similarly,whensyntheticaminoacidsourcespricecompetitivelywithfeedproteinsources,thenumberofsyntheticaminoacidsincludedinfinishingpigdietsincreases.Two(lysineandmethionine)ormore(threonine,perhapstryptophan)syntheticaminoacidsarefedcommonlytodaywiththebenefitofreducingtotalnitrogenfed,andthereforeexcreted,byswine.


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5.2.4.3 SwineHousingandManureHandling

Mostcommercially‐raisedfinishingswinearehousedindoorstoprovideabiosecureenvironmentandreducediseasepressures.Manureishandledasslurrywithlittleornobeddingaddedtothesystemandminimaladditionofwater.Asmallbutgrowingportionofthecommercialswineindustryhousebothfinishingpigsandsowsinhoopbarns.Inthesecases,beddingmaterial,oftenstraw,isprovided,andmanureplusbeddingishandledassolidmaterial.

5.2.5 PoultryProductionSystems

5.2.5.1 OverviewofPoultryProductionSystems

TheU.S.poultryproductionsystemiscomprisedofseveralkeyprocessesforpoultry,theirmanure/litter,andtheirendproducts(meat,eggs)asdepictedinFigure5‐6.

Thefigureprovidesanoverviewofthetypicalproductionsystems,followingboththelayerandbroilerphases.Thisconceptualmodelprovidesanoverviewofthetypicalpoultryproductionsystems,followingbirdsfrombirthtoslaughterandfollowingmanurefromtheanimalthroughamanagementsystem.Manureisproducedduringeachstageofactivitiesoccurringinthesystem,anddependingonthelocation,ismanageddifferently.TheemissionsfrommanuremanagementarediscussedindetailinSection5.3.

TheU.S.poultryindustryistheworld'slargestproducerandsecondlargestexporterofpoultrymeat.TheU.S.isalsoamajoreggproducer.Thepoultryandeggindustryisamajorfeedgrainuser,accountingforapproximately45.4billionkg(100billionlbs)offeedyearly.

Theeggincubationperiodforachickenis21days.Followinghatch,broilerchickensarerearedfor42to49days(sixtosevenflocksperyear),dependinguponthemarketintent(e.g.,roasters).U.S.eggoperationsproducemorethan90billioneggsannually.Morethan75percentofeggproductionisforhumanconsumption(thetable‐eggmarket).Theremainderofproductionisforthehatchingmarket.Theseeggsarehatchedtoprovidereplacementbirdsfortheegg‐layingflocksandtoproducebroilerchicksforgrow‐outoperations.Followinga16to22weekgrowthperiod,hensstartlayingeggs.

TheU.S.turkeyindustryproducesmorethanone‐quarterofabillionbirdsannually,withtheliveweightofeachbirdaveragingmorethan25lbs.Theeggincubationperiodforaturkeyis28days.Followinghatch,turkeypoultsarerearedfor15to22weeks(onetothreeflocksperyear)dependingonthemarketintent(e.g.,roasters).

5.2.5.2 DietandGrowthInformationforPoultry

Dietsformeatbirdsconsistlargelyofcornandsoybeanmeal(commonly85to92percentofthediet);however,alternateingredientssuchasdrieddistillersgrainswithsolubles(DDGS)andotherco‐products,andsyntheticaminoacidsareincreasinglyused.Hendietsaremostcommonlycomposedofcornandsoybeanmeal.Otheringredients,suchasDDGS,maybeincluded(rarelymorethan20percentofthediet).Ingredientvariabilityislargelyinsourcesofsupplementalenergy,minerals,andadditivestoimproveanimalhealthandperformance.Dietsareformulatedbasedongrowthrateandeggproductionandfedaseitheramashorapellet.Bonestrengthisanimportantcharacteristicofmeatbirdqualitythereforeprovisionofmineralssuchascalciumandphosphorusarecarefullyconsideredwhendietsareformulated.Similarly,eggshellqualityiskeyforlayinghens,andasaresult,calciumutilizationisakeyelementindietformulation.


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Figure5‐6:ConceptualModelofPoultryProductionSystemsintheUnitedStates


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Poultrybreedschangerapidly,demonstratingimprovedproductionefficiency,andassuch,dietsareincreasinglydensewithenergyandprotein.Thesechangesareduetoacombinationofgeneticsandmanagement,includingdietformulation.2WhiledietandgeneticinfluenceswereconsideredinastudybyHavensteinetal.(2007),theresultssuggestthatthedietchangesthatoccurredbetween1966and2003interactedwithotherfactors(flockage,ambienttemperature)toinfluencebirdgrowth.Someestimatethat85percentoftheimprovementinthegrowthrateofbroilerchickensisattributabletogenetics(Havensteinetal.,2003).3

IntheUnitedStatesthereisnoban,atpresent,onuseofantibioticgrowthpromoters(AGPs)inpoultryproduction(meatbirds).However,thetrendistowardconsumerswantingproductsthathavenotusedAGP.FindingreplacementsforAGPwilllikelyinvolvetheuseofmultipleproductsinthediet,eachwithsomeofthebenefitsofAGP,andmanagementchangeswillplayakeyroleinmaintaininganimalproductivityintheirabsence.Itisunlikelythatasinglereplacementwillbefoundthatwillprovetobeeconomicallyviable(DibnerandRichards,2005).

5.2.5.3 PoultryHousingandManureHandling

Thevastmajorityoftheindustryraisesbirdsonlitterinmechanicallyventilatedornaturallyventilatedhouses.Reuseoflitterandnumberofflocksgrownonthesamelitterisvariableacrossthecountry,andcanrangefromaslowasasingleflocktoasmanyas18flocksonthesamelittersource.Litterdrymattercontentcanvaryfrom40to80percent,dependingonmanagement.

Layinghenandpullethousingtypesrangefromhigh‐risehouseswherehensareincagesandmanureaccumulatesinabasementunderthecagesandisremovedannually,toamanure‐belthousewherehensareincagesandmanureisremoveddailyormorefrequentlyfromthebasementtoanexternalshedandstackedbeforeperiodicremovalforlandapplication(onceortwiceperyear),toaviarieswherehensareraisedonlitter(inlargeroomsasopposedtocages)thatisremovedfromtheaviaryannuallyormorefrequently.Whenmanureisremovedfromthehouseitmaybeimmediatelyappliedtofields,stockpiled,orcomposted.Moisturecontentmayvaryfrom80percentmoisturedownto20percentmoisture(aviaries).

5.3 EmissionsfromEntericFermentationandHousing

Emissionsfromanimalproductionsystemsincludethosefrombothentericfermentationandfromanimalhousing(includinganimalmanureinhousingareasthatmayultimatelybeflushedorscrapedandthentransportedtoanexternalmanuremanagementsystem).TheproductionofGHGsinlivestocksystemsoriginatesfromavarietyofsources,includingdirectlyfromtheanimalsthemselves;manureinlotsandbarns;stockpiledandcompostingmanures;manureslurriesorwatersintanks,pits,lagoons,retentionponds,settlingcells,etc.;andfromsoilsaftermanureapplication.Emissionsfromthesesourcesdependonanimalsizeandage,diet,manureproduction,handlingandstoragesystem,lotsurfaceandsoilcharacteristics,andambientweatherconditions(i.e.,temperature,wind,humidity,andprecipitation).Foreachanimaltype,thissectionsummarizes2Havensteinetal.(2007)compared1966strainsto2003strainsandobserveda20percentbettercumulativefeedconversionratiointhe2003tomturkeyfeda2003dietrelativetoa1966tomfedadiettypicalof1966.Feedefficiencyto11kgbodyweightwasapproximately50percentbetter(2.13at98daysofagein2003toms,comparedwith4.21at196daysfor1966toms).

3Havensteinetal.(2003)comparedthe1957Athens‐CanadianRandombredControlstrainandthe2001Ross308strainofbroilerswhenfedrepresentative1957and2001diets.The42‐dayfeedconversionsfortheRoss308birdsfedthe2001and1957feedswere1.62and1.92,respectively(withaveragebodyweightof2,672and2,126g).The42‐dayfeedconversionsfortheAthens‐CanadianRandombredControlwere2.14and2.34(averagebodyweightof578and539g,respectively).


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thecurrentunderstandingofentericfermentationandlivestockhousingemissionsandpresentsrecommendedmodelsforestimatingsuchemissions,includingtherationaleforselectingmethods.

ActualfieldmeasurementsofGHGsfromentericfermentationoverthepastseveraldecadeshavebeeninstrumentalinimprovingourunderstandingoftheunderlyingscienceandtheresultingmodelspresentedinthissection.Fordairyanimals,mostoftheemissionsestimatesavailablerepresentthelactatinganimal.Theequationsforgrowingbeefanimalsarelikelyappropriateforgrowingdairyanimalsifdietcompositionisconsidered.Thetextboxesonthefollowingpagessummarizeseveralofthekeytechniquesthathavebeenusedinmeasurementstudiesforbothindividualanimalsandgroupsofanimals.FurtherstudiesofthistypewillbeneededtoaddressresearchgapsinSection5.5.

ThissectionprovidestherecommendedmethodforestimatingGHGsfromentericfermentationandapplicablehousingemissions.Quantitativemethodsareprovidedfordairy,beef,sheep,swine,poultry,andotheranimals(i.e.,goats,Americanbison,llama,alpacas,andmanagedwildlife).Foreachsection,backgroundinformationisprovidedontherangeofemissionsandexistingmodelsforestimatingemissionsandtherationaleforthemethodselected.Forestimatingemissionsfromentericfermentation,theactivitydataisthesameforallanimaltypes.Ancillarydataincludesthepropertiesofthediets(e.g.,crudeprotein(CP),digestibleenergy(DE),neutraldetergentfiber(NDF)).Forsimplicity,activitydataandancillarydataarelistedinTable5‐2andarenotrepeatedbelowforeachanimaltype.

5.3.1 EntericFermentationandHousingEmissionsfromDairyProductionSystems

Althoughthedairyindustryisprimarilycomposedofthreelivestocktypes[growing(i.e.,calves,replacementheifers),lactatingcows,anddrycows],mostofthelimitedemissionsresearchconductedtodatehasbeentargetedatlactatingcows,whichtypicallyproduceatleast50percentmoreentericCH4perheadthanotherdairycattle.Fewemissionsdataexistforcalves,heifers,anddrycows.Therefore,thediscussionherefocusesprimarilyonlactatingcows.

Dataneededtoestimateemissionsincludehousingsystem(pasture,barntype,dry‐lot),animalcharacteristics(breed,bodyweight,growthpotential,stageoflactation,milkingfrequency,andmilkproduction)andpopulation,dietaryinformation(DMI,dietaryCP—alsoNDF,fat,DE,metabolizableenergy(ME),netenergy(NE),nutrientexcretion(N,C,andvolatilesolids),useofrecombinantbovinesomatotropin,useofmonensin,typeofmanurehandlingsystem,frequencyofmanureremoval,typeofbedding,andmanurecharacteristics(totalammoniumnitrogen,pH).

EntericFermentationEntericCH4productionvarieswithproductionstageindairycattle,withthehighestratesbeingproducedbylactatingcows(Table5‐4).Thistableillustrates,conceptually,theobservedvariationincattleatdifferentstagesofmaturityandactivity,butitisnotintendedtoprovideadepictionofabsolutedifferences.TherearemanyfactorsthataffectentericCH4production,andthereforealteringdairycattledietscouldhaveanimpactonentericCH4production.Foranin‐depthdiscussionofdietaryeffectsonentericCH4production,seeSection5.3.7(FactorsAffectingEntericFermentationEmissions).However,theresultsinTable5‐4clearlyillustratethedifferenceinentericemissions;inparticular,emissionsfromdairycattlearerelativelyhigherthanthosefromgrowing(i.e.,heifers)anddrycattle.

Table5‐4:ExamplesofCH4EmissionsMeasuredinDairyCattle

AnimalType CH4EmissionMethodUsedto

MeasureEmissions Reference

Dairycattle 260ganimal‐1day‐1CalculatedBlaxterandClapperton Crutzenetal.(1986)


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AnimalType CH4EmissionMethodUsedto

MeasureEmissionsReference

Heifer6‐24month 140gLU‐1day‐1 SeeaboveDairycattle,dryperiod 139gLU‐1day‐1 Respirationcalorimetry

Holter&Young(1992)Dairycattle,lactating 268gLU‐1day‐1 Seeabove

Dairycattle 257gLU‐1day‐1 Respirationcalorimetry Kirchgessneretal.(1991)

Dairycattle,lactating 429ganimal‐1 day‐1 WindtunnelSunetal.(2008)

Dairycattle,dryperiod 290ganimal‐1 day‐1 WindtunnelDairycattle,lactating 538–648ganimal‐1day‐1 Respirationcalorimetry Aguerreetal.(2011)LU,livestockunit=500kg

MethodsforMeasuringCH4 EmissionsfromEntericFermentation

IndividualAnimalsThestandardmethodofmeasuringCH4emissionsfromruminantsisbyrespirationcalorimetrychambers.Othertechniques,includingheadboxes,internaltracers,micrometeorology,isotopedilution,andpolyethylenetunnels,havebeenused(Kebreabetal.,2006;Harperetal.,2011).Severalnewtechnologieshavebeendevelopedtomeasureindividualanimalemissions.ToaddressthedifficultyinmeasuringentericCH4frommanyanimalsonpasture,alternatemethodsaresought.Asoneexample,Goopyetal.(2011)hasproposedaportablestaticchambermethodtomeasuredailyCH4production.Untilvalidated,resultsusingalternatemethodsshouldbeviewedwithcaution.

AvarietyofrespirationchambershavebeendevelopedtomeasureentericCH4lossesand/ortotalenergymetabolismoftheanimal.Ingeneral,airispulledfromthechamberataknownrateandreplacedwithoutsideair.FlowofairandconcentrationsofCH4,CO2,andoxygen(O2)intheairenteringandleavingthechamberaremeasuredtodeterminetotalCO2andCH4productionandO2consumption.Whenproperlycalibratedandused,respirationchambersgivehighlyaccurate,precisemeasurements.However,theyareexpensivetobuildandoperate,andrequiresignificantknowledge,skill,andlabor.

Feedintakeandproductionareusuallydepressedinanimalsinchambersandthemeasurementsdonotnecessarilyreflectintakeandproductionfromtypicalcommercialoperations.Thislimitationcanbepartiallyovercomebyfeedinganimalsatdifferentlevelsofintakeandmeasuringtheeffectsofintakelevel.Headboxesusethesameprinciplesasrespirationcalorimetry,andhavemanyofthesamelimitations.In‐barnchambersusingdrop‐downcurtainshavebeenusedtomeasure,atrelativelylowcost,emissionsofNH3,CH4,andothergassesfromgroupsofdairycows(Powelletal.,2007;Powelletal.,2008;Aguerreetal.,2011).

Internaltracertechniquessuchasthesulfurhexafluoride(SF6)tracermethod(Johnsonetal.,1994)weredevelopedtoallowmeasurementsfromfree‐ranginganimals,suchasthosemanagedunderpasturesituations,orwhenreal‐worldlevelsoffeedintakeareneeded.Thelimitationstothismethodaretheneedfortrainedanimals,theneedforlargersamplesizes(comparedwithchambers)todetecttheinfluenceofmitigationtechniques,andconcernsaboutinconsistentreleasesoftracergasfromSF6permeationtubesmanufacturedforlargereleaserates.Additionally,theSF6techniquegenerallyresultsinemissionestimatesthatarelowerthanchambermeasurements;possiblybecausetheSF6methoddoesnotmeasurealllowergutCH4production(McGinnetal.,2006).TheadvantagesandshortcomingsoftheSF6methodhavebeenrecentlyreviewed(Lasseyetal.,2011).


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Methods for Measuring CH4 Emissions from Enteric Fermentation

Group of Animals

MicrometeorologymethodshavebeenusedextensivelytomeasureCH4andNH3emissionsfrompastures,wholefeedyards,orportionsofthefeedyard(pens,retentionponds,manurestockpiles,etc.).Thesemethodshavebeenreviewed(Fowleretal.,2001;Fleschetal.,2005;Harperetal.,2011).Laubacketal.(2008)comparedtheSF6methodwiththreemicrometeorologicalmethods(integratedhorizontalflux,fluxgradient,andbackwardLagrangianstochastic(bLS))usingsteersgrazingpaddocks.Ingeneral,themicrometeorologicalmethodsgavehigherCH4measurementsthantheSF6method,withthedifferencebeinggreaterwhenanimalswerewithin22metersoftheCH4sampler.Thiseffectwasespeciallytrueforthefluxgradientmethod.ThelowervaluesfortheSF6methodcouldbedueinparttothefactthattheSF6methoddoesnotmeasureemissionsfromthelowergutorfromfermentationoffecesonthepaddocksurface.

Tomkinsetal.(2011)comparedentericCH4emissionsofsteersonpastureusingthebLSmethodandrespirationchambers.EmissionsestimatedusingthebLSmodelwereslightlygreaterthanwithrespirationchambers(136.1vs.114.3gheaddaily‐1).HoweveremissionspergramofDMIweresimilar(29.7vs.30.1gCH4kgDMI‐1,respectively),suggestingthatthebLSmodelmaybesuitableforestimatingentericemissions.

Mostdispersionmodelsandmicrometeorologicalmethodsassumethatemissionsareuniformlydistributedoverthesourcearea.Insomecases,suchasforindividualcattleinapenorfield,thisisnottrue.Therefore,McGinnetal.(2011)developedamethodthatusedapoint‐sourcedispersionmodelandatmosphericCH4concentrationsmeasuredusingmultipleopen‐pathlaserstomeasureCH4emissionsfromapaddockcontaining18cattle.MeasuredentericCH4emissionsweresimilartovaluesmeasuredusingothertechniques.However,recoveriesofknownCH4releasesaveragedonly77percentusingthismethod.Themethodgavemorereliablemeasurementsduringthedaytimewhenatmosphericconditionswereunstablethanatnightwhenatmosphericconditionswerestable.

Methods for Measuring Emissions from Manure

Estimatingemissionsfromlargeopensourceareastypicallyassociatedwithbothdairyandbeefcattleproductionisverychallenging,duetotheinabilitytocontainandmeasurethesourcearea.Instrumentsandtechniquestomeasureambientatmosphericgasesfromtheselargesourceareas(i.e.,dry‐lotbeefanddairycattleyards,freestalldairieswithnaturallyventilatedcurtainsidewallbarns,andgrazingland)mustbeabletodetectlowerconcentrationsthanthoseencounteredintypicalenclosedconfinedanimalproductionsystems,becauseofthelowconcentrationsandhighvariabilityresultingfromhighandvariableventilationrates.Alargerchallengewithmeasuringemissionsfromopenfacilitiesistheabilitytoestimateairflowduetothelackofadefined,constantairinletandairoutlet.ReportedbackgroundNH3concentrationstypicallyrangefrom<1.3to53.3partsperbillion(ppb)(Toddetal.,2005),backgroundatmosphericN2Oconcentrationsnearfeedyardsaverageabout319ppb(Michaletal.,2010),andbackgroundCH4concentrationstypicallyrunintheareaof1,780ppb(Michaletal.,2010).


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Methods for Measuring Emissions from Manure (Continued)

NumerousfactorscanaffectatmosphericconcentrationsofNH3andGHGnearlivestockoperationsincludingsamplingheight,atmosphericstability,windspeed,backgroundconcentrations,stockingdensity,samplingsite,samplingtime,temperature,andwinddirection(fetch).AveragedailyNH3concentrationsmeasuredatavarietyofsimilarsourceareasrangedfromapproximately100to2,000µgm‐3.Measuredmaximumconcentrationsrarelyexceed2,000µgm‐3.Ammoniaconcentrationsdecreaserapidlydownwindofsourceareas(Miner,1975),approachingbackgroundconcentrationsinlessthan800meters(McGinnetal.,2003;Sweeten,2004).

AtmosphericCH4concentrationsmeasuredatfeedlotsanddrylotdairieshaverangedfrom3.3to4.7partspermillion(ppm)(Michaletal.,2010),andfrombackground(approximately1.78ppm)to6.20ppm(Bjornebergetal.,2009),respectively.Nitrousoxideconcentrationsmeasuredatfeedlotsrangedfrom319ppb(background)to443ppbandaveraged396±16ppb(Michaletal.,2010).Nitrousoxideconcentrationswerehighestfollowingarainfallevent.Afterarain,CH4concentrationsaveraged3.7±0.1ppm.Atdry‐lotdairies,medianN2Oconcentrationsrangedfrom314ppbto330ppb,whichareveryclosetoglobalbackgroundvalues(Bjornebergetal.,2009).

SmallfluxchambersandwindtunnelshavebeenusedtoestimateemissionsofNH3,CH4,andN2Ofromfarmlands,pastures,pensurfaces,lagoons,andretentionponds(HutchinsonandMosier,1981;Ventereaetal.,2009;Venterea,2010;Harperetal.,2011;Hristovetal.,2011).Ingeneral,chambersalterthemicroenvironmentofthesurfaceandmayalteremissions.Thus,theaccuracyofthesemethodsfordeterminingemissionfactorsforsomegases(especiallyNH3)hasbeenquestioned(GaoandYates,1998;Harper,2005;Ventereaetal.,2009;Parkeretal.,2010;Venterea,2010;Harperetal.,2011).MeasuresofNH3emissionsusingfluxchambersandwindtunnelsarehighlydependentuponairflowandairturnoverratesinthechamber(Coleetal.,2007b;Parkeretal.,2010).Basedontheconventionaltwo‐filmmodelusedtodescribevolatilizationfromasolute‐solventmixture(Parkeretal.,2010),manygaseousemissionsarecontrolledbythegasfilmabovetheliquidortheupperportionoftheliquid(liquidfilm)definedbytheHenry’slawconstant.Ifvolatilizationisinhibitedbyhighconcentrationsinthegasphase(i.e.,gas‐filmcontrolled),increasesingaseousconcentration—suchaswithfluxchambers—willleadtosignificantunderestimationoftrueflux.Venterea(2010)reportedthatemissionsofN2Oestimatedusingstaticchamberswereunderestimatedbyapproximatelythreeto38percent,dependinguponsoilwatercontent,typeofregressionperformed(linearvs.quadraticvs.nonlinear),andotherfactors.Thepercentageofunderestimationtendedtobegreaterwithdrysoils,probablybecauseN2Ofluxislowerwhensoilsaredry.Sommeretal.(2004)reportedthatGHGemissionsfromcompoststockpilesmeasuredusingstaticchamberswereonly12to22percentofvaluesmeasuredusingtheintegratedhorizontalfluxmethod.

Becauseofthesefactors,fluxchambersshouldbeusedtoexaminerelativedifferences,ratherthanemissionfactorsofNH3,CH4,andN2Oemissionsfrompensurfaces,lagoons,retentionponds,manurestockpiles,orcompostwindrows.Inaddition,thesurfaceofpasturesandfeedlotpensistemporallyandspatiallyheterogeneous,withdryareas,areaswithfreshfeces,andareaswithurineofdifferentages(Woodburyetal.,2001;Coleetal.,2009a;Coleetal.,2009b).Toadequatelyrepresentthesurface,thenumberofchambermeasurementsrequired(estimatedasthecoefficientofvariationsquared/100:Kienbusch,1986)canbeverylarge(i.e.,onechamber/quaremeter:Coleetal.,2007b).


5-35

HousingThereareawidevarietyofdairycattlehousingsystemsduetovariationsinherdsizeandregionalpractices.InthenortheasternUnitedStates,herdsizetendstobesmallerandcattlearehousedinfreestallandtie‐stallbarnsandonpasture;inthewesternpartofthecountry,herdsizestendtobelargerandanimalsarehousedinfreestallbarnsordry‐lotswithfewproducersusingpasture‐basedsystems.ThesedifferencesinhousingcanleadtodifferencesinbothGHGandNH3emissions.ExamplesofreportedemissionsfromvaryinghousingsystemsarepresentedinTable5‐5.

Table5‐5:ExamplesofReportedOn‐FarmEmissionEstimatesforCH4,N2O,andNH3fromaVarietyofDairyCattleHousingSystems

Housing CountryEmissions(gcow‐1d‐1)

ReferenceCH4 N2O NH3

Barn Germany 402 64.8 Sahaetal.(2014)Tiestallbarn Austria 170‐232a 0.14‐1.2a 4‐7.4a Amonetal.(2001)Barn Germany 256 1.8 14.4 Jungbluthetal.(2001)Dry‐lot U.S. 41‐140 Casseletal.(2005)Hardstanding UK 0.03b 0.01 11 Ellisetal.(2001)Open‐freestall U.S. 410 22 80 Leytemetal.(2013)Tiestallbarn Canada 390 Kinsmanetal.(1995)Pasture NZ 300‐427 Laubach&Kelliher(2005)Dry‐lot U.S. 490 10 130 Leytemetal.(2011)Standoffpad NZ 1.66b 0.03 Luo&Saggar(2008)Barn Denmark 256 1.2 16 Zhangetal.(Zhangetal.,2005)Dry‐lot China 397 37 Zhuetal.(Zhuetal.,2014)Barn Sweden 216‐312a 21‐27a Ngwabieetal.(2009)Barn Germany 464 45 92.4 Sameretal.(Sameretal.,2011)Pasture Uruguay 372 Dinietal.(Dinietal.,2012)

*DenotesmeasurementsingLU‐1d‐1,whereaLU(livestockunit)=500kg.†MeasurementsdonotincludeentericCH4production.

Variationsinemissionsfromhousingareduetofactorssuchastemperature,dietcomposition,waterconsumption,ventilationflowrates,typeofmanurehandlingsystems,manureremovalfrequency,feces,andurinecharacteristics(i.e.,pHandtotalammoniacalnitrogen(TAN)),andtypeofbeddingused.Althoughdifferencescanbegreatbetweenemissionrates,therearesomeemissioncharacteristicsthatareconsistentacrossmoststudies.ManystudieshavereportedstrongdieltrendsinemissionsofCH4andNH3,withemissionstendingtobelowerinthelateeveningandearlymorningandthenhigherthroughoutthedaytillearlyevening(Amonetal.,2001;Casseletal.,2005;Powelletal.,2008;Sunetal.,2008;Bjornebergetal.,2009;Fleschetal.,2009;Ngwabieetal.,2009;Aguerreetal.,2011;Leytemetal.,2011).Thisstrongdieltrendinemissionscanbeassociatedwithwindspeedandtemperature,aswindstendtobelightinthelateeveningandearlymorningandthen,inmostinstances,steadilyincreasethroughoutthedaytoreachapeakinthelateafternoon.Temperaturealsoincreasesfromearlymorningtolateafternoon,andthendecreasesagain.Additionally,cattleactivitytendstoincreasefrommorningtolateafternoonasanimalswakeandbegintoeat,drink,ruminate,defecate,andurinate.Astheseactivitiesincrease,onewouldexpectanincreaseinCH4(andNH3)emissions.Therearealsoseasonaltrendsinemissions,themostprominentbeinginNH3emissions,withthelowestratesinwintercomparedwiththeotherseasons(Amonetal.,2001;Powelletal.,2008;Bjornebergetal.,2009;Fleschetal.,2009;Aguerreetal.,2011;Leytemetal.,2011).Powelletal.(2008),Fleschetal.(2009),andAguerreetal.(2011)reportedthatbarnemissionsofNH3inWisconsinwerelowestinwinter,withwinterratesaboutone‐halftoone‐thirdlowerthanthoseinthespringandsummer,whichwas


5-36

Ammonia Emissions in Dairy Cattle Housing

Asmentionedearlier,ammoniaisnotagreenhousegas,however,ammoniaemissionsareestimatedaspartofthenitrogenbalanceapproach.EmissionsofNH3fromdairycattlehousingsystemshavebeenstronglylinkedtodietarynitrogenintake,asthisaffectstheamountofureanitrogenexcretedinurine.Ofthenitrogeninthetotalcrudeprotein(CP)typicallyconsumedbyadairycowoncommercialdairyfarms,20to35percentissecretedinmilkandtheremainingnitrogenfromCPisexcretedaboutequallyinfecesandurine.Feednitrogen(N=CP÷6.25)useefficiency(percentageoffeednitrogensecretedasmilknitrogen)andthe50:50fecalnitrogen:urinarynitrogenexcretionratiocanbeinfluencedgreatly,however,bywhatisfedtothecow.Feedingnitrogeninexcessofnutritionalrequirementshasveryfewsignificantimpactsonmilkproductionorquality;itdecreasesfeednitrogenuseefficiencyandincreasestherelativeamountofureanitrogenexcretedinurine.Theureanitrogencontainedincowurine(whichis55to80percentofthenitrogencontainedinurine,dependingonconcentrationsofCPintheration)isthemajorsourceofNH3emissionfromdairyfarms.Ureaisproducedwhennitrogen‐richproteinsand/ornon‐proteinnitrogensourcesbreakdown(mainlyinthecowrumen),formingNH3gasthatmaybeusedbyruminalmicrobestoproducemicrobialproteinsorcanbeabsorbedthroughtheruminalwalltothebloodstream.Inthekidney,bloodNH3fromthedigestivetractortissuemetabolismiseventuallyconvertedtoureabeforebeingexcretedintheurine.Ureaseenzymes,whicharepresentinfecesandsoil,rapidlyconvertexcretedureatoammonium,whichcanbehydrolyzedquicklyintoNH3gasandlosttotheatmosphere.Thus,theincreaseinureanitrogenexcretionduetoexcessiverationCPincreasesNH3emissionsduringthecollection,storage,andlandapplicationofmanure(Rotz,2004;Misselbrooketal.,2005;Powelletal.,2008;Arriagaetal.,2010).

Pauletal.(1998)examinedtheeffectsofalteringdietaryCPonNH3lossesfromdairycows.TheyreportedthatNH3emissionsduringthefirst24hoursfollowingmanureexcretionwere38and23percentofthetotalmanurenitrogenfromdietswith16.4and12.3percentCPconcentrations,respectively,and22and15percentoftotalmanurenitrogenfromdietscontaining18.3and15.3percentdietaryCP,respectively.Misselbrooketal.(Misselbrooketal.,2005)reportedthatreducingdietaryCPcontentresultedinlesstotalnitrogenexcretionandasmallerproportionoftheexcretednitrogenbeingpresentinurine;urinenitrogenconcentrationwas90percentgreaterforthehigh‐CPthanthelow‐CPdiet.

However,Lietal.(2009)foundnoeffectofloweringdietaryCPinlactatingdairycattleonNH3emissionsfromthefloorofanaturallyventilatedfreestalldairybarnatlowandmoderatetemperatures(0to20°C).ThislackofresponsetoCPislikelyduetothefactthatureaseactivityisnegligibleattemperaturesbelow10°C(Bluteauetal.,2009).FactorsthatareessentialindeterminingNH3emissionsaremanureorurinepHandthetotalammoniacalnitrogencontent,bothofwhicharerelatedtothedietaryCPlevel.

ThemajorityofNH3emissionsfromhousingsystemsareduetothevolatilizationofNH3fromurinedeposition.Asdiscussedabove,nitrogenintakedrivestheamountofureathatisexcretedintheurine.Asthisurineisdepositedonbarnfloors,pastures,ordry‐lots,itmixeswithureasefromeitherfecesorsoilandisthenhydrolyzedtoammoniumand,viaeffectsofpH,convertedtoNH3andlosttotheatmosphere.ThelossofNH3happensrapidly,withmostNH3lossesoccurringwithin24hoursfollowingdeposition.Therefore,estimationofNH3emissionsneedstotakeintoaccounttheamountofureageneratedbythecow,pH(urine,manure,orsoil),temperature,andairflowoverthesource.StrategiesthatreducenitrogenexcretionwillbeverybeneficialinreducingNH3emissionsfromhousing/pasturesystems.


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attributedtocoldwintertemperatures.Ingeneral,N2Oemissionsfromhousingwerefoundtobelowandshowednodiscernibledielorseasonaltrends(Bjornebergetal.,2009;Ngwabieetal.,2009;Adviento‐Borbeetal.,2010;Leytemetal.,2011),suggestingthattheseemissionsfromthissectoroftheproductionsystemareofrelativelylittleconcern.ThereareconsistentreportsofbothdielandseasonalvariationsinbothCH4andNH3emissions,soitisimperativethatthesefactorsbecapturedinanyestimationofemissionsforagivenproductionsystem.

EmissionsofCH4aredominatedbyentericfermentationinhousing/pasturesystems.Amonetal.(2001)examinedCH4emissionsfromatie‐stalldairybarninAustriausingeitheraslurry‐basedsystemorstraw‐basedsystem.Inbothsystems,about80percentofthenetCH4emissionswereduetoentericfermentation,withtheremainingamountcomingfromthemanure.Sunetal.(2008)measuredCH4emissionfromdairycowsandfreshmanureinchambers,andreportedthatfreshmanurealonedidnotproducenoticeableCH4fluxes.Insomedairyproductionsystems,manureisremovedfromtheanimalhousingareafrequently;therefore,CH4emissionsfromanimalhousingareasofadairycanbelargelyattributedtoentericemissions.

N2Oemissionstendtobenegligiblefrombothanimalsandfreshmanure.ThemajorityofN2Oemissionsresultfrommanurestorage,pasture,andlandapplicationofmanures.Therefore,themainsourcesofN2Oemissionsfromanimalhousingwouldbefromdry‐lotdairiesandstand‐offpads,becausethereispotentialfordepositednitrogentobenitrifiedanddenitrifiedunderwetconditionsandlostasN2O.LuoandSaggar(2008)measuredN2OandCH4emissionsfromadairyfarmstand‐offpadinNewZealandandreportedN2Ofluxesfrom0to3gN2O‐Nday‐1,whichtheyattributedtotheconcentrationsofwaterandnitrateinthepadmaterials.Overall,only54gofN2O‐Nwasemittedfromthepadoverthetimeofuse,representing~0.01percentoftheexcretanitrogendepositedonthepad.

Whiletherehavebeenoverallimprovementsinmilkproductionwithbreedingprograms,thereisnoevidencethatanybreedofdairycowproduceslessentericCH4.MüngerandKreuzer(MüngerandKreuzer,2008)measuredentericCH4productionfromHolstein,Simmental,andJerseycowsandfoundnopersistentdifferencesinCH4yields,withaverageentericCH4beingapproximately25gCH4kgDMI‐1.

5.3.1.1 MethodforEstimatingEmissionsfromDairyProductionSystems

Methodfor Estimating CH4 Emissions from Enteric Fermentation in Dairy Cows

Millsetal.(2003)developedaseriesofsubmodelstoestimateentericCH4emissionsfromdairyandbeefcattle.TheoptimalmodelappearedtobeanonlinearMits3equation,whichisutilizedbytheDairyGEMModel(asubsetofIFSM)(Rotzetal.,2011b)andisshowninEquation5‐1(Mits3equation)isbasedprimarilyonmetabolizableenergyintake,aciddetergentfiber(ADF),andstarchcontentofdiet.

Datasourcesareuserinputondietaryintake,aswellasdietarydatafromtheFeedstuffsCompositionTable(Ewan,1989;Preston,2013).

UseoftheDairyGEM/Mits3equationisrecommendedovertheIPCCTier2equation(IPCC,2006)becauseithasproventobemoreaccurate,ingeneral,fordairycows.


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TheEmaxisconstantforallanimalsat45.98MJ/head/day.Theshapeparameter“c”iscalculatedfromthedietarynon‐fibercarbohydrate(NFC)toaciddetergentfiber(ADF)ratioinEquation5‐2.

Millsetal.(2003)notedthatnonlinearmodelshavetwoadvantagesoverlinearmodels:1)amaximumemissionisset;and2)itisexplainablefromabiologicalsense.Thefeedstuffcharacteristicsneededtocalculateemissionsfromdairycattleareincludedintheexamplebelow(Ewan,1989;Preston,2013).ThefulltablecanbefoundinAppendix5‐B.

Table5‐6:ExampleFeedstuffsTablea

FeedstuffDM%

Energy Protein FiberEE%

ASH%

Ca%

P%

K%

Cl%

S%

ZnppmTDN

%NEm NEg NEl

(Mcal/cwt.)

DE(%ofGE)*

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

AlfalfaCubes

x91 57 57 25 57 18 30 29 36 46 40 2.0 11 1.30 0.23 1.9 0.37 0.33 20

Alfalfadehydrated17%CP

92 61 62 31 61 65.16 19 60 26 34 45 6 3.0 11 1.42 0.25 2.5 0.45 0.28 21

Alfalfafresh

24 61 62 31 61 62.54 19 18 27 34 46 41 3.0 9 1.35 0.27 2.6 0.40 0.29 18

Source:Preston(2013).

Equation5‐1:Non‐LinearMits3Equation

exp .

Where:

CH4 =Entericmethaneemissionsperday(kgCH4head‐1day‐1)

Emax =MaximumpossibleCH4emissions(MJhead‐1day‐1)

c =Shapeparameterdeterminingemissionchangewithincreasingmetabolizableenergyintake(seeEquation5‐2)

x =Metabolizableenergyintake(MJhead‐1day‐1)

0.018=ConversionofMJtokgofCH4(kgCH4MJ‐1)

Equation5‐2:CalculatingShapeParameter

. .

Where:

c =Shapeparameterdeterminingemissionchangewithincreasingmetabolizableenergyintake(unitless)

NFC =[(100‐NDF+CP+EE)/100]xDMI(kghead‐1day‐1)

DMI =Drymatterintake(kgdryfeedanimal‐1day‐1)

ADF =AcidDetergentFiber(kghead‐1day‐1)

NDF =NeutralDetergentFiber(%)

CP =CrudeProtein(%)

EE =Etherextract(%)


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aColumnheadings:DM =Drymatter GE = Grossenergy ASH =AshTDN =Totaldigestiblenutrients CP =Crudeprotein Ca =CalciumNEm =Netenergyformaintenance UIP = Undegradableintakeprotein P =PhosphorousNEg =Netenergyforgrowth CF = Crudefiber K =PotassiumNEl =Netenergyforlactation ADF = Aciddetergentfiber Cl =ChlorineMcal =Megacalories NDF = Neutraldetergentfiber S =Sulfurcwt =Centumweight(hundredweight) eNDF = effectiveneutraldetergentfiber Zn =ZincDE =Digestibleenergy EE = Etherextract ppm =partspermillion

MethaneEmissionsfromDairyCows’Housing

TheDairyGEMModel(Rotzetal.,2011a)calculatesCH4emissionsfrombarnfloorsusinganempiricalmodeldevelopedfromthreefreestallbarns(Chianeseetal.,2009c).

Whenmanureisallowedtoaccumulateasastockpile,onadry‐lot,orinapitbelowtheanimalconfinement,theDairyGEMmodelusestheIPCC(2006)Tier2methodtoestimateCH4emissions(Equation5‐4).Thisisthesameequationusedforestimatingemissionsfrommanurethatismanagedoutsideofhousing(seeSection5.4.1TemporaryStackandLong‐TermStockpileand5.4.2 Composting for details).

Methodfor Estimating Dairy Cows’ GHG Emissions from Housing

Methane

TheDairyGEMModel(asubsetofIFSM)(Rotzetal.,2011a)calculatesCH4emissionsfromhousingsurfaces.

DairyGEMusestheIPCC(2006)Tier2methodtoestimateCH4emissionswhenmanureisallowedtoaccumulateinthehousing.

NitrousOxide

NitrogenexcretedestimatedusingequationsprovidedinASABED384.2. IPCC(2006)Tier2approachforN2Oemissionsfrommanureinhousing.

Equation5‐3:CalculatingCH4EmissionsfromBarnFloors (Chianeseetal.,2009c)

. , .

Where:

CH4=Methaneemissionsperday(kgCH4head‐1day‐1)

T =Barntemperature(˚C)

Abarn =Areaofthebarnfloorcoveredwithmanure(m2)


5-40

ThemaximumCH4producingcapacity(B0)formanurevariesbyanimalcategoryandisprovidedinTable5‐19.TheCH4conversionfactors(MCF)formanuredepositedonadry‐lot,storedinadeeppit,orfromcattlebeddingcanbefoundinTable5‐7.TheMCFsformanurestoredasastockpileareprovidedinTable5‐20throughTable5‐22.TheMCFsformanurecompostedwithinhousingareprovidedinTable5‐24.

Table5‐7:MethaneConversionFactorsforDry‐Lots,PitStorageBelowAnimalConfinement,andCattle/SwineBedding

Temperature Dry‐Lot

PitStorageBelowAnimalConfinementand

Cattle/SwineDeepBedding

<1month >1month

Cool

≤10°C

1% 3%

17%11°C 19%12°C 20%13°C 22%14°C 25%

Tem

perate

15°C

1.5% 3%

27%16°C 29%17°C 32%18°C 35%19°C 39%20°C 42%21°C 46%22°C 50%23°C 55%24°C 60%25°C 65%

Warm 26°C

2% 30%71%

27°C 78%≥28°C 80%

Source:IPCC(2006).

Equation5‐4:IPCCTier2ApproachforEstimatingCH4 EmissionsinHousing

.

Where:

ECH4 =CH4emissionsperday(kgCH4day‐1)

m =Totaldrymanureperdaya(kgdrymanureday‐1)

VS =Volatilesolids(kgVS(kgdrymanure)‐1)

B0 =MaximumCH4producingcapacityformanure(m3CH4(kgVS)‐1)

MCF =CH4conversionfactorforthemanuremanagementsystem(%)

0.67 =Conversionfactorofm3CH4tokgCH4


5-41

TheSommermodelisusedtoestimateemissionsfromanyliquidmanure(lessthan10percentdrymatter)storedinhousing.TheestimationmethodforliquidmanurecanbefoundinSection5.4.4AnaerobicLagoon,RunoffHoldingPond,StorageTanks.

NitrousOxideEmissionsfromDairyCows’Housing

Toestimatenitrogenlossesfromhousing,theamountofnitrogenexcreted(Nex)byeachanimalcategoryisfirstestimated.Equation5‐5,Equation5‐6,andEquation5‐7aretheequationsrecommendedbytheAmericanSocietyofAgriculturalandBiologicalEngineers(ASABE)forestimatingNex.

SomeofthenitrogenexcretedisvolatilizedasNH3,hence,theestimationofNH3lossesisnecessarytoestimateN2Oemissionsusinganitrogenbalanceapproach.TheNH3lostfrommanureinhousingisestimatedasafractionofNex,KoelschandStowell(2005)provideestimatesonthetypicalNH3lossfromdifferenthousingfacilitiesandanimalspeciesasafractionofNex(seeTable5‐8).Arange

Equation5‐5:ASABEApproachforEstimatingNitrogenExcretionfromLactatingCows

. . . ..

Where:

Nex =Totalnitrogenexcretionperanimalperday(gNanimal‐1day‐1)

Milk=Milkproductionperanimalperday(kgmilkanimal‐1day‐1)

DIM =Daysinmilk(days)

DMI =Drymatterintake(kganimal‐1day‐1)

CCP =Concentrationofcrudeproteinoftotalration(gcrudeprotein(gdryfeed)‐1)

BW =Averagelivebodyweight(kg)

Equation5‐6:ASABEApproachforEstimatingNitrogenExcretionfromDryCows

. . .

Where:




Equation5‐7:ASABEApproachforEstimatingNitrogenExcretionfromHeifers

. .

Where:





5-42

ofvalueshasbeenprovidedforeachfacilitytype;thelowervaluesshouldbeusedduringthewinter,thehighervaluesshouldbeusedduringthesummer,andintermediatevaluesshouldbeusedforthespringandautumn.

Table5‐8:TypicalAmmoniaLossesfromDairyHousingFacilities(PercentofNex)

FacilityDescription %Loss FacilityDescription %Loss

Opendirtlots(cool,humidregion) 15‐ 30 Roofedfacility(shallowpitunderfloor) 10‐ 20Opendirtlots(hot,aridregion) 30‐ 45 Roofedfacility(beddedpack) 20‐ 40Roofedfacility(flushedorscraped)Roofedfacility(dailyscrapeandhaul)

5‐15Roofedfacility(deeppitunderfloor‐includesstorageloss)

30‐40

Source:KoelshandStowell(2005).

N2Oislostfromtheexcretednitrogen.AquantitativemethodforestimatingN2OemissionsfromsolidmanureistheIPCCTier2approach,whichisalsousedfortheU.S.GreenhouseGasInventory(Equation5‐8).ThisestimationmethodisthesameasthemethodpresentintheTemporaryStackandLong‐TermStockpileandthe

Compostingsections(SeeSections5.4.1and0).Thisequationwillover‐estimatetheemissionsfromanimalhousingifsomeofthenitrogenexcretedismanagedoutsideofhousing(i.e.,theequationaccountsfornitrogenlossduetoNH3emissionsbutdoesnotaccountforthequantityofnitrogenthatismanagedinmanuremanagementsystems).

Formanureindeeppits,dry‐lots,ormixedwithbedding,theemissionfactorsareprovidedinTable5‐9.TheN2OemissionfactorsformanureinhousingthatisstoredinastockpileareprovidedinTable5‐23.TheemissionfactorsformanurethatiscompostedwithinahousingareaareprovidedinTable5‐25.

Table5‐9:N2OEmissionFactorsforManureStoredinHousing

Category N2OEmissionFactor(kgN2O‐N/kgN)CattleandSwineDeepBedding(ActiveMix) 0.07CattleandSwineDeepBedding(NoMix) 0.01PitStorageBelowAnimalConfinements 0.002Dry‐Lot 0.02

Source:IPCC(2006).

Equation5‐8:IPCCTier2ApproachforEstimatingN2OEmissionsfromHousing

, % /

Where:

EN2O,housing =Nitrousoxideemissionsfromhousingperday(kgN2Oday‐1)

N =Numberofheadoflivestockspecies(animal)


%NH3loss =PercentofNexlostasNH3inanimalhousing‐seeTable5‐8

EFN2O =N2Oemissionfactorformanureinhousing(kgN2O‐NkgN‐1)

=ConversionofN2O‐NemissionstoN2Oemissions

=Conversionofgramstokilograms


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TheremainingnitrogenexcretedthatisnotlostasN2OorvolatilizedasNH3inhousingthenentersmanurestorageandtreatment.Ifdataarenotavailabletotrackthenitrogenthatistransferredalongwiththemanure‐to‐manurestorageandtreatment,thenitrogencanbeestimatedasdescribedinEquation5‐9.However,thisequationisoverestimatingthenitrogentransferringtomanurestorageandtreatmentassomenitrogenwillbelostinhousing.ThisremainingtotalnitrogenvalueisaninputintotheN2Oequationsformanurestoredortreated.

TheDairyGEMModelprovidesdailyestimates;userscanrefertothatmodelforamorein‐depthanalysisoftheiremissions.

5.3.1.2 RationaleforSelectedMethodforEstimatingEmissionsfromDairyProductionSystems

Thereareavarietyofmethodsandmodelsavailabletoestimateemissionsfromdairyproductionsystems,rangingfromsimplecarbonfootprintmodelstohighlycomplexprocess‐basedmodelsforthedeterminationofNH3andGHGemissions.TheIPCCTier1methodologyprovidesasimplisticmethodusedforcountryinventorypurposes.Whenadditionaldataareavailable,thereareaseriesofequationsthatcanbeusedtodevelopIPCCTier2estimates.Thedatausedfortheseestimatesaretypicallyeasilyobtainablefromtheproductionfacilityoravailableinalookuptable.Whilethesemethodsprovideestimatesforemissionsthatmaybesuitableforaroughdeterminationofemissionsinventories,theyareinsomecasesbasedonverylimiteddataandmaynotbeveryrepresentativeofemissionsatthefarmlevel.Thedevelopmentofprocess‐basedmodelshasprovidedawaytoobtainamoredetailedanalysisofemissionsatthefarmscale.

Awidevarietyofmodelsapplicabletodairyproductionfacilitieswereidentifiedandevaluated,including:CarbonAccountingforLandManagers;ClimateFriendlyFoodCarbonCalculator;CoolFarmTool;CPLAN;DairyGEM;DairyWise;FarmingEnterpriseGHGCalculator;FarmGHG;Holos;IntegratedFarmSystemModel(IFSM);ManureAndNutrientReductionEstimator(MANURE);ManureDeNitrification‐DeComposition(ManureDNDC);OVERSEER;andSIMSDairy.

ThesemodelswereevaluatedtodeterminetheirsuitabilityforusetodetermineemissionsestimatesfordairyproductionfacilitiesintheUnitedStates.ElevencriteriawereusedtoidentifymodelsthatcouldbeusedtoestimateCH4fromentericCH4productionandCH4,N2O,andNH3fromanimalhousingsystems.Twoofthecriteriawereconsideredcritical:themodelhadtoberelevanttoU.S.climateanddairyproductionsystemsandithadtobepublicallyavailable.Ifthemodelsmetthesetwocriteriatheywerefurtherrankedbasedontheremainingninecriteria.Fourofthemodelsconsideredmetthecriticalcriteria:DairyGEM,IFSM,CoolFarmTool,andMANURE.AlthoughDairyGEMisasubsetofIFSM,itwasincludedseparatelybecauseDairyGEMonly

Equation5‐9:TotalNitrogenEnteringManureStorageandTreatment

% /

Where:

TNstorage =Totalnitrogenenteringmanurestorage(kgNday‐1)

N =Numberofheadoflivestockspecies(animal)


%NH3loss =PercentofNexlostasNH3inanimalhousing‐seeTable5‐8

=Conversionofgramstokilograms


5-44

estimatesemissionsfromtheanimalhousingandmanurestoragearea.Therefore,itislesscumbersometouseandrequiresfewerinputs.

Outofthesefourmodels,DairyGEMhadthemostflexibilityfordescribingtheproductionsystemandmetallofthespecifiedcriteria.Inaddition,thismodelimplementsemissionestimatemethodologiesthatareadvancedbeyondtheIPCCTier2determinations.ItmodelsCH4emissionsfromentericfermentationandmanuremanagementandthenitrogenbalanceassociatedwithnitrogenexcretedinmanure.TheunderlyingmethodsintheDairyGEMmodelarerecommendedfordeterminingCH4emissionsfromentericfermentationandhousingsystemsfordairycattle(seefurtherdiscussioninAppendix5‐E,Table5‐E‐1,andsubsequentrelevanttext).Theestimatesgeneratedfromthismodelcouldthenbemodifiedtoaccountformitigationstrategiesthatcouldaltertheemissionscurrentlybeinggeneratedon‐farm.Somemitigationstrategiesarealreadyembeddedinthemodel,suchasalternativefeeding,manurehandling/storage,andtheuseofbovinesomatotropin,whileotherscouldbeusedbydevelopingatablewithmodifiersbasedonliteraturevaluestodeterminehowon‐farmemissionscouldchangewiththeimplementationofthesestrategies.ForN2Oemissions,anitrogenbalanceapproach(basedontheconceptsinDairyGEM)usingnitrogenexcretionequationsfromASABEStandardD384.2isrecommended.TheuseoftheASABEequationstakesintoaccounttheimpactofdietarychangesonnitrogenexcretion.

5.3.2 EntericFermentationandHousingEmissionsfromBeefProductionSystems

Becauseofdifferencesinthediets,animalphysiologicalstateandage,andmanurehandling,theproportionsandsourcesofGHGsdifferamongthecow‐calf,stocker,andfinishingsegmentsofthebeefcattleindustry.AprimarysourceofGHGsfromthebeefcattleindustryisentericCH4,producedprimarilyintherumen,althoughsomeCH4isalsoproducedinthelowergut.Inaddition,CH4andN2Omaybeproducedfromfecesandurineonpasturesandfeedlotpensurfaces.Emissions

Model Evaluation Criteria for Dairy Production Systems

1. Themodelisbasedonwell‐established,scientificallysoundrelationshipsamongfarmmanagementinputs,emissionsoutputs(process‐based/mass‐balancemodelpreferable).

2. ThemodelisrelevanttoU.S.climateanddairyproductionsystems.3. ThemodelcanestimateCH4,andN2O,andNH3emissionsfromdairyhousingsystems

(includingentericCH4production).4. Thereisflexibilityinthemodeltodescribetheproductionsystem(animals,feed,

housing,andin‐housemanuremanagement).5. Themodeliseasytouseandisdesignedtouseeasilyobtainablefarminformationto

determineemissionsestimates.6. ModelemissionestimatesforbothentericCH4productionandemissionsassociated

withcattlehousingareeasilycaptured.7. Themodelincludessomemitigationstrategiesforreducingemissionsandproduces

realisticchangesinemissionsvalueswhenthesechangesaremadewithintheproductionsystem.

8. Thereistransparencyinthemodelcalculations,andtechnicalguidelinesareavailabletoelaboratethemethodologiesusedtoobtaintheemissionsestimates.

9. Themodelhasbeentested/validatedwithon‐farmdata.10. Themodelworksreliably(littletonoerrorsorprogramcrashes).11. Themodelispubliclyavailableandaccessible.


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fromhousingandmanurehandling(priortoenteringamanagementsystem)arediscussed,andequationsforstockpiledmanure(Section5.4)canbeappliedforemissionestimation.

Phetteplaceetal.(2001)estimatedGHGemissionsfromsimulatedbeefanddairy4systemsintheUnitedStatesusingmodificationsoftheIPCC(1997)methodology.Thesystemswerecomprisedofabaseherdofmaturecowspluscalvesandreplacements,stockercalves,afeedlot,andadairywith100lactatingcows.Theyalsoevaluatedemissionsfromcalvesthatwentthroughtheentirecow‐calf,stockerandfeedlotsystem(cow‐calftofeedlot).Greenhousegasemissionshead‐1(CO2‐eq)fromPhetteplaceetal.(2001)arepresentedinTable5‐10(withtheexceptionofthedairyherd).

Table5‐10:SimulatedGHGEmissionsforRuminantSystems(kgCO2‐eq/head/year)

Item Cow‐calf Stocker Feedlot Cow‐calfThroughFeedlot

DietaryTDN,% 62 57 88 62GHG(kgCO2‐eq/head/year)

EntericCH4 1,140 1,725 743 1,167ManureCH4 34 48 12 34TotalCH4 1,175 1,773 755 1,201N2O 1,487 1,721 1,294 1,490CO2 127 380 1,245 252

TotalCO2‐eq 2,788 3,874 3,294 2,944Source:Phetteplaceetal.(2001).

Elsewhere,Beaucheminetal.(2010)usedtheHolosmodel(Littleetal.,2008)toconductalife‐cycleassessmentofbeefproductioninwesternCanada.OftotalCO2‐eq,63percentwasfromentericCH4.5TheseareverysimilartovaluesreportedbytheU.S.DepartmentofAgriculture(2004b).Sixty‐onepercentofCO2‐eqemissionswerefromthecow‐calfherd,19percentwerefromreplacementheifers,eightpercentwerefrombackgroundingoperations,and12percentwerefromfeedlots.SeventyninepercentofentericCH4losseswerefromthecowherd,threepercentfrombulls,twopercentfromcalves,sevenpercentfrombackgrounders,andninepercentfromfeedlots.N2Ocontributions(CO2‐eq)asapercentoftotalGHGemissionswereasfollows:feedlotmanure–twopercent,feedlotsoil–twopercent,cow‐calfherdsoil–twopercent,andcow‐calfherdmanure–20percent.

Cow‐CalfandBullsThereisnoevidencethatanybreedofbeefcowproduceslessentericCH4thananother.Thereareafewreportssuggestingthatefficientcattle(thoseselectedforfeedefficiencyorresidualfeedintake(RFI))mayproducelessentericCH4(Nkrumahetal.,2006;Hegartyetal.,2007).However,FreetlyandBrown‐Brandl(2013)reportedthatcattlewithgreaterfeedefficiencyactuallyproducedmoreCH4;thusraisingsomequestionsaboutthegeneticfactorsassociatedwithfeedefficiencyandCH4emissions.Itisunclearwhetherthechangesobservedarearesultofalteredfeedintakeorareassociatedwithachangeinalteredruminalmicrobialpopulation.Additionally,recentinformationindicatesthatthereisaninteractionbetweendietqualityandfeedefficiencyonentericCH4emissions,whereefficientcowsproducelessCH4whengrazinghigh‐qualitypasturebutnotwhengrazingpoor‐qualityforage(Jonesetal.,2011).Residualfeedintakeismoderatelyheritable—(0.28to0.58;Mooreetal.,2009),thusitmightbepossibletogeneticallyselectforanimalswithlowerentericCH4production.AnexaminationofthevalueforselectionforlowentericCH4productionhasbeenconductedwithsheepinNewZealandandAustralia.Simulationsusingpublisheddata

4DiscussionofemissionsfromdairyproductionsystemscanbefoundinSection5.3.1.55%ofemissionswerefrommanureCH4,23%frommanureN2O,4%fromsoilN2O,and5%fromenergyCO2.


5-46

indicatethatwithoutaccuratefeedintakeinformationandamethodbywhichmanyanimalscanbescreenedforCH4emissions,selectionforlowerentericCH4productionisnotlikelytobeeconomicallyviable(Cottleetal.,2011).

MeasurementofentericCH4fromgrazingcattlehasbeenconductedprimarilyfromanimalsgrazingimprovedpasturesusingmicrometeorologicalmethodsandtracertechniques.Lassey(2007)summarizedmuchoftheCH4emissionsdatathathadbeencollectedusingtheSF6tracertechnique.Intakewaseithercalculatedfromarequirementsmodelorfromuseofmarkers(Cr2O3orYb2O3).Estimatedforagedigestibility(invitro)rangedfrom48.7to83percent,whichresultedinestimatedCH4conversionfactors[i.e.,entericCH4asapercentageofgrossenergyintake(GEI)]rangingfrom3.7to9.5percent.ThemeanYmfromallofthestudieswas6.25andagreesreasonablywellwiththatusedbyIPCC(2006)forcattleonpasture.Methaneemissionsfromcowsgrazingimprovedpasture,Kentuckyfescue,andBermudagrassinthesouthernUnitedStateswerereportedbyPavao‐Zuckermanetal.(1999)andDeRamusetal.(2003).InbothofthesestudiessignificantreductionsinentericCH4unit‐1ofanimalweightgainresultedfromtheimplementationofbestmanagementpracticesdesignedtoimprovepasturequality.

Entericemissionsestimatescanbemadeusingmicrometeorologicalmethodsandtracertechniques.OnereportinwhichCH4emissionsweremeasuredfrombeefcowsgrazingnativerangeinOctoberandMayillustratedalargevariationinentericemissions.InOctober,whencowswerelosingBW,theyproduced87gCH4headdaily‐1,andonthesamepastureinMaytheyproduced252gCH4headdaily‐1(Olsonetal.,2000).Westbergetal.(2001)measuredCH4fromcowsgrazingthesamepastureacrossseasonsandfoundsimilarresults,withhigherCH4emissionsfromcowsgrazinglushspringgrowthandthelowestemissionsfromgrazingstockpiledfallpasture.ThesedifferencesareattributabletodifferencesinbothDMIandforagequality.Ingeneral,asforagequalityincreases,DMIalsoincreases.Some"rulesofthumb"forDMIonpastureincludethefollowing:

Poorqualitypasture‐DMI=1to1.75percentofbodyweight; Mediumqualityforage‐DMI=1.75to2.25percentofbodyweight; HighqualityforageDMI=2.25to3percentofbodyweight.

StockersEntericCH4emissionsofstockerswhilegrazinghavebeenmeasuredbyLaubachetal.(2008),Tomkinsetal.(2011),McGinnetal.(2011),andBoadietal.(2002),usingavarietyoftechniquesincludingtheSF6tracer,andseveralmicrometeorologicalapproaches.ThesamefactorsthataffectCH4emissionsfromgrazingbeefcowsareimportantinstockercattle.Thosefactorsareleveloffeedintake,digestibilityofforageconsumed,supplementation,andthechemicalcompositionoftheplantsconsumed.Entericemissionsestimatescanbemadeusingmicrometeorologicalmethodsor,tracertechniquesorcanbepredictedfromIPCCTier2methods(seeentericdiscussion).Criticalvariablesincludemeasurementsorestimationsoffeedintakeandfeedquality(chemicalcomposition).Manyoftheequationscurrentlyavailablemaynotaccuratelypredictmeasuredentericemissionsfromgrazingcattle(Tomkinsetal.,2011).

FeedlotMostestimatesofentericmethaneemissionfromfinishingbeefcattlearebasedonworkusinganimalsconfinedtorespirationchambers,althoughafewstudieshaveusedmicrometeorologicalmethodsinopenfeedlots.EntericCH4lossesfromfinishingbeefcattlenormallyrangefrom50to200Lhead‐1daily(JohnsonandJohnson,1995;McGinnetal.,2004;Beaucheminetal.,2008;Lohetal.,2008;Halesetal.,2012;2013;Halesetal.,2014;Toddetal.,2014a;Toddetal.,2014b).InmoststudiesintheU.S.,dietshavebeenbasedonDRCorSFC;whereasmoststudiesinCanadathedietsarebasedonbarley.TheIPCCTier2(2006)entericCH4conversionfactor(Ym)forfeedlotcattleis


5-47

3±1percentofGEI.TherearefewstudiesthathavemeasuredemissionsofCH4andN2Ofromfeedlotpensurfacesandrunoffcontrolstructures.Theprimaryfactorsthatcontrolentericmethaneemissionsinfeedlotcattlearefeedintake,graintype,grainprocessingmethod,dietaryroughageconcentrationandcharacteristics,anddietaryfatconcentration.

5.3.2.1 MethodforEstimatingEmissionsfromBeefProductionSystems

aCalculatedusingEqn10.3inIPCC(2006)basedonbodyweight(“BW”).bCalculatedusingEqn10.4inIPCC(2006)basedonNEaandfeedingsituation.cCalculatedusingEqn10.8inIPCC(2006)basedonmilkproduction(“milk”)andmilkfat(“fat”).dCalculatedusingEqn10.11inIPCC(2006)basedoninformationondailyhoursofwork(“work”).eCalculatedusingEqn10.13inIPCC(2006)basedonNEmandpregnancystatus.fCalculatedusingEqn10.14inIPCC(2006)basedonDE.gCalculatedusingEqn10.13inIPCC(2006)basedonbodyweight,matureweight(“MW”),anddailyweightgain(“WG”).hCalculatedusingEqn10.15inIPCC(2006)basedonDE.

MethodforEstimatingCH4EmissionsfromEntericFermentationinBeefCattle

IPCCTier2approach,withsomeadjustmentfactors,basedondiet,animalweight,pregnancy/lactation,activity(IPCC,2006).

Datasourcesareuserinputsondietaryintake,lactationandpregnancyrates,animalweight,housing,andtheFeedstuffsCompositionTable(Ewan,1989;Preston,2013).

Althoughtheequationsutilizedarethesameasexistinginventorymethods,themethodtakesintoaccountalargedatabaseoffeedtypes(foundinAppendix5‐B,FeedstuffCompositionTable),aswellasreportingatthemonthly,ratherthanannual,temporalscale.

Equation5‐10:IPCCTier2EquationforCalculatingGrossEnergyRequirementsforBeefCattle

GE

NE NE NE NE NEREM

NEREG

DE%100

Where:GE =Grossenergy(MJday‐1)

NEm =Netenergyrequiredbytheanimalformaintenance(MJday‐1)a

NEa =Netenergyforanimalactivity(MJday‐1)b

NEl =Netenergyforlactation(MJday‐1)c

NEwork=Netenergyforwork(MJday‐1)d

NEp =Netenergyrequiredforpregnancy(MJday‐1)e

REM =Ratioofnetenergyavailableinadietformaintenancetodigestibleenergyconsumedf

NEg =Netenergyneededforgrowth(MJday‐1)g

REG =Ratioofnetenergyavailableforgrowthinadiettodigestibleenergyconsumedh

DE =Digestibleenergyexpressedasapercentofgrossenergy(%)


5-48

TheDEultimatelyusedintheIPCCTier2equation(inEquation5‐11)willbeweightedbasedonportionoftotalfeedintakefromaparticularfeedtype.TheDEdataforparticularfeedstuffscanbefoundinAppendix5‐B.TherecommendedYmforbeefreplacementheifers,steerstockers,heiferstockers,beefcows,andbullsis6.5percentforallregionsofthecountry.Forfeedlotcattle,theIPCC(2006)Ymof3percentisadjustedbasedondiets.AllfeedlotcattleinitiallystartwithabaselineYmofthreepercent(IPCC,2006).ThecorrectionfactorstoYmforfeedlotcattlefordifferentscenariosareprovidedbelow(seeAppendix5‐Aforadditionaldetails).TheYmusedforcalculatingemissionsforthesecattleismodifiedbasedonanimaldiets,asindicatedinTable5‐11.

Table5‐11:DeterminationofAdjustedMethaneConversionFactor(Ym)forFeedlotCattle

Variable Ym(asa%ofGEI)BaselineYm(IPCC,2006) 3%Ionophoreindiet(Tedeschietal.,2003;Guanetal.,2006): Yes Nochange

No IncreaseYmby4%(Ym=3%x1.04=3.12%ofGEI)

FatContent(ZinnandShen,1996;Beaucheminetal.,2008;Martinetal.,2010)(Foreachpercentofaddedfat(assupplementalfatorinbyproductssuchasdistillersgrainthatcontainabout10percentfat),decreasebyfourpercenttoamaximumofa16percentdecrease)

1%supplementalfatDecreaseYmby4%

(Ym=3%x0.96=2.88%)

2%supplementalfatDecreaseYmby8%

(Ym=3%x0.92=2.76%)

Fourorhigheraddedfatcontent DecreaseYmby16%(Ym=3%x0.84=2.52%)

GrainType(BeaucheminandMcGinn,2005;Archibequeetal.,2006;Halesetal.,2012): Graininanimaldietissteamflaked(SF)orhighmoisture(HM) NoChange

Graininanimaldietisunprocessed(UP)ordryrolled(DR)IncreaseYm20%

(Ym=3%x1.2=3.6%)

GrainindietisbarleyratherthancornorsorghumIncreaseYm30%

(Ym=3%x1.3=3.9)GrainConcentration(seeAppendix5‐A fordetailsandreferences): Dietcontainsmorethan60percentgrain NoChange

Dietcontains45to60percentgrain IncreaseYm10%(Ym=3%x1.1=3.3%)

Dietcontainslessthan45percentgrainIncreaseYm40%

(Ym=3%x1.40=4.2%)

Equation5‐11:IPCCTier2EquationforCalculatingEntericCH4 EmissionsfromBeefCattle

/.

Where:

DayEmit =Emissionfactor(kgCH4head‐1day‐1)

GE =Grossenergyintake(MJhead‐1day‐1)

Ym =CH4conversionfactor,whichisthefractionofGEinfeedconvertedtoCH4(%)

55.65 =Afactorfortheenergycontentofmethane(MJkgCH4‐1)


5-49

EmissionsfromFeedlotPenSurfacesTherearefew,ifany,studiesthathavemeasuredCH4orN2Oemissionsfrombeefcattlefeedlotpensurfacesandretentionponds.ThestudyofToddetal.(2014a;2014b)suggeststhereislittleCH4productionfromfeedlotpensurfaces.TheuseoftheIPCC(2006)methodologiesisrecommendedtoestimateemissionsfromfeedlotpensandretentionponds.

InordertoestimateCH4emissionsfrombeeffeedlotpensurfaces,thequantityofvolatilesolidsexcretedisfirstestimated.ThesecanbeestimatedbylabtestingsamplesfromthefacilityorusingvaluesfromtheASABEStandardD384.2(ASABE,2005).6CH4emissionsfromthepensurfacecanbeestimatedusingtheIPCC(2006)Tier2approachasoutlinedinsection5.4.1.2.Forcattlefeedlots,amaximumCH4productioncapacity(B0)of0.33m3/kgvolatilesolidsisassumed(Table5‐19)andtheCH4conversionfactorforpensurfacesrangesfrom1to2percentofB0,dependinguponenvironmentaltemperature(Table5‐20).Oncemanureisscrapedfromthepensandremoved,themethodsdescribedinsection5.4.1canbeusedtoestimateCH4emissionfrommanurethatiscompostedorstoredinstockpiles.

InordertoestimateN2Oemissionsfromthepensurfacesofbeeffeedlotsthequantityofnitrogenexcretedontothepensurfacemustbeknown.ThiscanbeestimatedusingEquation5‐12fromtheASABEStandardD384.2.Forabeeffeedlot,adefaultvalueof0.069kgofNkgdrymanure‐1canbeusedifNexisnotcalculated.

6Volatilesolidsvaluescanbeestimatedfromequations(1)or(2)insection4.3.1ofASABED384.2.DefaultvolatilesolidsvaluesarealsopresentedinTable5‐32ofthisdocument.

Methodfor Estimating Beef Cattle GHG Emissions from Housing

Methane

TheIPCC(2006)Tier2methodcanbeusedtoestimateCH4emissionswhenmanureisallowedtoaccumulateonfeedlotpensurfacesasdescribedbelow.

NitrousOxide

NitrogenexcretedestimatedusingequationsprovidedinASABED384.2. IPCC(2006)Tier2approachforN2Oemissionsfrommanureinhousing.


5-50

SomeofthenitrogenexcretedisvolatilizedasNH3,hence,theestimationofNH3lossesisnecessarytoestimateN2Oemissionsusinganitrogenbalanceapproach.TheNH3lostfrommanureinhousingisestimatedasafractionofNex.KoelschandStowell(2005)provideestimatesonthetypicalNH3lossfromdifferenthousingfacilitiesasafractionofNex(seeTable5‐12).Arangeofvalueshasbeenprovidedforeachfacilitytype;thelowervaluesshouldbeusedduringthewinter,thehighervaluesshouldbeusedduringthesummer,andintermediatevaluesshouldbeusedforthespringandautumn.

Table5‐12:TypicalAmmoniaLossesfromBeefCattleHousingFacilitiesExpressedasaPercentofNex

FacilityDescription %Loss FacilityDescription %Loss

Opendirtlots(cool,humidregion) 30–45 Roofedfacility(beddedpack) 20‐40

Opendirtlots(hot,aridregion) 40–60Roofedfacility(deeppitunderfloor,includingstorageloss)

30‐40


AnalternativeapproachistousetheequationofToddetal.(2013)whichcalculatesmonthlyfeedlotNH3emissionsasafunctionofdietarycrudeproteinandaveragemonthlytemperature(Equation5‐13).

Equation5‐12:ASABEApproachforEstimatingNitrogenExcretionfromBeefCattle

..

..

.

.

.

Where:


DMIx=DryMatterIntakeforrationx(kgdryfeedanimal‐1day‐1)

CCP‐x =Concentrationofcrudeproteinoftotalration(gcrudeproteingdryfeed‐1)

DOF =Daysonfeedforanindividualration(days)

BW =Livebodyweightatfinishoffeedingperiod(kg)

BW =Livebodyweightatthestartoffeedingperiod(kg)

DOFT=Totaldaysonfeedfromstarttofinishoffeedingperiods(days)

SRW =Standardreferenceweightforexpectedfinalbodyfat(kg)

x =Rationnumber

n =Totalnumberofrationsfed


5-51

N2OemissionsarecalculatedusingtheIPCC(2006)Tier2methodanddry‐lotemissionfactorsdescribedinEquation5‐8andTable5‐9.ThequantityofnitrogenthatleavesthefeedlotpensinmanurecanthenbecalculatedusingEquation5‐9.N2O‐NlossesfrommanurecollectedandremovedfromthepenscanbedeterminedfrommanurenitrogenusingEquation5‐27andEquation5‐29andtheemissionfactorsinTable5‐23andTable5‐25foundinSection5.4ManureManagement.NH3lossesfrommanurenitrogenremovedfromthepenscanbecalculatedasdescribedinAppendix5‐C.1and5‐C.3.

5.3.2.2 RationaleforSelectedMethodforEstimatingEmissionsfromBeefProductionSystems

Cow‐Calf,Bulls,andStockersThemostappropriatepredictionsavailableforentityscaleestimationareIPCCTier2methodsforgrazingcattle.Criticalvariablesthatareimportanttodefineinordertogeneratepredictionmethodsincludemeasurementsorestimationsoffeedintakeandfeedquality(chemicalcomposition)forpastureorrangelands.Iftheintakeisnotknown,intakepredictionequations/modelssuchasNRC(2000)canbeused.TheNRC(2000)providesanequationforthecalculationofDMIforgrazingbeefcowsandforstockercattle:NEmintake=SBW0.75*(0.04997*NEm2+0.04631)whereNEmistheestimatedMcalkg‐1ofthepasture,andSBWistheaverageshrunkbodyweightfortheperiodofgrazing(kg).TherequirementforknowledgeoftheNEmconcentrationofthepasturemaylimittheusefulnessofthepredictioninsomesituations.

Insituationsinwhichtheherdishousedinadry‐lotorbarnfacility,emissionfactorsforCH4andN2Oassociatedwithpensurfaces,manurestorage,andanimalmovement/manuredisturbancewouldbeappropriate.

FeedlotEllisetal.,(2009)reportedthatseveralequationsappearedtobegoodpredictorsofentericCH4lossesfromfeedlotcattlebasedonCanadianstudies.However,manyofthoseequationstendtogreatlyoverestimateentericlosseswhencomparedwithdatafromcattlefedatypicalsouthernplainsfinishingdiet(Halesetal.,2012;2013;Toddetal.,2014a;Toddetal.,2014b).AlthoughKebreabetal.(2008)reportedthatMOLLYandIPCCTier2(2006)gavepredictedvaluessimilartomeasuredvalueswithfeedlotcattle,therewasalargevariabilityinindividualanimalswitherrorsof75percentorgreater.Kebreabetal.(2008)notedtheaverageYm(MJentericCH4MJGEI‐1)forfeedlotcattlebasedonexperimentaldatawas3.88percentofGEI(range3.36to4.56),whichwashigherthantheIPCC(2006)valueof3.0percentandtherecentlyobtainedvalueswithtypicalfinishingdietsof2.85percent(Halesetal.,2012;2013).

Equation5‐13:MonthlyBeefFeedlotNH3 EmissionsasaFunctionofDietaryCrudeProteinandMonthlyTemperature

. .

Where:

NH3 =NH3emissionfromhousingperday(gNH3head‐1day‐1)

T =Averagemonthlytemperature(K)

CP =Dietarycrudeproteinasafractionofdrymatter(%)


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Currently,IPCCTier2maybethemostusefulmethodologyforpredictionofentericemissionsfromfeedlotbeefcattle.Unfortunately,theTier2methoddoesnotallowforestimatingchangesinentericemissionsrelatedtochangesindietormanagement.

Therefore,amodifiedIPCC(2006)methodisrecommendedtoestimateentericCH4emissionsfrombeefcattlefedhighconcentratefinishingdiets.TheCH4conversionfactor(Ym)willbeadjustedbyfactorsintheanimals’dietsasdescribedinSection5.3.2.1.AbaselinescenariobasedontypicalU.S.beefcattlefeedingconditionsisestablished,andtheYmvaluesareadjustedbasedonpublishedresearch.Emissionvaluesaremodifiedusingcorrectionfactorsthatarebasedonchangesinanimalmanagementandfeedingconditionsfromthebaselinescenario.

5.3.3 EntericFermentationandHousingEmissionsfromSheep

GHGemissionsassociatedwithsheepproductionincludeentericCH4emissions,manureandbeddingemissions,andemissionsassociatedwithgrazingandmanureapplicationtoland.

TheNewZealandMinistryfortheEnvironment(2010)estimatedthatsheepyoungerthanayearofageemit5.1percentofGEIasentericCH4,andadultsheepemit6.3percentoftheirGEIasCH4.Theseemissionfactors,whencombinedwithpopulationestimates,resultinbaselineentericemissionsof11.60kgCH4head‐1year‐1.Sheeparealsoestimatedtodeposit15.9kgNhead‐1year‐1.

Lassey(2007)summarizedtheentericemissionsmeasurementsfromgrazingsheeptrialsfromNewZealandandAustraliainwhichtheSF6tracertechniquewasused.Foragecharacteristicsrangedfromlush(invitrodigestibilityestimateof82percent)topoorquality(called“dead,”withaninvitrodigestibilityof54percent).Intakewasmeasuredusingcompletefecalcollectionoramarker(n‐alkane).EntericCH4emissionsrangedfrom11.7gday‐1forsheepfedforageofhigherquality(6.9percentofGEI)to35.2gday‐1forsheepfedforageoflowerquality(6.3percentofGEI).Theaverageentericemissionswere5.39percentofGEI,or23.5gday‐1.Ingeneral,lowerforagequalityresultedinagreateramountofCH4emittedasaproportionoftheenergyintakethandidhigherforagequality.

NewZealandpasturesgrazedbysheephadelevatedN2Oemissions(7.4gN2O‐Nha‐1day‐1vs.3.4gN2O‐Nha‐1day‐1)comparedwithcontrol,butsignificantlylessthanthatobservedwhencattlegrazed(32.0gN2O‐Nha‐1day‐1)(Saggaretal.,2007).ThedatawereusedtoevaluatetheNZ‐DNDCmodel,aprocess‐basedNewZealandwholefarmmodel.ToourknowledgetherearenopublishedestimatesofGHGemissionfromsheepmanuresystems.

5.3.3.1 MethodforEstimatingEmissionsfromSheep

MethodforEstimatingEntericFermentationCH4 EmissionsfromSheep

Howdenequation(Howdenetal.,1994),basedondietaryDMI. TheequationfromHowdenetal.(1994)estimatesemissionsbasedsolelyonDMI;hence,

emissionfactorsnotutilized.


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ThedrymatterdataforparticularfeedstuffscanbeobtainedfromAppendix5‐B.

Noemissionsestimationmethodshavebeenprovidedforhousingasmostsheeparekeptonpastureandminimalemissionsareexpected.

5.3.3.2 RationaleforSelectingMethodforEstimatingEmissionsfromSheep

Howdenetal.(1994)generatedanequationfromwhichtopredictCH4emissionsfromsheep.Equation5‐7resultedfromalinearextrapolationofDMItoemissions.IthassincebeenevaluatedandfoundtoberobustenoughtobetheequationusedintheAustralianNationalGreenhouseGasInventory.KleinandWright(2006)measuredCH4fromsheepinrespirationchambersandcomparedtheirresultstotheHowdenetal.(1994)equation.ActualCH4averaged1.1ghead‐1(SE±0.05)andpredictedCH4was1.1ghead‐1(SE±0.02).ApotentialconcernregardingtheHowdenequationisthatmuchofthedataincludedintheanalysiswasbasedontropicalforages.Nonetheless,whenintakedataareavailable,theHowdenequationpresentsthebestmethodbywhichtoestimatesheepentericemissions.Whenintakeisnotavailable,theIPCCTier2methodofestimationshouldbeused.EmissionsfromfeedlotsheepshouldusetheYmvaluesfromBlaxterandClapperton’soriginalpaper(1965)inwhichtheymeasuredCH4emissionsfromsheepwithrespirationcalorimetrychambers.Sheepfedhighlydigestibledietsatthreetimesmaintenanceproduced35percentlessCH4(kcal100Kkcaloffeedenergy‐1)thanthosefedsimilardietsatmaintenance;thus,areducedYmvalueiswarranted.TheequationisCH4=1.3+[0.112×(%digestibility/100)]+[MEintake/maintenanceMErequirement]×[2.37‐0.050×(%digestibility/100)].

5.3.4 EntericFermentationandHousingEmissionsfromSwineProductionSystems

SourcesofGHGemissionsincludeentericfermentation;manurestoredwithintheanimalhousing,whetheritisstoredasaliquidormixedwithbedding;emissionsthatoccurduringthetransportofmanuretoanexternalmanurestoragestructure;theoutsidemanurestoragestructure;emissionsthatoccurduringtransportofmanuretothefield;andemissionsfollowinglandapplicationofmanure.BecauseGHGmitigationhasnotbeenafocusofU.S.researchfortheswineindustrynorahighpriorityforswineproducers,dataarenotreadilyavailabletoidentifythemagnitudeofeachoftheabovepointsofemissionwithinafarm.However,emissionsofCH4areexpectedtooccurprimarilyduringmanurestorage,andemissionsofN2Oareexpectedtopredominatefollowinglandapplicationofmanure.7Oftenmanureisstoredunderneaththepighousinginadeeppit.Forthisreason,emissionsdiscussioninthissectionincludesin‐housemanurestorageandcomparisonofin‐housemanurestoragesystemswithsystemsthatstoremanureexternally.Becauseswinefeedsaredry,emissionsofGHGfromfeedstorageareasarebelievedtobenegligible.

7GreenhousegasemissionsresultingfollowinglandapplicationareaddressedseparatelyinthesectionsonChapter3:CroplandsandGrazingLands.

Equation5‐14:EquationforEntericFermentationEmissionsfromSheep(Howdenetal.,1994)

. .

Where:

CH4 =Methaneemissions(kgCH4head‐1day‐1)

Intake =DryMatterIntake(kghead‐1day‐1)


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Greenhousegasemissiondatafromswinefacilitiesissomewhatlimited.Liuetal.(2011a)reportedthatgrow/finishpigsemitted42to79mgCH4kgBW‐1dailyfromchamberswherepigswerehousedwithmanure.DailyemissionsofN2Orangedfrom11.4to12.4mgN2OkgBW‐1(Lietal.,2011).ThesevaluesaresomewhathigherthandatausedbyVergeetal.(2009)incalculatingGHGemissionsfromCanadianporkproduction(43mgCH4kgBW‐1and4mgN2OkgBW‐1).Philippeetal.(2007)observedGHGemissionsintherangereportedbyLietal.(2011)thoughtheirobservationswereinEuropeandeeplitterandslattedfloorsystems.Thereportedgaseousemissionsfrompigsraisedontheslattedfloorandonthedeeplitterwere,respectively,0.54and1.11gpig‐1day‐1forN2O,and16.3and16.0gpig‐1day‐1forCH4.

Liuetal.(2011a)conductedameta‐analysistoidentifyfactorsthatcontributetoGHGemissionsfromswineproduction.Findings,showninTable5‐13,illustratethattypeofemissionsource(swinebuildingsormanurestoragefacilities)wasnotsignificantforCH4andN2Oemissions.Swinecategory(stageofproduction)andgeographiclocationwassignificantforbothoftheGHGgases.Neithertemperaturenorsizeofoperationwassignificantintheoverallanalysis.

Withinthemeta‐analysis,Liuetal.(2011a)foundthatswinebuildingswithstraw‐flowsystemsgeneratedthelowestCH4andN2Oemissionsofsystemscompared,whilepitsystemsgeneratedthehighestCH4emissions,andbeddingsystemsgeneratedthehighestN2Oemissions.Emissionsfromlagoonsandslurrystoragebasin/tankswerecompared;lagoonsgeneratedsignificantlyhigherN2Oemissionsthanslurrystoragebasin/tanks,whileCH4emissionswerenotdifferent.Straw‐basedbeddingresultedinnumericallyhigherCH4butlowerN2Oemissionswhencomparedwithsawdustorcornstalkbeddingsystems.Liuetal.(2011a)observedanincreasingtrendforCH4emissionsasmanureremovalfrequencydecreased(P=0.13).DeeppitsandpitsflushedusinglagooneffluentalsogeneratedrelativelyhighCH4emissions.ResultsforN2Oemissionsshowedveryhighuncertainties(P=0.49).DeeppitsandpitswithmanureremovedeverythreeorfourmonthshadrelativelyhigherN2Oemissions.Asummaryofotherfindingsfromthemeta‐analysisconductedbyLiuetal.(2011a)showedthatCH4emissionsfromslurrystoragefacilitieswithoutcoversweresignificantlyhigherthanfromthosewithcovers.

ThehighestCH4emissionswerefromfarrowingswine,andweresignificantlyhigherthanthosefromfinishingandnurseryswine.Comparedwithfarrowingswine,thegestatingswinehadsignificantlylowerCH4emissions.ThehighestN2Oemissionswerefromgestatingswineandweresignificantlyhigherthanthosefromfinishingswine.

NorthAmericanstudiesreportedsignificantlyhigherCH4emissionsfromswineoperationsthanEuropeanandAsianstudies(Liuetal.,2011a).Thisisprobablyduetothedifferentprevailingmanurehandlingsystemsanddifferentmanurehandlingpracticesindifferentregions.EmissionsofCH4fromlagoonsandmanurestoragefacilitiesincreasedwithincreasingtemperature.Forswinebuildings,temperaturewasnotasignificantfactor.

Table5‐13:PValuesofMainEffectsonGHGEmissionsfromSwineOperations

CauseofVariation CH4(n=76)N2O

(n=53)Emissionsource 0.94 0.93Swinecategory 0.05 <0.01Geographicregion 0.04 0.02Temperature 0.20 0.95Sizeofoperation 0.89 0.24Source:Liuetal.(2011a).


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5.3.4.1 MethodforEstimatingEmissionsfromSwineProductionSystems

MethaneEmissionsfromSwineHousingTheIPCC(2006)Tier2equationisusedtoestimateCH4emissionswhenmanureisallowedtoaccumulateinapitbelowtheanimalconfinement.TheestimationmethodisprovidedinEquation5‐4.ThemaximumCH4producingcapacityforswineisprovidedinTable5‐19.TheMCFsformanurestoredinadeeppitorfromswinebeddingisprovidedinTable5‐7.

NitrousOxideEmissionsfromSwineHousingToestimatenitrogenlossesfromswinehousing,theamountofnitrogenexcreted(Nex)foreachanimalclassesarefirstestimated.Equation5‐16describestherelationshipbetweennitrogenintake,retention,andexcretionforswine.Equation5‐17,Equation5‐18,Equation5‐19,andEquation5‐20providethemethodsforestimatingthenitrogenintakeandretentionforthedifferentswineclassesasrecommendedbytheASABE.

MethodforEstimatingEntericFermentationCH4 EmissionsfromSwine

IPCCTier1approach,usingU.S.emissionfactorof1.5kgCH4/head/year.(IPCC,2006). SoledatasourceistheIPCCTierIemissionfactorforswine.Userinputistotalnumber

ofhead,regardlessofclassorweight.

Equation5‐15:EquationforEntericFermentationEmissionsfromSwine(IPCC,2006)

.

Where:

CH4 =Methaneemissionsperday(kgCH4day‐1)

Population =Numberofswine(head)

0.00411 =DailyCH4emissionsfromeachanimal(kghead‐1day‐1)

Methodfor Estimating Swine GHG Emissions from Housing

Methane

TheIPCC(2006)Tier2methodisusedtoestimateCH4emissionswhenmanureisallowedtoaccumulatebelowtheanimalconfinementasdescribedbelow.

NitrousOxide

Nitrogenintake,retention,andexcretionestimatedusingequationsprovidedinASABED384.2.

IPCC(2006)Tier2approachforN2Ofrommanureinhousing.


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Equation5‐16:ASABEApproachforEstimatingNitrogenExcretionfromSwine

Where:

Nex =Totalnitrogenexcretionperanimal(ganimal‐1)

Nintake =Nitrogenintakeperfinishedanimal(ganimal‐1)

Nretention =Nitrogenretainedperfinishedanimal(ganimal‐1)

Equation5‐17:ASABEApproachforEstimatingNitrogenExcretionfromGrow‐FinishPigs

. .

..

Where:



ADFIG =Averagedailyfeedintakeoverfinishingperiod(gday‐1)

CCP =Concentrationofcrudeproteinoftotal(wet)ration(%)

DOFG =Daysonfeedtofinishanimal(grow‐finishphase)(days)

BW =Final(market)bodyweight(kg)

DPF =Averagedressingpercent(yield)atfinalweight(%)

BW =Initialbodyweight(kg)

FFLPF =Averagefat‐freeleanpercentageatfinalweight(%)


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aRecommendedvaluesare:350gday‐1forhighleangrowthcapacitypigs;325gday‐1forhigh‐moderateleangrowthcapacitypigs;and300gday‐1formoderateleangrowthcapacitypigs.

aAssumedtobe115days.bRecommendedvaluefromASABEis19.205kg.

Equation5‐18:ASABEApproachforEstimatingNitrogenExcretionfromWeaningPigs

.

.

Where:



ADFIG =Averagedailyfeedintakeoverfinishingperiod(gday‐1)

CCP =Concentrationofcrudeproteinoftotal(wet)ration(%)

DOFN =Daysonfeedtofinishanimal(nurseryphase)(days)

FFLGG =Averagefat‐freeleangainfrom20to120kg(gday‐1)a

BW =Finalbodyweightinnurseryphase(kg)

BW =Initialbodyweightinnurseryphase(kg)

Equation5‐19:ASABEApproachforEstimatingNitrogen ExcretionfromGestatingSows

. .

Where:



ADFIS =Averagedailyfeedintakeduringgestation(gday‐1)

CCP =Concentrationofcrudeprotein(%)

GL =Gestationperiodlength(days)a

GLTG =Gestationleantissuegain(kg)b

LITTER =Numberofpigsinlitter(head)


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aRecommendedvaluefromASABEis‐4.20kg.

SomeofthenitrogenexcretedisvolatilizedasNH3,hence,theestimationofNH3lossesisnecessarytoestimateN2Oemissionsusinganitrogenbalanceapproach.TheNH3lostfrommanureinhousingisestimatedasafractionofNexaccordingtotherangesprovidedinTable5‐14.Arangeofvalueshasbeenprovidedforeachfacilitytype;thelowervaluesshouldbeusedduringthewinter,thehighervaluesshouldbeusedduringthesummer,andintermediatevaluesshouldbeusedforthespringandautumn.

Table5‐14:TypicalAmmoniaLossesfromSwineHousingFacilities(PercentofNex)

FacilityDescription %Loss FacilityDescription %LossRoofedfacility(flushedorscraped)Roofedfacility(dailyscrapeandhaul)

5‐15 Roofedfacility(beddedpack) 20‐40

Roofedfacility(shallowpitunderfloor) 10‐20Roofedfacility(deeppitunderfloor‐includesstorageloss) 30‐40


TheIPCC(2006)Tier2approachisusedforN2Oemissionsfrommanurestoredinhousing.TheestimationmethodisprovidedinEquation5‐8.TheN2OemissionfactorscanbefoundinTable5‐9.

TheremainingnitrogenexcretedthatisnotlostasN2OorvolatilizedasNH3inhousingthenentersmanurestorageandtreatment.Ifdataarenotavailabletotrackthenitrogenthatistransferredalongwiththemanuretomanurestorageandtreatment,thenitrogencanbeestimatedasdescribedinEquation5‐9.However,thisequationisoverestimatingthenitrogentransferringtomanurestorageandtreatmentassomenitrogenwillbelostinhousing.ThisremainingtotalnitrogenvalueisaninputintotheN2Oequationsformanurestoredortreated.

N2O‐NlossesfrommanurecollectedandremovedfromhousingcanbedeterminedfrommanurenitrogenusingequationsfromSection5.4ManureManagementfortheappropriatemanuremanagementsystem.NH3lossesfrommanurenitrogenremovedfromhousingcanbecalculatedusingthemethodologypresentedinAppendix5‐C.1and5‐C.3.

Equation5‐20:ASABEApproachforEstimatingNitrogenExcretionfromLactatingSows

. .

Where:



ADFILACT =Averagedailyfeedintakeduringlactation(gday‐1)

CCP =Concentrationofcrudeprotein(%)

LL =Lactationlength(daystoweaning)(days)

LLTG =Lactationleantissuegain(kg)a

LWEAN =Litterweightatweaning(kg)

LWBIRTH =Litterweightatbirth(kg)


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5.3.4.2 RationaleforSelectingMethodforEstimatingEmissionsfromSwine

Milesetal.(2006)suggestthatarobustmodelforentericandhousingemissionswouldincludefactorssuchashousemanagement,animalsizeandage,pH,andmanuremoisture.Duetothecurrentdatalimitations,anNH3andGHGestimationmodelshouldminimallyincludenumberofanimals,excretamoisturecontent,dietproteinandfibercontent,andexcretapH.Thechallengeisthatthesecriteriamaynotbereadilyavailabletothefarmmanager.

Liuetal.(2011a)comparedliteraturevalueswithIPCCvaluesandconcludedthatthevariationofthemeasuredCH4andN2OhousingemissionrateshasnotbeenadequatelycapturedbytheIPCCapproaches.ForCH4emissions,thedifferencesbetweentheIPCC‐estimatedemissionratesandmeasuredvaluesweresignificantlyinfluencedbytypeofemissionsource,geographicregion,andmeasurementmethods.LargerdifferencesbetweenestimatedandmeasuredCH4emissionrateswereobservedinNorthAmericanstudiesthaninEuropeanstudies.InNorthAmericanstudies,theresultsofmeta‐analysisindicatedanoverestimationbytheIPCCapproachesforCH4emissionsfromlagoons(pooledrelativedifference:‐33.9%;95%CI:‐66.8%to‐0.01%),andthediscrepancybetweentheIPCC‐estimatedemissionsandthemeasuredvaluesoccurredmainlyatlowertemperatures.InEuropeanstudies,theresultsindicatedanoverestimationoftheIPCCapproachesinswinebuildingswithpitsystems.ForN2Oemissionsfromswineoperations,anoverallunderestimationoftheIPCCapproacheswasobservedinEuropeanstudiesbutnotinNorthAmericanstudies.InEuropeanstudies,thepooledN2Oemissionfactorsforswinebuildingswithpitsystemswas1.6%(95%CI,0.6%to2.7%),whiletheIPCCdefaultemissionfactorforpitsystemsis0.2%.LargeruncertaintieswereobservedformeasuredN2Oemissionsfrombeddingsystemsandfromstrawflowsystems.

InordertoconsideranalternativetotheIPCCapproach,awidevarietyofmodelsapplicabletoswineproductionfacilitieswereidentifiedandevaluated,including1)CARLivestock,2)ManureAndNutrientReductionEstimator(MANURE),3)COOLFarmTool,4)CarbonAccountingforLand

Model Evaluation Criteria for SwineProduction Systems

1. Themodelisbasedonwell‐establishedscientificallysoundrelationshipsbetweenfarmmanagementinputsandemissionsoutputs(process‐basedmodelormass‐balancemodelpreferable);

2. ThemodelisrelevanttoU.S.climateandswineproductionsystems;3. ThemodelcanestimateCH4,N2O,andNH3emissionsfromentericfermentationand

swinehousingsystems;4. Thereisflexibilityinthemodeltodescribetheproductionsystem(animals,feed,

housing,andin‐housemanuremanagement);5. Themodeliseasytouseandisdesignedtouseeasilyobtainablefarminformationto

determineemissionsestimates;6. Themodelincludessomemitigationstrategiesforreducingemissions,andproduces

realisticchangesinemissionsvalueswhenthesechangesaremadewithintheproductionsystem;

7. Thereistransparencyinthemodelcalculations,andtechnicalguidelinesareavailabletoelaboratethemethodologiesusedtoobtaintheemissionsestimates;

8. Themodelhasbeentested/validatedwithon‐farmdata;9. Themodelworksreliably(littletonoerrorsorprogramcrashes);and10. Themodelispubliclyavailableandaccessible.


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Managers,5)FarmingEnterpriseGHGCalculator,6)CPLAN,and7)Holos.Thesemodelswereevaluatedby10criteria(seebox)todeterminetheirsuitabilityforuseindeterminingemissionsestimatesforswineproductionfacilitiesintheUnitedStates.Twoofthesecriteriawereconsideredtobecritical,inthatiftheywerenotmetbythemodel,theycouldnotbeconsideredforuse(i.e.,themodelhadtoberelevanttoU.S.climateandswineproductionsystemsandhadtobepubliclyavailable).

TheHolosmodelconsidereddiet(standard,lowcrudeprotein,orhigh‐digestibilityfeeds)andmanurehandlingoptions(anaerobicdigestion,coveredoruncoveredslurrystorage,deeppit,orsolidstorage).Inaddition,theHolosmodelprovidedanestimateofuncertaintyforthemodeloutput.TheMANUREmodel(WRI,2009)collectedthemostcomprehensivedataandallowedforeasycomparisonoftheimpactofchangesinmanurehandlinganduseonemissionsofNH3,N2O(directandindirect),andCH4.Ontheanimalside,MANUREbaseditscalculationssolelyonanimalnumbers;feedingwasnotconsidered.Theothermodelsconsidered,whilemeetingminimumcriteria,lackedanyimprovementsovertheIPCCapproach.Consequently,theIPCCmethodwasselected(i.e.,HolosutilizestheIPCCTier1approachforhousing)withnitrogenexcretionestimatedusingASABEequationsthataccountfordiets.

5.3.5 HousingEmissionsfromPoultryProductionSystems

MeatBirdsGreenhousegasemissionswithinthefarmboundaryofabroilerchickenfarmwilloriginatealmostexclusivelyfromtheanimalhousing,whichalsoservesasthestoragelocationformanure.Liuetal.(2011a)reportedthatfora20‐weekgrow‐outofturkeysonlitter,averagedailyN2Oemissionswere0.045g(kgbodyweight)‐1,anddailyCH4emissionswere0.08g(kgbodyweight)‐1.EmissionsourcesexternaltothehousingincludeGHGemissionsfromfarmvehicles.Ifahouseiscleanedordecaked(removalofthetop,crustedportionofthelitter)andstoredonthefarm,GHGandNH3productionandemissionscouldoccur;Appendix5‐CprovidesfurtherdiscussiononNH3emissionsfromhousing.Practicestodecakeandthetimingoflandapplicationofcakeandlittervaryfromsitetositeandmayormaynotincludefurthercomposting.

LayingHensGreenhousegasemissionswithinthefarmboundaryofaneggfarmmayoriginatefromthehousingorthemanurestoragelocation.EmissionsourcesexternaltothehousingincludeGHGemissionsfromfarmvehicles.Externaltothefarmitself,GHGemissionsresultfromlandapplicationoflitterorstockpilingofthelitterinfieldspriortolandapplication.

Layinghenhousingsystemswithoutlitterwouldlikelyexhibitgreateremissionsthanlittersystems,butcomparisonofestimatesaresparse.Layinghenhousestypicallystoreexcretainabasementormaymoveexcretaoutofthehousefrequently(dailyormoreoften);thiswouldrelocateemissionstoastorageshedratherthanchangethecumulativeemissionsunlesssomeformofprocessing(drying)tookplacepriortostorage.Lietal.(2010)reporteddailyCH4emissionsof39.3to45.4mghen‐1andN2Oemissionsof58.6mghen‐1(henbodyweightaverage=1.9kg)inabasement‐typesystem.Thiscomparestoalittersystemfora20‐weekgrow‐outofturkeyswhereaveragedailyN2Oemissionswere0.045gkg‐1bodyweightanddailyCH4emissionswere0.08gkg‐1bodyweight(Liuetal.,2011a).Basedonthecomparisonofthesetwostudies,differencesinGHGemissionsfromdrylittersystemsandwetter,stackedlayinghensystemswouldbeexpected.

ManagementpracticestoreducelittermoistureofferthemostpromiseforreducingemissionsofCH4andN2O.Quantitativeestimatesofhowemissionsvarywithlittermoisturearenotavailable,butwouldlikelyfollowsimilardynamicsassoilmoisturecontent.Reuseoflitteranddecaking


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proceduresmightalsobeusedasstrategiestoreduceemissionsinthefuture.However,dataarenotavailableatpresenttouseaspartofasystemsmodel.

Ammonia Emissions in Poultry Housing

Asmentionedearlier,ammoniaisnotagreenhousegas,however,ammoniaemissionsareestimatedaspartofthenitrogenbalanceapproach.Meatbirdsaretypicallyraisedinlittersystems.Littertemperature,pH,andmoisture,alongwiththeammoniumcontentandhouseventilationratearerecognizedasmajorfactorscontrollingNH3lossfrombroilerlitter(ElliotandCollins,1982;Carretal.,1990;Mooreetal.,2010).Thereareseasonalvariationsinemissions,withlossestendingtobegreaterinsummer(warmermonths)thaninwinter(Coufaletal.,2006).Birdage/sizecanaffectlittertemperature,whichmayinfluenceseasonaleffectsonemissions(Milesetal.,2008).Inaddition,theformationofcakeinthehousecanhavealargeimpactonemissions.Milesetal.(2008)reportedthatextremelycakedareasofthehousehadvirtuallynofluxesofNH3.AreasoflitterwhereanaerobicconditionsdevelopsuppressNH3formationandrelease(Carretal.,1990).Mooreetal.(2011)determinedthatNH3emissionsfrombroilerhousesaveraged37.5gbird‐1,or14.5gkgbirdmarketed‐1(50‐dayoldbirds).Thesameauthorsestimatedthatofthetotalnitrogenoutputfromtypicalbroilerhouses,approximately22percentcanbeassociatedwithNH3emissions,56percentfromharvestedbirds,and21percentfromlitterpluscake.Theadditionofaluminumsulfate(alum)atarateequivalenttofiveto10percentbyweight(alum/manure)reducesNH3emissionfrombroilerhousesby70percent(Mooreetal.,2000)andresultsinheaverbirds,betterfeedconversion,andlowermortality(Moore,2013).EmissionsofN2OandCH4aredependentuponlitterconditionsthatfavorananaerobicenvironment.LimiteddataareavailabledocumentinglittermoisturecontenteffectsonN2OandCH4emissions.Milesetal.(2011)demonstratedthatincrementalincreasesinlittermoisturecontentincreasedNH3volatilization.Similarly,CabreraandChiang(1994)demonstratedarangeinNH3volatilizationof32percentto139percentofinitialammoniumcontentaslitterwatercontentincreased.LittertemperatureisanotherfactorthatmayinfluenceGHGemissions.Milesetal.(2006)demonstratedthatlittertemperatureaffectedNH3flux,butthestudydidnotmeasureothergases.Milesetal.(2011)observedthatorganicbeddingmaterialsgeneratedtheleastamountofNH3attheoriginalmoisturecontentwhencomparedwiththeinorganicmaterials.Theinfluenceofbeddingmaterialatincreasedmoisturelevelswasnotclearacrossthetreatmentstested.ButthefindingssuggestthatchoiceofbeddingmaterialmayalsoinfluenceN2Oand/orCH4emissionsandcouldpotentiallybeusedasamitigationstrategy.


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5.3.5.1 MethodforEstimatingEmissionsfromPoultryProductionSystems

NitrousOxideandAmmoniaEmissionsfromPoultryHousingToestimatenitrogenlossesfromhousing,theamountofnitrogenexcreted(Nex)byeachanimalcategoryisfirstestimated.Equation5‐22andEquation5‐23aretheequationsrecommendedbytheAmericanSocietyofAgriculturalEngineers(ASABE)forestimatingNexfrombroilers,turkeys,ducks,andlayinghens.

Methodfor Estimating Emissions from Poultry Production Systems

Methane

IPCCTier1approach,utilizingbarncapacityandmanureCH4emissionsfactorsperpoultrytype.

IPCCemissionfactorforpoultryentericCH4productionis0.Emissionsfromhindgutfermentationaresmallandgenerallyconsideredpartofhousingemissions.

NitrousOxide

NitrogenexcretionestimatedusingequationsprovidedinASABED384.2. IPCC(2006)Tier2approachforN2Ofrommanureinhousing.

Equation5‐21:MethaneEmissionsfromPoultryHousing(IPCC,2006)

_

Where:

CH4 =Methaneemissionsperyear(kgCH4year‐1)

Rate =Manuremethaneemissions(kgCH4head‐1year‐1)

Barn_Capacity=Capacityofbarn(head)

Equation5‐22:ASABEApproachforEstimatingNitrogenExcretionfromBroilers,Turkeys,andDucks

.

Where:

Nex =Totalnitrogenexcretionperfinishedanimal(gN(finishedanimal)‐1)

FIx =Feedintakeperphase(gfeed(finishedanimal)‐1)

CCP‐X =Concentrationofcrudeproteinoftotalrationineachphase(gcrudeprotein(g(wet)feed)‐1)

NRF =Retentionfactorfornitrogen(fraction)


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aDefaulteggweightis60gforlightlayerstrainsand63gforheavylayerstrains.bDefaultfractionis0.80.

TheNH3lostfrommanureformeatandegg‐producingbirdsisestimatedasafractionofNex.KoelschandStowell(2005)provideestimatesonthetypicalNH3lossfromdifferenthousingfacilitiesasafractionofNex(seeTable5‐15).Arangeofvalueshasbeenprovidedforeachfacilitytype;thelowervaluesshouldbeusedduringthewinter,thehighervaluesshouldbeusedduringthesummer,andintermediatevaluesshouldbeusedforthespringandautumn.

Table5‐15:TypicalAmmoniaLossesfromPoultryHousingFacilities(PercentofNex)

FacilityDescriptionApplicableSpecies %Loss FacilityDescription

ApplicableSpecies %Loss

Roofedfacility(litter)Meat

producingbirds

25‐50Roofedfacility(stackedmanureunderfloor‐includesstorageloss)

Egg‐producingbirds

25‐50


N2O can also be lost from the excreted nitrogen. A quantitative method for estimating N2O emissions from solid manure is the IPCC Tier 2 approach, which is also used for the U.S. Greenhouse Gas Inventory (Equation 5-8). This estimation method is the same as the method present in the Temporary Stack and Long-Term Stockpile and the Composting sections (see sections 5.4.1 and 5.4.2 for more details). The N2O emission factors for poultry manure in housing is 0.001 (kg N2O-N/ kg N) for poultry manure with or without bedding IPCC (2006).

TheremainingnitrogenexcretedthatisnotvolatilizedasNH3orlostasN2Oinhousingthenentersmanurestorageandtreatment.Ifdataarenotavailabletotrackthenitrogenthatistransferredalongwiththemanuretomanurestorageandtreatment,thenitrogencanbeestimatedasdescribedinEquation5‐9.However,thisequationisoverestimatingthenitrogentransferringtomanurestorageandtreatmentassomenitrogenwillbelostinhousing.ThisremainingtotalnitrogenvalueisaninputintotheN2Oequationsformanurestoredortreated.

5.3.5.2 RationaleforSelectingMethodforEstimatingEmissionsfromPoultryProductionSystems

Milesetal.(2006)suggestthatarobustmodelwouldincludefactorssuchashousemanagement,birdsizeandage,cakemanagement,pH,andlittermoisture.Duetocurrentdatalimitations,anNH3andGHGestimationmodelshouldminimallyincludenumberofanimals,litter/excretamoisturecontent,dietaryproteinandfibercontent,andlitter/excretapH.Avarietyofmodelsapplicableto

Equation5‐23:ASABEApproachforEstimatingNitrogenExcretionfromLayingHens

..

Where:


FI =Feedintake(gfeed(finishedanimal)‐1)

CCP =Concentrationofcrudeproteinoftotalration(gcrudeprotein(g(wet)feed)‐1)

Egg =Eggweighta(g)

Egg =Fractionofeggsproducedeachdayb(eggshen‐1day‐1)


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poultryproductionfacilitieswereidentifiedandevaluated,includingCarbonAccountingforLandManagers;CFFCarbonCalculator;CPLAN;and4)Holos.Thesemodelswereevaluatedwithrespectto10criteria(seebox)todeterminetheirsuitabilityforuseindeterminingemissionsestimatesforpoultryproductionfacilitiesintheUnitedStates.

Twoofthesecriteriawereconsideredtobecritical,inthatiftheywerenotmetbythemodel,theycouldnotbeconsideredforuse(i.e.,themodelhadtoberelevanttoU.S.climateandpoultryproductionsystemsandhadtobepubliclyavailable).TheHolosmodeldidconsiderwetordrymanurehandlingforlayinghenoperations.Forallpoultrytypes,theCarbonAccountingforLandManagersmodelrequestedinformationrelatedtoburningofmanureandtimebirdsspendinafree‐rangesystem.Thisinformationwasthenusedtocalculatethemassofmanureavailablefordirectandindirectemissions.Nomodelrequestedinformationondietorin‐houselittermanagementpractices.ForCH4emissions,onlytheHolosmodelprovidedanestimateofconfidenceofmodeloutput.SpecifictoestimatesofpoultrymanureCH4emissions,themodelhadanuncertaintyunder20percentforbroilers,turkeys,layersinwetmanurehandlingsystems,andlayersindrymanurehandlingsystems.Consequently,theIPCCmethodwasselected(i.e.,HolosutilizestheIPCCTier1approachforhousing).ForN2Oemissions,theIPCCTier2wasusedwithnitrogenexcretionestimatedusingASABEequationsthataccountfordiets.

5.3.6 EntericFermentationandHousingEmissionsfromOtherAnimals

AlthoughthemajorityofemissionsfromlivestockintheUnitedStatesarefromcattle,sheep,swine,andpoultry,emissionsfromotheranimalscanalsobeimportanttoconsider,particularlyattheentitylevel.Overall,populationsoftheanimalsdiscussedinthissection(goats,Americanbison,llamas,alpacas,andmanagedwildlife)aremuchfewerthanthoseoftheanimalsdiscussedinpriorsections.However,theavailabilityofresearchonemissionsfromtheseanimalsallowsustoexplorethematleastatanintroductorylevel.Attheentitylevel,populationsoftheseanimalsmaybesignificantenoughtowarrantcalculatingtheiremissions.ThisreportrecommendsmethodsforestimatingCH4emissionsfromgoatsandAmericanbison(Equation5‐24andEquation5‐25).

Model Evaluation Criteria for Poultry Production Systems

1. Themodelisbasedonwell‐establishedscientificallysoundrelationshipsbetweenfarmmanagementinputsandemissionsoutputs(process‐basedmodelormass‐balancemodelpreferable).

2. ThemodelisrelevanttoU.S.climateandproductionsystems.3. ThemodelcanestimateCH4,N2O,andNH3emissionsfrompoultryhousingsystems.4. Thereisflexibilityinthemodeltodescribetheproductionsystem(animals,feed,

housing,andin‐housemanuremanagement).5. Themodeliseasytouseandisdesignedtouseeasilyobtainablefarminformationto

determineemissionsestimates.6. Themodelincludessomemitigationstrategiesforreducingemissionsandproduces

realisticchangesinemissionsvalueswhenthesechangesaremadewithintheproductionsystem.

7. Thereistransparencyinthemodelcalculations,andtechnicalguidelinesareavailabletoelaboratethemethodologiesusedtoobtaintheemissionsestimates.

8. Themodelhasbeentested/validatedwithon‐farmdata.9. Themodelworksreliably(littletonoerrorsorprogramcrashes).10. Themodelispubliclyavailableandaccessible.


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5.3.6.1 Goats

EntericemissionsfromgoatproductionsystemswereestimatedbyU.S.EPA(U.S.EPA,2011)usingIPCC(2006)methodstobe16GgCH4(ofatotalof6,655Gg).EmissionsofmanureCH4andN2OfromgoatproductionweremadeusingIPCC(2006)methods.Goatswereassociatedwith1GgofmanureCH4(ofatotalof2,356Gg)andlessthan0.5GgofN2O.

TheimpactofdietonJapanesegoatentericCH4emissionswasmeasuredbyBhattaetal.(2007).Goatsfedarangeofdietsfrom100percentforageto80percentconcentrateproducedfrom16.4to22gCH4day‐1(5.0to8.2percentofGEI).

TheIPCC(2006)Tier1equation,presentedinEquation5‐24,forestimatingentericfermentationemissionsfromgoatsisthebestoptionforcalculatingemissionsattheentitylevel.

5.3.6.2 AmericanBison,Llamas,Alpacas,andManagedWildlife

Galbraithetal.(1998)measuredentericCH4fromgrowingbison(n=5),wapiti(n=5),andwhite‐taileddeer(n=8)fedalfalfapelletsinthewinter‐spring(February‐March)andspring(April‐May)usingrespirationcalorimetrychambers.Thebisonproducedanaverageof86.4gday‐1(6.6percentGEI),thewapiti,62.1gday‐1(5.2percentGEI),andthedeer23.6gday‐1CH4(3.3percentGEI).Usingadetailedmethodofcalculationtoestimatehistoricalbisonemissions,KelliherandClark(2010)estimatedthatgrazingbisonwouldproduce72kgCH4year‐1or197gCH4day‐1.Hristov(2012)estimatedpresentdaybisonproduce21gCH4(kgDMI)‐1day‐1,eatapproximately12.8kgDMday‐1,andproduce268gCH4day‐1.Thedifferencesbetweentheseestimatesaredifferencesinanimalweights,DMI,limitedmeasurementsofbisonemissions,andassumedCH4conversionfactors.TheU.S.EPAusesIPCCTier1methodologiestoestimatebisonemissions,andcurrentlyTier1isthebestoptiontoestimateentericemissions.

TheIPCC(2006)Tier1equationforestimatingentericfermentationemissionsfromAmericanbisonisbasedontheemissionfactorforbuffaloandhasbeenmodifiedasrecommendedbyIPCCtoaccountforaverageweightasseeninEquation5‐25.

Equation5‐24:Tier1EquationforCalculatingMethaneEmissionsfromGoats

Where:

CH4 =Methaneemissionsperday(kgCH4day‐1)

Pop =Populationofgoats(head)

EFG =Emissionfactorforgoats(0.0137kgCH4head‐1day‐1)


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TheNewZealandMinistryfortheEnvironment(2010)usesafactorof6.4percentofGEItopredictentericCH4emissionsfromfarmedreddeerandprojectsanemissionrateperyearof23.7kgCH4

head‐1year‐1.Deerarealsoestimatedtoexcrete31.0kgNhead‐1year‐1contributingtowardN2Oproduction.ThevaluesusedtomakethesecalculationsarefrommeasurementsofdeerCH4emissionsusingtheSF6tracermethod.Elk,white‐tailed,andmuledeerentericemissionswereestimatedbyHristov(2012)tobe86.4,16,17gCH4head‐1day‐1respectively.IPCCTier1istherecommendedmethodbywhichtheseemissionsshouldbeestimated.

Adultllamasfedoathayinastudydesignedtodefineenergyrequirementswerefoundtolose7.1percentofGEIasentericCH4(Carmeanetal.,1992).Pinares‐Patinoetal.(2003)comparedentericCH4emissionsmeasuredwithrespirationcalorimetrychambersfromalpacaandsheepfedalfalfadietsandfoundthealpacaproduced14.9gCH4day‐1(5.1percentofGEI)andthesheepproduced18.8gCH4day‐1(4.7percentofGEI).Whengrazingaperennialryegrass/whitecloverpasture,thealpacaproduced22.6gCH4day‐1(9.4percentGEI)comparedto31.1gCH4day‐1(7.5percentGEI)forsheep.TheauthorsattributethehighconversionofGEItoCH4fromthealpacatograzingselectivityonpasture;thealpacawereobservedtoselect“morestructuralplantparts.”

5.3.7 FactorsAffectingEntericFermentationEmissions

AnumberoffactorsmayinfluenceentericfermentationandresultingCH4emissions.Athoroughreviewofsuchfactorsisoutsidethescopeofthisdocument,butkeyfactorshavebeenreviewedbyothers(Montenyetal.;(2006),Beaucheminetal.;(2008),Eckardetal.;(2010),andMartinetal.;(2010))andarediscussedbrieflybelow.

Benchaaretal.(2001)usedtherumendigestionmodelofDijkstraetal.(1992),asmodifiedbyBenchaaretal.(1998),andtheCH4predictionsystemofBaldwin(1995)toestimatetheeffectsofdietarymodificationsontheentericCH4productionofa500kgdairycow.ThemodelpredictedentericCH4productionbasedonaruminalHbalance.Inputsintothemodelincludedthefollowing:dailyDMI;chemicalcompositionofthediet;solubilityanddegradabilityofproteinandstarchinthediet;degradationratesofprotein,starch,andNDF;ruminalvolume;andfractionalpassageratesofsolidsandliquidfractionsfromtherumen.ValuesmodifiedinthesimulationswereDMI,dietaryforage,concentrateratio,starchavailability(barleyvs.corn),stageofmaturityofforage,formofforage(hayorsilage),particlesizeofalfalfa,andammonizationofcerealstraw.ThemodeledeffectsofdietarychangesonentericCH4emissionsindietsfedtodairycowsarepresentedinTable5‐16.

Equation5‐25:Tier1EquationforCalculatingMethaneEmissionsfromAmericanBison

Where:

CH4 =Methaneemissionsperday(kgCH4head‐1day‐1)

Pop =PopulationofAmericanbison(head)

EFAB=EmissionfactorforAmericanbison(kgCH4head‐1day‐1)

EFABistheIPCCemissionfactorforbuffalo(0.15kgCH4head‐1day‐1),adjustedforAmericanbisonbasedontheratioofliveweightsofAmericanbison(513kg)tobuffalo(300kg)tothe0.75power.

.


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TherearemanyfactorsthataffectentericCH4emissionsbutthemostcriticalfactorsarethelevelofdrymatterintake,thecompositionofthediet,andthedigestibilityofthedrymatter,asillustratedinTable5‐16.

Table5‐16:SummaryofEffectsofVariousDietaryStrategiesonEntericCH4ProductioninDairyCowsusingModeledSimulations

StrategyCH4Variation(perunitofGEI)

CH4Variation(perunitofDE)

IncreasingDMI ‐9to‐23% ‐7to‐17%Increasingconcentrateproportioninthediet ‐31% ‐40%Switchingfromfibrousconcentratetostarchyconcentrate ‐24% ‐22%Increasedforagematurity +15% ‐15%Alfalfavs.timothyhay +28% ‐21%Methodofforagepreservation(ensiledvsdried) ‐32% ‐28%Increasedforageprocessing(smallerparticlesize) ‐21% ‐13%Ammoniatedtreatmentofpoorqualityforage(straw)a x5 x2Proteinsupplementationofpoorqualityforage(straw) ×3 ×1.5

Source:Benchaaretal.,(2001),Table12.aEffectsareduetosignificantincreaseinhaydigestibilitywithnochangeinDMintake.

DietaryFat:ManystudieshavedemonstratedthatsupplementalfatcandecreaseentericCH4emissionsinruminants.Inareviewofstudies,Beaucheminetal.(2008)notedthatentericCH4emissions(g[kgDMI]‐1)decreasedbyapproximately5.6percentforeachonepercentincreaseinfataddedtothediet.Inalargerreview,Martinetal.(2010)reportedadecreaseof3.8percent(g[kgDMI]‐1)witheachonepercentadditionoffat.Lovettetal.(2003)reportedthattotaldailyemissionsdecreasedfrom0.19to0.12kgCH4head‐1(reportedas260to172LCH4head‐1)(6.6and4.8percentofGEI)fromsteersfeddietscontaining0or350gofcoconutoil,respectively.Thiseffectwasconsistentregardlessofdietaryforageconcentration(65,40,and10percentofDM).

AlthoughaddedfatmayreduceentericCH4emissions,ruminantshavealowtolerancefordietaryfat.Thus,totalfatlevelinthedietmustbemaintainedbeloweightpercentofdietaryDM.Somesourcesoffatappeartohavesomeprotectionagainstbiohydrogenationbyruminalmicrobesandthusmaybebettertolerated(Corriganetal.,2009;VanderPoletal.,2009).

GrainSource,GrainProcessing,StarchAvailability:GrainsourceandgrainprocessingmethodcanalsoaffectentericCH4losses.Ingeneral,thegreatertheruminalstarchdigestibility,thelowertheentericCH4emissions.Atconstantenergyintake(2xmaintenance),Halesetal.(2012)reportedapproximately20percentlower(2.5vs.3.0percentofGEI)entericCH4emissionincattlefedtypicalhigh‐concentrate(75percentcorn)steamflakedcorn(SFC)basedfinishingdietsthaninsteersfeddry‐rolledcorn(DRC)basedfinishingdiets.BasedontherumenstoichiometryofWolin(1960),ZinnandBarajas(1997)estimatedthatCH4productionperunitofglucoseequivalentfermentedintherumenalsodecreasedwithmoreintensivegrainprocessing(i.e.,coarse,medium,orfineflakes).Similarresponseswerenotedwiththefeedingofhigh‐moisturecorncomparedwithDRC(Archibequeetal.,2006).Somewhatincontrast,BeaucheminandMcGinn(2005)reportedlowerentericCH4emissionsfromfeedlotcattlefedDRC‐baseddiets(2.81percentofGEI)thanfromcattlefedsteam‐rolledbarley‐baseddiets(4.03percentofGEI),possiblytheresultoflowerruminalpHonthecorn‐baseddiet(5.7vs.6.2,respectively;(VanKesselandRussell,1996)and/orhigherNDFinthebarleydiet.EntericCH4emissionswere38percent(barley)to65percent(corn)lowerinhigh‐concentrate(ninepercentsilage)finishingdietsthanongrower(70percentsilage)diets.


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FeedingCoproductIngredients:Distillersgrainswithsolubles(DGS)andothercoproductsofthemillingandethanolindustriesarewidelyusedascattlefeeds.Theeffectsoffeeding30to35percentDGS(DMbasis)onentericCH4emissionhavebeenvariable,rangingfromasignificantdecreaseof25to30percent(McGinnetal.,2009)tonoeffect(Halesetal.,2012),toanincrease(Halesetal.,2013).Thesedifferingresultswereprobablyduetodifferencesinforageandfatintake.InthestudybyMcGinnetal.(2009)thedietcontained65percentsilage,anddietaryfatintakeincreasedbyapproximatelythreepercentageunits8whendriedDGSwereaddedtothediet.Incontrast,Halesetal.(2012;2013)feddietsthatcontainedonly10percentforageandwereequalintotalfatconcentration.

RoughageConcentrationandForm:TheconcentrationandformofroughageinthedietwillaffectbothentericandmanureCH4production(Halesetal.,2014).Usingaruminalvolatilefattyacids(VFA)stoichiometrymodel,Dijkstraetal.(2007)suggestedthatCH4lossesfromcarbohydratessubstrates(gkg‐1substrate)inaconcentratedietwithruminalpHvariationandapHof6.5were2.11,3.18,3.38,and3.10forstarch,solublesugars,hemicellulose,andcellulose,respectively.Similarly,withdairycows,MoeandTyrrell(1979)reportedthatentericCH4productionperunitcarbohydratedigestedwasthreetimesgreaterforcellulosethanforhemicellulose.Aguerreetal.(2011)foundthatlactatingdairycattleemittedmoreCH4whentheforage:concentrateratiowaschangedfrom47:53to68:32,0.54kgCH4day‐1vs.0.65kgCH4day‐1respectively.

Ingeneral,astheconcentrationofforageinthedietincreases,entericCH4productionincreasesandthequantityofvolatilesolidsexcretedincreases.Usingamicrometeorologicalmassdifferencemethod,Harperetal.(1999)reportedCH4emissionsof230ganimal‐1daily(7.7to8.1percentofGEI)infeedercattleonpasture,butonly70ghead‐1daily(1.9to2.2percentofGEI)incattlefedhigh‐concentratediets.MeasuredCH4lossesforpasturecattlewerehigherthanvaluespredictedusingtheIPCC(1997;2006)CH4conversionfactors(MCForYm),orAustralianmethodology(NGGIC,1996).Incontrast,measuredCH4lossesforfeedlotcattlewereabout67percentofthoseestimatedusingtheIPCC(2006)YmofthreepercentofGEIortheAustralianmethodology(NGGIC,1996),butweresimilartovaluesreportedbyBranineandJohnson(1990),BlaxterandWainman(1964),andHalesetal.(2012;2013;Halesetal.,2014).

EntericfermentationoftropicalgrassesandlegumesmayalsobedifferentthanpredictedbyIPCCornationalGHGinventorymethods.KennedyandCharmley(2012)measuredentericCH4productionofcattlefedAustraliantropicalgrassesandlegumestobe5.0to7.2percentofGEintakewhichissimilartoIPCC(2006)Tier2estimates(5.5to7.5percentofGEintake)ofcattlefedforagedietsbutsomewhatlowerthantheAustralianNationalGreenhouseAccountsNationalInventoryReport(2007)of8.7to9.6percentofGEintake.

BlaxterandWainman(1964)comparedtheeffectsoffeedingdietswithsixvaryinghay:flakedcornratios(100:0,80:20,60:40,40:60,20:80,5:95)onentericCH4emissionswhenfedattwotimesthemaintenancelevelofintake.CH4emissionsasapercentageofGEIincreasedslightlybetweenthe100:0diet(7.44percent)andthe60:40diet(8.17percent),thendecreasedtothe5:95diet(3.4percent).

InIreland,Lovettetal.(2003)reportedtotaldailyentericCH4emissionsof0.15,0.19,and0.12kghead‐1(reportedas207,270,and170Lhead‐1)forheifersfeddietscontaining65,40,and10percentforage(theremainderasconcentrate),respectively.AsapercentageofGEI,losseswere6.1,6.6,and4.4percent,respectively.

8Theterm“percentageunits”inthisdocumentisusedtorefertochangesindietsoremissionsthatarenotproportionaltotheirbaselines.Forexample,areductioninemissionsfromthreepercenttoonepercentisa2“percentageunit”reductionora67percentreduction.


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Usingsteersfedall‐foragediets,Ominskietal.(2006)reportedthat,withintherangeofforagequalitiestested(alfalfa‐grasssilagecontaining61,53,51,or46percentNDF,DMbasis),entericCH4emissionsofsteers,asapercentageofGEI,werenotsignificantlyaffectedbyNDFcontent(5.1to5.9percent),althoughdailyCH4productiontendedtobehighestforthe53percentNDFdiet(0.12,0.15,0.13,and0.14kghead‐1day‐1,respectively).Similarly,usinggrazingsheep,MilanoandClark(2008)reportednoeffectofforagequality(ryegrass–52or47percentNDF,77or67percentorganicmatter[OM]digestibility)onentericCH4emissions.

AlthoughdietaryforagequalitymaysometimesnotaffectentericCH4emissions,itwillaffectforagedigestibilityandthusfecalexcretionofvolatilesolids.Thus,feedingmoredigestibleforagesorconcentratesmaydecreaseGHGemissionsfrommanure.

LevelofIntake:BlaxterandWainman(1964)comparedtheeffectsoffeedingsixdietsattwolevelsofintake.EntericCH4emissions,asapercentofGEI,were23percentgreaterinsteersfedatmaintenancethaninsteersfedat2Xmaintenance(8.1vs.6.6percentofGEI,respectively).However,inastudyevaluatingemissionsfromcattlefedryegrassdiets,MilanoandClark(2008)reportedthatasDMIincreasedfrom0.75percentofmaintenanceto2Xmaintenance,entericCH4emissions(gday‐1)increasedlinearly(r2=0.80to0.84).EmissionsasapercentageofGEIwerenotaffectedbyDMI,andrangedfrom4.9to9.5percentofGEI(15.9to30.4g[kgDMI]‐1).

Usingahigh‐forage(70percentbarleysilage)ormedium‐forage(30percentsilage)dietfedatlevelsfrom1Xtoapproximately1.8Xmaintenance,BeaucheminandMcGinn(2006b)notedthatentericCH4emissions,asapercentofGEI,decreasedbyapproximately0.77percentageunitsforeachunitincreaseinfeedintake(expressedasleveloffeedintakeabovemaintenance).ThiswaslessthantheestimateusingtheBlaxterandClapperton(1965)equation(0.93to1.28percentpercentageunits)orthe1.6percentageunitssuggestedbyJohnsonandJohnson(1995).

FeedAdditivesandGrowthPromoters:Cooprideretal.(2011)notedthatthedailyCH4andmanureN2Oproductionofcattlefedthrougha“natural”programwithnouseofantibiotics,ionophores,orgrowthpromotersweresimilartocattlefedinmoretraditionalsystemsthatusedanabolicimplantsanddietsthatcontainedionophoresandbeta‐agonists.However,typicalcattlehadgreateraveragedailyweightgain(1.85vs.1.35kgday‐1)andthustook42fewerdaystoreachthesameendpoint(596kgbodyweight[BW]).Thus,overall,cattlefedusingmoderngrowthtechnologieshad31percentlowerGHGemissionsperhead.CH4emissionskgofBWgain‐1was1.1kggreaterforthe“natural”cattle(5.02vs.3.92CO2‐eqkgBWgain‐1)thanthetraditionalcattle.

MonensindecreasesentericCH4emissionsinfinishingcattleby10to25percent(Tedeschietal.,2003;McGinnetal.,2004).However,infeedlotcattletheeffectsappeartobetransitory,lastingfor30daysorless(Guanetal.,2006).Incontrast,Odongoetal.(2007)reportedthatmonensin(24ppm)indairydietsdecreasedentericCH4byseventoninepercentforuptosixmonths.Waghornetal.(2008)foundnoeffectofmonensincontrolled‐releasecapsulesonCH4productionofpasture‐feddairycows,andHamiltonetal.(2010)alsofoundnochangeinentericCH4productionfrommonensinfedtodairycowsofferedatotalmixedration.

AnumberofstudieshavedemonstratedthatavarietyofhalogenatedanalogueshavethepotentialtodramaticallydecreaseruminalCH4production(Johnson,1972;Treietal.,1972;Johnson,1974;ColeandMcCroskey,1975;TomkinsandHunter,2004;Tomkinsetal.,2009).Ingeneraltheeffectwasgreaterincattlefedhigh‐foragedietsthanincattlefedhigh‐concentratediets.WhenCH4lossesweredramaticallyreduced,asignificantquantityofhydrogencouldbelost(onetotwopercentofGEI)viaeructation,suggestinganalternativeelectronsinkisalsoneeded.Ingeneral,thecompoundsdidnotimproveproductionefficiencysignificantly.Inaddition,thepotentialtoxicityofthesecompoundsmadethemimpracticalforroutineuse.


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Anumberofnitrocompounds(nitropropanol,nitroethane,nitroethanol)havealsosignificantlydecreasedruminalCH4productioninvitro(Andersonetal.,2003),withaconcomitantincreaseinhydrogenproduction/release.Theeffectappearedtobeenhancedwhenanitratereducingbacteriumwasaddedtotheculture(AndersonandRasmussen,1998).

SeveralstudieshavesuggestedthatfeedingofcondensedtanninscandecreaseentericCH4productionby13to16percent;eitherthroughadirecttoxiceffectonruminalmethanogensorindirectlyviaadecreaseinfeedintakeanddietdigestibility(Eckardetal.,2010).Tanninsmayalsoshiftnitrogenexcretionawayfromurinetofecesandinhibitureaseactivityinfeces,whichcouldpotentiallydecreaseNH3andN2Oemissionsfrommanure(Powelletal.,2009;Powelletal.,2011).

Feedingyeastcultures,enzymes,dicarboxylicacids(fumarate,malate,acrylate),andplantsecondarycompounds,suchassaponins,maydecreaseentericCH4emissionsundersomefeedingconditions(McGinnetal.,2004;BeaucheminandMcGinn,2006a;Ungerfeldetal.,2007;Beaucheminetal.,2008;Eckardetal.,2010;Martinetal.,2010).

NovelMicroorganismsandtheirProducts:KlieveandHegarty(1999)notedthatentericCH4productionmaybebiocontrolleddirectlybyuseofvirusesandbacteriocins.Leeetal.(2002)reportedthatabacteriocin(BovicinHC5)fromStreptococcusbovisreducedinvitroCH4productionbyupto50percent.Itappeared,thatincontrasttoresultswithmonensin,theruminalmicroorganismsdidnotadapttothebacteriocin.

AustralianresearchershavesuggestedthatvaccinatingagainstmethanogenscandecreaseCH4emissions.However,theresultshavenotbeenconsistent(Wrightetal.,2004;Eckardetal.,2010)becauseefficacyisdependentonthespecificmethanogenpopulationandthatisdependentondiet,location,andotherfactors.

Genetics:Aspreviouslynoted,severalstudieshavesuggestedthatcattleselectedforlowerRFI(i.e.,increasedfeeduseefficiency)tendtohavelowerruminalentericCH4production(Nkrumahetal.,2006;Hegartyetal.,2007),althoughtheeffectmaydependonstageofproduction(lactationvs.dryandpregnant)and/orqualityofthedietconsumed(Jonesetal.,2011).RFIismoderatelyheritable(0.28to0.58)(Mooreetal.,2009),thusitmightbepossibletogeneticallyselectforanimalswithlowerentericCH4production.However,FreetlyandBrown‐Brandl(2013)foundhigherCH4emissionsfrommoreefficientanimals.Thus,moreinformationisneededtodefineunderwhatconditionsCH4emissionsarerelatedtofeedefficiencyortogenetics.

FactorsAffectingEmissionsfromSheepSheep,likecattle,areruminantanimalsandthusthesamedietaryfactorswillpositivelyornegativelyaffectemissionsfromentericfermentation.

FactorsAffectingEmissionsfromSwineDietarymodificationscaneffectivelyreducenitrogenexcretionsandmitigateairemissions(especiallyNH3,aprecursorforN2O)fromlivestockoperations(Suttonetal.,1996;Canhetal.,1998b).FeedingstrategiestoreducenitrogenexcretionsincludereducedCPdietssupplementedwithsyntheticaminoacids(AA)(Panettaetal.,2006),andmodifyingthedietaryelectrolytestoreduceurinarypH(Canhetal.,1998a).Inbothhogandpoultryoperations,reductionsinNH3emissionshavebeenreportedbysupplementingwithAAandreducingCPindiets.

ReducingdietaryCPcontenthasbeenshowntobeaneffectivewaytoreducetheamountofnitrogenexcreted(Lenis,1993;HartungandPhillips,1994).ThiscanbeachievedwithoutanynegativeeffectonanimalperformancebysupplementingwithanimprovedsyntheticAAbalance,resultinginareductionofexcessCPexcreted(Canhetal.,1998b;Ferketetal.,2002).InU.S.‐typediets(corn‐soybeanmealbased)themostlimitingaminoacidsareLysine,Methionine,Threonine,


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andTryptophan,followedbyIsoleucine,Valine,andHistidine(Outor‐Monteiroetal.,2010).Suttonetal.(1996)reportedthatnitrogenexcretiondecreasedby28percentwhendietCPcontentdecreasedfrom13percentto10percent(corn‐soybeanmeal)forgrowing‐finishingpigdietssupplementedwithLys,Met,Thr,andTrp.SeveralstudiesreportedreductionsinnitrogenexcretionandsubsequentdecreasesinNH3emissionsinnon‐ruminants(swineandpoultry)(HartungandPhillips,1994;Canhetal.,1997;Canhetal.,1998a;Canhetal.,1998b;Hayesetal.,2004).Powersetal.(2007)observedthat,asaresultoffeedingreducedCPdietswithincreasedamountsofsyntheticAA,NH3emissionswerereducedby22percent(threeAA)and48percent(fiveAA)comparedwiththecontroldietcontainingonlyoneAA,anddiethadnoeffectonpigperformance.

Canhetal.(1998b)andNdegwaetal.(2008)reportedthatsomenitrogenexcretioncouldbeshiftedfromurinetofecesbyincreasingdietaryfibercontent,orbyreducingdietarynitrogencontent,withnosignificantdifferencesinanimalperformanceorgrowth.Urinarynitrogenispredominantlyinorganicinnatureandfecalnitrogenismostlyorganic.TheconversionofureafromurinetoNH3isafastprocess,whileconversionoforganicnitrogentovolatileNH3infecesisaslowprocess.

ThereductioninNH3emissionassociatedwithlowerCPdietsnotonlycomesfromreductioninnitrogenexcretion,butalsofromlowermanurepH.Portejoieetal.(2004)reportedthatslurrypHdecreasedby1.3unitswhendietaryCPdecreasedfrom20to12percent,andslurryfrompigsfedthelowerCPdiethadahigherDMcontentandlowerTANandTKNcontents.Leetal.(2008),Hannietal.(2007),andCanhetal.(1998b)alsoreportedthatlowermanurepHresultedfromfeedinglowerCPindiets.Itshouldbenotedthatwaterintakewasoftenrestrictedinearlierstudies.

AarninkandVerstegen(2007)summarizedfourdietarystrategiestoreduceNH3emissions:1)loweringCPintakeincombinationwiththeadditionoflimitingAA;2)shiftingnitrogenexcretionfromurinetofecesbyincludingfermentablecarbohydratesinthediet;3)loweringurinarypHwiththeadditionofacidifyingsaltstothediet;and4)loweringfecespHwiththeinclusionoffermentablecarbohydratesinthediet.Theyclaimedthatbycombiningthesestrategies,NH3emissionsingrowing‐finishingpigscouldbereducedbyatotalof70percent.Toreduceodorfrompigmanure,Leetal.(2007)suggestthatdietarysulfur‐containingAAshouldbeminimizedtojustmeettherecommendedrequirements.

CurrentresearchhasconcentratedonfarmproductionefficiencyandreducingNH3emissions;littlehasfocusedonGHGemissionsmitigation(Bhattietal.,2005).BallandMöhn(2003)showedthatlowCPdietscanreducetotalGHGemissionsfromgrowingpigsby25to30percent(directlyfromtheanimalsaswellasfromthemanureafterexcretion)andfromsowsby10to15percent.Atakoraetal.(2003)reporteda27.3percentdecreaseinCH4emissionsinpigsfed16percentCP(supplementedwithAA)diets,comparedwith19.0percentCPdiets.Atakoraetal.(2004)reportedthattheCO2equivalentsemittedbyfinishingpigsandsowsfedwheat‐barley‐canoladietswerereducedby14.3to16.5percentwhenfeedingthereducedCP,AA‐supplementeddiets,andweresimilarforfinishingpigsandsows.Thereductionwasonly7.5percentwhenfeedingthecorn‐soybeanmeal‐basedreducedCPdiet.Misselbrooketal.(1998)foundthatCH4emissionsduringstoragewerelessatlowthanatahighdietaryCPcontent.TheemissionofCH4wassignificantlyrelatedtocontentofdrymatter,totalcarbon,andVFAinthemanure.Misselbrooketal.(1998)claimedthatthe50percentreductioninCH4emissionfromtheslurryobservedwhenpigswerefedthelowerCPdietwasprobablytheresultofthereducedvolatilefattyacids(VFA)contentoftheslurry,andCH4emissionsweremorecloselyrelatedtoVFAcontentthantototalcarboncontent.ThereappearstobeacloserelationshipbetweenfermentablecarbohydratesinthedietandCH4production(Kirchgessneretal.,1991).ManurepHalsoinfluencesCH4production.Kimetal.(2004)noteda14percentreductioninCH4emissionwhenidealpHwasreducedoneunitthroughaddition


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ofacidogeniccalciumandphosphorussourcestopigdiets.IncreasingfermentablecarbohydratelevelsinthediettolowerthepHofmanure,withthegoalofreducingNH3emissions,mightincreaseCH4production(AarninkandVerstegen,2007).Canhetal.(1998a)observedthatforeach100‐gincreaseintheintakeofdietarynon‐starchpolysaccharide(NSP),theslurrypHdecreasedbyapproximately0.12unitsandtheNH3emissionfromslurrydecreasedby5.4percentwhendietaryNSPrangedfrom150to340gNSPkgDM‐1.

Feedingofdrieddistillersgrainswithsolubles(DDGS)hasbecomecommonpracticeintheswineindustry.Lietal.(2011)demonstratedthatfeedingdietscontaining20percentDDGSincreasedemissionsofCH4butnotN2OwhencomparedtocontroldietswithoutDDGS.ObservedincreasesinCH4emissionsapproximated18percent.Ammoniaemissionsresultingfromfeeding20percentDDGSwereeitherhigherorlowerthandietswithoutDDGS,dependingontheformoftracemineralsincludedinthediet.DietsincludinginorganicformsoftracemineralshadsevenpercentgreaterNH3emissions,whilefeedingorganicformsoftracemineralsdecreasedNH3emissionsalmost20percentcomparedtocontroldiets(Liuetal.,2011a).

Inarecentmeta‐analysis,Liuetal.(2011a)used32datapointsinasubgroupofstudiesthatincludeddietCPinformationtoanalyzetheeffectofdietCPonGHGemissions.Threefactors(dietCP,geographicregion,andswineproductionphase)wereconsideredintheregressionanalysis.DietCPwasnotasignificantfactor.EmissionsofCH4arepositivelycorrelatedwithdietcrudeproteininswineproduction,mostsignificantlyforlagoonandslurrystoragesystems(Liuetal.,2011a).Clarketal.(2005)determinedthatreducingdietaryCPmayactuallyincreaseCH4emissions,soresultsarevaried.IthadbeenexpectedthatalowerCPdietmayresultinlowernitrogenexcretion,andthusmightbeabletoreduceN2Oemissionsfrommanure.However,thishypothesiswasnotsupportedbytheresultsofthemeta‐analysis.

Dietformulationateachstageofthelifecycleinfluencesnutrientsexcretedinmanure,aswellasemissionsthatresultfromthatmanureduringstorageandpotentiallyfollowinglandapplication.Fromamodelingperspective,thefocusneedstobeonmanagementfactors,includingdietformulationandmanurehandlingpractices.

Feedefficiencyimprovementscanreduceemissionsthroughouttheentirefoodproductioncyclebyreducingtheamountoffeedneededformeatproduction,therebyreducinginputsintofeedproductionaswellasreducingmanurenutrientsthatmustbemanaged.Feedefficiencyistheproductofgeneticsandenvironment(management).Geneticdifferencesaredifficulttoassess,becausethisinformationisretainedbycompanies.Geneticimprovementsarenotinsignificantovertimeandmayinfactbealargercontributortogainsthanmanagement.However,fromamodelingperspective,thefocusneedstobeonmanagementfactors,includingdietformulationandin‐housemanure/litterpractices.FeedefficiencycouldbeamodelcomponentinthefutureoncemoredataontheimpactsoffeedefficiencyonGHGemissionsareavailable.

FactorsAffectingEmissionsfromMeatBirdsEmissionsofbothN2OandNH3canberestrictedbyreducingthelitternitrogencontentthroughdietmodification.Fergusonetal.(1998a;1998b)fedreduceddietaryproteindietstobroilerchickens.Althoughperformancewashinderedinbothstudies,NH3concentrationandlitternitrogencontentwerereducedsignificantlyasaresultofthelow‐proteindiets.Applegateetal.(2008)reportedsimilarlitternitrogeneffectswhenturkeytomswerefedreduced‐proteindiets.Noperformancedifferenceswereobserved.ThesedietswerethenfedtoturkeytomsbyLiuetal.(2011a),whoobserveda12percentreductioninNH3emissionsasaresultofreducingcumulativenitrogenintakeby9percent.FeedingspecificAAallowedforsimilarnitrogenintakesacrosstreatments,butreducedNH3emissionsby25percent(Liuetal.,2011a)andnitrogeninlitterby12percent(Liuetal.,2011b),becausenitrogenwasbetterutilizedbythebirds.Acrossalldiets,N2O


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emissionsmadeuplessthanonepercentofnitrogenoutput(Liuetal.,2011b),suggestingthatreducingdietarynitrogenmayhavelessinfluenceonN2Oemissionsthanotherfactors.

FactorsAffectingEmissionsfromLayingHensDietfactorscanalterairemissionsfromlayinghenfacilities.MuchoftheworktodatehasfocusedonreducingNH3emissions.Robertsetal.(2007)showedthatinclusionofdietarycornDDGS,wheatmiddlings,orsoyhullsloweredtheseven‐daycumulativemanureNH3emissionfrom3.9gkgofdrymanure‐1forthecontrolto1.9,2.1,and2.3gkgofdrymanure‐1,respectively;italsoloweredthedailyNH3emissionrate.ReducingtheCPcontentbyonepercenthadnomeasurableeffectonNH3emission.Wu‐Haanetal.(2007b)fedareduced‐emissionsdietcontaining6.9percentofaCaSO4‐zeolitemixtureandslightlyreducedproteinto21‐,38‐,and59‐week‐oldHy‐LineW‐36hens;theyobservedthatdailyNH3emissionsfromhensfedthereduced‐emissionsdiets(185.5,312.2,and333.5mgbird‐1)werelessthanemissionsfromhensfedthecontroldiet(255.1,560.6,and616.3mgbird‐1)fortrials1,2,and3,respectively.Totalnitrogenexcretionfromhensfedthecontrolandreduced‐proteindietswasnotdifferent(Wu‐Haanetal.,2007a).Becauseoftheacidifyingnatureofthediets,themassofnitrogenremaininginexcretafollowingathree‐weekstorageperiodwaslessfromhensfedthecontroldietthanfromhensfedthereduced‐proteindiet(Wu‐Haanetal.,2007a).Lietal.(2010)foundthatfeedingcornDDGSdecreasedthemassofNH3emitteddailyby80mghen‐1(592vs.512mghen‐1day‐1forzeropercentand20percentDDGS,respectively),andby14percentpereggproduced,anddailyCH4emissionsby13to15percent(39.3vs.45.4mghen‐1day‐1;and0.70vs.0.82mggegg‐1day‐1).

5.3.8 LimitationsandUncertaintyinEntericFermentationandHousingEmissionsEstimates

Attheentitylevel,uncertaintyinentericCH4productionincattletypicallyresultsfrom,lackofprecisioninestimatingenergyintake,feedtypeandintake,characteristicsofparticularfeedstuffs(i.e.,aciddetergentfiber,starch,etc.),DE,maximumpossibleCH4emissions,CH4conversionfactors(Ym),synergiesorcountereffectsbetweenmitigationoptions,andnetenergyexpenditurebytheanimal.TheassumptionsaboutimplicationsofdietarychangesonentericCH4productionarebasedonliteraturevalues(includingempiricalfieldstudies)andmaynotbeindicativeoftruechangesinemissionsforparticularanimaltypes,asthiswillvarydependingonanindividualanimal’shealth,managementpractices,animalactivities,andbaselinediet.Forswine,goats,AmericanBison,llamas,alpacas,andmanagedwildlife,therecommendedestimationmethodsforemissionsfromentericfermentationarebasedontheIPCCTier1approach,whichhasanuncertaintyof30to50percent.

MethaneemissionsfromdairycattlehousingareasareestimatedusingequationsfromDairyGEM(IFSM).Inpredictingemissions,uncertaintywillresultfromalackofprecisioninestimatingexcretedvolatilesolidsandnitrogenexcreted,pH,temperature,airvelocity,andsurfaceareaofexposedmanure,beddingpack,CH4conversionfactors(MCFs),andmaximumCH4‐producingcapacityformanures.Comparisonofmodeledvalueswithon‐farmevaluationshasfoundthemodelpredictson‐farmemissionswithinfiveto20percent(unpublisheddata).

MethaneemissionsfrompoultryhousingareasareestimatedusingtheIPCCTier1method.Uncertaintyinpredictionsofemissionsresultfromalackofprecisioninestimatingfeedintake,nitrogenexcretedandvolatilesolids,MCF,volatilizationfraction,andinsomeinstancesemissionfactorsthatwerechoseninthemodel.UnfortunatelythereisalackofpublishedinformationrelatedtoGHGemissionsfrompoultryandtothebestofourknowledgethismodelhasnotbeenvalidated/testedusingon‐farmdata.


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Muchofthepublisheduncertaintyinformationininventoryguidance,suchasIPCCGoodPracticeGuidance(IPCC,2000)andintheU.S.NationalGHGInventory(U.S.EPA,2013),focusonuncertaintiespresentincalculatinginventoriesattheregionalornationalscale,manyofwhichdonottranslatetotheentitylevel.Someofthesourcesofuncertaintyattheregionalornationalscaleincludedvariabilityinnativevegetationeatenbygrazinganimals,assumptionsaboutthetypesoffeedfarmersprovideforanimals(includingthepracticeofincludingnutritionalsupplements),managementpracticessuchashousingoptionsanddailyanimalactivity,averageanimalweights,andanimalpopulations.Thequantityofuncertaintyatlargerscalesisdifficulttodefine,dependentonboththeaccuracyinreportingpracticesandexperts’understandingoftheimplicationsofmanagementpracticesandtheaccuracyofparticularestimationmethodologies.Consistentimprovementinreportingpracticescanhelpremovesomeofthisuncertainty.

AvailabledefaultvaluesanduncertaintyinformationisincludedinTable5‐17.

Table5‐17:AvailableUncertaintyDataforEmissionsfromHousingandEntericFermentation

Parameter

Abbreviation/Sym

bol

DataInputUnit

EstimatedValue

Relativeuncertainty

Low(%)

Relativeuncertainty

High(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DailyMilkProduction Milkkg

milk/animal/day

3% 5% ExpertAssessment

SupplementalFat(feedlot) S.Fat Percent 2 4 ExpertAssessment

Maximumdailyemissionsfordairycows Emax MJ/head 45.98 Millsetal.(2003)

TypicalAmmoniaLossesfromDairyHousingFacilities–Opendirtlots(cool,humidregion)

NH3loss

PercentofNex 15% 30%KoelshandStowell

(2005)TypicalAmmoniaLossesfromDairyHousingFacilities–Opendirtlots(hot,aridregion)

NH3loss


(2005)TypicalAmmoniaLossesfromDairyHousingFacilities–Roofedfacility(flushedorscraped)Roofedfacility(dailyscrapeandhaul)

NH3loss


(2005)

TypicalAmmoniaLossesfromDairyHousingFacilities–Roofedfacility(shallowpitunderfloor)

NH3loss


(2005)

TypicalAmmoniaLossesfromDairyHousingFacilities–Roofedfacility(beddedpack)

NH3loss


(2005)TypicalAmmoniaLossesfromDairyHousingFacilities–Roofedfacility(deeppitunderfloor,includesstorageloss)

NH3loss


(2005)

TypicalAmmoniaLossesfromBeefHousingFacilities–Opendirtlots(cool,humidregion)

NH3loss


(2005)

TypicalAmmoniaLossesfromBeefHousingFacilities–Opendirtlots(hot,aridregion)

NH3loss


(2005)

TypicalAmmoniaLossesfromBeefHousingFacilities–Roofedfacility(beddedpack)

NH3loss


(2005)

TypicalAmmoniaLossesfromBeefHousingFacilities–Roofedfacility(deeppitunderfloor,includesstorageloss)

NH3loss


(2005)

TypicalAmmoniaLossesfromSwineHousingFacilities–Roofedfacility(flushedorscraped)Roofedfacility(dailyscrapeandhaul)

%NH3loss


(2005)


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Parameter

Abbreviation/Sym

bol

DataInputUnit

EstimatedValue

Relativeuncertainty

Low(%)

Relativeuncertainty

High(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

TypicalAmmoniaLossesfromSwineHousingFacilities–Roofedfacility(shallowpitunderfloor)

%NH3loss


(2005)

TypicalAmmoniaLossesfromSwineHousingFacilities–Roofedfacility(beddedpack)

%NH3loss


(2005)TypicalAmmoniaLossesfromSwineHousingFacilities–Roofedfacility(deeppitunderfloor,includesstorageloss)

%NH3loss


(2005)

TypicalAmmoniaLossesfromPoultryHousing–Roofedfacility(litter)(MeatProducingbirds)

%NH3loss


(2005)TypicalAmmoniaLossesfromPoultryHousing–Roofedfacility(stackedmanureunderfloor,includesstorageloss)(Egg‐producingbirds)

%NH3loss


(2005)

MethaneEmissionsfromGoats–Emissionfactorforgoats

EFGkg

CH4/head/day0.0137 IPCC(2006)

5.4 ManureManagement

Useofmanureasasourceofplantnutrientsreducestheneedforpurchasedcommercialfertilizer.Manurestorageallowsformanureapplicationstolandtobesynchronizedwithcropculturalneeds.Thispracticereducesthepotentialforsoilcompactionduetopoortimingofmanureapplication(wetsoilconditions)andmakesmoreefficientuseoffarmlabor.ManyanimalmanurestorageortreatmentstructurescreateanaerobicconditionsthatresultintheproductionandreleaseofGHGsandodors.Manurethatisrecycledtothelandbasecanhavepotentialnegativeeffectsonwaterquality(bothsurfaceandgroundwater).

Manurestorageandtreatment,asacomponentofmanuremanagementsystems,playsacriticalroleinGHGemissions.Attheentitylevel,variousmanurestorageandtreatmentapproacheswillleadtodifferentamountsofGHGemission.Animalmanurecanbeclassifiedintotwocategoriesbasedontheirphysicalproperties:solid,definedasdrymatterabove15percent;andliquid,definedasdrymatteroflessthan15percent(includingliquidmanurewithadrymatteroflessthan10percentandslurrymanurewithadrymatterbetween10and15percent).Threesolidmanurestorage/treatmentpractices(temporarystack/long‐termstockpile,composting,andthermo‐chemicalconversion)andeightliquidmanurestorage/treatmentpractices(aerobiclagoon,anaerobiclagoon/runoffholdingpond/storagetanks,anaerobicdigestion,combinedaerobictreatmentsystem,sand‐manureseparation,nutrientremoval,solid‐liquidseparation,andconstructedwetland)wereevaluatedandtheemissionestimationmethodsarepresented.Atthefarmentitylevel,severalpracticesareoftenstrategicallycombinedtotreatmanure.Inordertoprovidetoolstoevaluatethesescenarios,activitydata(i.e.,massflowdataandchemicalandphysicalcharacteristicsofinfluentandeffluent,environmentaltemperature,pH,andtotalnitrogen)fromindividualpracticeswillbeusedtolinkpracticesinthecombinedsystemforindividualfarmentities.AschematicstructureofpossiblecombinationsofmanurestorageandtreatmentpracticesattheentitylevelispresentedinFigure5‐7.Asillustratedinthefigure,manurecanbehandledasasolidorliquid.Foreachstream,themanurecanbeapplieddirectlytoland,stored,ortreatedbeforestorageorlandapplication.Insomepractices,solidsareseparatedfromtheliquidmanurestreamandtreatedusingasolidshandlingsystem.


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Figure5‐7:SchematicStructureofPossibleCombinationofManureStorageandTreatmentPractices

Note:Individualpracticescouldbecombinedtogethertotreatmanurebasedontheneedattheentitylevel.

Eachmanuremanagementpracticeisdescribedasanindividualunitpracticeinthisdocument.ThereferencesforestimationofGHGemissionforindividualpracticearelistedinTable5‐18.

Table5‐18:ListofIndividualManureStorageandTreatmentPractices

Section StorageandTreatmentPracticesMajorReferencesforGHG

EstimationSolidmanure5.4.1 Temporaryandlong‐termstorage IPCC(2006);U.S.EPA(2011)0 Composting IPCC(2006);U.S.EPA(2011)Liquidmanure5.4.3 Aerobiclagoon IPCC(2006);U.S.EPA(2011)

5.4.4Anaerobiclagoon/runoffholdingponds/storagetanks

Sommeretal.(2004)

5.4.5 Anaerobicdigestionwithbiogasutilization IPCC(2006);CDM(2012)


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Section StorageandTreatmentPracticesMajorReferencesforGHG

Estimation5.4.6 Combinedaerobictreatmentsystem Vanottietal.(2008)5.4.7 Sand–manureseparation5.4.8 Nutrientremoval5.4.9 Solid–liquidseparation FordandFleming(2002)

5.4.10 ConstructedwetlandSteinetal.(2006;2007b)Stoneetal.(2002;2004)

5.4.11 Thermo‐chemicalconversion

TheremainderofthissectionpresentsthemethodforestimatingGHGsfromthesourceslistedinTable5‐18.ForeachsourceofGHGswithanestimationmethod,thefollowinginformationisprovided:

OverviewoftheGHGSourceandtheResultingGHGs.Thissectionprovidesanoverviewofmanuremanagementtechnology,theresultingGHGemissions,andthemethodologyproposedforestimatingtheemissions.

RationaleforSelectedMethod.Thissectionpresentsthereasoningfortheselectionofthemethodrecommendedinthisreport.

ActivityData.ThissectionliststheactivitydatarequiredforestimatingGHGsattheentitylevel.

AncillaryData.ThissectionlistsancillarydatasuchasCH4conversionfactors(MCF)andmaximumCH4productioncapacity(B0).

Method.Thissectionprovidesdetaileddescriptions,includingequationsfortheselectedmethods.

ForeachsourceofGHGswithoutanestimationmethod,aqualitativeoverviewisprovided.MethodsforestimatingNH3emissionsareprovidedinAppendix5‐C.

5.4.1 TemporaryStackandLong‐TermStockpile

5.4.1.1 OverviewofTemporaryStackandLong‐TermStockpiles

Managementmethodsforstoredmanurearedifferentiatedbythelengthoftimetheyarestockpiled(i.e.,temporarystackandlong‐termstorage).Temporarystackisashort‐termmanurestoragemethodthatisusedtotemporarilyholdsolidmanurewhenbadweatherprohibitslandapplication,and/orwhenthereislimitedavailabilityofcroplandformanureapplication.Withtemporarystack,

MethodforEstimatingEmissionsfromManureStorageandTreatment–TemporaryStackandLong‐TermStockpile

Methane

IPCCTier2approachusingIPCCandU.S.EPAInventoryemissionfactors,utilizingmonthlydataonvolatilesolidsanddrymanure.Volatilesolidscontentcanbeobtainedfromsamplingandlabtesting.

Methodisonlyreadilyavailablemethod.

NitrousOxide

IPCCTier2approachusingU.S.‐basedemissionfactorsandmonthlydataonvolatilesolids,totalnitrogen,anddrymanure.

Nospecificmodelsexist;methodistheonlyreadilyavailablemethod.


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themanureisremovedandappliedtolandwithinafewweeksofpiling.Temporarystorageisnotapreferredmethodtostoremanurebecauseitrequiresthemanuretobehandledtwice.

Long‐termstorageisapermanentmanurestoragemethodinwhichsolidmanureispiledonaconfinedareaorstoredinadeeppitforlongerthansixmonths.Inlow‐rainfallareas,thestockpilecanbepiledonthefieldwiththeinstallationofnutrientrunoffcontrol.Inhigherrainfallareas,aconcretepadandwallareconstructedtostoresolidmanureandpreventnutrientrunofffromheavyrain.

Greenhousegasesgeneratedfrombothstoragemethodshaveapatternsimilartothatofentericfermentation.CarbonandnitrogencompoundsinmanurearebrokendownbymicrobestoCH4,andN2O.ThemainfactorsinfluencingGHGemissionsfromstoragearetemperatureandstoragetime.Duetothelongerstoragetime,long‐termstockpilesolidmanurestoragegeneratesasignificantamountofGHGs.Temporarystack,asashort‐termmanurestoragemethod,generateslessGHGsthanthelong‐termstockpilesolidstorage.However,itisstillnecessarytoquantitativelydelineatetheemissionsinordertoassistlivestockandpoultryfarmsinevaluatingtheirmanuremanagementoperations.Temporarystackandlong‐termstockpilesofmanurealsoproduceNH3;proposedmethodstoestimateNH3emissionsarepresentedinAppendix5‐C.

TheIPCCTier2methodologyisprovidedforestimatingCH4emissionsfromtemporarystacksorlong‐termstockpiles.ThismethodologyusesacombinationofIPCCandcountry‐specificemissionfactorsfromtheU.S.EPAGHGInventory.Theamountofmanure,volatilesolidscontent,andtemperaturearespecifictotheentity.ThemethodforcalculatingN2OemissionsisthesameastheequationpresentedintheU.S.GHGInventory.

RationaleforSelectedMethodTheIPCCequationsaretheonlyavailablemethodsforestimatingCH4,andN2Oemissionsfromtemporarystackandlong‐termstockpiles.Thesemethodologiesbestdescribethequantitativerelationshipamongactivitydataattheentitylevel.

ActivityDataInordertoestimatethedailyCH4emissions,thefollowinginformationisneeded:9

Animaltype Totaldrymanure Volatilesolidsofdrymanure10 Temperatures(localambienttemperatureandmanuretemperature)

InordertoestimatethedailyN2Oemission,thefollowinginformationisneeded:

Totaldrymanure Totalnitrogencontentofthemanure

ThetotalnitrogencontentofthemanureenteringstoragesystemscanbeestimatedaccordingtothenitrogenbalancemethodasdescribedinEquation5‐9:TotalNitrogenEnteringManureStorageandTreatmentThefractionofnitrogenexcretedbyananimalthatisnotemittedasagasistheportionthatentersstorage.

9Althoughdailyestimatesfortheactivitydataareoptimal,trackingthislevelofdetailwouldbeburdensome.Annualestimatesdon’tallowforseasonalvariationindietsandclimate.Consequently,disaggregationofthedatabyseasonorbyperiodsofmajorshiftsinanimalpopulationissuggested.

10Volatilesolids,totalnitrogencontent,andammonia‐nitrogencontentshouldbeobtainedthroughsamplingandlabtesting.


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AncillaryDataTheancillarydatausedtoestimateCH4emissionfortemporarystorageandlongtermstockpilesare:maximumCH4producingcapacities(B0)andMCFs.TheB0valuesforsolidmanurestorageareobtainedfromtheIPCCandlistedinTable5‐19.Methaneconversionfactorsfordifferentmanuremanagementsystems(includingtemporarystorageofsolidmanure)arealsoobtainedfromtheIPCCandlistedinTable5‐20and5‐16.

TheancillarydatausedtoestimateN2OemissionsfortemporarystorageandlongtermstockpilesaretheN2OemissionfactorsforsolidmanurestoragesystemsarepresentedinTable5‐23(U.S.EPA,2011).

5.4.1.2 Method

MethaneEmissionsfromTemporaryStackandLong‐TermStockpileTheTier2approachbytheIPCCmodelisrecommendedtoestimateCH4emissionsandisdescribedinEquation5‐26(IPCC,2006).DailyCH4emissionisestimatedasafunctionofthevolatilesolidsinmanureplacedintothestorageandtheanimal‐specificMCF.

aDrymanurereferstomaterialremainingafterremovalofwater.Itisdeterminedthroughtheevaporationofwaterfromthemanuresampleat103‐105°C.Forcedairovenisthemostcommonequipmenttomeasurethedrymatter.

Table5‐19:MaximumCH4ProducingCapacities(B0)fromDifferentAnimals

Animal

MaximumCH4

ProducingCapacity(B0)

(m3/kgVS)

Animal

MaximumCH4

ProducingCapacity(B0)

(m3/kgVS)Beefreplacementheifers 0.33b Breedingswine 0.48Dairyreplacementheifers 0.17b Layer(dry) 0.39Maturebeefcows 0.33b Layer(wet) 0.39Steers(>500lbs) 0.33b Broiler 0.36Stockers(All) 0.17b Turkey 0.36Cattleonfeed 0.33b Duck 0.36Dairycow 0.24b Sheep 0.19bCattle 0.19b Feedlotsheep 0.36bBuffalo 0.1a Goat 0.17b

Marketswine 0.48Horse 0.3Mule/Ass 0.33

aTherearenodataforNorthAmericaregion;thedatafromWesternEuropeareusedtocalculatetheestimation.bNumbersarefromtheEPAU.S.Inventory:1990‐2009(U.S.EPA,2011).OthernumbersarefromIPCC(2006).

Equation5‐26:IPCCTier2ApproachforEstimatingCH4 Emissions

.

Where:ECH4 =CH4emissionsperday(kgCH4day‐1)m =Totaldrymanureperdaya(kgdrymanureday‐1)VS =Volatilesolids(kgVS(kgdrymanure)‐1)B0 =MaximumCH4producingcapacityformanure(m3CH4(kgVS)‐1)MCF =CH4conversionfactorforthemanuremanagementsystem(%)0.67=Conversionfactorofm3CH4 tokgCH4


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Table5‐20:MethaneConversionFactorsforTemporaryStorageofSolidManurefromDifferentAnimals

AnimalMethaneConversionFactor(%)

Temp=10‐14°C Temp=15‐25°C Temp=26‐28°CDairycow 1 1.5 2Cattle 1 1.5 2Buffalo 1 1.5 2Marketswine 1 1.5 2Breedingswine 1 1.5 2Layer(dry) 1.5 1.5 1.5Broiler 1.5 1.5 1.5Turkey 1.5 1.5 1.5Duck 1 1.5 2Sheep 1 1.5 2Goat 1 1.5 2Horse 1 1.5 2Mule/Ass 1 1.5 2

Source:IPCC(2006).

Table5‐21:MethaneConversionFactorsforLong‐TermStockStorageofSolidManurefromDifferentAnimals


Temp= 10‐14°C Temp=15‐25°C Temp=26‐28°CDairycow 2 4 5Cattle 2 4 5Buffalo 2 4 5Marketswine 2 4 5Breedingswine 2 4 5Layer(dry) 1.5 1.5 1.5Broiler 1.5 1.5 1.5Turkey 1.5 1.5 1.5Duck 1 1.5 2Sheep 1 1.5 2Goat 1 1.5 2Horse 1 1.5 2Mule/Ass 1 1.5 2

Source:IPCC(2006).

Table5‐22:MethaneConversionFactorsforLong‐TermStorageofSlurryManurefromBuffalo

Temperature(°C)Methane Conversion

Factor(%)Temperature(°C)

MethaneConversionFactor(%)

10 17 20 4211 19 21 4612 20 22 5013 22 23 5514 25 24 6015 27 25 6516 29 26 7117 32 27 7818 35 28 8019 39

Source:IPCC(2006).


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NitrousOxideEmissionsfromTemporaryStackandLong‐TermStockpileNitrousoxideemissionsaredependentonnitrificationanddenitrification.ManurestorageisoneofthemainsourcesofU.S.overallN2Oemissions.TheonlyquantitativemethodforestimatingN2OemissionsfromsolidmanureistheIPCCTier2approach,whichisalsousedfortheU.S.Inventory.ThisapproachisbasedontheuseofemissionfactorsfromthemostrecentIPCCGuidelinesandtotalnitrogenvaluesareestimatedaccordingtoEquation5‐9.Equation5‐27presentstheequationtoestimatetheN2Oemissionsforsolidmanure.


Table5‐23:N2OEmissionFactorsforSolidManureStorageTypeofStorage N2OEmissionFactor(kgN2O‐N/kgN)

Temporarystorageofsolid/slurrymanure 0.005

Long‐termstorageofsolidmanure 0.002

Long‐termstorageofslurrymanure 0.005Source:U.S.EPA(2011).

5.4.2 Composting

5.4.2.1 OverviewofComposting

Equation5‐27:IPCCTier2ApproachforEstimatingN2OEmissions

Where:

EN2O =Nitrousoxideemissionperday(kgN2Oday‐1)

m =Totaldrymanureperdaya(kgdrymanureday‐1)

EFN2O=N2Oemissionfactor(kgN2O‐NkgN‐1)

TN =Totalnitrogenatagivenday(kgN(kgdrymanure)‐1)


MethodforEstimatingEmissionsfromManureStorageandTreatment–Composting

Methane

IPCCTier2approach,utilizingmonthlydataonvolatilesolidsanddrymanure.Volatilesolidscontentcanbeobtainedfromsamplingandlabtesting.

Methodistheonlyreadilyavailablemethod.

NitrousOxide

IPCCTier2approach,utilizingdataonanitrousoxideemissionfactor,totalinitialnitrogen,anddrymanure.

Methoddependsonwhetherthesystemisinvessel,staticpile,intensivewindrow,orpassivewindrow.

Methodisonlyreadilyavailablemethod.


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Compostingisthecontrolledaerobicdecompositionoforganicmaterialintoastable,humus‐likeproduct(USDANRCS,2007).Animalmanuremaybecompostedinavarietyofdifferentsystems,includingin‐vesselsystems,windrows,orstaticpiles.In‐vesselsystemshandlecompostinaclosedsystemsuchasarotarydrumorboxthatincorporatesregularmovementtoensureproperaeration.Thelargestcompostingoperationsdivideupthecompostintolongheapsforwindrowcompostingorintoonelargepileforaeratedstaticpilecomposting.Intheformermethod,properoxygenflowcanbemaintainedviamanualturningorpipesystems,whereasinthelattermethod,itismaintainedthroughpipesystems.Compostinghasbecomeapopularmethodinsomeregionstodecreasethevolumeandweightoflivestockmanureandtoproduceaproductthatisoftenmoreacceptabletofarmersasafertilizer.Duringa100‐to120‐daycompostingperiod,theweightandvolumeofmanuremaybedecreasedby15to70percent(Eghballetal.,1997;Inbaretal.,1993;Lopez‐Real&Baptista,1996).Furthermore,theheatgeneratedthroughthecompostingprocesscankillparasites,pathogens,andweedseedsfoundinanimalwaste,creatingasaferproductforcropapplication.

ThequantityofGHGemissionsisaffectedbythecompostingmethodemployed.Haoetal.(2001)reportedthatGHGemissionsfromcattlemanurecompostincreasedabouttwofoldwhenthecompostwasactivelycompostedratherthanpassivelycompostedinwindrows.Activewindrowswereturnedsixtimes(days14,21,29,50,70,and84).Passivewindrowswereneverturned,butairwasintroducedintothewindrowsbyaseriesofopen‐endedperforatedsteelpipes.TotheextentthattherateofGHGformationdependsonoxygensaturationintheporespace,aerationmethod(i.e.,forced‐airvs.passive/convective)andrate(orturningfrequency)willaffectthemagnitudeofGHGemissionsduringthecompostingprocess.

Eghballetal.(1997)reportedthat19to45percentofthenitrogenpresentinmanurewaslostduringcomposting,withthemajorityofthispresumablyasNH3.Usingchangesinthenitrogen:phosphorusratiooffeedlotmanurethatwasplacedincompostwindrowsandthenitrogen:phosphorusratioof“finished”compost,Coleetal.(2011)estimatedthat10to20percentofnitrogenwaslostduringcomposting.TheU.S.EPAcurrentlyassumesthatoneto10percentofnitrogenenteringcompostsystemsislostasN2O(IPCC,2006;U.S.EPA,2009).

TheIPCCTier2methodologyisprovidedforestimatingCH4andN2Oemissionsfromcomposting.Thismethodologyusescountry‐specificemissionfactorsfromtheU.S.EPAGHGInventory.Theamountofmanure,volatilesolidscontent,andtemperaturearespecifictotheentity.TheGHGestimationmethodformanurecompostingdoesnotconsiderotherorganiccarbonsourcesthatmightbeaddedintomanurecomposting.

RationaleforSelectedMethodTheIPCCequationsaretheonlyavailablemethodsforestimatingCH4andN2Oemissionsfromcomposting.Thesemethodologiesbestdescribethequantitativerelationshipamongstactivitydataattheentitylevel.


InordertoestimatethedailyCH4emissions,thefollowinginformationisneeded:

Animaltype Totaldrymanure Volatilesolidsofdrymanure Temperatures(localambienttemperatureandmanuretemperature)

InordertoestimatethedailyN2Oemissions,thefollowinginformationisneeded:

Totaldrymanureinthestorage


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Totalnitrogeninmanure

ThetotalnitrogencontentofthemanureenteringstoragesystemscanbeestimatedaccordingtothenitrogenbalancemethodasdescribedinEquation5‐9:TotalNitrogenEnteringManureStorageandTreatmentThefractionofnitrogenexcretedbyananimalthatisnotemittedasagasistheportionthatentersstorage.


TheancillarydatausedtoestimateCH4emissionsformanurecompostingare:maximumCH4producingcapacities(B0)andMCFs.TheB0valuesareobtainedfromtheIPCC(2006)andlistedinTable5‐19.TheMCFvaluesareobtainedfromEPA(U.S.EPA,2011)andlistedinTable5‐24.

TheancillarydatausedtoestimateN2OemissionformanurecompostingaretheN2Oemissionfactors(Table5‐25).

5.4.2.4 Method

MethaneEmissionsfromCompostingTheTier2approachintheIPCCmodelisadaptedwithcountry‐specificfactorstoestimateCH4emissionsfromcompostingofsolidmanure.DailyCH4emissionsareestimatedasafunctionofthevolatilesolidsinmanureplacedintothestorageandtheMCF.


TheB0valuesforcompostingsolidmanureareobtainedfromtheIPCC(2006)andarelistedinTable5‐19.MethaneconversionfactorsfordifferentapproachesofcompostingsolidmanureareobtainedfromIPCC(2006).

Equation5‐28:IPCCTier2ApproachforCalculatingMethaneEmissionsfromCompostingSolidManure

.

Where:

ECH4 =Methaneemissionsperday(kgCH4day‐1)

m =Totaldrymanurea(kgdrymanureday‐1)


B0 =MaximumCH4producingcapacityformanure(m3CH4(kgVS)‐1)(seeTable5‐24)

MCF =Methaneconversionfactorforthemanuremanagementsystem(%)

0.67 =Conversionfactorofm3CH4tokgCH4


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Table5‐24:MethaneConversionFactorsforCompostingSolidManure


CoolClimate TemperateClimate

WarmClimate

Manurecomposting–invessel 0.5 0.5 0.5

Manurecomposting–staticpile 0.5 0.5 0.5Manurecomposting–intensivewindrow

0.5 1 1.5

Manurecomposting–passivewindrow 0.5 1 1.5Source:IPCC(2006).

NitrousOxideEmissionsfromCompostingATier2IPCCmodelisadaptedtoestimateN2Oemissionsfromcompostingofsolidmanure.Equation5‐29presentstheequationforestimatingN2Oemissionsfromcompostingofsolidmanure.EmissionfactorsfordifferentcompostingmethodsarelistedinTable5‐25andtotalnitrogenisestimatedaccordingtoEquation5‐9.11


Table5‐25:N2OConversionFactors(EFN2O)forCompostingSolidManure

Category N2OEmissionFactor(kgN2O‐N/kgTN)CattleandSwineDeepBedding(ActiveMix) 0.07

CattleandSwineDeepBedding(NoMix) 0.01

PitStorageBelowAnimalConfinements 0.002Source:IPCC(2006).

11SomestudieshavebeenconductedontherateofN2Oemissionsforswine(Fukummotoetal.,2003;Szantoetal.,2006)butthisdataislimitedandfurtherresearchisnecessary.SeeSection0ResearchGapsforfurtherdiscussion.

Equation5‐29:IPCCTier2ApproachforEstimatingN2OEmissionsfromCompostingofSolidManure

Where:

EN2O =Nitrousoxideemissionsperday(kgN2Oday‐1)

m =Totaldrymanurea(kgday‐1)

EFN2O=N2Oemission(loss)relativetototalnitrogeninmanure(kgN2O‐N(kgTN)‐1)

TN =Totalnitrogenintheinitial(fresh)manure(kgTN(kgdrymanure)‐1)



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5.4.3 AerobicLagoon

5.4.3.1 OverviewofAerobicLagoons

Aerobiclagoonsareman‐madeoutdoorbasinsthatholdanimalwastes.Theaerobictreatmentofmanureinvolvesthebiologicaloxidationofmanureasaliquid,witheitherforcedornaturalaeration.Naturalaerationislimitedtoaerobiclagoonswithphotosynthesisandisconsequentlyshallowtoallowforoxygentransferandlightpenetration.Thesesystemsbecomeanoxicduringlow‐sunlightperiods.Duetothedepthlimitation,naturallyaeratedaerobiclagoonshavelargesurfacearearequirementsandareimpracticalforlargeoperations.

TheIPCCTier2methodologyisprovidedforestimatingCH4andN2Oemissionsfromaerobiclagoons.ThismethodologyusesacombinationofIPCCandcountry‐specificemissionfactorsfromtheU.S.EPAGHGInventory.AerobicconditionsresultintheoxidationofcarbontoCO2,notthereductionofcarbontoCH4,thusCH4emissionsfromaerobiclagoonsisconsiderednegligibleandisdesignatedaszeroinaccordancewithIPCC.ThemethodforcalculatingN2Oemissionsaccountsforthevolumeofthelagoonaswellasthetotalnitrogencontentofthemanure.

5.4.3.2 RationaleforSelectedMethods

TheIPCCequationsaretheonlyavailablemethodsforestimatingCH4,andN2Oemissionsfromaerobiclagoons.Thesemethodologiesbestdescribethequantitativerelationshipamongactivitydataattheentitylevel.


Noactivitydataareneeded(MCF=0)fortheestimationofCH4gasemissions.

InordertoestimatethedailyN2Oemissions,thefollowinginformationisneeded:

Surfaceareaoflagoon Volumeofthematerialinthelagoon Totalnitrogencontentofthemanure

ThetotalnitrogencontentofthemanureenteringstoragesystemscanbeestimatedaccordingtothenitrogenbalancemethodasdescribedinEquation5‐9.Thefractionofnitrogenexcretedbyananimalthatisnotemittedasagasistheportionthatentersstorage.


TheancillarydatausedtoestimateN2OemissionsforaerobiclagoonareN2Oemissionfactors(U.S.EPA,2011).

MethodforEstimatingEmissionsfromManureStorageandTreatment–AerobicLagoon

Methane

TheMCFforaerobictreatmentisnegligibleandisdesignatedaszeropercentinaccordancewiththeIPCCGuidance.

NitrousOxide

IPCCTier2methodutilizingIPCCemissionfactors. Methodtakesintoaccountthevolumeofthelagoonandthetotalnitrogencontentof

themanure. Methodistheonlyreadilyavailablemethod.


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5.4.3.5 Method

MethaneEmissionsfromAerobicLagoonTheMCFforaerobictreatmentisnegligibleandwasdesignatedaszeropercentinaccordancewiththeIPCC(2006).ThesolidsfromthebottomofthelagoonhavesignificantvolatilesolidsandB0associatedwithlivestocktype;thecharacteristicsofthesolidsshouldbemeasuredandusedastheinputstoestimateemissionsofGHGsforsubsequentstorageandtreatmentoperations.

NitrousOxideEmissionsfromAerobicLagoonTheTier2approachintheIPCCmodelisadaptedtoestimateN2Oemissionsfromaerobiclagoons.TheN2OconversionfactorsfordifferentaerationsystemarelistedinTable5‐26.TheestimationmethodforN2OemissionsisprovidedinEquation5‐30.

5.4.4 AnaerobicLagoon,RunoffHoldingPond,StorageTanks

5.4.4.1 OverviewofAnaerobicLagoons,RunoffHoldingPonds,andStorageTanks

Table5‐26:N2OConversionFactors(EFN2O)forAerobicLagoons

AerationTypeN2OConversion Factor

(kgN2O‐N/kgN)Naturalaeration 0.01Forcedaeration 0.005Source:IPCC(2006).

Equation5‐30:CalculatingN2OemissionsfromAerobicLagoons

Where:


V =Totalvolumeofthelagoonliquid(m3day‐1)

EFN2O=Nitrousoxideemission(loss)relativetototalnitrogeninthelagoonliquid (kgN2O‐N(kgTN)‐1)

TN =Totalnitrogeninthelagoonliquid(kgTNm‐3)


MethodforEstimatingEmissionsfromManureStorageandTreatment–AnaerobicLagoons,RunoffHoldingPonds,StorageTanks

Methane

Sommermodel(Sommeretal.,2004)isusedwithdegradableandnondegradablefractionsofvolatilesolidsfromMølleretal.(2004).

Thismethodwasselectedasitaccountsformanuretemperatureandtotalvolatilesolidscontentofmanure.Volatilesolidscontentcanbeobtainedfromsamplingandlabtesting.

NitrousOxide

EmissionsareafunctionoftheexposedsurfaceareaandU.S.‐basedemissionfactors. Methodistheonlyreadilyavailableoption.


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Themostfrequentlyusedliquidmanurestoragesystemsareanaerobiclagoons(intheSouthernportionoftheUnitedStates),earthenorearthen‐linedstorages(intheNorthernportionofthecountry),runoffholdingponds,andabove‐gradestoragetanks.Anaerobiclagoonsareearthenbasinsthatprovideanenvironmentforanaerobicdigestionandstorageofanimalwaste.BoththeAmericanSocietyofAgriculturalandBiologicalEngineersandU.S.DepartmentofAgricultureNaturalResourcesConservationServicehaveengineeringdesignstandardsforconstructionandoperationofanaerobiclagoons.Inmostfeedlotsaholdingpondisconstructedtocollectrunoffforshort‐termstorage.Storagetanksrangefromlower‐costearthenbasinstohigher‐cost,glass‐linedsteeltanks.Themanurethatentersthesesystemsisusuallydilutedwithflushwater,waterwastedatstalls,andrainwater.

Allofthesestoragesystems(withoutaeration)arebiologically‐anaerobiclagoons,whichmeanthattheyhavesimilarpotential,aswithentericfermentation,toproduceCH4andN2O.DuetothelargequantityofliquidmanureproducedintheUnitedStates,liquidmanurestoragecanbeamajorsourceofGHGemissionsfromanimaloperations.IntermsofestimationofGHGemissionfromanaerobiclagoon/runoffholdingpond/storagetanks,thesestoragesystemsareclassifiedintofourcategories:1)coveredstoragewithacrustformedonthesurface;2)coveredstoragewithoutacrustformedonthesurface;3)uncoveredstoragewithacrustformedonthesurface;and4)uncoveredstoragewithoutacrustformedonthesurface.

ThealgorithmsforcalculatingCH4emissionsdescribedbySommeretal.(2004)arerecommendedforestimatingemissionsattheentity‐level.Themodelconsidersvolatilesolidstobethemainfactorinfluencingemissionsfrommanureandrelatesemissionstothecontentofdegradablevolatilesolids.Nitrousoxideisestimatedasafunctionoftheexposedsurfaceareaofthemanurestorageandwhetheracrustispresentonthesurface.

RationaleforSelectedMethodsTheSommeralgorithmslinkcarbonturnover,volatilesolids,temperature,andstoragetimetoCH4emissionsestimatesandisthebestavailablemethodforestimatingCH4emissionsattheentitylevel.ThemethodprovidedforN2Oistheonlyavailablemethodforestimatingemissions.Thesemethodologiesbestdescribethequantitativerelationshipamongactivitydataattheentitylevel.


InordertoestimatethedailyCH4emissions,thefollowinginformationisneeded:

Animaltype Totaldrymanure Volatilesolidsinthestorage Temperatures(localambienttemperatureandmanuretemperature)

InordertoestimatetheN2Oemission,thefollowinginformationisneeded:

Totaldrymanure Totalnitrogencontentofthemanure Theexposedsurfaceareaofthemanurestorage

ThetotalnitrogencontentofthemanureenteringstoragesystemscanbeestimatedaccordingtothenitrogenbalancemethodasdescribedinEquation5‐9.Thefractionofnitrogenexcretedbyananimalthatisnotemittedasagasistheportionthatentersstorage.


TheancillarydatausedtoestimateCH4emissionsforanaerobiclagoons,runoffholdingponds,andstoragetanksarethemaximumCH4producingcapacities(B0),potentialCH4yield(ECH4,pot),rate


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correctingfactors(b1andb2),Arrheniusconstant(A),activationenergy(E),gasconstant(r),andcollectionefficiency(η)forliquidmanurestoragefromdifferentanimals.ThesedataareavailablefromtheIPCC(2006)andSommeretal.(2004)andarelistedinTable5‐27.

TheancillarydatausedtoestimateN2Oemissionsforanaerobiclagoons,runoffholdingponds,andstoragetanksistheN2OemissionfactorfromTable5‐29(U.S.EPA,2011).

5.4.4.4 Method

MethaneEmissionsfromAnaerobicLagoons,RunoffHoldingPonds,StorageTanksTheSommermodel(Sommeretal.,2004)isusedastheestimationmethodforCH4emission(Rotzetal.,2011b).DailyCH4emissionsareestimatedasafunctionofmanuretemperatureandthevolatilesolidsinmanureplacedintoliquidstorages.TheparametersfortheestimationarelistedinTable5‐28.


ThedegradablefractionofthevolatilesolidsisdependentonthepotentialCH4yieldandthemaximumCH4producingcapacitiesandcanbecalculatedusingEquation5‐32.Thefractionofnondegradablevolatilesolids(materialthatisnotbrokendownbymicroorganisms)iscalculatedfromthetotalvolatilesolidscontentanddegradablefractionofthevolatilesolids,asdescribedbyEquation5‐33.TheB0valuesareobtainedfromtheIPCC(2006)andarelistedinTable5‐19.

Equation5‐31:UsingtheSommerModeltoCalculateDailyCH4Emissions

.

Where:

ECH4 =Methaneemissionperday(kgCH4day‐1)

m =Totaldrymanureperday(kgdrymanureday‐1)a

0.024 =DimensionlessfactortomodifytheSommermodelbasedonVS

VSdandVSnd =DegradableandnondegradableVSinthemanure,respectively (kg(kgdrymanure)‐1)

b1andb2 =Ratecorrectingfactors(dimensionless)

A =Arrheniusparameter(gCH4(kgVS)‐1hr‐1)

E =Activationenergy(Jmol‐1)

R =Gasconstant(JK‐1mol‐1)

T =Storagetemperature(K)

η =Collectionefficiencyofdifferentliquidstoragecategories


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Thecollectionefficiency(η)dependsondifferentliquidstoragecategoriesof:1)coveredstoragewithacrustformedonthesurface;2)coveredstoragewithoutacrustformedonthesurface;3)uncoveredstoragewithacrustformedonthesurface;and4)uncoveredstoragewithoutacrustformedonthesurface.AcrustallowsairandCH4toberetainedonthesurfaceofthemanurestorageandincreasesthepotentialforoxidationofCH4(Hansenetal.,2009;Nielsenetal.,2010).Whenacrustdoesnotform,CH4isdirectlyemittedwithoutrapidoxidation.Forcattleslurryandpigslurry,degradableandnondegradablevolatilesolids(asafractionofVST)aregiveninTable5‐28.

Table5‐27:ParametersforEstimatingCH4EmissionfromLiquidManureStorage

Parameters Cattle Swine

Arrheniusconstant(ln(A))–gCH4(kgVS)‐1hr‐1 43.33 43.21

Activationenergy(E)–Jmol‐1 1.127×105 1.127×105

Gasconstant(R)–JK‐1mol‐1 8.314 8.314

RatecorrectionfactorforVSd(b1) 1 1

RatecorrectionfactorforVSnd(b2) 0.01 0.01

Potentialmethaneyieldofthemanure(ECH4,pot)(kgCH4/kgVS) 0.48 0.50

Collectionefficiency(η)

Coveredstoragewithacrustformonthesurfacea 1 1

Coveredstoragewithoutacrustformonthesurfacea 1 1

Uncoveredstoragewithacrustformonthesurfaceb 0 0

Uncoveredstoragewithoutacrustformonthesurfacec ‐0.4 ‐0.4Source:Sommeretal.(2004)andIPCC(2006).aCH4gasfromcoveredstoragewithacrustformonthesurfaceiscollectedandflared.bUncoveredstoragewithacrustformonthesurfaceisusedforthederivationofEquation5‐22.cTheemissionforuncoveredstoragewithoutacrustis40percentgreaterthanuncoveredstoragewithacrust,sothecollectionefficiencyforthiscaseis‐40percent.

Equation5‐32:CalculatingtheDegradableFractionoftheVolatileSolids

,

Where:

VSd =DegradableVSfractionsinthemanureonagivenday(kg(kgdrymanure)‐1)

VST =Volatilesolidscontentinthestorageonagivenday(kg(kgdrymanure)‐1)

B0 =MaximumCH4producingcapacities(kgCH4(kgVS)‐1)

ECH4,pot =PotentialCH4yieldofthemanure(kgCH4(kgVS)‐1)

Equation5‐33:CalculatingtheNon‐DegradableFractionoftheVolatileSolids

Where:

VSdandVSnd =DegradableandnondegradableVSfractionsinthemanureonagivenday (kg(kgdrymanure)‐1),respectively

VST =Volatilesolidscontentinthestorageonagivenday (kg(kgdrymanure)‐1)


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Table5‐28:DegradableandNondegradableVolatileSolidsforCattleandSwineManure

TypeofManure VSd/VST VSnd/VST

Cattleliquidmanure 0.46 0.54

Swineliquidmanure 0.89 0.11Source:Mølleretal.(2004).

NitrousOxideEmissionsfromAnaerobicLagoon,RunoffHoldingPond,StorageTanksNitrousoxideemissionsfromliquidmanurestoragetypicallyrepresentarelativelysmallportionoftheN2Oemissionsfromfarms.MoststudiesindicatethecriticalityofthecrustfortheformationandemissionofN2O(PetersenandSommer,2011).Therefore,N2Oemissionsfromliquidmanurestorageareestimatedasafunctionoftheexposedsurfaceareaofthemanurestorageandthepresenceofacrustonthesurface.

TheemissionfactorofN2Oisdependentoncrustformationontheliquidstorage.Thecrustallowsairtoberetainedonthesurfaceofthemanurestorageandincreasesthepotentialfornitrificationanddenitrification(Hansenetal.,2009;Nielsenetal.,2010).Whenacrustdoesnotform,oxygenisnotretainedontheliquidsurfacewithnitrogenouscompounds,andthereforenoN2Oisformedandemitted.TheemissionfactorsofN2OfordifferentliquidstoragemethodsarelistedinTable5‐29.

Table5‐29:EmissionFactorofN2OforLiquidStoragewithDifferentCrustFormation

TypeofLiquidStorage EFN2O,man(gN2O/m2/day)

Uncoveredliquidmanurewithcrust 0.8

Uncoveredliquidmanurewithoutcrust 0

Coveredliquidmanure 0Source:Rotzetal.(2011a).

Equation5‐34:CalculatingN2OEmissionsfromLiquidManureStorage

Where:


EFN2O =EmissionrateofN2O(gN2Om‐2day‐1)

Asurface =Exposedsurfaceareaofthemanurestorage(m2)

1,000 =Conversionfactorforgramstokilograms


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5.4.5 AnaerobicDigesterwithBiogasUtilization

5.4.5.1 OverviewofAnaerobicDigesterwithBiogasUtilization

OneofthemostcommonlydiscussedwastemanagementalternativesforGHGreductionandenergygenerationisanaerobicdigestion.Anaerobicdigestionisanatural,biologicalconversionprocessthathasbeenproveneffectiveatconvertingwetorganicwastesintobiogas(approximately60percentCH4and40percentCO2).Biogascanbeusedasafuelsourceforengine‐generatorsets,producingrelativelycleanelectricitywhilealsoreducingsomeoftheenvironmentalconcernsassociatedwithmanure.Thedigestercanbeassimpleasacoveredanaerobiclagoon(Gould‐WellsandWilliams,2004)orassophisticatedasthermophilicormediamatrix(attachedgrowth)digesters(Cantrelletal.,2008a).Thereareawidevarietyofanaerobicdigestionconfigurations,suchascontinuousstirredtankreactor(CSTR),coveredlagoon,plug‐flow,temperaturephased,upflowanaerobicsludgeblanket(UASB),packed‐bed,andfixedfilm.Thedigestionisalsocategorizedbasedonculturetemperature:thermophilicdigestioninwhichmanureisfermentedatatemperatureofaround55°C,ormesophilicdigestionatatemperatureofaround35°C.Amongthesetechnologies,CSTR,plug‐flow,andcoveredlagoon,allundermesophilicconditions,arethemostoften‐usedmethods.

Duringanaerobicdigestion,agroupofmicrobesworktogethertoconvertorganicmatterintoCH4,

CO2,andothersimplemolecules.Themainadvantagesofapplyinganaerobicdigestiontoanimalmanuresareodorreduction,electricitygeneration,andthereductionofGHGemissionsandmanure‐bornepathogens.Anaerobicdigestionisalsoanexcellentpre‐treatmentprocessforsubsequentmanuretreatmenttoremoveorganicmatterandconcentratephosphorus.ConsideringthesmallamountofN2Oexistinginbiogas,N2Oemissionsarenotestimatedfortheanaerobicdigestionofliquidmanure.

Thechallengesassociatedwithanaerobicdigestionrelatetoinitialcapitalcost,operation,andmaintenanceandothergasesthatmaybegenerated(e.g.,nitricoxides).Theeconomicsrelatetoaccesstotheelectricalgridandsufficientgreen‐electricityoffsetstomaketheoperationprofitable.Profitableconditionsarerelativelyscarce.Finally,thedigestersludgemustbemanaged.Anotherconversionalternativewithenergycreationpotentialisthermochemicalconversion(Cantrelletal.,2008a).Systemsthatusethermochemicalconversionstosyngases,bio‐oil,andbiocharforelectricityandfuelareemerging,butarenotyetestablished.

SinceananaerobicdigestionsystemconvertsorganiccarboninmanureintoCH4andsubsequentlycombustsCH4intoCO2,theGHGemissionsfrommanureanaerobicdigestionoperationaremainly

MethodforEstimatingEmissionsfromManureStorageandTreatment–AnaerobicDigesterwithBiogasUtilization

Methane

IPCCTier2usingCleanDevelopmentMechanismEFsfordigestertypestoestimateCH4leakagefromdigesters.

AnaerobicdigestersystemsconvertorganicmatterinmanureintoCH4andsubsequentlycombustCH4intoCO2.

GasleakagefromdigestersisthemainsourceofGHGemission. LeakageofCH4fromtheanaerobicdigestersystemisestimated.

NitrousOxide N2Oleakagefromdigestersisfairlysmallandnegligible.


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fromtheleakageofdigesters.TheleakageofCH4canbeestimatedbasedontheIPCCTier2approachincombinationwithtechnology‐specificemissionfactors.


TheIPCCequationistheonlyavailablemethodforestimatingCH4emissionfromdigesters.Thismethodologybestdescribesthequantitativerelationshipamongactivitydataattheentitylevelandtakesintoaccountthespecifictechnologyemployed.


InordertoestimatetheCH4leakagefromanaerobicdigestion,thefollowinginformationisneeded:

Animaltype Totaldrymanureintothedigester Volatilesolidsinthemanure Digestertemperatures


AncillarydataforanaerobicdigestioneffluentareneededforfurtherestimationofCH4andN2Oemissionsfrompost‐treatmentapproachessuchasaerobicoranaerobiclagoons,nutrientremovaloperations,etc.Thus,thenecessarydatafortheeffluentincludeeffluentflowrate,totalsolids,volatilesolids,chemicaloxygendemand,effluenttemperature,environmentaltemperature,liquid/solidseparationmethods,andtotalnitrogen.

5.4.5.5 Method

Equation5‐35describestheIPCCTier2approachforestimatingCH4emissionsforanaerobicdigesters.TheCH4generatedfromdigestersisassumedtobeflaredorusedasabiogas;theonlyemissionsfromdigestersarefromsystemleakage.

TheB0valuesareobtainedfromtheIPCC(2006)andarelistedinTable5‐19.TheemissionfactorsfortheamountofCH4leakagebytechnologyarelistedinTable5‐30.

Equation5‐35:IPCCTier2ApproachforEstimatingCH4 Emissions

.,

Where:

ECH4 =CH4emissionsperday(kgCH4day‐1)

m =Totaldrymanureperday(kgday‐1)


B0 =MaximumCH4producingcapacityformanurefromdifferentanimal (m3CH4(kgVS)‐1)

0.67 =Conversionfactorfromweighttovolumeofmethane(kgCH4m‐3)

EFCH4,leakage=EmissionfactorforthefractionofCH4producedthatleaksfromtheanaerobicdigester(%)


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Table5‐30:EmissionFactorsfortheFractionofMethaneLeakingfromDigesters

DigesterConfigurations EFCH4,leakage(%)

Digesterswithsteelorlinedconcreteorfiberglassdigesterswithagasholdingsystem(eggshapeddigesters)andmonolithicconstruction

2.8

UASBtypedigesterswithfloatinggasholdersandnoexternalwaterseal 5Digesterswithunlinedconcrete/ferrocement/brickmasonryarchedtypegasholdingsection;monolithicfixeddomedigesters 10

Otherdigesterconfigurations 10Source:CDM(2012).

5.4.6 CombinedAerobicTreatmentSystems

Dealingwiththetotaltreatmentofwastewaterfromeitherswineordairyiscomplex,becausetheliquidandsolidphasesmustbetreated.Inmunicipalsewagetreatmentsystems,thewastewaterisverydilutesothetreatmentofthebiochemicaloxygendemandbyaerationisafundamentalprocess.Incontrast,thesolidscontentoflivestockwastewaterisquitehigh,asisthebiochemicaloxygendemand.Consequently,thecostofstabilizingthebiochemicaloxygendemandwithaerationhasproventobeuneconomical.AsuccessfulsolutiontothisproblemwasdevelopedbyVanottietal.(2007),whousedpolyacrylamideflocculationtoremovemorethan90percentofthesolids(VanottiandHunt,1999;Vanottietal.,2002).Thesolidfractionwasthencomposted(Vanotti,2006).Theremainingliquidwastransferredtoaseparatedwatertankwhereitwassubsequentlyaerated(VanottiandHunt,2000;Vanottietal.,2007;VanottiandSzogi,2008).Duringthesetwophasesoftreatment,morethan90percentoftheGHGemissionsfromstandardanaerobiclagoontreatmentwereavoided(Vanottietal.,2008).Theavoidancewasachievedbyaerobictreatmentofthesolidsviacompostingandnitrification/denitrificationintheliquideffluent.

Afternitrification/denitrification,thetreatedeffluentmovestothesettlingtankandsubsequentlyintothephosphorustreatmentchamber.Herethewastewater,whichhaslowalkalinity,isamendedwithliquidlime,andthepHisraisedtoapproximately10.InthepresenceofhighpHandcalcium,thephosphorusisprecipitatedandthepathogensarekilled(Vanottietal.,2003;Vanottietal.,2005;Vanottietal.,2009).Thetreatedwastewateristhenrecycledintothehouses.Thisprocessprovidesahealthierenvironmentforthepigs(Vanottietal.,2009).Thesystemmustbeoperatedtoensureproperandtimelyflushingofthehouse.Thepolyacrylamideadditionandthesolidsseparationunitsmustbeoperatedproperly.Aerationofthenitrificationtankmustbemaintained,asmusttheadditionofliquidlime.Thepumpsthatmaintaintheinternalrecyclingmustalsobemaintainedandoperatedcorrectly.ThissystemistheonlytreatmentsystemtomeetandbecertifiedforexpansionofswineproductioninNorthCarolina.

MethodforEstimatingEmissionsfromCombinedAerobicTreatmentSystems

Methodistoutilize10percentoftheemissionsresultingfromestimationofemissionsfromLiquidManureStorageandTreatment–AnaerobicLagoons,RunoffHoldingPonds,andStorageTanks.

Methodbasedonresearchfindingsthatsystemsavoid90percentoftheGHGemissionsfromstandardanaerobiclagoontreatment.


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Toestimateemissionsforcombinedaerobictreatmentsystems,themethodologyforanaerobiclagoons,runoffholdingponds,andstoragetanksisappliedtothesystem.GasemissionsofCH4andN2Oareestimatedusing10percentofthevaluesforemissionsfromanaerobiclagoontreatment.

5.4.7 Sand‐ManureSeparation

Sandisoneofthestandardmaterialsfordairycowbedding.Itprovidessuperiorcowcomfort,environmentforudderhealth(andconsequentlybettermilkquality),andtractionwhencomparedwithorganicbeddingmaterials.Sandseparationsystemscanbeclassifiedasmechanicalseparationandsedimentationseparation.Sedimentationseparationusesdilutionwaterandgravitytoallowsandtopassivelysettleinsandtraps.Duetothehighorganicmaterialcontentcontainedinthesettledsand,thesandrecoveredfromthesandtrapneedstobedrainedmultipletimesanddriedpriortoreuse.Mechanicalsand‐manureseparationsystemsuserecycledliquidmanureandaerationtosuspendmanuresolids,settlesandatthebottomoftheseparator,andrecoverthesandusingaheavydutyauger.Sandisgenerallydischargedwithlessthantwopercentorganicmatter.Themechanicallyseparatedsandcanbereusedforbedding.

Sincesand‐manureseparationisrelativelyquick(comparedwithotherstorageandtreatmentmethods),GHGemissionsfromtheoperationareminimal.TheprocessofseparatingsandandmanureisnotassumedtocontributetoGHGemissions.Aftersand‐manureseparation,theseparatedliquidmanureistreatedastheinfluentforthenextstepofstorageandtreatmentoperations.ThevariousstorageandtreatmentoperationoptionsareshowninFigure5‐7.Theparametersofvolatilesolids,totalnitrogen,organicnitrogen,andmanuretemperatureoftheseparatedliquidmanureshouldbemeasured,andusedastheinputstoestimateemissionsofGHGs.

5.4.8 NutrientRemoval

Nitrogenandphosphorusaretheprimaryelementsthatcauseeutrophicationinsurfacewaters.WithincreasedFederal,Stateandlocalattentiononnon‐pointwastesources,moreandmoreanimaloperationswilllikelyusenutrientremovalapproachestotreatliquidmanurebeforelandapplicationandotheruses.Comparedtophosphorus,nitrogeninmanurecontributestoN2Oemission;removingitcansignificantlyalleviateemissions.NitrogeninmanurecomprisesNH3,particulateorganicnitrogen,andsolubleorganicnitrogen.Fivemainnitrogenremoval

MethodforEstimatingEmissionsfromLiquidManureStorageandTreatment–Sand/ManureSeparation

NomethodisprovidedasGHGemissionsarenegligiblefromthesand/manureseparationprocess.However,resultingvolatilesolids,totalnitrogen,organicnitrogen,andmanuretemperatureoftheseparatedliquidmanureshouldbemeasuredandusedastheinputstoestimateemissionsofGHGsforsubsequentstorageandtreatmentoperations.

MethodforEstimatingEmissionsfromLiquidManureStorageandTreatment–NutrientRemoval

NotestimatedduetolimitedquantitativeinformationonGHGsfromnitrogenremovalprocesses.


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approaches—BiologicalNitrogenRemoval(BNR),Anamox,NH3stripping,ionexchange,andstruvitecrystallization—havebeenappliedformunicipalandindustrialwastewater,aswellasforanimalwastestreams.BecauseN2Ooriginatesfromnitrogensources,quantificationofnitrogenremovalisimportanttoestimateemissionsfromanimalmanure.

Becausemostnitrogenremovalmethodsforliquidmanurearecurrentlyintheresearchanddevelopmentstage,verylittlequantitativeinformationisavailableonthenitrogenremovalmethodsmentionedaboveforanimalmanureunderdifferentoperationconditions.Thesuggestedestimationmethodistoconsidertheliquidmanureafternutrientremovalastheinfluentforstorageandtreatmentapproachesthatentitieswillusetofurthertreatliquidmanure.Measurementsofvolatilesolids,totalnitrogen,organicnitrogen,andmanuretemperatureofthetreatedliquidmanureareneededtoestimateCH4andN2Oemissions.

5.4.9 Solid–LiquidSeparation

Solid–liquidmanureseparationhasbeenusedwidelybydairyfarms.Onepurposeofsolid–liquidseparationistophysicallyseparateandremovethelargersolidsfromliquidmanureinordertostoreandtreatthemseparately.Theavailablecommercialmethodsincludegravitysedimentationandmechanicalseparation(withorwithoutcoagulationflocculation).Sedimentationandmechanicalseparationwithoutcoagulationflocculationarethemostpopularmethodsusedbyanimalfarms.Similartosand–liquidmanureseparation,GHGemissionsfromtheoperationareminimal;however,separationhasanimpactonnutrientdistributioninseparatedsolidandliquidmanure,whichwillinfluenceGHGemissionsfromthenextstageofmanurestorageandtreatmentforsolidandliquidmanure.Theseparatedliquidmanureistreatedastheinfluentforthenextstepofstorageandtreatmentoperations.ThepossiblestorageandtreatmentoptionsaredelineatedinFigure5‐7.

Theparametersoftotalsolids(drymanure),totalnitrogen,organicnitrogen,andmanuretemperatureoftheseparatedliquidandsolidmanureshouldbemeasured,andusedastheinputstoestimateGHGsemissioninthesubsequentstorageandtreatmentoperations.Thedistributionoftotalsolidsaftersolid–liquidseparationfortypicalmechanicalseparatorsarelistedinTable5‐317(FordandFleming,2002).

Table5‐31:EfficiencyofDifferentMechanicalSolid‐LiquidSeparation

SeparationTechnique

ManureType

ScreenSize(mm)

Influent(%DM)

TotalSolidRemoval

Efficiency(%)Source

ScreenStationaryinclinedscreen

Swine 1.0 0.0‐0.7 35.2 Shuttetal.(1975)Beef 0.5 0.97‐4.41 1‐13 Heggetal.(1981)Dairy 1.5 3.83 60.9 Chastainetal.(2001)

Vibratingscreen

Swine 0.39 0.2‐0.7 22.2 Shuttetal.(1975)Beef 0.52‐1.91 5.5‐7.4 4‐44 GilbertsonandNienaber(1978)

MethodforEstimatingEmissionsfromLiquidManureStorageandTreatment–Solid–LiquidSeparation

NomethodisprovidedasGHGemissionsarenegligible.However,resultingvolatilesolids,totalnitrogen,organicnitrogen,andmanuretemperatureoftheseparatedliquidandsolidmanureshouldbemeasuredandusedastheinputstoestimateemissionsofGHGsandNH3forsubsequentstorageandtreatmentoperations.


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SeparationTechnique

ManureType

ScreenSize(mm)

Influent(%DM)

TotalSolidRemoval

Efficiency(%)Source

Beef 0.64‐1.57 1.55‐3.19 6‐16 Heggetal.(1981)Dairy 0.64‐1.57 0.95‐1.9 8‐16 Heggetal.(1981)Swine 0.64‐1.57 1.55‐2.88 3‐27 Heggetal.(1981)Swine 0.10‐2.45 1.5‐5.4 11‐67 Holmbergetal.(1983)

Rotatingscreen

Beef 0.75 1.56‐3.68 4‐6 Heggetal.(1981)Dairy 0.75 0.52‐2.95 0‐14 Heggetal.(1981)Swine 0.75 2.54‐4.12 4‐8 Heggetal.(1981)

In‐channelflightedconveyorscreen

Dairy 3 7.1 4.22 Mølleretal.(2000)

Swine 3 5.66 25.8 Mølleretal.(2000)CentrifugalCentrifuge Beef 7.5 25 Glerumetal.(1971)Centrisieve Swine 5‐8 30‐40 Glerumetal.(1971)

Decantercentrifuge

Beef 6.9 64 Chiumentietal.(1987)Beef 6.0 45 Chiumentietal.(1987)Swine 7.58 66 Glerumetal.(1971)Swine 1.9‐8.0 47.4‐56.2 Sneathetal.(1988)

Liquidcyclone

Swine 26.5 Shuttetal.(1975)

Filtration/pressing

RollerpressSwine 5.2 17.3 Posetal.(1984)Dairy 4.8 25 Posetal.(1984)Beef 4.5 13.3 Posetal.(1984)

BeltpressDairy 1‐2 7.1 32.4 Mølleretal.(2000)Swine 1‐2 5.7 22.3 Mølleretal.(2000)

Screwpress

Swine 5 16 Chastainetal.(1998)Swine 1‐5 15‐30 Converseetal.(1999)Dairy 1‐10 15.8‐47 Converseetal.(1999)Dairy 2.6 23.8 Converseetal.(1999)Dairy 4.9 33.4 Converseetal.(1999)

Fournierrotarypressa

Swine 85 FordandFleming(2002)Fournier(2010)

Rotaryvacuumfilter Swine 7.5 51 Glerumetal.(1971)

Pressurefilter Beef 7 76 Chiumentietal.(1987)ContinuousBeltMicroscreeningUnit

Swine 2‐8 40‐60 Fernandesetal.(1988)aWithpolymeraddition.


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5.4.10 ConstructedWetland

Globally,constructedwetlandsareusedforthetreatmentofwastewaters,captureofsediments,

anddrainagewaterabatement(Hammer,1989;KadlecandKnight,1996;Tanneretal.,1997;Huntetal.,2002;Huntetal.,2003;Piceketal.,2007;HarringtonandMcInnes,2009;Mustafaetal.,2009;Soosaaretal.,2009;Elgoodetal.,2010;HarringtonandScholz,2010;VanderZaagetal.,2010;Chenetal.,2011;Lockeetal.,2011;TannerandHeadley,2011;TannerandSukias,2011;Vymazal,2011).Constructedwetlandsaregenerallyclassifiedassub‐surfaceorsurfaceflowwetlands(KadlecandKnight,1996).Thesub‐surfacewetlandstypicallyconsistofwetlandplantsgrowinginabedofhighlyporousmedia,suchasgravelorwoodchips.Theyarecommonlyusedtoimprovedrainagewaterquality.Thesewetlandsaregenerallyrectangularinshapeandonetotwometersindepth.Thereislackofagreementabouttherelativeimpactofmicrobialandplantprocessesinthefunctionofsubsurfacewetlands,includingGHGproductionandemissions.However,itisaccuratetosaythatplantsandmicrobesaretypicallyinterdependentlyinvolved(Piceketal.,2007;Zhuetal.,2007;Wangetal.,2008;Faubertetal.,2010;Luetal.,2010;TannerandHeadley,2011).Themicrobialcommunityadvancesbiogeochemicalprocesses(Tanneretal.,1997;Huntetal.,2003;Zhuetal.,2007;Dodlaetal.,2008;Faulwetteretal.,2009),whiletheplantcommunityadvancestransportedoxygenintothedepthofthewetlands,providesrootsurfacesforrhizospherereactions,andventsgasestotheatmosphere.Theplantprocessesaresignificantlyaffectedbyplantcommunitycompositionandweatherconditions(Towleretal.,2004;SteinandHook,2005;Steinetal.,2006;Zhuetal.,2007;Wangetal.,2008;Tayloretal.,2010).

Surfaceflowwetlandshaveamuchmoredirectinterchangewiththeatmosphereforthesupplyofoxygenandnitrogen,aswellastheemissionsofGHGs.Theycanbevariableinshapeandaregenerallylessthan0.5metersdeep.Surfacewetlandsminimizecloggingproblems,buttheycanhavesignificantlossoftreatmentasaresultofchannelflow.Therearereasonablyfunctionalmodelsforwetlanddesignoptimizedforeithercarbonornitrogenremoval(Stoneetal.,2002;Stoneetal.,2004;Steinetal.,2006;Steinetal.,2007a).ThemanagementofGHGs(principallyCH4andN2O)fromtreatmentwetlandsissomewhatsimilartomanagingGHGsinrice(Freemanetal.,1997;Tanneretal.,1997;Feyetal.,1999;Johanssonetal.,2003;Manderetal.,2005a;Manderetal.,2005b;TeiterandMander,2005;Piceketal.,2007;Maltais‐Landryetal.,2009;Wuetal.,2009).

Ofparticularimportanceisthemaintenanceofwetlandoxidative/reductivepotentialconditionssufficientlypositivetoavoidCH4production(Tanneretal.,1997;InsamandWett,2008;SeoandDeLaune,2010).Thisrequireshigherlevelsofoxygenandlowerlevelsofavailablecarbon.IthasbeenreportedthatthefluxesofN2OandCH4fromtreatmentwetlandsaregenerallybelow10mgN2O‐Nm‐2d‐1and300mgCH4‐Cm‐2d‐1(Manderetal.,2005a;Søviketal.,2006).ThemanagementofN2Oemissionsiscomplicatedbythefactthatnitratesareoftenpresentinthewastewatersordrainagewaters.Thisnitratewillbedenitrifiedundertheprevailinganaerobicconditionofthetreatmentwetlands—itisoneoftreatmentwetland’scriticalfunctions.However,itisimportantthatthepreponderanceofdenitrificationproceedstocompletion,withtheultimateproductionofinertdi‐nitrogengas.Completedenitrificationrequireshighercarbon/nitrogenratios

MethodforEstimatingEmissionsfromLiquidManureStorageandTreatment–ConstructedWetland

Currentlynomethodisprovidedtoestimategasemissionfromconstructedwetlandofanimalmanure,althoughGHGsinksarenotedtolikelybegreaterthanCH4andN2Oemissions,whichareconsiderednegligible.


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(Klemedtssonetal.,2005;Hwangetal.,2006;Huntetal.,2007).Thus,thereisanimportantbalancebetweensufficientcarbonforcompletedenitrificationandcopiouscarbonthatcandrivewetlandsintothelowreduction/oxidationconditionsassociatedwithCH4production.

Estimationmethodsareverycomplicatedandcase‐based.Inanapproximateestimationmannerthatconsiderswetlandsverysimilartocropland,treatmentwetlandsofanimalmanureareGHGsinksmorethansources.TheCH4andN2Oemissionfromwetlandtreatmentofanimalmanurecouldbenegligible.Thecriticalactivitydataincludehydraulicload;inflowwatercomposition,especiallycarbonandnitrogen;pretreatmentssuchassolidsremovalornitrification;amendments;anddryingcycles.Criticalancillarydataincluderainfall,temperature,windspeed,stormevents,changesinlivestockstockingrates,cropping/tillagesystems,andfertilizationtiming/rates.

5.4.11 Thermo‐ChemicalConversion

Combustion,themostprimitiveandexothermicformofthermochemicaltreatmentoflivestockwaste,hasbeeninusesinceantiquity;however,itsuseforlarge‐scalelivestockwastetreatmenthasgenerallybeenhamperedbyeconomic,health,andenvironmentalqualityissues(Florinetal.,2009).Principalamongtheseissueshasbeencomponentsthatdegradeairquality,includingGHGs(mainlyCO2).Nonetheless,thermochemicaltreatmentoflivestockmanurehasattributesthatcontinuetoattracteffortstomakeiteconomicallyandenvironmentallyeffective(Ramanetal.,1980;Heetal.,2000;Heetal.,2001;Ocfemiaetal.,2006;Roetal.,2007;Cantrelletal.,2008a;Cantrelletal.,2008b;Powlsonetal.,2008;Cantrelletal.,2009;Dongetal.,2009;Jinetal.,2009;Roetal.,2009;Xiuetal.,2009;Cantrelletal.,2010a;Cantrelletal.,2010b;Stoneetal.,2010;Wangetal.,2011;Xiuetal.,2011).

Recently,pyrolysis/gasificationhasreceivedmuchinterestforitstreatmentoflivestockwaste.Therehavealsobeenadvancesinthecleaningofexhaustgases(Heetal.,2001;Roetal.,2007;Cantrelletal.,2008a;Dongetal.,2009;Xiuetal.,2009;Xiuetal.,2011).Pyrolysis/gasificationoffersthreeprincipalendproducts:syngas,bio‐oil,andbiochar(Cantrelletal.,2008a;Xiuetal.,2011).Thequalityandquantityofendproductswillvarywithfeedstock,exposuretime,andpyrolysis/gasificationtemperature.Thesyngascanbeusedfordirectcombustionortorunanelectricalgenerator(Roetal.,2010).ItcanalsobeusedviaFischer‐Tropschconversionforproductionofliquidfuel(Cantrelletal.,2008a).Pyrolysis/gasificationforsyngasandeventualliquidfuelproductionisaveryattractivepotentialbusinessmodelforspecificagriculturalfuels.

IntermsofGHGemission,treatmentoffluegasfromcombustionandutilizationofsyngasfrompyrolysis/gasificationarecritical.Thethermalprocesseswithafluegasclean‐upunitandsyngasutilizationunitshouldminimizetheGHGemissionfromthethermalconversionprocesses.

InordertoestimatethedailyemissionsofCH4andN2Othefollowinginformationisneeded:typeofthermalconversionprocesses;detailedinformationontheprocess,suchaswith/withoutfluegasclean‐upunitorsyngasutilizationunit;inflowcomposition,suchasmoisture,carbon,andnitrogen;andmassflowthroughtheprocess,includingmassin,fluegas/syngas,andash/biochar.Themeasurementscanbebasedondietarychangesorseasonaltimeframe,whichisdecidedbyindividualfarmentity.However,duetothedynamicnatureofmanurepilesandtherapidchangesthatcanoccurinchemicalandphysicalcomposition,frequentmeasurementsarerecommendedtoensureaccuracyoftheestimation.Thetotalenergybalanceofthesystemshouldalsobeknown.For

MethodforEstimatingEmissionsfromSolidManureStorageandTreatment–ThermochemicalConversion

NomethodisprovidedasCH4andN2Oemissionsareconsiderednegligible.


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instance,thecarboncreditsofbiocharcannotbeclaimedwhileignoringtheenergyrequiredtocreatethebiochar.Theeffectivenessoftheexhaustgascleaningprocessinremovingairqualitydegradingcomponentsmustbecertified.

Duetothenatureofthermalconversion,muchloweremissions(CH4andN2O,)aregeneratedfromthethermalconversioncomparedwithotherstorageortreatmentmethods.TheCH4andN2Oemissionsfromcompletethermalconversionprocessesarerelativelysmallandnegligible.

5.4.12 LimitationsandUncertaintyinManureManagementEmissionsEstimates

Fortemporaryandlong‐termstorage,composting,andaerobiclagoons,theIPCCTier2methodologyisusedtoestimateCH4emissions.ThemaximumCH4productioncapabilities(B0)forruminantanimalsareU.S.specificvaluesfromtheU.S.EPAInventoryofU.S.GHGEmissionsandSinks.IPCCestimatesthattheuncertaintyassociatedwiththesecountry‐specificfactorsis±20percent.B0valuesforotheranimalvaluesareIPCCdefaultsandhaveanassociateduncertaintyof±30percent.TheMCFsprovidedintheGuidelinesforsolid,slurry,andsolid/slurrymanurearefromtheIPCCGuidanceandhaveanestimateduncertaintyof±30percent.TheB0andMCFvaluesprovidedareintendedforuseatthenationallevel,thusapplicationofthesefactorsattheentitylevelmayresultinhigheruncertainty.

AmodifiedTier2approachisprovidedforestimatingCH4emissionsfromanaerobicdigesters.TheleakratesfordifferentdigestertypesistakenfromtheCleanDevelopmentMechanism’smethodologicaltoolforprojectandleakageemissionsfromanaerobicdigesters(CDM,2012).TheCleanDevelopmentMechanism’sleakratesarebasedonIPCC(2006),Fleschetal.(2011),andKurup(2003).TheleakageratetakenfromFleschetal.(2011)isbasedonmeasurementstakenfromanIntegratedManureUtilizationSysteminstalledinAlberta,Canada.Thesystemprocesses100metrictonsofmanuredailyandwasthemosttechnologicallyadvancedsystemavailableatthetimeofthestudy.ThestudiesperformedbyKurup(2003)werebasedonasystemlocatedinKerala,India.Nouncertaintyestimatesareprovidedfortheseleakrates;however,theactualleakrateofanentitymaydifferduetodifferencesintechnology,maintenance,orotherfactors.

TheSommermodel(Sommeretal.,2004)isrecommendedforestimatingCH4emissionsfromanaerobiclagoons,runoffholdingponds,andstoragetanks.SimilartotheIPCCTier2methodsusedforstockpiles,composting,andaerobiclagoons,theSommermodelrequiresB0valuesfromIPCC.ThedegradableandnondegradablevolatilesolidscanbecalculatedusingtheB0andpotentialCH4yieldoradefaultvaluefromMølleratal.(2004).ThedefaultvaluespresentedarebasedontypicalconcentrationsonDanishcattleandpigslurries;valuesdonotdifferentiatebetweentypeofcattleordietoftheanimalandthusthereishigherrelativeuncertaintyassociatedwithusingthedefaultvalues.

Sommeretal.(2004)performedananalysistodeterminethesensitivityofemissionestimatestowardsdifferentfactors.OnefactorconsideredistheeffectofslurrystoragetemperatureonCH4emissions.Sommeretal.(2004)appliedaveragemonthlytemperaturesforsevendifferentlocations(allNordiccountries)atconstantvolatilesolidsandmanagement.WhencomparedtothemodelresultsforDenmark(whicharecalibratedtocorrespondwithIPCCmethodology),theemissionsestimatesvariedfrom‐1to+36percentforpigslurryand‐23to+1percentforcattleslurry.GiventhattheclimaticconditionsoftheUnitedStatesdiffersfromNordiccountries,thevariationasaresultofslurrystoragetemperatureisexpectedtobegreater.

IPCCmethodologyormodifiedmethodologyisusedtoestimatetheN2Oemissionsfromtemporarystackandlong‐termstorage,composting,andaerobiclagoons.IPCCreportslargeuncertaintieswiththedefaultemissionfactorsapplied(‐50percentto+100percent).Theseemissionfactorswereintendedforuseatthenationallevelanddonottakeintoaccountvaryingtemperature,moisture


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content,aeration,manurenitrogencontent,metabolizablecarbon,durationofstorage,andotheraspectsoftreatmentfordifferententities,thustheuncertaintyisexpectedtobehigherthanreportedbyIPCC.

Themethodsrecommendthattheusersendmanuresamplestoalaboratorytoobtainanestimateofthevolatilesolids,NH3,andnitrogencontentofmanure.Ameasurementofmanurecharacteristicscanhelpminimizeuncertaintybyprovidinganentity‐specificvaluethattakesintoaccountanimalanddietcharacteristics.Iflaboratory‐testedvolatilesolidsvaluesarenotavailable,defaultvaluesfromtheAmericanSocietyofAgriculturalandBiologicalEngineers(ASABE)canbeapplied.ASABEprovidesdefaultmanurecharacteristicsbasedondatafrompublishedandunpublishedinformation.Thesevaluesarearithmeticaveragesandmaynotrepresentthedifferencesinanimalage,diet,usage,productivity,andmanagement.ThereisahigheramountofuncertaintyassociatedwiththeuseofASABEvaluesbutthereisnoquantifieduncertaintyprovidedforthesevalues.Notethatwithinthestandardcitedbelowthereareequationsprovidedthatallowforfarm‐specificvaluestobedeterminedbasedonanimalcharacteristicsanddietcomposition.Thetablebelowisintendedtoprovide‘average’values,butwherefarmdataareavailable,equationsshouldbeusedinordertoprovidemoreestimatesthatbetterreflectfarmconditionsandpractices.

AvailabledefaultvaluesanduncertaintyinformationisincludedinTable5‐32.

Table5‐32:AvailableUncertaintyDataforEmissionsfromManureManagement

Parameter

Abbreviation/Sym

bol

DataInputUnit

EstimatedValue

RelativeuncertaintyLow

(%

)

RelativeuncertaintyHigh

(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

TotalDryManure–BeefFinishingCattle

kgdrymanure/animal/day 2.4 ‐20 20 ASABE(2005)

TotalDryManure–BeefCow(confinement)


TotalDryManure–BeefGrowingcalf(confinement)


TotalDryManure–DairyLactatingcow

kgdrymanure/animal/day 8.9 ‐20 20 8.7 11.3 ASABE(2005)

TotalDryManure–DairyDrycow kgdrymanure/animal/day 4.9 ‐20 20 8.8 11.2 ASABE(2005)

TotalDryManure–DairyHeifer kgdrymanure/animal/day 3.7 ‐20 20 ASABE(2005)

TotalDryManure–DairyVeal118kg kgdrymanure/animal/day 0.12 ‐20 20 ASABE(2005)

TotalDryManure–HorseSedentary500kg


TotalDryManure–HorseIntenseexercise500kg


TotalDryManure–PoultryBroiler kgdrymanure/animal/day 0.03 ‐20 20 ASABE(2005)TotalDryManure–PoultryTurkey(male)


TotalDryManure–PoultryTurkey(females)


TotalDryManure–PoultryDuck kgdrymanure/animal/day 0.04 ‐20 20 ASABE(2005)TotalDryManure–Layer kgdrymanure/animal/day 0.02 ‐20 20 ASABE(2005)TotalDryManure–SwineNurserypig(12.5kg)



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Parameter

Abbreviation/Sym

bol

DataInputUnit

EstimatedValue


(%

)


(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

TotalDryManure–SwineGrowfinish(70kg)


TotalDryManure–Swinegestatingsow200kg


TotalDryManure–SwineLactatingsow192kg


TotalDryManure–SwineBoar200kg


Volatilesolids–BeefFinishingcattle VS kgVS/kgdrymanure 0.81 ‐25 25 ASABE(2005)Volatilesolids–BeefCow(confinement)

VS kgVS/kgdrymanure 0.89 ‐25 25 ASABE(2005)

Volatilesolids–BeefGrowingcalf(confinement)


Volatilesolids–DairyLactatingcow VS kgVS/kgdrymanure 0.84 ‐25 25 ASABE(2005)Volatilesolids–DairyDrycow VS kgVS/kgdrymanure 0.85 ‐25 25 ASABE(2005)Volatilesolids–DairyHeifer VS kgVS/kgdrymanure 0.86 ‐25 25 ASABE(2005)

Volatilesolids–DairyVeal118kg VS kgVS/kgdrymanure ‐25 25 ASABE(2005)

Volatilesolids–HorseSedentary500kg


Volatilesolids–HorseIntenseexercise500kg


Volatilesolids–PoultryBroiler VS kgVS/kgdrymanure 0.73 ‐25 25 ASABE(2005)Volatilesolids–PoultryTurkey(male)


Volatilesolids–PoultryTurkey(females)


Volatilesolids–PoultryDuck VS kgVS/kgdrymanure 0.58 ‐25 25 ASABE(2005)Volatilesolids–Layer VS kgVS/kgdrymanure 0.73 ‐25 25 ASABE(2005)Volatilesolids–SwineNurserypig(12.5kg)


Volatilesolids–SwineGrowfinish(70kg)


Volatilesolids–Swinegestatingsow200kg


Volatilesolids–SwineLactatingsow192kg


Volatilesolids–SwineBoar200kg VS kgVS/kgdrymanure 0.89 ‐25 25 ASABE(2005)Totalnitrogenatagivenday–beeffinishingcattle

kgN/kgdrymanure 0.07 ASABE(2005)

Totalnitrogenatagivenday–beefcow(confinement)


Totalnitrogenatagivenday–beefgrowingcalf(confinement)


Totalnitrogenatagivenday–dairylactatingcow


Totalnitrogenatagivenday–dairydrycow


Totalnitrogenatagivenday–dairyheifer


Totalnitrogenatagivenday–dairyveal118kg


Totalnitrogenatagivenday–HorseSedentary500kg


Totalnitrogenatagivenday–HorseIntenseExercise



5-102

Parameter

Abbreviation/Sym

bol

DataInputUnit

EstimatedValue


(%

)


(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Totalnitrogenatagivenday–poultry,broiler


Totalnitrogenatagivenday–poultry,turkey(male)


Totalnitrogenatagivenday–poultry,turkey(females)


Totalnitrogenatagivenday–poultry,duck


Totalnitrogenatagivenday–layer kgN/kgdrymanure 0.07 ASABE(2005)Totalnitrogenatagivenday–swinenurserypig(12.5kg)


Totalnitrogenatagivenday–swinegrowfinish(70kg)


Totalnitrogenatagivenday–swinegestatingsow200kg


Totalnitrogenatagivenday–swinelactatingsow192kg


Totalnitrogenatagivenday–swineboar200kg


MethaneConversionFactor(MCF)a–DairyCow

MCF % ‐30 30 IPCC(2006)

MethaneConversionFactora–Cattle MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–Buffalo MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–MarketSwine

MCF % ‐30 30 IPCC(2006)

MethaneConversionFactora–BreedingSwine

MCF % ‐30 30 IPCC(2006)

MethaneConversionFactora–Layer(Dry)

MCF % ‐30 30 IPCC(2006)

MethaneConversionFactora–Broiler MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–Turkey MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–Duck MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–Sheep MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–Goat MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–Horse MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–Mule/Ass

MCF % ‐30 30 IPCC(2006)

MethaneConversionFactora–Buffalo MCF % ‐30 30 IPCC(2006)MethaneConversionFactora–Invesselmanurecomposting

MCF % ‐30 30 IPCC(2006)

MethaneConversionFactora–Staticpilemanurecomposting

MCF % ‐30 30 IPCC(2006)

MethaneConversionFactora–Intensivewindrow

MCF % ‐30 30 IPCC(2006)

MethaneConversionFactora–Passivewindrow

MCF % ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–BeefReplacementHeifers

Bo m3CH4/kgVS 0.33 ‐20 20 U.S.EPA(2011)

MaximumMethaneProducingCapacities–DairyReplacement Bo m3CH4/kgVS 0.17 ‐20 20 U.S.EPA(2011)

MaximumMethaneProducingCapacities–MatureBeefCows

Bo m3CH4/kgVS 0.33 ‐20 20 U.S.EPA(2011)

MaximumMethaneProducingCapacities–Steers(>500lbs)

Bo m3CH4/kgVS 0.33 ‐20 20 U.S.EPA(2011)


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Parameter

Abbreviation/Sym

bol

DataInputUnit

EstimatedValue


(%

)


(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

MaximumMethaneProducingCapacities–Stockers(All)

Bo m3CH4/kgVS 0.17 ‐20 20 U.S.EPA(2011)

MaximumMethaneProducingCapacities–CattleonFeed

Bo m3CH4/kgVS 0.33 ‐20 20 U.S.EPA(2011)

MaximumMethaneProducingCapacities–DairyCow

Bo m3CH4/kgVS 0.24 ‐20 20 U.S.EPA(2011)

MaximumMethaneProducingCapacities–Cattle

Bo m3CH4/kgVS 0.19 ‐20 20 U.S.EPA(2011)

MaximumMethaneProducingCapacities–Buffalob

Bo m3CH4/kgVS 0.1 IPCC(2006)

MaximumMethaneProducingCapacities–MarketSwine Bo m3CH4/kgVS 0.48 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–BreedingSwine

Bo m3CH4/kgVS 0.48 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–Layer(dry) Bo m3CH4/kgVS 0.39 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–Layer(wet)

Bo m3CH4/kgVS 0.39 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–Broiler Bo m3CH4/kgVS 0.36 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–Turkey

Bo m3CH4/kgVS 0.36 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–Duck Bo m3CH4/kgVS 0.36 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–Sheep

Bo m3CH4/kgVS 0.19 ‐20 20 IPCC(2006)

MaximumMethaneProducingCapacities–Feedlotsheep Bo m3CH4/kgVS 0.36 ‐20 20 IPCC(2006)

MaximumMethaneProducingCapacities–Goat

Bo m3CH4/kgVS 0.17 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–Horse Bo m3CH4/kgVS 0.3 ‐30 30 IPCC(2006)

MaximumMethaneProducingCapacities–Mule/Ass

Bo m3CH4/kgVS 0.33 ‐30 30 IPCC(2006)

EmissionfactorforthefractionofCH4producedthatleaksfromtheanaerobicdigester–Digesterswithsteelorlinedconcreteorfiberglassdigesterswithagasholdingsystem(eggshapeddigesters)andmonolithicconstruction

EFCH4,leakage

% 2.8 CDM(2012)

EmissionfactorforthefractionofCH4producedthatleaksfromtheanaerobicdigester–UASBtypedigesterswithfloatinggasholdersandnoexternalwaterseal

EFCH4,leakage

% 5 CDM(2012)

EmissionfactorforthefractionofCH4producedthatleaksfromtheanaerobicdigester–Digesterswithunlinedconcrete/ferrocement/brickmasonryarchedtypegasholdingsection;monolithicfixeddomedigesters

EFCH4,leakage

% 10 CDM(2012)


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Parameter

Abbreviation/Sym

bol

DataInputUnit

EstimatedValue


(%

)


(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

EmissionfactorforthefractionofCH4producedthatleaksfromtheanaerobicdigester–Otherdigesterconfigurations

EFCH4,leakage

% 10 CDM(2012)

Temporarystorageofliquid/slurrymanure–N2Oemissionfactorc

EFN20 kgN2O‐N/kgN 0.005 ‐50 100 U.S.EPA(2011)

Long‐termstorageofsolidmanure–N2Oemissionfactorc


Long‐termstorageofslurrymanure–N2Oemissionfactorc


CattleandSwineDeepBedding(ActiveMix)‐N2Oemissionfactorc

EFN20 kgN2O‐N/kgN 0.07 IPCC(2006)

CattleandSwineDeepBedding(NoMix)‐N2Oemissionfactorc


PitStorageBelowAnimalConfinements‐N2Oemissionfactorc


Naturalaerationaerobiclagoons–N2Oconversionfactorc

EFN20 kgN2O‐N/kgN 0.01 ‐50 100 IPCC(2006)

Forcedaerationaerobiclagoons–N2Oconversionfactorc


N2Oemissionfactorforliquidstorage–uncoveredliquidmanurewithacrustc


N2Oemissionfactorforliquidstorage–uncoveredliquidmanurewithoutacrustc

EFN20 kgN2O‐N/kgN 0 ‐50 100 IPCC(2006)

N2Oemissionfactorforliquidstorage–coveredliquidmanurec

EFN20 kgN2O‐N/kgN 0 ‐50 100 IPCC(2006)

ManureManagement–MultipleSources–collectionefficiency,coveredstorage(withorwithoutcrust)

η Percentage 1 Sommeretal.(2004)

ManureManagement–MultipleSources–collectionefficiency,uncoveredstoragewithcrustformation

η Percentage 0

Sommeretal.(2004)

ManureManagement–MultipleSources–collectionefficiency,uncoveredstoragewithoutcrustformation

η Percentage ‐0.40

Sommeretal.(2004)

ManureManagement–MultipleSources–Ratecorrectingfactors(b1)

b1 Dimensionless 1 Sommeretal.(2004)

ManureManagement–MultipleSources–Ratecorrectingfactors(b2)

b2 Dimensionless 0.01Sommeretal.(2004)

ManureManagement–MultipleSources–Arrheniusparameter,cattle

A gCH4/kgVS/hr 43.33 Sommeretal.(2004)

ManureManagement–MultipleSources–Arrheniusparameter,swine

A gCH4/kgVS/hr 43.21Sommeretal.(2004)

Potentialmethaneyieldofthemanurecattle

ECH4,pot‐

kgCH4/kgVS 0.48 Sommeretal.(2004)

Potentialmethaneyieldofthemanure‐swine

ECH4,pot

kgCH4/kgVS 0.5Sommeretal.(2004)


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Parameter

Abbreviation/Sym

bol

DataInputUnit

EstimatedValue


(%

)


(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Temporarystackandlong‐termstockpile–Ratiodegradablevolatilesolidstototalvolatilesolids‐cattleliquidmanure

VSd/VST

Unitless 0.46 Mølleretal.(2004)

Temporarystackandlong‐termstockpile–Ratiodegradablevolatilesolidstototalvolatilesolids‐swineliquidmanure

VSd/VST


Temporarystackandlong‐termstockpile–RatioNon‐degradablevolatilesolidstototalvolatilesolids‐cattleliquidmanure

VSnd/VST


Temporarystackandlong‐termstockpile–Rationon‐degradablevolatilesolidstototalvolatilesolids–swineliquidmanure

VSnd/VST


aThevaluesformethaneconversionfactor(MCF)varydependingonthetemperatureandthemanuremanagementsystem.IPCC(2006)providesestimateduncertaintyrangesfortheseMCFs.bTherearenodataforNorthAmericaregion;thedatafromWesternEuropeareusedtocalculatetheestimation.Thereisnoreporteduncertaintyforthisadaptedvalue.cIPCC(2006)reportslargeuncertaintieswithdefaultN2Oemissionfactors.TheN2OEFvaluesvarydependingontheanimalspeciesandtemperatureofthemanuremanagementsystem.

5.5 ResearchGaps

Researchgapshavebeenidentifiedforanimalproductionsystems,coveringactivitydata,aswellaskeyareasthatwouldfacilitatemoreaccurateestimationofemissionsfromentericfermentationandmanuremanagementsystems.Recommendationsarediscussedbelow.

5.5.1 EntericFermentation

CattleFutureresearchrelatedtoimprovingemissionsestimatesshouldbeaimedatexpandingtheoptionswithinexistingmodelstobetterdescribeanindividualfarmsystemandincorporatemoreoptionsformitigationstrategiestoseehowemissionsmightchangewithimplementationofthesestrategiesaswellasconsidertheinteractiveeffectsofmultiplestrategies.

BeefCow‐Calf,Bulls,Stocker,andSheepKeydataneedsincludemeasurement/predictionoffeedintakeonpasture,measurement/predictionofCH4fromgrazinganimals(largernumbersofanimals),andmethodsbywhichtocharacterizerangeforageandintakeunderproductionconditions.

FeedlotThereisaneedforequationsandmodelstoaccuratelypredictentericCH4emissionsfromcattleandsheepfedhigh‐concentratefinishingdiets.


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DairyOneofthelargestresearchgapsisthelackofbasicdatarelatedtoemissionsfromcalves,heifers,anddrycowhousingsystems.Inaddition,thereisaneedforequallyconsistentandreliablemethodsformeasuringrelativedifferencesinemissionsassociatedwiththeimplementationofavarietyofmanagementpractices.Furthermodeldevelopmentforestimatingemissionsshouldincludeanexpansionofoptionstodescribetheproductionfacilityandinclusionofmanagementpracticesthatcanbeadoptedtomitigateemissions.

SwineFutureresearchrelatedtoimprovingemissionsestimatesshouldbeaimedatexpandingtheoptionswithinthesemodelstobetterdescribeanindividualfarmsystemandincorporatemoreoptionsformanagementandmitigationstrategiestoseehowemissionsmightchangewithimplementationofdifferentpractices.Minimally,thedietconsiderationsinHolosneedtobeincorporatedintotheMANUREmodelandexpandedtoreflectproductionphase.

PoultryFutureresearchrelatedtoimprovingemissionsestimatesshouldbeaimedatexpandingtheoptionswithinthesemodelstobetterdescribeanindividualfarmsystemandincorporatemoreoptionsformanagementandmitigationstrategiestoseehowemissionsmightchangewithimplementationofdifferentpractices.

5.5.2 ManureManagement

Greenhousegasemissionsfromavarietyofmanuremanagementsystemshavebeendevelopedfromalimitednumberofstudiesandalimitednumberofpotentialvariationsinmanagementandtheenvironmentalconditionsaroundaparticularmanuremanagementsystem.ThelargestdeficiencyinthecurrentGHGstudiesisthelackofcharacterizationofthetemporalvariationintheGHGemissionsfromdifferentsystemsandthespatialvariationinGHGemissionsinducedbymeteorologicalconditionsamongspecificlocations.Ingeneral,theresearchneededtodevelopamorecompleteunderstandingoftheGHGemissionscanbesummarizedas:

Developdatabasesfromresearchobservationsofcommercialfacilitiesthatcharacterizethestoragesystem,timeinstorage,environmentalconditionsandlocation,andtheattributesofthemanuresource,e.g.,typeofanimal,diet,loadingrate.

UtilizethedatabasestoderivesimulationmodelstoquantifytheGHGemissionsfromdifferentmanuremanagementsystems.

ValidatethemodelsusingindependentobservationsfrommanuremanagementsystemsdistributedaroundtheUnitedStates.

Developoperationalmodelscapableofbeingappliedtoproductionscalesystemswhichutilizesimpleparametersasinputvariablesandproduceresultsinagreementwiththemorecomplexsimulationmodels.

UtilizethesemodelstodeveloppotentialstrategieswhichcouldbeemployedtomitigateGHGemissionsfrommanuremanagementsystems.

TemporaryStackandLong‐TermStockpileMethaneemissiondatafromsolidstoragesindifferentregionsunderdifferentclimatesarelimited.InordertodevelopamoreaccuratemodeltoestimatetheCH4emissionfromsolidmanurestorages,in‐depthstudiesareneededtointegratetemperature,storagetime,storagemethod,andmassflowwithCH4emissionindifferentregions.AsforN2Oemission,systematicallycollectingmoreintensedata(avarietyofspatialandtemporalscales)fromdifferentregionswillbeagoodfirststeptowardaccurateN2Oemissionmodels.Oncethesedataarecollectedandusedtodevelop/validatemodels,workwilllikelybeneededtodevelopfarmer‐friendlymodelsusing


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simplefarmparametersasinputvariables,resultinginemissionsestimatesthatarecorrelatedwiththoseofmorecomplexmodels.Forexample,thesemodels,ifsynchronized,couldformpartofacomprehensivemanurestewardshiptoolkit.

ThereisapaucityofdataonCH4andN2Oemissionsfromopenlot(beeffeedlotsanddairies)pensurfacesandrunoffcontrolstructuresandonthechemicalandphysicalfactorscontrollingthoseemissions.

CompostingGreenhousegasemissionsdatafromcompostingindifferentregionsunderdifferentoperationalconditionsarelimited.AgoodfirststeptowardanaccurateGHGemissionsmodelwouldbetocollectmoredatafromdifferentregionsanddifferentoperationalconditions.Consequently,in‐depthstudiesintegratingcompostpilesize/surfacearea,pileshape,aerationrate,storagetime,compostingtemperature,etc.,withGHGemissionsneedtobeconductedtodevelopcomplexmodelsdescribingGHGemissionsfromcomposting.Furthermore,workwilllikelybeneededtodevelopfarmer‐friendlymodelsusingsimplefarmparametersasinputvariables,resultinginemissionestimationsthatarecorrelatedwiththoseofmorecomplexmodels.

TherehavebeensomestudiesperformedtoestimatetheemissionfactorsforN2Ofromcompostingmanureindifferentsystemsandfordifferentlivestockcategories.(Fukummotoetal.,2003;Szantoetal.,2006)haveconductedstudiesoncompostingswinemanureatspecificambienttemperatures.Factorshavebeenpresentedinthestudiesbutthereissignificantuncertaintyduetothelimiteddataavailable.Furtherresearchisneededtorefinetheseemissionfactorsaswellasdevelopfactorsforotheranimals.

AerobicLagoonIn‐depthstudiesareneededtointegratelagoondepths,aerationrate,pH,temperature,andnutrientconditionsofmanurewithGHGemissions,whichwillfacilitatethedevelopmentofcomprehensivemodelstopredictGHGemissionsunderdifferentoperationalandclimateconditions.Simplifiedandfarm‐friendlymodelsusingfarmoperationalparametersasinputsshouldbedevelopedtohelpfarmsestimatetheGHGemissionsattheentitylevel.

AnaerobicLagoon,RunoffHoldingPond,andStorageTanksAllmodelstoestimateGHGemissionsfromliquidmanurestoragearerelativelyinaccurate,duetothecomplexityandvarietyoflivestockmanureoperations.Inordertodevelopamoreaccuratemodeltoestimateemissionsfromliquidmanurestorages,in‐depthstudiesareneededtointegratemanurestorageconfiguration,temperature,storagetime,storagemethod,massflow,andsurfaceturbulencewithemissionsindifferentregions.Inaddition,systematicallycollectingmoredatafromdifferentregionswillbeveryhelpfultodevelopmorestatisticallyaccuratemodelstoestimateGHGemissions.

AnaerobicDigestionChangesinchemicaloxygendemand,volatilesolids,totalsolids,andnitrogenintheanaerobicdigestionprocessareindirectlylinkedtoGHGemissionsfrompost‐treatmentofanaerobicdigestioneffluent.TheeffectivenessofanaerobicdigestionatmitigatingGHGemissionshasbeenstudiedintensively.However,anaerobicdigestioneffluentcanleadtoGHGemissions.Morein‐depthstudiesareneededtodevelopintegratedmodelsthatcanaccuratelypredicttheoverallGHGemissionfromthecombinationofanaerobicdigestionandpost‐treatmentapproaches.

CombinedAerobicTreatmentSystemsMethodsandtechniquestoreducethecapitalandoperatingcostsareneeded.Thereisalsoaneedtodevelopbetterwaystoconserveandderiveenergyfromthewastematerial.Thereisapaucityof


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dataonGHGemissionsfromthesesystemsanddevelopmentofemissionmodelswillrequireintegrationofdatacharacterizingthesesystemsandtheclimaticconditionsinordertodevelopthesemodels.Thesemodelswillneedtobevalidatedagainstobserveddata.

NutrientRemovalVariousmethodsofnitrogenremoval,suchasbiologicalnitrogenremoval,Anamox,NH3stripping,ionexchange,andstruvitecrystallization,shouldbeinvestigatedatcommercial‐scaleanimaloperationsunderdifferentclimateconditions.Characteristicsofmanure,massflow,andgasemissionsshouldbecloselymonitoredinordertoprovidethedataneededtoconstructrelativelypreciseestimationmodels.Inaddition,furtherresearchisneededtopilotinnovativebeefanddairyGHGemissionreductionstrategiesinfeedlotsanddairies.

ConstructedWetlandAlthoughtherearenumerouspaperspublishedaboutvariousaspectsoftreatmentwetlandeffectivenessandemissions,therecurrentlyisnotanestablishedmethodforcalculationofGHGemissionsfromanyofthetreatmentwetlandtypes.Moreover,therearenotsufficientunifyingpublicationstosuggestthatareliablemethodcouldbeestablishedwithinthescopeofthisreport.AmorerobustandextensivedatabaseonGHGemissionsfromtreatmentwetlandsisneeded.Concomitantly,thereisaneedforbetterpredictiveequationandmodels.

Thermo‐ChemicalConversionMorestudiesareneededontheeffectsofthermalconversionofanimalmanureonGHGemissioninordertoconcludedetailedemissionprofilescorrespondingtodifferenttypeofmanure.ThesestudieswouldentaildetailedobservationsofthemanureconversionsystemalongwithGHGemissionsandinformationontheenvironmentalconditions.


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Appendix5‐A:EntericCH4fromFeedlotCattle–MethaneConversionFactor(Ym)AsnotedintheBeefProductionSystemssection(Section5.3.2.2),amodifiedIPCC(2006)methodisproposedtoestimateentericCH4emissionsfromfinishingbeefcattle.Forthisreport,abaselinescenariobasedontypicalU.S.beefcattlefeedingconditionswasestablishedandbaselinevaluesweresetbasedonpublishedresearch.Toestimatemethaneemissions,emissionvaluesaremodifiedusingadjustmentfactorsthatarebasedonchangesinanimalmanagementandfeedingconditionsfromthebaselinescenario.Thisappendixpresentsbackgroundinformationonthebaselinescenarioandadjustmentfactors.

ThefollowingbaselinescenariosareestablishedforbeefcattleinU.S.feedlots:

1. Mediumtolargeframesteer(orheifer)yearlingsarefedahighconcentratefinishingdietcontaining<=10percentforageindietdrymatter(=to8to18percentNDF)indry‐lot,soil‐surfacedpens.

2. Thegrainportionofthedietisatleast70percentofdietdrymatter.3. Thegrainsourceissteamflaked(SFC)orhighmoisturecorn(HMC).4. Thedietarycrudeproteinconcentrationis12.5to13.5percentofdietdrymatter

(VasconcelosandGalyean,2007).5. Thedietaryruminallydegradableprotein(DIPorRDP)concentrationis7.5to9percentof

dietdrymatter(VasconcelosandGalyean,2007).6. Thedietcontainsmonensin(Rumensin,ElancoAnimalHealth)atrecommended

concentrations(VasconcelosandGalyean,2007).7. Dietsforheiferscontainmelengestrolacetate(MGA)attherecommendedconcentrations

(VasconcelosandGalyean,2007).8. Cattleareimplantedwithanestrogenicimplantthroughoutthefeedingperiod(Vasconcelos

andGalyean,2007).9. Nobeta‐agonistisfed.10. Thedietcontainsnosupplementalfat(vegetableoil,yellowgrease,etc.)andhasatotalfat

concentrationoflessthan4.5percentofdietdrymatter.11. EntericCH4emissionisthreepercentofgrossenergyintake(GEI:(IPCC,2006).12. Thedietaryforageischoppedalfalfa,sorghum,orgrasshayatsevento10percentofdiet

drymatter.13. Thedietcontainsmineralsandvitaminsattherecommendedlevel(NRC,2000).14. Temperaturesaremild/moderateduringthefeedingperiod.15. Cattleareslaughteredatanaveragebodyweightofapproximately582kg(1,280lb.)(KSU,

2012).16. Averagedressingpercentis61percent.17. Cattlearefed150days.

TheYmadjustmentfactorsforfeedlotcattlefedhigh‐concentratedietsinTable5‐11weredeterminedbasedonthefollowingliteraturereviewsandanalyses.

Ionophores:Onaverage,thefeedingofionophoresdecreasesDMIbyaboutfivepercent(Delfinoetal.,1988;Vogel,1995;RobinsonandOkine,2001;Tedeschietal.,2003)anddecreasesADGbyabouttwopercent(Delfinoetal.,1988;Tedeschietal.,2003).Feedingionophoresdecreasesentericmethaneemissionsapproximately20percentforthefirsttwotofourweeksonfeed(Tedeschietal.,2003;Guanetal.,2006).Therefore,overa150‐dayfeedingperiod,overallentericmethane


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emissionsaredecreasedapproximately4percent.Becauseofanincreaseinthegain:feedratio,entericmethaneemissionsperunitofproductionaredecreasedwhenionophoresarefed.

SupplementalFat:Foreachonepercentincreaseinsupplementalfat(uptoamaximumoffourpercentaddedfat),entericmethaneemissions(asapercentageofgrossenergyintake)decreaseapproximately3.8to5.6percent(ZinnandShen,1996;Beaucheminetal.,2008;Martinetal.,2010).AconservativevalueoffourpercentperonepercentincreaseinsupplementalfatisrecommendedbecausemanyfatsourcesusedintheindustryarepartiallysaturatedandmayhavelesseffectonentericCH4productionthanthehighlyunsaturatedfatsusedinmoststudies.Forexampleifthreepercentsupplementalfatisaddedtothediet,thenCH4productionisdecreased12percent(threepercentaddedfattimesfourpercentisequivalenttoa12percentdecrease).TherevisedentericCH4emissionis2.64percentofGEI(threepercentbaseline*0.88=2.64percentofGEI).Manydistiller’sgrainscontainapproximately8to12percentfat.Additionofdistiller’sgrainmayserveasasourceofsupplementalfat,andthusdecreaseentericCH4(McGinnetal.,2009).HoweverHalesetal.(2013)notedthatfeedingincreasingconcentrationsofWetDistillersGrainswithSolubles(WDGS)inequal‐fatdietsincreasedentericCH4,likelyduetotheincreasedNDFintake.12

Grainprocessing&Grainsource:GrainprocessingdirectlyaffectsentericCH4productionviaitseffectsonruminalfermentation.EntericCH4emissions,asapercentofGEI,are20percentgreaterwithdietsbasedonDRCthanindietsbasedonsteam‐flakedcorn(SFC)orhighmoisturecorn(HMC)(Archibequeetal.,2006;Halesetal.,2012).Moreextensivegrainprocessingmayalsoimprovethegain:feedratioabout10percent(Owensetal.,1997;ZinnandBarajas,1997)andmay,decreasemanureCH4emissionsviadecreasedfecalstarchexcretion(ZinnandBarajas,1997;Halesetal.,2012).EntericCH4emissionsare20to40percentgreaterwithfinishingdietsbasedonbarleythandietsbasedoncorn;presumablybecauseofthelowerstarchandhigherfibercontentofbarley(Benchaaretal.,2001;BeaucheminandMcGinn,2005).Amean(30%)forthesestudiesisrecommendedforabarleyadjustmentfactor.

DietaryForageandGrainConcentrationeffects:LimiteddataexiststoevaluateeffectsofdietaryforageandgrainconcentrationonentericmethaneproductionfrombeefcattlethatarefedtypicalU.S‐based,highconcentratefinishingdiets.EquationsfromEllisetal.(2007;2009)illustratetheeffectsofdietaryforage,NDF,andstarchonentericCH4production.Inparticular,thefollowing10equationsillustratetherelationships:

CH4(MJ/day)=3.96+0.561×DMI(kg/day) CH4(MJ/day)=4.79+0.0492×Forage(%) CH4(MJ/day)=5.58+0.848×NDF(kg/day) CH4(MJ/day)=5.70+1.41×ADF(kg/day) CH4(MJ/day)=2.29+0.670×DMI(kg/day) CH4(MJ/day)=4.72+1.13×Starch(kg/day) CH4(MJ/day)=‐1.01+2.76×NDF(kg/day)+0.722×Starch(kg/day) CH4(MJ/day)=2.68–1.14(Starch:NDF)+0.786×DMI(kg/day) CH4(MJ/day)=2.50=0.367×Starch(kg/day)+0.766×DMI(kg/day) CH4(MJ/day)=2.70+(1.16×DMI(kg/day))–(15.8×etherextract(kg/day))

12Ymisadjustedfordistillergrainsbychangesinfatcontentandgrainconcentration.Forexample,a30percentconcentrationofdistillergrainsinthefinishingdietwilltypicallyincreasethedietaryfatlevelby2to3percentanddecreasethegraincontentby25to30percent.TheresultingchangeinYmisadecreaseby8percenttoaccountforincreaseinfatcontentandanincreaseof10percenttoaccountforadecreaseingraincontent(i.e.,Ym=3%x0.92x1.10=3.036%).


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Todevelopadjustmentfactorsforgrainconcentrationsindiets,artificialdatasetswerecreatedthatvariedinforage(rangeof5to25percent),NDF(range10to20percent),fat(rangeof3to6percent),andstarch(rangeof30to60percentofdietdrymatter)content.Usingthesedatasets,entericCH4emissionswereestimatedusingtheappropriateequation(s)ofEllisetal.(2007;2009).EffectsofdietarychangesonentericCH4werethendeterminedbylinearregressionanalysis.Onaverage,entericCH4production(MJ/day)increasedfivepercentforeachonepercentincreaseindietaryforageconcentration;increased13percentforeachonekgincreaseindietaryNDFintake,increasedfivepercentforeachonekgincreaseinstarchintakeanddecreasedfivepercentforeachoneunitincreaseinthedietarystarch:NDFratio.SmallincreasesinforageconcentrationfromthebaselinevaluehadsmalleffectsonYm;whereas,greaterincreaseshadalargereffect(Halesetal.,2012;Halesetal.,2014).AnevaluationofthesefactorsindicatedanentericCH4Ymadjustmentfactorof10%forsmallincreasesinforage(anddecreasesingrainconcentration)andalargercorrectionfactorof40percentforgreaterchanges(dietconcentratelessthan45percent).Thesefactorsarerecommendedforaccountingforthegrainconcentrationinfinishingdiets.

NoYmadjustmentfactorwasexplicitlymodeledtoaccountforthefollowingdietarymanagementfactors:13

Beta‐agonists:Beta‐agonistsdonotdirectlyaffecttheYm(i.e.,entericCH4emissionsperunitofgrossenergyintake),thereforenoadjustmentfactorisrecommended.However,becauseofa4percentincreaseinfeedefficiency,a2.5to3.5%increaseinhotcarcassweight(HCW),andanincreaseinlivebodyweight(Vasconcelosetal.,2008;Elametal.,2009;Montgomeryetal.,2009;Delmoreetal.,2010;Radunz,2011),entericCH4emissionsperunitofproductionaredecreasedwhenbeta‐agonistsarefed.

Melengestrolacetate(MGA:heifersonly):FeedingMGAtoheifersdoesnotdirectlyaffectentericCH4emissions.However,becauseofaninepercentincreaseinthegain:feedratio(Hilletal.,1988;KreikemeierandMader,2004)entericCH4emissionsperunitofproductionaredecreasedwhenMGAisfed.

DirectFedMicrobials:MostdirectfedmicrobialsdonotappeartodirectlyaffectentericCH4emissionsandeffectsonanimalperformancearesomewhatvariable(Krehbieletal.,2003).Noadjustmentfactorisrecommendedforthefeedingofdirectfedmicrobials.

DietaryCrudeProteinandRuminalDegradableProtein(RDP):DietaryproteinmaypotentiallyaffectanimalperformanceandentericCH4emissionsviaeffectsonruminalfermentation.However,thereisnoreadilyavailabledatawithmodernfeedlotdietswithwhichtocompare(BergerandMerchen,1995;RobinsonandOkine,2001;Gleghornetal.,2004;Coleetal.,2006;Wagneretal.,2010).ThereisnorecommendedYmadjustmentfactorfordietaryprotein.Dietaryproteinmayaffectemissionsofmanuregreenhousegases(N2O)anddefinitelyaffectsNH3emissions(Toddetal.,2013).

Implantingregimens:ImplantsdonotdirectlyaffectentericCH4emissions.Howeverbecauseofanincreaseinfeedefficiency,livebodyweight,andHCW(Herschleretal.,1995;RobinsonandOkine,2001;Wilemanetal.,2009),entericCH4emissionsperunitofproductionaredecreasedwhenimplantsareused.

Ambienttemperature:ColdandhottemperaturesmaypotentiallyaffectentericCH4emissionduetoeffectsonfeedintake,ruminaldigestionandrateofpassage(Young,1981);however,theactualeffectsarenotclear.Thereforenoadjustmentfactorforenvironmentaltemperatureisused.ColdtemperaturesmaydecreaseCH4,N2OandNH3lossesfrompen

13AlthoughthesemanagementfactorsarenotmodeledtoimpactYm,someofthemdoimpactentericCH4perunitofproduction.Hence,inevaluatingmethaneintensityperunitofproduction,thesefactorswouldhaveanimpact.


5-112

surfacesviaeffectsonmicrobialactivityinthemanure.Conversely,warmtemperaturesmayincreaseemissionsfrommanureviaincreasedmicrobialactivity.

TheIPCCTier2modeliscurrentlythemostusefulforpredictingemissionsfromcow‐calfandstockerproduction,aswell,asnotedintheearliercow‐calfandstockerSections(5.3.2.2).EntericemissionsfromallcattleotherthandairycowsanddairyheifersareestimatedusingtheIPCCTier2equationorthemodifiedIPCCTier2previouslydiscussedforfeedlotcattle.Tousetheseequations,itisnecessarytomakesuretheinputstotheequationsareasaccurateaspossible.ForDE(asapercentageofGE),werecommendusingthefeedstuffscompositiontableprovidedinNRC(1989)andEwan(1989).SeveralfeedstuffsfromthetableareincludedinTable5‐C‐1.Afterreviewofthemodels,theirstrengthsandlimitations,modelsbasedontheMillsequations(e.g.,DairyGEM,COWPOLL,IFSM)appeartobethemostusefulforpredictingemissionsfromdairycattle.TheMits3equationrecommendedforcalculatingentericCH4emissionsfromdairycowsanddairyheifers(usedinDairyGEM/IFSM)requiresdifferentdietaryinputinformationthanthatrequiredfortheIPCCTierIIequation.Specifically,DairyGEM/IFSMrequiresthestarchandADFcontentoffeeds.BecausestarchisnearlyequivalenttoNFC(whichisstarch+sugar+pectin)inhighforagediets(dairydiets),weuseNFCintheMits3equation(NFC=100–(NDF+CP+EE+Ash)).ThesevaluescanbefoundinAppendix5‐B.


5-113

Appendix5‐B:FeedstuffsCompositionTableThistableprovideddatainputsforentericfermentationemissionscalculationsforcattleandsheep.

Table5‐B‐1:FeedstuffsCompositionTable(Preston2013,exceptwherenotedfordigestibleenergy)

Feedstuff DM %

Energy Protein Fiber

EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

% NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Alfalfa Cubes x91 57 57 25 57 18 30 29 36 46 40 2.0 11 1.30 0.23 1.9 0.37 0.33 20

Alfalfa Dehydrated

17% CP 92 61 62 31 61 65.16 19 60 26 34 45 6 3.0 11 1.42 0.25 2.5 0.45 0.28 21

Alfalfa Fresh 24 61 62 31 61 62.54

b 19 18 27 34 46 41 3.0 9 1.35 0.27 2.6 0.40 0.29 18

Alfalfa Hay Early Bloom

90 59 59 28 59 63.72 19 20 28 35 45 92 2.5 8 1.41 0.26 2.5 0.38 0.28 22

Alfalfa Hay Midbloom

89 58 58 26 58 61.79 17 23 30 36 47 92 2.3 9 1.40 0.24 2.0 0.38 0.27 24

Alfalfa Hay Full Bloom

88 54 54 20 54 55.71 16 25 34 40 52 92 2.0 8 1.20 0.23 1.7 0.37 0.25 23

Alfalfa Hay Mature

88 50 50 12 49 54.18 13 30 38 45 59 92 1.3 8 1.18 0.19 1.5 0.35 0.21 23

Alfalfa Seed Screenings

91 84 92 61 87 34 13 15 10.7 6 0.30 0.67

Alfalfa Silage 30 55 55 21 55 60.71

c 18 19 28 37 49 82 3.0 9 1.40 0.29 2.6 0.41 0.29 26

Alfalfa Silage Wilted

39 58 58 26 58 60.71

d 18 22 28 37 49 82 3.0 9 1.40 0.29 2.6 0.41 0.29 26

Alfalfa Leaf Meal

89 60 60 30 60 26 15 16 24 34 35 3.0 10 2.88 0.34 2.2 0.32 39

Alfalfa Stems 89 47 47 7 46 11 44 44 51 68 100 1.3 6 0.90 0.18 2.5

Almond Hulls 89 56 56 23 56 59.90 3 60 16 29 36 100 3.1 7 0.24 0.10 2.0 0.03 0.07 20

Ammonium Chloride

99 0 0 0 0 163 0 0 0 0 0 0.0 0.00 0.00 0.0 66.00 0.00 0

Ammonium Sulfate

99 0 0 0 0 132 0 0 0 0 0 0.0 24.15

Apples 17 70 73 44 71 3 10 7 9 25 10 2.2 2 0.06 0.60 0.8

Apple Pomace Wet

20 68 70 41 69 5 10 18 27 36 27 5.2 3 0.13 0.12 0.5 0.04 11

Apple Pomace Dried

89 67 69 40 68 56.69 5 15 18 28 38 29 5.2 3 0.13 0.12 0.5 0.04 11

Artichoke Tops (Jerusalem)

27 61 62 31 61 6 18 30 41 40 1.1 10 1.62 0.11 1.4

Avocado Seed Meal

91 52 52 16 51 20 19 24 1.2 16

Bahiagrass Hay 90 53 53 18 53 54.85 6 37 32 41 72 98 1.8 7 0.47 0.20 1.4 0.21

Bakery Product Dried

90 90 100 68 94 81.31 11 30 3 9 30 0 11.5 4 0.16 0.27 0.4 2.25 0.15 33

Bananas 24 84 92 61 87 4 4 5 0.8 3 0.03 0.11 1.5 8

Barley Hay 90 57 57 25 57 60.89 9 28 37 65 98 2.1 8 0.30 0.28 1.6 0.19 25

Barley Silage 35 59 58 26 58 12 22 34 37 58 61 3.0 9 0.46 0.30 2.4 0.22 28

Barley Silage Mature

35 58 58 26 58 12 25 30 34 50 61 3.5 9 0.30 0.20 1.5 0.15 25

Barley Straw 90 44 44 1 43 43.98 4 70 42 55 78 100 1.9 7 0.32 0.08 2.2 0.67 0.16 7

Barley Grain 89 84 92 61 87 12 28 5 7 20 34 2.1 3 0.06 0.38 0.6 0.18 0.16 23

Barley Grain, Steam Flaked

85 90 100 70 100 12 39 5 7 20 30 2.1 3 0.06 0.35 0.6 0.18 0.16 23


5-114

Feedstuff DM %


EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

% NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Barley Grain Steam Rolled

86 84 92 61 87 12 38 5 7 20 27 2.1 3 0.06 0.41 0.6 0.18 0.17 30

Barley Grain 2-row

87 84 92 61 87 12 6 8 24 34 2.3 2 0.05 0.31 0.6 0.18 0.17

Barley Grain 6-row

87 84 92 61 87 11 6 8 24 34 2.2 3 0.05 0.36 0.6 0.18 0.15

Barley Grain Lt. Wt. (42-44

lb/bu) 88 78 83 54 80 13 30 9 12 30 34 2.3 4

Barley Feed Pearl Byproduct

90 74 78 49 76 15 25 12 15 3.9 5 0.05 0.45 0.7 0.06

Barley Bran 91 59 59 28 59 12 28 21 27 36 6 4.3 7

Barley Grain Screenings

89 71 74 46 73 12 9 11 2.6 4 0.35 0.33 0.9 0.15

Beans Navy Cull

90 84 92 61 87 84.52 24 25 5 8 20 0 1.4 5 0.15 0.60 1.4 0.06 0.26 45

Beet Pulp Wet 17 77 82 53 79 75.09 9 35 20 25 45 30 0.7 5 0.65 0.08 0.9 0.40 0.22 21

Beet Pulp Dried 91 76 81 52 78 79.81 9 44 21 26 46 33 0.7 5 0.65 0.08 0.9 0.40 0.22 21

Beet Pulp Wet with Molasses

24 77 82 53 79 11 25 16 21 39 33 0.6 6 0.60 0.10 1.8 0.42 11

Beet Pulp Dried with Molasses

92 77 82 53 79 82.52 11 34 17 23 40 33 0.6 6 0.60 0.10 1.8 0.42 11

Beet Root (Sugar)

23 80 86 56 83 4 5 7 16 0.4 3

Beet Tops (Sugar)

19 58 58 26 58 14 11 14 25 41 1.3 24 1.10 0.22 5.2 0.20 0.45 20

Beet Top Silage 25 52 52 16 51 12 12 2.0 32 1.38 0.22 5.7 0.57 20

Bermudagrass Coastal

Dehydrated 90 62 63 33 63 16 40 26 29 40 10 3.8 7 0.40 0.25 1.8 0.72 0.23 18

Bermudagrass Coastal Hay

89 56 56 23 56 53.05 10 20 30 36 73 98 2.1 6 0.47 0.21 1.5 0.70 0.22 16

Bermudagrass Hay

89 53 53 18 53 50.79 10 18 29 37 72 98 1.9 8 0.46 0.20 1.5 0.70 0.25 31

Bermudagrass Silage

26 50 50 12 49 10 15 28 35 71 48 1.9 8 0.46 0.20 1.5 0.72 0.25 31

Birdsfoot Trefoil Fresh

22 66 68 38 67 21 20 21 31 47 41 4.4 9 1.78 0.25 2.6 0.25 31

Birdsfoot Trefoil Hay

89 57 57 25 57 16 22 31 38 50 92 2.2 8 1.73 0.24 1.8 0.25 28

Biuret 99 0 0 0 0 248 0 0 0 0 0 0.0 0 0.00 0.00 0.0 0.00 0.00 0

Blood Meal, Swine/Poultry

91 66 68 38 67 92 82 1 2 10 0 1.4 3 0.32 0.28 0.2 0.30 0.70 22

Bluegrass KY Fresh Early

Bloom 36 69 71 43 70 75.62 15 20 27 32 60 41 3.9 7 0.37 0.30 1.9 0.42 0.19 25

Bluegrass Straw 93 45 45 3 44 6 40 50 78 90 1.1 6 0.20 0.10

Bluestem Fresh Mature

61 50 50 12 49 56.82 6 34 2.5 5 0.40 0.12 0.8 0.05 28

Bread Byproduct

68 90 100 68 94 14 24 1 2 3 0 3.0 3 0.10 0.18 0.2 0.76 0.15 40

Brewers Grains Wet

23 85 93 62 88 62.66 26 52 13 21 45 18 7.5 4 0.30 0.58 0.1 0.15 0.32 78

Brewers Grains Dried

92 84 92 61 87 60.43 25 54 14 24 49 18 7.5 4 0.30 0.58 0.1 0.15 0.32 78

Brewers Yeast Dried

94 79 85 55 81 48 3 1.0 7 0.10 1.56 1.8 0.41 41

Bromegrass Fresh Immature

30 64 65 36 65 78.57 15 22 28 33 54 40 4.1 10 0.45 0.34 2.3 0.21 20


5-115

FeedstuffDM %


EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

%NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Bromegrass Hay

89 55 55 21 55 62.19

e 10 33 35 41 66 98 2.3 9 0.40 0.23 1.9 0.40 0.19 19

Bromegrass Haylage

35 57 57 25 57 11 26 36 44 69 61 2.5 8 0.38 0.30 2.0 0.20 19

Buckwheat Grain

88 75 79 50 77 72.27 12 13 17 2.8 2 0.11 0.36 0.5 0.05 0.16 10

Buttermilk Dried 92 88 98 65 91 34 0 5 0 0 0 5.0 10 1.44 1.00 0.9 0.09 44

Cactus, Prickly Pear

23 61 62 31 62 5 16 20 28 2.1 18 4.00 0.10 1.5 0.20

Calcium Carbonate

99 0 0 0 0 0 0 0 0 0 0.0 99 38.50 0.04 0.1 0.00 0

Canarygrass Hay

91 53 53 18 53 9 26 32 34 67 98 2.7 8 0.38 0.25 2.7 0.14 18

Canola Meal, Solv. Ext.

90 72 75 47 74 41 30 11 19 29 23 2.0 8 0.74 1.14 1.1 0.07 0.78 68

Carrot Pulp 14 62 63 33 63 6 19 23 40 0 7.8 9

Carrot Root Fresh

12 83 90 60 86 92.29 10 9 11 20 0 1.4 10 0.55 0.32 2.5 0.50 0.17

Carrot Tops 16 73 77 48 75 13 18 23 45 41 3.8 15 1.94 0.19 1.9

Cattle Manure Dried

92 38 40 0 36 30.58 15 35 42 55 0 2.5 14 1.15 1.20 0.6 1.78 240

Cheatgrass Fresh Immature

21 68 70 41 69 16 23 2.7 10 0.60 0.28

Citrus Pulp Dried

90 78 83 54 80 7 38 13 20 21 33 2.9 7 1.81 0.12 0.8 0.04 0.08 14

Clover Ladino Fresh

19 69 71 43 70 73.22 25 20 14 33 35 41 4.8 11 1.27 0.38 2.4 0.20 20

Clover Ladino Hay

90 61 62 31 61 63.40 21 25 22 32 36 92 2.0 9 1.35 0.32 2.4 0.30 0.20 17

Clover Red Fresh

24 64 65 36 65 18 21 24 33 44 41 4.0 9 1.70 0.30 2.0 0.60 0.17 23

Clover Red Hay 88 55 55 21 55 58.33 15 28 30 39 51 92 2.5 8 1.50 0.25 1.7 0.32 0.17 17

Clover Sweet Hay

91 53 53 18 53 16 30 30 38 50 92 2.4 9 1.27 0.25 1.8 0.37 0.46

Coconut Meal, Mech. Ext.

92 76 81 52 78 79.66 21 56 13 21 56 23 6.8 7 0.40 0.30 1.0 0.33 0.04

Coffee Grounds 88 20 36 0 16 13 41 68 77 10 15.0 2 0.10 0.08

Corn Whole Plant Pelleted

91 63 64 34 64 9 45 21 24 40 6 2.4 6 0.50 0.24 0.9 0.14

Corn Fodder 80 65 66 37 66 9 45 25 29 48 100 2.4 7 0.50 0.25 0.9 0.20 0.14

Corn Stover Mature (Stalks)

80 54 54 20 54 5 30 35 43 70 100 1.3 7 0.45 0.15 1.2 0.30 0.14 22

Corn Silage, Milk Stage

26 65 66 37 66 8 18 26 32 54 60 2.8 6 0.40 0.27 1.6 0.11 20

Corn Silage, Mature Well

Eared 34 72 75 47 74 72.88 8 28 21 27 46 70 3.1 5 0.28 0.23 1.1 0.20 0.13 22

Corn Silage, Sweet Corn

24 65 66 37 66 11 20 32 57 60 5.0 5 0.24 0.26 1.2 0.17 0.16 39

Corn Grain, Whole

88 88 98 65 91 88.85 9 58 2 3 9 60 4.3 2 0.02 0.30 0.4 0.05 0.14 18

Corn Grain, Rolled

88 88 98 65 91 9 54 2 3 9 34 4.3 2 0.02 0.30 0.4 0.05 0.14 18

Corn Grain, Steam Flaked

85 93 104 71 97 95.44 9 59 2 3 9 40 4.1 2 0.02 0.27 0.4 0.05 0.14 18

Corn Grain, High Moisture

74 93 104 71 97 91.64 10 42 2 3 9 0 4.0 2 0.02 0.30 0.4 0.06 0.14 20

Corn Grain, High Oil

88 91 102 69 95 8 54 2 3 8 60 6.9 2 0.01 0.30 0.3 0.05 0.13 18


5-116

Feedstuff DM %


EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

% NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Corn Grain, Hi-Lysine

92 87 96 64 90 12 58 4 4 11 60 4.4 2 0.03 0.24 0.4 0.05 0.11 18

Corn and Cob Meal

87 82 89 59 85 83.15 9 52 9 11 26 56 3.7 2 0.06 0.27 0.5 0.05 0.13 16

Corn Cobs 90 48 48 9 47 53.18 3 70 36 39 88 56 0.6 2 0.12 0.04 0.8 0.27 5

Corn Screenings

86 91 102 69 95 10 52 3 4 9 20 4.3 2 0.04 0.27 0.4 0.05 0.12 16

Corn Bran 91 76 81 52 78 11 10 17 51 0 6.3 3 0.04 0.15 0.1 0.13 0.08 18

Corn Germ, Full-fat

97 135 198 160 198 12 55 6 11 36 20 44.9 2 0.02 0.28 0.1 0.02 0.17 60

Corn Gluten Feed

90 80 86 56 83 78.47 22 25 9 12 38 36 3.2 7 0.11 0.84 1.3 0.25 0.47 84

Corn Gluten Meal 41% CP

91 85 93 62 88 46 63 5 9 32 23 3.2 3 0.13 0.55 0.2 0.07 0.62 35

Corn Gluten Meal 60% CP

91 89 99 67 93 75.29 67 65 3 6 11 23 2.5 2 0.06 0.54 0.2 0.10 0.90 40

Corn Cannery Waste

29 68 70 41 69 8 15 28 36 59 0 3.0 5 0.10 0.29 1.0 0.13 25

Cottonseed, Whole

91 95 107 73 99 23 38 27 37 47 100 19.4 5 0.16 0.64 1.0 0.06 0.24 34

Cottonseed, Whole, Delinted

90 95 107 73 99 24 39 19 28 40 100 22.9 5 0.12 0.54 1.2 0.24 36

Cottonseed, Whole,

Extruded 92 87 98 67 91 26 50 32 44 53 33 9.5 5 0.17 0.68 1.3 0.24 38

Cotton Gin Trash (Burrs)

91 42 43 0 40 9 35 50 70 100 2.0 14 1.40 0.18 1.9 0.14 25

Cottonseed Hulls

90 45 45 3 44 44.30 5 45 48 70 87 100 1.8 3 0.15 0.08 1.0 0.02 0.05 10

Cottonseed Meal, Solv. Ext.

41% CP 90 77 82 53 79 72.85 47 42 13 18 25 23 1.5 7 0.22 1.23 1.6 0.05 0.44 66

Cottonseed Meal, Mech. Ext. 41% CP

92 79 85 55 81 71.71 46 50 13 19 31 23 5.0 7 0.21 1.18 1.6 0.05 0.42 64

Crab Waste Meal

91 29 37 0 30 32 65 11 13 3.0 43 15.00 1.88 0.5 1.63 0.27 107

Crambe Meal, Solv. Ext.

91 81 88 58 84 31 45 25 35 47 23 1.4 8 1.27 0.86 1.1 0.70 1.26 44

Crambe Meal, Mech. Ext.

92 88 98 65 91 28 50 24 33 42 25 17.0 7 1.22 0.78 1.0 0.65 1.18 41

Cranberry Pulp Meal

88 49 49 11 48 7 26 47 54 33 15.7 2

Crawfish Waste Meal

94 25 36 0 29 35 74 12 15 42 13.10 0.85

Curacao Phosphate

99 0 0 0 0 0 0 0 0 0 0.0 95 34.00 15.00

Defluorinated Phosphate

99 0 0 0 0 0 0 0 0 0 0.0 95 32.60 18.07 1.0 100

Diammonium Phosphate

98 0 0 0 0 115 0 0 0 0 0 0.0 35 0.52 20.41 0.0 2.16

Dicalcium Phosphate

96 0 0 0 0 0 0 0 0 0 0.0 94 22.00 18.65 0.1 1.00 70

Distillers Grains, Wet

25 91 102 69 95 28 52 8 18 40 4 9.6 5 0.10 0.70 1.0 0.20 0.60 95

Distillers Grain, Barley

90 75 79 50 77 30 56 16 20 44 4 8.5 4 0.15 0.67 1.0 0.18 0.43 50

Distillers Grain, Corn, Dry

91 95 106 72 99 76.86 30 58 8 16 44 4 9.5 4 0.09 0.75 0.9 0.14 0.70 65


5-117

Feedstuff DM %


EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

% NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Distillers Grain, Corn, Wet

36 97 109 74 102 30 47 8 16 44 4 9.5 4 0.09 0.75 0.9 0.14 0.70 65

Distillers Grain, Corn with Solubles

89 98 111 76 103 81.50 30 54 8 16 38 4 11.9 6 0.20 0.75 0.9 0.18 0.80 85

Distillers Dried Solubles

93 87 96 64 91 79.45 31 47 4 7 22 4 13.0 8 0.35 1.20 1.8 0.28 1.10 91

Distillers Corn Stillage

7 92 103 70 96 22 55 8 10 21 0 8.1 5 0.14 0.72 0.2 0.60 60

Distillers Grain, Sorghum, Dry

91 84 92 61 87 72.85 33 62 13 20 44 4 10.0 4 0.20 0.68 0.3 0.50 50

Distillers Grain, Sorghum, Wet

35 86 95 63 89 33 55 13 19 43 4 10.0 4 0.20 0.68 0.3 0.50 50

Distillers Grain, Sorghum with

Solubles 92 85 93 62 88 33 53 12 18 42 4 10.0 4 0.23 0.70 0.5 0.70 55

Elephant (Napier) Grass Hay, Chopped

92 55 55 21 54 9 24 46 63 85 2.0 10 0.35 0.30 1.3 0.10

Fat, Animal, Poultry,

Vegetable 99 195 285 230 285

80.08f

0 0 0 0 0 99.0 0 0.00 0.00 0.0

Feather Meal Hydrolyzed

93 67 69 40 68 87 68 1 14 42 23 7.0 3 0.48 0.45 0.1 0.20 1.82 90

Fescue KY 31 Fresh

29 64 65 36 65 15 20 25 32 64 40 5.5 9 0.48 0.37 2.5 0.18 22

Fescue KY 31 Hay Early

Bloom 88 60 60 30 60 53.57 18 22 25 31 64 98 6.6 8 0.48 0.36 2.6 0.27 24

Fescue KY 31 Hay Mature

88 52 52 16 51 11 30 30 42 73 98 5.0 6 0.45 0.26 1.7 0.14 22

Fescue (Red) Straw

94 43 44 0 41 4 41 1.1 6 0.00 0.06

Fish Meal 90 74 78 49 76 66 60 1 2 12 10 9.0 20 5.55 3.15 0.7 0.76 0.80 130

Flax Seed Hulls 91 38 40 0 36 9 32 39 50 98 1.5 10

Garbage Municipal Cooked

23 80 86 56 83 16 9 50 59 30 20.0 10 1.20 0.43 0.6 0.67

Glycerol (Glycerin)

88 90 100 68 94 0 0 0 0 0 0 0.0 6 4.00

Grain Screenings

90 65 66 37 66 14 14 5.5 9 0.25 0.34 30

Grain Dust 92 73 77 48 75 10 11 2.2 10 0.30 0.18 42

Grape Pomace Stemless

91 40 42 0 38 27.50 12 45 32 46 54 34 7.6 9 0.55 0.07 0.6 0.01 24

Grass Hay 88 58 58 26 58 10 30 33 41 63 98 3.0 6 0.60 0.21 2.0 0.20 28

Grass Silage 30 61 62 31 61 11 24 32 39 60 61 3.4 8 0.70 0.24 2.1 0.22 29

Guar Meal 90 72 75 47 74 39 34 16 3.9 5

Hominy Feed 90 89 99 67 93 11 48 5 8 21 9 6.5 3 0.04 0.55 0.6 0.06 0.10 32

Hop Leaves 37 49 49 11 48 15 15 3.6 35 2.80 0.64

Hop Vine Silage 30 53 53 18 53 15 21 24 3.1 20 3.30 0.37 1.8 0.22 44

Hops Spent 89 35 39 0 33 23 26 30 4.6 7 1.60 0.60

Kelp Dried 91 32 38 0 29 54.67 7 7 10 0.5 39 2.72 0.31

Kenaf Hay 92 48 48 9 47 10 31 44 56 98 2.9 12

Kochia Fresh 29 55 55 21 55 65.11 16 23 1.2 18 1.10 0.30

Kochia Hay 90 53 53 18 53 14 27 1.7 14 1.00 0.20

Kudzu Hay 90 54 54 20 54 16 33 2.6 7 3.00 0.23


5-118

FeedstuffDM %


EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

%NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Lespedeza Fresh Early

Bloom 25 60 60 30 60 16 50 32 2.0 10 1.20 0.24 1.1 0.21

Lespedeza Hay 92 54 54 20 54 14 60 30 3.0 7 1.10 0.22 1.0 0.19 29

Limestone Ground

98 0 0 0 0 0 0 0 0 0 0 0.0 98 34.00 0.02 0.03

Limestone Dolomitic Ground

99 0 0 0 0 0 0 0 0 0 0 0.0 98 22.30 0.04 0.4

Linseed Meal, Solv. Ext.

91 77 82 53 79 38 36 10 18 25 23 1.7 6 0.43 0.91 1.5 0.04 0.47 60

Linseed Meal, Mech. Ext.

91 82 89 59 85 37 40 10 17 24 23 6.0 6 0.42 0.90 1.4 0.04 0.46 59

Meadow Hay 90 50 50 12 49 63.37 7 23 33 44 70 98 2.5 9 0.61 0.18 1.6 0.17 24

Meat Meal, Swine/Poultry

93 71 74 46 73 56 64 2 7 48 0 10.5 24 9.00 4.42 0.5 1.27 0.48 190

Meat and Bone Meal,

Swine/Poultry 93 72 75 47 74 56 24 1 5 34 0 10.0 29 13.50 6.50

Milk, Dry, Skim 94 87 96 64 90 36 0 0 0 0 0 0.9 8 1.36 1.09 1.7 0.96 0.34 41

Mint Slug Silage 27 55 55 21 55 14 24 1.8 16 1.10 0.57

Molasses Beet 77 75 79 50 77 91.95 8 0 0 0 0 0 0.2 12 0.14 0.03 6.0 1.64 0.60 18

Molasses Cane 77 74 78 49 76 86.63 6 0 0 0 0 0 0.5 14 0.95 0.09 4.2 2.30 0.68 15

Molasses Cane Dried

94 74 78 49 76 82.12 9 0 2 3 7 0 0.3 14 1.10 0.15 3.6 3.00 30

Molasses, Cond.

Fermentation Solubles

43 69 71 43 70 16 0 0 0 0 0 1.0 26 2.12 0.14 7.5 2.73 0.93 30

Molasses Citrus 65 75 79 50 77 84.11 9 0 0 0 0 0 0.3 8 1.84 0.15 0.2 0.11 0.23 137

Molasses Wood,

Hemicellulose 61 70 73 44 71 1 0 1 2 4 0 0.6 7 1.10 0.10 0.1 0.05

Monoammonium Phosphate

98 0 0 0 0 70 0 0 0 0 0 0.0 24 0.30 24.70 0.0 1.42 81

Mono-Dicalcium Phosphate

97 0 0 0 0 0 0 0 0 0 0.0 94 16.70 21.10 0.1 1.20 70

Oat Hay 90 54 54 20 54 59.36 10 25 31 39 63 98 2.3 8 0.40 0.27 1.6 0.42 0.21 28

Oat Silage 35 60 60 30 60 64.00

g 12 21 31 39 59 61 3.2 10 0.34 0.30 2.4 0.50 0.25 27

Oat Straw 91 48 48 9 47 49.64 4 40 41 48 73 98 2.3 8 0.24 0.07 2.5 0.78 0.22 6

Oat Grain 89 76 81 52 78 75.63 13 18 11 15 28 34 5.0 4 0.05 0.41 0.5 0.11 0.20 40

Oat Grain, Steam Flaked

84 88 98 65 91 13 26 11 15 30 32 4.9 4 0.05 0.37 0.5 0.11 0.20 40

Oat Groats 91 91 102 69 95 88.29 18 15 3 6.6 2 0.08 0.47 0.4 0.10 0.20

Oat Middlings 90 91 102 69 95 16 20 4 6 6.0 3 0.07 0.48 0.5 0.23

Oat Mill Byproduct

89 33 38 0 30 7 27 37 2.4 6 0.13 0.22 0.6 0.24

Oat Hulls 93 38 40 0 36 38.39 4 25 33 41 75 90 1.6 7 0.16 0.15 0.6 0.08 0.14 31

Orange Pulp Dried

89 79 85 55 81 9 9 16 20 33 1.8 4 0.71 0.11 0.6 0.05

Orchardgrass Fresh Early

Bloom 24 65 66 37 66 60.13 14 23 30 32 54 41 4.0 9 0.33 0.39 2.7 0.08 0.20 21

Orchardgrass Hay

88 59 59 28 59 64.29

h 10 27 34 40 67 98 3.3 8 0.32 0.30 2.6 0.41 0.20 26

Pea Vine Hay 89 59 59 28 59 11 32 50 62 92 2.0 7 1.25 0.24 1.3 0.20 20


5-119

FeedstuffDM %


EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

%NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Pea Vine Silage 25 58 58 26 58 16 29 44 55 61 3.3 8 1.25 0.28 1.6 0.29 32

Pea Vine Straw 89 51 51 14 50 49.62 7 41 49 72 98 1.4 7 0.75 0.13 1.1 0.15

Peas Cull 88 85 93 62 88 23 22 7 9 12 0 1.4 4 0.14 0.46 1.1 0.06 0.26 30

Peanut Hulls 91 22 36 0 18 23.17 7 63 65 74 98 1.5 5 0.20 0.07 0.9

Peanut Meal, Solv. Ext.

91 77 82 53 79 71.90 51 27 9 16 27 23 2.5 6 0.26 0.62 1.1 0.03 0.30 38

Peanut Skins 92 0 0 0 0 17 13 20 28 0 22.0 3 0.19 0.20

Pearl Millet Grain

87 82 89 59 85 68.04 13 2 6 18 34 4.5 3 0.03 0.36 0.5

Pineapple Greenchop

17 47 47 7 46 8 24 35 64 41 2.4 7 0.28 0.08

Pineapple Bran 89 71 74 46 73 72.43 5 20 33 66 20 1.5 3 0.26 0.12

Pineapple Presscake

21 71 74 46 73 5 24 35 69 20 0.8 3 0.25 0.09

Potato Vine Silage

15 59 59 28 59 15 26 3.7 19 2.10 0.29 4.0 0.37

Potatoes Cull 21 80 86 56 83 10 0 2 3 4 0 0.4 5 0.03 0.24 2.2 0.30 0.09

Potato Waste Wet

14 82 89 59 85 7 0 9 11 18 0 1.5 3 0.16 0.25 1.2 0.36 0.11 12

Potato Waste Dried

89 85 93 62 88 95.85 8 0 7 9 15 0 0.5 5 0.16 0.25 1.2 0.39 0.11 12

Potato Waste Wet with Lime

17 80 86 56 83 5 0 10 12 16 0 0.3 9 4.20 0.18

Potato Waste Filter Cake

14 77 82 53 79 5 0 2 7.7 3 0.10 0.19 0.2

Poultry Byproduct Meal

93 79 85 55 81 62 49 2 14.5 17 4.00 2.25 0.5 0.58 0.56 129

Poultry Manure Dried

89 38 40 0 36 67.83 28 22 13 15 35 0 2.1 33 10.20 2.80 2.3 1.05 0.20 520

Prairie Hay 91 50 50 12 49 55.53 7 37 34 47 67 98 2.0 8 0.40 0.15 1.1 0.06 0.06 34

Pumpkins, Cull 11 80 86 56 83 15 14 21 30 0 8.9 9 0.24 0.43 3.3

Rice Straw 91 40 42 0 38 51.16 4 38 47 72 100 1.4 13 0.23 0.08 1.2 0.11

Rice Straw Ammoniated

87 45 45 3 44 9 39 53 68 100 1.3 12 0.25 0.08 1.1 0.11

Rice Grain 89 79 85 55 81 83.86 8 30 10 12 16 34 1.9 5 0.07 0.32 0.4 0.09 0.05 17

Rice Polishings 90 90 100 68 94 14 4 5 14.0 9 0.05 1.34 1.2 0.12 0.19 28

Rice Bran 91 71 74 46 73 66.64 14 30 13 18 24 0 16.0 11 0.07 1.70 1.8 0.09 0.19 40

Rice Hulls 92 13 35 0 8 15.91 3 45 44 70 81 90 0.9 20 0.12 0.07 0.5 0.08 0.08 24

Rice Mill Byproduct

91 39 41 0 37 7 32 50 60 0 5.7 19 0.25 0.48 2.2 0.30 31

Rye Grass Hay 90 58 58 26 58 66.07

i 10 30 33 38 65 98 3.3 8 0.45 0.30 2.2 0.18 27

Rye Grass Silage

32 59 59 28 59 14 25 22 37 59 61 3.3 8 0.43 0.38 2.9 0.73 0.23 29

Rye Straw 89 44 44 1 43 33.72 4 44 55 71 100 1.5 6 0.24 0.09 1.0 0.24 0.11

Rye Grain 89 80 86 56 83 84.83 14 20 3 9 19 34 2.5 3 0.07 0.55 0.5 0.03 0.17 33

Safflower Meal, Solv. Ext.

91 56 56 23 56 57.72 24 33 41 57 36 1.3 6 0.35 0.79 0.9 0.21 0.23 65

Safflower Meal Dehulled, Solv.

Ext. 91 75 79 50 77 70.55 47 11 20 27 30 0.8 7 0.38 1.50 1.2 0.18 0.22 36

Safflower Hulls 91 14 35 0 34 4 58 73 90 100 3.7 2

Sagebrush Fresh

50 50 50 12 49 59.04

j 13 25 30 38 9.2 10 1.00 0.25 0.22

Sanfoin Hay 88 61 62 31 62 14 60 24 3.1 9


5-120

Feedstuff DM %


EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

% NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Shrimp Waste Meal

90 48 48 9 47 50 60 11 5.5 25 8.50 1.75 1.15

Sodium Tripolyphosphat

e 96 0 0 0 0 0 0 0 0 0 0.0 96 0.00 25.98 0.0 0.00

Sorghum Stover 87 54 54 20 54 5 33 41 65 100 1.8 10 0.50 0.12 1.2

Sorghum Silage 32 59 59 28 59 65.58 9 25 27 38 59 70 2.7 6 0.48 0.21 1.7 0.45 0.11 30

Sorghum Grain (Milo), Ground

89 82 89 59 85 11 55 3 6 15 5 3.1 2 0.04 0.32 0.4 0.10 0.14 18

Sorghum Grain (Milo), Flaked

82 90 100 68 94 11 62 3 6 15 38 3.1 2 0.04 0.28 0.4 0.10 0.14 18

Soybean Hay 89 52 52 16 51 54.10 16 33 40 55 92 3.5 8 1.28 0.29 1.0 0.15 0.24 24

Soybean Straw 88 42 43 0 40 45.98 5 44 54 70 100 1.4 6 1.59 0.06 0.6 0.26

Soybeans Whole

88 92 103 70 96 41 28 8 11 15 100 18.8 5 0.27 0.64 1.9 0.03 0.34 56

Soybeans Whole,

Extruded 88 93 104 71 97 40 35 9 11 15 100 18.8 5 0.27 0.64 2.0 0.03 0.34 56

Soybeans Whole, Roasted

88 93 104 71 97 40 48 9 11 15 100 18.8 5 0.27 0.64 2.0 0.03 0.34 56

Soybean Hulls 90 77 82 52 79 66.86 13 28 39 48 62 28 2.3 5 0.60 0.19 1.3 0.02 0.12 38

Soybean Meal, Solv. Ext. 44%

CP 89 84 92 61 87 79.50 49 35 7 10 15 23 1.5 7 0.36 0.70 2.2 0.07 0.41 62

Soybean Meal, Solv. Ext. 49%

CP 89 87 96 64 90 54 36 4 6 8 23 1.3 7 0.28 0.71 2.2 0.08 0.45 61

Soybean Mill Feed

90 50 50 12 49 15 36 46 1.9 6 0.46 0.19 1.7 0.07

Spelt Grain 88 75 79 50 77 77.18 13 27 10 17 21 34 2.1 4 0.04 0.40 0.4 0.15 47

Sudangrass Fresh Immature

18 70 73 44 71 73.27 17 23 29 55 41 3.9 9 0.46 0.36 2.0 0.11 24

Sudangrass Hay

88 57 57 25 57 62.67 9 30 36 43 67 98 1.8 10 0.50 0.22 2.2 0.80 0.12 26

Sudangrass Silage

31 58 58 26 58 60.29 10 28 30 42 64 61 3.1 10 0.58 0.27 2.4 0.52 0.14 29

Sunflower Meal, Solv. Ext.

92 65 66 37 66 44.89 40 27 18 22 36 23 2.8 8 0.44 0.97 1.1 0.15 0.33 55

Sunflower Meal with Hulls

91 57 57 25 57 31 35 27 32 44 37 2.4 7 0.40 1.03 1.0 0.30 85

Sunflower Seed Hulls

90 40 42 0 38 4 65 52 63 73 90 2.2 3 0.00 0.11 0.2 0.19 200

Sugar Cane Bagasse

91 39 41 0 37 52.15 1 49 60 86 100 0.6 4 0.90 0.29 0.5 0.10

Tapioca Meal, Cassava

Byproduct 89 82 89 59 85 1 5 8 34 0.8 3 0.03 0.05

Timothy Fresh Pre-Bloom

26 64 65 36 65 11 20 31 36 59 41 3.8 7 0.40 0.28 1.9 0.57 0.15 28

Timothy Hay Early Bloom

88 59 59 28 59 60.75 11 22 32 39 63 98 2.7 6 0.58 0.26 1.9 0.51 0.21 30

Timothy Hay Full Bloom

88 57 57 25 57 58.68 8 30 34 40 65 98 2.6 5 0.43 0.20 1.8 0.62 0.13 25

Timothy Silage 34 59 59 28 59 59.32 10 25 34 45 70 61 3.4 7 0.50 0.27 1.7 0.15

Tomatoes 6 69 71 43 70 16 9 11 4.0 6 0.14 0.35 4.2

Tomato Pomace Dried

92 64 65 36 65 53.98 23 26 50 55 34 10.6 6 0.43 0.59 3.6

Triticale Hay 90 56 56 23 56 10 34 41 69 98 0.30 0.26 2.3 25


5-121

Feedstuff DM %


EE%

ASH%

Ca %

P %

K %

Cl%

S %

Zn ppmTDN

% NEm NEg NEl

(Mcal/cwt.)

DE (% of

GE)a

CP%

UIP%

CF%

ADF%

NDF%

eNDF%

Triticale Silage 34 58 58 26 58 14 30 39 56 61 3.6 0.58 0.34 2.7 0.28 36

Triticale Grain 89 85 93 62 88 83.82 14 25 4 5 22 34 2.4 2 0.07 0.39 0.5 0.17 37

Turnip Tops (Purple)

18 68 70 41 69 18 10 13 2.6 14 3.10 0.40 3.0 1.80 0.27

Turnip Roots 9 86 95 63 89 92.94 12 0 11 34 44 40 1.6 9 0.65 0.31 3.1 0.65 0.43 40

Urea 46% N 99 0 0 0 0 288 0 0 0 0 0 0.0 0 0.00 0.00 0.0 0.00 0.00 0

Vetch Hay 89 58 58 26 58 59.44 18 14 30 33 48 92 1.8 8 1.25 0.34 2.4 0.13

Wheat Fresh, Pasture

21 71 74 46 73 76.07 20 16 18 30 50 41 4.0 13 0.35 0.36 3.1 0.67 0.22

Wheat Hay 90 57 57 25 57 62.73 9 25 29 38 66 98 2.0 8 0.21 0.22 1.4 0.50 0.19 23

Wheat Silage 33 59 59 28 59 63.99 12 21 28 37 62 61 3.2 8 0.40 0.28 2.1 0.50 0.21 27

Wheat Straw 91 43 44 0 41 45.77 3 60 43 57 81 98 1.8 8 0.17 0.06 1.3 0.32 0.17 6

Wheat Straw Ammoniated

85 50 50 12 49 9 25 40 55 76 98 1.5 9 0.15 0.05 1.3 0.30 0.16 6

Wheat Grain 89 88 98 65 91 86.45

k 14 23 3 4 12 0 2.3 2 0.05 0.43 0.4 0.09 0.15 40

Wheat Grain Hard

89 88 98 65 91 88.54

l 14 28 3 6 14 0 2.0 2 0.05 0.43 0.5 0.16 45

Wheat Grain Soft

89 88 98 65 91 89.96

m 12 23 3 4 12 0 2.0 2 0.06 0.40 0.4 0.15 30

Wheat Grain, Steam Flaked

85 91 102 69 95 14 29 3 4 12 0 2.3 2 0.05 0.39 0.4 0.15 40

Wheat Grain Sprouted

86 88 98 65 91 12 18 3 4 13 0 2.0 2 0.04 0.36 0.4 0.17 45

Wheat Bran 89 70 73 44 71 71.16 17 28 11 14 46 4 4.4 7 0.13 1.32 1.4 0.05 0.24 96

Wheat Middlings

89 80 86 56 83 18 22 8 11 36 2 4.7 5 0.14 1.00 1.3 0.05 0.20 98

Wheat Mill Run 90 76 81 52 78 79.11 17 28 9 12 37 0 4.5 6 0.11 1.10 1.2 0.07 0.22 90

Wheat Shorts 89 78 83 54 80 19 25 8 10 30 0 5.3 5 0.10 0.93 1.1 0.08 0.20 118

Wheatgrass Crested Fresh Early Bloom

37 60 60 30 60 79.78 11 25 26 28 50 41 1.6 7 0.46 0.32 2.4

Wheatgrass Crested Fresh

Full Bloom 50 55 55 21 55 65.89 10 33 33 36 65 41 1.6 7 0.39 0.28 2.1

Wheatgrass Crested Hay

92 54 54 20 54 56.51 10 33 33 36 65 98 2.4 7 0.33 0.20 2.0 32

Whey Dried 94 82 89 59 85 91.47

n 14 15 0 0 0 0 0.9 10 0.98 0.88 1.3 1.20 0.92 10

Yeast, Brewer's 92 79 85 55 81 73.76 47 30 3 4 0 0.9 7 0.13 1.49 1.8

DM =Drymatter ADF = AciddetergentfiberTDN =Totaldigestiblenutrients NDF = NeutraldetergentfiberNEm =Netenergyformaintenance eNDF = effectiveneutraldetergentfiberNEg =Netenergyforgrowth EE = EtherextractNEl =Netenergyforlactation ASH =AshMcal =Megacalories Ca =Calciumcwt =Centumweight(hundredweight) P =PhosphorousDE =Digestibleenergy K = PotassiumGE =Grossenergy Cl =ChlorineCP =Crudeprotein S =SulfurUIP =Undegradableintakeprotein Zn =ZincCF =Crudefiber ppm =partspermillionaDE(%ofGE)valuesfromEwan(1989)bAverageoffresh,latevegetative;fresh,earlybloom;fresh,midbloom;fresh,fullbloomcAverageofsilagewilted–earlybloom;silagewilted–midbloom;silagewilted–fullbloomdAverageofsilagewilted–earlybloom;silagewilted–midbloom;silagewilted–fullbloom


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eAverageofhay–sun‐cured,latevegetative;hay–sun‐cured,latebloomfAverageoffat,animalpoultry;oil,vegetablegAverageofsilage,latevegetative;silage,doughstagehAverageofhay,sun‐cured,earlybloom;hay,sun‐cured,latebloomiAverageofryegrass,ItalianLoliummultiflorum:hay,sun‐cured,latevegetative;hay,sun‐cured,earlybloom;averageofryegrass,perennialLoliumperenne:hay,sun‐curedjAverageofsagebrush,bigArtemisiatridentate:browse,fresh,stem‐cured;sagebrush,budArtemisiaspinescens:browse,fresh,earlyvegetative;browse,fresh,latevegetative;andsagebrush,fringedArtemisiafrigida:browse,fresh,midbloom;browse,fresh,maturekAverageofwheat,DurumTriticumdurumandwheatTriticumaestivumgrainlAverageofgrain,hardredspring;grain,hardwintermAverageofgrain,softredwinter;grain,softwhitewinter;grain,softwhitewinter,pacificcoastnAverageofdehydrated(cattle)andlowlactose,lowlactose,dehydrated(driedwheyproduct)(cattle)


5-123

Appendix5‐C:EstimationMethodsforAmmoniaEmissionsfromManureManagementSystemsThisappendixpresentsmethodsforestimatingNH3frommanuremanagementsystems.NH3,althoughnotaGHG,isemittedinlargequantitiesfromanimalhousingandmanuremanagementsystemsandisanindirectprecursortonitrousoxide(N2O)emissionsaswellasanenvironmentalconcern.

5‐C.1 MethodforEstimatingAmmoniaEmissionsUsingEquationsfromIntegratedFarmSystemModel

Ammoniaemissionsfrommanurestoragearemainlyfromtotalammoniacalnitrogen(TAN).Formanyanimalconfinementsystems,ithasbeenreportedthatmostoftheureainmanurehasbeenconvertedtoTANandlostasNH3bythetimemanureistransferredtostorage(Rotzetal.,2011b);therefore,onlyorganicnitrogeninthemanureatthestoragestage,whichismineralizedtoTAN,isusedtoestimateNH3release.TherearefourmainstepsrelatedtoNH3releasetotheatmosphere:diffusion,dissociation,aqueoustogaspartitioning,andmasstransportawayfromthemanuresurface(Rotzetal.,2011b).Forsolidmanure,diffusionthroughthemanureisamainconstrainttotheemissionrate.Forliquidmanure,NH3emissionsareafunctionoftheoverallmasstransferrateandthedifferenceintheNH3concentrationbetweenthelagoonandthesurroundingatmosphere.

5‐C.1.1 RationaleforSelectedMethod

Ammoniaemissionsfromtemporarystackandlongtermstockpiles,aerobiclagoons,anaerobiclagoons,runoffholdingponds,andstoragetankscanbecalculatedusingequationsfromtheDairyGEMModel(asubsetoftheIntegratedFarmSystemModel)(Rotzetal.,2011b).TheequationsfromRotzetal.aretheonlyavailablemethodsforestimatingNH3emissionsfromthesesystemsandbestdescribesthequantitativerelationshipamongstactivitydataattheentitylevel.

5‐C.1.2 ActivityData

InordertoestimatethedailyNH3emissionfromtemporarystack,long‐termstockpiles,anaerobiclagoons,runoffholdingponds,andstoragetanks,thefollowinginformationisneeded:

Totalnitrogencontentofmanure ManuretotalNH3‐Ncontent Surfaceareaofmanurepile Temperatures(localambienttemperatureandmanuretemperature) Localambientairvelocity Foraerobiclagoons,thepHofthelagoonisalsoneeded.

Thetimingofmeasurementscanbebasedondietarychangesorseasonaltimeframe,whichisdecidedbyindividualfarmentity.However,duetothedynamicnatureofmanurepilescausingthe

Ammonia

Methodisafunctionofthesurfaceareaofthestorageunit,resistancetomasstransfer,ambientairvelocity,totalNH3andorganicnitrogencontent,rateoforganicnitrogentransformationtototalammoniacalnitrogen,andmanuretemperatureasdefinedbyRotzetal.(2011b).

Ammoniaandorganicnitrogencontentcanbeobtainedfromsamplingandlabtesting.


5-124

changesofthevariables,frequentmeasurementsofmanurecharacteristicsarerecommendedtoensureaccuracyoftheestimation.

5‐C.1.3 AncillaryData

TheancillarydatausedtoestimateNH3emissionfortemporarystoragearekinematicviscosityofair,massdiffusivityofNH3,andresistancetomasstransfer.ThekinematicviscosityofairatstandardatmosphericpressureislistedinTable5‐C‐1.ThemassdiffusivityofNH3isobtainedfromreferences(PaulandWatson,1966;Baker,1969)andlistedinTable5‐C‐2.TheresistancetomasstransferfordifferentsolidmanurestoragesareobtainedfromtheDairyGEMmodel(Rotzetal.,2011a).

5‐C.2 MethodforAmmoniaEmissionsfromTemporaryStack,Long‐TermStockpile,AnaerobicLagoons/RunoffHoldingPonds/StorageTanks,andAerobicLagoons

TemporaryStack,Long‐TermStockpile,andAnaerobicLagoons/RunoffHoldingPonds/StorageTanksAsindicatedinEquation5‐C‐1,NH3emissionsareafunctionoftheoverallmasstransferrateandthedifferenceinNH3concentrationbetweenthemanureandsurroundingatmosphere.ThemeanambientairNH3concentrationis1.3µg/m3basedonpassivemeasurementsfrom35locationsacross24StatesintheU.S.withoneyearormoreofmeasurements(AmmoniaMonitoringNetwork,NationalAtmosphericDepositionProgram).TheHenry’sLawconstantisusedtodefinetheratioofNH3concentrationinasolutioninequilibriumwithgaseousNH3concentrationinairandisexponentiallyrelatedtotemperature.

aAmmoniaconcentrationinambientaircanbeobtainedfromNationalAtmosphericDepositionProgram(nadp.sws.uiuc.edu/amon/).bShapefactors( )arelistedinAppendix5‐D.

Equation5‐C‐2describesthecalculationforHenry’sLawConstant.Themanuretemperatureiscalculatedastheaverageambienttemperatureovertheprevious10days.

Equation5‐C‐1:AmmoniaEmissionsfromTemporaryStack,LongTermStockpiles,andAnaerobicLagoons/RunoffHoldingPonds/StorageTanks

Where:

ENH3 =NH3emissionsperday(kgNH3day‐1)

24 =Hoursperday(hrday‐1)

3,600 =Secondsperhour(shr‐1)

Asurface =Footprintofmanurestorage(m2)×shapefactorb

K =Overallmasstransfercoefficient(ms‐1)asdefinedinEquation5‐C‐3

TANm =Totalammoniacalnitrogeninthemanure(kgm‐3)

TANa =NH3concentrationinambientaira(kgm‐3)

H =Henry’sLawconstantasdefinedinEquation5‐C‐2


5-125

Theoverallmasstransfercoefficientisexpressedasthereciprocaloftheoveralleffectiveresistanceofthemanure.ThemasstransfercoefficientthroughgaseousphaseonthetopofmanureiscalculatedusingEquation5‐C‐3.TheresistancetomasstransferiscalculatedinEquation5‐C‐5.IthasbeenreportedthatthemasstransfercoefficientthroughmanurehasrelativelylittleeffectonthemasstransferofNH3(Ni,1999)andthusthe1/Klisconsiderednegligibleinthefollowingequation.

Themasstransfercoefficientthroughgaseousphase(Equation5‐C‐4)isestimatedfromtheairfrictionvelocityandSchmidtnumberofair.TheTurbulentSchmidtnumberisdependentonthecharacteristicsofthegasandthescalesofatmosphericturbulence.Sinceturbulenceishighlydependentonmanycomplexinteractions,theTurbulentSchmidtnumberwasapproximatedbyonlyaccountingforthegascharacteristics.ThesecharacteristicsareexpressedinthemolecularSchmidtnumber,definedasSC=ν/D,whereνisthekinematicviscosityofair(m2s‐1),andDisthemassdiffusivityofNH3(m2s‐1).InordertocalculateSchmidtnumber,thedynamicviscosityofair,thedensityoftheair,andthemassdiffusivityofNH3aregivenbasedonairtemperatureinTable5‐C‐1andTable5‐C‐2.

Equation5‐C‐2:CalculationHenry’sLawConstant

..

Where:

H=Henry’sLawconstantforNH3(aqueoustogas)

T=Manuretemperature(Kelvindegree)

Equation5‐C‐3:OverallMassTransferCoefficient

Where:

K =Overallmasstransfercoefficient(ms‐1)

H =Henry’sLawconstantforNH3(aqueoustogas)

Rm =Resistancetomasstransfer(sm‐1)

Kg =Masstransfercoefficientthroughgaseousphaseonthetopofmanure(ms‐1)

Kl =Masstransfercoefficientthroughmanure(ms‐1)


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ThemasstransfercoefficientthroughmanurehaslittleeffectonthemasstransferofNH3,soitisnegligible.Theresistancetomasstransferisthesumoftheresistancethroughthemanureandtheresistanceofcovermaterialsoverthemanure(Equation5‐C‐5).ThevaluesforresistancetomasstransferthroughthemanureandresistancetomasstransferthroughthecoverarelistedinTable5‐C‐3fortemporarystackandlong‐termstockpileandinTable5‐C‐4foranaerobiclagoons,runoffholdingponds,andstoragetanks.

Table5‐C‐3:ResistancetoMassTransferforSolidManureStorageTypeofManureStorage Rs(sm‐1) Rc(sm‐1)

Uncoveredsolidmanure(drymatter>15%) 3×105 0Coveredsolidmanure(drymatter>15%) 3×105 2×105Uncoveredslurrymanure(drymater,10‐15%) 2×105 0

Table5‐C‐1:KinematicViscosityofAiratDifferentTemperatureatStandard

AtmosphericPressure

Temperature(°C)KinematicViscosity

(m2/s)x10‐5‐40 1.04‐20 1.170 1.325 1.3610 1.4115 1.4720 1.5125 1.5630 1.6040 1.6650 1.76

Source:White(1999).

Table5‐C‐2:Mass DiffusivityofAmmoniaatStandardAtmosphericPressure

Temperature(°C)DiffusivityofAmmonia

(m2/s)x10‐4‐40 0.1060 0.11030 0.20040 0.20950 0.233

Source:PaulandWatson (1966)andBaker(1969).

Equation5‐C‐4:CalculatingMassTransferCoefficientthroughGaseousPhase

. . . . .

Where:

Kg=Masstransfercoefficientthroughgaseousphaseonthetopofmanure(ms‐1)

Va=Ambientairvelocity(ms‐1)thatcanbeobtainedfromNationalWeatherServicebysearchingthetargetlocation

SC=TurbulentSchmidtnumberofNH3intheairabovemanuresurface(dimensionless)

Equation5‐C‐5:CalculationofResistancetoMassTransfer

Where:

Rm=Resistancetomasstransfer(sm‐1)

Rs=Resistancetomasstransferthroughthemanure(sm‐1)

Rc=Resistancetomasstransferthroughthecover(sm‐1)


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TypeofManureStorage Rs(sm‐1) Rc(sm‐1)Coveredslurrymanure(drymater,10‐15%) 2×105 2×105

Source:Rotzetal.(2011b).Table5‐C‐4:ResistancetoMassTransferForAnaerobicLagoons,RunoffHoldingPonds,andStorageTanks

TypeofCover Rs(sm‐1) Rc(sm‐1)Uncoveredliquidmanure 0 0

Coveredliquidmanure 0 2×105Source:Rotzetal.(2011a).

AerobicLagoonsThemethodforestimatingNH3emissionsfromaerobiclagoons(Equation5‐C‐6)issimilartothatforstockpilesandanaerobiclagoonsbutaccountsfortheconcentrationofNH3intheliquid.

TheoverallmasstransfercoefficientiscalculatedusingEquation5‐C‐3withresistancetomasstransferassumedtobezero.Henry’sLawConstantiscalculatedusingEquation5‐C‐2andthemasstransfercoefficientthroughagaseousphaseiscalculatedusingEquation5‐C‐4.ThemasstransferthroughtheliquidfilmlayeriscalculatedusingEquation5‐C‐7.

Equation5‐C‐8describestheestimationmethodforNH3concentrationintheliquid.TheNH3fractionofTANinthelagoonliquidisafunctionofpHandadissociationconstantaccordingtoEquation5‐C‐9.

Equation5‐C‐6:ResistancetoMassTransferforSolidManureStorage(Rotzetal.,2011b)

Where:

ENH3 =NH3emissionsperday(kgday‐1)

24 =Hoursperday(hrday‐1)

3,600=Secondsperhour(sh‐1)

K =Overallmasstransfercoefficient(ms‐1)

Asurface=Surfaceareaoflagoon(m2)

NH3 =Concentrationintheliquid(kgm‐3)

Equation5‐C‐7:CalculatingtheMassTransferCoefficientthroughtheLiquidFilmLayer

Kl 1.417 10‐12 T4

Where:

Kl=Masstransfercoefficientthroughtheliquidfilmlayer(ms‐1)

T=Manuretemperature(Kelvin)


5-128

5‐C.3 MethodforEstimatingAmmoniaEmissionsfromCompostingUsingIPCCTier2Equations

Compostingisthecontrolledaerobicdecompositionoforganicmaterialintoastable,humus‐likeproduct(USDANRCS,2007).Eghballetal.(1997)reportedthat19to45percentofthenitrogenpresentinmanurewaslostduringcomposting,withthemajorityofthispresumablyasNH3.

5‐C.3.1 RationaleforSelectedMethod

TheIPCCmethodisadaptedforestimatingNH3emissionsandincorporatesNH3emissionfactorsfromastudyofcompostingcattleandswinemanure(HellebrandandKalk,2000).TheIPCCequationistheonlyavailablemethodforestimatingNH3emissionsfromcomposting.Thismethodologybestdescribesthequantitativerelationshipamongstactivitydataattheentitylevel.

Equation5‐C‐8:CalculatingtheAmmoniaConcentrationintheLiquid

Where:

NH3 =Concentrationintheliquid(kgm‐3)

F =NH3ofTANinthelagoonliquid

TAN =Totalammonianitrogeninthemanureliquid(kgm‐3)

Equation5‐C‐9:CalculatingtheAmmoniaFractionofTANintheLagoonLiquid

Where:

F =NH3ofTANinthelagoonliquid

pH =Hydrogenionconcentration

Ka =Dissociationconstant,whereK 10 .

T =Temperature(Kelvin)

Ammonia

IPCCTier2approachadjustedtoestimateNH3emissionsutilizingdataonanNH3emissionfactor,totalinitialnitrogen,anddrymanure.

TheNH3emissionfactorisobtainedfromastudyofcompostingmixtureofcattleandswinemanurebyHellebrandandKalk(2000).

Nitrogencontentcanbeobtainedfromsamplingandlabtesting. Methodistheonlyreadilyavailablemethod.


5-129

5‐C.3.2 ActivityData

InordertoestimatethedailyNH3emissions,thefollowinginformationisneeded:

Totaldrymanureinthestorage Totalnitrogeninmanure

Thetimingofmeasurementscanbebasedondietarychangesoronaseasonaltimeframe,whichisdecidedbyindividualfarmentity.However,duetothedynamicnatureofmanurestoragecausingchangesinthevariables,frequentmeasurementsofmanurecharacteristics(e.g.,volatilesolids,temperature,totaldrymanure)arerecommendedtoimproveaccuracyoftheestimation.

5‐C.3.3 AncillaryData

TheancillarydatausedtoestimateNH3emissionformanurecompostingisNH3emissionfactor(HellebrandandKalk,2000).

5‐C.4 MethodforAmmoniaEmissionsfromComposting

Ammoniaemissionsfromcompostingaredependentonvolatilizationandmineralizationafternitrification,decompositionoforganicnitrogencompounds,orureahydrolysis.AnIPCCTier2approachforestimatingN2OemissionsisadaptedtoestimateNH3emissionsfromcompostingofsolidmanure.TheNH3emissionfactorof0.05isobtainedfromastudyofcompostingmixtureofcattleandswinemanure(HellebrandandKalk,2000).Equation5‐C‐10providestheequationsforestimatingNH3emissions.

5‐C.5 UncertaintyinAmmoniaEmissionsEstimates

EstimationmethodsfromRotzetal.(2011b)areusedtoestimateNH3emissionsfromtemporarystackandlong‐termstockpilesandaerobiclagoons.Rotzetal.takesintoaccounttheamountofemissivesurfaceareaofthepileorlagoon.Giventhedifficultyofmeasuringthesurfaceareaofamanurepile,shapefactorshavebeendevelopedtoapproximatesurfaceareabasedongeneralshapeandfootprint.Theseshapefactorsprovideanestimatetotalsurfaceareaonly;thereisassociateduncertaintybasedontheaccurracyofthefootprintmeasurementsandhowwelltheshapeofthepilematchestheshapefactorsdefined.

TheRotzetal.equationsrequiretheNH3concentrationintheambientaironsite.NationaldataonambientNH3concentrationsareavailablefromtheNationalAtmosphericDepositionProgram.The

Equation5‐C‐10:IPCCTier2ApproachforCalculatingNH3 EmissionsfromCompostingofSolidManure

Where:

ENH3 =NH3emissionsperday(kgNH3day‐1)

m =Totaldrymanure(kgday‐1)

EFNH3 =NH3emission(loss)relativetototalnitrogeninmanure(kgNH3‐N(kgTN)‐1;=0.05)

TN =Totalnitrogenintheinitial(fresh)manure(kgTN(kgdrymanure)‐1)

=ConversionofNH3tonitrogen


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ProgramprovidesambientNH3concentrationsfromapproximately60activemonitoringsitesacrossthecountry.Giventhedearthofmonitoringsitesandthepotentiallylongdistancesbetweentheentityandthenearestmeasurement,therecanbealargeamountofuncertaintyassociatedwiththeambientairNH3concentrationsusedforestimatingNH3emissions.

Table5‐C‐5:AvailableUncertaintyDataforAmmoniaEmissionsEstimates

ParameterAbbreviation/Sym

bol

DataInputUnit

EstimatedValue


(%

)


(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

pH pH ‐ 7.5 6.5 8.5 ExpertAssessment

Totalammonianitrogeninthemanure–beefearthenlot

TAN kgNH3/m3 0.1 0 0.02 ASABE(2005)

Totalammonianitrogeninthemanure–poultry,leghornpullets

TAN kgNH3/m3 0.85 0.66 1.04 ASABE(2005)

Totalammonianitrogeninthemanure–poultry,leghornhen

TAN kgNH3/m3 0.88 0.54 1.22 ASABE(2005)

Totalammonianitrogeninthemanure–poultry,broiler

TAN kgNH3/m3 0.75 ASABE(2005)

Ammoniaconcentrationintheliquid–dairylagooneffluent

NH3 kgNH3/m3 0.08 ASABE(2005)

Ammoniaconcentrationintheliquid–dairyslurry(liquid)


Ammoniaconcentrationintheliquid–SwineFinisher‐Slurrywet‐dryfeeders


Ammoniaconcentrationintheliquid–SwineSlurrystorage‐dryfeeders

NH3 kgNH3/m3 0.34 0.19 0.49 ASABE(2005)

Ammoniaconcentrationintheliquid–Swineflushbuilding


Ammoniaconcentrationintheliquid–Swineagitatedsolidsandwater


Ammoniaconcentrationintheliquid–SwineLagoonsurfacewater


Ammoniaconcentrationintheliquid–SwineLagoonsludge


Composting–Ammoniaemission(loss)relativetototalnitrogeninmanure

EFNH3 kgNH3‐N/kgN 0.05 HellebrandandKalk(2000)


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Appendix5‐D:ManureManagementSystemsShapeFactors( )Factorscanbeappliedtoaccountforthedifferencesinemissivesurfaceareasfordifferentshapesofmanurepiles.Theequationsprovidedbelowprovideestimatesforthesurfaceareaforcommonpileshapes;theseestimatesareappliedforcalculatingNH3emissionsfromtemporarystacks.

Figure5‐D‐1:EquationsforCalculatingtheShapeFactorfora2‐SidedStorageBinwithQuarter‐ConePile


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Figure5‐D‐2:EquationsforCalculatingtheShapeFactorfora3‐SidedStorageBin

Figure5‐D‐3:EquationsforCalculatingtheShapeFactorforaConicalManurePile


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Figure5‐D‐4:EquationsforCalculatingtheShapeFactorforaFree‐Standing,TruncatedConicalStack

Figure5‐D‐5:EquationsforCalculatingtheShapeFactorforaWindrowwithTriangularCrossSection


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Appendix5‐E:ModelReview:ReviewofEntericFermentationModelsAnumberofempiricalandmechanisticmodelshavebeendevelopedtoestimateentericCH4

production(Table5‐E‐1).TwoofthefactorsthataffectentericCH4productiontothegreatestextentaredietcompositionandlevelofintake.PredictionequationsandmodelsconstructedtopredictentericCH4aregenerallybasedonthesefactors.MoststatisticalequationsdevelopedtoestimateentericCH4emissionshavebeendevelopedusingdatasetsofanimalsfedhigh‐foragedietsormixeddiets;fewstudieshavefedhigh‐concentratedietstypicaloftoday’sU.S.feedlots.

Table5‐E‐1:ModelsPotentiallyUsefulinEstimatingEntericCH4EmissionsfromTypicalU.S.RuminantAnimals

Reference Variablemodeled Inputs/CommentsEmpiricalModels

IPCC(2006) EntericCH4No.ofanimals,animalspecies,animaltype,emissionfactorforeachanimaltype(Tier2CH4conversionfactor;Ym)

Kriss(1930) EntericCH4 Drymatterintake(DMI)Axelsson(1949) EntericCH4 DMIBratzler&Forbes(1940)

EntericCH4 Digestedcarbohydrate

Millsetal.(2003) EntericCH4Metabolizableenergy(ME)intake,starchandaciddetergentfiber(ADF)intake

Blaxter&Clapperton(1965)

EntericCH4Digestibleenergy(DE)(%)atmaintenanceintake,grossenergyintake(GEI),feedinglevel(multipleofmaintenance)

Moe&Tyrrell(1979) EntericCH4Digestiblesolublecarbohydrates,digestiblehemicellulose,digestiblecellulose

Holter&Young(1992) EntericCH4Digestiblesolublecarbohydrates,cellulose,hemicellulose,fatintake

Yanetal.(2009) EntericCH4Digestibleenergy,silage,andtotalDMI,silage,anddietADF

Ellisetal.(2007) EntericCH4 Metabolizableenergyintake,ADF,ligninintake

Ellisetal.(2009) EntericCH4Metabolizableenergyintake,cellulose,hemicellulose,andfatintake;non‐fibercarbohydrate,neutraldetergentfiber(NDF),andDMI

Millsetal.(2001) EntericCH4 DMIHolos(Littleetal.,2008)

EntericCH4,manureCH4

BasedonIPCC(2006)

CNCPS(2010)

EntericCH4,DMI,nutrientexcretion,urinenitrogenexcretion;

UsesequationofMillsetal.(2003)fordairyandEllisetal.(2007)forbeef.Animalcharacteristics,dietnutrientcomposition,feedproteinfractions,animalperformance,animalmanagement,insitudegradabilityoffeeds

IntegratedFarmSystemModel(Rotzetal.,2011b)

EntericCH4,nutrientexcretion,urinenitrogen,DMI,manureNH3,CH4,andN2O

UsestheMits3equationofMillsetal.(2003)forentericCH4,IPCC(2006)formanureCH4,andeitherDAYCENT(Chianeseetal.,2009d)orIPCC(2006)formanureN2O

Phetteplaceetal.(2001)

EntericCH4,manureCH4

Animalclass,animalageandbodyweight,quantityofmeat/mileproduced,feedtype,feedintake,manuremanagement

Process‐basedModels


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Reference Variablemodeled Inputs/Comments

Kebreabetal.,(2004;2009)

EntericCH4,nutrientexcretion

DMI,NDF,degradableNDF,totalstarch,degradablestarch,solublesugarsindiet,dietnitrogen,NHx‐Nindiet,indigestibleprotein,rateofdegradationofstarch,andprotein

COWPOLL(Dijkstraetal.,1992;Millsetal.,2003;Banninketal.,2006;Kebreabetal.,2008)

EntericCH4

DMI,NDF,degradableNDF,totalstarch,degradablestarch,solublesugarsindiet,dietnitrogen,NHx‐Nindiet,indigestibleprotein,rateofdegradationofstarch,andprotein

MOLLY(Baldwin,1995)

EntericCH4 SimilartoCOWPOLL

Predictionmodelsforentericemissions.Thefollowingisabriefsummaryofthemodelsevaluatedandtheirstrengthsandlimitations.

SimpleRegressionModelBasedonDigestibleEnergy.BlaxterandClapperton(1965)developedasimpleregressionequationtoestimateentericCH4basedondigestibleenergy,feedintakeasapercentageofmaintenanceandGEI.Thedatasetusedtocreatethisempiricalmodelwascomposedmostlyofdatafromsheepfedlow‐concentratedietsinrespirationchambers,whichmayaccountforitslimitedaccuracyinpredictingCH4emissionsacrossruminantdiets(Johnsonetal.,1991).

EmpiricalModel.MoeandTyrrell(1979)developedanempiricalmodeltoestimateentericCH4emissionfromdairycowsbasedondietcomposition.Thisempiricalmodelwasdevelopedwithhigh‐foragedietsindairycowsfedinrespirationchambers;itsuseforestimatingbeefcattleentericemissionsisthereforelimited.

RegressionModel.Yanetal.(2000)developedregressionequationstopredictentericCH4emissionsfrombeefanddairycattlefeddietsbasedongrasssilage.Concentratesrepresentedfrom0to81.5percentoftheDMI,withameanof46.7percentofdietDMI.Whencorrectedtoequalfeedintakes,animalbodyweighthadnoeffectonentericCH4emissions.(Yanetal.,2000)validatedtheirequationsusingdatafromtheliterature,mostlydairystudieswithalldietsbasedongrasssilage.

RegressionEquations.Ellisetal.(2009)developedregressionequationstoestimateentericCH4productionfrombeefcattlebasedonstudiesinwhichcattlewerefedhigh‐concentrateormoderate‐concentrate(50percent)diets.Theseequationswerecomparedwith14equationsdevelopedearlierbyEllisetal.(2007),sevendevelopedbyMillsetal.(2003),theBlaxterandClapperton(1965)equation,andtheMoeandTyrrell(1979)equation.ThemeanentericCH4production(MJday‐1andpercentofGEI)inall12ofthestudieswasgreaterthanvaluesnotedmorerecently(Halesetal.,2012),possiblybecauseofdifferencesindietarygraincontentandfatsupplementation.However,someoftheEllis(2007;2009)equationsestimatedCH4emissionssimilartothosereportedbyToddetal.(2014a;2014b)inopenlotfeedlots.

Thelinearmodelwiththelowestresidualmeansquarepredictionerror(RMSPE)wasEquation5‐E‐1asfollows:


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Apossibleadvantagetousingthisequation,comparedwithotherempiricalequations,isthatthevariablesrequiredforthecalculationscanbereadilyobtainedwithsometraininginnutrition.Anotheristhattheindependentvariablesinthemodel(energy,fiber,andfatintake)aretheprimarydifferencesthatwouldoccurinvariousbeefanddairycattlediets.However,amajorconcernwiththeiruseforfinishingcattleisthatanumberofthestudiesusedtodeveloptheequationswerehigh‐foragedietsand/ordidnotuseeithersupplementalfatormonensininthediet.Aspreviouslynoted,whencomparedwithemissionsfromcattlefedtypicalfinishingdietsbasedonsteam‐flakedcorn(SFC)ordry‐rolledcorn(DRC),thisequationgreatlyoverestimatedCH4emissions(Halesetal.,2012).Linearequationsusingnutrientratios(starch:NDF,etc.)werealsodeveloped,butallhadgreaterRMSPEthanthepreviousequation(Ellisetal.,2009).Nonlinearequationswerealsodeveloped.Despitebeingmorebiologicallydefendable,thenonlinearequationsallhadgreaterRMSPEthanthelinearequation.

Inalaterstudy,Yanetal.(2009)developedadditionalequationsusingadatabaseof108measurementsforbeefsteersofvariedbreedinginrespirationchambersandfeddietsthatrangedfrom100to30percentroughage.Theyalsocomparedanumberofequationsdevelopedelsewhere.Equationswere“validated”usingone‐thirdoftheoriginaldataset.Emissionswerehighlycorrelatedtolivebodyweight,DMI,andGEI,butlivebodyweightwasapoorpredictorofentericCH4emissions.TheabilityofanumberofequationstopredictentericCH4measuredinthestudywasvaried(eightpercentoverpredicted,to33percentunderpredicted).ThepoorestresultswerewithfourlinearequationsdevelopedbyEllisetal.(2007)thatusedDMI,MEI,and/orforageintakeasindependentvariables.TheyattributedthepoorresponsetothefactthatagoodportionofthedataforEllisetal.(2007)wasfromgrazinganimalsusingtheSF6technique,whichwouldnotincludeCH4fromthelowergut.TheBlaxterandClapperton(1965)equationdidarespectablejob(93percentofactualwithR2=0.69;meanpredictionerror=0.12;and63percentofmeanssquarepredictionerrorduetorandomeffects,and29percentduetoameanbias).

EmpiricalandMechanisticModel.TheIFSMModel(anditssubsetDairyGEM)(Rotzetal.,2005;Chianeseetal.,2009b;2009c;2009a;2009d)isacombinationempiricalandmechanisticmodelofwholefarmnutrientmanagement.ThesubmodeltoestimateentericCH4emissionsfrombeefordairycattleusestheMits3equationofMillsetal.(2003).Ellisetal.(2007)reportedthattheMillsetal.(2003)equationswerepooratpredictingCH4frombeefcattle,probablybecausetheyweredevelopedfromdairydata.Infact,oneequationthatworkedwellwithdairycowsactuallypredictednegativeCH4emissionsfrombeefcattlefedhigh‐concentrate,low‐foragediets.Thus,thecurrentIFSMmaynotbeappropriatetoestimateentericCH4emissionsfrombeefcattle,especiallyfeedlotcattle.

MechanisticModels.MOLLY(Baldwinetal.,1987;Baldwin,1995)isamechanisticmodelthatestimatesruminalCH4productionbasedonahydrogenbalancewithintherumen.Input

Equation5‐E‐1:LinearModelwiththeLowestRMSPE

. . . . .

Where:

CH4 =Methaneperday(MJday‐1)

MEintake=MEintakein(MJday‐1)

CELL =Celluloseintake(kgday‐1)

HC =Hemicelluloseintake(kgday‐1)

Fat =Fatintake(kgday‐1)


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parameterstothemodelaredailyDMI,chemicalcompositionofthediet,solubilityofproteinandstarch,degradability,ruminalpassagerates,ruminalvolume,andruminalpH.COWPOLL(Dijkstraetal.,1992;Millsetal.,2001)isanothermechanisticmodel.InputparameterstothemodelaresimilartoMOLLY.MOLLYandCOWPOLLbothuseanH‐balancetoestimateentericCH4production.However,theyusedifferentVFAstoichiometrysubmodels.Bothmodelsrequiresignificantinputsthatareprobablybeyondthescopeoftypicalproducers.However,theyareexcellentresearchtools.

TheCornellNetCarbohydrateandProteinSystemmodel(CNCPS,2010)calculatesnutrientrequirements,nutrientinputs,animalproduction(weightgainand/ormilkproduction),andnutrientexcretioninbeefanddairycattle.Itrecentlyaddedasubmodel(VanAmburghetal.,2010)tocalculateentericCH4emissions.ThesubmodelusesanequationofMillsetal.(2003)toestimateentericemissionsfromdairycowsandanequationofEllisetal.(2007)toestimateentericemissionsfrombeefcattle.Atpresent,toourknowledgetherearenocomparisonsorindependentvalidationsofthenewsubmodelsthathavebeenpublished,andtheextenttowhichthemodelisresponsivetomitigationstrategiesisunclear.

ComparativeAnalysesusingIndependentDataSets.SeveralstudieshaveattemptedtoevaluatethepredictiveabilityofentericCH4modelsbyusinganindependentdataset.Benchaaretal.(1998)comparedtwomechanistic(Baldwinetal.,1987;Dijkstraetal.,1992;Baldwin,1995);andtwolinear(BlaxterandClapperton,1965;MoeandTyrrell,1979)modelswithadatasetof32dietsfrom13publicationsintheliterature.Theynotedthatthemechanisticmodelswerebetterpredictorsthantheregressionequations.Thelinearregressionmodelscouldonlyexplain42to57percentofthevariationinpredictedvalues,whereasthemechanisticmodelsexplainedmorethan70percentofthevariation.ThemodelofDijkstraetal.(1992)tendedtounderestimateactualCH4

production(meanerror=0.30Mcalday‐1),withtheerrorbeinggreaterathigherCH4productions.ThemodelofBaldwin(Baldwinetal.,1987;Baldwin,1995)overestimatedCH4productionbyabout0.93Mcalday‐1,primarilyduetoahighintercept.TheequationsofMoeandTyrrell(1979)andBlaxterandClapperton(1965)tendedtooverestimateCH4production,especiallyatlowproductionrates.

ComparativeAnalysis/LactatingandNonlactatingCows.Wilkersonetal.(1995)comparedseveralpublishedequations(Kriss,1930;BratzlerandForbes,1940;Axelsson,1949;BlaxterandClapperton,1965;MoeandTyrrell,1979;HolterandYoung,1992)fortheirabilitytopredictentericCH4productionfromlactatingandnonlactatingHolsteincows.Ingeneral,equationsthatwerebasedontotalDMIoronintakeofdigestedcellulose,hemicellulose,andnonfibercarbohydrates,providedthehighestcorrelationandlowesterrorsofprediction.PredictionequationsthatusedaquadraticfunctionofDMIwerepooratpredictingentericCH4.Ingeneral,theequationspredictedemissionsfromnonlactatingcowsmoreaccuratelythanfromlactatingcows.

ComparativeAnalysisLinearModels.Kebreabetal.(2006)comparedtwolinearmodels(MoeandTyrrell,1979;Millsetal.,2003),anonlinearmodel(Millsetal.,2003),theIPCCTier1andTier2models(IPCC,1997),andadynamicmechanisticmodel(Kebreabetal.,2004)usingdatafromstudiesconductedinNorthAmerica.Theyrecommendedthatthelinearmodelsbeusedwhenthereislimitedinformationonnutrientintakeandwhentheexpectedemissionsarewithintherangeofdatafromwhichthemodelwasdeveloped.ThenonlinearmodelofMillsetal.(2003)couldbeusedforextrapolatingbeyondtherangeofdatausedtodeveloptheequation,butthemechanisticmodelwasrecommendedforevaluationofmitigationoptions.TheIPCCTier1modelwasfoundtobeadequateforgeneralinventorypurposes.ThepredictiveabilityoftheTier2model,whilemostuseful,waslimited.


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ComparativeAnalysisMechanisticModels.Kebreabetal.(2008)alsocomparedtwomechanisticmodels,MOLLY(Baldwinetal.,1987;Baldwin,1995)andCOWPOLL(Dijkstraetal.,1992;Millsetal.,2001;Banninketal.,2006),totheIPCCTier2(2006)andlinearequationofMoeandTyrrell(1979).Usingabeefcattledataset,MOLLYandIPCCtendedtobemoreaccuratethantheothermodels,althoughMOLLYwasmoreprecise.MOLLYandIPCCTier2hadminimalmeanbias,whereasCOWPOLLandtheMoeandTyrrell(1979)equationgreatlyoverpredictedaverageemissions.COWPOLL,whichisbasedontheentericCH4predictionequationsofMillsetal.(2001)andtheupdatedrumenstoichiometryforlactatingcows(Banninketal.,2006),hadthepoorestabilitytopredictentericCH4emissionfromfeedlotcattleandtendedtooverpredictCH4emissions(MJday‐1)byasmuchas50percent.AlthoughonaverageMOLLYandIPCCTier2(2006)gavepredictedvaluessimilartomeasuredvalues,therewasalargevariabilityinindividualanimals,witherrorsof75percentorgreater.Thelargevariabilityinpredictedvaluesindicatesthattherecanbelargeanimal‐to‐animalvariationinentericCH4production,evenwhenanimalsarefedthesamedietsatsimilarfeedintakes.

ComparativeAnalysis/Feedlots.McGinnetal.(2008)comparedmeasured(usingbLSmodel)CH4emissions(entericpluspensurface)fromfeedlotsinAustraliaandCanadawithestimatesusingtheIPCCTier1,IPCCTier2,BlaxterandClapperton(1965),andMoeandTyrrell(1979)equations.TheTier2methodunderestimatedCH4atbothlocations.EstimatesusingtheIPCCTier1methodswereclosetomeasuredvaluesinAustralia;however,Tier1underestimatedvaluesfortheCanadafeedlot.EstimatesmadeusingtheBlaxterandClapperton(1965)andMoeandTyrrell(1979)equationswereclosetomeasuredvaluesinCanada,butoverestimatedvaluesinAustralia.Methaneemissionshadasignificantdielpatternindicatingthatshort‐termmeasurementofCH4emissionsatfeedlotsmayoverestimateorunderestimatedailyemissions.

ComparativeAnalysisofStoichiometricModels.Alemuetal.(2011)comparedentericCH4emissionsfromdairycowsusingavarietyofstoichiometricmodelsofruminalfermentation(Murphyetal.,1982;Banninketal.,2006;Sveinbjornssonetal.,2006;Nozièreetal.,2010),andnotedthatmechanisticmodelssuchasBanninketal.(2006)aremoreaccurateforpredictingentericCH4fromdairycowsthantheIPCCTier2(2006)method.However,thesemodelsrequiredaconsiderablequantityofdataregardingtheanimalsandtheirdiet.

ComparativeAnalysisMeasurementDataandModels.Tomkinsetal.(2011)measuredentericCH4emissionsofsteersonpastureusingamicrometeorologicalmethodandrespirationchambers.EmissionsestimatedusinganEllis(2009)equation(CH4,MJday‐1=3.272+0.736(DMI,kgday‐1))weresimilar(112.7gday‐1)tomeasuredemissions.EstimatesusingtheequationsofKuriharaetal.(1999)asmodifiedbyHunter(2007)(109.1gday‐1),Yanetal.(2009)(105.6gday‐1),andCharmleyetal.(2008)(2008:NABCEMS;100.2gday‐1)wereslightlylower,butnotaslowastheIPCC(2006)model(82.7gday‐1).

ComparativeAnalyses/Models.Legesseetal.(2011)comparedentericCH4emissionestimatesusingMOLLY,COWPOLL,IPCCTier2,andoneequationofEllisetal.(2007)undervariousCanadianbeefcow‐calfmanagementsystems.Differencesamongthemodels(26to35percent)weremuchgreaterthandifferencesamongmanagementsystems(threetofivepercent).Theauthorssuggestedthatthesedifferenceslimitedthemodel’sutilityinpredictingCH4emissionfrombeefcowsystems.

EvaluationofModels.Yanetal.(2000;2009)notedthatCH4production(percentofGEIordigestibleenergy)decreasedwithincreasingDMI(asmultiplesofmaintenance)andwithincreasingforageinthediet.Thus,theysuggestedthatmodelsthatdonotconsiderfeedinglevelwillunderpredictCH4atlowplanesofnutritionandoverpredictentericCH4athighlevelsoffeeding.Similarly,Kebreabetal.(2006)notedthatlinearmodelstendtogiveunrealisticallyhigh


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emissionvalueswhenDMIincreases,whereasnonlinearmodelsgavevaluesapproachingthetheoreticalmaximumemission,whichisbiologicallyreasonable.

AlthoughseveralequationsofEllisetal.(2009)appearedtobegoodpredictorsofentericCH4lossesfromfeedlotcattlebasedonCanadianstudies,whencomparedwithdatafromcattlefedatypicalcorn‐basedfinishingdiet(Halesetal.,2012)mosttendedtogreatlyoverestimateentericlosses.Atthepresenttime,theIPCCTier2modelwithsomemodificationsmaybethemostusefulforpredictionofentericemissionsfromfeedlotbeefcattle.

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Authors:

CoeliHoover,USDAForestService(LeadAuthor)RichardBirdsey,USDAForestService(Co‐LeadAuthor)BruceGoines,USDAForestServicePeterLahm,USDAForestServiceGreggMarland,AppalachianStateUniversityDavidNowak,USDAForestServiceStephenPrisley,VirginiaPolytechnicInstituteandStateUniversityElizabethReinhardt,USDAForestServiceKenSkog,USDAForestServiceDavidSkole,MichiganStateUniversityJamesSmith,USDAForestServiceCarlTrettin,USDAForestServiceChristopherWoodall,USDAForestService

Contents:

6 QuantifyingGreenhouseGasSourcesandSinksinManagedForestSystems....................6‐46.1 Overview.........................................................................................................................................................6‐5

6.1.1 OverviewofManagementPracticesandResultingGHGEmissions.........................6‐66.1.2 SystemBoundariesandTemporalScale..............................................................................6‐96.1.3 SummaryofSelectedMethods/Models..............................................................................6‐106.1.4 SourcesofData..............................................................................................................................6‐116.1.5 OrganizationofChapter/Roadmap......................................................................................6‐12

6.2 ForestCarbonAccounting......................................................................................................................6‐156.2.1 DescriptionofForestCarbonAccounting..........................................................................6‐156.2.2 DataCollectionforForestCarbonAccounting.................................................................6‐236.2.3 EstimationMethods....................................................................................................................6‐256.2.4 Limitations,Uncertainty,andResearchGaps...................................................................6‐28

6.3 Establishing,Re‐establishing,andClearingForests....................................................................6‐296.3.1 Description.....................................................................................................................................6‐296.3.2 ActivityDataCollection.............................................................................................................6‐336.3.3 EstimationMethods....................................................................................................................6‐346.3.4 SpecificProtocolforComputation........................................................................................6‐376.3.5 ActualGHGRemovalsandEmissionsbySourcesandSinksfromForestClearing...

.............................................................................................................................................................6‐436.3.6 LimitationsandUncertainty....................................................................................................6‐44

6.4 ForestManagement..................................................................................................................................6‐45

Chapter 6Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

Chapter 6: Quantifying Greenhouse Gas Sources and Sinks in Managed Forest Systems

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6.4.1 Description.....................................................................................................................................6‐456.4.2 ActivityData...................................................................................................................................6‐536.4.3 ManagementIntensityCategories........................................................................................6‐576.4.4 EstimationMethods....................................................................................................................6‐646.4.5 LimitationsandUncertainty....................................................................................................6‐66

6.5 HarvestedWoodProducts.....................................................................................................................6‐666.5.1 GeneralAccountingIssues.......................................................................................................6‐666.5.2 EstimationMethods....................................................................................................................6‐686.5.3 ActivityDataCollection.............................................................................................................6‐696.5.4 Limitations,Uncertainty,andResearchGaps...................................................................6‐70

6.6 UrbanForests..............................................................................................................................................6‐716.6.1 Description.....................................................................................................................................6‐716.6.2 ActivityDataCollection.............................................................................................................6‐736.6.3 EstimationMethods....................................................................................................................6‐746.6.4 LimitationsandUncertainty....................................................................................................6‐80

6.7 NaturalDisturbance–WildfireandPrescribedFire...................................................................6‐826.7.1 Description.....................................................................................................................................6‐826.7.2 ActivityDataCollection.............................................................................................................6‐826.7.3 EstimationMethods....................................................................................................................6‐826.7.4 LimitationsandUncertainty....................................................................................................6‐87

Appendix6‐A:HarvestedWoodProductsLookupTables.....................................................................6‐88Chapter6References..........................................................................................................................................6‐107

SuggestedChapterCitation:Hoover,C.,R.Birdsey,B.Goines,P.Lahm,GMarland,D.Nowak,S.Prisley,E.Reinhardt,K.Skog,D.Skole,J.Smith,C.Trettin,C.Woodall,2014.Chapter6:QuantifyingGreenhouseGasSourcesandSinksinManagedForestSystems.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.



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Acronyms,ChemicalFormulae,andUnitsBA BasalareaC CarbonCH4 Methanecm CentimetersCO2 CarbondioxideCO2‐eq CarbondioxideequivalentsCOLE CarbonOnLineEstimatorCRM ComponentratiomethodDBH DiameteratbreastheightDDW DowndeadwoodDOE DepartmentofEnergyEPA EnvironmentalProtectionAgencyFFE FireandFuelsExtensionFIA ForestInventoryandAnalysisFIADB ForestInventoryandAnalysisDatabaseFIDO ForestInventoryDataOnlineFOFEM FirstOrderFireEffectsModelFVS ForestVegetationSimulator modelft Feetg GramGHG GreenhousegasH Heightha Hectarehp Horsepowerhr HourHW HardwoodHWP Harvestedwoodproductsin Incheslbs PoundsIPCC IntergovernmentalPanelonClimateChangem Metersmm MillimetersMcf ThousandcubicfeetN2O NitrousoxideNOx Mono‐nitrousoxidesO2 OxygenPW PulpwoodSL SawlogsSOC SoilorganiccarbonSSURGO SoilSurveyGeographicdatabaseSTATSGO StateSoilGeographicdatabaseSW SoftwoodTg TeragramsUFORE UrbanForestEffectsmodelUNFCCC UnitedNationsFrameworkConventiononClimateChangeUSDA U.S.DepartmentofAgriculture


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6-5

6 QuantifyingGreenhouseGasSourcesandSinksinManagedForestSystems

Thischapterprovidesguidanceforreportinggreenhousegas(GHG)emissionsassociatedwithentity‐levelfluxesfromtheforestrysector.Inparticular,itfocusesonmethodsforestimatingcarbonstocksandstockchangefrommanagedforestsystems.Section6.1providesanoverviewofthesector.Section6.2describesthemethodsforforestcarbonstockaccounting.Section6.3describesthemethodsforestimatingcarbonstocksandstockchangefromestablishingandclearingforest.Section6.4describesmethodsforestimatingcarbonstocksandstockchangefromforestmanagement.Section6.5describesmethodsforestimatingcarbonstocksandstockchangefromharvestedwoodproducts.Section6.6describesmethodsforestimatingcarbonstocksandstockchangefromurbanforests(i.e.,treesoutsideofforests).Finally,Section6.7describesmethodsforestimatingemissionsfromnaturaldisturbancesincludingforestfires.

6.1 Overview

AsummaryofproposedmethodsandmodelsforestimatingGHGemissionsfrommanagedforestsystemsisprovidedinTable6‐1.

Table6‐1:OverviewofManagedForestSystemsSources,MethodandSection


6.2.3 ForestCarbonAccounting

Rangeofoptionsdependentonthesizeoftheentities’forestlandincluding:ForestVegetationSimulatormodelwithFireandFuelsExtension(FVS‐FFE)(entitiesthatfitthelargelandownerdefinition);anddefaultlookuptables(entitiesfittingthesmalllandownerdefinition).

6.3.3Establishing,Re‐establishing,andClearingForests

IntergovernmentalPanelonClimateChange (IPCC) algorithmsdevelopedbyAaldeetal.(2006).Theseoptionsuse:allometricequationsfromJenkinsetal.(2003a),orFVSwiththeJenkinsetal.equationswhereapplicable;anddefaultlookuptablesfromSmithetal.(2006;GTRNE‐343)—defaultregionalvaluesbasedonforesttypeandageclassdevelopedfromFIAdata.

6.4.4 ForestManagement

Rangeofoptionsdependentonthesize/managementintensity/dataavailabilityoftheentity’sforestlandincluding:FVS‐FFEwithJenkins(2003a)allometricequations;Defaultlookuptablesofmanagementpracticescenarios;andFVSmaybeusedtodevelopasupportingproductprovidingdefaultlookuptablesofcarbonstocksovertimebyregion;foresttypecategories,includingspeciesgroup(e.g.,hardwood,softwood,mixed);regeneration(e.g.,planted,naturallyregenerated);managementintensity(e.g.,low,moderate,high,veryhigh);andsiteproductivity(e.g.,low,high).

6.5.2 HarvestedWoodProducts

MethodusesU.S.‐specificharvestedwoodproducts(HWPs)tables.TheHWPstablesarebasedonWOODCARBIImodelusedtoestimateannualchangeincarbonstoredinproductsandlandfills(Skog,2008).TheentityusesthesetablestoestimatetheaverageamountofHWPcarbonfromthecurrentyear’sharvestthatremainsstoredinendusesandlandfillsoverthenext100years.

6.6.3 UrbanForests

Rangeofoptionsdependsondataavailabilityoftheentity’surbanforestland.Theseoptionsuse:i‐TreeEcomodel(http://www.itreetools.org)toassesscarbonfromfielddataontreepopulations;andi‐TreeCanopymodel(http://www.itreetools.org/canopy/index.php)toassesstreecoverfromaerialimagesandlookuptablestoassesscarbon.Quantitativemethodsarealsodescribedformaintenanceemissionsandalteredbuildingenergyuseandincludedforinformationpurposesonly.


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6.7.3

NaturalDisturbance—WildfireandPrescribedFire

Rangeofoptionsdepends onthedataavailabilityoftheentity’sforestlandincluding:FirstOrderFireEffectsModel(FOFEM)enteringmeasuredbiomass;andFOFEMmodelusingdefaultvaluesgeneratedbyvegetationtype.TheseoptionsuseReinhardtetal.(1997).



ForestryactivitiesrepresentsignificantopportunitiestomanageGHGs(Caldeiraetal.,2004;PacalaandSocolow,2004).TherearemanykindsofforestryactivitiesthatmaybeconsideredbyentitiesasameanstoreduceGHGs,suchasestablishingnewforests,agroforestry,improvedforestmanagement,andavoidedforestclearing.Costisamajorfactorguidingdecisionsaboutwhichactivitiesinforestrytopursue(Lewandrowskietal.,2004;StavinsandRichards,2005;U.S.EPA,2005).IntheannualGHGinventoryreportedbytheU.S.DepartmentofAgriculture(USDA)andtheU.S.EnvironmentalProtectionAgency(EPA),forestsandforestproductssequesteranaverageof790millionmetrictonscarbondioxide(CO2)peryearon253millionhectares(ha)offorestland,makingitthemainlandcategorysequesteringcarbon(U.S.EPA,2012b;USDA,2011).Mostofthecarbonsequestered(89percent)isintheforestecosystem,withtheremainderaddedtothepoolofcarboninwoodproducts.


Forestsremovecarbonfromtheatmosphereandstoreitinvegetativetissuesuchasstems,roots,barks,andleaves.Throughphotosynthesis,allgreenvegetationremovesCO2andreleasesoxygen(O2)totheatmosphere.Theremainingcarbonisusedtocreateplanttissuesandstoreenergy.Duringrespiration,carbon‐containingcompoundsarebrokendowntoproduceenergy,releasingCO2intheprocess.AnyremainingcarbonissequestereduntilthenaturaldecompositionofdeadvegetativematterorcombustionreleasesitasCO2totheatmosphere.Thenetcarbonstockinforestsincreaseswhentheamountofcarbonwithdrawalfromtheatmosphereduringphotosynthesisexceedsthereleaseofcarbontotheatmosphereduringrespiration.Thenetcarbonstockdecreaseswhenbiomassisburned.

OtherGHGs,suchasnitrousoxide(N2O)andmethane(CH4),arealsoexchangedbyforestecosystems.N2Omaybeemittedfromsoilsunderwetconditionsorafternitrogenfertilization;itisalsoreleasedwhenbiomassisburned.CH4isoftenabsorbedbythemicrobialcommunityinforestsoilsbutmayalsobeemittedbywetlandforestsoils.Whenbiomassisburnedineitheraprescribedfire/controlburnorinawildfire,precursorpollutantsthatcancontributetoozoneandothershort‐livedclimateforcersaswellasCH4areemitted.Awildfireisanunplannedignitioncausedbylightning,volcanoes,unauthorizedactivity,accidentalhuman‐causedactions,andescapedprescribedfires.Aprescribedfire/controlburnisanyfireintentionallyignitedbymanagementunderanapprovedplantomeetspecificobjectives.

Someofthecarboninforestsisreleasedtotheatmosphereaftertheharvestoftimber.However,theamountofthecarbonreleased,andwhen,dependsonthefateoftheharvestedtimber.Ifthetimberisusedtomakewoodproducts,aportionofthesequesteredcarbonwillremainstoredforuptoseveraldecadesorlonger.Iftheharvestedtreesareburnedandusedtoproduceenergy,carbonwillbereleasedthroughcombustionbutmayalsopreventcarbonemissionsthatwouldhavebeenreleasedthroughtheburningoffossilfuels.Suchemissionsfrombiomassenergyuseare


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typicallycombustedwithhigherefficiencyascomparedtoopenbiomassburningaswouldoccurinawildfiresituationnettinglowercarbonemissions.

6.1.1.3 ForestSectorSchematic

Figure6‐1isasimplifiedrepresentationofthekeyforestcarbonpools,carbontransfers,andGHGfluxesfortheforestsystem.Atthistime,CO2isthemainGHGrepresentedcomprehensively.Emissionsofnon‐CO2GHGsinteractwithothersectors;atthistime,potentialfluxesofnon‐CO2GHGsarerepresentedinageneralmannerontheschematic.Theproportionoftotalsystemcarbonineachpoolcanvaryovertimedependingonavarietyoffactors;ratesofcarbontransferarealsovariable.

6.1.1.4 ManagementInteractions

Forestrypracticestypicallytriggerecosystemresponsesthatchangeovertime.Forexample,anewlyestablishedforestwilltakeupcarbonatalowrateinitially,andthenpassintoaperiodofrelativelyrapidcarbonaccumulation.Thecarbonuptakeratewillthentypicallydeclineasheterotrophicandautotrophicrespirationincreaseandgrowthisbalancedagainstmortalityintheolderforest.Fromthispointintime,standinglivetreebiomassmaynotincrease,butevidencesuggeststhatcarbonmaycontinuetoflowintootherforestcarbonpoolsuntiltheforestisremovedbyharvestoranaturaldisturbanceevent.

Theneteffectsofmanagementactivitiesoncarbonflowsinforestecosystemsincludechangesinmanydifferentpoolsofcarbon(suchasabovegroundbiomass,belowgroundbiomass,litter,soil,etc.).Carbonaccountingshouldbecomprehensive,addressingtheneteffectsofactivitiesonallcarbonflows.Forestryactivitiescausecarbontomovebetweenthevariouspoolsandto/fromtheatmosphere.Forexample,forestmanagementmaybeveryeffectiveatincreasingtheaccumulationofbiomassincommerciallyvaluableforms—thatis,inthetrunksofcommercialtreespecies.Thisincreasedgrowthmaysimplyresultfromreducingcompetitionfromothertypesoftrees,causingatransferofcarbonuptakefromonegroupoftreestoanother.Forestryactivitiescanalsohaveeffectsonforestsoils,woodydebris,andtheamountofcarboninwoodproducts.Thenetcarbonfloweffectsofanyactivitywillbethesumofalltheindividualeffectsonthedifferentcarbonpools.

Inaddition,theremaybeinteractionsbetweenbiologicalandphysicalprocessesthatareaffectedbyforestmanagementtreatmentsornaturaldisturbances(e.g.,changesinalbedoduringforestregeneration,afterwildfires).Whiletheseinteractionsoccur,researchinthisfieldisintheearlystagesandsuchinteractionsarebeyondthescopeofthisguidance.


Carbonthatissequesteredinsoils,vegetation,orwoodproductsisnotnecessarilypermanentlyremovedfromtheatmosphere.Forestryactivitiesintendedforonepurposemaybechangedbyadifferentlandownerorachangeinmanagementobjectives.Landownersmaychangetheirpractices,causingthereleaseofstoredcarbon,ornaturaldisturbancesmaycausethelossofstoredcarbontotheatmosphere.Insectepidemics,drought,orwildfiremayhappenatanytimeandmayaffectalloronlyaportionofthelandareawithinactivityorentityboundaries.Naturaldisturbancesmayberareevents,inwhichcasetheeffectsonestimatedcarbonflowsmaybesmallwhenaveragedoverlargeforestedareasorlongperiodsoftime.Catastrophicdisturbancessuchaswindstormsmaycauseobviousandeasilyestimatedchangesincarbonstocks,whileinothercases,suchasaone‐yearperiodofinsectdefoliation,itmaybedifficultafterafewyearstoseparatetheeffectsofthenaturaldisturbancefromotherfactors.ItshouldbenotedthatGHGregistriesgenerallyrequireentitiestocalculatecarbonstocksandfluxesandgenerallyrequireentitiestoconductanassessmentofriskofreversalofprojectedcarbonvalues.Suchassessmentsgenerally

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includeriskofnaturaldisturbancessuchasfire,drought,insectanddiseasemortality,windthrow(hurricane,tornado,highwindevents),aswellasfinancialrisks,managementrisks,andsocialpoliticalrisks.Theseriskassessmentsarecommonlyusedtogenerateavaluethatdiscountstheprojectedcarbonvalueofmanagementactivitiesandtoprovidean“insurancepolicy”againstreversalsthatmaybeusedtoensurethataprogram’sclimatebenefitsarerealized.Manyforestmanagementpracticescanreducethesenaturalhazardrisks(suchasfuelhazardreduction,forestthinningforgrowthorresiliencetodroughts,climatechange,insectordiseaseagents,anduseofprescribedfiretoreduceriskoffires).Reducingtheriskofreversalthroughmanagementmayleadtoreducedemissions,long‐termnetincreaseincarbonstocks,andimprovedresultsinariskassessment.


Forthisreport,thenominalsystemboundariesaretheextentofthelandowner’sproperty.Estimationmethodspresentedinthissectionarefortheforestsector;however,wheretheforestsectormayinteractwiththeanimalagricultureorcroplandsandgrazinglandssectors,theseinstancesarenotedandlandownersshouldrefertotherelevantsectorguidance.AlandownermayneedtouseestimationmethodsforseveralsectorstoachieveacomprehensivereportofGHGsourcesandsinksfortheirproperty,ensuringthatdoublecountingdoesnotoccur.Inaddition,ifland‐usetransitionsoccurwithintheproperty,thesemustbeaccountedforsothatapparentchangesincarbonstocksorfluxesare“real”andnottheresultofanunrecordedtransferfromonesectortoanother.WhileGHGfluxeswilloccuracrossthesystemboundary,thesearegenerallynotestimatedexceptintheinstanceofharvestedwoodproducts(HWPs).

Theforestsectorpresentsanaccountingchallengerelatedtotemporalscalethatmaynotoccurinothersectors.Whilemanyfarmsoperateonanannualcycle,forestryoperations,bytheirnature,occurovermultipleyearsanddecades.Whileannualestimationandreportingarerequired,annualmeasurementsofforestcarbonpoolsarenoteconomicallyfeasible,norarechangesincarbonstocksgenerallydetectablewithinacceptableerrorlevelsonanannualbasis.ThisnecessitatestheuseofmodelsandprojectionstoassessthecarbonconsequencesofmanagementpracticesandevaluatethepossibleGHGbenefitsofachangeinmanagementpractices.Throughouttheforestguidance,referenceswillbemadetoseveraltypesofestimatesthatmaybegenerated.ATypeIestimateistheestimateofthecarbonstockinthecurrentyear(orarecentpastyear)basedonfieldmeasurementsandotherdata.Toassessthecarbonimpactsofapracticeovertime,anecessarysteptogenerateanannualestimate,projectionsoffuturecarbonstocksmustbemade.ThiswillbereferredtoasaTypeIIestimateandwillrequiretheuseoflookuptables,simulationmodels,orothertools.ATypeIIIestimateisusedtoassessthechangeintheGHGfootprintasaresultofachangeinmanagementpractice.TogenerateaTypeIIIestimate,alandownerwillneedtoproduceTypeIIestimatesforthecurrentpracticeandthepracticeunderconsiderationandcomparethetwo.Whilesomelandownersmayrequireonlyanestimateofcurrentcarbonstocks(TypeIestimate),manywillbeinterestedingeneratingestimatesoftherateofcarbonstorageovertime(TypeIIestimate),whichnecessitatestheuseofmodelstoprojectforestgrowth.TheoverallgoalofthisguidanceistoenablealandownertodevelopanestimateoftheirGHGfootprintandtoassessthepotentialeffectsofchangesinmanagementpracticesorlanduseonthisfootprint(forforestsystems,thiswillbedominatedbycarbon).TypeIIestimatescanbegeneratedandcomparedforthecurrentmanagementschemeandmultiplealternatives(whichmayincludea“noaction”scenario).Comparingtheestimatespermitslandownerstoevaluatethepotentialimpactsofawiderangeofpossiblefactors,includingforegonegrowth,land‐usechange,andchangesinmanagementpractices.

Generally,entitiesreportannuallyforthelifeofaproject.Sinceforestsmaylastindefinitely,thereisnobiologicalending,althougheventssuchasland‐usechange,anaturaldisturbance,orbiome


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shiftfromclimatechangemayeffectivelyendthelifeofaspecificforestorforesttype.Variousprogramsmayimposetimelimitsforreporting,ortheentitymaychooseaprojectlengththatisconsistentwithmanagementobjectives.Theaccountingmethodsarenotaffectedbyprojectorreportingperiodlength;thereforenospecificrecommendationsaremadeinthisguidance.

6.1.3 SummaryofSelectedMethods/Models

6.1.3.1 FieldMeasurementsofCarbonPoolsandFluxes

Methodsforestimatingthekeyforestcarbonpoolsarewelldevelopedandfairlystandard.PoolsaredefinedinSection6.2,althoughdetailedmethodsarenotgiven.Methodsformeasuringforestcarbonstocksaredescribedinavarietyofpublications,includingtheIPCCGoodPracticeGuidanceforLandUse,LandUseChange,andForestry(IPCC,2003),Pearsonetal.(2007),andHoover(2008),amongothers.AstheForestInventoryandAnalysis(FIA)programoftheUSDAForestServiceistheFederalprogramtaskedwithprovidingnational‐scaleestimatesoftheU.S.forestcarbonstocks/flux(Heathetal.,2011),documentedinventoryproceduresfromthisprogram(USDAForestService,2010a;2010b)serveasabasisformanyfacetsofentitylevelcarbonreportingprescribedinthisdocument.

6.1.3.2 LookupTablesandRegionalEstimates

ThemostcomprehensivecollectionoftablesofcarbonstockestimatesisSmithetal.(2006).Estimationmethodsaredescribed,andestimatesforeachcarbonpoolareprovidedbyforesttypeforeachregionoftheconterminousUnitedStates.ThevolumeincludesmethodsandtablestoestimatecarboninHWPs.

6.1.3.3 Models

Avarietyofmodelsmaybeusedtoassistintheestimationofforestcarbonstocksandstockchanges.Modelswillbedescribedinmoredetailinthesectionsthatfollow,butforreferencepurposes,briefsummariesofthemostcommonlyusedmodelsareprovidedbelow.Someofthesemodelsarecomplexandmayrequireasubstantialtimeinvestment.Interactingwithsomeofthesemodelsoftenrequiresspecialistknowledgeortrainingorboth.Forsuchmodels,anonlineestimationtoolcouldbedevelopedsothatlandownerswouldnotneedtolearneachindividualmodel,butwouldinteractwiththemthroughtheinterfaceofanestimationtool,whilethecomponentsoperateinthebackground.Whileallmodelshavestrengthsandlimitations,themodelsrecommendedforuseineachsectionofthisreportwereselectedbecauseoftheirnationwidecoverage,historyofperformance,andsuitabilityforthistask.

ForestVegetationSimulatorandFireandFuelsExtensionCarbonReports.TheForestVegetationSimulator(FVS)isanationalsystemofgrowthandyieldmodels,withmultipleregionalvariants,thatcanbeusedtosimulategrowthandyieldforU.S.forests.FVSisastand‐levelmodelandcansimulatenearlyanytypeofforestmanagementpractice.TheFireandFuelsExtension(FFE)toFVScanbeusedtogeneratereportsofallcarbonpoolsexceptsoilbutincludingHWPs;nonCO2GHGsarenotincluded.1Anumberofgeographicvariantsareavailable,eachwithregionallyspecificequationsanddefaultvalues.2

i‐Tree.Twoofthetoolsini‐Treeestimatecarbonstoragewithinurbantrees,annualcarbonsequestration,andcarbonemissionsavoidedthroughenergyconservationduetourbantrees.Onetool,theUrbanForestEffects(UFORE)model,focusesonanentireurbanforest.Theothertool,

1Seehttp://www.fs.fed.us/fmsc/fvs/index.shtml2Suggestedvariantsmaybefoundhere:http://www.fs.fed.us/fmsc/fvs/whatis/index.shtml


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STRATUM,focusesonstreettreepopulations.Treesample(e.g.,fromrandomfieldplots)orinventorydataarerequiredtorunthemodel.Modelstoestimatefuturecarboneffectsbasedonlocalfielddataanduser‐definedmortalityandplantingrateshavealsobeendeveloped.3

FirstOrderFireEffectModel.TheFirstOrderFireEffectsModel(FOFEM)isanationallevelmodelwithgeographicvariants,designedtopredicttreemortality,fuelconsumption,smokeproduction,andsoilheatingcausedbyprescribedfireorwildfire.4

COMSUME.CONSUMEisadecision‐makingtooldesignedtoassistresourcemanagersinplanningforprescribedfireandimpactsofwildfire.CONSUMEpredictsfuelconsumption,pollutantemissions,andheatreleasebasedonfuelloadings,fuelmoisture,andotherenvironmentalfactors.5ItallowsestimationofGHGemissionsandconsumptionfrompost‐harvestandthinningactivities.

6.1.4 SourcesofData

SourcesofavailabledatathatmaybeappropriateforuseindevelopingestimatesofGHGemissionsandcarbonsequestrationvarybycarbonpool(orflux).Inallcases,fieldcollectionofdataispossible,andmaybetheonlyavailableapproachforthoseinstanceswherecredibledefaultvalueshavenotbeendevelopedand/orlookuptablesarenotavailable;thismaybeparticularlyrelevantforagroforestryandurbanforestryapplications.Inthecaseofmanyofthenon‐livingforestcarbonpools,regionaldefaultvaluesareavailablefordowndeadwood(DDW),forestfloor,andstandingdeadwoodthroughtheFIAprogram,aswellasanumberofdocumentsdevelopedinsupportofofficialU.S.governmentestimates.AllFIAdataareavailablethroughanumberofportals,includingtheFIAdatabasetools—ForestInventoryDateOnline(FIDO)andEVALIDator—andtheCarbonOnLineEstimator(COLE),6whichinteractsdirectlywiththeFIAdatabase.SeeTable6‐2forapartiallistofpotentialdatasources.

Currently,valuesforsoilorganiccarbon(SOC)stocksaredrawnfromtheStateSoilGeographic(STATSGO)database,andareofcoarsespatialresolution.Alimitedamountoffield‐sampledSOCdataarealsoavailablethroughtheFIAdatabaseaspartoftheForestHealthMonitoringportionoftheinventoryprocess.CarboninlivetreebiomassisalsoavailablefromFIAandlikeothervariablescanberetrievedatthecountylevel.TheFIAsamplingdesignisintendedtomeetaspecifiederrortargetatlargeareasofforestland;soFIAdatamaynotbeappropriateforuseatsmallerspatialscales.Estimatesbasedonasmallnumberofplotsmaypresentanunacceptableerrorlevel.COLEandEVALIDatorprovideerrorestimatesforallvariables;thesevaluesshouldbecarefullyconsideredbeforethedataareusedtodevelopestimatesforaparticularsite.

DataforemissionsofotherGHGsfromforestsarenotwidelyavailable,althoughestimatesandcalculationmethodsarebetterdevelopedforN2OthanCH4.TheU.S.EPAandIPCCprovideestimationmethodsandemissionsfactorsforbothgasesfromwildfires,andforN2Ofromforestfertilization(IPCC,2006;U.S.EPA,2011).TheU.S.EPApublishesaNationalEmissionsInventoryeverythreeyears,whichprovidesestimatesforwildfireaswellasprescribedfireforcriteriapollutantsaswellashazardousairpollutants,includingsomeGHGspecies(U.S.EPA,2012a).

3Seehttp://www.itreetools.org/4Seehttp://www.firelab.org/science‐applications/fire‐fuel/111‐fofem5Seehttp://www.fs.fed.us/pnw/fera/research/smoke/consume/index.shtml6Seehttp://www.ncasi2.org/COLE/index.html.COLEwasdevelopedthroughUSDAForestServicefinancialsupport,butiscurrentlyhostedbyNCASI.


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ThischapterprovidesguidanceonestimatingcarbonsequestrationandGHGemissionsfortheforestsector.Incaseswherealandowner’sholdingsinvolvemultiplelanduses,guidancefortheothersectorsshouldbeconsulted.Inthischapter,attemptstonoteareaswherecross‐sectorinteractionsarelikelytooccurhavebeenmade.Wetlandsandhydrologicallymanagedsoilsareimportantinseveralsectors,andforthisreasonguidanceforestimatingGHGemissionsandsequestrationfromwetlandsystemsiscoveredinaseparatesection,outsideofthecroplands/grazinglandsandforestsectors.

Thechapterisorganizedtoprovideanoverviewoftheelementsofforestcarbonaccounting,includingdefinitionsofthekeycarbonpoolsandbasicmethodsfortheirestimation.Nextisasectionrelatingtoestimationmethodsincaseswhereforestshavebeenestablished,re‐established,and/orcleared.TheforestmanagementsectionconsiderstheGHGimplicationsofavarietyofcommonlyemployedmanagementpractices,andisfollowedbyguidanceontheestimationofcarboninHWPs.Whileagroforestrysystemsandurbanforestsmaynotbeconsideredastraditionalforestlandscapes,theworkinggrouprecognizestheimportanceoftreeslocatedoutsideofforests.Sincethemostimportantcomponentinthesesystemsisoftenthelivebiomass,urbansystemshavebeenincludedintheforestsector.Agroforestryisacomplextopic,combiningaspectsofforestry,croplandagriculture,andanimalagriculture.Sinceagroforestryismostlikelytobepracticedonlandsprimarilyusedforagriculture,theestimationguidanceisprovidedinthecroplandsandgrazinglandssectionofthedocument.Itisimportanttonotethatagroforestryhasmanycross‐sectorlinkages,andacompleteestimateoftheGHGimplicationsofagroforestrypracticesmaynecessitateconsultationoftheforestmethodsprovidedhere.Asnotedabove,naturaldisturbanceisoneoftheimportantrisksofreversalintheforestsector,andthefinalsectionprovidesguidanceonestimatingtheimpactsfromnaturaldisturbanceinforestedsystems.

Theremainderofthischapterisorganizedasfollows:

Section6.2:ForestCarbonAccounting

Section6.3:Establishing,Re‐establishing,andClearingForest

Section6.4:ForestManagement

Section6.5:HarvestedWoodProducts

Section6.6:UrbanForests

Section6.7:NaturalDisturbances

Table6‐2showsinternetsitesavailableforinformationoncarbonestimation.Figure6‐2showsadecisiontreefortheforestsectorshowingwhichforestchaptersections(i.e.,sourcecategories)arerelevantdependingonwhichforestactivitiesaretakingplaceforanentity.


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Table6‐2:InternetSitesAvailableforInformationonCEstimation

Internetsite Organization RelevantContent

http://fia.fs.fed.us/ USDAForestService,ForestInventoryandAnalysis

Foreststatisticsbystate,includingcarbonestimates

Sampleplotandtreedata Forestinventorymethodsandbasicdefinitions

http://www.fhm.fs.fed.us/

USDAForestService,ForestHealthMonitoring

Foresthealthstatus Regionaldataonsoilsanddeadwoodstocks Foresthealthmonitoringmethods

http://www.usda.gov/oce/climate_change/greenhouse.htm

USDAGHGInventory State‐by‐Stateforestcarbonestimates

http://unfccc.int/http://www.ipcc.ch/

UNFCCCandIPCC Internationalguidanceoncarbonaccounting

andestimationhttp://soildatamart.nrcs.usda.gov/

USDANaturalResourcesConservationService

SoilDataMart:accesstoavarietyofsoildata

http://www.nrs.fs.fed.us/carbon/tools/

USDAForestService,NorthernResearchStation

Accountingandreportingprocedures Softwaretoolsforcarbonestimation

http://www.eia.gov/oiaf/1605/gdlins.html

U.S.EnergyInformationAdministration,VoluntaryGHGReporting

Methodsandinformationforcalculatingsequestrationandemissionsfromforestry;seePartI,Appendix

http://www.epa.gov/climatechange/emissions/usinventoryreport.html

U.S.EnvironmentalProtectionAgency

MethodsandestimatesforGHGemissionsandsequestration

http://www.comet2.colostate.edu/

USDANaturalResourcesConservationServiceandColoradoStateUniversityNaturalResourcesEcologyLab

Web‐basedtoolforestimatingcarbonsequestrationandnetGHGemissionsfromsoilsandbiomassforU.S.farmsandranches


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Figure6‐2:DecisionTreeforForestSectorShowingRelevantChapterSectionsDependingonApplicableSourceCategories

NO

NO

YES

YES

NO

See Section 6.7:Natural Disturbances

YES

YES

NO

Start

Chapter 6 is not applicable for your

entity.

Did you have any natural disturbances

(e.g., fires, pests, storms) in your forest

stands?

Did you establish new,

re‐establish, or clear forest stands on your

land?

Did you initiateany improved forest

management practices on your forest stands?

Did youharvest any wood for

products from your forest stands?

Do you have forest stands that are located

in an urban area?

See Section 6.3:Establishing,

Re‐establishing, and Clearing Forest

See Section 6.4:Forest Management

See Section 6.2:Forest Carbon Accounting

See Section 6.6:Urban Forestry

See Section 6.5: Harvested Wood

Products

YES

NO

NO

YES

NO

YES

YES

Do you have forest stands on your

land?

Do you have additional forest

stands ?

End

NO

Did you useagroforestry: e.g.,

windbreaks, riparian forest buffers, alley cropping,

silvopastures?

See Section 3.4: Agroforestry


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6.2 ForestCarbonAccounting

6.2.1 DescriptionofForestCarbonAccounting

Thebasicquestioninherentwithinthebroadercontextofforestcarbonestimationis:“Howmuchcarbonisinthisforest?”AnydiscussionofforestsorforestryactivitiesinthecontextofGHGsdependsonquantifyingforestcarbon.Forestecosystemsaregenerallyrecognizedassignificantstocksofcarbon,andaggrading,orgrowing,forestscanbestrongcarbonsinks.Disturbancesandforestmanagementinfluencethesizeandratesofchangeofthesestocks.Itisimportanttonotethatforestcarbongenerallyisnotmeasureddirectly(e.g.,collectingforestbiomasssamplesforlaboratorydeterminationofcarboncontent).Itisusuallyquantifiedindirectlyfromstandardforestinventoriesandassociatedcarbonmodels(e.g.,littercarbondependentonforesttypeandstandage).Forlivetreepools,forestinventoriesoftenonlymeasurelimiteddimensionalattributes(e.g.,diameterandheight)ofindividualtreesandusebiomasscomponentmodels(e.g.,boleandcrowns)andwooddensityvaluestoconvertthesevaluesintoanestimateoftotaltreebiomass.Onceanestimateofbiomassisattained,astandardcarbonconversionconstantisappliedtoproduceacarbonstockestimate.Carbonconversionsvaryslightly,but50percentofdryweightisausefulroundvalueapplicabletoallvegetationandsoundwood(IPCC,2006).Forotherpools,suchaslitterlayersandsoilorganicmatter,specificcarboncontentperunitvolumedependsondecayandcompositionofthematerialandisgenerallylessthan50percentcarbon.Giventhediversityofestimationproceduresandcarbonpooldefinitions,areasonableselectionofmethodologiesshouldbeavailableforentitieswishingtoassesstheirforestcarbon.

Amajorattributeofcarbon“accounting”istoexplicitlydocumentanddefineaccountingproceduressuchthatforestcarbonreportsarecomparableacrossownershipsandforestecosystems.Absolutequantitiesofcarbon,orcarbonmass,arenotonlyafunctionofaspecificforestbutalsodependentonhowpoolsaredefinedandhowthemassofcarbonwithinthepoolisestimated.Forexample,bothremotelysensedimagesandground‐basedtreemeasurementscanprovideseparateestimatesofthesameforest.Thesetwotechniquesareunlikelytoprovideidenticalestimatesduetomethodologicaldifferences,includingthefactthateachapproachmaydefinedifferentpopulationsofinterestandthusaccountfordifferentsetsoftrees.Identifyingandresolvingsuchissuesisanobjectiveofforestcarbonresearch.Notallforestcarbonassessmentsormanagementplansneedtoencompassallcarbon(orGHGs)poolsifthecarbonisproperlyidentified.Measuringthecurrentstateofaforest’scarbonstocksandrecentchangesisapartof

MethodsforForestCarbonAccountingUtilizedinthisGuidance

Rangeofoptionsdependentonthesizeoftheentities’forestlandincluding:

− FVS‐FFEmodule(entitiesthatfitthelargelandownerdefinition),and

− Defaultlookuptables(entitiesfittingthesmalllandownerdefinition).

Theseoptionsuse:

− AllometricequationsfromJenkinsetal.(2003a),and

− DefaultlookuptablesfromSmithetal.(2006;GTRNE‐343)—defaultregionalvaluesbasedonforesttypeandageclassdevelopedfromFIAdata.

Thesemethodswereselectedbecausetheyprovidearangeofoptionsdependentonthesizeoftheentities'forestland.


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developingabaseline,whichcanthenbeusedforadditionalanalysis.Abaselineofpastcarbonstocksandchangecanbeconstructedandusedwithmodelingtodetermineprojectionsoflikelyfuturecarbon.Similarly,abaselineisnecessaryforanalysisofalternatemanagementoptionstoevaluatepotentialforsequestration/emission.Thetechnicalspecificationsofbaselines(e.g.,startingyearandincludedstockcategories)areoftenasocial/politicaldecision,andarebeyondthepurviewofthisdocument.However,tostandardizeforestcarbonaccountingoptionsforthepurposeofentityreporting(e.g.,woodlandowners),thisdocumentwillproposeasinglesetofforestcarbonpooldefinitions.Thespecificrecommendationsincludedhereareintendedtodirectlandownerstotoolsanddatasourcesspeciallydevelopedforquantifyingforestcarbon.Notethattheselistedprocessesarenotintendedtoexcludealternativedatasummariesthatmaybeavailabletoentities.Detailsarediscussedbelowinthediscussionoftherespectiveforestcarbonpools,butthegeneraloptionslistedindecreasingaccuracy(andcost)includethefollowing:

(1) Measure/sampleyourforestandestimatecarbonfromthesedata(reducesampledatasoastothenapplyavailablebiomassequationsorothercarbonconversionfactors);

(2) Characterizeyourforestaccordingtoclassifications(i.e.,lookuptables)basedonstandorsiteattributesderivedfromrecordsinthenation’sforestinventorydatabase(FIADB)(Woodalletal.,2010;Woudenbergetal.,2010);or

(3) Useassociatedmodels(FIDO,COLE,etc.),whichbaseyourforest’scarbonestimatesonrepresentativedatasampledbyotherswithcriticaldependentuservariableinput(e.g.,standage).

Notethattheabovethreeoptionsarenotnecessarilymutuallyexclusive.Forexample,FIADBdataorsimilarmodels(Option2)arebasedonpermanentinventoryplotsamplingandcarbonconversion(Option1),andlookuptables(Option3)arebasedontheFIADB(Option2).Therecommendedforestcarboninventoryoptionsinvolvetradeoffsincostsandlevelofinformationuniquetotheentities’forestland.

Theprocessofobtainingforestcarbonestimatesdependsoncircumstancesuniquetoeachentity,butmostlydependsontheintendedaudienceandtheresourcesavailableforforestinventory.Forthisguidance,atwo‐tiersystemisinplace.Thegoalistobeasinclusiveaspossiblewhilenotcreatingameasurementburden.Smallerholdingsthatarenotactivelymanagedareunlikelytobeinventoried;atwo‐tierapproachpermitsownersofsuchholdingstoestimatetheirfootprintandthepotentialchangesfromchangesinpracticesappliedwithoutincurringthecostsofmeasurement.Smallerlandownerswhohaveinventorydataorwhowishtoacquireitshouldusethetoolsandprotocolsdescribedforlargelandowners.

Landownersizeclassesaredefinedasfollows:

Landownerswhohold200ormoreacres(80.9hectares[ha])offorestlandshouldfollowthemethodsforlargelandowners.Also,landownerswhoholdlessthan200acres(80.9ha)offorestlandshouldfollowthemethodsforlargelandownersifthreeormoreofthefollowingaretrue:

Landownerownsormanagesmorethan50forestedacres(20.2ha)

Landowner’sforestiscertified

Landownerhasdevelopedaforestmanagementplan

Landowner’sforestedpropertyhasahistoryoftimberharvesting

LandownerparticipatesinStateforesttaxabatementprograms

Landownersnotmeetingthedefinitionoflargelandownershouldfollowthemethodsforsmalllandowners.


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Recommendedmethodsdependonforestlandownersize.Smalllandownersmayusegeneralizedlookuptablesbasedonregion,foresttype,andageclasstoestimatecarbonstocks.LargelandownersshouldcollectstandardforestinventorydataandusetheFVS‐FFEmodulewithJenkinsetal.(2003a)allometricequations.ItshouldbenotedthatFVSandtheFFEarelargeandcomplicatedmodels;anytoolthatimplementsthesemethodswillrequiredevelopmentofasimplifieduserinterfacethatinteractswithFVSandFFE.

Atthistime,theJenkinsetal.(2003a)equationsarespecifiedsincetheyarenationallyconsistent.FuturedevelopmentislikelytoincludetheimplementationofamorerecentFIAbiomassestimationmethodinFVS,enablingtheproductionofestimatesthatmatchtheofficialU.S.forestcarbonestimates.Whilelocalvolumeorbiomassequationsmaybemoreaccurateforagivenlocation,useofsuchequationswillresultinadditionalinconsistenciesinresults,sonootherequationsareapprovedforuseatthistimeunderthismethodology.

Althoughcarbonreportingbeyondthatoftheentitylevel(e.g.,majortimberlandownerornationalforest)mayuserefinedmeasurementprotocols,expandedcarbonpooldefinitions,and/orancillarydata(e.g.,remotelysensedimagery),theproposedpoolsandinventorymethodologiesinthisdocumentserveasastartingpoint.Classificationofcarbonestimateswithinmulti‐tieredsystems,andlinkstomodelstoprojectfuturechangeunderalternatescenariosareaddressedattheendofSection6.2.

Tofacilitateaccounting,forestcarbonistypicallyclassifiedintoafewdiscretepools,whichshouldbecomprehensive(allorganiccarbon)withnogapsandnooverlap.Thepurposeofestablishingtheseseparatepools,orbins,offorestcarbonistwofold:(1)toalignappropriatedatawithecosystem/productcomponents(e.g.,treeinventoriesandlivetreecarbonpool),oralternativelytoidentifygaps;and(2)asapartoftheaccountingprocess,notallreportedstockorchangenecessarilyneedstoincludeallofthecarbonpools,butwhatisincludedmustbeunambiguouslyidentified.Notethatthecarbonpools(orbinsorclassifications)focusoncarbonfromphytomass.Strictlyspeaking,totalcarbonstockswithinaforestincludeanon‐plant(notoriginatingfromtheplantkingdom)percentage,butsuchpoolsarenotdefinedbecausethisisgenerallyaninsignificantproportion.Exceptionsaretheforestfloorandsoilpools,whichincludedecomposersandsoilfauna.Asometimessignificantamountofcarbonisremovedfromforestsaswoodisharvestedandusedinwoodproducts.Someofthatcarbonremainssequesteredforlongperiodsoftime,dependingontheproducts.Thus,harvestedwoodshouldbeincludedinforestcarbonestimates.

Figure6‐3isadecisiontreefortheforestcarbonaccountingsourcecategoryshowingwhichcarbonaccountingassumptions(e.g.,simulationmodels,allometricequations,biomassexpansionfactors,lookuptables)arerecommendedforanentitydependingonthetypeofactivitydataavailable.However,itshouldbenotedthatfornationalreporting—i.e.,theannualGHGinventoryreportedbyUSDAandU.S.EPA—whereindividualtreemeasurementsfromFIA’sinventoryplotsareavailable,thecomponentratiomethod(CRM)forestimatingbiomass(Woodalletal.,2011)iscurrentlyused.Again,futuredevelopmentwilllikelybringthesemethodsintoalignment.


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Figure6‐3:DecisionTreeforForestCarbonAccountingShowingMethodsAppropriateforEstimatingForestCarbonStocks

1Smalllandowners(asdefinedinSection6.2.1)mayusegeneralizedlookuptablesbasedonregion,foresttype,andageclasstoestimatecarbonstocks.Largelandownersshouldcollectstandardforestinventorydataanduseallometricequationstoestimatelivetreebiomasscarbon(othercarbonpoolsmaybeobtainedfromlookuptables).2Jenkinsetal.(2003a).3Notethatvolumeequationsusedbylandownersshouldalignwith“meanvolume”specifications(e.g.,rotten/culldeductions)ofSmithetal.(2006).Differentvolumeequationsanddeductionswillproducevolumeestimatesthatdifferfromthoseusedinthetables.4Smithetal.(2006).


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Anotheraspectofacarbonaccountingframeworkisconsistentorcomparablerepresentationofchange,whichgoesbeyondtheidentificationofcarbonpools.Changeisaffectedbyprocessesofrecruitmentandgrowthaswellasdisturbance,mortality,andharvest.Inthemostbasicsense,changecanbethedifferencebetweentwosuccessivestockestimates.ThisiscommonforGHGreportingbasedonstandardforestinventories.Somecomponentsofchangecanbemeasuredwithintensivesamplingatsmallscales,butingeneralchangeisestimatedfrommeasurementsattwosuccessiveinventorytimes(e.g.,totalstockchange,orgrowth/removals/mortalityestimates,orremotelysenseddata),orbasedonmodelsofecosystemorbiogeochemicalchange.Abasicapproachtoquantifyingchangeinforestcarbonisbasedonthequantitiesdefinedforforestcarbonstocks.Netannualcarbonstockchangesarecalculatedbytakingthedifferencebetweentheinventoriesanddividingbythenumberofyearsbetweentheinventoriesforaselectedforestorforestarea(e.g.,Δstock=(stock2–stock1)/time).Thisstock‐changeapproach(IPCC,2006)isthechangemethodappliedtoFIAstrategic‐scaleinventoriesforthestock‐changevaluesreportedintheU.S.NationalGHGInventories(e.g.,U.S.EPA,2011).

SixStepstoForestEntityCarbonEstimation

Theapproachtoestimationofcarbonstocksandfluxesintheforestsectorisasfollows:

Step1:Determinelandownersizeclassbasedonforestarea.Basedontheacreageunderconsideration,landownersaredividedintotwogroups:“small”landownersand“large”landownersasdefinedinSection6.2.1.

Step2:Collectforestdata.Forbothsizeclassesoflandowners,somelevelofforestinventory(i.e.,fieldsurvey)dataisrequired.However,therearedifferingdatarequirementsforsmalllandownersandlargelandowners.

Smalllandownersshouldcollectbasicdataonspeciesmix(i.e.,typeofforest)andstandage(ortimesincelastmajordisturbance)withintheirforest.Greaterinventorydetailcanleadtomorepreciseestimatesofcarbon,butevenbroadgeneralizationsabouttheregion,age(and/ormeanvolume),andtypeofforestcanleadtoacarbonestimate.Theobjectiveistoobtainreasonableandconsistentestimatesovertimeatthelowestcost.Ifasmalllandownerwishestoconductaninventoryandfollowtherecommendedguidanceforlargelandowners,theyarefreetochoosethisoption.Theprincipaltradeoffisbetweencostandaccuracy;collectinginventorydataincreasesthecostofdevelopingestimatesbutincreasesaccuracy.

Largelandownersshouldgathermoreextensivedataaboutforestandstandcharacteristics.AthoroughforestinventoryiscreatedusingindustrystandardsandpracticesofthetypedescribedinGTRNRS‐18:MeasurementGuidelinesfortheSequestrationofForestCarbon.Variablesconsideredmustincludedominantspecies,dominantageclass,standdensity,andsiteclass.Inclusionofadditionalvariables,whilenotrequired,willimproveaccuracyofcarbonestimates.

Step3:Estimateinitialforestcarbonstockandannualfluxes.Quantitiesofcarbonchangeovertime.Forestcarbonestimatesaredividedintosixdiscrete,mutuallyexclusivepools,includinglivetrees,standingdeadtrees,understoryvegetation,downdeadwood,forestfloor,andsoilorganiccarbon.Anumberofpool‐specificcarbonconversionmethodsareavailable;thesemethodsusetheinventorydatagatheredinStep2toquantifycarbonforeachpool.However,thespecificmethodstobeuseddifferdependingonthelandownersizeclass.

(Continued)


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(Continued)

Smalllandowners,aftercollectingobservationaldata,canuselookuptablesfromSmithetal.(2006)(alsoknownasGTR‐NE‐343:MethodsforcalculatingforestecosystemandharvestedcarbonwithstandardestimatesforforesttypesoftheUnitedStates)toestimatecarbonstocksandcarbonstockchanges.Thelookuptablesarecategorizedbyregion,foresttype,previouslanduse,andinsomecases,managementactivity.Usersmustidentifythecategoriesfortheirforestsandestimatetheareaofforestland.TofacilitateuseofthedatafromGTR‐NE‐343,atoolcouldincorporatethedatasuchthat,inmostcases,landownerswouldbeabletoselecttheirstandcharacteristicsfromadrop‐downmenuofdefaults.Basedonthelandowner’sselectionsfromthedefaultmenus,thetoolwouldproduceestimatesofcarbonstocksineachofthesixcarbonpools.

LargelandownersshouldusethedatacollectedintheirforestsurveystoperformmodelrunsusingtheFVSmodel.FVSwillusethesite‐andstand‐specificdatatoprovidemoreaccurateestimatesofcarbonstocksineachofthecarbonpools(excludingsoilcarbon,whichFVSdoesnotestimate).Soilcarbonestimatescanbedeterminedfromarangeofmethodsincludingsamplingorexistingforestsoilcarbonestimatedatasetsdependingonaspecificentity’scircumstances.

Thoughthemethodsdifferforsmalllandownersandlargelandowners,bothcalculateinitialcarbonstocksandexpectedannualratesofaccumulationunderaverageconditions(repeatingthefieldsurveyatprescribedintervalswillhelpcalibrateorvalidatethestockchangeestimates).

ThemethodsalsoallowforadjustmentsduetoHWPs(Step4),forestmanagementpractices(Step5),andnaturaldisturbances(Step6).

Step4:AdjustcarbonestimatesduetoHWPs.Harvestingactivitiescanhaveconsiderableimpactoncarbonquantityacrossthesixforestcarbonpools.Intermsofemissions,thefateoftheharvestedmaterialmustbeconsideredaswell,includingwhetherthematerialisusedinHWPsorforenergy.Asabove,themethodsforestimatingtheseimpactsdifferdependingonthelandownersizeclass.

ForHWPs,smalllandownersshouldrelyondataprovidedinlookuptablesinGTR‐NE‐343,whichprovidesfactorsforcalculationofcarboninHWPsbasedonregion,timbertype,andindustrialroundwoodcategory.Thelookuptablesdividetheharvestedforestmaterialspoolintofourdistinctfates:productsinuse,landfill,emittedwithenergycapture,andemittedwithoutenergycapture.Carbonemissionsdifferdependingonthefate,whichinturndependsontheregionandharvestmaterialcharacteristics.Byusingthelookuptables,landownerscanadjustcarbonestimatesaccordingly.

LargelandownersshouldrelyonFVStomodelforestmanagementpractices,resultinginestimatesofthecarbonimpactofthesepractices(e.g.,harvesting).Forexample,FVScanconsiderthetypeofharvest(e.g.,clearcutversusstrategicthinning)andprojecttheresultsofthisharvestoncarbonstocks,thusallowinguserstoquantifythecarbonimpactofvariousharvestingactivities,aswellasadjustingfortheultimatefateofharvestedmaterials.TheharvestedforestmaterialpoolisdividedbyFVSintothesamefourdistinctfatesasforGTR‐NE‐343:productsinuse,landfill,emittedwithenergycapture,andemittedwithoutenergycapture.Harvestsalsoimpactforestgrowthovertime,whichismodeledbyFVS.

(Continued)


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6.2.1.1 ForestCarbonPools

Carbonreporting—suchasfortheU.S.reportingcommitmenttotheUnitedNationsFrameworkConventiononClimateChange(UNFCCC),whichismetbytheU.S.EPA’sofficialGHGinventory(e.g.,

(Continued)

Step5:Adjustcarbonestimatesduetoimprovedforestmanagement.Forestmanagementpractices,suchasthinningorfertilization,mayimpactcarbonfluxesaswell.Asabove,themethodsforestimatingtheseimpactsdifferdependingonthelandownersizeclass.

FVSallowslargelandownerstoquantifytheimpactofvariousforestmanagementpractices.Forexample,usingkeywords(orcombinationsofkeywords)providedbyFVS,userscangenerateestimatesfortheimpactofstanddensitymanagement,sitepreparationmethods,vegetationcontrols,variousdensitiesofplantingstock,fertilization,rotationlengthmanagement,prescribedfire/controlburnsandfuelloadmanagement,andpestanddiseasecontrol.Withgivenstandandtree‐listdata,userscandevelopabaseline,whichcanthenbecomparedtoalternativemanagementstrategies.Thisallowsforassessmentofcarbonimpactofimplementingthosemanagementpractices.ItshouldbenotedthatFVSistherecommendedmethod,evenifalargelandownerhasitsowncustominventoryandmodelingsystem,whichmightbeconsideredsuperiortoregionalmodelssuchasFVS.Theadoptionofasingle,recommendedmethodforlandownersallowsfortransparent,consistent,comparable,andcompleteestimatesacrosslandownersappreciatingthattherewillbealikelytradeoffintheaccuracy,costeffectiveness,andeaseofuseofthemethodforthoselandownerswithcustomsystems.Futuredevelopmentmayincludeameansforlargelandownerstousecustommodelsinthisframework,butthisoptionisnotavailableatthistime.

Unfortunately,thelookuptablesdonotallowforestimatesassociatedwithimprovedforestmanagement.Ifprescribedfire/controlburningisusedbyeitherlandownertype,itisrecommendedthattheemissionsfortheactivitybecalculatedasguidedinStep6.

Step6:Adjustcarbonestimatesduetoforestfiresandothernaturaldisturbances.Naturaldisturbances,suchasforestfires,storms,wind,drought,orpest/insectinfestation,canalsohaveconsiderableimpactoncarbonquantitiesacrossthesixforestcarbonpools.Landownersshouldestimatethecarbonimpactofnaturaldisturbances.

Forforestfires,wildfires,andprescribed/controlledburns,bothsmallandlargelandownersshouldrelyonFOFEMtogeneratecarbonestimates.FOFEMinputrequirementsincludebasicforesttype,sitelocation,anddominantspeciesdata,butalsoallowsuserstoinputadditionalinformation,dependingonaspecificentity’scircumstances,onamountofduff,moisturecontent,andothervariablesassociatedwithfire.Theseverityofthefirecanbecategorizedbypercentofthelandaffected.Theresultingoutputincludesestimatesofcarbonemissions.

Themethodsassumesmalllandownerscanprovideobservationalestimatesfortheimpactsofnaturaldisturbancessuchaspests,basedonthepercentageofforestlandaffectedbythedisturbance.LargelandownersmaymodelimpactsofpeststhroughavailablekeywordsandextensionsprovidedbyFVS.

Thephilosophybehindthesesixstepsisthattheyallowtheentitytoassesswhatcarbonstockstheyhaveunderanypresentconditionsandwhatstockstheymightexpectgivenimplementationofaparticularharvestingregime,changeinforestmanagementpractices,and/oravarietyofnaturaldisturbances.


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U.S.EPA,2011)—providesaframeworkforthepoolsdescribedhere.However,thepoolsaremodifiedtomorecloselycorrespondtotypesofforestinventorydata.Forexample,forestcarboncanbeeasilycategorizedaccordingtoabovegroundversusbelowground,orlivingversusdeadplantmaterial.Inpractice,classificationsofcarbonpoolsdependontheforestdataandhowtheyareused.Assuch,thepoolsdescribedbelowarejointlydefinedbyUNFCCCreportingrequirementsandtheuseofFIAforestinventoryastheprimarydatasource.Inotherwords,thepoolsdefinedbelowareaconvenientset,butdefinitionsandboundariesaroundpoolscanvaryaccordingtospecificcarbonestimationprocedures/capabilitiesandreportingneeds(seeFigure6‐4).

Figure6‐4:ForestCarbonPoolHierarchyShowingHowForestCarbonPoolsCanBeDelineatedintoEvenSmallerPoolsDependentontheEntityNeedsandInventoryCapabilities

Livetrees:Alargewoodyperennialplant(capableofreachingatleast15feet(4.6m)inheight)withadiameteratbreastheight(DBH)oratrootcollar(ifmultistemmedwoodlandspecies)greaterthan1inch(2.5centimeters[cm]).Includesthecarbonmassinroots(i.e.,livebelowgroundbiomass)withdiametersgreaterthan0.08in(2millimeters[mm],stems,branches,andfoliage.

Understory:Roots,stems,branches,andfoliageoftreeseedlings,shrubs,herbs,forbs,andgrasses.

Standingdeadtrees:Deadtreesofatleast1inch(2.5cm)DBHthathavenotyetfallen,includingcarbonmassofcoarseroots,stems,andbranches,butthatdonotleanmorethan45degreesfromvertical(Woudenbergetal.,2010),includingcoarsenonlivingrootsmorethan0.08in(2mm)indiameter.

Downdeadwood(alsoknownascoarsewoodydebris):Allnonlivingwoodybiomasswithadiameterofatleast3inches(7.6cm)attransectintersection,lyingontheground.Thispoolalso


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includessomeless‐than‐obviouscomponentsofDDW:(1)debrispiles,usuallyfrompastlogging;and(2)previouslystandingdeadtreesthathavelostenoughheightorvolume,orleangreaterthan45degreesfromvertical,sotheydonotqualifyasstandingdeadtrees.

Forestfloor:Thelitter,fulvic,andhumiclayers,andallfinewoodydebriswithadiameterlessthan3inches(7.6cm)attransectintersection,lyingonthegroundabovethemineralsoil.

SoilorganicC:Allorganicmaterialinsoiltoadepthofgenerally3.3feet(1meter[m]),includingthefineroots(e.g.,lessthan0.08in(2mm)indiameter)oftheliveandstandingdeadtreepools,butexcludingthecoarserootsofthepoolsmentionedearlier.

Harvestedwood:Woodremovedfromtheforestecosystemforprocessingintoproducts,notincludingloggingdebris(slash)leftintheforestafterharvesting.

Thesepooldefinitionsaredevelopedaroundacommonsetinusebyanumberofpublications(e.g.,Smithetal.,2006)andattheforeststandlevel,whichinturndifferfromstockdefinitionsusedbytheUnitedStatestomeetUNFCCCnationalreportingrequirements.

Alsonotable(inthereportinglist)istheinclusionofHWP(coveredindetailinSection6.5),whichassumesthatameasurableportionofwoodremovedatharvestremainssequesteredfromreemissiontotheatmosphereforaperiodoftimethatcanbeestimated.PoolsandestimationofstocksareorganizedprimarilyaccordingtodatacollectionandestimationwithFIA’spermanentinventoryplots(phasetwo(P2),thestandardinventorymeasurements;andphasethree(P3),theforesthealthmeasurements).Notethatpooldefinitionsarenotindependentofrelatedestimators;detailsrelatedtoestimationarenotaddresseduntilsubsequentsectionsofthisguidance.

6.2.2 DataCollectionforForestCarbonAccounting

Forestcarbonistypicallyestimatedindirectly,throughapplyingconversionconstantstoastandardforestinventory,usingalocalizedbiogeochemicalmodel,orsimplylookingupspecificforestattributes(e.g.,standage,foresttype)inalookuptable(e.g.,Smithetal.,2006).Forthepurposesofthisdocumentation,astandardsetofcarbonpooldefinitionsthatarepartofFIA’snationalinventoryaredelineatedthatcorrespondtoavailablelookuptables(Smithetal.,2006).

6.2.2.1 LiveTrees

Thetreecarbonpoolsincludeabovegroundandbelowground(coarseroot)carbonmassoflivetrees.Separateestimatesaremadeforfull‐treeandaboveground‐onlybiomasstoestimatethebelowgroundcomponent.TreecarbonestimateswithintheFIADB(USDAForestService,2012;Woudenbergetal.,2010)arebasedonWoodalletal.(2011)andJenkinsetal.(2003a).Theper‐treecarbonestimatesareafunctionoftreespecies,diameter,height,andvolumeofwood.Belowgroundbiomassiscalculatedasavaryingproportionofabovegroundbiomass.Again,thisisdependentonspeciesandsizeofindividualtrees.ThepooloflivetreeswithintheFIADBisdefinedastrees,orwoodybiomasswithgreaterorequalto1inch(2.5cm)DBH.However,treeslessthan5inches(12.7cm)DBHaresampleddifferentlythanthosethatare5inches(12.7cm)ormore.Thesedifferencesshouldnotaffectprecisionintheoverallamountoftreecarbonorstandleveldensity.Saplingsaretreesatleast1inch(2.5cm)butlessthan5inches(12.7cm)DBH.The“sapling”versuslargertreedistinctionisbasedonsamplingdifferencesontheFIAplots.Thisillustratesthatpoolclassificationisdependentonboththeobviousphysicalandspatialseparationinastandaswellasdatasources.

6.2.2.2 Understory

Understoryvegetationisaminorcomponentofbiomassortheliveplantcomponent.Understoryvegetationisdefinedasallbiomassofundergrowthplantsinaforest,includingwoodyshrubsand


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treeslessthan1inch(2.5cm)DBH.InFIADB‐basedcarboninventory,itisassumedthat10percentofunderstorycarbonmassisbelowground.Thisgeneralroot‐to‐shootratio(0.11)isnearthelowerrangeoftemperateforestvaluesprovidedinIPCC(2006)andwasselectedbasedontwogeneralassumptions:ratiosarelikelytobelowerforlight‐limitedunderstoryvegetationcomparedwithlargertrees,andagreaterproportionofallrootmasswillbelessthan0.08in(2mm)indiameter.EstimatesofcarbondensityarebasedoninformationinBirdsey(1996),whichwasappliedtoFIApermanentplots.

6.2.2.3 StandingDead

Thestandingdeadtreecarbonpoolsincludeabovegroundandbelowground(coarseroot)mass.Estimatesandallometryareessentiallysimilartothoseforlivetrees,withsomeadditionalconsiderationsfordecayandmechanical/structuraldamage(Domkeetal.,2011;Harmonetal.,2011).Carbonconversionsvaryslightly,but50percentisausefulroundvaluefordeadwood.However,specificcarboncontentislessforthelitterandorganiclayersoftheforestfloor.Thereisnotadeadplantmaterialpoolcorrespondingtounderstory;itisassumedtheseveryquicklybecomelitterorsmallwoodydebris.Pairingpooldefinitions(boundaries)withdatasourcesisalsoveryimportantwiththepoolsofdeadplantmaterial,becausemeasurementsspecifictoestimatesaremuchlesslikelyforDDW,forestfloor,etc.IntheFIADBthedistinctionbetween“standing”and“down”deadwoodisbasedonangleofleanandisappliedtoP2(phasetwo,“standard”forestinventoryplot)andP3(phasethree,asmallernumberofplotsthatincludeadditionalmeasurementssuchassoilsandforestfloor)data;otherdefinitionsmayvary.Forsmalldiameterstandingdeadtrees,estimatesexistbutareproblematic:FIAdataonlyprovidesamplesofstandingdeadtreesat5inches(12.7cm)DBHorlarger.Estimatesofsaplings(1–5inch(2.5—12.7cm)DBHtrees)necessarilywillbemodeled(Woodalletal.,2012).

6.2.2.4 DownDeadWood

DDWisdefinedaspiecesofdeadwoodnolongerapartofstandingdeadorsnags,yetdistinctfromsmalleroradvanceddecayedwoodoftheforestfloor.ThedefinitionlargelycorrespondstotheP3downwoodymaterialpool,andrepresentsaslightchangefromthepastdefinition.Thispoolalsoincludessomeless‐than‐obviouscomponentsofDDW:(1)debrispiles,usuallyfrompastlogging;(2)previouslystandingdeadtreesthathavelostenoughheightorvolumeorleangreaterthan45degreesfromverticalsotheydonotqualifyasstandingdead;(3)stumpswithcoarseroots(aspreviouslydefined);and(4)nonlivingvegetationthatotherwisewouldfallunderthedefinitionofunderstory.

6.2.2.5 ForestFloororLitter

Theforestflooristhelayersoflitter,oftenclassifiedasthefibric(Oi),hemic(Oe),andsapric(Oa)organiclayersabovethemineralsoilandsmallerthanDDW.Thisclassificationrepresentsachangefromthepastdefinition,whichalsoincludedthesmallwoodydebrisfromtheDDWpool.Organicsoilspresentadditionalchallengeswhendelimitingthispool.

6.2.2.6 ForestSoilOrganicCarbon(SOC)

Thispoolisorganiccarbonwithinthesoilbutexcludingcoarserootsasdefinedforlivetrees,understory,standingdeadtrees,andstumps—allasdefinedabove.Byconvention,largepiecesofwoodymaterialthatareseparatelyandindependentlyestimatedthroughsamplingandallometryareexcluded.Depthisarbitraryandsofarhasbeendefinedbythedatasetinuse.Thedatasetshouldrepresentsamplesofasmuchoftheorganiccarbonaspossible,althoughpeatlandspresentauniqueproblem.Acommonsamplingdepthis1m,althoughthisisnotanIPCCstandard.Adequatesamplingdepthmaybeascertainedthroughlocalknowledge;3.9to7.9inches(10to20


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cm)maybeadequateforsomeforestecosystems,whileothersrequiregreaterdepths.Datasetsofsoilmapsfromsurveysareanothersourceofdata(inadditiontoP3plots).SOCvariabilityextendstorelativelylarge‐scalemapssuchaslocationssurroundingP2/P3plots.Thatis,soilsmapsarebasedondatawiththesamevariabilityasseenintheP3subplot‐to‐subplotprecision.

NotethatthepooldefinitionsusedbyFVSdonotmatchdefinitionsusedbyFIAinallcases.Whilethemaincategoriesofliveanddeadbiomasswillincludethesameelements,theFIAdefinitionofforestfloorincludesfinewoodydebris,whiletheFVS‐FFEdefinitionplacesfinewoodydebrisintheDDWcategory.FIAconsiderstreesunder1inch(2.5cm)DBHtobepartoftheunderstorypool,whileFVStrackstheseastreesregardlessofsize.FutureworkislikelytoincludethecapabilityofFVS‐FFEtogenerateacarbonreportwithpoolscorrespondingtothedefinitionsusedbyFIAinnationalaccounting.

6.2.3 EstimationMethods

Theflexibilityinusingthebestobtainabledatabalancedwiththeneedsandresourcesofeachindividualforestownercanprovidegood/validforestcarbonestimatesifsomebasicguidelinesarefollowed:

Carbonpoolsshouldbeexplicitlyidentifiedtomakeitpossibletoidentifypossiblegapsoroverlapsbetweenpools.Identifyingandrecognizingthatagapexists(forexample,therearenoseedlingdata,orstandingdeadtreeswerenotmeasured)ismoreusefulthanfuzzyboundariesbetweenpools.

Consistentpooldefinitionsandmethodsforcarbonestimationwithinthosepoolsarerequiredforvalidestimatesofchange.Thatis,changeshouldbebasedonthesamepoolsandmethodsatbothtime1andtime2.

6.2.3.1 LiveTrees

Variousapproachesareusedforestimatesoftreebiomassorcarboncontent;ultimately,eachreliesonallometricrelationshipsdevelopedfromacharacteristicsubsetoftrees.Here,livetreesincludestemswithDBHofatleast1inch(2.5cm).Allometrycanincorporatewholetreesorcomponentssuchascoarseroots(greaterthan0.08to0.20inches(0.2to0.5cm);publisheddistinctionsbetweenfineandcoarserootsarenotalwaysclear),stems,branches,andfoliage.Livetreebelowgroundcarbonestimatescanbetroublesome,butoverallaccuracyisbestiftheboundaryissettoconformtoavailabledataratherthanapredefinedthreshold.

Recommendedoptionsforobtainingestimatesofcarbonstockoflivetreesare:

Smalllandowners(asdefinedinSection6.2.1):Valuesobtainedfromlookuptables(e.g.,eitherthoseinSmithetal.,2006,orasotherwiseprovided)categorizedbygeographicregion,foresttype,andageclass.

Largelandowners(asdefinedinSection6.2.1):Standardforestinventory,estimatescalculatedusingindividualtreemeasurement(diameter)andtheFVS‐FFEmodulewiththeJenkinsbiomassequations(Jenkinsetal.,2003a).

Biomassequationsmustbeappliedappropriately;usingequationsoutsidethediameterorgeographicrangesforwhichtheyweredevelopedwillintroduceadditionalerrortotheestimates.GiventhehundredsofdifferenttreespeciesgrowingindiversehabitatsacrosstheUnitedStates,itisbeyondthescopeofthisdocumenttosuggestthemagnitudeoftheeffectofalternativetreevolumemodelsbeyondthenational‐scalemodelssuggestedherein.Regardlessoftheestimationapproachselected,itiscriticaltousethatmethodconsistentlyovertime.Estimatesproducedfromdifferentmethodswillvary;changingestimationmethodsovertimewillintroduceadditionalerror.


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AlthoughwearecurrentlyspecifyingonlytheuseofbiomassequationsbyJenkinsetal.(2003a),itisunderstoodthattheseequationsmaynotbethemostappropriateinallcircumstances.Forexample,usingequationsoutsidethediameterorgeographicrangesforwhichtheyweredevelopedwillintroduceadditionalerrortotheestimates.SomeJenkinsequationshavelimitstotheallowablediameters.SpecificguidancewillbedevelopedinthefuturetofacilitatetheuseofdifferentbiomassequationssuchasthoseusedbyFIAbasedontheCRMandlocally‐specificequations.RefertoFigure6‐3foradecisiontreefortheforestcarbonaccountingsourcecategoryshowingwhichcarbonaccountingassumptions(e.g.,simulationmodel,allometricequations,andlookuptables)arerecommendedforanentitydependingonthesizeclassandtypeofactivitydataavailable.

SamplingandAllometry.Recommendedapproachesarebasedontheapplicationofallometricrelationshiptosampledinventorydata.TheFIADB‐basedestimatesoflivetreecarbonarebasedontheplotdata–P2dataandCRMbiomassestimation(Woodalletal.,2011).Inaddition,alargenumberofotherallometricrelationshipshavebeendevelopedfortreebiomass(biomassregressionequations).Manybiomassequationsareavailableforavarietyofforesttypes;forexample,possibleoldercitationsareTer‐MikaelinandKorzukhin(1997);seealsocitationsinJenkinsetal.(2003b).TheequationsrecommendedinthisreportaretheJenkinsetal.(2003a)equations,whicharenationallyconsistentandstraightforwardtoapply.FuturedevelopmentorintegrationofthismethodintoasoftwaretoolshouldconsiderimplementationoftheCRMbiomassestimationmethodinordertobetteralignwiththemethodsusedforU.S.GHGinventoryreporting.TheCRMapproachiscomputationallycomplex,andisnotincludedatthistime.

Inventorydesignsandprotocolsarewelldocumentedbyavarietyofauthorsandwillnotbediscussedfurtherhere.AgoodexampleisPearsonetal.(2007),whichiswrittenspecificallyforcarboninventories.

LookupTables.Publishedsummaryvaluesofsimilarorrepresentativeforestsprovidequickandinexpensivemeansofroughlyassessinglikelyforestcarbon.Agoodexampleofsuchlookupvaluesarethepastrevised1605(b)guidelines,withtheforesttablespublishedasSmithetal.(2006).AlternativeversionsofrepresentativevaluesincludeFIAonlineapplicationssuchasFIDOorEVALIDator,FIA‐relatedapplicationssuchasCOLE,ormodelsfromspatialdatasuchastheFIAbiomassmaportheNationalLandCoverDatasetlayers.

Simulations/Modeling.Notonlydoforestbiometricalmodelsprovideaplatformforestimatingfuturescenariosofforestcarbonstocks,buttheycanalsobearapidmethodologyforentity‐levelcalculationofcurrentforestcarbonstocks.TheFVSisonesuchsimulationtoolthatcanprovideestimatesofcurrentforestcarbonstocksgivenanelementaryforestinventorywasconducted(e.g.,numberoftrees,size,andspecies).Inaddition,andperhapsamorepowerfulaspectofsuchatool,isthatprojectionsoffuturestandattributescanbeacquired(e.g.,forestcarbonstocks50yearsfrompresent)asdescribedinDixon(2002)andHooverandRebain(2008;2011).

6.2.3.2 Understory

Estimationproceduresanddatasourcesarelimitedforthispool.Unlessanentityhasthecapabilitytodeveloplocalizedunderstorymodelsandallometricrelationships,thedevelopmentofcarbonestimatesforthesepoolswillbelimitedtolookuptablesandsimulations/modeling.ValuesareprovidedintheSmithetal.(2006)lookuptables,whicharebasedonBirdsey(1996)andmodifiedtoapplytoFIAdata;seeU.S.EPAAnnex3.12(2010)foradditionaldetails.TheFIADBconditiontableincludesestimatesbasedonthismodel,soestimatesbasedonsimilarstandscanbeobtainedfromtheFIADB.UnderstoryvaluesareprovidedinthecarbonreportsinFVSandareregionaldefaultvaluessetwithinthemodel.


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6.2.3.3 StandingDead

Theprevailingdifferenceinvolume/biomass/carbonestimationofstandingdeadtreesfromlivetreesistheincorporationofdecayreductionfactorsandrotting/missing/cullcomponents(Domkeetal.,2011;Harmonetal.,2011).

SamplingandAllometry.FIAinventory‐basedestimationforstandingdeadtreesisfromP2plot,condition,andtreerecords.TreemassintheFIADBiscalculatedaccordingtoCRMmethods(Woodalletal.,2011)withrefinementstotheCRMapproachspecifictostandingdeadtreesproposedbyDomkeetal.(2011).Duringastandardforestinventory,standingdeadtreesaremeasuredandtallied,andlargelandownerscanusethisinformationwithFVStoproduceestimatesofthebiomassandcarboninthispool.

LookupTables.Publishedsummaryvaluesofsimilarorrepresentativeforestsprovidequickandinexpensivemeansofroughlyassessinglikelyforestcarbon.Agoodexampleofsuchlookupvaluesarethepastrevised1605(b)guidelines,withtheforesttablespublishedasSmithetal.(2006).AlternativeversionsofrepresentativevaluesincludeFIAonlineapplicationssuchasFIDOorEVALIDATOR,andFIA‐relatedapplicationssuchasCOLE.Notethatsomedifferencesmayappearamongpoolestimatescomparedtothesampleestimates,becausesomeorallarebasedonempiricalmodels(regressions)andnotthedirectplot‐levelmeasurementsthatarenowavailablewithintheFIADB.SmalllandownerscanobtainestimatesofthestandingdeadpoolusingtheSmithetal.(2006)lookuptables.

6.2.3.4 DownDeadWood

TherecommendedmethodforobtainingestimatesofcarbonstockofDDWforlargelandownersisestimationfromtransectdatacollectedduringtheinventory.CareshouldbetakentoadheretotheboundsbetweentheDDWandforestfloorpools(notingthatfinewoodydebrisisconsideredpartoftheforestfloorpoolinthisguidance).Smalllandownersmayrefertothelookuptablesforpoolestimates.

SamplingandAllometry.AvarietyofsamplingandestimationprotocolsisavailablefortheDDWpool;astraightforwardandcommonlyusedapproachcanbefoundinPearsonetal.(2007).

LookupTables.RegionalaveragesbyforesttypeareasdescribedinSmithetal.(2006),orestimatescanbesummarizedandextractedfromtheFIADBconditiontabletocorrespondtotheentity’sforest.However,notethatthecurrentFIADB’sDDWfromtheconditiontableisamodelindependentofP3sampling.SeeSmithetal.(2006),U.S.EPAAnnex3.12(2010),Woodalletal.(2013),andDomkeetal.(2013)fordetails.

Simulations/Modeling.DDWcarbonvaluesareprovidedinthecarbonreportsinFVS.Valuesmaybesuppliedbythelandowner;ifthesedataarenotavailable,regionaldefaultvaluesbasedonP3dataoravailabledatafortheregionandforesttypeareautomaticallyinputbythemodel.

6.2.3.5 ForestFloororLitter

Recommendedoptionsforobtainingestimatesofcarbonstockofforestfloorforalllandownersistheuseoflookuptablesbasedonforesttype,region,andstandage.Largelandownerswhoarechanginglandusesfromnon‐foresttoforestmaywishtocollectdataforthispool.

SamplingandAllometry.LandownerswishingtoestimatethesepoolsfromfielddatacanusefinewoodydebrissamplingandcarbonconversionaccordingtoWoodallandMonleon(2008),andforestfloorusingtheapproachdescribedbyPearsonetal.(2007).NotethatwhilePearsonetal.(2007)applyamasstocarbonconversionfactorof0.5(Smithetal.,2006)),othersuseaconversion


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factorof0.37.Landownerswhoareestimatingtheforestfloorpoolusingfielddatashouldapplythe0.37conversionfactor.

LookupTables.RegionalaveragesbyforesttypeareasdescribedinSmithetal.(2006);estimatescanalsobesummarizedandextractedfromtheFIADBconditiontabletocorrespondtotheentity’sforest.TheseestimatesarebasedonsimulationsdescribedinSmithandHeath(2002).NotethatthecurrentFIADBconditiontableestimatesofforestfloorarethesemodeledvaluesindependentoftheP3sampling.

Simulations/Modeling.ForestfloorcarbonvaluesareprovidedinthecarbonreportsinFVS.Valuesmaybesuppliedbythelandowner;ifthesedataarenotavailable,regionaldefaultvaluesbasedonP3dataoravailabledatafortheregionandforesttypeareautomaticallyinputbythemodel(FVSemploysthe0.37masstocarbonconversionfactorwhenestimatingthispool).

6.2.3.6 SoilOrganicCarbon

PossibleoptionsforobtainingestimatesofSOCstocksare:

Sampling,followingstandardfieldmethods;

DatasetssuchastheSoilSurveyGeographic(SSURGO)Database,StateSoilGeographic(STATSGO)Database,ortheDigitalGeneralSoilMapoftheUnitedStates(STATSGO2);and

Stand/forestclassification:extractrangeofmodeledestimatesfromFIADBconditiontable.

SamplingandAllometry.SoilsamplingandcarbonestimationaccordingtoFIAP3plotprotocolscanbefoundattheUSDAForestServiceFIALibrary:FieldGuidesforStandards(Phase3)Measurements;7methodsarealsoavailableinPearsonetal.(2007),Hoover(2008),andothers.

Soilsdataaregenerallyconsidereddifficulttomeasureandspatiallyquitevariable.Theconsequenceisthatthecostsarehighandthepayoffislikelylow.Ourrecommendationisthatsamplingisonlyusefulifthereisanimportantreasontodoso,suchasachangefromnon‐foresttoforestorviceversa.Ifawildfireoccursandthereissignificantconsumptionofpeatlands,samplingshouldbeconductedandemissionscalculatedusingFOFEMand/orCONSUMEmodels.ThissituationismostlikelytobefoundintheSoutheastorNorthCentralStates.

LookupTables.Forestsoilorganiccarbonestimates—representativevaluesorlookuptables.DatasetssuchasSTATSGOorSSURGOarepossiblesources.EstimatescanbesummarizedandextractedfromtheFIADBconditiontabletocorrespondtotheentity’sforest;thesearebasedonaSTATSGO/P2overlay(Smithetal.,2006;U.S.EPA,2010).

6.2.4 Limitations,Uncertainty,andResearchGaps

Thereisoftentremendousuncertaintyassociatedwithestimatesofforestcarbonbaselines,suchthatevenatlargescales(e.g.,state‐level)thepowertodetectstatisticallysignificantchangesinforestcarbonstocksislimitedtomajordisturbances(Westfalletal.,2013).Compoundingthesamplingerroroftenassociatedwithforestinventories,thereismeasurementandmodelerrorthatmaynotbeacknowledged.Usersofanyinventories,lookuptables,ormodelsshouldremainawareofthesepotentialerrorsduringtheirapplicationofinformation.

Thereisalevelofuncertaintyassociatedwithnotonlytreevolume/biomassequations,butalsowiththevariousforestcarbonpools(e.g.,belowgroundtoforestfloor)foundacrossadiversityofforestecosystems(e.g.,tropicaltoboreal)intheUnitedStates.Researchtorefineapproachestoforestcarbonaccountingandrefinementsofassociatedmodelsiscurrentlyinprogress.Perhaps7http://fia.fs.fed.us/library/field‐guides‐methods‐proc/


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someofthemostneededimprovementsareforindividualtreevolume/biomassequations,especiallyfortraditionallynon‐commercialspecies.Anotherforestcarbonpoolthatisbeinginvestigatedissoilorganiccarbon.Althoughthesoilcarbonpoolisnotexpectedtochangequicklyincomparisontolivetreepools,inmanyareasoftheUnitedStatesitisthelargestcarbonstock(e.g.,northernMinnesota).Beyondreducingtheuncertaintyassociatedwithestimatesofcarbonpools,researchisbeingconductedtorefineunderstandingoftheeffectsofdisturbanceandclimatechangeoncarbonpools.

6.3 Establishing,Re‐establishing,andClearingForests

6.3.1 Description

Conventionalparlanceattributeschangesofcarbononasiteundergoingland‐usechangeintothreedirectionalprocesses:establishing(i.e.,afforestation),re‐establishing(i.e.,reforestation),andclearingforest(i.e.,deforestation).Inrecentyears,thetermforestdegradationhasbeenusedtoacknowledgethatanexistingforestcanbesignificantlyreducedincarbonstocksandcanbeconsideredasourceofemissions,aslongasthereductionincarbonstocksisnotanaspectofnormalforestmanagement.However,thisisnotaformofland‐usechangebecausethelandremainsinforests.Thisisanimportantconsiderationunderforestmanagement,butmayalsobeimportantwhenhumanuseandremovalsofforeststockstakeplaceevenwhennotprescribedbyamanagementregime.ThemostimportantsourceofGHGemissionsfromforestsisassociatedwithforestclearing(IPCC,2007).Theconversionofforeststootherlandusesimmediatelyreducesthestockofcarboninabovegroundbiomassandsoilorganicmatter,andislikelytoreducethelong‐termcarbonstoragepotentialoftheland.Thecarbonthatwasoncestoredinforestbiomassandsoilisreducedthroughrapidoxidationbyfireorslowlyovertimebymicrobialdecomposition.Someofthebiomasscanalsoberemovedfromthesiteandconvertedtoforestproductssuchaslumber,paper,pulp,andotherproductsthathavelongertermbutvariabledecompositionrates—andhencelongertermandvariableemissionsovertime.Allofthesecomponentsofland‐usechangeneedtobeaccountedforwhendeterminingthechangesinsitecarbonstocksduetoland‐usechange.

Aparceloflandcanbeconvertedtoforest,plantation,orothertreedlandscapeeitherthroughintentionalplantingorthenaturalprocessofsecondarysuccession.Landthathadoncebeeninforestisreturnedtoforestthroughre‐establishment.Notethatthisappliestolandthatisnotcurrentlyinforest,nottoforestlandthatisregeneratedaspartofforestmanagement.Landthathadnotbeeninforest,suchasgrasslands,canbeconvertedtoforeststhroughestablishment.In

MethodsforEstablishing,Re‐establishing,andClearingForest

IPCCalgorithmsdevelopedbyAaldeetal.(2006).

Theseoptionsuse:

− AllometricequationsfromJenkinsetal.(2003a),orFVSwiththeJenkinsetal.equationswhereapplicable;and

− DefaultlookuptablesfromSmithetal.(2006;GTRNE‐343)—defaultregionalvaluesbasedonforesttypeandageclassdevelopedfromFIAdata.

Thesemethodswereselectedbecausetheyprovidearangeofoptionsdependentonthesizeofanentity'sforestland.


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eithercase,generallyspeaking,thestockofcarboninbiomassandsoilorganicmatterwillincreaseovertimeasaresultofthistypeofland‐usechange.Biomassincreasespredictablyastreesandothervegetationareestablishedonthesite.Soilorganicmatteralsochanges,butinlesspredictableways.Forinstance,theestablishmentofaforestplantationongrasslandincooltemperateregionsmayresultinatemporarylossofcarboninsoilorganicmatterbeforeitbuildsupagainaftertheplantationisfullyestablished.Forbothaccountingandplanningpurposes,thesechangesinstocksofcarbonmustbeestimatedandaccountedforwhenassessingtheeffectsofland‐usechange.

Currentinternationaldefinitionsarepresentedbelowanddrawadistinctionbetweenlandsthathaveneverbeenunderforestcoverandthosewhichwereinforestcoverinthepastbuthavenotbeenforestedrecently(e.g.,forthelast50years).Thesedefinitionsarepresentedherebecausetheyarecommonlyusedintheliterature;however,intermsofcarbonaccountingforlivebiomass,thereisnopracticaldifferencebetweenthetwocategories.Thegreatestimpactisonthesoilcarbonpool.Wheretheaimistoestimateentity‐levelGHGfluxes,thesetwocategorieswillbetreatedtogetherandtermed“establishingforest”inthisguidance.

6.3.1.1 EstablishingForest

Establishmentistheconversionofanon‐forestsitethatisnotnaturallyaforestedortreedecosystemorhadneverbeeninforesttoaforestorsimilartree‐dominatedlandcover.Examplesofestablishmentincludetheconversionofbarelandtoaforestandconversionofgrasslandstoforestsorplantation.Inpracticalterms,andforthesakeofthisguidance,landthathadbeeninagricultureorothernon‐forestlandcoverforalongtime(e.g.,morethan50years)thatisconvertedtotreecovercanalsobeviewedasestablishment.Hence,establishedforestlandisthatwhichhasnotbeendominatedbytreesformorethan50years.

6.3.1.2 Re‐establishingForest

Re‐establishmentisthereversionofforestsortreecoveronsitesthathadformerlyandrecentlybeen(e.g.,lessthan50years)inforestordominatedbytreecover.Examplesofre‐establishmentincludenaturalregenerationofadisturbedorclearedparcelofforesttoasecondaryforest,conversionofagriculturallandtoaforest,andestablishmentofaplantationonasitethathadoncebeenforestbutisnowinanotherlanduse(suchascropland).Itisimportanttodistinguishbetweenre‐establishmentasaland‐usechangeandforestregrowthaspartofforestmanagementortheresultofanaturaldisturbance.Forexample,aland‐usechangefromagriculturetoforestisconsideredhereasre‐establishment,whereforestregenerationfollowingawindthroworclear‐cuttingisnotconsideredaland‐usechangeresultinginre‐establishment.

Intheinternationalconventions,theIPCCSpecialReportonLandUse,Land‐UseChange,andForestry(IPCC,2000),whichwasdevelopedexplicitlyforcarboninventory,definesre‐establishmentas"theestablishmentoftreesonlandthathasbeenclearedofforestwithintherelativelyrecentpast;theplantingofforestsonlandswhichhave,historically,previouslycontainedforestsbutwhichhavebeenconvertedtosomeotheruse." Establishmentandre‐establishmentbothrefertoestablishmentoftreesonnon‐treedland.Re‐establishmentreferstocreationofforestonlandthathadrecenttreecover,whereasestablishmentreferstolandthathasbeenwithoutforestformuchlonger.Avarietyofdefinitionsdifferentiatebetweenthesetwoprocesses.Somedefinitionsofestablishmentarebasedonphrasessuchas"hasnotsupportedforestinhistoricaltime;"othersrefertoaspecificperiodofyears,andsomemakereferencetootherprocesses,suchas"undercurrentclimateconditions."TheIPCCGuidelinesdefineestablishmentasthe"plantingofnewforestsonlandswhich,historically,havenotcontainedforests"(IPCC,2000).

Asnotedabove,forthepracticalpurposesofreportingunderthesemethods,achangefromnon‐foresttoforestcoverwillbetermedestablishingforest,andthe50yeartimehorizonwillnotapply.


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6.3.1.3 ClearingForest

Clearingistheconversionofaforestortree‐dominatedsitetoanotherlanduseotherthanforestoratree‐dominatedsite.Oftenclearingresultsinthecompleteremovalofabovegroundlivebiomass.Examplesofclearingincludetheconversionofaforestwoodlottocroplandorpasture,conversionofaforestwoodlottocommercialorresidentialuse,andconversionofanaturalforesttoagriculture.

6.3.1.4 OtherImportantConsiderations

DistinctionbetweenLand‐UseChangeandLand‐CoverChange.Itisveryimportanttounderstandanddelineatethedifferencebetweenland‐coverchangeandland‐usechange.Becausetheterms“landuse”and“landmanagement”areoftenconfusedorusedinterchangeablythedistinctionisdefinedhere.Abasicdefinitionoflandcoveris“theobservedphysicalandbiologicalcoveroftheEarth’slandasvegetationorhuman‐madefeatures.”Abasicdefinitionoflanduseis“thetotalofarrangements,activities,andinputsundertakeninacertainland‐covertype(asetofhumanactions).Thesocialandeconomicpurposesforwhichlandismanaged(e.g.,grazing,timberextraction,conservation).”Theconventionsfoundintheliterature—Turneretal.(1994),Skole(1994),andLambinetal.(2006)—arefollowedandwereadoptedbytheIPCCin2000.Itisrecognizedthatinadoptionoftheterminologyoflanduse,land‐usechange,andforestry,theIPCCGoodPracticeGuidancedocument(IPCC,2006)generalizedtheuseoftermstoincludethesixbroadland‐usecategoriesdefinedinIPCC(2003)Chapter2andrecognizedthattheseland‐usecategoriesareamixtureoflandcover(e.g.,forest,grassland,wetlands)andlanduse(e.g.,cropland,settlements)classes.Forconvenience,theyareherereferredtoasland‐usecategories.

Werecognizeherethatthetermland‐usechangecanbeadoptedtoincludeland‐coverchanges,aswellasland‐usechanges.Thus,forthisguidance,aswithIPCC,land‐usechangewillbetheconversionofthe“typeofvegetation”fromonecovertype,suchasaforestdominatedbytrees,toacompletelydifferentcovertype,suchascroplanddominatedbynon‐woodyfoodcrops.Thedirectionofcoverchangedeterminesthenatureofthechangeincarbonstocks(e.g.,forestclearingversusestablishment).Generallyspeaking,land‐usechangeisthemostimportantconsiderationforalandowner,sincethisprocessusuallyresultsinthelargestchangeinonsitecarbon.

However,wealsorecognizethatlandownerswillhaveimportantchangestotheirlandsthroughthemanagementactivitiesthattheydeploy,andtheseactivitiescanhaveimportantimplicationsforcarbonstocksandGHGemissionsandremovals.Thus,wealsorecognizetheconceptandterminologyofland‐managementchange,whichisachangeinthetypeofactivitybeingcarriedoutonaunitofland,andthushowitismanagedorused,suchaschangingthemanagementpracticeswithinaforestfromselectiveharvesttoprotection.Land‐managementchangemayormaynothaveasignificantimpactoncarbonandotherGHGs.

Landmanagementexplicitlyreferstohowthelandisbeingmanagedorused,whilelandusereferstowhatisontheland.Anexampleoflandmanagementisatree‐dominatedsitethatisusedasaworkingforestorwoodlot.Assuch,alandownercanchangethemanagementplanforthesite—forinstance,changingitsusetoaforestreserve—withoutradicallychangingitscover.Nonetheless,evensuchchangeinusecanaffecttheamountofcarbonstoredonthesiteandinthesoils.Typically,whenaforeststandlandmanagementischangedwithoutaffectingitscovertypeitisconsideredamanagedforest,anditsaccountingprotocolsfollowthoseforforestmanagementratherthanforestablishingforests.Thusitisimportanttodetermineanddocumentboththeland‐useandland‐managementchangesthatoccuronthesite,andexplicitlyassociatethecarbonestimationapproachtoeitherestablishing/clearingforests(Section6.3)orforestmanagement(Section6.4),butnotboth.


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EstablishingandClearingForestversusForestManagement.Forreasonsoforderandconsistency,establishingandclearingforestisdistinguishedfrommanagement,whichisaddressedinSection6.4.Forestryoperationssuchasthinning,artificialregeneration,andharvestingareassociatedwithmanagedforestsystems.Unlessforestryactivitiesleadtoachangefromonelandusetoanotherland‐use,theseactivitiesarenottreatedusingestablishingandclearingforestaccountingprinciples.Theinitialconversionfromforesttoagriculture,forexample,wouldusetheestablishingandclearingforestrules,followedbytheapplicationofrulesforagriculture.Similarly,whenanon‐forestlandcoverisconvertedtoamanagedforesttheinitialconversionwouldbetreatedasestablishingforestandusethesemethods,butsubsequentmanagementofthestandwouldfollowforestmanagement(e.g.,forestcarbonaccountingandforestmanagement)methods.

TypesofForest.Fromastrictcarbonaccountingpointofview,theland‐coverdesignationdoesnotmatter,nordoesitschangeincovertypeaslongasonehasgoodestimatesofcarbonstocks,andcanmeasureorestimatetheirchanges.However,datausedtoestimatechangesincarbonareoftenreportedandorganizedbyforesttype,sothecompositionandstructureoftheforestoftencomesintothecomputationmethods.Moreover,toavoiddoublecounting,itisimportanttodefinewhattypeoflandscapescanbeconsideredasaforestforestablishingandclearingforest.Therearetwoelementsofadefinitionofforeststhatarewarranted.Thefirstisabasicdefinitionofaforest.Therearearangeofconditionsoftreedlandscapeswhereestablishingandclearingforestactivitiescantakeplace,frompreservedforeststowoodlotstoopenandwidelyspacedtreelandscapesandurbantreedlandscapes.Therearehundredsofvariationsofdefinitionsofforest(Lund,1999)andforeachofthesetherearesubtypes.Examiningtheimplicationsofeachvariantwouldnotbefruitful;theresultwouldbegreaterconfusion,ratherthantheclaritysought.Inastrictsense,aforestisdefinedhereusingtheU.S.‐specificdefinitionofforestland(Smithetal.,2009).Thesearelandswithtreecrowncover(orequivalentstockinglevel)ofmorethan10percent,widthofatleast120feet(36.6m),andareaof1acre(0.4ha).Treesshouldbeabletoreachaminimumheightof6.6–16.4feet(2–5m)atmaturityinsitu.Aforest‐landunitmayconsistofclosedforestformationswheretreesofvariousstoriesandundergrowthcoverahighproportionofground,oropenforestformationswithacontinuousvegetationcoverinwhichtreecrowncoverexceeds10percent.

Second,landownersmayhaveadiverselandbasethatisaffectedbydifferentforestryactivities,managedatdifferentintensities,orthathasavarietyofexistingdata.Oneofthefirststepsinpreparingentity‐wideorsub‐entityestimatesofcarbonfluxesfromforestsistoorganizetheunderlyingdataonlandconditionsintomanageableunits,referredtohereasforeststrata.Landshouldbegroupedintoforeststratausingalogicalframeworkthataggregatessimilarlandunits.Forexample,landcouldbepartitionedbyaveragetreeage,foresttype,productivityclass,andmanagementintensity.Inmanycasesforeststratawillbecontiguous,althoughthisisnotanecessarycondition.Thelandownercanselectthetypeofstratificationschemetoemploy;andthereareseveralguidesavailabletodothis.Thebetterthestratification,themoreaccurateandprecisearethecarbonestimationswiththeminimalamountofdatacollection.

Thedefinitionofaforestisusefulforconsistencyinreportingandcoversawiderangeofconditions.However,notethatthetechnicalmethodscanapplytoanytreedlandscape.Theadoptionoftheinternationalnomenclatureforforestsallowstheconsiderationofarangeofsiteconditionsandsituations.ForestsintheUnitedStatesarevaried,fromscrubwoodlandsinsemi‐aridzonestomaturedeciduousandconiferouscomplexesinthehumidzones.Inaddition,humanmanagedsystems,suchaswoodlotsandplantations,areconsideredasforests.

SimilarModalitiesandVariantsofEstablishing,Re‐establishing,andClearingForest.Thissectionrecognizesthatestablishingandclearingforestaresimilartoandindeedconceptuallyrelatedtoseveralotherland‐coverchangemodalities,whicharetreatedinotherprotocols.Theseincludebutarenotlimitedtoagro‐forestry,whichinvolvestheuseoftreesonfarms;urbanforests


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andwidelyspacedtreecomplexes;treesonlandscapesoutsideofforests;woodlandsandsavannasystems;orchards;andpalmandhorticulturecomplexes.Althoughthemeasurementandestimationmethodsdescribedheremaybeeasilyadaptedtotheselandcoversandlanduses,theyarenottreatedinthissection.

6.3.2 ActivityDataCollection

Activitydataaremeasurementsorestimationsofmagnitudeofhumanactivityresultinginemissionsorremovalstakingplaceduringagivenperiodoftime.Mostoftentheareaoflandthatisconvertedfromonelandusetoanotheristhemostimportanttypeofactivitydata.Dataonareaburned,managementpractices,andlimeandfertilizeruseareotherexamplesofactivitydata.Forestablishingandclearingforest,activitydataconsistsmostlyofinformation,preferablyinmapformwithdelineatedboundaries.Forsmalllandowners,itispossibletodelineateanareaofland‐coverchangebyfootusingsimpledistancemeasurementsorwiththeaidofaGPS.Alandownermayhavedifferentactivitiesoccurringonasingleproperty,andthuseachoftheforeststratashouldbemappedandhaveseparatelydelineatedactivities.Remotesensingoraerialphotographycanbeusefulforanylandownerwithaccesstothesedata,butareespeciallyusefulforlargerlandunits.Historicalinformationonchangesintheareasoflandusesonapropertyisalsoimportant,andthesedataarefrequentlyfoundinairphotoarchivesorothermaprecords.Inadditiontotheareasandratesofclearingand/orestablishment,itisnecessarytocollectdataonspecificaspectsanddetailsoftheseactivities.Thismayincludedataontreetypes,biomass,clearingintensity,woodremovals,treeplantingdensities,andotherfactorsthatdescribedthemodalityoftheestablishingandclearingforestactivities.

6.3.2.1 EstablishingForest

Foranestablishmentactivity,itisimportanttogatherbasicinformationontheareaandlocationofeachstratumoflandusethatisbeingestablished.Forthemostpartanestablishmentactivitywillbeaplantationorsimilartypeofestablishment/forestationactivity.Thus,basicinformationonsitepreparation,speciesselection,anddensitiesofplantingscanbeusedwithaprojectionofthelong‐termplanforthesitetomakeareasonableex‐antecalculation.Ifnaturalregenerationistheprimarymeansofestablishment,estimatesofseedlingcountscanbeusedtodevelopagrowthprojection.Alternatively,regionalyieldtablesmaybeusedtoestimateprojectedstocks.Theprioruseandmanagementofthestratumorlanduseshouldalsobedocumented,sincethehistoricaluseofthelandinfluencescarbonstockandstockchangeestimates.Forinstanceestablishmentofaforeststandongrasslandwillhaveadifferentresultintermsofcarbonthanestablishmentonarowcropagriculturalfield.Onceaforestiswellestablished,forallpracticalpurposesitbecomesamanagedforestandshouldbetreatedusingthemethodsinthenextsectiononforestmanagement.Weconsidertheland‐usestratumtobeaforestwhenthecharacteristicsofthestandmeetthedefinitionofaforest.Mostoftenthiswillbewhenthesiteiswellstockedtothedefinitionalcrowncoverandheightoftrees.

6.3.2.2 ClearingForest

Themostimportantactivitydatatocollectaretheareaandratesofforestclearingforeachstratumorparcelintheprojectarea.Itisalsoimportanttoknowtheintensityofclearingandifthereareremainingtreesorothervegetationleftonsiteafterclearing.Toestimateemissions,itisnecessarytoknowalsothecharacteristicsofthestratumthatistobecleared,includingthebiomassandsoilorganicmatterofthesite.Theprocessofclearingasiteisanactivitythatcanalsobecharacterized.Informationneededincludesthefractionoftheabovegroundbiomassthatwouldbeburned,thefractionthatisleftbehindonsiteasslashanddebris,thefractionthatwouldberemovedintheformofwoodproducts,andthefractionthatisremovedintheformofotherproducts.


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Thissectionlaysouttheminimumnecessarypartsofacomputationschemeforestimatingcarbonstocksandcarbonemissionsinbiomassandsoilassociatedwithestablishingandclearingforest.Thedescriptionslaidoutherearegeneralized.Thebasicconceptbehindthemissimple:thestock,ormass,ofcarbononasitechanges,andthetaskofestimationistocomputethedifferenceinstocksbetweenthelandusebeforeandaftertheinterventionordisturbance.Whenasiteiscleared,stocksgodownandthisresultsinemissionstotheatmosphere.Whenasiteisestablished,stocksgoupandthisresultsinremovalsfromtheatmosphere.

6.3.3.1 UnitsofMeasurement

Allstockcomputationsareperformedintermsofmassofcarboninkilogramsormetrictonsperunitareainmetricsystemunits(carbonperhectareorCha−1).Ratedataarereportedintermsofchangeincarbonperhaovertime,asincarbonperhectareperyear(Cha−1year−1).Allcarbonbiomassisreferencedtoitsdryweightbasisandthefractionofbiomassincarbon.Forthepurposeofthisguidance,thefractionofdrybiomassthatiscarbonis0.5.Anexamplestockis100metrictonsCha−1,andanexamplestockchangeis100metrictonsCha−1year−1.ItisimportanttodifferentiatebetweenunitsofcarbonandCO2equivalents(CO2‐eq)andreporttheappropriateunitstothereportingentity.Forexample,somereportingprograms(e.g.,carbonmarkets)requiretheconversionofmetrictonsofcarbontometrictonsCO2‐eq.ThisconventionplacesallcarbonmassestimatesintounitsofCO2,whichcanbederivedbymultiplyingthecarbonmassby44/12.

6.3.3.2 StocksandFluxes

Thestockofcarbonistheamountofcarboninbiomassandsoilonasite.Thestockchangeisthedifferenceinthestocksfromonetimeperiodtothenext.Thischangecanbepositiveornegative,dependingonwhetherthesiteisexperiencingclearing,degradation,restoration,orestablishment.Decliningstocksovertimefromclearingordegradationresultinemissions,whileaccumulatingstocksovertimefromestablishmentorrestorationarereferredtoassequestration.

6.3.3.3 DelineatingandCharacterizingtheSiteUsedinComputation

Toestimatecarbonstocksandfluxes,itisnecessarytodefinethemappedextentandthefeaturesofthesite.Forsmallareas,suchasafarmwoodlotorforeststand,theboundariesaredefinedgeographicallyusingaGPSdevice.Ifsurveyors’reportsorotherformsofmapsandphotossuchasaerialimageryareavailable,theycanbeused.Thereareagrowingnumberofonlinetoolsthatareavailable(e.g.,GoogleMaps)thatprovidedetailedimageryoflandthatcanbeusedtodrawboundariesoftheproposedsites.Afterdefiningthepreciseboundaries,aland‐coverclassificationshouldbeperformedtodefinethevariousvegetation,cover,orsoilstratawithinthesite.Forinstance,are‐establishmentprojectwithtwozoneswithintheboundaries,oneforacommercialplantationandtheotherfornaturalregeneration,wouldbestratifiedintotwostands.Iftheprojectorpropertyistobeasinglecover,suchasanaturalregenerationforestoraplantationforest,theprojectsitecanbeasinglestratum;butotherfactorsmaybeimportant,suchaslandslopeorsoilconditions.Iftherewillbeafuturemanagementactivityassociatedwiththeproject,thisstratumshouldalsobedelineated.Inshort,anyareawithintheprojectboundarythatwouldhavedifferentcoverorcarboncharacteristicsshouldbeseparatelydelineated.Standardmappingcoordinates,projections,andgeodeticdatumsshouldbeused.

6.3.3.4 CarbonPoolsunderConsideration

Generally,IPCCandothersourcesreferencefivepoolsofcarbontomeasure—abovegroundlivebiomass,belowgroundlivebiomass,standingdeadanddowneddebris,litter,andsoilorganic


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carbon.Thelandownerorprojectdevelopershouldidentifyfromthebeginningthepoolsthatwillbeaccounted.Allpoolsshouldbeincluded,unlessonecanshowthatapool’sstockchangesaresmallandunimportant—thedeminimisassumption(lessthan10percentofthetotalbaselinestock,seemorebelow)—orcanshowthatapoolwouldnothavestocklossesoremissions(e.g.,forestclearing).Inthesecases,thelandownerischoosingtobeconservativeinestimationoftheimpactoftheestablishingandclearingforestontheatmosphereforthatpool.Forinstance,inanestablishmentprojectwheretheestimationofsoilcarbonchangemaybedifficult,timeconsuming,orcostly,andthesoilcarbonchangeisassumedtobedeminimisinmagnitude,itmaybeeliminated.Or,ifitcanbedemonstratedthatthesoilpoolwillbeaccumulatingcarbon,thelandownermayselecttonotcountthatpoolandthusbeconservativeinthesequestrationpotentialoftheproject.Woodproductsthatareremovedfromthesitethroughharvestarenotbythemselvesconsideredaseparatepool,butthelandownerisadvisedtodocumentthisamountanditsfate,wherebyfatecanbe,forexamplehardwoodproducts,paperproducts,orfirewood(seeSection6.5).

6.3.3.5 InitialCarbonStockMeasurement

Thecarbonstocksinthemeasuredpoolsthataretobereportedneedtobedeterminedatthebeginningoftheprojectinordertodefineareferencecarbonamounttowhichfuturechangeswillbecompared.Whetherthesiteisaforestbeforeitsconversionoragriculturallandbeforere‐establishmentoftreecover,theinitialconditionsintermsofcarbonmustbereported.Theinitialcarbonstocksinallstrataareindividuallydeterminedfromlookuptables,satelliteimagery,orFIAdatabase,oraremeasuredandreportedaccordingtothedetailedmeasurementmethodsgivenbelow.Thereportingofthebaselinecangetcomplicatedinsomecases.Typicallythebaselineisthecurrentcarbonstocks.However,insituationswherethecarbonstocksarechanging,thebaselineiscomputedovertimeastheforwardlookingcarbonstocksthatwouldoccurintheabsenceoftheprojectorintervention.

6.3.3.6 TheEx‐AnteComputation

Onceinitialcarbonstocksaredetermined(theTypeIestimate),theprojectdeveloperneedstomakeaforwardprojectionoftheexpectedcarbonstockchanges,anditsdeviationfromwhatwouldhaveoccurredonthesitewithouttheinterventionofaprojectorland‐coverchange(TypeIIandIIIestimates).Thisissomewhatproblematicsinceitisnotpossibletopredictthefuturewithcertainty.However,anumberoftoolsandmethodsareavailabletomaketheseprojectionswithreasonablecertainty(seeTable6‐3).Animportantreasonformakingthiscomputationisthatthecarbonstockwouldchangeovertimeintheabsenceoftheproject’sintervention.Forexample,anabandonedfarmfieldcouldbeexpectedtonaturallygothroughold‐fieldsuccessionevenwithoutareestablishmentproject.Hence,theproject‐relatedcarbonchangesneedtobecomparedwiththenointervention/noactionestimateovertime,notjustfromthestartoftheproject,togetatrueaccountingofnetcarbonbenefits.Landownerswouldwanttomaketheex‐antecomputationsothattheycanevaluatearangeoffutureestablishment,clearing,ormanagementoptionstoselecttheonethatbestsuitstheircarbonandotheroutcomeneeds.

6.3.3.7 MeasurementandMonitoring

Aftertheinitiationoftheprojectintervention(e.g.,treeplanting),ongoingmeasurementsofactualcarbonstockchangesneedtooccur.Thisisoftenreferredtoasthemonitoringphaseoftheproject.Methodsforongoingmeasurementaredescribedbelow.Theprojectdevelopershouldkeeporganizedrecordsofthemeasurementsmadeoveraroutineandstandardtimeframe.Annualmeasurementsareusuallyeithernotlogisticallypossibleortootime‐consumingandexpensive.


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Thus,itisrecommendedthataftertheinitialmeasurement,thesemeasurementsarerepeatedevery5years.

6.3.3.8 PermanentSamplePlots

Forsmallprojectssuchasfarmwoodlots,ortreeandforeststands,acompleteinventoryofcarboninthereportingpools,strata,andprojectlandcanbeperformed.However,forlargeareas,installinganddelineatinganumberofsampleplotsisrequired.Thesesampleplotsareestablishedintheprojectareaonastratifiedbasis,laidoutrandomlyorsystematically—i.e.,eachlandcoverstratumhasanestablishednumberofsystematicallyorrandomlyplacedplots.Methodsforforestinventoryarewelldescribedandavailablefromavarietyofsourcesandwillnotbefurtherdescribedhere(e.g.,Pearsonetal.,2007).Boththenumberandlocationoftheplotsneedtobeconsidered.Itisimportanttorememberthattheplotsareestablishedforthepurposeofsamplingaforeststandorprojectstratum.Thesampleestimatewillbeasaccurateasthenumberandlocationofthesampleplotspermit.Thenumberofplotswillrelatetotheaccuracyoftheestimates;insimplestratasuchasplantations,thenumberofsampleplotscanbeextremelylow,butincomplexnaturalstandsthenumberwillhavetobegreater.Agoodstratificationwillreducethenecessarynumberofplots.Thelocationoftheplotsisimportanttocapturethespatialheterogeneityofthestand.Theplotsaretobewellmarkedandmadepermanentforrepeatmeasurementsovermanyyears.Forforestclearingcomputations,itisnotnecessarytomakepermanentplotsunlesstheprocessofclearingisselectivedegradationoveralongperiodoftime.Forforestclearing,lotsonlyneedtobemeasuredoncebeforetheinterventionandonceaftertheinterventionhasbeencompleted.

6.3.3.9 MeasurementversusEstimation

Insomecases,itwillnotbepossibletomeasuretheinitialcarbonstocksorpost‐interventioncarbondirectly.Forinstance,aforestclearingeventmayoccurwithouttheopportunitytoestablishplotsintheforest,oritmaynotbepossibletomeasurealarge‐areaestablishmentevent.Inthesecases,regionalsummaryvaluesoftheforestcarbonstocksmaybeofuse(Smithetal.,2006).

6.3.3.10 Allometry,BiomassExpansionFactors,andStandardValues

Theconventionalapproachtobiomassestimationistouseallometricequationsbasedonspecies‐specificinformation(Jenkinsetal.,2003b;2003a).AnallometricapproachcanbebasedonDBHoracombinationofDBH,canopyheight(H),andwooddensityonanindividualtreebasisfortheentirestandorfortreesinthepermanentplots.Theallometricequationpredictseithervolumeofwoodinthemainstemorwholetreebiomassorcarbon.Intheformercase,itisthennecessarytoestimateawholetreebiomassexpansionfactor(Smithetal.,2003).Alternatively,theentitycanusestandardvaluesforstocksandgrowthratesbasedonlookuptables(DOE,1992;Smithetal.,2006).Forlargeareasofforestsconvertedthroughclearing,itmaybeacceptabletousestandardvaluesforstocksperunitarea,suchasthosepublishedbyIPCC(2003;2006).

6.3.3.11 StocksversusChangeinStocksoverTime

Forestimationofforestestablishmentitisnecessarytocomputethechangeinstocksovertime,whichwillbeameasurementofnetsinksofcarbonthroughsequestration.Forestclearingcomputationisessentiallythesamebutwiththeoppositesigntoindicateemissions.Thesubtledifferenceisthatestablishmentrequiressomemeanstoestimatetheaccumulationofcarbonontheprojectsiteovertime.Thisisaccomplishedusingeitherdirectmeasuresoryieldmodels.Forforestclearing,itisnecessarytoknowtheinitialstockofcarbonintheforeststand,andhowitthenchangeswithdisturbance.Thelatterrequiresdataonthepartitioningofpost‐disturbancecarboncomponents,asremovals,andslashanddebrisleftonsite.


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6.3.3.12 ForestClearingRemovalsandDeadMaterialonSite

Thedifferenceofcarbonstocksbeforeandafterforestclearingisthecarbonthathasbeenremovedbyharvestaswoodproductsorotherproducts(e.g.,energyfeedstocks),andthatleftbehindonthesiteasslashanddebris(Skog,2008).Ifthesemassamountsareknown,theycanbeincludeddirectlyintothecomputations.Iftheyarenotknown,theycanbeestimatedandrepresentedasfractionsoftheoriginalstandingstockspriortodisturbance.Allremovalssuchastheseconstituteimmediateandfutureemissionsources,astheydecayoverdifferenttimescales.Therefore,itisnecessarytoassignmassamountstofourlong‐termdecaypoolswithturnovertimesof1,10,100,and1,000years.Theemissionsarecomputedalonganexponentialdecayfunctionrelatedtotheturnovertimeofthepool.Forexample,carbonlostduetoimmediateoxidationbyfireisplacedintothe1‐yearpool,andthecharcoalcomponentisplacedintothe1,000‐yearpool.Otherremovalsareplacedintothe10‐and100‐yearpools.

6.3.4 SpecificProtocolforComputation

6.3.4.1 ActualCarbonRemovalsbySinksinEstablishingForest

Thebasicapproachtoestimationofemissionsto,orremovalsfrom,theatmosphereistomultiplytheactivitydatabyemissionfactorsor,inthiscase,multiplytheland‐usechangeareabysitebiomasscarbonandsoilorganicmattercarbon.Theseproceduresdescribetherecommendedmethodofestimatingcarbon—usingallometricequationstoestimatebiomassdirectlyfromDBHusingtheequationsofJenkinsetal.(2003a).

Stratificationoftheprojectareamaybecarriedouttoimprovetheaccuracyandtheprecisionofthecarbonestimates.Whererequired,stratificationcouldbemadeaccordingtotreespecies,ageclasses,orforestmanagementpractices.Figure6‐5showsadecisiontreeindicatingwhichmethodismoreapplicableforaparticularlandowner.

Thisprotocolwillfollowthetwo‐tierapproachdescribedearlierinthedocument.Smalllandownerscanusedefaulttables(i.e.,Smithetal.,2006)andequationsfortheappropriateregionandforesttypegrouptoestimatebiomassoftheirforestsystems.Largelandownersshouldusebasicforestdatacollectedinthefieldonsampleplotswithallometricequations(Jenkinsetal.,2003a)toestimatethebiomassofindividualtreesandentirestands.Ifsmalllandownerswanttousesampleplotsandallometricequations,theyarefreetodoso.Smalllandownersshouldcontactaconsultingforesterorperhapsauniversityextensionpersontobestunderstandrequirementsforfieldsampling.

Whilemostofthefluxesfromanestablishmentprojectareremovalsfromtheatmosphere,theremaybesomeemissionsassociatedwithsomeaspectsoftheproject.TheactualnetCO2removalsbysinkscanbeestimatedusingtheequationsinthissection.Whenapplyingtheseequationsforex‐antecalculationsofnetanthropogenicCO2removalsbysinks,landownerswillprovideestimatesofthevaluesofthoseparametersthatarenotavailablebeforethestartoftheprojectperiodandcommencementofthemonitoringactivities.Participantsshouldretainaconservativeapproachinapplyingtheseestimates.


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Figure6‐5:DecisionTreeforEstablishing,Re‐establishing,andClearingForestsShowingMethodsAppropriateforEstimatingForestCarbonStocks

1Smalllandowners(seeSection6.2fordefinition)mayusegeneralizedlookuptablesbasedonregion,foresttype,andageclasstoestimatecarbonstocks.Largelandowners(seeSection6.2fordefinition)shouldcollectstandardforestinventorydataanduseallometricequationstoestimatelivetreebiomasscarbon(othercarbonpoolsmaybeobtainedfromlookuptables).However,largelandownerswhodonotengageinanymanagementactivitiesorplantomanagetheirholdingsmayuselookuptablesforallpools;butifactivemanagementoccurs,theinventoryapproachshouldbeused.2Jenkinsetal.(2003a).3Smithetal.(2006).


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TheactualnetCO2removalsbysinksinyeartareequalto:

EstimationofCarbonStockinLivingBiomassofTreesattheStratumLevel.Thecarbonstockinlivingbiomassoftreesforstratumi(Ctrees,i,t)isestimatedusingthefollowingapproach:Themeancarbonstockinabovegroundbiomassperunitareaisestimatedbasedonfieldmeasurementsinpermanentsampleplots.

Step1:Determinebasedonmeasurements(expost),theDBHattypically4.3feet(1.3m)abovegroundlevel,andalsopreferablyheight(H),ofallthetreesabovesomeminimumDBHinthepermanentsampleplots.

Step2:Calculatetheabovegroundbiomassforeachindividualtreeofaspecies,usingallometricequationsappropriatetothetreespecies(orgroupsofthemifseveraltreespecieshavesimilargrowthhabits)inthestratum.

Step3:Estimatecarbonstockinabovegroundbiomassforeachindividualtreelofspeciesjinthesampleplotlocatedinstratumiusingtheselectedordevelopedallometricequationappliedtothe

Equation6‐1:TheActualNetCO2 RemovalsbySinksinYeart

ΔCACTUAL,t=ΔCPJ,t

Where:

ΔCACTUAL,t =ActualnetCO2removalsbysinksinyeart(metrictonsCO2eqyear−1)

ΔCPJ,t =ProjectCO2removalsbysinksinyeart(metrictonsCO2eqyear−1)

Equation6‐2:ProjectCO2RemovalsbySinksareCalculatedasFollows(betweentwodatesforatimeperiodoft)

tΔCPJ,t=ΣΔCproject,i,t×44/12

i=1

ΔCproject,i,t=[(Ctrees,i,t2–Ctrees,i,t1)/T]+ΔCsoil,i,t

Where:

ΔCPJ,t =ProjectCO2removalsbysinksinyeart(metrictonsCO2eqyear−1)

ΔCproject,i,t =AverageCO2removalsbylivingbiomassoftreesandsoilforstratumi,foryeart(metrictonscarbonyear−1)

Ctrees,i,t =Carbonstockinlivingbiomassoftreesforstratumi,inyeart(metrictonscarbon)

ΔCsoil,t =Averageannualchangeincarbonstockinsoilorganicmatterforstratumi,foryeart(metrictonscarbonyear−1)

T =Numberofyearsbetweenyearst2andt1(years)


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treedimensionsresultingfromStep1,ormultiplytheresultofStep2by0.5(i.e.,thefractionofdrybiomasstocarbonconversionfactor),andsumthecarbonstocksinthesampleplot.

Step4:ConvertthecarbonstockinabovegroundbiomasstothecarbonstockinbelowgroundbiomassusingtheequationsprovidedinJenkinsetal.(2003a)orbymultiplyingtheresultofStep3by0.26(i.e.,theroot‐to‐shootratio).Sumtheabovegroundcarbonstockandbelowgroundcarbonstocks.

Step5:Calculatetotalcarbonstockinthelivingbiomassofalltreespresentinthesampleplotspinstratumiattimet.

Step6:Calculatethemeancarbonstockinlivingbiomassoftreesforeachstratum,asperEquation6‐6.

Equation6‐3:EstimateCarbonStockinAbovegroundBiomassforEachIndividualTree

Nj,sp

CAB,i,sp,j,t=ΣCFj×ƒj(DBH,H)t=1

Where:

CAB,i,sp,j,t =Carbonstockinabovegroundbiomassoftreesofspeciesj,onsampleplotsp,forstratumi(metrictonscarbon)

CFj =Carbonfractionofdrymatter(dm)forspeciesorgroupofspeciestypej(metrictonscarbon(metrictondm)‐1)

fj(DBH,H)=Anallometricequationlinkingabovegroundbiomassofalivingtree(metrictonsdm)toDBHandpossiblytreeheight(H)forspeciesj,inyeart(metrictonsdm)

Note:Forex‐anteestimations,meanDBHandHvaluesshouldbeestimatedforstratumi,inyeartusingagrowthmodeloryieldtablethatgivestheexpectedtreedimensionsasafunctionoftreeage.TheallometricrelationshipbetweenabovegroundbiomassandDBHandpossiblyHisafunctionofthespeciesconsidered.AlternativelythereareestimatorsandtoolsthatprojectcarbongrowthratesdirectlywithoutinputofDBH.

i=1,2,3,…MPSstrataintheprojectscenario

j=1,2,3,…SPStreespeciesintheprojectscenario

l=1,2,3,…Nj,spsequencenumberofindividualtreesofspeciesj,insampleplotsp

t=1,2,3,…t*yearselapsedsincethestartoftheprojectactivity


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Equation6‐4:ConverttheCarbonStockinAbovegroundBiomasstotheCarbonStockinBelowgroundBiomass

CBB,i,sp,j,t=CAB,i,sp,j,t×Rj

Where:

CBB,i,sp,j,t =Carbonstockinbelowgroundbiomass(BB)oftreesofspeciesj,inplotsp,instratumi,foryeart(metrictonscarbon)

CAB,i,sp,j,t =Carbonstockinabovegroundbiomass(AB)oftreesofspeciesj,inplotsp,instratumi,foryeart(metrictonscarbon)

Rj =Root:shootratioappropriateforbiomassstock,forspeciesj(dimensionless)

Equation6‐5:CalculateTotalCarbonStockintheLivingBiomassofAllTreesPresentintheSamplePlot

Sps

Ctree,i,sp,t=Σ(CAB,i,sp,j,t+CBB,i,sp,j,t)

j=1

Where:

Ctree,i,sp,t =Carbonstockinlivingbiomassoftreesonplotspofstratumi,foryeart(metrictonscarbon)

CAB,i,sp,j,t =Carbonstockinabovegroundbiomass(AB)oftreesofspeciesj,inplotsp,instratumi,foryeart(metrictonscarbontree−1)

CBB,i,sp,j,t =Carbonstockinbelowgroundbiomass(BB)oftreesofspeciesj,inplotsp,instratumi,foryeart(metrictonscarbontree−1)

i =1,2,3,…MPSstrataintheprojectscenario(PS)

j =1,2,3,…SPStreespeciesintheprojectscenario(PS)

t =1,2,3,…t*yearselapsedsincethestartoftheprojectactivity


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SoilOrganicCarbon.Forstratathatcontainonlymineralsoils,ex‐anteandex‐postΔCsoil,i,tchangeisestimatedfromEquation6‐7.

ThedefaultvalueofΔCforesti=0.5metrictonsCha−1year−1,andatequilibriumof20years,ishallbeused.

Changesincarbonstockinsoilorganicmatterarenotmonitoredex‐post(i.e.,measuredbeforeandaftertheequilibriumperiod),butareinsteadestimatedex‐ante(i.e.,predictedbasedonthespecifieddefaultvalueandequilibriumperiod).

OtherPools.Sampleplotsneedtobesetupinsuchawaysthatthesmallherbsandbushes,aswellasforestfloorlitterisalsomeasured.Todothis,establishseveralsmallcollectionplotsmeasuring3.3feetby3.3feet(1mby1m)ontheforestfloor.Collectallliter,herbs,andsmalldebrisinthesubplotandweighitusingafieldscale,anddrysmallsampletogetthedryweightfraction.

Equation6‐6:CalculateMeanCarbonStockinTreeBiomassforEachStratum

Pi

Ctree,i,t=(Ai/Aspi)ΣCtree,i,sp,t sp=1

Where:

Ctree,i,t =Carbonstockinlivingbiomassoftreesinstratumi,foryeart(metrictonscarbon)

Ctree,i,sp,t =Carbonstockinlivingbiomassoftreesonplotsp,ofstratumi,foryeart(metrictonscarbon)

Aspi =Totalareaofallsampleplotsinstratumi(ha)

Ai =Areaofstratumi(ha)

sp=1,2,3,… =Pisampleplotsinstratumiintheprojectscenario

i=1,2,3,… =MPSstrataintheprojectscenario(PS)

t=1,2,3,… =t*yearselapsedsincethestartoftheprojectactivity

Equation6‐7:EstimatingChangeinCarbonStocksforStrataThatContainOnlyMineralSoils

ΔCsoil,i,t=Ai*ΔCforest,ifort≤tequilibrium,i

ΔCsoil,i,t=0fort>tequilibrium,i

Where:

ΔCsoil,i,t =Averageannualchangeincarbonstockinsoilorganicmatterforstratumi,foryeart(metrictonsCyear−1)

Ai =Areaofstratumi;hectare(ha)

ΔCforest,i =Averageannualincreaseincarbonstockinsoilorganiccarbonpoolforforestsysteminstratumi(metrictonsCha−1year−1)

tequilibrium,i=Timefromstartoftheprojectactivityuntilanewequilibriumincarbonstockinsoilorganicmatterisreachedforforestsysteminstratumi(years)


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Multiplytheaveragedryweightoflitterby0.37tocomputetheplotlittercarbon,andby0.5tocomputetheplotherbsandseedlingcarbon.Forsmalltreesandbushesestablishafewsmallplotsmeasuring16.4feetby16.4feet(5mby5m)inthesampleplot.Cutandweighallsmalltreesandbushes.Establishadryweightbasisandmultiplythedryweightby0.5tocomputeasubsamplecarbonvalue.Standingdeadwoodalsoneedstobeestimated.Mostpublishedstudiessuggestthispoolissmallandcanbeignored.

Non‐CO2GHGs.Non‐CO2GHGs,includingCH4andN2Oarecalculatedbasedonemissionfactorsappliedtotheparcelbiomass.Thus,theparcelbiomassismultipliedbyafactorfromdefaultvaluesforthattimeofstandorplantingactivity.Theseemissionsandremovalswillvarydependingonthemanagementpractice,e.g.,naturalsuccession,plantations,fertilization.

6.3.5 ActualGHGRemovalsandEmissionsbySourcesandSinksfromForestClearing

Theabovesuiteofequationscanbeusedtoestimatethesourcesandsinksofcarbonfromforestclearing,withtheresultshavingadifferentsignthanestablishmentandre‐establishment.ThefundamentalcomputationisinEquation6‐8.

TheprecisecomputationinEquation6‐9requiresthemeasurementorestimationofthedifferencesincarbonstocksintheforestsystemandtheland‐coversystemthatitisconvertedto.Italsorequiresanunderstandingacomputationofthepartitioningoftheproductsthatwereremovedfromthesiteorleftasslashanddebris.Formaterialleftonsiteandburned,GHGemissionsshouldbecalculatedusingtheCONSUMEmodel.Hence,Cfisestimatedfromstandardper‐areaforesttypecarbonstocksorfromplotdata.Thefractionsfyanddyareestimatedordirectlymeasured(forsimplicityitispossibletoassumethatdyisthefractionoftheturnovertime,asin1/1,1/10,1/100or1/1,000).Esisthesoilfluxthatisrepresentedinlookuptables,andbasedonthetime‐varyingrateofcarbonlossasapercentageoftheoriginalforestsoilcarbon.

Equation6‐8:ComputingEmissionsofCarbonfromaForestClearing

Ed=f(D×C/ha)

Where:

Ed =Emissionsofcarbonfromforestclearing,D(metrictonscarbonyear‐1)

D =Therateofforestclearing(hayear‐1)

C/ha =Thestockofcarbonintheforestsystempriortoclearing(metrictonscarbonha‐1)


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6.3.6 LimitationsandUncertainty

Therearepublishedmethodsforformallyestimatinguncertaintyoftheestimation,generallybasedonthenumberanddistributionofthepermanentplots,andhowtheyareappliedtothewholestratum.Theseuncertaintyestimatescanbeusedaprioritoestablishthenumberofplotsneededtoachievealevelofaccuracy.Theycanalsobeusedtoattachanuncertaintyvaluetothefinalestimate.Butperhapsthemostchallengingcomponentofuncertaintyliesintheuseofvariousexpansionfactorswhereprecisefieldestimatesarenotknown.Inparticular,theestimationofnon‐CO2GHGfluxesisveryuncertain,andmustbeusedwithsomedegreeofcaution.ThisisespeciallytrueforN2OinallactivitiesandCH4incasesofforestestablishment.Considerablymoreresearchisnecessarytomaketheseestimates.

Anotheruncertaintyinmostestimatesisthefractionofstandingdeadbiomass.Basedonsomework(WoodallandMonleon,2008),itisbelievedtobesmall,butthevariationwithforesttypes,standage,conditions,andactivitiesislarge.Whenusingdefaultvaluesthismaybeachallengetothefinalestimation.Inthecasewheredirectmeasurementsaretobemadeonsite,thestandingdeadcanbemeasuredalongwithstandinglivebiomass.Thismaybeanapproachthathasspecialbenefitifthesitebeingclearedhasbeenintenselydamagedbypestsordisease.

Perhapsthemostproblematicareaisthecomputationofwholetreebiomassfromallometry.ThereisaverygoodNorthAmericanliteratureonallometryforstemvolumesandbiomassbutlessonwholetreevolumeandbiomass.Mostallometryisbasedonvolumesratherthanwholetreebiomassorcarbon.Frequentlyalimitednumberofsimpleexpansionfactorsaredeployedtoexpandthevolumeofthemainstemtothebiomassofthewholetreeincludingitsbranches.Thesemodelsneedtoberefinedtobettermaketheestimation.Thismaybeimportantsincemostlandownerswillnothavetheabilityorinteresttoconducttheirowndestructivetreesamplingtoextractlocalwholetreebiomassallometry(i.e.,aTier3approach).

Equation6‐9:ComputingthePartitioningoftheProductsThatWereRemovedfromtheSiteorLeftasSlashorDebrisin1Year

Ed=D[(Cf–Cc)×∑ ]+Es

Where:

Ed=Emissionsofcarbonfromforestclearing,D(metrictonscarbonyear‐1)

D=Therateofforestclearing(hayear‐1)

Cf=Thecarbonstockpriortoforestclearing(metrictonscarbonha‐1)

Cc=Thecarbonstockafterforestclearing(metrictonscarbonha‐1)

fy=Thefractionoforiginalcarbonstockinlong‐termdecaypooly

dy=Thedecayfunctionforthemassquantitiesindecaypooly (long‐termdecaypoolsare1‐,10‐,100‐and1,000‐yearturnovertimes)

Es=Emissionsfromsoil(metrictonscarbonyear‐1)


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Table6‐3:ExamplesofForestCarbonCalculators

Developer WebsiteUSDAForestServicetoolsforcarboninventory,management,andreporting

http://www.nrs.fs.fed.us/carbon/tools/

FAOExACT http://www.fao.org/tc/exact/en/TARAM(BioCFandCATIE) http://wbcarbonfinance.org/Router.cfm?Page=DocLib&Catalog

ID=31252CO2Fix http://www.efi.int/projects/casfor/models.htmGORCAM http://www.joanneum.at/gorcam.htmCASS http://www.steverox.info/software_downloads.htmFullCam http://www.ieabioenergy‐

task38.org/workshops/canberra01/cansession1.pdfCOLE http://www.ncasi2.org/COLE/Reforestation/AfforestationProjectCarbonOnlineEstimator

http://ecoserver.env.duke.edu/RAPCOEv1/

WinrockAFOLUCalculator http://winrock.stage.datarg.net/CarbonReporting/Welcome

6.4 ForestManagement

6.4.1 Description

Forestmanagementisconcernedwithmeetinglandownerobjectivesforaforestwhilesatisfyingbiological,economic,andsocialconstraints.Forestmanagersuseawidevarietyofsilviculturaltechniquestoachievemanagementobjectives,mostofwhichwillhaveimpactsonthecarbondynamics(seeTable6‐4).Theprimaryimpactsofsilviculturalpracticesonforestcarbonincludeenhancementofforestgrowth(whichincreasestherateofcarbonsequestration)andforestharvestingpractices(whichtransferscarbonfromstandingtreesintowoodproductsandresidues,whicheventuallydecay).Someforestmanagementactivitieswillresultinacceleratedlossofforestcarbon,suchaswhensoildisturbanceincreasestheoxidationofsoilorganicmatter,orwhenprescribedburningreleasesCO2.Furthermore,someforestmanagementactivitiesresultinfossilfuelemissions(e.g.,fromtheutilizationofmechanizedequipment,transportation).However,recentevidencesuggeststheseemissionsarefairlyminor.Markewitz(2006)estimatedthatfossilemissionsfromsilviculturalactivitiesinintensivelymanagedpineplantationswereabout3MgCha−1overa25‐yearrotation.Theseemissionswereverylowrelativetothesubsequent

MethodsforForestManagement

Rangeofoptionsdependentonthesize/managementintensity/dataavailabilityoftheentity’sforestlandincluding:

− FVS‐FFEwithJenkins(2003a)allometricequations;

− Defaultlookuptablesofmanagementpracticescenarios;and

− FVSmaybeusedtodevelopasupportingproductprovidingdefaultlookuptablesofcarbonstocksovertimebyregion;foresttypecategories,includingspeciesgroup(e.g.,hardwood,softwood,mixed);regeneration(e.g.,planted,naturallyregenerated);managementintensity(e.g.,low,moderate,high,veryhigh);andsiteproductivity(e.g.,low,high).

Themethodswereselectedbecausetheyprovideaconsistentandcomparablesetofcarbonstocksovertimeundermanagementscenarioscommontotheforesttypesandmanagementintensities.


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sequestrationofcarbonintheforestandinwoodproducts.Côtéetal.(2002)reportemissionsfromsilviculturalactivitiestotaledabout9percentoftotalemissionsfromapulpandpaperoperationandabout4percentofgrossforestsequestration.Inalife‐cycleanalysisfromthePacificNorthwest,Johnsonetal.(2005)reportedfossilemissionsofCO2fromforestryoperationsamountedto8.02to8.12kgCO2‐eqm−3ofharvestedlogs,orlessthan1percentofthe935kgCO2‐eqcontainedinacubicmeterofaDouglas‐firlog.InthedryPonderosapineforestsofArizona,athinningtreatmentresultedinCO2emissionsfromfossilfuelsof334kgCO2‐eqha−1,about1.1percentofthe30,213kgCO2‐eqha−1offirewoodremovedinthethinningoperation(FinkralandEvans,2008).

Thissectiondescribesgeneralcategoriesofforestmanagementactivitiesandtheirimpactsoncarbonstorage.ThedetailsvarywidelyacrosstheUnitedStateswithdifferentforesttypes,ownershipobjectives,andforeststandconditions.Itisimportanttoengageprofessionalforesterswhenconsideringharvestsorothersilviculturalpractices.Animportantdistinctiontobemadeattheoutsetisbetweenplantedforests,orplantations,andforeststhathavebeennaturallyregenerated.Productivityrates,silviculturalpractices,andmanagementobjectivesmaybemarkedlydifferentforplantedversusnaturalforests.Inplantedforests,conditionsaretypicallyoptimizedforincreasedgrowth,whichincreasescarbonsequestrationoverslowergrowing,naturallyregeneratedforests.However,methodsforinventorying,monitoring,andassessingcarbonstorageinbothplantedandnaturalforestsarethesame;variabilitymaybelessinsingle‐speciesplantations,butapproachesareidentical.SmalllandownerswillusetheregionaldefaulttablestoestimatethepotentialchangesinGHGfluxesfromchangesinforestmanagement,whilelargelandownerswillusestandardforestinventorydataincombinationwiththesimulationfeatureoftheFVS‐FFEtoassesschangesinsequestrationandemissionsfromchangesinpractice.

Table6‐4:CommonForestManagementPractices

Practice Description Benefits

Standdensitymanagement

Controllingthenumbersoftreesperunitareainastandthroughavarietyoftechniques,suchasunderplanting,precommercialthinning,andcommercialthinning

Maintainsstandatatreedensitythatprovidesoptimalgrowingspacepertreeforbestutilizationofsiteresources

Allowsconcentrationofsiteresourceson“crop”trees

Sitepreparation Preparinganareaoflandforforestestablishmentbyremovingdebris,removingcompetingvegetation,and/orscarifyingsoilwhenneeded

Improvessurvivalandinitialgrowthofplantedornaturallyregeneratedseedlingsorsprouts

Enhancesregenerationofdesiredspecies Providesconditionsfavorableforplanting

ofseedlingsVegetationcontrol

Removing,throughchemicalormechanicalmeans,undesirablevegetationthatwouldcompetewiththedesiredspeciesbeingregenerated

Improvessurvivalandgrowthofdesiredtrees/species

Planting Plantingofseedlingsbyhandormachinetoestablishanewforeststand

Controlsspeciescompositionandgeneticsofnewlyestablishedstand

Controlsstocking(density)oftreesperunitareaforoptimalgrowth/survival


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Practice Description Benefits

Naturalregeneration

Establishinganewforeststandbyallowing/enhancingnaturalseedingorsprouting

Resultsinmixofspecies Speciesthatsproutfromstumpsand

rootswillrapidlyrecapturethesite Lowcostrelativetoplanting Mayinvolvelesssoildisturbancethereby

reducingerosionFertilization Augmentingsitenutrientsthroughthe

applicationofnitrogen,phosphorous,orotherelementsessentialtotreegrowth

Enhancesgrowthoftrees Reducesthetimefortreestoreach

merchantablesize Eliminatesorreducesnutrient

deficienciesthatwouldimpairforestgrowth/survival

Selectionofrotationlength

Choosingthetimingoffinalharvestsoastooptimizethemixofforestproductsthatcanbeobtainedfromthestand

Controlstherelativeamountsofpulpwoodandsawtimberproducts

Allowslandownertorespondtowoodproductsmarketsbyoptimizingproductmix

Harvestingandutilization

Removaloftreesfromtheforest,andcuttingandseparatinglogsforforestproductsmarkets

Selectionofappropriateharvestingsystemscanprovidelogsformarketswhileminimizingdamagetoresidualtreesordisturbanceofsoil

Choiceofharvestingandsilviculturalcuttingsystemwillimpactsubsequentregenerationofthestand;systemscanbechosentoinfluencethespeciescompositionoftheregeneratedstand

Fireandfuelloadmanagement

Reducingtheriskoflosstowildfirebycontrollingthequantityoffuelsinaforeststandbycontrolledfireormechanicaltreatments

Reducesthedamagecausedbyseverewildfiresbyeliminatingexcessivelyhighfuelloads

Mayinfluencethespeciescompositionoftheunderstory

Reducingriskofemissionsfrompestsanddisease

Recoveringvalueoftimberafterdamagingeventsand/orpreventingfurtherdamagebyinterruptingspreadofpests/diseases

Salvageharvestsrecoversvalueindamagedtimberbyremovingitbeforeitisunusable

Sanitationharvestspreventspreadofpests/diseases

Short‐rotationwoodycrops

Producingmerchantabletreesinveryshorttimeperiodsthroughintensivemanagement(genetics,herbicide,fertilization)

Reducesthetimefortreestoreachmerchantablesize

Theremainderofthissectiondescribestheseforestmanagementpracticesandtheirimpactoncarbonstocks.

6.4.1.1 StandDensityManagement

Managementofforeststanddensity(numberoftreesperunitarea)isimportanttoachieveoptimalgrowth.Overstockedstands(toomanytrees)orunderstockedstands(toofewtrees)willgrowlessfiber,andthereforestorelesscarbon,thanmightbedesirable.Inoverstockedstands,treescompetewitheachotherforscarceresources(nutrients,water,andlight),andsuchstandsmayhavehighnumbersoftreesofpoorsizeandqualityandarehighlysusceptibletowildfireorotherreversaldisturbances.Reducingthestockinginoverstockedstandswillconcentrategrowthintreesofmore


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desirablespeciesandquality.Understockedstandsdonotfullyutilizetheresourcesofthesiteandthereforedonotachievethegrowthpotentialofafullystockedstand.Standdensitymanagementseekstomaintainafullystockedstand.

Densityofanexistingforeststandmaybeincreasedbyunderplanting,whichinvolvesplantingadditionaltrees(possiblyofdifferentspecies)beneathanexistingtreecanopy.Thistreatmentmaybedesirableforstandsinwhichadequateadvancedregenerationofdesiredspeciesislacking.Underplantingisdesignedtoincreasethelikelihoodofsuccessfulregenerationfollowingtheeventualharvestoftheoverstory.Thus,whiletheimmediatecarbonimpactofthistreatmentislow,theremaybesubstantialeventualimprovementincarbonstockscomparedwithastandwithoutunderplanting.

Decreasingthedensityofaforeststandisaccomplishedthroughthinning,orcuttingsomeproportionofthetreesinastand.Thismaybedoneasprecommercialthinning,inwhichcasemostofthetreestobecutaretoosmalltoeconomicallyjustifytheirremovalfromtheforest,andtheyareleftinthestandtodecaynaturally.Whileprecommercialthinningprovidesnoimmediateeconomicbenefits,itmaybeusedtoimprovethestockinglevel,speciescomposition,andoverallhealthofastand;itrepresentsaninvestmentincreatingamorevaluable,productiveforest.Precommercialthinningandstanddensitymanagementalsocanreducetheriskofreversalfromdrought,insects,disease,andpossiblyfire.Fromacarbonstandpoint,precommercialthinningwillremovecarbonfromthelivetreepoolandincreasethecarboninthedeadwoodpool.Iftheslashisburned,theGHGemissionsshouldbeaccountedforusingtheCONSUMEmodelwhentheburnoccurred.

Iftreestobethinnedareofproperspecies,size,andquality,commercialthinningmaybeperformed.Incommercialthinning,treesaretargetedforremovalbasedontheirspecies,size,andthemanagementobjectives.Thinnedtreesareremovedfromthestandandsoldtoappropriateforestproductsmarkets.Thus,commercialthinningwillshiftcarbonfromthelivetreepoolandintodeadwoodandlitter(branches,foliage,andstumpsremaininginthestandafterharvest),andHWPpools.

6.4.1.2 SitePreparationTechniques

Regeneratingaforeststandafterharvestmayrequiretreatmentstocreatethemostdesirableconditionsfordevelopmentofthenewstand.Thismayinvolveremovingdebrisfromthepriorstand,removingundesirablecompetingvegetation,scarifyingordisturbingthesoilforenhancedregenerationofspeciesthatrequiresuchconditions,andcreatingspaceorproperconditionsforplantingtrees.

Awidevarietyoftechniquesareavailabletomeetthespecificregenerationobjectives;theyvaryconsiderablyacrossgeographicregions,topography,siteconditions,andforestspeciesundermanagement.Generalcategoriesofsitepreparationtechniquesincludemechanicalmethods,chemicalapplications,andprescribedfire.

Mechanicalmethodsdisplaceunwantedvegetation,moveorbreakdownloggingresidues,and/orcultivatethesoil(Nyland,2002).Mechanicalsitepreparationusesavarietyofmachinesandequipment,andmaybelimitedbysitefactorssuchasterrainandsoilconditions.Becausemechanicalsitepreparationinvolvessoildisturbance,thereisincreasedoxidationandemissionofCO2fromthesoilorganicmatterforaperiodoftimeaftersitepreparation.

Chemicalapplicationsinvolvetheuseofherbicidestargetedatcontrollingundesirablevegetationsothatthepreferredspeciesoftreeshaveimprovedsurvival.Chemicalsmaybeappliedthroughgroundorairsprayingorinjectionintoindividualtrees.Chemicalsitepreparationinvolveslittletonosoildisturbanceandhasminimaleffectonsoilcarbonemissions.


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Prescribedburningmaybeusedtoreducetheamountofdebris(limbs,tops,andfoliage)frompriorharvests,killadvancedregenerationoftreesofundesirablespecies,andcontrolpeststhatinhabitdecayingwoodleftfromthepriorstand.Somefire‐adaptedspeciesrequireburningtoopenconesanddisperseseedforthenewstand.Clearly,prescribedfireforsitepreparationwillresultincombustionandemissionofCO2fromwoodymaterialsleftonthesite,butwillavoidthesoildisturbanceofmechanicaltechniques.TheFOFEMmodelfornaturalfuelsandtheCOMSUMEmodelforactivitygeneratedfuelscanbeusedtoaddressthistypeofburningandallowsestimationofGHGemissionsandconsumption.

6.4.1.3 VegetationControl

Controlofcompetingvegetationisonemeansofenhancingthegrowthofdesirabletreesinaforest.Forexample,inapineplantation,wherepinetreesarethespeciesofprimaryinterest,growthofpinesisincreasedwhenhardwoodcompetitionisremoved.Vegetationcontrolmaybeaccomplishedmechanically(suchasgirdlingundesirabletrees)orchemically.Vegetationcontrolisespeciallyimportantattwostagesinthelifeofastand:atestablishment(plantingorregeneration)andlaterintherotationbutbeforetreesarepastthesaplingstage.

Atestablishment(e.g.,ofaplantation),theprimarycompetitionmaycomefromherbaceousvegetationthatcanquicklyoutgrowtheplantedtreesandsuppresstheirgrowthorincreasemortality.Herbicidesmaybeeffectiveatcontrollingherbaceouscompetitionandprovidingthenewlyplantedtreesachancetogrowsufficientlytocapturethesite.Mid‐rotationreleaseoftreesmayrequireanadditionalapplicationofchemicalcontroltoreducecompetitionandfocusgrowthondesirabletrees.

Vegetationcontrolhasbeenestimatedtohavecontributed35percentofthesubstantialgaininplantationproductivityrelativetounimprovedplantations(Stanturfetal.,2003).Theprimarycarbonstockimpactofvegetationcontrolisatransferofcarbonstockfromthelivetreetostandingdeadbiomasspool.Treesreleasedfromcompetitionwillusuallyexhibitagrowthresponsetobalancethelossofgrowthonthevegetationremoved(i.e.,overallforestproductivityandsequestrationwillremainunchanged).

6.4.1.4 Planting

Onepopularformofregeneratingaforeststandfollowingclearcuttingistoestablishaplantationbyplantingtreesofadesirable,fast‐growingspecies,potentiallyutilizinganimprovedgeneticsource,ataconsistentspacingselectedtooptimizegrowth.Plantationmanagementpracticesincludecombinationsoftreatmentstocontrolcompetingvegetationandmanagetreenutritionthroughfertilization,thinning,anduseofgeneticallyimprovedstock(Vanceetal.,2010).Becauseoftheseefforts,plantationsmaybeuptosixtimesmoreproductivethannaturallyregeneratedstandsofthesamespecies(CarterandFoster,2006).Successfulplantationestablishmententailscarefulselectionofspecies,genetics,andspacing(plantingdensity).

Speciesusedinplantedstandstypicallyareselectedforhighgrowthrates,lowsusceptibilitytodamagefrominsectsanddisease,andqualityandvalue.Forexample,intheU.S.South,loblollypineisthemostwidelyplantedtreespeciesbecauseitisnativetothearea,fast‐growingrelativetootherpines,andresistanttodisease(Schultz,1997).Longstandinggeneticimprovementprogramshaveledtotheproductionofimprovedgeneticsourcesforforestplantationspecies.Geneticallyimprovedseedlingsareavailablefromcommercialandstatetreenurseries;essentiallyallofthe1.2billionloblollypineseedlingsplantedannuallyintheU.S.Southaretheresultoftreeimprovementprograms(McKeandetal.,2003).InthePacificNorthwest,geneticimprovementinDouglasfirtreeshasledtoincreasesinproductivity(volumeproduction)inexcessof25percent(St.Clairetal.,2004).Finally,selectionofplantingdensity(treesperunitarea)canaffectoverallstand


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productivity,necessityforthinning,abilitytoaccessthestandwithequipmenttoconductsilviculturaloperations,andtimerequireduntiltreesreachmerchantablediameters.Allofthesefactorscombinetodeterminethelikelysurvivalandgrowthratesofaforestplantation.Plantationproductivityisdirectlyrelatedtorateofforestsequestration.Anyactivityincreasingproductivitywillimprovesequestrationrates.

6.4.1.5 NaturalRegeneration

Certainforesttypesareregeneratedmostefficientlyusingnaturalregeneration,inwhichseedlingsandsproutsfromarecentlyharvestedordisturbedforestwillgrowquicklyafterremovalofaportionoralloftheforestoverstory.Inthiscase,thespecieswillbepredictablebasedonthespeciescompositionofadvancedregenerationfromthepreviousstand,orifspeciespresentinthepreviousstandareprolificinsprouting.Thespeciescanalsobepredictedbasedonpost‐harvestregenerationofseedlingsfromresidualoverstorytreesorfromsurroundingstands.Densitywillnotbecontrolledduringtheregenerationprocess;frequentlynaturalregenerationresultsinverydensevegetationthatthengoesthroughanaturalprocessofcompetition.

Becauseneitherthegeneticsourcenordensityarecontrolledduringnaturalregeneration,thesestandsarefrequentlylessproductivethanplantationsbutmaybemoredesirablebasedontheobjectivesofthelandowner(e.g.,forrecreation,wildlife,ordifferentproductsthanplantationswouldprovide).Theprocessofnaturalregenerationmayentailminimal(ifany)sitepreparationandlesssoildisturbanceandcostthanwouldplantations.Dependingonthelevelofsoildisturbancefromtheharvestofthepreviousstand,earlysoilCO2emissionsmaybelowerthaninplantedstands.


Fertilizationhasbeenshowntodramaticallyimprovetheproductivityofforeststandsinwhichnutrientsarelimitingplantgrowth.Forexample,intheU.S.South,nitrogenandphosphorusarecommonlydeficientinpineplantations(Foxetal.,2007).Intheseareas,phosphorusfertilizationmayincreasevolumeproductionbymorethan100percent(Jokelaetal.,1991).Nitrogenandphosphorusfertilizationhasbeenshowntoincreasegrowthby1.6tonsacre−1year−1(Foxetal.,2007).

ThetwoprimarytypesofforestfertilizationcurrentlypracticedintheSoutharephosphorus‐fertilizationondeficientsites(usuallyatorneartimeofplanting),andnitrogenandphosphorusfertilizationinmid‐rotationstands(e.g.,ages8to12).Volumegainsvary,withhighestgainswherestandsaremostnutrient‐limited.

Thedirectcarbonimpactoffertilizationofforestsistheobservableincreaseingrowthandthereforesequestration.Otherimpactshavebeennotedinagriculturalsettings,includingincreasedemissionsofotherGHGssuchasNOxandN2O.Resultsfromagriculturalfertilizerapplicationsmaynotbedirectlyapplicabletoforestryoperations.RecentresearchinwesternCanadianforestsshowedsoilGHGfluxeswereneutralfollowingfertilization(Basilikoetal.,2009).InananalysisoffertilizationofpineplantationsinthesoutheasternUnitedStates,Albaughetal.(2012)foundthatcarbonsequestrationinforestgrowthfarexceededtheemissionsassociatedwithfertilizerproduction,transport,andapplication(8.70Tgyear−1CO2sequestrationversus0.36Tgyear−1emissions).Thus,forestfertilizationwhenappliedappropriatelycandramaticallyincreasecarbonsequestrationwhencomparedtounfertilizedstands.

6.4.1.7 SelectionofRotationLength

Onesignificantdecisionthatforestmanagersmakeistheselectionoftherotationlength,ortargetageatwhicharegenerationharvest(finalharvest;oftenbutnotnecessarilyaclearcut)willoccur.


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Thedecisionaffectsthetimingofotherstandtreatments.Forexample,thinningsandsomefertilizationtreatmentsaretargetedforacertaintimebeforefinalharvest.Italsoaffectsthemixofforestproductsthatmightbeexpectedfromtheharvestedstand.Standsharvestedatrelativelyyoungageswillyieldprimarilytreessuitableforpulpwoodmarkets,whilelongerrotationsmayinvolvemorethinningsandwillincreasetheproportionofsawtimber‐sizedtreesinthestand.Becausethesedifferentproductshavedifferentlongevities(seeSection6.5),therotationlengthwillhaveasignificantimpactontheoverallcarbondynamicsofaforest(anditssubsequentpoolofcarboninHWPs).Furthermore,longerrotationsresultingreateraveragecarbonstorageintheforest,withresultinghigherlevelsofsequestration(StainbackandAlavalapati,2002).Itiswidelyrecognizedthatincreasingrotationsfromharvestingatfinancialmaturitytoharvestingclosertoagesatwhichstandsreachasteadystatebetweengrowthandmortalitycanbebeneficialforcarbonstorage(vanKootenetal.,1995).

Avarietyofdecisioncriteriaareavailableforidentifyingtheoptimalrotationlengthfordifferentsetsofobjectives.Ifcarbonstorageisoneoftheimportantobjectives,longerrotationswillbebeneficial(Liskietal.,2001).

6.4.1.8 HarvestingandUtilizationTechniques

Regenerationharvests(alsocalledrotationharvestsorfinalharvests)areconductedtoharvesttreesforforestproductsmarketsandtopromotetheregenerationofdesirablespeciesforthenextstand.Tomeetthetwinobjectivesofregenerationandproductionofmerchantabletimber,forestmanagersmaychoosefromawidearrayoftechniquesandoperationalapproaches.Thesilviculturalsystemwillbechosentodeterminewhichtreesaretoberemovedfromthestand,andaharvestingsystemwillbechosentodeterminethebestloggingapproachtodoso.

Thesilviculturalsystemdetermineswhatproportionoftheforeststandistoberemovedintheharvest,andwilldictatewhethertheresultingstandwillbeeven‐aged(astandoftreesofasingleageclass)oruneven‐aged(astandoftreeswiththreeormoreageclasses)(Helms,1998).Harvestsrangefromclearcuts,inwhichmostoralloftheoverstoryisremoved,toavarietyofpartialharvests.Partialharvestsincludesystemssuchasseed‐tree,shelterwood,groupselection,individualtreeselection,diameter‐limit,andothers.Harvesttechniquesthatopenmostorallofthecanopy(suchasclearcuttingorseed‐treeharvests)willpromotetheregenerationofspeciesthatthriveinsunlightanddonottolerateshade.Clearcuttingisalsothepreferredtechniquewhenthenextstandistobeestablishedbyplantingratherthannaturalregeneration.

Afterselectionofasilviculturalsystemforregeneration,forestmanagerswillselectaharvestingsystemforthefellingandextractionoftreesfromthesite.Againawidevarietyofsystemsareavailable,fromindividualtree‐fellingbychainsawwithextractionbyhorseteams,tohighlymechanizedsystemsinvolvingskidders,feller‐bunchers,forwarders,andothertypesofequipment.Whenterrainconditionspreventground‐basedvehicularextractionoffelledtrees,itmaybedoneusingcableyardingsystemsorhelicopters.Loggingsystemsthatminimizesoildisturbanceandimpactsonunharvestedtreesandunderstorymayreducetheseharvest‐associatedemissions.

Whentreesareharvestedfromaforest,theymayproduceavarietyofproductsforspecificmarkets.Forexample,large‐diametertreesofcertainspeciesarepreferredforsawtimbermarkets,whilepulpwoodmarketsacceptroundwoodwithsmallerdiametersorevenchips.Thus,aharvestingoperationwillofteninvolvemerchandising—thesorting,cutting,andseparatingoflogsfordeliverytodifferentmarkets.Dependingonthesilviculturalsystemchosen,treeswithoutmarketvalue(e.g.,toosmall,poorform,orundesirablespecies)maybecutandleftonsitetodecay.Inaddition,agreatdealoflogging“slash”maybeproduced;thismaterialmayconsistofbranches,portionsoftreesbeyondmerchantabilitylimits(tops),roots,andfoliage.Wherebiomassenergymarketsexist,someofthismaterialmayberemovedandusedtoreplacefossilenergyGHGsources;


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otherwiseitmaybeleftonsitetodecayorbeburnedduringsitepreparationwithassociatedGHGemissions.Theproportionofwoodymaterialremovedfromaharvestingoperationistermedutilization;highlevelsofutilizationmeanmorewoodybiomassisremovedandlessremainsonsite.

Therearemanycarbonconsequencestotheselectionofasilviculturalandharvestsystem.Partialharvestswillleavesubstantialcarboninlivetreesonthesite,whereasclearcutharvestwillleaveverylittle.Oncertainsoils,mechanizedsystemsforfellingandextractingtreeswillresultinmoresoildisturbanceandsubsequentCO2emissionsthanlow‐impactsystems(Naveetal.,2010).Theharvestingimpactonsoilcarbonisgreaterfortheforestfloorthanforcarboninthemineralsoil,buttheseeffectsareshorterlivedandmaybemodestoverlongertimeintervals(Naveetal.,2010).Theavailabilityofmarketsforsmaller‐diametermaterialortreesofnonmerchantablespecieswillaffecthowmuchresidue(slash)isleftonthesite.Availabilityofstrongmarketswillgenerallyleadtohigherutilizationandlessresidue.Itisimportanttokeepaccountingboundariesinmindtoensurethatthereisnoomissionordoublecountingofemissionsorremovals.TheIPCCmethodologieshaveadoptedtheconventionthatemissionsfromburningbiomassforenergyshouldnotbeaccountedintheenergysector,butshouldbeaccountedintheland‐usesector.Weconformtothisconvention.If,forexample,forestresiduesareburnedforenergy,theCO2emissionsarenotcountedintheenergysector,andthereshouldbeareductionintheamountoffossilfuelburned.ButtheCO2emissionsfromtheburnedresiduewillbeaccountedasadecreaseincarbonstocksintheland‐usesector,andemissionswillbenodifferentthaniftheresidueshadbeenpiledandburnedintheforest.Thatis,acompleteaccountingofemissionswhenresiduesareburnedforenergywillshowemissionssavedintheenergysectorbutnochangeintheland‐usesector.

6.4.1.9 FireandFuelLoadManagement

Manyforesttypeshaveanaturaldependenceondisturbancefromfire.Asmentionedpreviously,itmayplayaroleinnaturalregeneration,butithasmanyotherfunctionsincludingnutrientrelease,naturalthinningandpruning,aswellasmodifyingfuelstructureandloading.Withoutprescribedfire,manyforesttypesmaybeatamuchhigherriskofreversalofgrowingcarbonstock.Inregionsofthecountrywherewildfireisaconcern,forestmanagersmaytakeamoreactiveroleinmanagingthelevelsofpotentialfuelsinaforest.Fuelmanagementcannotpreventignitionsofwildfires,butcandecreaselevelsofintensity,severity,andspread.Twocommonapproachestofuelloadmanagementareprescribedburningandmechanicalfueltreatments.

Prescribedfireisanyfireintentionallyignitedbymanagementunderanapprovedplantomeetspecificobjectives.Whenforestfuelsareburnedundercarefullyselectedconditions(weather,fuel,moisture,etc.),fuelscanbereducedtolevelsthatdecreasetheriskofdamagingwildfires.Otherobjectivesforuseoffireandcontrolledburnmaybetoreducethreatfromnon‐nativeinvasivespeciesandmaintenanceofmanyendangeredspeciesthroughouttheUnitedStates.

Mechanicalfueltreatmentsaresimilartoharvestingoperations,inthatspecificclassesoftreesarecutandremoved.Forexample,alltreesbelowathresholddiametermayberemovedinathinning(Johnsonetal.,2007).Theresultshouldbedecreasedavailabilityoffuelsthatwouldincreasewildfireseverity.

Thecarbonimpactoffueltreatmentsistwo‐fold.First,itinevitablyresultsinemissionsofCO2fromthematerialremovedorburned.However,second,itsgoalistoreducethepotentialformuchlargerfutureemissions(andincreasedenvironmentaldamage)fromwildfiresinareaswheretheyareathreat.Awildfirecouldresultinareversalofthepreviousgainsincarbononthesite.Wildfireintensityandresultantlossofcarbonishighlyvariableanddependsuponsitespecificconditionsandeffects.Wildfirecanoccuratlowtomoderateintensity,whichlikeaprescribedfiremayresultinamoreresilientandproductivesiteoverthelongterm.ThechallengeisthattheimmediateCO2


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emissionsfromawildfireorprescribedfire/controlburnarereadilyquantifiable,whereastheavoidedemissionsfrompotentialwildfiresarenotand,becausetreatmentsmaynottakeplaceintheareaswherewildfireoccurs,theycouldcreateextraemissionsthatwouldnototherwisehavehappened.Recentresearchindicatesthatprescribedburninghasaminimalimpactonforestcarbonbudgets,especiallyintheeasternUnitedStates.Impactsobservedfrommechanicalandfiretreatmentswerealsofairlyshort‐lived(Boerneretal.,2008).DispositionofremovedmaterialsisakeyfactortoconsiderwhenassessingtheGHGimplicationsoffuelmanagementtreatments.Prescribedfirecanhavesignificanteffectsonreducingtheriskofreversalthatcouldresultfromawildfire.

6.4.1.10 ReducingRiskofEmissionsfromPestsandDisease

Silviculturalinterventionmayalsobecalledforwhenforestsaredamagedbyweather,insects,ordisease.Forexample,wheninsectoutbreakssuchaspinebeetleinfestationskillpatchesoftrees,removaloftreesatorneartheinfestationsitemaypreventpopulationsofharmfulinsectsfromspreadingfurther.Whenharvestsaredesignedtorespondtopestanddiseaseproblems,theymaybecalledsanitationharvests.

Whenweathereventssuchasicestorms,hurricanes,orseverewinds(orawildfire)causeextensivedamagetoforeststands,quickremovalofthedownedtimbermayprovideanopportunitytorecoversomeofthefinancialvalueofthetimberandmaypreventthebuildupofverylargefuelloads.Wheneconomicvalueiscapturedfromaharvestofdamagedtimber,itistermedasalvageharvest.

Bothsalvageandsanitationharvestsremovetrees,sometimeswithmarketvalueandsometimeswithout.Thecarbonimpactsarereflectedintheamountofwoodymaterialremovedfromtheforestandwhetherthematerialremovedentersmarketsforwoodproductsorforenergy.Similartowildfiretreatments,inbothsanitationandsalvageharvests,however,theremovalofbiomassmaybecomparedwiththealternativeofleavingthematerialintheforesttodecayorburn,resultinginCO2emissions.Forsomecarbonaccountingsystems,thisdifferenceiscrucial;theassumptionthatemissionswouldhaveoccurredwithouttheactivityaffectsbaselineassumptionsagainstwhichcarbonsequestrationismeasured.

6.4.1.11 Short‐RotationWoodyCrops

Short‐rotationwoodycrops,alsocalledbiomassplantationsorbiomassenergyplantations,aretreeplantationsmanagedwithaveryhighintensitytoproducefibercropsinarelativelyshorttimeframe(e.g.,5–10years).Theseplantationsaremorelikeagriculturalcropsinthelevelofintensityoftreatments(e.g.,fertilization,weedcontrol,andsometimesirrigation).Woodgrowninthismannerisusuallysuitableforusebybiomassenergyfacilitiesorpossiblypulpmills,butthecosttoproducethiswoodisveryhighcomparedwithtraditionalplantations.Forsomespecies,itispossibletoregeneratethesestandsbycoppicing,orcuttingtopromotesproutingfromintactrootsystems,whichavoidsthecostofplantingnewtrees.Regenerationbysproutscanresultindensestandsexhibitingveryfastgrowth.

Thecarbondynamicsinashort‐rotationwoodycropsystemaresimilartoconventionalplantations,exceptfortheacceleratedgrowthandreducedrotationlength.Insomeshort‐rotationwoodycropsystems,covercropsmaybegrowntopreventerosionandmaintainsoilfertility.Covercropswouldalsoservetoincreasecarbonstorageonsite.

6.4.2 ActivityData

Carbonstoragefromforestmanagementactivitiesisestimatedapplyingthreedifferenttypesofestimates.EstimateTypeIfocusesontheeffectsofmanagementactivitiesoncarbonstocksfora


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givenyear.EstimateTypeIIfocusesontheeffectsofmanagementactivitiesoncarbonstocksoveraperiodofyearsinthefutureandmustbebasedonprojections.EstimateTypeIIIexaminesthedifferenceinprojectedcarbonstocksbetweensetsofalternativescenariosofpotentialmanagement.Thissectionwilldiscusstheactivitydataneedsforeachofthetypesofestimatesforthevariousforestmanagementactivities.Ingeneral,however,theestimationapproachesanddataneedswillbeoftwotypes:(1)forestinventorydata;and(2)standprojectionmodels.

ForTypeI,incaseswhereamanagementactivityhasalteredthecarbonstockinspecificpools,thebestestimatesmaybeobtainedbyhavingforestinventorydatabeforeandafterthetreatment,suchthatthedifferencecanbeattributedtothemanagementactivity.Forestinventorydatashouldincludemeasurementsobtainedintheforestataseriesofplots,withlistsofthetreesineachplot.Usuallyforeachtreeitisnecessarytoknowthespecies,diameter,andsometimesheight.Fromthesemeasurements,stand‐levelestimatesoftreedensity(treesperunitarea),basalarea(cross‐sectionalboleareaat4.5feet(1.4m)fromtheground),speciescomposition,andtreevolumeandbiomasscanbecomputed.

Anotherapproach,usedforTypeIIandTypeIIIestimates,requirestheuseofstandprojectionmodelstoestimatetheresponsesoftheforesttomanagementactivities.Suchmodelshavebeencreatedforawidevarietyofforesttypesandtreatments;anexampleistheFVSfamilyofmodelsdiscussedearlier.Projectionmodelsforforecastingforestconditions(andcarbonstocks)typicallyrequiremeasuresorindicesofforestproductivity.Acommonlyusedmeasureofforestproductivityissiteindex,whichrepresentstheheightthattreesonasitewillreachbyacertainbaseage.Forexample,onlandwithasiteindexof65(baseage25),theaverageheightofdominantandco‐dominanttreesinastandwillbe65feet(19.8m)whenthetreesreachage25.

ThemostaccurateTypeIIandTypeIIIestimatesarefrommodelsdevelopedspecificallyforagivenplantationspeciesornarrowlydefinedforesttype.Forexample,therearemanymodelsavailabletoestimateeffectsofmanagementoncommonlyplantedandhighlyresearchedspeciessuchasDouglasfirorloblollypine(e.g.,AmateisandBurkhart,2005;Burkhart,2008;Carlsonetal.,2008;Lietal.,2007;Sucreetal.,2008).Atthistime,theFVSfamilyofmodelsistherecommendedmethodforestimatingforestcarbonstocks.Inincorporatingthismethodintoanysoftwaretool,adataportalthatallowstheusertoloadtheirexistingstanddataandmanagementactivitydatafortranslationintotheFVSformatisrecommendedandwouldproveuseful.Futuredevelopmentmayalsopermitcustommodelstointerfacewithanestimationtool.Atthistime,however,suchcapabilityisnotavailable.Incaseswheresuchmodelsarenotavailable,itmaybenecessarytogeneralizebyaggregatingforesttypesandmanagementactivitiesandperformprojectionsbasedoncategoriesofmanagementintensityforgeneralforesttypes.ManagementintensitycategoriesaredefinedinSection6.4.3.

Theremainderofthissectionisorganizedasfollows:

StandDensityManagement

SitePreparationTechniques

VegetationControl

Planting

NaturalRegeneration

Fertilization

SelectionofRotationLength

HarvestingandUtilizationTechniques

FireandFuelLoadManagement


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ReducingRiskofEmissionsfromPestsandDisease

Short‐RotationWoodyCrops


Standdensitymanagementactivitiesincludeunderplanting,precommercialthinning,andcommercialthinning.Ineachcase,theprimarydatarequirementsforTypeIestimatesaretreeinventoriesbeforeandafterthetreatment,whichcanindicatethechangeinstockinglevelsandthequantityofbiomassremovedduringthinnings.Inthecaseofthinnings,itisimportanttoknowthevolumeorbiomassdirectedtodifferentwoodproductsmarkets(e.g.,pulpwood,sawtimber,orenergy)toproperlyaccountforthecarboninHWPs.

ForTypeIIandIIIestimatesofthefuturecarbondynamicsofthestandafterthesetreatments,standprojectionmodelswillrequireameasureofsiteindexinadditiontotheinventoryinformationcollectedforTypeIestimates.

6.4.2.2 SitePreparation

Theprimaryinformationrequirementforestimatesofstockchangesduetositepreparationiswhethersoildisturbancehasoccurredduringsitepreparation.Mechanicalsitepreparationtechniquesthatinvolvesoildisturbancewillbeassumedtoleadtoashort‐termlossofsoilcarbonstoragefollowedbyarecovery.Chemicalorothertreatmentsthatdon’tinvolvesoildisturbancewillnotresultinsoilCO2emissionsbeyondwhatmayhaveoccurredduringharvesting.ForTypeIIandIIIestimates,thesitepreparationtechniqueshouldberecordedintheeventthatmodelsmaydifferentiatebetweengrowthratescorrespondingtovarioussitepreparationtechniques.


ForTypeIestimates,itisnecessarytohaveinventoryinformationbeforeandaftervegetationcontroltreatmentsifthevegetationcontrolinvolveswoodymaterial.(Carbonstocksarenotexpectedtobesubstantiallydifferentforherbaceouscontroltreatmentsneartimeofplanting.)Whenvegetationiskilledbutnotremoved,thecarbonstockimpactsinvolveprimarilytheredirectionofstockfromonepool(livetrees)toanother(standingdeadtrees).

ForTypeIIandIIIestimates,somemodelsmayprojectstandgrowthdifferentlyifcompetingvegetationisremoved.Insuchcases,similarinventoryinformationbeforeandaftertreatmentwillbenecessary.

6.4.2.4 Planting

Theactofplantingitselfinvolvesanegligiblecarbonstockchangefortheyearofplanting.Thus,aTypeIestimatewouldshownocarbonstockchangefollowingaplanting.

Forallsubsequentyears,however,criticalparametersarethespeciesplanted,theoriginalplantingdensity(treesperacre),andthesurvivalrate(inpercent)afteronegrowingseason.Becausemostearlymortalityoccurswithinoneyearofplanting,thepercentageoftreessurvivingatyearoneprovidesarobustestimateofstanddensityforgrowthprojections.ItwillalsobeimportantforTypeIIandIIIestimatestorecordthegeneticstockused(e.g.,firstgeneration,open‐pollinated,mass‐controlledpollinated,clonal)intheeventthatprojectionmodelsaredevelopedforspecificgeneticsources.Somemeasureofsiteproductivity(e.g.,siteindex)willbeneededaswell.


Asinthecaseofplantationestablishment,carbonstockchangesatthetimeofnaturalregenerationarenegligible.


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TypeIIandIIIestimateswillrequireinformationonspeciesmix,standdensity,andsomeinformationonstandproductivity.Incasesinwhichstandproductivitycannotbemeasureddirectly(bymeasuringexistingtreesforsiteindex),someestimatescanbederivedfromsoilsdatabasessuchasSSURGO,orfromfieldcharacterizationofsoilseriesandreferencetosoilmapsandmanuals.


TypeIestimateswillshownoimmediatecarbonstockchangesrelativetofertilizationfortheyearinwhichtheactivityoccurred.N2Oemissionswilloccurattimeoffertilization;activitydatashouldincludenumberofacresfertilized,applicationrate,andtypeofnitrogenapplied.

TypeIIandIIIestimatesinvolvingstandprojectionmaymakeuseofmodelswhichincorporateinformationaboutthefertilizationtreatment.Applicationrates(poundsperacre)andelementalcomposition(nitrogen,phosphorus,potassium)shouldberecorded.

6.4.2.7 SelectionofRotationLength

TypeIestimatesarenotapplicabletoselectionofrotationlength.TypeIIandIIIestimatesmayentailexperimentationwithrotationlengthsinmodelingexercisestotestthecarbonstockimplicationsofdifferentrotationlengthstrategies.Suchexperimentationwillsimplyinvolvethecomparisonofmodelsrunwithallparametersheldconstantexceptforrotationlength.

6.4.2.8 HarvestingandUtilizationTechniques

Harvestinghasthelargestimmediateimpactonforestcarbonstocks.Consequently,forTypeIestimates,thelandownerneedstocollectaccurateandsufficientlydetailedforestinventoryinformationbeforeharvestandafterharvestinthecaseofpartialcutting.Becauseongoingsequestrationofcarbonstocksfollowsdifferentpathwaysfordifferentforestproducts,thedispositionoftheharvestedmaterialintodifferentproductpools(e.g.,pulpwood,sawtimber)needstoberecorded.Thisinformationshouldbereadilyavailableaspartofsalesrecords.Defaultfactorsareavailabletoestimatecarboninharvestingresidues(slash).

Inthecaseofpartialharvests(wherethereisaresidualstandtoproject),orprojectionsofimpactsofdifferentharvestingorsilviculturalsystems,completeinventorydataandproductivityestimates(e.g.,siteindex)forthestandareneeded.


ForTypeIestimates,pre‐treatmentdataonfuelloadingwithfocusonthematerialtoberemovedinthetreatmentneedstobecollected.AnexampleofdatacollectionprotocolsforfueldatacanbefoundinBrown(1974).Post‐treatmentassessmentofresidualmaterialwillindicatetheamountremovedinthetreatment.Thetypeoftreatment(burnormechanical)andthedispositionoffuel(consumed,leftonsite,removed)shouldberecorded.Ifconsumed,FOFEMorCONSUMEcanbeusedtocalculatetheGHGemissionsfromaprescribedburn.

TypeIIandIIIestimatesofthecarbonstockimpactsoffueltreatmentswillrequirespecializedfiremodelsthatcouldindicatelikelyoutcomesofthefueltreatmentrelativetonotreatmentandasubsequentwildfire;availabletoolsincludemodelssuchasCONSUME(JointFireScienceProgram,2009)andtheFVS‐FFEmodule(ReinhardtandCrookston,2003).SeeTable6‐13wherealow‐severityfirecouldbecomparedtothecrownfireeffectbasedonFOFEMoutputs.


Forestimatesofcarbonstockimpactsofsanitationandsalvageharvests,pretreatmentandpost‐


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treatmentinventoriesarerequired.Inthepretreatmentinventory,theextentandnatureofdamageareneededtoestimatethecarbonstockthathasshiftedfromlivetodeadbiomasspriortotreatment.

ModelingforTypeIIandIIIestimatesmayentailsimplyprojectingtheresidual(post‐treatment)stand.Tofullyevaluatethecarbonstockimpactsofthetreatment,modelsorassumptionsareneededforestimatingthespreadoftheinsectordiseaseabsentthetreatment.Toolsforsuchmodelingorassumptionsmaybehardtoobtain.


Estimationofcarbonstockimpactsfromplantationsofshort‐rotationwoodycropswouldfollowthesamegeneralprocedureasotherplantationestimates.Nostockchangeswouldbeexpectedattimeofplanting(carboninseedlingsorplantingstockisnegligible).ProjectionsforTypeIIandIIIestimatesrequiretheavailabilityofmodelstoprojectgrowthandyieldofthespeciesplantedunderthemanagementscenariosenvisioned.

6.4.3 ManagementIntensityCategories

Intheprevioussection,theuseofmodelstopredictforestresponsestomanagementactivitieswasdiscussed.Manysuchmodelsareavailableforspecificmanagementpracticesinplantationsofcertainspeciesorinspecificforesttypes.Thesemodelsarevariedintheirinputrequirementsandtheirapplications.Todevelopanationallyconsistentapproach,theinfinitecombinationsofsequencesofspecificmanagementactivitiesandforesttypesneedtobegeneralized.Usingasinglemodelingframework,suchasFVS(Dixon,2002)andcategoriesofmanagementintensities,allowsforthesimulationofsuitesofmanagementactivitiesinawidevarietyofforesttypesandconditionswithasinglesetofinputs.ThisapproachtodefiningmanagementintensitycategoriesissimilartothatusedbySiry(2002).

Therefore,inthissectioncategoriesofforesttypesandmanagementintensitiesthatrepresentbroadcombinationsofcommonlyappliedactivitiesintheforesttypesoftheUnitedStatesaredefined.Defaulttablesofcarbonstocksforthesecategoriescouldthenbedevelopedtoprovideconsistentandusefulinformationaboutlikelycarbonstockimplicationsofforestmanagementactivitiesacrossthecountry.

6.4.3.1 DefiningForestTypeCategories

Thefirstdistinctionindefiningmanagementintensitycategoriesistheidentificationofthebroadspeciesgrouping:hardwood,softwood,ormixed.Hardwoodforesttypesaredominatedbyhardwoodtreespeciessuchasoak,maple,cottonwood,birch.Softwoodtypesaredominatedbysoftwoodtreespeciessuchaspine,spruce,orDouglasfir.Mixedtypesexhibitnocleardominanceofonespeciesgroup.Thesecondmajordistinctioniswhetherthestandwasplantedornaturallyregenerated.Certainmanagementactivitiesarefarmorelikelytobeappliedtoplantationsthannaturalstands.Mostplantationsaresoftwoods,withtheexceptionofsomeshort‐rotationwoodycropsofhardwoodtypessuchascottonwood,willow,hybridpoplar,oraspen.

6.4.3.2 DefiningCategoriesofManagementIntensity

Fourcategoriesofmanagementintensityaredefinedbasedoncommonlyencounteredpractices.Forexample,almostallforestfertilizationisappliedtoplantationsratherthannaturallyregeneratedstands,sofertilizationwillbeconsideredpartofmanagementintensitiesrelatedonlytoplantations.Similarly,standsthatarefertilizedareusuallyalsotreatedwithherbicidetocontrolcompetingvegetationsothatthefertilizationbenefitaccruestothedesiredcropspecies.


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Thefourcategoriesofmanagementintensityarelow,moderate,high,andveryhigh.Lowintensitygenerallyreferstominimalmanagementintervention(e.g.,naturalregenerationoroldersoftwoodplantationswithoutgeneticallyimprovedstock).Moderateintensityincorporatessomelevelofactivemanagementsuchasintermediateharvests(e.g.,thinnings).Highintensityappliesonlytoplantationsandincorporatestheuseofsuperiorgeneticstockandvegetationcontrol.Veryhighintensitymanagementappliestoaggressivelymanagedsoftwoodorhardwoodplantationsinwhichsubstantialeffortismadetomaximizegrowthusinggenetics,vegetationcontrol,andfertilization.Theresultingcombinationsofforesttypes,intensities,andmanagementpracticesaresummarizedinTable6‐5.

Table6‐5:ManagementIntensityCategories

ForestTypea/ManagementIntensityb

StandDensityMgmt

Planting SuperiorGenetics

VegetationControl

Fertilization

Hardwood/low

Hardwood/moderate X Mixed/low Mixed/moderate X Softwood(Nat)/low Softwood(Nat)/moderate X Softwood(Plt)/low X Softwood(Plt)/moderate X X X Softwood(Plt)/high X X X XSoftwood(Plt)/veryhigh X X X X XHardwood(Plt)/veryhighc X X X X XaForesttypereferstothecombinationofspeciesgroupandregeneration(Nat=naturallyregenerated;Plt=Planted).bAnXindicatesthatthepracticeindicatedisappliedforthemanagementintensitycategory.cVeryhighintensityhardwoodplantationsareusuallyencounteredinthecontextofshort‐rotationwoodcropsorbiomassplantations.

Figure6‐6showsthespecificregions(e.g.,PacificNorthwest,West;PacificNorthwest,East;PacificSouthwest;RockyMountainNorth;RockyMountainSouth;GreatPlains;NorthernLakeStates;Central;SouthCentral;Northeast;andSoutheast)forwhichsilviculturaloptionsbythemostcommonlymanagedforesttypeweredeveloped.


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Figure6‐6:MapofSpecificRegionsofForestManagement

ForthemanagementintensitycategoriesillustratedinTable6‐5,commonsilviculturaloptionsbythemostcommonlymanagedforesttypesforspecificregionsofforestmanagement(seeTable6‐6)aredescribed.Thislistisnotexhaustive,sincesilviculturalprescriptionsmayoftenbetailoredtositespecificconditions;however,thelistprovidesthepracticesfrequentlyappliedincommonlymanagedforesttypes.Themanagementobjectivemaynotnecessarilybetimberproduction;insomeregionsandtypeshabitatrestoration,rangelands,orforesthealthmaybetheprimarymanagementobjectives.Table6‐6providesalistofcommonlyusedsilviculturalprescriptionsforcommonforesttypesineachregion.

Table6‐6:CommonSilviculturalOptionsbyMostCommonlyManagedForestType

Region ForestType GeneralizedPractice

Northeasta

Northernhardwoods:beech,sugarmaple,yellowbirch,andassociates

Singletreeselection:harvest40–50ft2peracreevery20yearsacrossarangeofsizeclassesinstandswith120–130ft2basalarea(BA)Clearcut:when120–130ft2,thencommercialthinningCommercialthin:Atage90–100(120ft2)thinto70–80ft2

Standardshelterwood:Harvest40–50ft2frombelow,leaving80ft2inoverstory;removeoverstoryin10–15years

Spruce–fir:red/whitespruce,balsamfir

Shelterwood:Harvest60ft2 frombelow(leave100ft2);harvestremainderin10–15yearsSingletreeselection:At160ft2,remove50ft2inallsizes,every20years


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Commercialthinning:Atage50–60,thinfrom150downto100ft2

Centralb

Oak–hickory

ClearcutShelterwood: followinglocalguidelinesGroupselectionwithcommercialthinningtoB‐levelstockingonGingrichGuide(Gingrich,1967)Precommercial/commercialthinningtoB‐levelstockingonGingrichGuideDiameterlimitcut:To12inchesDBHPrescribedfire:topromoteoakregenerationorwoodlandrestoration

Elm–ash–cottonwoodClearcutIndividualtreeselection: followinglocalguidanceDiameterlimitcut:To12inchesDBH

Maple–beech–birch

ClearcutShelterwood: followinglocalguidanceGroupselectionwithcommercialthinningtoB‐levelstockingonGingrichGuideIndividualtreeselection:CommercialthinningtoB‐levelstockingonGingrichGuideDiameterlimitcut:To12inchesDBH

Oak–pine

Clearcut:Shelterwood:GroupselectionwithcommercialthinningtoB‐levelstockingonGingrichGuideDiameterlimitcut:To12inchesDBHPrescribedfire:Topromote woodlandrestoration

RockyMountainSouthc

Drymontane:ponderosapine,Douglasfir

Selectioncutting:Harvest20–30ft2peracreevery20–30yearsacrosssizeclassesinstandsto40–80ft2BACommercialthinning:Atage60–80thinto50–60ft2 BAShelterwood:Harvest60–80ft2 BAfrombelow;leave30ft2inoverstory;removeoverstoryin5–10years

Aspen Coppice: Atage100Lodgepolepine Clearcut: Atage120–150

Spruce–firSingletreeselection:Harvest20–30ft2peracreevery20–30yearsacrosssizeclassesinstandsto80–120ft2BA

Woodlandtypes:pinyon–juniper,Gambreloak Selectioncutting:Harvestto40–60ft2BA

Southeastd

UplandhardwoodClearcut:Atage35–50Singletreeselection:Harvest40–60ft2peracreinstandswith100–140ft2peracre

BottomlandhardwoodSingletreeselection:Harvest40–60ft2peracreinstandswith100–140ft2peracre

Pineplantation–lowintensity

Plantwithnon‐improvedseedlings600–700peracre,thinto60–70ft2peracreatage18–24,clearcutatage25–35

Pineplantation–mediumintensity

Plantwithimprovedseedlings600–700peracre,thinto60–70ft2peracreatage18–22,fertilizeafterthinningwithnitrogenandphosphorus(ifneeded),clearcut5–7yearsafterthinning


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Pineplantation–highintensity

Plantwithimprovedseedlings600–700peracre,herbaceousweedcontrolage2–4,thinto60–70ft2peracreatage16–20,fertilizeafterthinningwithnitrogenandphosphorus(ifneeded),clearcut5–7yearsafterthinning

SouthCentrald

UplandhardwoodClearcut:Atage35–50Singletreeselection:Harvest40–60ft2peracreinstandswith100–140ft2peracre

Bottomlandhardwood Singletreeselection:Harvest40–60ft2peracreinstandswith100–140ft2peracre

Pineplantation–lowintensity

Plantwithnon‐improvedseedlings450–700peracre, onlowerqualitysites,thinto60–70ft2peracreatage18–20;onhigherqualitysites,thinto60–70ft2peracreatage12–16,onhigherqualitysites,thinagainatage20–24,clearcut5–7yearsafterthinning

Pineplantation–mediumintensity

Plantwithimprovedseedlings600–700peracre,onlowerqualitysites,thinto60–70ft2peracreatage18–20;onhigherqualitysites,thinto60–70ft2peracreatage12–16,fertilizeafterthinningwithnitrogenandphosphorus(ifneeded),onhigherqualitysites,thinagainage20–24,clearcut5–7yearsafterthinning

Pineplantation–highintensity

Plantwithimprovedseedlings600–700peracre,herbaceousweedcontrolage2–4,onlowerqualitysites,thinto60–70ft2peracreatage18–20;onhigherqualitysites,thinto60–70ft2peracreatage12–16,fertilizeafterthinningwithnitrogenandphosphorus(ifneeded),onhigherqualitysites,thinagainatage20–24,clearcut5–7yearsafterthinning

NorthernLakeStatese

Aspen–birch

Clearcut:50–60yearrotationShelterwood:Whenbirchismaincomponent:twocutsystem,commercialthinningatage40–50onhighqualitysites

NorthernhardwoodsShelterwood:twostage;firstcut20yearspriortorotationage;commercialthinningasrequiredSingletree/groupselectionwith10–20yearcuttingcycle

Oak

Clearcut:Onlowerqualitysites,andonhighqualitysiteswhereadequateadvancedregenerationispresent;commercialthinningasrequiredShelterwood:Onhighqualitysiteswhenadequateadvancedregenerationisnotpresent;commercialthinningasrequired

JackpineClearcut:50–60yearrotation (notethatjackpinemanagedforKirtland’swarblerhabitatwillhaveadditionalmanagementrequirements)

Redpine

Clearcut:Commonlyfollowedbysitepreparationandplanting900peracre,commercialthinningbeginningatage25–40Shelterwood:Wherediseaseriskislow;oftenusedwithprescribedfire;commercialthinningbeginningatage25–40

WhitepineShelterwood:Twostagesystem;commercialthinningbeginningatage40


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Whitespruce/balsamfirClearcut:WhenadequateregenerationispresentShelterwood:Twostagesystem,whenadequateregenerationisnotpresent

Lowlandconifer

Clearcut:Whenadequateregenerationispresent; patchandstripclearcutsmaybeusedinsomecasesShelterwood:Twostagesystem,whenadequateregenerationisnotpresent

GreatPlainsf Ponderosapine

Two‐cutShelterwood:reducebasalareatobelow60ft2peracre,thenremoveremainingoverstoryafteradequateregenerationispresentPrecommercialthinningasnecessarytomaintaindesireddensitiesArtificialregenerationmayberequiredaftercatastrophicdisturbancesortoestablishforestsonpreviouslyunforestedland;thismaybedonethroughbroadcastseedingorplanting

RockyMountainNorthg

PonderosapinePlant400–500trees peracre,precommercialthinto200–300treesperacre,commercialthinto150–200treesperacreatage30–40;clearcutharvestatage60–80

LodgepolepineSitepreparetoexposemineralsoilseedbed,naturalregenerationbyseeding,precommercialthinto200–400treesperacre,patchclearcutharvestatage80–100

PacificSouthwesth

Mixedconifer:ponderosapine,sugarpine,Douglasfir,incensecedar,whitefir,Jeffreypine,andCaliforniablackoak

Commercialthin:Startingatagesnear40andcontinuingatvariousperiodiccyclesuntilregeneration;post‐thinningstockinggenerallyrangesbetween150–250ft2;variablerotationlength,dependingonobjectivesCommercialthinningwithbothpatchregenerationandreservedareas:Similartoabove,butwithhigherlevelsofvariationinpost‐thinningstockinglevels,smallpatchesofregeneration,primarilytoincreasepinespecies,andsmallareasreservedfromharvest,maintaininglarger/oldertreesprovidingrelativelyuniquewildlifehabitats;variablerotationlength,dependingonobjectives

PacificNorthwest,Easti

Douglasfir/Ponderosapine–lowintensity

Sitepreparationbysitescarificationinsmallspots,naturalregeneration,precommercialthinatage20–25yearsto100–250treesperacre,patchclearcutorseed‐treeharvestatage50–70

Douglasfir/Ponderosapine–mediumintensity(onmoreproductivesites)

Mechanicalsitepreparationtoscarifysoilandremovecompetingvegetation,plantwithimprovedseedlingsatapprox.400–500peracre,precommercialthinatage15–20,commercialthinatage30–40,patchclearcutorseed‐treeharvestatage50–70

PacificNorthwest,Westj

Douglasfir

Sitepreparestandwithpre‐emergentherbicides,plantwithimprovedseedlingsatapprox.450peracre,commercialthinningasneededatage20–30,fertilizeasneededatage30–40,clearcutharvestatage40–50

DBH=DiameteratbreastheightaPersonalcommunication:BillLeak.bPersonalcommunication:SteveShifley.cPersonalcommunication:JamesYoutz,JimThinnes.dPersonalcommunication:StevePrisley.ePlanningdocumentsandsilvicultureguides,andpersonalcommunicationwithstaffontheHuron‐Manistee,Ottawa,and


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HiawathaNationalForests.fSeeShepperdandBattaglia(2002).gSeeYoungblood(2005).hPersonalcommunication:JoeSherlock.iSeeBriggs(2007).jSeeHanleyandBaumgartner(2005).

6.4.3.3 ApplyingDefaultTablesofManagementPracticeScenarios

Oncethegeneralcategoriesofforesttypesandmanagementintensitiesaredefined,amodelingframeworksuchasFVScouldbeusedtodevelopsetsofdefaulttablesofcarbonstocksinvariouspoolsovertimeundermanagementscenarioscommontotheforesttypesandmanagementintensities.Notethatatthistime,theselookuptablesarenotavailable;developingdefaultcarbonstockvaluesforforestmanagementpracticesisataskrequiringasignificantleveloftimeandeffort.Intheabsenceofsuchtables,smalllandownerswishingtoestimatetheeffectsofchangingmanagementpractices(aTypeIIIestimate)willneedtousethemethodsdescribedforlargelandowners.

Table6‐7showsanunpopulatedexampleforthedefaultlookuptablesofmanagementpracticescenarios.Thedefaulttableswouldprovideregionalestimatesoftimbervolumeandcarbonstocksforaspecificforesttypegroup(e.g.,loblolly‐shortleafpinestands)underaspecific(e.g.,Softwood(planted)/veryhigh)managementintensityonforestlandafterclearcutharvestinaspecificregion(e.g.,theSoutheast)forlowproductivityandhighproductivitysites.

Table6‐7:RegionalEstimatesofTimberVolumeandCarbonStocksforaSpecificForestTypeGroup(e.g.,Loblolly‐ShortleafPineStands)UnderaSpecific(e.g.,Softwood(Planted)/VeryHigh)ManagementIntensityonForestLandafterClearcutHarvestinaSpecificRegion(e.g.,theSoutheast)forLowProductivityandHighProductivitySites

Note:Atthistime,populatedtablesarenotavailable;developmentofsuchtablesisnotcertain.

AgeMeanVolume

MeanCarbonDensity

LiveTree StandingDeadTree

DownDeadWood

ForestFloororLitter

TotalNonsoil

Years m3ha−1 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐MetricTonsCha−1(LowProductivity)‐‐‐‐‐‐‐‐‐‐‐‐‐0 ‐ ‐ ‐ ‐ ‐ ‐5 ‐ ‐ ‐ ‐ ‐ ‐10 ‐ ‐ ‐ ‐ ‐ ‐15 ‐ ‐ ‐ ‐ ‐ ‐20 ‐ ‐ ‐ ‐ ‐ ‐25 ‐ ‐ ‐ ‐ ‐ ‐30 ‐ ‐ ‐ ‐ ‐ ‐35 ‐ ‐ ‐ ‐ ‐ ‐40 ‐ ‐ ‐ ‐ ‐ ‐45 ‐ ‐ ‐ ‐ ‐ ‐50 ‐ ‐ ‐ ‐ ‐ ‐55 ‐ ‐ ‐ ‐ ‐ ‐60 ‐ ‐ ‐ ‐ ‐ ‐65 ‐ ‐ ‐ ‐ ‐ ‐70 ‐ ‐ ‐ ‐ ‐ ‐75 ‐ ‐ ‐ ‐ ‐ ‐80 ‐ ‐ ‐ ‐ ‐ ‐85 ‐ ‐ ‐ ‐ ‐ ‐90 ‐ ‐ ‐ ‐ ‐ ‐


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AgeMeanVolume

MeanCarbonDensity

LiveTree StandingDeadTree

DownDeadWood

ForestFloororLitter

TotalNonsoil

Years m3ha−1 ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐MetrictonsCha−1(highproductivity)‐‐‐‐‐‐‐‐‐‐‐‐ 0 ‐ ‐ ‐ ‐ ‐ ‐5 ‐ ‐ ‐ ‐ ‐ ‐10 ‐ ‐ ‐ ‐ ‐ ‐15 ‐ ‐ ‐ ‐ ‐ ‐20 ‐ ‐ ‐ ‐ ‐ ‐25 ‐ ‐ ‐ ‐ ‐ ‐30 ‐ ‐ ‐ ‐ ‐ ‐35 ‐ ‐ ‐ ‐ ‐ ‐40 ‐ ‐ ‐ ‐ ‐ ‐45 ‐ ‐ ‐ ‐ ‐ ‐50 ‐ ‐ ‐ ‐ ‐ ‐55 ‐ ‐ ‐ ‐ ‐ ‐60 ‐ ‐ ‐ ‐ ‐ ‐65 ‐ ‐ ‐ ‐ ‐ ‐70 ‐ ‐ ‐ ‐ ‐ ‐75 ‐ ‐ ‐ ‐ ‐ ‐80 ‐ ‐ ‐ ‐ ‐ ‐85 ‐ ‐ ‐ ‐ ‐ ‐90 ‐ ‐ ‐ ‐ ‐ ‐



TypeIestimatesmaybedevelopedforstanddensitymanagement.Forunderplanting,carbonstocksareessentiallyunchangedimmediatelyafterthetreatment.Forprecommercialthinnings,carbonismovedfromthelivetreepooltothestandingdeadpooland/orforestfloorpool;quantitieswillbelowandessentiallyjustacceleratethenaturalmortalityofthesesmallertrees,thusaccountingforthisactivitymaybeunnecessary.Forcommercialthinning,thelivetreecarbonstockisreducedandcarbonismovedintoHWPs,sothesepoolsneedtobeestimatedusingproceduresoutlinedinSection6.2andSection6.5.

TypeIIandIIIestimatesmaybedevelopedusingforestgrowthmodels(i.e.,FVS)specifictotheforesttypeandpracticesused.

6.4.4.2 SitePreparationTechniques

Carbonstockchangesthatareduetomechanicalsitepreparationtechniqueswillconsistofsomeoxidationofsoilorganiccarbonthatwillbereplacedovertimebyforestgrowth.Forlong‐termmonitoring,itmaybeassumedthatsoilcarbonstockswillbestableundersustainableforestmanagement(Smithetal.,2006).Thus,TypeIestimatescouldreflectshort‐termlossesofsoilcarbonstocksbasedonassumptionsappropriatetotheforesttypeandregion.


Controlofwoodyvegetationwillexhibitpatternssimilartoprecommercialthinning:transferofcarbonstocksfromlivetreetodeadtreepools.Quantitieswilllikelybesmallandtheeffectofshortduration;henceaccountingfortheseimpactsusingTypeIestimatesmaybeunnecessary.

ForTypeIIandIIIestimates,vegetationcontrolmaybeexpectedtohaveabeneficialimpactonthe


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growthoftheresidualstandthatshouldbemodeledaccordingly.

6.4.4.4 Planting

Negligiblecarbonstockchangesareexpectedatthetimeofestablishmentofanewplantation,soTypeIestimateswillshownostockchanges.ForTypeIIandIIIestimates,theplantationactivityestablishesanewstandthatcanthenbemodeledbasedonspecies,siteindex,andinitialstocking(plantingdensitytimesyear1survivalpercent).


Asinthecaseofplantationestablishment,carbonstockchangesatthetimeofnaturalregenerationarenegligible.ForTypeIIandIIIestimatesinvolvingprojectionsofstandgrowthovertime,initialstocking,speciesmix,andsiteproductivitywilldefinethestandparametersforgrowthprojections.

6.4.4.6 HarvestingandUtilization

Dependingontheharvestingandsilviculturalsystemused,multiplestockchangesoccurwitharotationharvest.Livetreebiomassstocksarereducedbytheamountofharvestedwood(upto100percentofthelivetreebiomasspool).TheseremovalsshouldbebalancedbyadditionstoHWPpoolsandslash/residueintheforestflooranddeadwoodpools.Becauselossestosoilorganiccarbonpoolsfromdisturbancebymechanizedharvestingsystemsareofrelativelyshortduration,itiscommontoconsiderthelossandrecaptureasasteadystate(e.g.,Smithetal.,2006),thoughthismaydifferdependingonsoilcharacteristics.

Inthecaseofpartialharvests,thereisaresidualstandforwhichcarbonstocksremaintobeprojectedovertime.Post‐harvestinventoryinformationprovidesthecriticalstandparameterstobeinputintogrowthmodels.Intheabsenceofapost‐harvestinventory,pre‐harvestinventorydatacanbeadjustedtoreflectthelossoftreesremovedbytheharvest(e.g.,bydecreasingthenumbersoftreesbyspeciesanddiameterclassbasedonharvestrecords).


TypeIestimatesofcarbonstockchangesduetofueltreatmentsorprescribedfireshouldreflectlossestolivetreebiomassaccordingtothematerialburned,killed,orremoved(frompreandpost‐treatmentinventorydata).Foraprescribedfire,emissionscanbecalculatedusingFOFEM.Ifslashisleftfromthefueltreatment,CONSUMEmayalsobeused.

TypeIIandIIIestimatessimplyinvolveprojectingthestandbasedoninformationfromthepost‐treatmentinventory.


TypeIcarbonstockestimateswillinvolvecomputationoflossestolivetreebiomassfromthesanitationorsalvageharvest,withadditionstoHWPpoolsasappropriate.

TypeIIandIIIestimatessimplyinvolveprojectingthestandbasedoninformationfromthepost‐treatmentinventory.


Negligiblecarbonstockchangesareexpectedatthetimeofestablishmentofanewplantation,soTypeIestimateswillshownostockchanges.ForTypeIIandIIIestimates,theplantationactivityestablishesanewstandthatcanthenbemodeledbasedonspecies,siteindex,andinitialstocking(plantingdensitytimesyear1survivalpercent).


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6.4.5.1 MeasurementUncertainties

Forestinventorydata,fromwhichmostestimatesinthissectionarederived,containuncertaintyasaresultofsamplingandmeasurementerror.Furthermore,equationsareusedtoestimatebiomassfromtreemeasurements(species,diameters,heights),andtheseequationsintroduceadditionalerrors.Theseuncertainties,however,arewelldocumentedandcanbequantified.

6.4.5.2 ModelUncertainties

ForthedevelopmentofTypeIIandTypeIIIestimates,modelsareusedtoprojectcurrentconditionsintothefuture.Thesetypesofestimatesarebasedinitiallyoninventorydataandaresubjecttothemeasurementuncertaintiesdiscussedabove,butarealsosubjecttomodelingerror.Modelingerrorcanbedocumentedinpartbasedonthediagnosticsreported(ifany)fromthemodeldevelopmentprocess.Greateruncertaintiesareintroducedwhenmodelsareappliedbeyondtheconditionsforwhichtheyweredeveloped(e.g.,biomassequationsappliedtospeciesforwhichnobiomassdatawerecollected,forestgrowthmodelsappliedtostandsreceivingdifferentmanagementthanthestandsusedformodeldevelopment,etc.).Modeluncertaintiesalsoincreasewiththeprojectionperiod(thedistanceintothefutureforwhichestimatesareobtained).Someofthemodeluncertaintiesarecancelledoutwhenresultsfromtwosimilarmodelrunsarecompared(i.e.,aTypeIIIestimate).Forexample,ifamodelslightlyoverestimatescarbonstockinaforestwithandwithoutsometreatment,thedifferencebetweenthetwomodelestimatesmaybeaccurateeveniftheindividualestimatesarenot.

6.4.5.3 GeneralizationUncertainties

ForthepurposeofapplyingnationallyconsistentestimationmethodstoTypeIIandIIIestimates,itisnecessarytogeneralizesituationsintobroadforesttypesandmanagementintensities.Thus,someprecisionislostinapplyingageneralized,aggregatedestimatetoaparticularsetofmanagementactivities.

6.5 HarvestedWoodProducts

6.5.1 GeneralAccountingIssues

Whenforestlandownersharvestwoodforproducts,aportionofthewoodcarbonendsupinsolidwoodorpaperproductsinenduses,andeventuallyinlandfills,andcanremainstoredforyearsordecades.Thisreportsuggestsaspecificmeasure,alongwithestimationmethods,that

MethodforHarvestedWoodProducts

MethodusesU.S.‐specificHWPstables.

TheHWPstablesarebasedonWOODCARBIImodelusedtoestimateannualchangeincarbonstoredinproductsandlandfills(Skog,2008).

TheentityusesthesetablestoestimatetheaverageamountofHWPcarbonfromthecurrentyear’sharvestthatremainsstoredinendusesandlandfillsoverthenext100years.

Thismethodwasselectedbecauseitissuitabletorepresenttheamountofcarbonstoredinproductsinuseandinlandfills.


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forestlandownerscanusetoreportcarbonadditionstothestockofHWPsfromwoodtheyharvest.TheaccountingframeworkusedtotrackHWPcarbonissimilartotheframeworkthattheUnitedStatesusestoreportnational‐levelannualchangesinHWPcarbonstocksunderUNFCCC.

Thenationalaccountingframeworkandthesemethodsadopttheproductionapproach,whichentailsthefollowing:(1)estimatingtheannualcarbonadditionstoandremovalsfromthestockofcarbonheldinwoodproductsinuseandinlandfills,(2)trackingonlycarboninwoodthatwasharvestedintheUnitedStates(U.S.EPA,2011),and(3)providingestimatesthattrackwoodcarbonheldinproducts,evenifistheproductsareexportedtoothercountries.

EstimatesoftheannualcontributionofHWPstocarbonstocksmaybemadeforTypeI,TypeII,andTypeIIIestimatesofforestcarbonchangeasoutlinedinSection6.2:

ForTypeIestimates,thefocusisonestimatingtheannualcontributionofHWPstocarbonstocksforagivencurrentyearorrecentpastyears.

ForTypeIIestimates,thefocusisonestimatingtheannualcontributionofHWPstocarbonstocksforaprojectedperiodofyearsinthefuture.

ForTypeIIIestimates,thefocusisonestimatingthechangeintheannualcontributionofHWPstocarbonstocksbetween:(1)abasecasewithonescenarioforforestmanagement(andharvest);and(2)asecondscenarioforforestmanagement(andharvest)thatisintendedtochangecarbonflux.

ForeachoftheTypeI,II,orIIIestimates,thesemethodsrecommendthatforestlandownersreporttheannualcontributionofHWPstocarbonstocksusingaspecificmeasureintendedtoapproximatetheclimatemitigationbenefitassociatedwithstoringcarboninHWPsovertime.TherecommendedmeasureistheestimatedaverageamountofHWPcarbonfromthecurrentyear’sharvestthatremainsstoredinendusesandlandfillsoverthesubsequent100years.

Theintentofthismeasureistoapproximatetheaverageannualclimatebenefitofwithholdingcarbonfromtheatmospherebyacertainamounteachyearfor100yearsasdescribedbya“decay”curve.Thisaveragebenefitisonethatcanbecreditedintheyearofharvest.ThisestimateofaverageeffectisconceptuallysimilartothemeasureoftheradiativeforcingimpactofacurrentyearemissionofCO2,CH4,orotherGHG.OnetonofCO2emissions—inGHGaccounting—isequatedtotheradiativeforcingitcausesoverthe100yearsfollowingtheemission.Theradiativeforcingcausedineachyearisweightedthesameovereachofthe100years.Wearesuggestingthesameconventioninweightingthecarbonstorageinwoodproductsequallyforeachof100years.

AnestimateofaveragefractionofHWPcarbonstoredover100years(averageamountstoredover100yearsdividedbytheoriginalproductcarbonproduced)isnotexactlythesameasthefractionofradiativeforcingavoidedbystoringwoodproductscarbon(andemittingcarbonslowly)over100years.FordecaycurveswhereaconstantfractionofremainingHWPcarbonisemittedeachyearthefractionofradiativeforcingavoidedover100yearscanbe0to14percentlessthantheaveragefractionofHWPcarbonstoredover100yearsdependingonthedecayrate.8Estimatesofthefractionofradiativeforcingavoidedover100yearscouldbeusedinplaceoftheaveragecarbonstorage.Giventheuncertaintyindecayratesasaninfluenceonestimatesandthegreatercomplexityoftheradiativeforcingmeasure,werecommendthemeasureofaveragecarbonstoredasanadequateproxyfortheeffectofwoodproductsproducedinthecurrentyearandstoredover

8Thefractionofradiativeforcingavoidedover100yearswasestimated(andcomparedtoaveragecarbonstoredover100years)assumingarangeofdecayratesforfirstorderdecaycurvesforwoodproductsandusingtheCO2radiativeforcingresponsecurvefromtheIPCCWorkingI4thAssessmentReport(footnotea,p.213)(IPCC,2007).


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100years.

Themeasure—averagecarbonstoredinHWPover100years(withvariationsonhowlandfillcarbonisincluded)—isusedintheClimateActionReserve(2010)ForestProjectProtocolsadoptedbytheCaliforniaAirResourcesBoard.Theprotocolsindicatehowtocalculatethelevelofannualcarboncreditsthatmaybesoldbyforestlandownerswhoentercarboncontracts.

Notethatuseoftheproductionapproachtoaccountingisnotalife‐cycleassessmentaccountingapproachthatcouldtakeintoaccounthowcarbonemissionsfromincreasedwoodburningorincreaseduseofwoodproductsmightoffsetfossilfuelemissionsoremissionsfrommakingnon‐woodproductsovertime.TheestimatesofannualchangeincarboninHWPsarenotintendedtoindicatethetotalimpactonGHGlevelsintheatmosphereofusingHWPs(includinguseofwoodforenergy),noraretheyintendedtoindicatethattheemissiontotheatmospheretookplaceintheUnitedStatesversusothercountrieswhereproductswereexported.EstimationofTypeIIIsecondaryGHGreductioneffectsofsubstitutionofwoodforfossilfuelsornon‐woodconstructionproductsarecomplexandwouldrequirespecificationofabaselinefromwhichchangeismeasuredandotherassumptionsthatarebeyondthescopeofthesemethods.

Theproductionapproachisusedtoacknowledgethatharvestingofforestsdoesnotimmediatelyreleaseallofthecontainedcarbontotheatmosphere;theaccountingcountsonlythecarbonchangeinHWPsinordertoallowannualcarbonchangesinHWPstobedeductedoraddedtoannualemissionsintheenergyandmanufacturingsectorsandcarbonchangesinforests,sotherewillbenoomissionordoublecountingofsequestrationoremissionstotheatmosphere.Inthenationalaccountingframework,theannualemissionsfromwoodenergyareaccountedforaspartoftheaggregatedannualchangeinforestplusHWPcarbon.


6.5.2.1 WoodProductsFate/Longevity

ToallowforestlandownerstoestimatecarbonadditionstoHWPstocks—usingaveragecarbonstoredinHWPover100years—lookuptablesareprovidedthatgiveestimatesofcarbonremainingstoredafterharvestoutto100years.

Therearetwotypesoflookuptables:a“roundwood”typeanda“primaryproduct”type.

Fortheroundwoodtype,thelandownerneedsestimatesofthecarboninharvestedamountsofindustrialroundwood:hardwood(HW)orsoftwood(SW),sawlogs(SL),orpulpwood(PW).Industrialroundwoodiswoodusedforsolidwoodorpaperproductsandexcludesbarkandfuelwood.Thelandownercanbeginwithestimatesincubicunitsandconvertthemtocarbonweightorwoodweightunitsthenconvertthemtocarbonweight(assuming0.5metrictonscarbonpermetrictondrywood).Separatelookup“decay”tablesareprovidedbymajorU.S.regionandroundwoodtype(HWorSW,SL,orPW)thatshowthefractionofcarboninwoodtypicallystoredinwoodproductsinuseandinlandfills,outto100yearsaftertheyearofharvest,andtheaveragefractionstoredover100years.

Fortheprimaryproducttypeoflookuptables,thelandownerneedsestimatesoftheprimarywoodproductsmadefromthewoodharvested;i.e.,SWorHWlumber,SWorHWplywood,orientedstrandboard,orpaper(inconventionalproductunits).Thelandownerthenconvertstheseamountstocarbonweight.Foreachprimaryproduct,thelookup“decay”tablesshowthefractionofwoodcarbonthatistypicallystoredinwoodproductsinuseandinlandfills,fromtheyearofharvestoutto100years,andtheaveragefractionofcarbonstoredover100years.


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6.5.3.1 PrimaryProductDecayTables

Inordertoconstructtheprimaryproducttypedecaytables,dataareusedforeachU.S.regionon:

Thedispositionofeachprimaryproduct(e.g.,lumber,structuralpanels)tomajorenduses(e.g.,percentageofproductgoingtoresidentialhousing,non‐residentialhousing,manufacturing(furniture)),andpercentagegoingtoexports;

Thedecayfunctionsindicatinghowquicklyproductsgooutofuseforeachenduse;

Thefractionofmaterialgoingoutofusethatgoestolandfills;and

Thefractionofmaterialinlandfillsthatdoesnotdecay,andthedecayrateformaterialinlandfillsthatdoesdecay.

ItisassumedthatthereisanationalmarketforprimaryproductsandthepercentageofprimaryproductsgoingtoeachendusewillbethesameforeachU.S.region.ItisalsoassumedthatprimaryproductsexportedfromtheUnitedStatesareusedinthesamewayasdomesticproducts.Thatis,thereisanationalmarketforeachoftheprimarywoodandpaperproducts.Dataforitems(1)through(4)comefromtheWOODCARBIImodelusedtoestimateannualchangeincarbonstoredinproductsandlandfillsfortheU.S.InventoryofGHGEmissionsandSinksreport(Skog,2008;U.S.EPA,2010).

Ifalandownerknowsthetraditionalnumberofunitsofprimaryproducts(e.g.,thousandboardfeetoflumber)thatweremadefromthetimberharvestedfromtheirlandinagivenyear,theycanuseTables6‐A‐1,6‐A‐2,and6‐A‐3toestimatethecarboncontentsintheseproducts(Table6‐A‐1)andestimatetheamountofcarbonstoredintheseproducts(inuseandinlandfills)outto100yearsandtheaverageamountofcarbonstoredover100years(Table6‐A‐2[inuse]andTable6‐A‐3[inlandfills]).

Theaverageamountofcarbonstoredover100yearsforaparticularprimaryproductisthetotaloftheaveragesforproductsinuseandproductsinlandfillsshowninTables6‐A‐2(inuse)andTable6‐A‐3(inlandfills).

6.5.3.2 RoundwoodDecayTables

Inordertoconstructtheroundwoodtypeofdecaytables,dataareneededforeachregiononthepercentageofHWorSW,SL,orPWthatgoestovariousprimarywoodproducts;forexample,thefractionofSWSLsintheSouththatgoestolumber,panels,andpaper.Aftertheamountsofprimarywoodproductsareestimated,theprimaryproductstypedecaytablescanbeusedtoconstructroundwooddecaytables.DataneededtodivideroundwoodintoprimaryproductsforeachregionincludeForestServiceFIAtimberproductoutputdataandnationaldataonprimarywoodproductsproduction(Howard,2012;Smithetal.,2007).

Ifalandownerknowsthecubicfeetofroundwood,intheformofHWorSWSLsorPWthatisharvestedfromtheirlandinagivenyear,theycanuseTable6‐A‐4and6‐A‐5to(1)estimatetheweightofwoodharvested;(2)convertweightofwoodtocarbonbymultiplyingby0.5(i.e.,thefractionofdrybiomasstocarbonconversionfactor);and(3)estimatethetotalamountofcarbonstoredintheproducts(thesumofamountsinuseandinlandfills)eachyearoutto100years,andtheaveragestoredover100years.

Ifthelandownerknowstheweightofroundwoodharvestedratherthancubicfeet,itwouldusesteps2and3above.

AnnualHWPcarbon(averagestoredover100years)isgivenforeachregionandroundwoodtype


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inTable6‐A‐5.

Ifthelandownerismakingforestgrowthandharvestprojections(TypeIIandTypeIIIestimates)andonlyknowsthecubicfeet(orweight)ofgrowingstockofHWandSWSLsandPWthatwillbeharvestedingivenfutureyears,thenTable6‐A‐6canbeusedtoestimatethetotalamountofroundwoodthatcanexpectedtobeharvested(growingstockandnon‐growingstock).Thesetotalamountsofroundwood(HWandSWSLsandPWmaythenconvertedtocarbonandtocarbonstored(andaveragecarbonstoredover100years)usingTable6‐A‐4andTable6‐A‐5,asdiscussedabove.Toconvert1cubicfootofdrywoodtopoundsmultiplydensityby62.4lbsft−3.Toconvert1cubicfoottokilogramsmultiplydensityby28.3kgft−3.

Aspreadsheetisavailableshowingalltheparametersandcalculationsthatproducethecarbonstoragetablesthatstartwithprimaryproductsorroundwoodharvest(Skog,2013).

6.5.4 Limitations,Uncertainty,andResearchGaps

6.5.4.1 UncertaintyinCchangeestimate

Generalestimatesofuncertainty,givenasthe95percentconfidenceintervals,canbemadeforHWPmeasureusedinTypeIcarbonchangeestimates(currentyearorrecentpastyears).Theseestimatesofuncertaintycouldbeprovidedwitheachofthetwotypesoflookuptables,andcanbemadeusingMonteCarlosimulationsandassumptionsaboutHWPuncertaintythatareusedfortheInventoryofU.S.GreenhouseGasEmissionsandSinksreport(U.S.EPA,2011).Uncertaintycouldbespecifiedforkeyvariablesincluding:(1)fractionsofSLsPWgoingtovariousprimaryproducts;(2)fractionsofprimaryproductsgoingtovariousenduses;(3)rateatwhichproductsarediscardedfromeachenduse;(4)fractionofdiscardedwoodorpaperthatgoestolandfills;(5)fractionofwoodorpapersettolandfillsthatissubjecttodecay;and(6)rateofdecayinlandfillsofdegradablewood/papercarbon.

AspreadsheetisavailablethecouldbeusedasabasisforMonteCarlosimulationstoestimateoveralluncertaintyforestimatesofaveragecarbonstoredover100years(Skog,2013).

ItwouldbepossiblebutmorecomplextomakeuncertaintyestimatesforTypeIIandTypeIIIcarbonchangeestimatesbyaddingestimatesofuncertaintyinparametersusedtomakeprojectionsofharvest.

Additionalresearchisneededtoimprovedifferentiationofthevariousratesatwhichsolidwoodproductsarediscardedfromusessuchaspallets,railroad,railcars,andfurniturethatarecurrentlygroupedintoonecategory.Thisfurtherdifferentiationwouldrefineestimatesofaveragecarbonstoredwhenthelandownerknowswhichprimarywoodproductsaremadefromthewoodthatisharvestedontheirland.Alternatecurvesfordiscardratesfromenduses,particularlydiscardsfromhousing,ifempiricallyverified,couldimproveestimatesofaveragecarbonstored.Estimatesofuncertaintyinparametersover100yearprojectionsareneededtogiveasoundestimateoftheuncertaintyinaveragecarbonstoredover100years.


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6.6 UrbanForests

6.6.1 Description

6.6.1.1 DefiningUrbanAreasandForests

Urbanforestsarecomposedofapopulationofalltreeswithinanurbanarea.Todelimittheextentofanurbanforest,theboundariesoftheurbanareamustbedrawn.Thisboundaryissuecanbeproblematic,aspeoplemayconceiveordefine“urban”differently.TodelimiturbanareasintheUnitedStates,U.S.Censusbureaudefinitionsareused.ThesedefinitionsdifferfromthoseusedintheNationalResourcesInventory,whichaimstoidentifyareasthatareremovedfromtherurallandbaseandincludeslandusessuchastransportationcorridors.

TheU.S.CensusBureau(2007)definesurbanasallterritory,population,andhousingunitslocatedwithinurbanizedareasorurbanclusters.Urbanizedareaandurbanclusterboundariesencompassdenselysettledterritories,whicharedescribedbyoneofthefollowing:(1)oneormoreblockgroupsorcensusblockswithapopulationdensityofatleast386.1peoplekm−2

Figure6‐7:UrbanandCommunityLandinConnecticut

Source:U.S.CensusBureau(2007).

MethodsforUrbanForests

Rangeofoptionsdependsondataavailabilityoftheentity’surbanforestland.

Theseoptionsuse:

− i‐TreeEcomodel(http://www.itreetools.org)toassesscarbonfromfielddataontreepopulations;and

− i‐TreeCanopymodel(http://www.itreetools.org/canopy/index.php)toassesstreecoverfromaerialimagesandlookuptablestoassesscarbon.

Quantitativemethodsarealsodescribedformaintenanceemissionsandalteredbuildingenergyuseandincludedforinformationpurposesonly.

Themethodswereselectedbecausetheyprovidearangeofoptionsdependentonthedataavailabilityfortheentity'surbanforestland.


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(1,000peoplemile−2);(2)surroundingblockgroupsandcensusblockswithapopulationdensityof193.1peoplekm−2(500peoplemile−2);and(3)lessdenselysettledblocksthatformenclavesorindentationsorareusedtoconnectdiscontinuousareas.Morespecifically,urbanizedareasconsistofterritoriesof50,000ormorepeople.Urbanclusters,aconceptnewtothe2000Census,consistofterritorieswithatleast2,500peoplebutfewerthan50,000people.

Inadditiontourbanland,theCensusBureaudesignatesplacesthatdelimitpopulationconcentrationsbasedonincorporatedorunincorporatedplaces,suchasacity,town,village,andcensus‐designatedplace.Theseplaces,or“communities,”alsodefineareaswherepeoplereside,butoftenwithalowerpopulationdensity.Thegeographicareasofurbanandcommunitiesoverlap(seeFigure6‐7),andeitherorbothcouldbeusedtodefineurbanforests.Theurbanlanddesignationdelimitshigherpopulationdensities,butdoesnotfollowtheboundariesofcitiesortownsthatmostpeoplecanrelateto.Theplaceorcommunityboundariesfollowthesepoliticalboundaries,butoftenincludebothruralandurbanland.

Urbanlandisdefinedbasedonpopulationdensity,andcommunitylandisoftenbasedonpoliticalboundaries.Thus,urbanforestlandoverlapswithforestlands.Thatis,forestedstandsthataremeasuredaspartofotherprogramscanexistwithinurbanorcommunityboundaries.Assessmentsofurbanforesteffectsthushavethepotentialtodouble‐counteffectsfoundinforestswithinregionalornationalscaleassessments.Theamountofthisoverlapisestimatedas13.8percentofurbanareaor1.5percentofforestareaintheconterminousUnitedStates(Nowaketal.,2013)andisanimportantconsiderationforlargerscaleassessments.ThissectionfocusesonassessingthecarboneffectsofurbanorcommunitytreesandforestsintheUnitedStates.

Urbanorcommunityforests(hereafterreferredtoasurbanforests)affectthecarboncyclebydirectlystoringatmosphericcarbonwithinthewoodyvegetation,butalsobyaffectingthelocalclimateandtherebyalteringcarbonemissionsaffectedbylocalclimaticconditions.Urbantreemaintenanceactivitiesalsoaffectcarbonemissionsinurbanareas.Foratrueaccountingofcarboneffects,allofthesefactorsneedtobeconsidered.Thisreportfocusesontrees(definedaswoodyvegetationwithadiameterofatleast1inch(2.5cm)DBH),butsimilaraccountingcouldbeconductedforallurbanvegetation.

6.6.1.2 AccountingforPrimaryUrbanForestCarbonEffects

Treessequesterandstorecarbonintheirtissueatdifferingratesandamounts,basedonsuchfactorsastreesize,lifespan,andgrowthrate.Afteratreeisremoved,thetreecandecomposewiththecarbonstoredinthattreeemittedbacktotheatmosphere,orthecarbonmaybestoredinwoodproductsorthesoil.Thus,inordertoaccountforthetotalcarboninthesystematonetime,oneneedstounderstandhowmanytreesthereareinthesystemalongwithinformationsuchasspeciesandsize(e.g.,NowakandCrane,2002).Toaccountforhowthecarbonstockwillchangethroughtime,onemustalsoaccountforgrowthrates,treemortalityandremovals,andthedispositionofthewoodafterremoval(e.g.,chipping,burning,products),whichaffectdecompositionratesandcarbonemissions.Inaddition,thenumberofnewtreesenteringthesystemthroughtreeplantingandnaturalregenerationmustbeconsidered.

6.6.1.3 AccountingforSecondaryEffects

Inadditiontothecarbonstoredintrees,theurbanforesthassecondaryimpactsonatmosphericcarbonbyaffectingcarbonemissionsfromurbanareas.Treecareandmaintenancepracticesoftenreleasecarbonbacktotheatmosphereviafossil‐fuelemissionsfrommaintenanceequipment(e.g.,chainsaws,trucks,chippers).Thus,someofthecarbongainsfromtreegrowthareoffsetbycarbonlossestotheatmosphereviafossilfuelsusedinmaintenanceactivities(Nowaketal.,2002).Treesstrategicallylocatedaroundbuildingscanreducebuildingenergyuse(e.g.,Heisler,1986),and


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consequentlyreducecarbonemissionsfromfossil‐fuel‐burningpowerplants.Theseenergyeffectsarecausedprimarilybytreetranspiration(loweringofairtemperatures),blockingofwinds,andshadingofbuildingsandothersurfaces.Treestypicallylowerbuildingenergyuseinsummer,butcaneitherlowerorincreasebuildingenergyuseinthewinterdependinguponthetree’slocationrelativetoabuilding.

“Alteredbuildingenergyuse”and“maintenanceemissions”forurbantreesaredescribedinSection6.6.3.1.However,whilequantitativemethodsaredescribedforestimatingalteredbuildingenergyuseandmaintenanceemissionsforurbanforestry,theyareincludedforinformationpurposesonly,sincetheyhavealreadybeendevelopedaspartofthei‐Treesoftwaresuite.However,aspreviouslymentionedinChapter1,thescopeofthisguidancedoesnotincludeotherenergy‐relatedsourcecategoriesthatareassociatedwithmanagementactivitiesrelatedtocertainagricultureandforestryactivities(e.g.,transportation,fueluse,heatingfueluse).


Toestimatecarbonstorage,annualsequestration,andlong‐termcarbonchanges,twogeneralapproachescouldbeused.Thefirstmethodisbasedoncollectingdataontreesintheurbanareaofinterest;thesecondmethodinvolvescollectingaerialdataontreecoverinthearea,andusingtablestoestimateeffectsbasedonfielddatafromotherareas.Thefirstmethod,usinglocalfielddata,willproducethemostaccurateestimatesforthelocalarea,butatincreasedcostsandtimespentbythelandowner.Thesecondmethodismorecost‐effectiveandmorestraightforward,butitsaccuracyismorelimited(seeTable6‐8).

Table6‐8:Comparisonofthe“FieldData”and“Aerial”MethodsforEstimatingtheChangesinCarbonStocksforUrbanForests

FieldDataMethod AerialMethod

Requiressignificanttimecommitmenttotakefieldmeasurements

Requireslesstimetoextractnecessaryaerialdatafromanexistingdatabase

Requiresaccesstoseveralsampleplotsacrossanarea

Doesnotrequirefieldmeasurements,onlyacomputerwithinternetaccess

Increasesspecificityandaccuracy ReturnsamoreapproximateestimateProvidesavarietyofoutputdataincludingcurrentcarbonstock,annualcarbonsequestration,andlongtermeffects

Providesonlyinformationontotalcarbonstoredandannualcarbonsequestration

Theoutputdatafromthefielddatamethodincludescurrentcarbonstock(existingcarbonstorage),annualcarbonsequestrationbytrees,andlongtermeffectsoftheforest(accountingforchangesintreepopulationanddispositionofcarbonfromtrees).Forthefielddatamethod(orforproducingthedefaulttablesthatareusedintheaerialapproach)thefollowingitemsneedtobemeasuredandinputbythelandowner:

CurrentStock:

− Numberoftreesbyspeciesandsizeclass(species,DBH,height,condition,competitionfactor)

AnnualSequestration:


− Annualgrowthratesforeachtreebasedontreeandsiteconditions(inchesperyear)

LongTermEffects:


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− Annualgrowthratesforeachtreebasedontreeandsiteconditions(inchesperyear)

− Changesintreepopulationduetotreedeathandremovals,andnewtreesplantedornaturallyregenerated(numbersoftreesdyingbyspeciesandsizeclass,numberofnewtreesbyspeciesandsizeclass)(numberperyear)

Proportionofremovedtreebiomassthatis:

− Chipped/mulched

− Burned

− Burnedtoproduceenergy(e.g.,heatbuildings)

− Belowthegroundinroots

− Usedforlong‐termwoodproducts

− Leftonthegroundtodecomposenaturally

− Putinlandfills

Decomposed;decompositionratesforwoodfromremovedtrees:

− Percentageofbiomassperyeardecomposedperremovalclassabove

MaintenanceEmissions:

− Amount(numberandhoursperyear)ofmaintenanceequipmentused(e.g.,vehicles,chippers,chainsaws)forvegetationmaintenance(e.g.,planting,maintenance,treeremoval)

− Emissionfactors(gChr−1)foreachmaintenanceequipmentused

AlteredBuildingEnergyUse:

− Numberoftreesbyspeciesandsizeclasswithin60feet(18.3m)ofresidentialbuildingbycardinalandordinaldirection

Forestimatingtreecoverusingtheaerialapproach,onewouldneedtoknowtheextent(ha)oftheurbanareaandthepercentageoftreecoverwithinthearea,anduseadefaulttableofvaluestoconverthaoftreecovertoprimaryandsecondarytreeeffectsinacity.Toestimatechangeinthepopulation,thetreecoverwouldneedtobere‐measuredthroughtime.

Aspreviouslymentioned,alteredbuildingenergyuseandmaintenanceemissionsforurbantreesaredescribedinSection6.6.3.1.However,whilequantitativemethodsaredescribedforestimatingalteredbuildingenergyuseandmaintenanceemissionsforurbanforestry,theyareincludedforinformationpurposesonly,astheyarepartofthei‐Treesoftwaresuiteorcanbecalculatedfromi‐Treedata.


Themethodsforestimatingcarboneffectsinanurbanforestwillbedetailedforthefielddataandaerialapproachesseparately.ThefielddatamethodandaerialmethodforurbanforestsaredescribedinSection6.6.3.1andSection6.6.3.2.Figure6‐8showsadecisiontreeindicatingwhichmethodismoreapplicableforeachtypeofactivitydata.


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Figure6‐8:DecisionTreeforUrbanForestsShowingMethodsAppropriateforEstimatingUrbanForestCarbonStocks

1TheU.S.CensusBureau(2007)definesurbanasallterritory,population,andhousingunitslocatedwithinurbanizedareasorurbanclusters.Urbanizedareaandurbanclusterboundariesencompassdenselysettledterritories,whicharedescribedbyoneofthefollowing:(1)oneormoreblockgroupsorcensusblockswithapopulationdensityofatleast386.1peoplekm−1(1,000peoplemile−2);(2)surroundingblockgroupsandcensusblockswithapopulationdensityof193.1peoplekm−2(500peoplemile−2);and(3)lessdenselysettledblocksthatformenclavesorindentations,orareusedtoconnectdiscontinuousareas.Morespecifically,urbanizedareasconsistofterritoriesof50,000ormorepeople.Urbanclusters,aconceptnewtothe2000Census,consistofterritorywithatleast2,500peoplebutfewerthan50,000people.

6.6.3.1 FieldDataMethodforEstimatingCarbonStorageandAnnualSequestration

Thefielddatamethodinvolvesusingfieldmeasurementsofurbantrees(i.e.,a“treelist”)tobuildatailored,accurateestimateofcarbonstorageandsequestrationinanurbanforest.Thevariousstepsforestimatingcarbon(andalteredbuildingenergyuse)effectsfromanurbanforestare:

(1)Delimitboundaryofurbanareatobeanalyzed.Thisinformationisessentialtosettheboundaryoftheanalysis.U.S.Censusboundaryfilesofurbanareasorplacescanbeusedtodelimittheboundaries(U.S.CensusBureau,2011).Informationontheseboundariescanbeusedtodetermineareasofpotentialoverlapwithothercarbonestimates(e.g.,non‐urbanforests),andtohelpsetupa


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samplingdesigntocollectnecessaryfielddataasdesiredbythelandowner.

(2)Measurealltreeswithintheurbanareaorsamplethetreepopulation.Withinthedefinedgeography,alltreescanbemeasured,orarandomdistributionoffieldplotscanbemeasuredtoquantifytheurbantreepopulationasdesiredbythelandowner.Toconductthisfieldsamplingandanalysis,thei‐TreeEcomodel(formerlyUFOREmodel)isavailablefreeofchargeatwww.itreetools.org.Fieldmanualsexistonhowtorandomlyselectplotslocationsandcollecttheneededfielddata(http://www.itreetools.org/resources/manuals.php).Detailsonmodelmethodsalsoexist(e.g.,NowakandCrane,2002;Nowaketal.,2008).

Thebasicfielddataprocedureistorecordinformationonalltreeswithinthefieldplots.Thisinformationincludes:

Treespecies

DBH

Treeheight

Dieback

Crownlightexposure

Distanceanddirectiontobuildings

Thesevariablesareneededtoassesscarboneffects,butothertreevariables(e.g.,crownwidth,percentageofcrownmissing)canalsobecollectedtoassessotherecosystemservices(e.g.,airpollutionremoval,volatileorganiccompoundemissions,effectsonbuildingenergyuse,rainfallinterception,andrunoff).

(3)Enterdataintoi‐Treeandrunanalyses.Afterfielddataarecollected(viapaperformsoronamobiledevice),dataareenteredintoi‐Tree,andtheprogramproducesstandardtables,graphs,andreportsthatdetailcarbonandotherecosystemserviceinformation.Inrelationtocarbon,resultsalongwithsamplingstandarderrorsarespecificallyproducedbyspeciesandlanduseregarding:

Carbonstorage:amountofcarboncurrentlyintheexistingtreestock;

Grossannualcarbonsequestration:one‐yearestimateassequestrationbasedonestimatedannualtreegrowth,whichvariesbylocation,treecondition,andcrowncompetition;and

Netannualcarbonsequestration:grosssequestrationminusestimatedcarbonlostfromdeadorremovedtreesduetodecomposition.

AlteredBuildingEnergyUse.Inadditiontothecarboneffectsestimatedbythefielddatamethod,thei‐TreeprogramcanestimatetreeeffectsonresidentialbuildingenergyuseandconsequentcarbonemissionsusingmethodsdetailedinMcPhersonandSimpson(1999).

MaintenanceEmissions.Forestimatingmaintenanceemissioneffects,thefollowingstepsaresuggested:


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(1)Determinevehicleuserelatedtotreemaintenance.Determinethenumberofmilesdrivenbyvariousvehicletypes.

(2)Calculatecarbonemissionsfromvehicles.Toestimatecarbonemissionsfromvehicles,thelatestfuelefficiencyinformation(mpg)willbeneededforeachvehicleclass.Dividethemilesdrivenbythevehicleclassmpgtodeterminethetotalgallonsofgasoline(orotherfuel)used.Multiplytotalgallons(orotherunits)usedbytheemissionsfactorinTable6‐9toestimatecarbonemissionsfromvehicleuse(Nowaketal.,2002).

Table6‐9:EmissionFactorsforCommonTransportationFuels

Fuel Emissions(lbsCO2perunitvolume)

B20biodiesel 17.71 pergallonB10biodiesel 19.93 pergallonDieselfuel(No.1andNo.2) 22.15 pergallonE85ethanol 2.9 pergallonE10ethanol 17.41 pergallonGasoline 19.36 pergallonNaturalgas 119.90 perMcfPropane 5.74 pergallonSource:Table1.D.1,U.S.DOE(2007).

(3)Determinemaintenanceequipmentuse.Estimatethenumberofrunhoursusedforallfossil‐fuel‐basedmaintenanceequipmentusedontrees(e.g.,chainsaws,chippers,aeriallifts,backhoes,andstumpgrinders).EstimatesofruntimeforvariouspruningandremovalequipmentaregiveninTable6‐10.

Table6‐10:TotalHoursofEquipmentRun‐TimebyDBHClassforTreePruningandRemoval

DBH

Pruning Removal2.3hp

3.7hp

BucketChipperb

2.3hp

3.7hp

7.5hp

BucketChipperb

Stump

Saw Saw Trucka Saw Saw Saw Trucka Grinderb

1–6 0.05 NA NA 0.05 0.3 NA NA 0.2 0.1 0.257–12 0.1 NA 0.2 0.1 0.3 0.2 NA 0.4 0.25 0.3313–18 0.2 NA 0.5 0.2 0.5 0.5 0.1 0.75 0.4 0.519–24 0.5 NA 1.0 0.3 1.5 1.0 0.5 2.2 0.75 0.725–30 1.0 NA 2.0 0.35 1.8 1.5 0.8 3.0 1.0 1.031–36 1.5 0.2 3.0 0.4 2.2 1.8 1.0 5.5 2.0 1.5+36+ 1.5 0.2 4.0 0.4 2.2 2.3 1.5 7.5 2.5 2.0

Note:TableisbasedonACRTdata(WadeandDubish,1995)andassumesthatcrewsworkefficientlyandequipmentisnotrunidle(Nowaketal.,2002).hp = Horsepower DBH = Diameter at breast height a Mean hp = 43 (U.S. EPA, 1991) b Mean hp = 99 (U.S. EPA, 1991)

(4)Calculatecarbonemissionsfrommaintenanceequipment.Calculationsforemissionsfromequipmentarebasedontheformula:


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TypicalloadfactorsandaveragecarbonemissionsforequipmentaregiveninTable6‐11.

Table6‐11:TypicalLoadFactors(U.S.EPA,1991),AverageCarbonEmissions,andTotalCarbonEmissionsforVariousMaintenanceEquipment(fromNowaketal.,2002)

Equipment TypicalLoadFactora

Average CarbonEmission

(ghp−1hr−1)b

TotalCarbonEmission(kghr−1)c

Aeriallift 0.505 147.2 3.2dBackhoe 0.465 147.3 5.3eChainsaw<4hp 0.500 1,264.4 1.5fChainsaw>4hp 0.500 847.5 3.2gChipper/stumpgrinder 0.370 146.4 5.4h

aAveragevaluefromtwoinventories(conservativeloadfactorof0.5frominventoryBwasusedforchainsaws>4hpduetodisparateinventoryestimates;inventoryaverageforthischainsawtypewas0.71).bCalculatedfromestimatesofcarbonmonoxide(U.S.EPA,1991),hydrocarboncrankcaseandexhaust(U.S.EPA,1991),andcarbondioxideemissions(Charmley,1995),adjustedforin‐useeffects.Totalcarbonemissionswerecalculatedbasedontheproportionofcarbonofthetotalatomicweightofthechemicalemission.Multiplyby0.0022toconverttolbshp−1hr−1.cMultiplyby2.2toconverttolbshr−1.dMeanhp=43(U.S.EPA,1991).eMeanhp=77(U.S.EPA,1991).fhp=2.3.ghp=7.5.hMeanhp=99(U.S.EPA,1991).

(5)Calculatetotalmaintenancecarbonemissions.Addresultsofcarbonemissionsfromvehiclesandmaintenanceequipment.

CombinedCarbonSequestration,AlteredBuildingEnergyUse,andMaintenanceEmissions.Todeterminecurrentnetannualurbanforesteffectoncarbon,thecarbonemissionsfromtreemaintenanceshouldbecontrastedtonetcarbonsequestrationfromtreesandalteredcarbonemissionsfromalteredbuildingenergyuseeffects.

ChangesinCarbonSequestration,AlteredBuildingEnergyUse,andMaintenanceEmissions.Todeterminehowtreeandmaintenanceeffectsoncarbonchangethroughtime,thefieldplotsortreesinventoriedcanbere‐measured,andresultsbetweentheyearscontrastedtoestimatechangesincarbonstock,netannualcarboneffects,andalteredbuildingenergyuseeffects.In

Equation6‐10:CalculateCarbon EmissionsfromMaintenanceEquipment

C=N×HRS×HP×LF×E

Where:

C =Carbonemissions(g)

N =Numberofunits(dimensionless)

HRS =Hoursused(hr)

HP =Averageratedhorsepower(hp)

LF =Typicalloadfactor(dimensionless)

E =Averagecarbonemissionsperunitofuse(ghp−1hr−1)(U.S.EPA1991)


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addition,maintenanceactivityestimatesshouldbeupdatedwhenthere‐measurementoccurs.

6.6.3.2 AerialDataMethod

Theaerialdatamethodusesaerialtreecoverestimatesandlookuptablestoprovideamoreapproximate(i.e.,higherdegreeofuncertainty),butlessresourceintensiveestimateofannualcarbonsequestrationinanurbanforestcomparedtothefielddatamethod.Thevariousstepsforestimatingcarboneffectsfromanurbanforestare:

(1)Delimitboundaryofurbanareatobeanalyzed.Thisinformationisessentialtosettheboundaryoftheanalysis.U.S.Censusboundaryfilesofurbanorplacescanbeusedtodelimittheboundaries(U.S.CensusBureau,2011).Informationontheseboundariescanbeusedtodetermineareasofpotentialoverlapwithothercarbonestimates(e.g.,non‐urbanforests).

(2)Conductphotointerpretationoftreecoverinurbanarea.Todeterminepercentageoftreecover,theurbanareacanbephotointerpretedusingi‐TreeCanopy(http://www.itreetools.org/canopy/index.php).Thiswebtoolallowsuserstoimportashapefileof,ormanuallydelimittheirarea,andthenrandomlylocatepointswithintheareaonGoogle®aerialimagery.Theuserthenclassifieseachpointaccordingtoitscoverclass(e.g.,treeornon‐tree).Theprogramproducesestimatesofpercentagecoverandassociatedstandarderrorforthecoverclasses.ThissametypeofanalysiscouldalsobeperformedwithdigitalaerialimagesusingaGeographicInformationSystem.

(3)Estimatetotaltreecoverinurbanarea.Multiplythepercentageoftreecoverandstandarderrorbyurbanarea(ha)toproduceanestimateoftotaltreecoverandstandarderror(ha).Notethati‐TreeCanopywillmakethesecalculations.

(4)Estimatecarboneffects.Multiplytotaltreecover(ha)byaveragecarbonstorageorannualsequestrationperhaoftreecoverinplacesorurbanareas(Table6‐12).i‐TreeCanopywillmakethesecalculationsbasedonaveragestateornationaldata.

Notethattoestimateeffectsformaintenanceemissionsandalteredbuildingenergyusebasedontotaltreecover,atablesimilartoTable6‐12wouldneedtobedevelopedcontainingemissionratesforthesesourcecategories.

Table6‐12:MetricTonsCarbonStorageandAnnualSequestrationperHectareofTreeCoverinSelectedCitiesandUrbanAreasofSelectedStates(fromNowaketal.,2013)

City,StateStorage Sequestration

MetrictonsCha−2 StandardError

MetrictonsCha−2year−1

StandardError

Arlington,TXa 63.7 7.3 2.9 0.28Atlanta,GAa 66.3 5.4 2.3 0.17Baltimore,MDa 87.6 10.9 2.8 0.36Boston,MAa 70.2 9.6 2.3 0.25Casper,WYb 69.7 15.0 2.2 0.39Chicago,ILc 60.3 6.4 2.1 0.21Freehold,NJa 115.0 17.8 3.1 0.45Gainesville,FLd 63.3 9.9 2.2 0.32Golden,COa 58.8 13.3 2.3 0.45Hartford,CTa 108.9 16.2 3.3 0.46JerseyCity,NJa 43.7 8.8 1.8 0.34Lincoln,NEa 106.4 17.4 4.1 0.63LosAngeles,CAe 45.9 5.1 1.8 0.17Milwaukee,WIa 72.6 11.8 2.6 0.33


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City,StateStorage Sequestration

MetrictonsCha−2 StandardError

MetrictonsCha−2year−1

StandardError

Minneapolis,MNf 44.1 7.4 1.6 0.23Moorestown,NJa 99.5 9.3 3.2 0.30Morgantown,WVg 95.2 11.6 3.0 0.37NewYork,NYh 73.3 10.1 2.3 0.29Oakland,CAi 52.4 1.9 na naOmaha,NEa 141.4 22.9 5.1 0.81Philadelphia,PAj 67.7 9.0 2.1 0.27Roanoke,VIa 92.0 13.3 4.0 0.58Sacramento,CAk 78.2 15.7 3.8 0.64SanFrancisco,CAl 91.8 22.5 2.4 0.50Scranton,PAm 92.4 12.8 4.0 0.52Syracuse,NYa 85.9 10.4 2.9 0.30Washington,DCn 85.2 10.4 2.6 0.30Woodbridge,NJa 81.9 8.2 2.9 0.28Indianao 88.0 26.8 2.9 0.77Kansasp 74.2 13.0 2.8 0.48Nebraskap 66.7 18.6 2.7 0.74NorthDakotap 77.8 24.7 2.8 0.79SouthDakotap 30.6 6.6 1.3 0.26Tennesseeq 64.7 5.0 3.4 0.21Average 76.9 13.6 2.8 0.45aUnpublisheddataanalyzedusingtheUFOREmodel.bNowaketal.(2006a).cNowaketal.(2011).dEscobedoetal.(2009).eNowaketal.(2011).fNowaketal.(2006c).gNowaketal.(2012c).hNowaketal.(2007d).iNowak(1991).

j Nowaketal.(2007c).kNowaketal.(Inreview).lNowaketal.(2007b).mNowaketal.(2010).nNowaketal.(2006b).oNowaketal.(2007a).pNowaketal.(2012b).qNowaketal(2012a).

CombinedCarbonSequestration,AlteredBuildingEnergyUse,andMaintenanceEmissions.Todeterminecurrentnetannualurbanforesteffectoncarbon,thecarbonemissionsfromtreemaintenance,ifavailable,shouldbecontrastedtothenetcarbonsequestrationfromtreesandalteredcarbonemissionsfromalteredbuildingenergyuseeffects.

ChangesinCarbonSequestration,AlteredBuildingEnergyUse,andMaintenanceEmissions.Todeterminetreeeffectsoncarbonchangethroughtime,thephoto‐interpretationpointscanbere‐measuredwhennewerphotosbecomeavailabletoassesschangeintreecover(e.g.,NowakandGreenfield,2012).Thei‐TreeCanopyprogramsavesthegeographiccoordinatesofeachpointsothepointscanbere‐measuredinthefuture.Changesintreecoverandassociatedcarboneffectsbetweentheyearscanbecontrastedtoestimatechangesincarbonstockandnetannualcarboneffects.Changesinalteredbuildingenergyuseeffectsandmaintenanceeffectscouldalsobeestimatediftheappropriatetablesaredeveloped.


Fielddatacollectionestimateshavefewerlimitationsthantheaerialapproach,butsomelimitationsexist(Nowaketal.,2008).Themainadvantageofcarbonestimationusingthefielddata


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approachandi‐Treeishavingaccurateestimatesofthetreepopulation(e.g.,species,size,distribution)withacalculatedlevelofprecision.Themodeledcarbonvaluesareestimatesbasedonforest‐derivedallometricequations(Nowak,1994;NowakandCrane,2002).Thecarbonestimatesyieldastandarderroroftheestimatebasedonsamplingerror,ratherthanerrorofestimation.Estimationerrorisunknown,andlikelylargerthanthereportedsamplingerror.Estimationerrorincludestheuncertaintyofusingbiomassequationsandconversionfactors,whichmaybelarge,aswellasmeasurementerror,whichistypicallysmall.Thestandardizedcarbonvalues(e.g.,kgCha−1orlbsC(acreoftreecover)−1)fallinlinewithvaluesforforests(BirdseyandHeath,1995),butvaluesforcities(places)canbehigher(Table6‐12),likelyduetoalargerproportionoflargetreesincityenvironmentsandrelativelyfastgrowthratesduetoamoreopenurbanforeststructure(NowakandCrane,2002).

Therearevariousmeanstohelpimprovethecarbonstorageandsequestrationestimatesforurbantrees.Carbonestimatesforopen‐grownurbantreesareadjusteddownwardbasedonfieldmeasurementsoftreesintheChicagoarea(Nowak,1994).Thisadjustmentmayleadtoconservativeestimatesofcarbon.Moreresearchisneededontheapplicabilityofforest‐derivedequationstourbantrees.Inaddition,moreurbantreegrowthdataareneededtobetterunderstandregionalvariabilityofurbantreegrowthunderdifferingsiteconditions(e.g.,treecompetition)forbetterannualsequestrationestimates.Averageregionalgrowthestimatesareusedbasedonlimitedmeasuredurbantreegrowthdatastandardizedtolengthofgrowingseasonandcrowncompetition.

Therearecurrentlyaverylimitednumberofbiomassequationsfortropicaltreesini‐Tree.Themodelneedstobeupdatedwithtropicaltreebiomassequationsformoreaccurateestimatesintropicalcities.Futureresearchisneededtoobtainbiomassequationsforurbanorornamentaltreespecies.Estimatesoftreedecayandnetannualsequestrationini‐Treearequiterudimentary(Nowaketal.,2010),andcanbeimprovedwithfutureresearch.Thedegreeofuncertaintyofthenetcarbonsequestrationestimatesisunknown.

Estimatesofmaintenanceemissionsandalteredbuildingenergyuseeffectsarealsorathercrude.Accuratemaintenanceemissionsestimatesrequiregoodestimatesofvehicleandmaintenanceequipmentuse;thentheyrelyonanaveragemultiplierforemissionsfromtheliterature.Energyeffectsestimatesarebasedonsamplingproximityoftreesnearbuildingswithinvarioustreesize,distance,anddirectionclassesfromabuilding.Energyfactors,convertedtocarbonemissionfactorsbasedonstateaverageenergydistribution(e.g.,electricity,oil)areappliedtotreesineachbuildinglocationclassbasedonU.S.climatezoneandaveragebuildingtypesinastatetoestimateenergyeffects(seeMcPhersonandSimpson,1999).Thoughtheseestimatesarecrude,withanunknowncertainty,theyarebasedonreasonableapproachesthatprovidefirst‐orderestimatesofeffects.Itshouldbenotedthatemissionreductionsfromalteredbuildingenergyuseeffectsmightalsobeimplicitlyincludedinanyemissionestimationanentitymightperformbasedonactualenergyusedata(e.g.,meterreadings)forthebuildinginquestion.

Estimatesbasedonaerialtreecanopyeffectshavethesamelimitationsasfielddataapproaches,plussomeadditionallimitationsandadvantages.Theadvantagesincludeasimple,quick,andaccuratemeanstoassesstheamountofcanopycoverinanarea,withmeasuresthatarerepeatablethroughtime.Thedisadvantagesarethattheusermustusealookupvaluefromatable(e.g.,meanvalueperunitofcanopycover)toestimatecarboneffects.Thoughthetreecoverestimatewillbeaccuratewithknownuncertainty(i.e.,standarderror),thecarbonmultipliersmaybeoffdependingupontheurbanforestcharacteristics.Ifaveragemultipliersareused,theaccuracyofthoseestimateswilldeclineasthedifferenceincreasesbetweenthelocalurbancharacteristicsandthevaluesoftheaveragemultipliers.Iflocalfielddataarenotcollected,thenthediscrepancybetweentheurbanforest’scharacteristicsandthoseofaveragevaluesisunknown.Iftheaveragevaluesin


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Table6‐12trulyrepresentaverages,theestimatesoveralargepopulationofurbanareasshouldbereliable.However,localestimatesmaybeinaccuratedependingupontheextenttowhichcharacteristicsofthelocalurbanforestdivergefromtheaveragevalues.

Bothapproachescanprovidecarbonestimatesforurbanareas,withdifferingdegreesofuncertaintyandworkrequired.Bothapproachescanalsobeimprovedwithmorefielddatacollectioninurbanareas,andwithmodelandmethodimprovementsrelatedtocarbonestimation.

6.7 NaturalDisturbance–WildfireandPrescribedFire

6.7.1 Description

FireproducesGHGemissionsdirectlythroughfuelconsumption.Emissionsproducedaredirectlyproportionaltofuelconsumed.Fuelconsumptionisinturninfluencedbyfuelquantityandfuelcharacteristicssuchassize,moisturecontent,fireweather,andfireseverity.Algorithmsexistforestimatingfuelconsumptionforavarietyoffueltypesandconditions.Fireandotherdisturbancesalsoconvertlivevegetationtodead,alteringsubsequentcarbondynamicsonthesitebyreducingcarboncapturedbyphotosynthesisintheshortrunduetoreducedvegetativecover,andincreasingemissionsfromdecompositionofdeadvegetation.Fireseverity,whichisdrivenbytheonsitefactorsthatdriveconsumptionaswellasotherphysicalfactors,willdrivethesubsequentcarbondynamicsandareawherereversalofcarbonretentionmayoccur.


Foralldisturbances,keyactivitydataistheareaaffected.Asimpledescriptorisusedtocharacterizetheseverityoftheevent.Forbothwildfireandprescribedfire/controlburns,descriptorsofseverityincludecrownfire,stand‐replacementunderburn,mixed‐severityunderburn(sometreemortality),andlow‐severityunderburn.Typicallywildfirewillbemoreweightedtowardscrownfireandhigherseverityversuslowerseverityfromprescribedfire.Forotherdisturbances,thepercentageoflivetreeskilled(orpercentagebasalareamortality)andthepercentageofkilledtreesthatarestillstandingaswascoveredpreviouslyinSection6.4.2.10andSection6.4.4.8areused.


FOFEM9(Reinhardtetal.,1997)isrecommendedforestimatingGHGemissions,becauseitisapplicablenationally,computercodeisavailablethatcanbelinkedtoorincorporatedintoother

9http://www.firelab.org/science‐applications/fire‐fuel/111‐fofem

MethodsforEmissionsfromNaturalDisturbances

Rangeofoptionsdependsonthedataavailabilityoftheentity’sforestlandincluding:

− FOFEMmodelenteringmeasuredbiomass;and

− FOFEMmodelusingdefaultvaluesgeneratedbyvegetationtype.

TheseoptionsuseReinhardtetal.(1997).

Themethodswereselectedbecausetheyprovidearangeofoptionsdependentonthedataavailabilityoftheentity'sdisturbedforestland.


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code,andinputsaredefinedsothatmeasuredbiomasscanbeenteredordefaultvaluesgeneratedbyvegetationtype.FOFEMproducesdirectestimatesoftotalCO2,CO,CH4,andNOxemitted,aswellasestimatesoffuelconsumptionbycomponent,whichcanbeusedtodetermineresidualfuelquantitiesforestimatingsubsequentdecomposition.FOFEMand/orCONSUME(JointFireScienceProgram,2009)canalsobeuseddirectlytocomputeemissionsandconsumptionfromfire.FOFEMalgorithmscanalsobeusedtocomputetreemortalityinordertoupdateestimatesofliveanddeadbiomass.AlthoughanotheroptionistouseFVS‐FFE10(Rebain,2010;ReinhardtandCrookston,2003),itisnottherecommendedapproachforwildfireGHGcalculation.FVS‐FFEusesmanyofthesameinternalalgorithmsforestimatingtreemortality,fuelconsumption,andemissionsasFOFEM,butalsosimulatesstand,fuel,andcarbondynamicsovertime.Itisamorepowerfulpredictivetool,butsubstantiallymoreworkisinvolvedinunderstandingthemodelingframework,settingupmodelrunsanddatapreparation.Alternatively,lookuptablescanbebuiltusingthesetoolsforarangeofvegetationtypes,fuelloadingsfromnaturaland/ormanagementprocesses,andfireseverities,orasimplifiedalgorithmcanbedevelopedasinthe2006IPCCGuidelinesforNationalGHGInventories(IPCC,2006).

10http://www.fs.fed.us/fmsc/fvs/whatis/index.shtml


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Figure6‐9:DecisionTreeforNaturalDisturbancesShowingMethodsAppropriateforEstimatingEmissionsfromForestFiresDependingontheDataAvailable

6.7.3.1 EstimationofGreenhouseGasEmissionsfromFire

ThecalculationofGHGemissionsfromfirescanbeseeninEquation6‐11below.


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Inordertousethisalgorithm,anestimateofAbyfireseverityisused.ForMB,theunderstory,DDW,andforestfloorareassumedtobeavailableforcombustion.Inaddition,anestimateofwhatportionofthelivetreebiomassisavailableforcombustion(typicallyonlyfoliageandfinebranchwood)isused.ForCf,IPCC(2006)protocolsuse0.45fortemperateforests.SeparatevaluesforCfforbiomasspoolsforcrownfire,stand‐replacementunderburn,mixed‐severityunderburn,andlow‐severityunderburn,byforesttype,usingFOFEMareprovided(seeTable6‐13).ForGefemissionfactorsfromUrbanskietal.(2009)arerecommended:1619g(kgdrymatterburntforCO2)−1,89.6g(kgdrymatterburntforCO)−1,3.4g(kgdrymatterburntforCH4)−1,andfromAkagietal.(2011),2.5g(kgdrymatterburntforNOx)−1.Notethatnotallbiomassisavailableforcombustion;inparticular,standinglivetreebolesarenotavailable.

Forsubsequenteffects,theGHGestimationmethodsadoptedshouldmatchascloselyaspossiblethoseusedinothersections(e.g.,HWPs).Decompositionofdeadmaterialovertimewillbeprojectedusingafixedannuallossrate.Theconversionofstandingdeadtodead‐and‐downshouldalsobeprojectedusingafixedrateandapproximatingthemethodsinFVS‐FFE.

GHGemissionsfromnaturaldisturbancewildfiresandprescribedfiresusedforsitemaintenanceandrestorationshouldbereportedseparatelyfromemissionsresultingfrommanagement(siteswiththinningslash,machineorhandpiles,orloggingslash)tofacilitatetheuseoftheestimatesindecisionmakingregardingmanagementpractices.

Table6‐13showsanexampleforthedefaultlookuptablesforconsumptionfraction(Cf).RegionsarethoseshowninTable6‐13,withtheexceptionoftheWestregion,whichrepresentsanaverageofallwesternregions.

Table6‐13:CfConsumptionFraction

Region ForestTypeCfCrownFire

Cf StandReplacementUnderburn

CfMixedSeverity

Cf LowSeverity

Underburn

%

Northeast

Aspen–birch 84 69 59 45Elm–ash–cottonwood 74 47 35 20Maple–beech–birch 77 60 44 35Oak–hickory 63 49 41 32Oak–pine 80 61 50 38Spruce–fir 73 73 69 62White–red–jackpine 55 45 37 26

Equation6‐11:CalculateGHGEmissionsfromFire

Lfire=A×MB×Cf×Gef×10−3

Where:

Lfire =Amountofgreenhousegasemissionsfromfire(metrictonsofeachGHG,i.e.,CH4,N2O,etc.)

A =Areaburned(ha)

MB =Massoffuelavailableforcombustion(metrictonsha−1)

Cf =Combustionfactor(dimensionless)

Gef =Emissionfactor(g(kgdrymatterburnt)−1)


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Region ForestTypeCfCrownFire

Cf StandReplacementUnderburn

CfMixedSeverity

Cf LowSeverity

Underburn

%

NorthernLakeStates

Aspen–birch 84 69 59 45Elm–ash–cottonwood 74 47 35 20Maple–beech–birch 77 60 44 35Oak–hickory 80 61 50 38Spruce–fir 73 73 69 62White–red–jackpine 55 45 37 26

NorthernPrairieStates

Elm–ash–cottonwood 74 47 35 20Maple–beech–birch 77 60 44 35Oak–hickory 80 61 50 38Ponderosapine 60 53 47 37

PacificNorthwest,East

Douglasfir 85 79 72 60Fir–spruce–m.hemlock 67 64 58 44Lodgepolepine 77 72 64 52Ponderosapine 78 53 41 27

PacificNorthwest,West

Alder–maple 82 67 48 42Douglasfir 71 62 55 43Fir–spruce–m.hemlock 67 64 58 44Hemlock–Sitkaspruce 85 77 69 55

PacificSouthwest

Mixedconifer 79 69 50 46Douglasfir 66 42 30 17Fir–spruce–m.hemlock 67 64 58 44PonderosaPine 78 53 41 27Redwood 82 76 69 56

RockyMountain,NorthandSouth

Aspen–birch 80 61 50 35Douglasfir 85 79 72 60Fir–spruce–m.hemlock 67 64 58 44Lodgepolepine 77 72 64 52Ponderosapine 78 53 41 27Mixedconifer 79 69 50 46

Southeast

Elm–ash–cottonwood 76 45 29 19Loblolly–shortleafpine 66 52 44 35Oak–hickory 61 50 44 36Oak–pine 62 55 51 45

SouthCentral

Elm–ash–cottonwood 76 45 29 19Loblolly–shortleafpine 66 52 44 35Longleaf–slashpine 69 63 57 47Oak–hickory 61 50 44 36Oak–pine 62 55 51 45

Westa

Pinyon–juniper 64 55 49 41Tanoak–laurel 70 52 43 32Westernlarch 76 68 60 47Westernoak 65 62 56 48Westernwhitepine 68 56 47 33

aRepresentsanaverageoverallwesternregionsforthespecifiedforesttypes(PNW‐W,PNW‐E,PSW,RMN,RMS).


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6.7.3.2 EstimationofGreenhouseGasEmissionsfromOtherDisturbances

Forotherdisturbances,theprimaryeffectsareindirect:byconvertinglivebiomasstodead—andinsomecasesstandingtreestodead,downtrees—decompositionisaccelerated.Currentlygroupingnon‐firedisturbanceintotwocategoriesissuggested:disturbancesthatleavedeadtreesstanding(insectanddisease‐causedmortality)anddisturbancethatleavesthetreesontheground(windoricestorms).Thelandownerwillhavetoestimatemortality(Section6.7.2);thenasindecompositionoffire‐killedtrees,afixeddecompositionrate(defaultvalue0.015)willbeusedtosimulatesubsequentdecomposition.

Forinsectorpathogen‐causedmortality,thetreesareassumedtobeinitiallystandingafterdeath.Conversionofstandingdeadtodead‐and‐downwillbeprojectedusingafixedrateandapproximatingthemethodsinFVS‐FEE.Oncedown,thedefaultdecompositionratefromFVS‐FFEof0.015fordeadanddownwoodwillbeusedtosimulatedecomposition.Forblowdownsoricestorms,theimpactedtreesareassumedtobedeadanddown.Inthiscasedecompositionbeginsimmediately.


Amajorsourceofuncertaintyinpredictingfireemissionsisthepreburnfuelquantities.Iflandownersaredoingsomekindofinventoryofliveanddeadbiomass(seeSection6.7.2)theywillhaverelativelyrobustestimatesofavailablefuel.Iftheyareusinglookuptablevaluesbyforesttype,therewillbemoreuncertaintyassociatedwiththeestimatessincefuelquantitiesvarygreatlywithinforesttype.

Arelatedchallengeisdeterminingtheappropriatedegreeofspecificityfortrackingbiomassbypools(e.g.,live,dead).Anykindofmanagementordisturbancechangesbiomassatthetimeofoccurrence,andalsothesubsequenttrajectory.Subsequentmanagementordisturbanceshouldbeappliedtothechangedandchangingvalues,nottheoriginalvalues.ThiscanresultinacomplicatedsimulationmodellikeFVS,ratherthanacalculator.Sinceprefirefuelquantityisthestrongestpredictoroffuelconsumption,determiningtheappropriatedegreeofspecificityfortrackingbiomassbypoolsisnotacompletelyacademicquestion.


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Appendix6‐A:HarvestedWoodProductsLookupTables

Table6‐A‐1:FactorstoConvertPrimaryWoodProductstoCarbonMassfromtheUnitsCharacteristicofEachProduct

SolidwoodProductorPaper UnitFactortoConvertUnitstoTons(2,000lbs)C

FactortoConvertUnitstoMetricTonsC

Softwoodlumber/laminatedveneerlumber/glulamlumber/I‐joists

Thousandboardfeet 0.488 0.443

Hardwoodlumber Thousandboardfeet 0.844 0.765

Softwoodplywood Thousandsquarefeet,3/8‐inchbasis

0.260 0.236

Orientedstrandboard Thousandsquarefeet,3/8‐inchbasis

0.303 0.275

Non‐structuralpanels(average)Thousandsquarefeet,3/8‐inchbasis 0.319 0.289

Hardwoodveneer/plywoodThousandsquarefeet,3/8‐inchbasis 0.315 0.286

Particleboard/mediumdensityfiberboard

Thousandsquarefeet,3/4‐inchbasis 0.647 0.587

HardboardThousandsquarefeet,1/8‐inchbasis

0.152 0.138

InsulationboardThousandsquarefeet,1/2‐inchbasis

0.242 0.220

Otherindustrialproducts Thousandcubicfeet 8.250 7.484Paper Tons,airdry 0.450 0.496


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Table6‐A‐2:FractionofCarboninPrimaryWoodProductsRemaininginEndUsesupto100YearsAfterProduction(year0indicatesfractionattimeofproduction)

YearafterProduction

SoftwoodLumber

HardwoodLumber

SoftwoodPlywood

OrientedStrandboard

Non‐StructuralPanels

Misc.Products Paper

0 1.000 1.000 1.000 1.000 1.000 1.000 1.0001 0.908 0.909 0.908 0.908 0.908 0.903 0.8802 0.892 0.893 0.893 0.896 0.892 0.887 0.7753 0.877 0.877 0.878 0.884 0.876 0.871 0.6824 0.863 0.861 0.863 0.872 0.861 0.855 0.6005 0.848 0.845 0.848 0.860 0.845 0.840 0.5286 0.834 0.830 0.834 0.848 0.830 0.825 0.4657 0.820 0.815 0.820 0.837 0.816 0.810 0.3548 0.806 0.801 0.807 0.826 0.801 0.795 0.2699 0.793 0.786 0.794 0.815 0.787 0.781 0.20510 0.780 0.772 0.781 0.804 0.774 0.767 0.15615 0.718 0.705 0.719 0.753 0.708 0.700 0.04020 0.662 0.644 0.663 0.706 0.649 0.639 0.01025 0.611 0.589 0.613 0.662 0.595 0.583 0.00330 0.565 0.538 0.567 0.622 0.546 0.532 0.00135 0.523 0.492 0.525 0.585 0.501 0.486 0.00040 0.485 0.450 0.487 0.551 0.460 0.444 0.00045 0.450 0.411 0.452 0.519 0.423 0.405 0.00050 0.418 0.376 0.420 0.490 0.389 0.370 0.00055 0.389 0.344 0.391 0.462 0.358 0.338 0.00060 0.362 0.315 0.364 0.437 0.329 0.308 0.00065 0.338 0.288 0.340 0.413 0.303 0.281 0.00070 0.315 0.264 0.317 0.391 0.280 0.257 0.00075 0.294 0.242 0.296 0.370 0.258 0.234 0.00080 0.276 0.221 0.277 0.351 0.238 0.214 0.00085 0.258 0.203 0.260 0.333 0.220 0.195 0.00090 0.242 0.186 0.244 0.316 0.203 0.178 0.00095 0.227 0.170 0.229 0.300 0.188 0.163 0.000100 0.213 0.156 0.215 0.285 0.174 0.149 0.000Average 0.466 0.430 0.468 0.526 0.441 0.424 0.059


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Table6‐A‐3:FractionofCarboninPrimaryWoodProductsRemaininginLandfillsupto100YearsafterProduction(year0indicatesfractionattimeofproduction)

YearafterProductio

n

Softwood

Lumber

HardwoodLumber

SoftwoodPlywood

OrientedStrandboar

d

Non‐StructuralPanels

Misc.Products Paper

0 0.000 0.000 0.000 0.000 0.000 0.000 0.0001 0.061 0.060 0.061 0.061 0.061 0.064 0.0402 0.071 0.070 0.071 0.068 0.071 0.074 0.0733 0.080 0.080 0.080 0.076 0.081 0.084 0.1024 0.089 0.090 0.089 0.083 0.090 0.094 0.1275 0.098 0.099 0.097 0.090 0.099 0.103 0.1476 0.106 0.109 0.106 0.097 0.108 0.112 0.1647 0.114 0.117 0.114 0.103 0.117 0.121 0.1978 0.122 0.126 0.122 0.110 0.125 0.129 0.2209 0.130 0.134 0.130 0.116 0.134 0.138 0.23610 0.138 0.143 0.137 0.122 0.142 0.146 0.24715 0.173 0.181 0.172 0.151 0.179 0.184 0.25620 0.203 0.214 0.202 0.176 0.211 0.217 0.24125 0.230 0.243 0.229 0.199 0.239 0.246 0.22330 0.253 0.269 0.252 0.220 0.265 0.272 0.20735 0.274 0.292 0.273 0.238 0.287 0.296 0.19540 0.293 0.313 0.292 0.255 0.307 0.316 0.18545 0.310 0.332 0.308 0.271 0.325 0.335 0.17750 0.325 0.348 0.324 0.285 0.341 0.352 0.17155 0.338 0.363 0.337 0.298 0.356 0.367 0.16660 0.351 0.377 0.349 0.310 0.369 0.380 0.16365 0.362 0.389 0.361 0.321 0.381 0.393 0.16070 0.372 0.400 0.371 0.331 0.391 0.404 0.15875 0.381 0.410 0.380 0.341 0.401 0.414 0.15680 0.390 0.419 0.389 0.350 0.410 0.423 0.15485 0.398 0.427 0.397 0.359 0.418 0.431 0.15390 0.405 0.435 0.404 0.366 0.426 0.439 0.15395 0.412 0.442 0.411 0.374 0.432 0.446 0.152100 0.418 0.448 0.417 0.381 0.438 0.452 0.151Average 0.297 0.317 0.296 0.264 0.311 0.321 0.178


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Table6‐A‐4:DensityofSoftwoodandHardwoodSawlogs/VeneerLogsandPulpwoodbyRegionandForestTypeGroupa

Region ForesttypeSpecific Gravitydof

SoftwoodsSpecificGravitydof

Hardwoods

Northeast

Aspen–birch 0.353 0.428Elm–ash–cottonwood 0.358 0.470Maple–beech–birch 0.369 0.518Oak–hickory 0.388 0.534Oak–pine 0.371 0.516Spruce–fir 0.353 0.481White–red–jackpine 0.361 0.510

NorthernLakeStates

Aspen–birch 0.351 0.397Elm–ash–cottonwood 0.335 0.460Maple–beech–birch 0.356 0.496Oak–hickory 0.369 0.534Spruce–fir 0.344 0.444White–red–jackpine 0.389 0.473

NorthernPrairieStates

Elm–ash–cottonwood 0.424 0.453Loblolly–shortleafpine 0.468 0.544Maple–beech–birch 0.437 0.508Oak–hickory 0.448 0.565Oak–pine 0.451 0.566Ponderosapine 0.381 0.473

PacificNorthwest,East

Douglasfir 0.429 0.391Fir–spruce–m.hemlock 0.370 0.361Lodgepolepine 0.380 0.345Ponderosapine 0.385 0.513

PacificNorthwest,West

Alder–maple 0.402 0.385Douglasfir 0.440 0.426Fir–spruce–m.hemlock 0.399 0.417Hemlock–Sitkaspruce 0.405 0.380

PacificSouthwest

Mixedconifer 0.394 0.521Douglasfir 0.429 0.483Fir–spruce–m.hemlock 0.372 0.510PonderosaPine 0.380 0.510Redwood 0.376 0.449

RockyMountain,North

Douglasfir 0.428 0.370Fir–spruce–m.hemlock 0.355 0.457Hemlock–sitkaspruce 0.375 0.441Lodgepolepine 0.383 0.391Ponderosapine 0.391 0.374

RockyMountain,South

Aspen–birch 0.355 0.350Douglasfir 0.431 0.350Fir–spruce–m.hemlock 0.342 0.350Lodgepolepine 0.377 0.350Ponderosapine 0.383 0.386


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Region ForesttypeSpecific Gravitydof

SoftwoodsSpecificGravitydof

Hardwoods

Southeast

Elm–ash–cottonwood 0.433 0.499Loblolly–shortleafpine 0.469 0.494Longleaf–slashpine 0.536 0.503Oak–gum–cypress 0.441 0.484Oak–hickory 0.438 0.524Oak–pine 0.462 0.516

SouthCentral

Elm–ash–cottonwood 0.427 0.494Loblolly–shortleafpine 0.470 0.516Longleaf–slashpine 0.531 0.504Oak–gum–cypress 0.440 0.513Oak–hickory 0.451 0.544Oak–pine 0.467 0.537

Weste

Pinyon–juniper 0.422 0.620Tanoak–laurel 0.430 0.459Westernlarch 0.433 0.430Westernoak 0.416 0.590Westernwhitepine 0.376 ‐‐

‐‐=Nohardwoodtreesinthistypeinthisregion.aEstimatesbasedonsurveydatafortheconterminousUnitedStatesfromUSDAForestService,FIAProgram’sdatabaseofforestsurveys(FIADB)(USDAForestService,2005)andincludegrowingstockontimberlandstandsclassifiedasmedium‐orlarge‐diameterstands.Proportionsarebasedonvolumeofgrowingstocktrees.dAveragewoodspecificgravityisthedensityofwooddividedbythedensityofwaterbasedonwooddrymassassociatedwithgreentreevolume.eWestrepresentsanaverageoverallwesternregionsfortheseforesttypes.


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Table6‐A‐5:AverageDispositionPatternsofCarbon asFractions inRoundwoodby RegionandRoundwoodCategory;FactorsAssumeNoBarkonRoundwoodandExcludeFuelwood

YearafterProduction

Northeast,Softwood

InUse

SawlogTotal

Emissions InUse

PulpwoodTotal

EmissionsInLandfills

TotalStored

InLandfills

TotalStored

0 0.569 0.000 0.569 0.431 0.513 0.000 0.513 0.4871 0.521 0.029 0.550 0.450 0.452 0.021 0.473 0.5272 0.505 0.037 0.542 0.458 0.400 0.038 0.438 0.5623 0.491 0.044 0.535 0.465 0.355 0.052 0.407 0.5934 0.478 0.050 0.528 0.472 0.315 0.064 0.379 0.6215 0.465 0.056 0.522 0.478 0.279 0.074 0.354 0.6466 0.453 0.062 0.516 0.484 0.248 0.083 0.331 0.6697 0.438 0.069 0.507 0.493 0.193 0.099 0.293 0.7078 0.425 0.075 0.500 0.500 0.152 0.111 0.263 0.7379 0.414 0.080 0.494 0.506 0.120 0.119 0.239 0.76110 0.403 0.085 0.489 0.511 0.096 0.124 0.220 0.78015 0.363 0.105 0.468 0.532 0.038 0.130 0.168 0.83220 0.332 0.121 0.453 0.547 0.022 0.124 0.146 0.85425 0.306 0.134 0.440 0.560 0.017 0.116 0.133 0.86730 0.282 0.146 0.428 0.572 0.015 0.109 0.124 0.87635 0.260 0.156 0.417 0.583 0.014 0.103 0.117 0.88340 0.240 0.166 0.406 0.594 0.013 0.099 0.111 0.88945 0.222 0.174 0.397 0.603 0.012 0.095 0.107 0.89350 0.206 0.182 0.388 0.612 0.011 0.093 0.104 0.89655 0.191 0.189 0.380 0.620 0.010 0.091 0.101 0.89960 0.177 0.195 0.372 0.628 0.009 0.089 0.099 0.90165 0.165 0.201 0.365 0.635 0.009 0.088 0.097 0.90370 0.153 0.206 0.359 0.641 0.008 0.087 0.095 0.90575 0.143 0.210 0.353 0.647 0.008 0.086 0.094 0.90680 0.133 0.214 0.347 0.653 0.007 0.086 0.093 0.90785 0.124 0.218 0.342 0.658 0.007 0.085 0.092 0.90890 0.116 0.221 0.337 0.663 0.006 0.085 0.091 0.90995 0.108 0.224 0.332 0.668 0.006 0.085 0.091 0.909100 0.101 0.227 0.328 0.672 0.006 0.085 0.090 0.910Average 0.235 0.166 0.402 0.041 0.095 0.136


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Table6‐A‐5—continued

YearafterProduction

Northeast,Hardwood

InUseSawlog

TotalEmissions

InUse

PulpwoodTotal


TotalStored

InLandfills

TotalStored

0 0.614 0.000 0.614 0.386 0.650 0.000 0.650 0.3501 0.559 0.034 0.594 0.406 0.580 0.032 0.613 0.3872 0.544 0.042 0.586 0.414 0.540 0.046 0.586 0.4143 0.530 0.049 0.579 0.421 0.503 0.059 0.562 0.4384 0.516 0.056 0.573 0.427 0.471 0.070 0.541 0.4595 0.504 0.063 0.567 0.433 0.443 0.079 0.522 0.4786 0.491 0.069 0.561 0.439 0.417 0.087 0.504 0.4967 0.477 0.076 0.553 0.447 0.374 0.101 0.475 0.5258 0.463 0.083 0.546 0.454 0.341 0.111 0.453 0.5479 0.452 0.089 0.540 0.460 0.316 0.119 0.434 0.56610 0.441 0.094 0.535 0.465 0.295 0.125 0.420 0.58015 0.397 0.117 0.514 0.486 0.239 0.137 0.376 0.62420 0.361 0.136 0.497 0.503 0.215 0.140 0.355 0.64525 0.330 0.152 0.482 0.518 0.199 0.141 0.340 0.66030 0.301 0.167 0.468 0.532 0.186 0.142 0.328 0.67235 0.275 0.180 0.455 0.545 0.175 0.144 0.319 0.68140 0.252 0.192 0.444 0.556 0.164 0.146 0.310 0.69045 0.230 0.202 0.432 0.568 0.155 0.148 0.302 0.69850 0.211 0.211 0.422 0.578 0.146 0.150 0.296 0.70455 0.193 0.220 0.412 0.588 0.138 0.152 0.290 0.71060 0.176 0.227 0.403 0.597 0.130 0.154 0.285 0.71565 0.162 0.234 0.395 0.605 0.123 0.157 0.280 0.72070 0.148 0.240 0.388 0.612 0.116 0.159 0.275 0.72575 0.136 0.245 0.380 0.620 0.110 0.161 0.271 0.72980 0.124 0.250 0.374 0.626 0.104 0.163 0.268 0.73285 0.114 0.254 0.368 0.632 0.099 0.165 0.264 0.73690 0.104 0.258 0.362 0.638 0.094 0.167 0.261 0.73995 0.096 0.261 0.357 0.643 0.089 0.169 0.258 0.742100 0.088 0.264 0.352 0.648 0.085 0.171 0.255 0.745Average 0.244 0.192 0.437 0.178 0.145 0.323


6-95


YearafterProduction

NorthCentral,Softwood

InUse

Sawlog

TotalEmissions

InUse

Pulpwood

TotalEmissionsIn

LandfillsTotalStored

InLandfills

TotalStored

0 0.630 0.000 0.630 0.370 0.514 0.000 0.514 0.4861 0.579 0.031 0.610 0.390 0.454 0.021 0.475 0.5252 0.561 0.039 0.601 0.399 0.402 0.038 0.440 0.5603 0.545 0.047 0.592 0.408 0.357 0.052 0.409 0.5914 0.530 0.055 0.585 0.415 0.317 0.064 0.381 0.6195 0.516 0.062 0.577 0.423 0.281 0.074 0.356 0.6446 0.502 0.068 0.570 0.430 0.250 0.083 0.333 0.6677 0.485 0.076 0.561 0.439 0.196 0.099 0.295 0.7058 0.470 0.083 0.553 0.447 0.154 0.111 0.265 0.7359 0.457 0.089 0.546 0.454 0.123 0.119 0.241 0.75910 0.446 0.094 0.540 0.460 0.098 0.124 0.223 0.77715 0.401 0.116 0.517 0.483 0.041 0.130 0.171 0.82920 0.366 0.133 0.500 0.500 0.025 0.124 0.148 0.85225 0.336 0.148 0.485 0.515 0.020 0.116 0.135 0.86530 0.310 0.162 0.471 0.529 0.018 0.109 0.126 0.87435 0.286 0.173 0.459 0.541 0.016 0.103 0.120 0.88040 0.264 0.184 0.447 0.553 0.015 0.099 0.114 0.88645 0.243 0.193 0.437 0.563 0.014 0.096 0.110 0.89050 0.225 0.202 0.427 0.573 0.013 0.093 0.106 0.89455 0.208 0.209 0.418 0.582 0.012 0.091 0.103 0.89760 0.193 0.216 0.409 0.591 0.012 0.089 0.101 0.89965 0.179 0.222 0.401 0.599 0.011 0.088 0.099 0.90170 0.166 0.228 0.394 0.606 0.010 0.087 0.098 0.90275 0.154 0.233 0.387 0.613 0.010 0.087 0.097 0.90380 0.144 0.237 0.381 0.619 0.009 0.086 0.095 0.90585 0.134 0.242 0.375 0.625 0.009 0.086 0.095 0.90590 0.125 0.245 0.370 0.630 0.008 0.086 0.094 0.90695 0.116 0.249 0.365 0.635 0.008 0.086 0.093 0.907100 0.108 0.252 0.360 0.640 0.007 0.086 0.093 0.907Average 0.258 0.184 0.442 0.043 0.095 0.138


6-96


YearafterProduction

NorthCentral,Hardwood

InUseSawlog

TotalEmissions

InUse

PulpwoodTotal


TotalStored

InLandfills

TotalStored

0 0.585 0.000 0.585 0.415 0.685 0.000 0.685 0.3151 0.533 0.032 0.565 0.435 0.613 0.035 0.648 0.3522 0.518 0.040 0.558 0.442 0.575 0.049 0.624 0.3763 0.504 0.047 0.550 0.450 0.541 0.061 0.602 0.3984 0.490 0.054 0.544 0.456 0.511 0.071 0.582 0.4185 0.477 0.060 0.537 0.463 0.484 0.080 0.565 0.4356 0.465 0.066 0.531 0.469 0.460 0.089 0.548 0.4527 0.450 0.073 0.523 0.477 0.421 0.101 0.522 0.4788 0.437 0.080 0.517 0.483 0.390 0.111 0.501 0.4999 0.425 0.085 0.511 0.489 0.365 0.119 0.484 0.51610 0.415 0.090 0.505 0.495 0.346 0.125 0.471 0.52915 0.372 0.112 0.484 0.516 0.290 0.139 0.429 0.57120 0.339 0.130 0.468 0.532 0.263 0.144 0.408 0.59225 0.309 0.145 0.454 0.546 0.245 0.148 0.393 0.60730 0.282 0.158 0.441 0.559 0.229 0.151 0.380 0.62035 0.258 0.170 0.428 0.572 0.216 0.154 0.370 0.63040 0.236 0.181 0.417 0.583 0.203 0.158 0.360 0.64045 0.216 0.191 0.407 0.593 0.191 0.161 0.352 0.64850 0.197 0.199 0.397 0.603 0.180 0.165 0.345 0.65555 0.181 0.207 0.388 0.612 0.170 0.168 0.338 0.66260 0.165 0.214 0.379 0.621 0.160 0.171 0.332 0.66865 0.151 0.220 0.372 0.628 0.152 0.174 0.326 0.67470 0.138 0.226 0.364 0.636 0.143 0.178 0.321 0.67975 0.127 0.231 0.358 0.642 0.136 0.180 0.316 0.68480 0.116 0.235 0.351 0.649 0.129 0.183 0.312 0.68885 0.106 0.239 0.346 0.654 0.122 0.186 0.308 0.69290 0.098 0.243 0.340 0.660 0.116 0.188 0.304 0.69695 0.089 0.246 0.336 0.664 0.110 0.191 0.300 0.700100 0.082 0.249 0.331 0.669 0.104 0.193 0.297 0.703Average 0.229 0.182 0.411 0.212 0.158 0.370


6-97


YearafterProduction

PacificNorthwest,East,Softwood

InUse

AllTotal

Emissions

InLandfills

TotalStored

0 0.637 0.000 0.637 0.363 1 0.574 0.036 0.610 0.390 2 0.551 0.046 0.597 0.403 3 0.530 0.055 0.585 0.415 4 0.511 0.063 0.574 0.426 5 0.494 0.070 0.564 0.436 6 0.478 0.077 0.555 0.445 7 0.455 0.086 0.541 0.459 8 0.436 0.093 0.529 0.471 9 0.420 0.100 0.520 0.480 10 0.406 0.105 0.512 0.488 15 0.359 0.125 0.484 0.516 20 0.327 0.139 0.466 0.534 25 0.301 0.150 0.451 0.549 30 0.278 0.160 0.438 0.562 35 0.258 0.169 0.427 0.573 40 0.239 0.177 0.416 0.584 45 0.222 0.185 0.406 0.594 50 0.206 0.191 0.397 0.603 55 0.191 0.198 0.389 0.611 60 0.178 0.203 0.381 0.619 65 0.166 0.208 0.374 0.626 70 0.155 0.213 0.368 0.632 75 0.145 0.217 0.362 0.638 80 0.136 0.221 0.356 0.644 85 0.127 0.224 0.351 0.649 90 0.119 0.227 0.347 0.653 95 0.112 0.230 0.342 0.658 100 0.105 0.233 0.338 0.662 Average 0.238 0.177 0.415


6-98


YearafterProduction

PacificNorthwest,West,Softwoods

InUse

SawlogTotal

Emissions InUse

PulpwoodTotal


TotalStored

InLandfills

TotalStored

0 0.740 0.000 0.740 0.260 0.500 0.000 0.500 0.500

1 0.674 0.039 0.713 0.287 0.440 0.020 0.460 0.540

2 0.652 0.049 0.702 0.298 0.387 0.037 0.424 0.576

3 0.632 0.059 0.691 0.309 0.341 0.051 0.392 0.608

4 0.613 0.068 0.681 0.319 0.300 0.063 0.364 0.636

5 0.596 0.076 0.672 0.328 0.264 0.074 0.338 0.662

6 0.579 0.083 0.663 0.337 0.233 0.082 0.315 0.685

7 0.558 0.093 0.651 0.349 0.177 0.099 0.276 0.724

8 0.539 0.101 0.640 0.360 0.134 0.111 0.245 0.755

9 0.524 0.108 0.631 0.369 0.102 0.119 0.221 0.779

10 0.510 0.114 0.624 0.376 0.078 0.124 0.202 0.798

15 0.457 0.139 0.596 0.404 0.020 0.129 0.149 0.851

20 0.418 0.158 0.576 0.424 0.005 0.122 0.127 0.873

25 0.384 0.174 0.558 0.442 0.001 0.113 0.114 0.886

30 0.355 0.188 0.543 0.457 0 0.105 0.105 0.895

35 0.328 0.201 0.529 0.471 0 0.098 0.099 0.901

40 0.303 0.213 0.516 0.484 0 0.093 0.093 0.907

45 0.281 0.223 0.504 0.496 0 0.090 0.090 0.910

50 0.260 0.232 0.493 0.507 0 0.086 0.086 0.914

55 0.242 0.241 0.482 0.518 0 0.084 0.084 0.916

60 0.224 0.248 0.473 0.527 0 0.082 0.082 0.918

65 0.209 0.255 0.464 0.536 0 0.080 0.080 0.920

70 0.194 0.261 0.456 0.544 0 0.079 0.079 0.921

75 0.181 0.267 0.448 0.552 0 0.078 0.078 0.922

80 0.169 0.272 0.441 0.559 0 0.078 0.078 0.922

85 0.158 0.276 0.434 0.566 0 0.077 0.077 0.923

90 0.148 0.281 0.428 0.572 0 0.077 0.077 0.923

95 0.138 0.285 0.423 0.577 0 0.076 0.076 0.924

100 0.129 0.288 0.417 0.583 0 0.076 0.076 0.924

Average 0.298 0.213 0.511 0.030 0.090 0.119


6-99


YearafterProduction

PacificNorthwest,West,Hardwood PacificSouthwest,Softwood

InUseAll

TotalEmissions

InUse

AllTotal


TotalStored

InLandfills

TotalStored

0 0.531 0.000 0.531 0.469 0.675 0.000 0.675 0.325

1 0.476 0.027 0.503 0.497 0.611 0.036 0.647 0.353

2 0.447 0.038 0.485 0.515 0.587 0.047 0.634 0.366

3 0.421 0.048 0.469 0.531 0.566 0.056 0.622 0.378

4 0.397 0.057 0.454 0.546 0.546 0.065 0.611 0.389

5 0.376 0.064 0.440 0.560 0.528 0.072 0.600 0.400

6 0.357 0.071 0.428 0.572 0.511 0.080 0.591 0.409

7 0.327 0.081 0.408 0.592 0.488 0.089 0.577 0.423

8 0.303 0.089 0.393 0.607 0.468 0.097 0.565 0.435

9 0.284 0.096 0.380 0.620 0.451 0.104 0.555 0.445

10 0.269 0.101 0.369 0.631 0.437 0.110 0.547 0.453

15 0.222 0.115 0.337 0.663 0.387 0.131 0.518 0.482

20 0.197 0.122 0.319 0.681 0.353 0.146 0.499 0.501

25 0.179 0.127 0.306 0.694 0.324 0.159 0.483 0.517

30 0.164 0.132 0.295 0.705 0.299 0.170 0.469 0.531

35 0.150 0.136 0.286 0.714 0.276 0.180 0.457 0.543

40 0.137 0.140 0.278 0.722 0.256 0.190 0.445 0.555

45 0.126 0.144 0.270 0.730 0.237 0.198 0.435 0.565

50 0.115 0.148 0.263 0.737 0.220 0.205 0.425 0.575

55 0.106 0.151 0.257 0.743 0.204 0.212 0.416 0.584

60 0.097 0.155 0.252 0.748 0.189 0.218 0.408 0.592

65 0.089 0.157 0.247 0.753 0.176 0.224 0.400 0.600

70 0.082 0.160 0.242 0.758 0.164 0.229 0.393 0.607

75 0.075 0.163 0.238 0.762 0.153 0.233 0.387 0.613

80 0.069 0.165 0.234 0.766 0.143 0.238 0.381 0.619

85 0.064 0.167 0.231 0.769 0.133 0.241 0.375 0.625

90 0.059 0.169 0.227 0.773 0.125 0.245 0.370 0.630

95 0.054 0.171 0.224 0.776 0.117 0.248 0.365 0.635

100 0.050 0.172 0.222 0.778 0.109 0.251 0.361 0.639

Average 0.145 0.139 0.284 0.254 0.190 0.444


6-100


YearafterProduction

RockyMountain,Softwood

InUse

All Total

Emissions

In


0 0.704 0.000 0.704 0.296 1 0.640 0.037 0.677 0.323 2 0.615 0.048 0.663 0.337 3 0.592 0.057 0.650 0.350 4 0.572 0.066 0.638 0.362 5 0.552 0.075 0.627 0.373 6 0.535 0.082 0.617 0.383 7 0.510 0.092 0.602 0.398 8 0.489 0.101 0.590 0.410 9 0.472 0.108 0.579 0.421 10 0.457 0.114 0.571 0.429 15 0.404 0.136 0.540 0.460 20 0.368 0.152 0.520 0.480 25 0.338 0.166 0.504 0.496 30 0.312 0.177 0.489 0.511 35 0.288 0.188 0.476 0.524 40 0.266 0.198 0.464 0.536 45 0.247 0.206 0.453 0.547 50 0.229 0.214 0.443 0.557 55 0.212 0.221 0.433 0.567 60 0.197 0.228 0.425 0.575 65 0.183 0.234 0.417 0.583 70 0.170 0.239 0.409 0.591 75 0.159 0.244 0.403 0.597 80 0.148 0.248 0.396 0.604 85 0.138 0.252 0.390 0.610 90 0.129 0.256 0.385 0.615 95 0.121 0.259 0.380 0.620 100 0.113 0.262 0.375 0.625 Average 0.265 0.198 0.463


6-101


YearafterProduction

Southeast,Softwood

InUseSawlog

TotalEmissions

InUse

PulpwoodTotal


TotalStored

InLandfills

TotalStored

0 0.636 0.000 0.636 0.364 0.553 0.000 0.553 0.447

1 0.578 0.034 0.612 0.388 0.490 0.024 0.514 0.486

2 0.557 0.043 0.600 0.400 0.442 0.040 0.482 0.518

3 0.537 0.052 0.589 0.411 0.399 0.054 0.453 0.547

4 0.519 0.060 0.578 0.422 0.361 0.066 0.427 0.573

5 0.502 0.067 0.569 0.431 0.328 0.076 0.403 0.597

6 0.486 0.074 0.560 0.440 0.298 0.084 0.382 0.618

7 0.465 0.083 0.547 0.453 0.247 0.100 0.347 0.653

8 0.447 0.090 0.537 0.463 0.208 0.111 0.319 0.681

9 0.432 0.096 0.528 0.472 0.178 0.119 0.297 0.703

10 0.418 0.102 0.520 0.480 0.155 0.124 0.279 0.721

15 0.371 0.122 0.494 0.506 0.098 0.132 0.230 0.770

20 0.339 0.137 0.476 0.524 0.079 0.128 0.208 0.792

25 0.311 0.150 0.461 0.539 0.071 0.123 0.194 0.806

30 0.287 0.161 0.448 0.552 0.066 0.118 0.184 0.816

35 0.265 0.171 0.436 0.564 0.062 0.115 0.177 0.823

40 0.245 0.180 0.425 0.575 0.058 0.112 0.170 0.830

45 0.227 0.188 0.415 0.585 0.055 0.110 0.165 0.835

50 0.210 0.195 0.405 0.595 0.052 0.109 0.161 0.839

55 0.195 0.202 0.397 0.603 0.049 0.108 0.157 0.843

60 0.181 0.208 0.389 0.611 0.046 0.108 0.154 0.846

65 0.169 0.213 0.382 0.618 0.044 0.108 0.151 0.849

70 0.157 0.218 0.375 0.625 0.041 0.108 0.149 0.851

75 0.146 0.222 0.369 0.631 0.039 0.108 0.147 0.853

80 0.137 0.226 0.363 0.637 0.037 0.108 0.145 0.855

85 0.127 0.230 0.358 0.642 0.035 0.108 0.143 0.857

90 0.119 0.233 0.353 0.647 0.033 0.109 0.142 0.858

95 0.111 0.236 0.348 0.652 0.031 0.109 0.141 0.859

100 0.104 0.239 0.344 0.656 0.030 0.110 0.140 0.860

Average 0.243 0.180 0.423 0.082 0.109 0.191


6-102


YearafterProduction

Southeast,Hardwood

InUse

SawlogTotal

Emissions InUse

PulpwoodTotal


TotalStored

InLandfills

TotalStored

0 0.609 0.000 0.609 0.391 0.591 0.000 0.591 0.409

1 0.552 0.035 0.587 0.413 0.525 0.027 0.552 0.448

2 0.534 0.043 0.577 0.423 0.480 0.043 0.522 0.478

3 0.518 0.051 0.569 0.431 0.439 0.056 0.495 0.505

4 0.503 0.058 0.561 0.439 0.404 0.067 0.471 0.529

5 0.488 0.065 0.553 0.447 0.372 0.077 0.449 0.551

6 0.475 0.071 0.546 0.454 0.344 0.085 0.430 0.570

7 0.457 0.079 0.537 0.463 0.296 0.100 0.397 0.603

8 0.442 0.086 0.528 0.472 0.260 0.111 0.371 0.629

9 0.429 0.092 0.521 0.479 0.231 0.119 0.350 0.650

10 0.418 0.097 0.515 0.485 0.209 0.124 0.334 0.666

15 0.373 0.119 0.492 0.508 0.153 0.134 0.287 0.713

20 0.338 0.136 0.475 0.525 0.132 0.133 0.265 0.735

25 0.309 0.151 0.460 0.540 0.121 0.130 0.251 0.749

30 0.282 0.164 0.446 0.554 0.113 0.127 0.240 0.760

35 0.258 0.176 0.434 0.566 0.106 0.126 0.232 0.768

40 0.236 0.186 0.422 0.578 0.100 0.125 0.225 0.775

45 0.216 0.196 0.412 0.588 0.094 0.125 0.218 0.782

50 0.198 0.204 0.402 0.598 0.089 0.125 0.213 0.787

55 0.181 0.212 0.393 0.607 0.084 0.125 0.209 0.791

60 0.166 0.218 0.384 0.616 0.079 0.126 0.205 0.795

65 0.152 0.224 0.376 0.624 0.075 0.126 0.201 0.799

70 0.139 0.230 0.369 0.631 0.071 0.127 0.198 0.802

75 0.127 0.235 0.362 0.638 0.067 0.128 0.195 0.805

80 0.117 0.239 0.356 0.644 0.063 0.129 0.193 0.807

85 0.107 0.243 0.350 0.650 0.060 0.130 0.190 0.810

90 0.098 0.247 0.345 0.655 0.057 0.131 0.188 0.812

95 0.090 0.250 0.340 0.660 0.054 0.132 0.186 0.814

100 0.083 0.253 0.336 0.664 0.051 0.133 0.185 0.815

Average 0.231 0.187 0.417 0.119 0.123 0.242


6-103


YearafterProduction

SouthCentral,Softwood

InUseSawlog

TotalEmissions

InUse

PulpwoodTotal


TotalStored

InLandfills

TotalStored

0 0.629 0.000 0.629 0.371 0.570 0.000 0.570 0.430

1 0.569 0.035 0.603 0.397 0.506 0.026 0.532 0.468

2 0.547 0.044 0.591 0.409 0.459 0.041 0.500 0.500

3 0.527 0.053 0.580 0.420 0.417 0.055 0.472 0.528

4 0.509 0.061 0.569 0.431 0.380 0.066 0.447 0.553

5 0.492 0.068 0.560 0.440 0.348 0.076 0.424 0.576

6 0.477 0.075 0.551 0.449 0.319 0.085 0.404 0.596

7 0.455 0.083 0.538 0.462 0.270 0.100 0.370 0.630

8 0.437 0.091 0.527 0.473 0.232 0.111 0.343 0.657

9 0.421 0.097 0.518 0.482 0.202 0.119 0.321 0.679

10 0.408 0.102 0.510 0.490 0.180 0.124 0.304 0.696

15 0.362 0.122 0.484 0.516 0.123 0.133 0.256 0.744

20 0.330 0.136 0.466 0.534 0.103 0.130 0.234 0.766

25 0.303 0.148 0.451 0.549 0.094 0.126 0.220 0.780

30 0.280 0.158 0.439 0.561 0.087 0.122 0.210 0.790

35 0.259 0.168 0.427 0.573 0.082 0.120 0.202 0.798

40 0.240 0.176 0.416 0.584 0.077 0.118 0.195 0.805

45 0.222 0.184 0.406 0.594 0.072 0.117 0.189 0.811

50 0.206 0.191 0.397 0.603 0.068 0.116 0.185 0.815

55 0.192 0.197 0.389 0.611 0.064 0.116 0.181 0.819

60 0.178 0.203 0.381 0.619 0.061 0.116 0.177 0.823

65 0.166 0.208 0.374 0.626 0.058 0.116 0.174 0.826

70 0.155 0.213 0.368 0.632 0.054 0.117 0.171 0.829

75 0.145 0.217 0.362 0.638 0.051 0.117 0.169 0.831

80 0.135 0.221 0.356 0.644 0.049 0.118 0.167 0.833

85 0.126 0.225 0.351 0.649 0.046 0.119 0.165 0.835

90 0.118 0.228 0.346 0.654 0.044 0.119 0.163 0.837

95 0.111 0.231 0.342 0.658 0.042 0.120 0.161 0.839

100 0.104 0.234 0.338 0.662 0.039 0.121 0.160 0.840

Average 0.239 0.176 0.415 0.099 0.116 0.215


6-104


YearafterProduction

SouthCentral,Hardwood

InUseSawlog

TotalEmissions

InUse

PulpwoodTotal


TotalStored

InLandfills

TotalStored

0 0.587 0.000 0.587 0.413 0.581 0.000 0.581 0.419

1 0.531 0.033 0.564 0.436 0.516 0.027 0.542 0.458

2 0.512 0.042 0.554 0.446 0.470 0.042 0.512 0.488

3 0.495 0.050 0.545 0.455 0.429 0.055 0.484 0.516

4 0.479 0.057 0.536 0.464 0.392 0.067 0.459 0.541

5 0.464 0.064 0.528 0.472 0.360 0.077 0.437 0.563

6 0.450 0.070 0.521 0.479 0.332 0.085 0.417 0.583

7 0.432 0.078 0.510 0.490 0.283 0.100 0.383 0.617

8 0.416 0.085 0.501 0.499 0.246 0.111 0.357 0.643

9 0.403 0.091 0.493 0.507 0.217 0.119 0.336 0.664

10 0.391 0.096 0.487 0.513 0.195 0.124 0.319 0.681

15 0.347 0.116 0.463 0.537 0.138 0.133 0.272 0.728

20 0.314 0.132 0.446 0.554 0.118 0.131 0.250 0.750

25 0.286 0.145 0.432 0.568 0.108 0.128 0.236 0.764

30 0.262 0.157 0.419 0.581 0.101 0.125 0.226 0.774

35 0.239 0.168 0.407 0.593 0.095 0.123 0.217 0.783

40 0.219 0.177 0.396 0.604 0.089 0.121 0.210 0.790

45 0.200 0.186 0.386 0.614 0.084 0.121 0.204 0.796

50 0.183 0.193 0.377 0.623 0.079 0.120 0.199 0.801

55 0.168 0.200 0.368 0.632 0.075 0.121 0.195 0.805

60 0.154 0.206 0.360 0.640 0.070 0.121 0.191 0.809

65 0.141 0.212 0.353 0.647 0.067 0.121 0.188 0.812

70 0.129 0.217 0.346 0.654 0.063 0.122 0.185 0.815

75 0.118 0.222 0.340 0.660 0.060 0.123 0.182 0.818

80 0.108 0.226 0.334 0.666 0.057 0.124 0.180 0.820

85 0.099 0.229 0.329 0.671 0.054 0.124 0.178 0.822

90 0.091 0.233 0.324 0.676 0.051 0.125 0.176 0.824

95 0.084 0.236 0.319 0.681 0.048 0.126 0.174 0.826

100 0.077 0.238 0.315 0.685 0.046 0.127 0.173 0.827

Average 0.215 0.177 0.393 0.110 0.119 0.229


6-105


YearafterProduction

OtherWest,Hardwood

InUseAll

TotalEmissions

In


0 0.568 0.000 0.568 0.432 1 0.516 0.028 0.544 0.456 2 0.494 0.038 0.532 0.468 3 0.473 0.046 0.520 0.480 4 0.455 0.054 0.509 0.491 5 0.438 0.061 0.499 0.501 6 0.422 0.068 0.490 0.510 7 0.399 0.077 0.476 0.524 8 0.381 0.084 0.465 0.535 9 0.365 0.090 0.455 0.545 10 0.352 0.095 0.447 0.553 15 0.307 0.113 0.421 0.579 20 0.277 0.126 0.403 0.597 25 0.253 0.136 0.389 0.611 30 0.232 0.146 0.377 0.623 35 0.212 0.154 0.366 0.634 40 0.195 0.162 0.356 0.644 45 0.179 0.169 0.347 0.653 50 0.164 0.175 0.339 0.661 55 0.151 0.181 0.331 0.669 60 0.138 0.186 0.324 0.676 65 0.127 0.190 0.318 0.682 70 0.117 0.195 0.312 0.688 75 0.108 0.198 0.306 0.694 80 0.099 0.202 0.301 0.699 85 0.091 0.205 0.296 0.704 90 0.084 0.208 0.292 0.708 95 0.078 0.210 0.288 0.712 100 0.072 0.213 0.284 0.716 Average 0.195 0.161 0.357


6-106

Table6‐A‐6:RegionalFactorstoEstimateCarboninRoundwoodLogs,BarkonLogs,andFuelwood

RegionaTimberType

RoundwoodCategory

RatioofRoundwoodtoGrowing‐StockVolumethatisRoundwoodb

RatioofCarboninBarktoCarboninWoodc

FractionofGrowing‐StockVolumethatisRoundwoodd

RatioofFuelwoodtoGrowing‐StockVolumethatisRoundwoodb

NortheastSW

Sawlog 0.991 0.1820.948 0.136

Pulpwood 3.079 0.185

HWSawlog 0.927 0.199

0.879 0.547Pulpwood 2.177 0.218

NorthCentral

SWSawlog 0.985 0.182

0.931 0.066Pulpwood 1.285 0.185


0.831 0.348Pulpwood 1.387 0.218

PacificCoast


0.929 0.096Pulpwood 1.099 0.185


0.947 0.957Pulpwood 0.324 0.219

RockyMountain


0.907 0.217Pulpwood 2.413 0.185


0.755 3.165Pulpwood 1.336 0.219

South


0.891 0.019Pulpwood 1.246 0.185


0.752 0.301Pulpwood 1.191 0.218SW=Softwood,HW=Hardwood.aNorthCentralincludestheNorthernPrairieStatesandtheNorthernLakeStates;PacificCoastincludesthePacificNorthwest(WestandEast)andthePacificSouthwest;RockyMountainincludesRockyMountain,NorthandSouth;andSouthincludestheSoutheastandSouthCentral.bValuesandclassificationsarebasedondatainTables2.2,3.2,4.2,5.2,and6.2ofJohnson(2001).cRatiosarecalculatedfromcarbonmassbasedonbiomasscomponentequationsinJenkinsetal.(2003a),appliedtoalllivetreesidentifiedasgrowingstockontimberlandstandsclassifiedasmedium‐orlarge‐diameterstandsinthesurveydatafortheconterminousUnitedStatesfromUSDAForestService,FIAProgram’sdatabaseofforestsurveys(FIADB)(Alerichetal.,2005;USDAForestService,2005).Carbonmassiscalculatedforbolesfromstumpto4‐inch(10.2cm)top,outsidediameter.dValuesandclassificationsarebasedondatainTables2.9,3.9,4.9,5.9,and6.9ofJohnson(2001).


6-107

Chapter6References


Akagi,S.K.,R.J.Yokelson,C.Wiedinmyer,M.J.Alvarado,etal.2011.Emissionfactorsforopenanddomesticbiomassburningforuseinatmosphericmodels.Atmos.Chem.Phys.,11:4039‐4072.

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Author:StephenM.Ogle,ColoradoStateUniversity

Contents:

7 QuantifyingGreenhouseGasSourcesandSinksfromLand‐UseChange.............................7‐37.1 Overview...........................................................................................................................................................7‐37.2 DefinitionsofLandUse...............................................................................................................................7‐47.3 Caveats...............................................................................................................................................................7‐67.4 EstimatingGHGFluxfromLand‐UseChange.....................................................................................7‐6

7.4.1 CarbonPoolsinLiveBiomass,DeadBiomass,andSoilOrganicCarbon..7‐87.4.2 ChangesinSoilCarbon..................................................................................................7‐87.4.3 ChangesinotherGHGemissions.............................................................................7‐13

Chapter7References.............................................................................................................................................7‐14

SuggestedChapterCitation:Ogle,S.M.,2014.Chapter7:QuantifyingGreenhouseGasSourcesandSinksfromLandUseChange.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.


Chapter 7

Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change

Chapter 7: Quantifying Greenhouse Gas Sources and Sinks from Land-Use Change

7-2


C CarbonCH4 MethaneCO2 CarbondioxideCO2‐eq CarbondioxideequivalentsDOM DeadorganicmatterEPA EnvironmentalProtectionAgencyFIA ForestInventoryandAnalysisGHG Greenhousegasha HectareIPCC IntergovernmentalPanelonClimateChangeN2O NitrousOxideNRI NaturalResourcesInventoryPRISM Parameter‐ElevationRegressionsonIndependentSlopesModelSOC SoilorganiccarbonSSURGO SoilSurveyGeographicDatabaseUSDA U.S.DepartmentofAgriculture


7-3

7 QuantifyingGreenhouseGasSourcesandSinksfromLand‐UseChange

Thischapterprovidesguidanceonestimatingthenetgreenhousegas(GHG)fluxresultingfromchangesbetweenlandusetypes—i.e.,conversionsintoandoutofcropland,wetland,grazingland,orforestland—attheentityscale.Insomecases,itissufficienttoestimatethenetGHGfluxassociatedwiththenewlanduse.Ifchangingfromonelandusetoanotherhasasignificanteffectoncarbonstocks(e.g.,changesinforestcarbonstocks,changesinsoilcarbon),itwillbenecessarytorepresentthatinfluenceassociatedwithaspecificland‐usechange(e.g.,wetlandtocropland,grazinglandtocropland,forestlandtocropland).Table7‐1providesasummaryanddescriptionofthesourcescoveredinthischapter.

Table7‐1:OverviewofLand‐UseChangeSourcesandAssociatedGHGs


CO2 N2O CH4Annualchangeincarbonstocksindeadwoodandlitterduetolandconversion

Liveanddeadbiomasscarbonstocksandsoilorganiccarbonconstitutesasignificantcarbonsinkinmanyforestandagriculturallands.Followingland‐useconversion,theestimationofdeadbiomasscarbonstockchangesduringtransitionperiodsrequiresthattheareasubjecttoland‐usechangeontheentity’soperationbetrackedforthedurationofthe20‐yeartransitionperiod.

Changeinsoilorganiccarbonstocksformineralsoils

Soilorganiccarbonstocksareinfluencedbyland‐usechange(Aaldeetal.,2006)duetochangesinproductivitythatinfluencecarboninputs,andtochangesinsoilmanagementthatinfluencecarbonoutputs(DavidsonandAckerman,1993;Ogleetal.,2005;PostandKwon,2000).Themostsignificantchangesinsoilorganiccarbonoccurwithland‐usechange,particularlyconversionstocroplands,duetochangesinthedisturbanceregimesandassociatedeffectsonsoilaggregatedynamics(Sixetal.,2000).

7.1 Overview

Inmanycases,themethodsproposedtoestimatecontributionstotheGHGfluxresultingfromland‐usechangearethesameasthoseusedtoestimatecarbonstockchangesintheindividualchaptersonCroplandandGrazingLand,Forestry,andWetlands;although,inspecificcasesguidanceisalsoprovidedonreconcilingcarbon‐stockestimatesbetweendiscretedatasetsandestimationmethods(e.g.,reconcilingforestsoilcarbonestimatesandcroplandsoilcarbonestimatesforland‐usechangefromforestlandtocropland).Table7‐2presentsthemethodologiesforeachsourceandindicatestheirsection.


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Table7‐2:OverviewofCroplandandGrazingLandSystemsSources,MethodandSection

Section Source ProposedMethod

7.4.1 Annualchangeincarbonstocksindeadwoodandlitterduetolandconversion

Thechangeincarbonstocksindeadwoodandlitterduetolandconversionisestimatedasthedifferenceincarbonstocksintheoldandnewland‐usecategoriesappliedintheyearoftheconversion(carbonlosses),ordistributeduniformlyoverthelengthofthetransitionperiod(carbongains)(Aaldeetal.,2006).

7.4.2 Changeinsoilorganiccarbonstocksformineralsoils

ThemethodologiestoestimatesoilcarbonstockchangesfororganicsoilsandmineralsoilsareadoptedfromIPCC(Aaldeetal.,2006).

Theremainderofthischapterisorganizedasfollows:

Definitions

Caveats

StepsforestimatingGHGfluxfromland‐usechange

Overlaps,issues,andassemblyinstructionsforGHGfluxestimationfromland‐usechange

7.2 DefinitionsofLandUse

Aland‐usecategorizationsystemthatisconsistentandcomplete(bothtemporallyandspatially)isneededtoassesslanduseandland‐usechangestatuswithinanentity’sboundaries.Eachentityshouldensurethatitcharacterizesallofthelandwithinitsboundaryaccordingtothefollowingland‐usetypes:cropland,grazingland,forestland,wetland,settlements(e.g.,residential,farm,andcommercialbuildings),andotherland(e.g.,baresoil,rock).Theland‐usedefinitionsprovidedbelowareexpectedtobeadoptedbyentitiesusingthisreport.Itiscriticalthatindividualparcelareasareestimatedaccuratelyandwhencombinedadduptothetotallandareareportedbytheentitybeforeandaftertheland‐usechange.

CurrentdefinitionsforlandusethatareconsistentwithotherpolicyprogramsrelatedtoGHGestimation(e.g.,IntergovernmentalPanelonClimateChange(IPCC),NaturalResourcesInventory(NRI))areprovidedbelow.ThesedefinitionsarespecifictotheUnitedStatesandarebasedpredominantlyoncriteriausedintheland‐usesurveysfortheUnitedStates.Specifically,thedefinitionofforestlandisbasedontheForestInventoryandAnalysis(FIA)definitionofforest,1whiledefinitionsofcropland,grazingland,andsettlementsarebasedontheNRI.2ThedefinitionsforotherlandandwetlandsarebasedontheIPCC(2006)definitionsforthesecategories.

ForestLand:Aland‐usecategorythatincludesareasatleast36.6meterswideand0.4hectaresinsizewithatleast10percentcover(orequivalentstocking)bylivetreesofanysize,includinglandthatformerlyhadsuchtreecoverandthatwillbenaturallyorartificiallyregenerated.Forestlandincludestransitionzones,suchasareasbetweenforestandnon‐forestlandsthathaveatleast10percentcover(orequivalentstocking)withlivetreesandforestareasadjacenttourbanandbuilt‐uplands.Roadside,streamside,andshelterbeltstripsoftreesmusthaveacrownwidthofatleast36.6metersandcontinuouslengthofatleast110.6meterstoqualifyasforestland.Unimprovedroadsandtrails,

1SeeFIAGlossaryhttp://socrates.lv‐hrc.nevada.edu/fia/ab/issues/pending/glossary/Glossary_5_30_06.pdf2SeeNationalResourceInventoryGlossaryofSelectedTerms(p.9)http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1041379.pdf


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streams,andclearingsinforestareasareclassifiedasforestsiftheyarelessthan36.6meterswideor0.4hectaresinsize;otherwisetheyareexcludedfromforestlandandclassifiedassettlements.Tree‐coveredareasinagriculturalproductionsettings,suchasfruitorchards,ortree‐coveredareasinurbansettings,suchascityparks,arenotconsideredforestland(Smithetal.,2009).

Cropland:Aland‐usecategorythatincludesareasusedfortheproductionofadaptedcropsforharvest;thiscategoryincludesbothcultivatedandnon‐cultivatedlands.Cultivatedcropsincluderowcropsorclosegrowncropsandalsohayorpastureinrotationwithcultivatedcrops.Non‐cultivatedcroplandincludescontinuoushay,perennialcrops(e.g.,orchards),andhorticulturalcropland.Croplandalsoincludeslandwithalleycroppingandwindbreaks,aswellaslandsintemporaryfalloworenrolledinconservationreserveprograms(i.e.,set‐asides3).Roadsthroughcropland,includinginterstatehighways,statehighways,otherpavedroads,gravelroads,dirtroads,andrailroadsareexcludedfromcroplandareaestimatesandare,instead,classifiedassettlements.

GrazingLand:4Aland‐usecategoryunderwhichtheplantcoveriscomposedprincipallyofgrasses,grass‐likeplants,forbs,orshrubssuitableforgrazingandbrowsing.Thiscategoryincludesbothpasturesandnativerangelandsandareaswherepracticessuchasclearing,burning,chaining,and/orchemicalsareappliedtomaintainthegrassvegetation.Savannas,somewetlandsanddeserts,andtundraareconsideredgrazingland.Woodyplantcommunitiesoflowforbsandshrubs,suchasmesquite,chaparral,mountainshrub,andpinyon‐juniper,arealsoclassifiedasgrazinglandiftheydonotmeetthecriteriaforforestland.Grazinglandincludeslandmanagedwithagroforestrypracticessuchassilvopastureandwindbreaks,assumingthestandorwoodlotdoesnotmeetthecriteriaforforestland.Roadsthroughgrazingland,includinginterstatehighways,statehighways,otherpavedroads,gravelroads,dirtroads,andrailroadsareexcludedfromgrazinglandareaestimatesandare,instead,classifiedasSettlements.

Wetlands:5Aland‐usecategorythatincludeslandwithhydricsoils,nativeoradaptedhydrophyticvegetation,andahydrologicregimewerethesoilissaturatedduringthegrowingseasoninmostyears.Wetlandvegetationtypesmayincludemarshes,grasslands,orforests.Wetlandsmayhavewaterlevelsthatareartificiallychanged,orwherethevegetationcompositionorproductivityismanipulated.Theselandsincludeundrainedforestedwetlands,grazedwoodlandsandgrasslands,impoundmentsmanagedforwildlife,andlandsthatarebeingrestoredfollowingconversiontoanon‐wetlandcondition(typicallyasaresultofagriculturaldrainage).Provisionsforengineeredwetlandsincluding

3Aset‐asideiscroplandthathasbeentakenoutofactivecroppingandconvertedtosometypeofvegetativecover,including,forexample,nativegrassesortrees.4Notethatthisdefinitionisthe“grassland”definitionfromtheNIRwith“grassland”replacedwith“grazingland.”5Thejurisdictionaldefinitionofawetlandis“thoseareasthatareinundatedorsaturatedbysurfaceorgroundwateratafrequencyanddurationsufficienttosupport,andthatundernormalcircumstancesdosupport,aprevalenceofvegetationtypicallyadaptedforlifeinsaturatedsoilconditions.Wetlandsgenerallyincludeswamps,marshes,bogs,andsimilarareas”(EPA,1980).The1987CorpsofEngineersWetlandDelimitationManual&RegionalSupplements(U.S.ArmyCorpsofEngineers,1987)isusedtoidentifywetlandsinthefield.


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stormwaterdetentionponds,constructedwetlandsforwatertreatment,andfarmpondsorreservoirsarenotincluded.Naturallakesandstreamsarealsonotincluded.

Settlements:Aland‐usecategoryrepresentingdevelopedareasconsistingofunitsof0.25acres(0.1ha)ormorethatincludesresidential,industrial,commercial,andinstitutionalland;constructionsites;publicadministrativesites;railroadyards;cemeteries;airports;golfcourses;sanitarylandfills;sewagetreatmentplants;watercontrolstructuresandspillways;parkswithinurbanandbuilt‐upareas;andhighways,railroads,andothertransportationfacilities.Alsoincludedaretractsoflessthan10acres(4.05ha)thatmaymeetthedefinitionsforForestLand,Cropland,Grassland,orOtherLandbutarecompletelysurroundedbyurbanorbuilt‐upland,andsoareincludedinthesettlementcategory.Ruraltransportationcorridorslocatedwithinotherlanduses(e.g.,ForestLand,Cropland,andGrassland)arealsoincludedinSettlements.

OtherLand:Aland‐usecategorythatincludesbaresoil,rock,ice,andalllandareasthatdonotfallintoanyoftheotherfiveland‐usecategories,whichallowsthetotalofidentifiedlandareastomatchthemanagedlandbase.

7.3 Caveats

ThemethodspresentedhereforquantifyingGHGfluxfromland‐usechangeareintendedforuseattheentityscaleonlandsmanagedtoenhancetheproductionoffood,feed,fiber,andrenewableenergy.Methodsarecurrentlynotprovidedforestimatingemissionsfromenergyusedwhenconvertinglandusefromonecategorytoanother.Methodsarealsonotprovidedforland‐usechangefromsettlementsorthe“otherland”categorytoforestland,cropland,grazingland,orwetlands.ThesemethodshavebeendevelopedforU.S.conditionsandareconsideredapplicabletoagriculturalandforestryproductionsystemsintheUnitedStates.

7.4 EstimatingGHGFluxfromLand‐UseChange

RationaleforSelectedMethodThismethodisbasedontheIPCC2006Guidelines(IPCC,2006)andrepresentsthemostconsistentmethodforestimatingemissionsfromland‐usechange.Othermethodsareprovidedforlandparcelsthatarenotundergoinglandusechange,andarguablythosemethodsaremorecomprehensiveforestimatingemissionsforthespecificlanduse.However,itiscriticalthatanindividuallandparcelhasaconsistent,seamlessmethodforestimatingcarbonstockchangesthroughoutthetimeseries.Otherwiseartificialchangesinstockscanbeestimatedduetoachange

MethodforEstimatingGHGFluxfromLand‐UseChange

TheGHGfluxassociatedwithland‐usechangeisestimatedbasedonthebalanceofcarbonlossesfromthepreviouslandusefollowingconversionandthecarbongainswiththecurrentlanduse.

Thissectiononlycoversmethodologiesfordeadorganicmattercarbonandsoilorganicmattercarbon.Guidanceonbiomasscarbonmethodsareprovidedintheland‐use‐specificsections(Cropland,Grazingland,ForestLand,andWetlands).

Thechangeincarbonstocksindeadwoodandlitterduetolandconversionisestimatedasthedifferenceincarbonstocksintheoldandnewland‐usecategoriesappliedintheyearoftheconversion(carbonlosses),ordistributeduniformlyoverthelengthofthetransitionperiod(carbongains).

ThemethodologiestoestimatesoilcarbonstockchangesfororganicsoilsandmineralsoilsareadoptedfromIPCC(Aaldeetal.,2006).


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inthemethod.ThemethodsbasedontheIPCC2006Guidelines(IPCC,2006)providethisconsistencyandseamlessintegration.Furthertestinganddevelopmentwillbeneededbeforethemorecomprehensivemethodsprovidedineachlandusesectioncanbeintegratedintoaseamlessapproachforestimatingthecarbonstockchanges.

DescriptionofMethodForinventorypurposes,changesincarbonstockinbiomassshouldbeestimatedfor:(1)landremaininginthesameland‐usecategory;and(2)landconvertedtoanewland‐usecategory.Themethodsprovidedinthissectionarestrictlyforportionsofanentity’soperationthathaveundergonealandusechange.Thesoilcarbonchangesmustbeaddressedovera20‐yearperiod.Abovegroundandbelowgroundbiomassareestimatedonanannualbasis.Notethatthissectiononlyaddressesdeadorganicmattercarbonandsoilorganicmattercarbon.Biomasscarbonmethodsshouldfollowtheguidanceprovidedintheland‐use‐specificsections(Cropland,Grazingland,ForestLand,andWetlands).

Thereportingconventionisthatallcarbonstockchangesandnon‐CO2GHGemissionsassociatedwithaland‐usechangearereportedinthenewland‐usecategory.Forexample,inthecaseofconversionofforestlandtocropland,boththecarbonstockchangesassociatedwiththeclearingoftheforestaswellasanysubsequentcarbonstockchangesthatresultfromtheconversion,arereportedundercropland(IPCC,2006).

TheGHGfluxassociatedwithland‐usechangeisessentiallythesumoftheGHGfluxesassociatedwithprevious(i.e.,old)land‐usecategoriesplusthesumoftheGHGfluxesassociatedwiththecurrent(i.e.,new)land‐usecategoriesforaspecifiedareaundergoingconversionfromtheoldtonewland‐usecategory.GHGemissionsandstockchangesnotresultingfromaland‐usechangeareestimatedwithmethodsintheland‐use‐specificsections.

Foreachland‐usecategoryundergoingaland‐usechange,itisimportanttoestimatetheannualcarbonstockchangeoccurringwithineachstratumorsubdivision(e.g.,carbonpool,managementregime)forthatland‐usecategory.

Equation7‐1:AnnualCarbonStockChangesforaLand‐UseChangeEstimatedastheSumofChangesinAllLand‐UseCategories

ΔCLUC=ΔCLUCo+ΔCLUCn

and

ΔCLUC=ΔCLUCFL+ΔCLUCCL+ΔCLUCGL+ΔCLUCWL

Where:

ΔC=carbonstockchange(metrictonsCO2‐eqha‐1year‐1)

Indicesdenotethefollowingland‐usecategories:

LUC =land‐usechangeo =oldland‐usecategoryn =newland‐usecategoryFL =forestlandCL =croplandGL =grazinglandWL =wetlands


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Forexample,inthecaseofconversionofforestlandtocropland,thecarbonstockchangesassociatedwitheachoftheforestcarbonpoolsplusharvestedwoodproductsshouldbeassessed,aswellasanysubsequentcarbonstockchangesthatresultfromtheconversion(specificannualizedchangesindeadorganicmatter,soilcarbon,etc.).

7.4.1 CarbonPoolsinLiveBiomass,DeadBiomass,andSoilOrganicCarbonLiveanddeadbiomasscarbonstocksandsoilorganiccarbonconstituteasignificantcarbonsinkinmanyforestandagriculturallands.Sector‐specificmethodsforestimatingchangesinbiomasscarbonstocksaredetailedintheindividualsectorchaptersandshouldbeusedwhenestimatingtheeffectofland‐usechange.Inadditiontoestimatingthechangesinbiomasscarbonstocksbeforeandaftertheland‐usechangeusingthesector‐specificmethods,itisalsoimportanttoestimateanyincreaseintheharvestedwoodpoolresultingfromclearing/harvestoftheforestfollowingthemethodsoutlinedinChapter6,Forestry.Anybiomassthatisretainedonthelandduringtheland‐useconversionwillneedtobeincludedintheestimation,suchasconversionofforesttograsslands,wheresometreesarelefttoprovideshadeforgrazinglivestock.

Followingland‐useconversion,theestimationofdeadbiomasscarbonstockchangesduringtransitionperiodsrequiresthattheareasubjecttoland‐usechangeontheentity’soperationbetrackedforthedurationofthe20‐yeartransitionperiod.Forexample,deadorganicmatter(DOM)stocksareassumedtoincreasefor20yearsafterconversiontoforestland.After20years,theareaconvertedbecomesforestandremainingforestland,andnofurtherDOMchangesareassumed.Theconceptualapproachtoestimatingchangesincarbonstocksindeadwoodandlitterpoolsistoestimatethedifferenceincarbonstocksintheoldandnewland‐usecategoriesandtoapplythischangeintheyearoftheconversion(carbonlosses),ortodistributeituniformlyoverthelengthofthetransitionperiod(carbongains).

7.4.2 ChangesinSoilCarbonSoilorganiccarbonstocksareinfluencedbyland‐usechange(Aaldeetal.,2006)duetochangesinproductivitythatinfluencecarboninputs,andtochangesinsoilmanagementthatinfluencecarbonoutputs(DavidsonandAckerman,1993;Ogleetal.,2005;PostandKwon,2000).Themostsignificantchangesinsoilorganiccarbonoccurwithland‐usechange,particularlyconversionstocroplands,duetochangesinthedisturbanceregimesandassociatedeffectsonsoilaggregatedynamics(Sixetal.,2000).Whilethereisconsiderableevidenceandmechanisticunderstandingabouttheinfluenceofland‐usechangeonsoilorganiccarbon,thereislessknownabouttheeffectonsoilinorganiccarbon.Consequently,currentmethodsdonotincludeimpactsoninorganiccarbonuncertaintyassociatedwithestimatesoflanduseandmanagementimpactsonsoilcarbonstocks.

Equation7‐2:AnnualCarbonStockChangesforaLand‐UseChangeasaSumofChangesinEachStratumWithinaLand‐UseChange

ΔCLUC=∑i∆CLUCI

Where:

ΔCLUC =carbonstockchangesforaland‐usechangeasdefinedinEquation7.1(metrictonsCO2‐eqha‐1year‐1)

i =denotesaspecificstratumorsubdivisionwithinthelandusesundergoingland‐useconversion(byanycombinationofspecies,climaticzone,ecotype,managementregime,etc.),i=1ton


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EstimatingchangesinGHGemissions,includingcarbonstocks,requireconsistencyinthemethodsthatareappliedacrossatimeseries.Applyingdifferentmethodstoaccountforchangesincarbonstocksasthelandshiftsfromonelandusetoanotherwillleadtoartificialchangesinthestocksbeyondtheactualchangeoccurringontheland.Thus,inordertoensureconsistency,changesinsoilorganiccarbonstockswillbeestimatedfortheentiretimeseriesbeingreported,usingthemethoddescribedinthissection.Asnotedearlier,estimatesshouldbemadeseparatelyforeachparceloflandthatundergoesachangeinlanduse.However,thestockchangeswillonlybereportedasaland‐usechangeeffectfora20‐yeartransitionperiod.Applyingthesamemethodacrosstheentiretimeserieswilllimiterrorsintheestimationofmineralsoilorganiccarbonstockchangesthatwouldresultfromchangingmethodsafterthe20‐yeartransitionperiod.

7.4.2.1 DescriptionofMethodModelshavebeenadoptedfromtheIPCCmethodstoestimatesoilorganiccarbonstockchange(Aaldeetal.,2006).Formineralsoils,themethodwillrequireestimatesofcarbonstocksatthebeginningandendoftheyearinordertoestimatetheannualchangeusingtheequationbelow.Emissionsoccurinorganicsoilsfollowingdrainageduetotheconversionofananaerobicenvironmentwithahighwatertabletoaerobicconditions(ArmentanoandMenges,1986),resultinginasignificantlossofcarbontotheatmosphere(Ogleetal.,2003).Emissionestimationmethodsfromorganicsoilsshouldbeconsistentwiththeirappropriatesectormethodologies(i.e.,forestry,croplands,grazinglands,orwetlands).

MineralSoils:ThemodeltoestimatechangesinsoilorganiccarbonstocksformineralsoilshasbeenadoptedfromthemethoddevelopedbyIPCC(Aaldeetal.,2006).Thechangewouldneedtobeestimatedseparatelyforeachareaintheentity’soperationthatisconvertedfromonelandusetoanother.Thechangeinstocksforeachareaisestimatedoverfiveyearintervalsfortheentirereportingtimeseries,usingthefollowingequation:

Equation7‐3:AnnualChangeinCarbonStocksinDeadWoodandLitterDuetoLandConversion

ΔCDOM=(Cn‐Co)×Aon÷Ton

Where:

ΔCDOM =annualchangeincarbonstocksindeadwoodorlitter(metrictonsCyear‐1)

Co =deadwood/litterstock,undertheoldland‐usecategory(metrictonsCha‐1)

Cn =deadwood/litterstock,underthenewland‐usecategorymetrictonsCha‐1)

Aon =areaundergoingconversionfromoldtonewland‐usecategory(ha)

Ton =timeperiodofthetransitionfromoldtonewland‐usecategory(year)(Thedefaultis20yearsforcarbonstockincreasesand1yearforcarbonlosses.)


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CarbonstocksareestimatedusingthefollowingequationadaptedfromtheIPCC(Aaldeetal.,2006):

Thestockchangefactors(FLU,FMG,FI)andreferencecarbonstocks(SOCREF)arecountry‐specificvaluesdevelopedfortheUnitedStates(EPA,2011;Ogleetal.,2003;Ogleetal.,2006).ThereferencestocksarebasedontheSOCstocksincroplands(Table7‐3),whiletheland‐usefactorsrepresenttherelativechangeinSOCbetweencroplandandgrazinglands,forestland,andset‐asidecropland(Table7‐4).Themanagementfactorsrepresenttheinfluenceoftillageincroplandsandgrasslandconditioningrazinglands.Theinputfactorsrepresentinfluencesofchangingplantproductivityoncarboninputtosoils.Managementandinputfactorsarenotneededforforestlands(Factorsaresettoavalueof1).

OrganicSoils:ThemethodologyforestimatingsoilcarbonstockchangesinorganicsoilshasbeenadoptedfromIPCC(Aaldeetal.,2006),andisdescribedaccordinglyinChapter4,Wetlands,andChapter3,Croplands,andGrazingLands.Chapter6,Forestry,recommendssoilsamplingincaseswheretherehavebeensignificantchangesinsoilcarbon(e.g.,landconversion).

Equation7‐4:ChangeinSoilOrganicCarbon StocksforMineralSoils

ΔCMineral=[(SOCf−SOCi)×CO2MW]÷D

Where:

ΔCMineral =annualchangeinmineralsoilorganiccarbonstock(metrictonsCO2‐eqyear‐1)

SOCf =soilorganiccarbonstockattheendofyear5(metrictonsC)

SOCi =soilorganiccarbonstockatthebeginningofyear1(metrictonsC)

CO2MW =ratioofmolecularweightofCO2toC=44/12(dimensionless)

D =timedependenceofstockchangefactors(20years)

Equation7‐5:SoilOrganicCarbon StockforMineralSoils

SOC=SOCREF×FLU×FMG×FI×A

Where:

SOC =soilorganiccarbonstockatthebeginning(SOCi)andendofthefiveyears(SOCf)(metrictonsC)

SOCREF =referencesoilorganiccarbonstock(metrictonsCha‐1)

FLU =stockchangefactorforlanduse(dimensionless)

FMG =stockchangefactorformanagement(dimensionless)

FI =stockchangefactorforinput(dimensionless)

A =areaofland‐usechange(ha)


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Table7‐3:ReferenceCarbonStocks(MgCha‐1)(±1SE)ToEstimateSoilOrganicCarbonStockChangesforMineralSoils

SoilTypeUSDA

Taxonomy

CoolTemperate

Dry

CoolTemperateMoist

WarmTemperate

Dry

WarmTemperateMoist

Sub‐TropicalDry

Sub‐TropicalMoist

Highactivityclaysoils

Vertisols,Mollisols,Inceptisols,Aridisols,andhighbasestatusAlfisols

42±1.4 65±1.1 37±1.1 51±1.0 42±2.6 57±13.0

Lowactivityclaysoils

Ultisols,Oxisols,acidicAlfisols,andmanyentisols

45±3.0 52±2.3 25±1.4 40±1.2 39±4.8 47±13.9

Sandysoils

Anysoilswithgreaterthan70%sandandlessthan8%clay(oftenEntisols)

24±4.8 40±3.7 16±2.4 30±2.0 33±1.9 50±7.9

Volcanicsoils

Andisols 124±11.4 114±16.7 124±11.4 124±11.4 124±11.4 128±15.0

Spodicsoils

Spodosols 86±6.5 74±6.8 86±6.5 107±8.3 86±6.5 86±6.5

Wetlandsoils

SoilswithAquicsuborder

86± 89± 48± 51± 63± 48±

Source:EPA(2011)andOgleetal.(2003)Ogleetal.(2006).

Table7‐4:CarbonStockChangeFactors(±1SE)toEstimateSoilOrganicCarbonStockChangesforMineralSoils

FactorWarmTemperateMoist/Subtropical

Moist

WarmTemperate

Dry/SubtropicalDry

CoolTemperateMoist

CoolTemperateDry

Land‐UseFactorLong‐termcultivated 1 1 1 1Forest/grassland 1.42±0.06 1.37±0.05 1.24±0.06 1.20±0.06Set‐aside 1.31±0.06 1.26±0.04 1.14±0.06 1.10±0.05CroplandManagementFulltill 1 1 1 1Reducedtill 1.08±0.03 1.01±0.03 1.08±0.03 1.01±0.03No‐till 1.13±0.02 1.05±0.03 1.13±0.02 1.05±0.03GrasslandManagementa


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FactorWarmTemperateMoist/Subtropical

Moist

WarmTemperate

Dry/SubtropicalDry

CoolTemperateMoist

CoolTemperateDry

Non‐degraded 1 1 1 1Moderatelydegraded 0.95±0.06 0.95±0.06 0.95±0.06 0.95±0.06Severelydegraded 0.7±0.14 0.7±0.14 0.7±0.14 0.7±0.14Improved 1.14±0.06 1.14±0.06 1.14±0.06 1.14±0.06CroplandinputLow 0.94±0.01 0.94±0.01 0.94±0.01 0.94±0.01Medium 1 1 1 1High 1.07±0.02 1.07±0.02 1.07±0.02 1.07±0.02Highwithamendmenta 1.38±0.06 1.34±0.08 1.38±0.06 1.34±0.08GrasslandinputaMedium 1 1 1 1High 1.11±0.04 1.11±0.04 1.11±0.04 1.11±0.04aGrasslandmanagementandinputfactorsarefromthe2006IPCCGuidelines(Verchotetal.,2006)aswellasthehighinputsystemswithmanureincroplands(Lascoetal.,2006).

7.4.2.2 ActivityDataMineralsoilsrequirethefollowingactivityforcroplands:

Cropselectionandrotationsequence;

Residuemanagement,includingharvested,burned,grazed,orleftinthefield;

Irrigation,yesorno;

Mineralfertilization,yesorno;

Limeamendments,yesorno;

Organicamendments,yesorno;

Tillageimplements,whichcanbeusedtodeterminetillageclassification(i.e.,fulltillage,reducedtillage,andno‐till);and

Covercrops,yesorno.Themethodforgrazinglandrequiresthefollowingmanagementactivitydata:

Degradationstatus,non‐degraded,moderatelydegraded,severelydegraded;

Irrigation,yesorno;

Mineralfertilization,yesorno;

Seedinglegumes,yesorno;

Limeamendments,yesorno;and

Organicamendments,yesorno.Themethodforforestlanddoesnotrequireanymanagementactivitydatabecausethemethodprovidedhereassumeslimitedinfluenceonsoilorganiccarbonstockchangesassociatedwithforestmanagementafteraland‐usechange(i.e.,theland‐usechangehasthelargestimpact).

Theactivitydataareusedtoclassifyland‐use,management,andinputclasses.TheclassificationscanbefoundinLascoetal.(2006)forcropland(Figure5.1),andVerchotetal.(2006)forgrassland(Figure6.1).


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7.4.2.3 AncillaryDataAncillarydataincludeclimateregionsandsoiltypes,consistentwiththemethoddevelopedbytheIPCC(Bickeletal.,2006).WeatherdatamaybebasedonnationaldatasetssuchastheParameter‐ElevationRegressionsonIndependentSlopesModel(PRISM)data(Dalyetal.,2008)andareclassifiedaccordingtotheIPCCclassificationasrefinedfortheUnitedStates(Table7‐5).SoilsdatamayalsobebasedonnationaldatasetssuchastheSoilSurveyGeographicDatabase(SSURGO)(SoilSurveyStaff,2011),andareclassifiedaccordingtotheIPCCclassification(Bickeletal.,2006;Figure3A.5.3).However,entitiesmayalsosubstitutefield‐specificsoilsdata,aslongasentitiescharacterizethesoilpedonsnecessaryforuseoftheIPCCclassifications.Thesecharacteristicsincludesandandclaycontent,soilorder,andsuborder(SeeTable7‐3).

Table7‐5:ClimateClassificationfortheSoilOrganicCarbonMethodsAssociatedwithLand‐UseChange

ClimateType MeanAnnualTemperature(°C) MeanAnnualPrecipitation(mm)

Cooltemperatedry <10 <PotentialevapotranspirationCooltemperatemoist <10 ≥PotentialevapotranspirationWarmtemperatedry 10‐20 <PotentialevapotranspirationWarmtemperatemoist 10‐20 ≥PotentialevapotranspirationSubtropicaldry >20 <1000Subtropicalmoist >20 1000‐2000Source:Bickeletal.(2006).

7.4.2.4 ModelOutputModeloutputisgeneratedasanabsolutequantityofemissions.Thechangeinmineralsoilorganiccarbonstocksisestimatedbasedonstockchangesoverfive‐yeartimeperiods(Equation7.4).Inaddition,trendsinsoilorganiccarbonwillbeestimatedfortheentiretimeseriesassociatedwiththeparcelofland,including20previousyearsofhistory,inordertopresentthelongertermtrendsandprovideanadequatebaselineofdataandconsistencyinthetimeseriesforreportingpurposes.

7.4.2.5 LimitationsandUncertaintyThelimitationsofthemineralsoilorganiccarbonmethodincludenoassessmentoftheeffectofland‐usechangeatdeeperdepthsintheprofile(IPCCmethodonlyaddresseschangesintop30cmofsoilprofile;Aaldeetal.,2006),andnoassessmentoferosion,transport,anddepositionofcarbon.Uncertaintiesinthemineralsoilmethodsincludeimprecisionintheemissionfactors,inadditiontouncertaintiesintheactivityandancillarydata.Uncertaintyintheemissionfactorsisprovidedinthisguidance(Ogleetal.,2003;Ogleetal.,2006).Uncertaintyintheactivitydataisbasedontheentityinput,aswellastheancillarydatatotheextentthatthisinformationisprovidedbytheentity.UncertaintiescanbecombinedusingaMonteCarlosimulationapproach.

7.4.3 ChangesinotherGHGemissionsAspreviouslymentioned,changesinotherGHGemissions—i.e.,non‐CO2emissions—associatedwithaland‐usechangeshouldbeincludedinanyestimationoftheGHGfluxstrataassociatedwiththeoutgoingorincomingland‐usechange.WhilechangesinbiomassandsoilcarbonstocksarelikelytodominatetheGHGflux,thereareanumberofactivitiesthatmayoccurduringland‐useconversionthatmightresultinnon‐CO2emission.Forexample,ifforestharvestresidues(andotherdeadorganicmatter)arepiledandburntaspartoftheconversionofforestlandtoanotherlanduse,inadditiontothechangeincarbonstocktheresidueburningwillresultinemissionsofN2OandCH4;andifwetlandsareclearedanddrainedpriortoconversiontoanotherlanduse(e.g.,grazinglands,peatextraction),inadditiontothechangeincarbonstockfromclearing,thedraining


7-14

willresultinareductioninemissionsofCH4,andapossibleincreaseinemissionsofN2O,dependingonthenitrogencontentofthewetlandsoil(i.e.,peat).

Sector‐specificmethodsforestimatingchangesinbiomassburningnon‐CO2emissions(e.g.,forcroplandandgrazinglandsystems)andsoilnon‐CO2emissions(e.g.,forwetlandsystems)aredetailedintheindividualsectorchapters.

Chapter7References


Armentano,T.V.,andE.S.Menges.1986.Patternsofchangeinthecarbonbalanceoforganicsoil.Bickel,K.,G.Richards,M.Kohl,R.L.V.Rodrigues,etal.2006.Chapter3:Consistentrepresentationof

land.In2006IPCCGuidelinesforNationalGreenhouseGasInventories,S.Eggleston,L.Buendia,K.Miwa,T.NgaraandK.Tanabe(eds.).Japan:IGES.

Daly,C.,M.Halbleib,J.I.Smith,W.P.Gibson,etal.2008.PhysiographicallysensitivemappingofclimatologicaltemperatureandprecipitationacrosstheconterminousUnitedStates.InternationalJournalofClimatology,28:2031‐2064.


EPA.1980.USEPARegulation40CFR230.3(t):U.S.EnvironmentalProtectionAgency.EPA.2011.InventoryofU.S.greenhousegasemissionsandsinks:1990‐2009.Washington,D.C.:

EnvironmentalProtectionAgency.IPCC.2006.IPCCGuidelinesforNationalGreenhouseGasInventories.TheNationalGreenhouseGas

InventoriesProgramme.Hayama,Kanagawa,Japan:TheIntergovernmentalPanelonClimateChange.


Ogle,S.M.,F.J.Breidt,M.D.Eve,andK.Paustian.2003.UncertaintyinestimatinglanduseandmanagementimpactsonsoilorganiccarbonstorageforUSagriculturallandsbetween1982and1997.GlobalChangeBiology,9(11):1521‐1542.

Ogle,S.M.,F.J.Breidt,andK.Paustian.2005.Agriculturalmanagementimpactsonsoilorganiccarbonstorageundermoistanddryclimaticconditionsoftemperateandtropicalregions.Biogeochemistry,72(1):87–121.

Ogle,S.M.,F.J.Breidt,andK.Paustian.2006.Biasandvarianceinmodelresultsassociatedwithspatialscalingofmeasurementsforparameterizationinregionalassessments.GlobalChangeBiology,12:516‐523.

Post,W.M.,andK.C.Kwon.2000.SoilCarbonSequestrationandLand‐UseChange:ProcessesandPotential.GlobalChangeBiology,6:317‐327.

Six,J.,E.T.Elliot,andK.Paustian.2000.Soilmacroaggregateturnoverandmicroaggregateformation:amechanismforCsequestrationunderno‐tillageagriculture.SoilBiol.Biochem.,32:2099‐2103.

Smith,W.B.,P.D.Miles,C.H.Perry,andS.A.Pugh.2009.ForestResourcesoftheUnitedStates,2007.Washington,DC:U.S.DepartmentofAgricultureForestService.

SoilSurveyStaff.2011.SoilSurveyGeographic(SSURGO)Database:NaturalResourceConservationServiceandUnitedStatesDepartmentofAgriculture.


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U.S.ArmyCorpsofEngineers.1987.WetlandDelimitationManual&RegionalSupplementsWetlandsResearchProgramTechnicalReportY‐87‐1:U.S.ArmyCorpsofEngineers,EnvironmentalLaboratory,.http://www.usace.army.mil/Missions/CivilWorks/RegulatoryProgramandPermits/reg_supp.aspx.



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E

Authors:1JayBreidt,ColoradoStateUniversityStephenM.Ogle,ColoradoStateUniversityWendyPowers,MichiganStateUniversityCoeliHoover,USDAForestService

Contents:

8 UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks...................8‐3 ComponentsandInputstoanEntity‐ScaleMonteCarloUncertaintyAssessment............8‐48.1

8.1.1 ParameterUncertainty..................................................................................................8‐58.1.2 SamplingMethodUncertainty....................................................................................8‐68.1.3 LargeDatasetUncertainty............................................................................................8‐98.1.4 ModelUncertainty.........................................................................................................8‐16

ResearchGaps...............................................................................................................................................8‐208.2Appendix8‐A:ExampleOutputFilefromFVSSamplingUncertaintyBootstrappingApplicationFVSBoot(asprovidedinGreggandHummel,2002)................................................................................8‐21Appendix8‐B:UncertaintyTables....................................................................................................................8‐22Chapter8References.............................................................................................................................................8‐55

SuggestedChapterCitation:Breidt,F.J.,Ogle,S.M.,Powers,W.,Hover,C.,2014.Chapter8:UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks.InQuantifyingGreenhouseGasFluxesinAgricultureandForestry:MethodsforEntity‐ScaleInventory.TechnicalBulletinNumber1939.OfficeoftheChiefEconomist,U.S.DepartmentofAgriculture,Washington,DC.606pages.July2014.Eve,M.,D.Pape,M.Flugge,R.Steele,D.Man,M.Riley‐Gilbert,andS.Biggar,Eds.


1AllauthorsofChapters3,4,5,and6providedstrategicinputintheparametersintheuncertaintychapter.

Chapter 8

Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks

Chapter 8: Uncertainty Assessment for Quantifying Greenhouse Gas Sources and Sinks

8-2

Acronyms,ChemicalFormulae,andUnitsCONUSSTATSGO ContinentalUnitedStatesSoilGeographicDatabaseDBH DiameteratbreastheightDNDC DeNitrification‐DeCompositionEPA U.S.EnvironmentalProtectionAgencyERS USDAEconomicResearchServiceFOFEM FirstOrderFireEffectsModelFVS ForestVegetationSimulatorGHG GreenhousegasIPCC IntergovernmentalPanelonClimateChangeNADP NationalAtmosphericDepositionProgramNARR NorthAmericanRegionalReanalysisNASS USDANationalAgriculturalStatisticsServiceNCEP NationalCentersforEnvironmentalPredictionNLCD NationalLandCoverDatabaseNOAA NationalOceanicandAtmosphericAdministrationNRCS USDANaturalResourcesConservationServiceNRI NationalResourceInventoryPDF ProbabilitydensityfunctionPRISM Parameter‐elevationRegressionsonIndependentSlopesModelSOC SoilorganiccarbonSSURGO SoilSurveyGeographicUSDA U.S.DepartmentofAgriculture


8-3

8 UncertaintyAssessmentforQuantifyingGreenhouseGasSourcesandSinks

Quantifyingtheuncertaintyofgreenhousegas(GHG)emissionsandreductionsfromagricultureandforestrypracticesisanimportantaspectofdecision‐makingforfarmers,ranchersandforestlandownersastheuncertaintyrangeforeachGHGestimatecommunicatesourlevelofconfidencethattheestimatereflectstheactualbalanceofGHGexchangebetweenthebiosphereandtheatmosphere.Inparticular,afarm,ranch,orforestlandownermaybemoreinclinedtoinvestinmanagementpracticesthatreducenetGHGemissionsiftheuncertaintyrangeforanestimateislow,meaningthathigherconfidenceintheestimatesexists.Thischapterpresentstheapproachforaccountingfortheuncertaintyintheestimatednetemissionsbasedonthemethodspresentedinthisreport.2AMonteCarloapproachwasselectedasthemethodforestimatingtheuncertaintyaroundtheoutputsfromthemethodologiesinthisreportasitiscurrentlythemostcomprehensive,soundmethodavailabletoassesstheuncertaintyattheentityscale.Limitationsanddatagapsexist;however,asnewdatabecomeavailablethemethodcanbeimprovedovertime.ImplementationofaMonteCarloanalysisiscomplicatedandrequirestheuseofastatisticaltooltoproduceaprobabilitydensityfunction(PDF)3aroundtheGHGemissionsestimate.4FromthePDF,theuncertaintyestimatecanbederivedandreported.

2TheIPCCGoodPracticeGuidance(IPCC,2000)recommendstwoapproaches—Tier1andTier2—fordevelopingquantitativeestimatesofuncertaintyforemissionsestimatesforsourcecategories.TheTier1methoduseserrorpropagationequations.Theseequationscombinetheuncertaintyassociatedwiththeactivitydataandtheuncertaintyassociatedwiththeemission(orother)factors.Thisapproachisappropriatewhereemissions(orremovals)areestimatedastheproductofactivitydataandanemissionfactororasthesumofindividualsub‐sourcecategoryvalues.TheTier2methodutilizestheMonteCarloStochasticSimulationtechnique.Usingthistechnique,anestimateofemission(orremoval)foraparticularsourcecategoryisgeneratedmanytimesviaanuncertaintymodel,resultinginanapproximatePDFfortheestimate.Wheresufficientandreliableuncertaintydatafortheinputvariablesareavailable,theTier2methodisthepreferredoption.3TheintegralofaPDFoveragivenintervalofvaluesistheprobabilityforarandomvariabletotakeonsomevalueintheinterval.Thatis,thePDFisafunctiongivingprobability“densities”anditsintegralgivesprobabilities.AnarrowerPDFforanestimateindicatessmallervariancearoundthecentral/mostlikelyvalue,i.e.,ahigherprobabilityofthevaluetobeclosertothecentral/mostlikelyvalue.Theuncertaintyforsuchanestimateislower.4GiventhecomplexityofMonteCarloanalysisandthenecessityforatool,theapproachpresentedhereisnotintendedfordevelopmentbyalandowner,ratheritisintendedforuseindevelopingatoolthatalandownerwouldusetoassessuncertaintyestimates.


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UncertaintyinGHGemissionsestimationarisesbecauseofunknownorincompletelyknownfactorsassociatedwith:

Parameters–Duetolimitationsassociatedwithavailableinputdata(e.g.,activitydataandemissionfactors).

Samplingmethods–Duetoeithermeasurementerrorsduringsamplecollectionorpotentialvariationsinvaluesobtainedfromsampling(i.e.,whenthechosensampleisnotfullyrepresentativeoftheentirepopulation).

Largedatasets–Duetomeasurementserrorsduringdatacollection,andvariationsindatasetvaluesforagivensetofconditions.

Models–Duetoapproximationerrorsandestimationerrors.Approximationerrorarisesbecausethemodelisasimplificationoftherealsystem,whileestimationerrorarisesbecausethetheoreticalmodelisfittedusinglimiteddata.

Concepts–Thisiscloselyrelatedtomodelapproximationerrorandoccursbecausetheconceptualscopedoesnotcapturetheactual/realscopethuscreatingabias.Foranentity,thisconceptualizationuncertaintymayberelativelysmall.

Theapproachtoaddressuncertaintydoesnotaddressconceptualuncertaintybecauseitisexpectedtobesmallanddifficulttoquantify.Thischapteraddressesparameteruncertainty,samplinguncertainty,largedatasetuncertainty,andmodelapproximationuncertainty.WheredataarecurrentlyunavailableorincompleteforestablishingPDF’sandestimatinguncertainty,theauthorsprovideexpertjudgmentand/oraqualitativedescriptionofuncertaintyintheinterestsofmakingtheGHGmanagementmethodsastransparentandcompleteaspossible.Inthefuture,newdatacanbeusedtorefineandimprovetheestimationofuncertainty.

Inthischapter,Section8.1includesthecomponentsandinputstoanentity‐scaleMonteCarlouncertaintyassessment,andSection8.2highlightsresearchgaps.

ComponentsandInputstoanEntity‐ScaleMonteCarloUncertainty8.1Assessment

ToconductaMonteCarlouncertaintyanalysisforeachoftheGHGquantificationmethodsandresultingnetGHGemissions,informationisrequiredabouttheuncertaintyassociatedwith:(1)theinputvariables(i.e.,parameters);(2)samplingmethodsusedtoobtaindata;(3)existinglargedatasetsusedasdatasources;and(4)externalmodelsused.Ideally,thisinformationwouldconsistofspecificPDFs(e.g.,normal,triangular,uniform,beta).Alternatively,theuncertaintymightbe

MonteCarloAnalysisforAssessingUncertainty IntheMonteCarlomethod,uncertaininputs(parametersandotherdata)anduncertainmodel

structurearedescribedviaPDFs.ByrandomlyselectingfromeachofthesePDFs,andrunningtheselectedinputsthroughtheselectedmodel,anuncertaintymodeloutputisobtained.CombiningthesemodeloutputsacrossmanyrandomselectionsleadstoanapproximatePDFdescribinguncertaintyinthemodeloutput,reflectingknownsourcesofuncertaintyintheinputsandmodelstructure.

AtoolisneededtorunaMonteCarloanalysistoassesstheuncertaintyformodeloutputs.FarmersandlandownersarenotexpectedtoperformaMonteCarloanalysisontheirown.

Acentralizeddatabaseisneededtostoreinformationontheknownuncertaintiesassociatedwiththeactivityandemissionfactordataforeachemissionssource.Thisreportpresentsreadilyavailabledatathatcanformtheinitialfoundationforsuchadatabase.


8-5

describedwithsummarystatistics,suchaslowerandupperboundsforintervalswithspecifiedconfidence,minimum,maximum,mean,andstandarddeviation.ThissummaryinformationformsthebasisforconstructingapproximatePDFsfortheMonteCarlomethod.RepeatedselectionsaremadefromthesePDFs.TheseselectionsrepresenttherangeofpossibleoutcomesfromeachPDF.RandomsamplingfromthePDFswillensuresuchrepresentativeness.5ByrandomlyselectingfromeachofthesePDFsandrunningtheselectedinputsthroughthemodel,arangeofoutputsisobtained.CombiningthesemodeloutputsacrossmanyrandomselectionsleadstoanoutputPDFthatcanbeusedtodescribeuncertaintyintheestimate,accountingforknownsourcesofuncertaintyintheinputsandmodelstructure.

Thissectionpresentsreadilyavailableinformationoneachofthekeycomponentsofuncertainty.Insummary,althoughinformationonallthecomponentsaredescribedhere,theMonteCarlomethodforassessingnetGHGemissionsuncertaintyreliesmostheavilyonparameteruncertainty,forwhichthebestPDFdataandinformationareavailable.Othercomponentsofuncertaintyarediscussed,includinglimitationssuchascharacterizingtheuncertaintyassociatedwithothercomponents.Thesecomponentscanbereadilyimprovedorrefinedintheuncertaintyanalysisasadditionalinformationbecomesavailable.Overalluncertaintyistypicallygreaterthananyparticularuncertaintycomponent(e.g.,sampling,largedatasets,models)andcanbereadilyimprovedorrefinedasadditionalinformationbecomesavailable.Astheuncertaintyassociatedwiththeothercomponentsisaddressed,theuncertaintywillincrease(i.e.,addressingonlyparameteruncertaintysetsalowerboundforoveralluncertainty).Therefore,thequantificationofparameteruncertaintysetsalowerboundforoveralluncertainty.

8.1.1 ParameterUncertainty

ParameteruncertaintyistheprimarysourceofuncertaintyinthenetGHGestimates.ThissectionpresentsreadilyavailableinformationonparametersusedtoestimatenetGHGemissionsfromanimalproductionsystems,croplandsandgrazinglands,andforestryGHGestimationmethods.Foreachinputvariable,readilyavailableinformationwascollectedontheprobabilitydistribution;variance;standarddeviation;expectedmean,median,andmode;mostlikelyvalue;minimum;maximum;relativeuncertaintyabsolutevalues;confidenceinterval;anddatasources.Theinformationwascollectedprimarilyfrompublishedliterature,suchastheIntergovernmentalPanelonClimateChange(IPCC)Guidelines(2006),theU.S.NationalGHGInventoryReport(U.S.EPA,2012),andpeer‐reviewedjournals.Intheabsenceofpublisheddata,defaultfactorsareindicatedbasedonexpertjudgmentobtainedfromtheWorkingGroups.TheinformationobtainedtodateispresentedinAppendix8‐B.6,7,8

5AnalternativeapproachtoselectingfromthePDFsisLatinhypercubesampling(McKayetal.,1979;HeltonandDavis,2003).6Uncertaintyfortheforestrysectorismainlydrivenbymodelingandsamplinguncertainty;consequently,onlyafewparametershavebeenlistedinAppendix8‐B.7TheWetlandsChaptermethodssuggestuseoftheFVSandDNDCmodelsincombinationwiththelookuptablesfordominantshrubandgrasslandvegetationtypesfoundinChapter3,forestimatingbiomasscarbon,soilcarbon,N2O,andCH4emissionsandremovalsinwetlands.Descriptionsofthesemodelsandtheuncertaintyassociatedwiththelook‐uptablesareincludedintheUncertaintyAssessment(Chapter8).8AnuncertaintyassessmentwasnotcompletedfortheLand‐useChangeChaptermethods(i.e.,annualchangeincarbonstocksindeadwoodandlitterduetolandconversion,changeinsoilorganiccarbonstocksformineralsoils)astheyarebaseduponIPCC2006GuidanceandnoU.S.specificcustomizationsweremadetothesemethods.Uncertaintyassessmentsforeachland‐useandtransitionintooroutofaland‐usecategory


8-6

Inordertomaketheuncertaintyestimationprocessfeasibleandconsistentattheentityscaleforusebyanentityorlandowner,atoolwillbeneededthatprovidesthefollowinguncertaintyinformationforinputvariables:

PDFsordistributions–Default

Emissionfactors–Default

Activitydata–Default,butcustomizable

Withdefaultuncertaintyinformationavailable,itisfeasibletoquantifyparameteruncertaintyviaPDFsandtocombinetheuncertaintyviaMonteCarlomethods.ThesePDFsareoftenrelativelycrude,relyingondefaultvaluesandconservativeexpertjudgment.OptionstoimprovethePDFs(i.e.,improveparameteruncertaintyquantification)areto:(1)developamethodtohelpelicitandrefinetheseuncertaintydistributionsatanentityscale;and(2)conductnewresearchtobetterunderstandthekeyparametersidentifiedinthisreportandtoquantifytheiruncertainties.9

TheuncertaintyassociatedwiththevariousinputstotheGHGestimationequationormodelsarecombinedtoestimateoveralluncertaintyattheentitylevelfor:(1)eachsourcecategoryemissionestimate;and(2)totalemissionestimatearrivedatbyaggregatingeachsourcecategory’sestimate.Althoughmostinputswithinacategoryandacrosscategoriesareindependent,certainvariablesmightbethesame,similar,orhighlyco‐related,andwillneedtobeaccountedforappropriatelyintheuncertaintyanalysis.

8.1.2 SamplingMethodUncertainty

Somesamplingmethods(i.e.,fieldmeasurements)willbeconductedtosupporttheestimationofemissionsusingtheGHGquantificationmethods.Forexample,fortheforestrysector,conductingfieldmeasurementsonsamplingplotsforlargeforestandonurbanforestsisusedtodetermineaggregateforestcharacteristics(e.g.,treecover).Additionally,somelargedatasetsandexternalmodelsthatthemethodsusealsoutilizedatathatwereobtainedfromavarietyofoutsidesamplingmethods.Forexample,forestinventorydatausedintheforestvegetationsimulator(FVS)modelandthe averagecarbonsequestrationratesusedinthei‐Treemodelusedataobtainedthroughsamplingmethods.Inaddition,thereareinstancesintheseexternalmodelsandlargedatasetswherethevariationinmeasurementsobtainedfromthesamplingmethodsisnottakenintoaccount,buttheycanimpactuncertainty.

Ifforeststandsamplingisconductedatanentitylevelusingaformalprobabilitysamplingdesign,thenunbiasedestimatesofsamplingerrorvariancecanbecomputedviastandardtechniquesfromthefieldofsurveystatistics.Theexactformofthevarianceestimatedependsontheparticulardesignusedforthestandsampling.Thoughadditionaluncertaintiesarisefromtheactualmeasurementprotocolsusedinthefield,thesamplingerrorvarianceisamajorpartofthesamplinguncertainty.AcurrentlyfeasibleapproachtoincorporatinginformationonsamplingerrorvariancesintotheuncertaintyanalysisistomodelthesamplingerrorPDFasanormaldistributionwithzeroasitsmeanandtheestimatedsamplingerrorvarianceasitsvariance.10Thissection

arecontainedintheassociatedland‐usecategorychapterofthe2006IPCCGuidelines.http://www.ipcc‐nggip.iges.or.jp/public/2006gl/vol4.html9Atoolcouldprovideanoptiontousepre‐definedvaluessuchasthoseprovidedinAppendix8‐Boruser(i.e.,landowner)suppliedvaluestodefinePDFs.10Similarly,estimatesderivedfromexistingsurveysandtoolssuchasForestInventoryDataOnlineintheForestInventoryandAnalysisNationalProgramortheUSDANaturalResourcesInventory(NRI)SummaryReportshaveassociatedsamplingerrorvariancesfromwell‐establishedstatisticalprocedures.Inthesecases,


8-7

providesthesamplingmethodsandtheirpotentialsourcesofuncertainty.However,giventhecomplexityinincorporatinginformationonuncertaintyforsamplingdataintheMonteCarlouncertaintyassessment,atoolwillbeneededtoquantifytheimpactofsamplinguncertaintyontheestimateofnetGHGemissions.11

8.1.2.1 ForestStandSamplingwithPlotsforUsewiththeForestVegetationSimulatorModel

TheFVSmodelisafamilyofforestgrowthsimulationmodelvariants.FVSestimatesforestcarbonstocksbasedonsampledataparameters(e.g.,thediameter,height,species,andcanopydensityoftreesfromrepresentativesampleplotsestablishedacrosstheforest).Forsamplingpurposes,anumberofplotsareestablishedwithinaforestthatcanserveasarepresentativesampleoftheentireforest.Asthevarianceinforesttypesincreases,thenumberofplotswillincrease.Thesizeandnumberofplotsshouldbedeterminedbasedonthevarianceincarbonstocksbetweenplots.Completeforestestimateshavesamplinguncertainty,butlargerandmorenumerousplotshelptocreateamorerepresentativesampleandlowertheuncertaintyassociatedwithcarbonstockestimatesproducedbyFVS.Bothpermanentandtemporaryplotscanbeusedinsampling;however,alargeruncertaintyisassociatedwithtemporaryplots.Notethattheuseofpermanentplotsisrecommendedinthisreport.Thistypeofsamplingmethodologyiscommonlyreferredtoasaforestinventory.

Onceplotshavebeendefined,alltreesaboveacertaindiameteratbreastheight(DBH)(commonly2.5cm.or1in.)aremeasuredandrecorded.12DBH,height,andavarietyofothermeasurementsarerecorded,butDBHaloneissufficientforusewithFVS.FVSusesDBHandavailableinformationtodevelopcarbondensityestimatesfortheentireforest.Ifprovided,FVSmodelsgrowthestimatesforfutureyearsbasedonaveragegrowthratesandvariablessuchasthinning.13SelectingplotstorepresententireforestsmeansFVSoutputsaresubjecttothesamplingmethoduncertainty.However,uncertaintyintheFVSoutputscanbelowered(i.e.,morerepresentativecarbonstockestimatescanbeobtained)bycollectingmoredetailedtreedatabeyondDBHaswellasensuringthatsampleplotsarelargeandnumerousenoughtocoverthevarietyoftreegrowthsettingsinaforest.

TheforestinventorydatarecommendedforusewithFVSinthisreportisbasedonthesamplingmethodsdescribedintheU.S.DepartmentofAgriculture(USDA)MeasurementGuidelinesfortheSequestrationofCarbon(Pearsonetal.,2007).Theseguidelinesalsodescribethepotentialuncertaintyassociatedwithsuchsamplingmethods.Accordingtothesemeasurementguidelines,areasonableestimateofthenetchangeincarbonstockswouldbewithin10percentofthetruevalueofthemeanatthe95percentconfidencelevelthatcanbeachievedbyhavingasufficientlylargesamplesize(Pearsonetal.,2007).Differentcarbonpoolsinaforestcanhavedifferentvariances;however,focusingonthestandinglivetreecomponentforforestryactivitiescancapturemostofthetotalvariance.

itmaybefeasibletousetheseestimatedvariancesfairlydirectly.Inothercases,itmightbenecessarytoconsidersmallareaestimationtechniquestodescribeuncertaintyaslarge‐scalesurveydataaredownscaledtoentitylevels.Describingthistypeofuncertaintywouldrequirebuildingstatisticalmodelsforcomplexsurveydataand,hence,isnotaddressedinthisreportasadditionalresearchisrequired.11Forexample,inaddressinguncertaintyingrowthofforestbiomass,algorithmstoaccountfornonlineargrowthpatternswillbeneeded.12Undercommonstandexams,eventreeslessthan2.5cmor1incanbemeasured,butthesetreesareoftenconsideredtobepartoftheunderstory(e.g.,byFIA).13Othervariables,suchasfertilization,onlyapplytoafewFVSvariants.


8-8

TheforestinventorydatathatisusedformodelingthechangesinforeststandsislikelythelargestsourceofparameteruncertaintyfortheinputsandassumptionsusedintheFVSmodel;inaddition,therearemanycomponentsintheFVSmodelwherevariationinmeasurementsforthisdataisnottakenintoaccount.Forexample,thepotentialerrordistributionfrommonthtomonthincarbonstorageassociatedwithleavesandfoliageisnotaccountedforintheFVSmodel.

InaresearchpaperonobtainingsamplinguncertaintyinFVS,expertsnotethechallengeofreportingdistributionsofmodelinputsthatincludesamplinguncertaintyinFVSprojections(GreggandHummel,2002).Theystateintheirintroductionthat,“ithasn’tbeenpossibletocomputetheeffectsofsamplinguncertaintybecauseclassicalstatisticalmethodsarenotavailabletomakeinferencesaboutFVSprojections.Avarianceestimatorisnotavailablefortheresultsofsimulation.”

AsprovidedinAppendix8‐A,theFVSmodelprovidesquantitativeinformationontherangeandvariabilityofsamplingdata.ThisFVSapplication,calledFVSBoot,uses“bootstraps”todeterminefluctuationinestimateoutcomes(GreggandHummel,2002)(i.e.,allowsmodelertoempiricallyapproximatethesamplingdistributionofanystatistic/FVSattributeforwhichthemodelerwantstomakeinferences).

Bootstrapsampling14usingtheFVSBootprogramcanbeusedtoempiricallyapproximatethesamplingdistributionofstatisticsforwhichinferencesaretobedrawn.Newsamplesofstandconditionscanbegeneratedbysamplingtheoriginalplotswithreplacementtocreateabootstrapsample.Abootstrapmeancanbegeneratedfromthebootstrapsample.RepeatingthisprocessmultipletimeswillgenerateaMonteCarloapproximationofthedistributionofbootstrapmeans.Thestandarddeviationofthisapproximationwillbeanestimationofthetruestandarddeviationfortheentirepopulation.FVSBootdoesnotcoverallpotentialsourcesofvariationbutitcangiveameasureofimportantcomponentsofuncertaintyinFVSmodelprojections.WhiletheFVSBootprogramcanbeusedtodeterminethesamplinguncertaintyinFVS,itwasnotdevelopedoriginallytoproduceanoveralluncertaintyestimateforFVSoutputs.However,FVSBoothasbeenusedforsensitivityanalysisofsomeFVSoutputs(Hummeletal.,2013).AtoolwouldbeneededtofacilitatedevelopinganestimateoftheuncertaintybasedonacombinationoftheresultsfromtheFVSBootprogramandunderlyingequationsusedintheFVSmodel.

8.1.2.2 UrbanTreePopulationSamplingforUsewiththeFieldDataMethodUsingthei‐TreeEcoModel

Thei‐TreeEcomodelestimatesurbanforestcarbonstocksandgrossandnetannualcarbonsequestrationbasedonsampledataparameters(e.g.,thetreespecies,diameter,height,dieback,andcrownlightexposure)withacalculatedlevelofprecision.Asdesiredbythelandowner,alltreescanbemeasuredorarandomdistributionoffieldplotscanbemeasuredtoquantifytheurbantreepopulation.Largerandmorenumerousplotshelptocreateamorerepresentativesampleandlowertheuncertaintyassociatedwiththesampling.Thei‐TreeEcomodelusesthesampledataparametersandforest‐derivedallometricequationstoestimatecarbonvalues.Themodelalsoestimatesthestandarderroroftheestimatedcarbonvalue,whichisbasedonthesamplinguncertainty,ratherthantheerrorofestimationfromapplyingtheallometricequations.Estimationerrorisunknownandlikelylargerthanthereportedsamplingerror.AMonteCarlo

14Bootstrappingistheprocessofestimatingvariancebyrepeatedrandomsamplingwithreplacementofanexistingdataset.Forexample,todeterminetheprobabilitydistributionofaverageDBHforasampleof100trees,resamplesofthedatasetof100treescanbetakentoapproximatethevariance.


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analysistoolcouldusethestandarderroroftheestimatedcarbonvaluetoevaluatetheuncertaintyassociatedwithanentity’stotalnetGHGemissions.

8.1.2.3 SamplingandDAYCENTforEstimatingBiomassCarboninGrazingLandandAgroforestrySystems

SamplinguncertaintywillexistwhenestimatingbiomasscarbonusingthemethodprovidedinChapter3.Forexample,peakforageestimatesforgrazinglandscanbesampledusingthebiomassclippingmethod.15Thismethodisdestructivewiththeremovalofforagesamplesfromthefield.Thismethodhasbeenshowntoproduceestimateswithlowuncertainty(Lauenrothetal.,2006;Byrneetal.,2011).Non‐destructivemethodscanalsobeusedincludingthecomparativeyieldmethodforrangelands,16ortherobelpolemethodonrangelandsorpastures(Harmoneyetal.,1997;Vermeireetal.,2002).Thebiomassclippingmethodandcomparativeyieldmethodshavelessuncertaintythantherobelpoleorvisualobstructionmethod.Destructivesamplingmethods,however,aremoretimeandlaborintensive.UncertaintyassociatedwiththerobelpolemethodwasassessedintheBlackHillsofSouthDakotainastudybyUreskandBenzon(2007).Theauthorscompareddestructive(clipping)methodsforestimatingbiomassandtherobelpolemethod.Theyfoundtherewasalinearrelationshipbetweenthetwomethodsandthestandarderroroftherobelpoleestimateforasinglemeanwas373kgha‐1.Thestudyfurtherrecommendsthataminimumofthreetransectsbesampledformonitoringareaslessthan259hatobewithin20percentofthemeanand80percentconfident(UreskandBenzon,2007).InasimilarstudybyVermeireetal.,(2002),asinglevisualobstructionmodel(i.e.,robelpolemethod)effectivelyestimatedherbagestandingcropacrossrangetypesandproducedacoefficientofdeterminationof0.93.Anysamplingthatisdone,whetherdestructiveornon‐destructive,shouldoccuratlocationsthatarerepresentativeofthelandparcel.Ifsamplingtheforageisnotfeasible,defaultforageproductionvaluesareprovidedbytheUSDANaturalResourcesConservationService(USDA‐NRCS)inEcologicalSiteDescriptions.17Thesamplinguncertaintywilldependonthemethodusedtocollectthesampleandshouldbeprovidedbythefarmerorlandowner.

8.1.3 LargeDatasetUncertainty

InformationfromseverallargedatasetswillbeusedwiththeGHGquantificationmethodstoestimateemissionsfromtheanimalagriculture,croplandandgrazingland,andforestrysectors.Largedatasetscanbeconsideredanygroupingofdatapointsthatcoverawidetime‐seriesand/orlevelofreportedvariables.Thesedatasetsincludemultipledatalayers,GISdata,databases,andothersuchreportingcatalogues.

ThelargedatasetstobeusedincludetheSmithetal.(2006,alsoknownasGTR‐NE‐343),ForestInventoryandAnalysis,FVS,Daymet,andthedatasetfromthei‐Treemodel.Thesedatasetsprovidevaluesforestimationequationandmodelinputs.Theseinputsincluderegion‐andspecies‐specifictreegrowthrates,landandtreecover,inferredandobservedmeteorologicaldata,soiltypeanddistribution,ammoniacontent,andhistoricalclimatedatafortheNorthAmericancontinent.Thesedatawillbeusedtoinformthecarbondensitiesofsmallforestholdings,coverageofurbantrees,directandindirectN2Oemissions,soilpH,organicmattervalues,ambientairammoniaconcentrations,anddailyairtemperatureandvelocity.

15SeeSection15,“StandingBiomass”(USDANRCS,2011b).16SeeSection13,“DryWeightRank”(USDANRCS,2011b).17SeeUSDANRCS(2011a).


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Thissectionincludesadescriptionofthelargedatasetsusedforestimatingforestryandagroforestrysectorcarbonstocksandstockchanges,GHGemissionsandremovalsfromwetlands,soilcarbonstocks,andammoniaemissions.Thesectionalsoprovidesuncertaintyinformationobtainedfromthedatasetdevelopersandanapproachtoincorporatinguncertaintyassociatedwiththesedatasetsintotheoveralluncertaintyanalysis.

Manyofthelargedatasetsarecomplexandcovermultipleparameters.Insomeinstancesuncertaintyinformationisavailableforsomevariablesbutnotforothers,makingitdifficulttoassesstheuncertaintyoftheentiredataset.Table8‐1belowsummarizestheuncertaintydocumentationavailableforeachlargedatasetused.Themajorityofdatasetsdidnothavepublicallyavailabledocumentationcharacterizingtheassociateduncertainty.

Table8‐1:AvailabilityofUncertaintyInformationforLargeDatasets

DatasetName DatasetAbbreviation

AvailabilityofUncertaintyDocumentation

MethodsforCalculatingForestEcosystemandHarvestedCarbonwithStandardEstimatesforForestTypesoftheUnitedStates(Smithetal.,2006)

GTR‐NE‐343

Nopublishedquantificationofuncertainty.Standarderrorsavailableforcarbondensityforliveandstandingdeadtreesatthe50thand99thpercentileofvolume.

NationalLandCoverDatabase NLCDNopublishedquantitativeuncertaintyinformationfound.Authorsonlyprovideinformationoncontributingfactors.

DailySurfaceWeatherandClimatologicalSummaries

Daymet Nopublishedquantitativeuncertaintyinformationfound.

ContiguousUnitedStatesSoilGeographicDatabase

CONUSSTATSGO

Nopublishedquantitativeuncertaintyinformationfound.

SoilSurveyGeographicDatabase SSURGO Nopublishedquantitativeuncertaintyinformationfound.

AmmoniaMonitoringNetwork AMoNNopublishedquantitativeuncertaintyinformationfound.

Parameter‐ElevationRegressionsonIndependentSlopesModel PRISM


NorthAmericanRegionalReanalysis NARRRegional‐scaleaccuracyandbiasreportedbyMesinger(2006).

NaturalResourcesInventory NRI

Dataarecollectedusingatwo‐stagesamplingprocess.StatisticallyvaliduncertaintiesinmanagementpracticesarecomputableatMajorLandResourceAreasorStatelevel.

NationalAgriculturalStatisticsServiceAgriculturalCensus

NASS‐agriculturalcensus


NationalAgriculturalStatisticsService–CroplandDataLayer

NASS–CroplandDataLayer

NASSprovides accuracyinformationanderrormatrices(totalaccuracy,errorsofomissionandco‐mission),butnotonanannualbasisforcropsandStates.

EconomicResearchServiceCroppingPracticesSurvey

ERS‐CPS Nopublishedquantitativeuncertaintyinformationfound.

EconomicResourceServiceAgriculturalResourceManagementSurvey

ERS‐ARMSNopublishedquantitativeuncertaintyinformationfound.


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DatasetNameDataset

AbbreviationAvailabilityofUncertaintyDocumentation

NationalClimaticDataCenteroftheNationalOceanicandAtmosphericAdministration

NCDC(NOAA)

NCDCprovidesvaluesthatdescribetherangeoftheuncertainty,orsimply"range,"ofeachmonth’s,season's,oryear'sglobaltemperatureanomaly.Thesevaluesareprovidedasplus/minusvalues.

Modern‐EraRetrospectiveAnalysisforResearchandApplications

MERRA(NASA) Nopublishedquantitativeuncertaintyinformationfound.

NationalCentersforEnvironmentalPrediction NCEP(NOAA)


NationalAtmosphericDepositionProgram NADP

Regional‐scaleuncertaintywasassessedinDennisetal.(2011).

8.1.3.1 GTR‐NE‐343CarbonDensityValues

Estimatesofcarbonstocksandstockchangesfromthereport,“MethodsforCalculatingForestEcosystemandHarvestedCarbonwithStandardEstimatesforForestTypesoftheUnitedStates”(Smithetal.,2006)(USDAForestService,GeneralTechnicalReportNE‐343),arebasedonregionalaveragesandreflectthecurrentbestavailabledata.However,accordingtoGTR‐NE‐343,“quantitativeexpressionsofuncertaintyarenotavailableformostdatasummaries,coefficients,ormodelresultspresentedinthe[GTR‐NE‐343]tables.”GTR‐NE‐343lookuptablesincludesomeinformationabouttheconfidenceintervalsforliveandstandingdeadtreecarbondensitiesattwodifferentaveragevolumes(seeTable20ofGTR‐NE‐343),butitdoesnotprescribeamethodforapplyingthesesummaryuncertaintystatisticstostandlevelcarbonstockestimates.

Theuncertaintyassociatedwiththesereportedregionalaveragecarbonstockvaluesislikelyhigherasthesevaluesareappliedtosmaller‐scaleprojectsratherthanregions.Samplinguncertaintyassociatedwiththeregionalaverages,thatarebasedondatasummariesormodels,caninfluenceestimatesforspecificprojects.Theseprojectsaregenerallysmallsubsetsofaregion.Yet,variabilitywithinaregionforvaluesinadatasetwilllikelyhaveamuchgreaterinfluenceonuncertaintythantheactualsamplinguncertaintyassociatedwithcollectingregionalvalues(Smithetal.,2006).

OncetheuserfindsthetableinGTR‐NE‐343thatdescribestheforest’sspeciesmixandregion,theusercanusetheage(orvolume)oftheforeststand(whichisalsocollectedwithahighlevelofuncertainty)tofindoutthemetrictonsofcarbonperacredensityvalueforlivetreecarbon,downdeadwood,organicsoilcarbon,andothercategories.Theuncertaintyinformationisgivenas95percentconfidenceintervalsforthecarbondensityofliveandstandingdeadtrees,attwodifferentgrowingstockvolumes—the50thpercentileandthe99thpercentile.Theseconfidenceintervalsaregivenforeachforesttypeandregion.Tousethisinformationinanuncertaintyanalysisrequiresextrapolationtoothergrowingstockvolumes,whichrequiresmodelingtherelationshipbetweengrowingstockvolumeandvariationincarbondensity.Whilethesetablesaresimpleandeasytouse,theuncertaintyofresultsobtainedbyusingrepresentativeaveragevaluesmaybehighrelativetoothertechniquesthatusesite‐orproject‐specificdata.AdditionalresearchisneededtoincludethisuncertaintyintoaMonteCarloanalysisframework.

8.1.3.2 NationalLandCoverDatabaseMap

TheNationalLandCoverDatabase(NLCD)MapistheproductoftheMulti‐ResolutionLandCharacterizationpartnership,aconsortiumofFederalagenciesincludingtheU.S.GeologicalSurvey,EnvironmentalProtectionAgency(EPA),NationalOceanicandAtmosphericAdministration(NOAA),andtheUSDAForestServicethatarecontinuouslydevelopingdigitallandcoverdata.This


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associationhassuccessfullyprovidedlandcoverdataforthelower48States,Hawaii,Alaska,andPuertoRicofromdecadalLandsatsatelliteimageryandotherassociatedimagingdatasets.ThedatabaseprovidesLandsat‐based,30‐meterresolution,landcoveragecharacteristicsincludingthematicclass(e.g.,urban,agriculture,andforest),percentimpervioussurface,andpercenttreecanopycover.

Regardinguncertainty,theNLCDmapdocumentationindicates,“Unfortunately,thereisnoreadilyavailablereferencedatasetwithwhichtocomparetheinventorytogenerateaccuracystatistics.Referencedatahavetobespecificallygeneratedthroughmanualinterpretationofremotesensingdataforasampleoflocations,ashasbeendoneforaccuracyassessmentoflandcovermaps.Inlieuofsuchanapproach,whichisoutsidethescopeofthisstudy,thebestthatcanbedonecurrentlytodescribetheuncertaintyoftheinventorydataistoidentifytheknownconditionsthatcontributetoit”(NationalLandCoverDatabase,2008).

8.1.3.3 ContinentalUnitedStatesSoilGeographicDatabase

TheContinentalUnitedStatesSoilGeographicDatabase(CONUSSTATSGO)isadigitalgeneralsoilassociationmapthathasbeendevelopedbytheNationalCooperativeSoilSurveyanddistributedbytheUSDANRCS.Itconsistsofbroadbasedinventoryofsoilsandnon‐soilareasthatoccurinarepeatablepatternonthelandscapeandthatcanbecartographicallyshownatscaleandmapped.Noinformationisreadilyavailableontheuncertaintyassociatedwiththisdataset.

8.1.3.4 SoilSurveyGeographicDatabase

TheSoilSurveyGeographic(SSURGO)databasehasbeendevelopedbytheNationalGeospatialManagementCenter,formerlytheNationalCartographyandGeospatialCenter.TheSSURGOdatabasedepictsinformationaboutthekindsofsoilsanddistributionofsoilsonthelandscape.Thisdatasetisadigitalsoilsurveyandgenerallyisthemostdetailedlevelofsoilgeographicdataavailable.Uncertaintyinformationwasnotreadilyavailableforthisdatabasebeyondthedisclaimerthattheaccuracyofdatapoints‘metnationalmapaccuracystandards.’

8.1.3.5 AmmoniaMonitoringNetwork

TheAmmoniaMonitoringNetworkispartoftheNationalAtmosphericDepositionProgram(NADP),andwasoriginallyinitiatedbytheU.S.StateAgriculturalExperimentStations.Thedatasetprovidesconsistent,longtermrecordofammoniagasconcentrationsintheUnitedStates,drawingfrom50monitoringsitesacross37statesintotal.Uncertaintywasnotdirectlyaddressedinthedatasetmaterials,asidefromthedisclaimerthattheNADP’sCentralAnalyticalLaboratory(CAL)analyzes,qualityassures,andprovidestheanalyticaldatatotheNADP(2011).

8.1.3.6 Parameter‐elevationRegressionsonIndependentSlopesModel

TheParameter‐elevationRegressionsonIndependentSlopesModel(PRISM)isaclimatemappingsystemdevelopedbythePRISMClimateGroup.PRISMisaknowledge‐basedsystemthatusespointmeasurementsofprecipitation,temperature,andotherclimaticfactorstoproducecontinuous,digitalgridestimatesofmonthly,yearly,andevent‐basedclimaticparameters.Noinformationisreadilyavailableontheuncertaintyassociatedwiththisdataset.

8.1.3.7 DaymetWeatherDataset

DaymetisaweathermodeldevelopedbyOakRidgeNationalLaboratorythatprovidesinterpolationsextractedfromdailymeteorologicalobservationsontoagriddeddatasetwherenosuchobservationsarepresent.Daymetprovidesoutputparametersincludingtemperature,precipitation,humidity,solarradiation,andsnowwaterequivalent.TheDaymetdatasetisbasedon


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thespatialconvolutionofatruncatedGaussianweightingfilterwiththesetofstationlocations.Sensitivitytothetypicalheterogeneousdistributionofstationsincomplexterrainisaccomplishedwithaniterativestationdensityalgorithm.TheweatherdatasetsareproducedasoutputsfromtheDaymetmodelrun.ThisdatasetisusedasaninputforestimatingGHGemissionsfromcroplandsandgrazinglands,andammoniaemissionsfrommanuremanagement.Noinformationisreadilyavailableonuncertaintyassociatedwiththisdataset.

8.1.3.8 NorthAmericanRegionalReanalysisWeatherDataset

TheDAYCENTmodelsimulationsusetheNorthAmericanRegionalReanalysis(NARR)dataproductfordailytemperatureandprecipitation.TheNARRdatasetwaschosenbecauseitprovidesfull,gap‐filledcoveragefortheconterminousU.S.andismaintainedandupdatedregularly.AsdescribedbyMesinger(2006),“TheNationalCentersforEnvironmentalPrediction(NCEP)NorthAmericanRegionalReanalysis(NARR)isalong‐term,dynamicallyconsistent,high‐resolution,high‐frequency,atmosphericandlandsurfacehydrologydatasetfortheNorthAmericandomain.Itcoversthe25‐yearperiod1979–2003,andisbeingcontinuedinnear‐realtimeastheRegionalClimateDataAssimilationSystem,R‐CDAS.EssentialcomponentsofthesystemusedtogenerateNARRarethelateralboundariesfromandthedatausedfortheNCEP/DOEGlobalReanalysis,theNCEPEtaModelanditsDataAssimilationSystem,arecentversionoftheNOAAlandsurfacemodel,andtheuseofnumerousdatasetsadditionaltoorimprovedcomparedtothoseoftheGlobalReanalyses.Inparticular,NARRhassuccessfullyassimilatedhighqualityanddetailedprecipitationobservationsintotheatmosphericanalysis.Consequently,theforcingtothelandsurfacemodelcomponentofthesystemismoreaccuratethaninpreviousreanalyses,sothatNARRprovidesamuchimprovedanalysisoflandhydrologyandland‐atmosphereinteraction.”Noquantitativeinformationisreadilyavailableonuncertaintyassociatedwiththisdataset.

8.1.3.9 DAYCENTLandManagementDataSets

Dataonpastlanduseandmanagement(priortotheyear2000)arethebasisforrepresentativecroplandmanagementsystems,selectedbytheentitylandowner,thatareusedtoinitialize(“spinup”)theDAYCENTmodelforcomputingsoilorganiccarbonstockchanges.TheattributesofthemanagementsystemsarebasedprimarilyonthreelargedatasetsfortheUS:theNationalResourcesInventory(NRI),theNationalAgriculturalStatisticsService(NASS)croplandsurveys,andUSDAEconomicResearchServiceCroppingPracticesSurvey.TheuseofrepresentativecropmanagementsystemsfortheDAYCENTinitializationprocessintroducessomeuncertaintywhenappliedtoaspecificfarmorranchentity(whichhasauniquemanagementhistorythatmaybedifferentfromtheregionally‐basedrepresentativemanagementhistoriesspecifiedbyMajorLandResourceAreas.However,themajoruncertaintyforthemodelinitializationisdrivenbythetimingofmajorland‐coverchange(e.g.,conversionofgrasslandtocropland)whichcanbeuser‐specifiedfortheparticularentityandlandparcel.

NationalResourcesInventory.TheNRIisaninventoryoflandcoveranduse,soilerosion,primefarmland,wetlands,andothernaturalresourcecharacteristics.NRIwasdesignedasatooltoassessconditionsandtrendsforsoil,water,andrelatednaturalresourcesprimarilyonnon‐FederallandsoftheUnitedStates(NusserandGoebel,1997).TheNRIisastratifiedtwo‐stageareasampleofoverseveralhundredthousandpointsdistributedacrosstheUnitedStatesandCaribbean.Eachpointinthesurveyisassignedanareaweight(i.e.,expansionfactor)basedonotherknownareasandland‐useinformationsothateachpointhasastatisticallyassignedareathatitrepresents(NusserandGoebel,1997).Itshouldbenotedthatthereissomeuncertaintyassociatedwithscalingthepointdatatoaregionorthecountryusingtheexpansionfactors.Ingeneral,thoseuncertaintiesdeclineatlargerscales,suchasStatescomparedtosmallercountyunits,becauseofalargersamplesize.


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NationalAgriculturalStatisticsServiceCropSurveys.DatafromtheNASScountyagriculturalproductionsurveyswereusedtoconstructrepresentativecroprotationsfortheperiodpriorto(i.e.,before1979)thedatarecordintheNRI.NASSconductsthousandsofsurveyseachyearcoveringmanyfacetsofU.S.agriculture.Estimatesincludecropacreage,yield,production,irrigation,andlivestocknumbers.State‐levelcropestimatesareavailablefromasearlyas1866dependingontheStateandvariableofinterest.Somecounty‐levelcropdataisavailablefromasearlyas1915,withmostcropsavailableformostStatesbyabout1960.Dataaggregatedtothecountylevelaresubjecttoahighlevelofqualitycontrol,includingdatascreeningforoutliers,doublecheckingwithprimarydatacollectorsandcomparisonswithotheraggregatedatasetssuchasfromtheUSDAFarmServicesAgency.

USDAEconomicResearchService(ERS)CroppingPracticesSurvey.AncillarydataonhistoricalmanagementpracticesusedintheDAYCENTmodelinitializationincludenitrogenfertilizerrates(USDAERS,1997;2011).Meanfertilizerratessince1990wereestimatedforallmajorcrops,summarizedbyERSattheState‐level.IfaStatewasnotsurveyedforaparticularcroporiftherewerenotenoughdatatoproduceaStatelevelestimate,thendatawereaggregatedtoUSDAFarmProductionRegionsinordertoestimateameanandstandarddeviationforfertilizationrates(FarmProductionRegionsaregroupsofStateswithsimilaragriculturalcommodities).Crop‐specificregionalfertilizerratespriorto1990werebasedlargelyonextrapolationorinterpolationoffertilizerratesfromtheyearswithavailabledata.Forcropsinsomeagriculturalregions,littleornodatawereavailable,and,therefore,ageographicregionalmeanwasusedtosimulatenitrogenfertilizationrates(e.g.,nodataareavailablefortheStateofAlabamaduringthe1970sand1980sforcornfertilizationrates;therefore,meanvaluesfromthesoutheasternUnitedStateswereusedtosimulatefertilizationtocornfields).Nouncertaintydataareavailableforthisdataset.

8.1.3.10 DNDCInputDatasets

TheDeNitrification‐DeComposition(DNDC)modelisproposedtoestimateGHGemissionsandremovalsfromwetlandssystems.DNDCisasoilbiochemistrymodelthatsimulatesthermodynamicandreactionkineticprocessesofcarbon,nitrogen,andwaterdrivenbytheplantandmicrobialactivitiesinecosystems(OlanderandHaugen‐Kozyra,2011).TheDNDCmodelreliesonspecificinputdatasetsthatcanbecategorizedintofivesources:(1)cropland/land‐usedata;(2)cropmanagementdata;(3)soilsdata;(4)weatherdata;and(5)atmosphericdepositiondata(Salasetal.,2012).Theseprimarysourcesofdataanduncertaintyassociatedwiththedatasetareprovidedbelow.

NationalAgriculturalStatisticsServiceCroplandDataLayerdataset.TheDNDCmodelusestheNASSCroplandDataLayerasasourceofcropland/land‐usedata.TheNASSCroplandDataLayerisanonlinegeospatialexploringtoolgeneratedfromsatelliteimageobservationsata30meterresolution.NASSprovidesaccuracyinformationanderrormatrices(totalaccuracy,errorsofomissionandco‐mission),butnotonanannualbasisforcropsandStates.NASSAgriculturalCensus.Thecensusisavailableeveryfiveyears,andusedatthecountyscale.ItprovidesinformationonU.S.farmsandranchesandistheonlysourceofuniform,comprehensiveagriculturaldataatthecountylevel.Farmersandranchersareaskedtorespondtothecensusbymailoronline.Informationincludingproductionexpenses,marketvalueofproducts,andoperationcharacteristicsareafewofthecategoriesofdata.Uncertaintyisnotassessedforthesedata.RemoteSensing.DNDCusesremotesensingtobuildregionaldatabasesoncroplandonaprojectandasneededbasis.TherangeofsensorsusedincludesRapidEye,Landsat,MODIS,andSAR(PALSAR,Radarsat,ENVISAT,etc.).Remotesensingisusedforestimatinghydroperiods(i.e.,wherethewatertableisatanygiventime).AsDNDCdoesnothaveagroundwatermodelingcomponent,


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remotesensingisusedtoidentifywhenwetlandsareflooded.Uncertaintyisnotassessedforthesedata.USDA,ERSAgriculturalResourceManagementSurvey(ARMS).ARMSdataareusedtopopulatethecropmanagementcomponentoftheDNDCmodule.USDAERSARMSprovidedataonthefinancialcondition,productionpractices,andresourceuseoffarmersatthefieldlevelwithintheUnitedStates.ARMSdataarereleasedand/orrevisedtwiceayear.Uncertaintyisnotassessedforthesedata.CONUSSTATSGO(Seedescriptionabove).Thesedataareusedtoassociatesoiltypesanduncertaintyofsoilsdatawithinthemodel.SSURGO(Seedescriptionabove).SSURGOdataareretrievedbyDNDCviaanautomatedretrievalscriptandextractfourkeysoilattributes:claycontent(texture),bulkdensity,organicmatter(soilorganiccarbon),andpH.NOAANationalClimaticDataCenter.DNDCusesstationdatafromtheNOAANationalClimaticDataCenter(NCDC)toinputtemperature,dewpoint,relativehumidity,precipitation,windspeedanddirection,visibility,andatmosphericpressure.Dataareprovidedatthesubhourly,hourly,daily,monthly,annual,andmultiyeartimescale.NCDCprovidesvaluesthatdescribetherangeoftheuncertainty,orsimply"range,"ofeachmonth,season,oryearglobaltemperatureanomaly.Thesevaluescanbeusedasplus/minusvalueswithinanoverallMonteCarloframework;however,atoolisneededtoutilizethisinformation.Daymet(Seedescriptionabove).TheseweatherdataareusedbyDNDCandhavebeenavailableformuchofNorthAmericafrom1980to2012.Uncertaintyinformationisnotavailableforthisdataset.NationalAeronauticsandSpaceAdministrationModern‐EraRetrospectiveAnalysisforResearchandApplications(MERRA).TheDNDCmodelreliesonMERRAsatellitedataasinputforthehydrologicalcycle.MERRAprovidesglobaldataonvariousaspectsofmoisturedistributionandvariability.Nearly30yearsofdataareavailableandhasundergoneanonlinebiascorrectionforsatelliteradianceobservations.Thiswasdonetocalibrateobservationsfromdifferentsatellites.UncertaintydataarenotavailableforMERRAoutput.NationalOceanicandAtmosphericAdministrationNationalCenterforEnvironmentalPrediction(NCEP).DNDCinputsNCEPnationalweather,water,andclimatedataintotheNCEPmodel.NCEPcreatesclimate,water,ocean,space,andenvironmentalhazardoutputs.UncertaintydataarenotavailableforNCEPoutput.NationalAtmosphericDepositionProgramNationalTrendsNetwork(NTN)Stations.DNDCrequirestotalnitrogendepositionandestimatesofaverageconcentration.DNDCreliesontheNADPNTNstationstoinputtotalnitrogendeposition(NO3andNH4)intothemodel.NADPNTNstationscollectprecipitationandchemistrysamplesawayfromurbanareaandpointsourcesofpollution.Thestation’sCentralAnalyticalLaboratoryreviewsdataforcompletenessandaccuracyandflagssamplesthatweremishandledorcompromised.SampledataarefurtherreviewedbytheNADPprogramofficetodoafinalchecktoresolvediscrepancies.Oncedataaremadeavailableonline,DNDCcalculatesmeannitrogendepositionforthesimulationtimeperiodandincorporatesthedataintotheprojectdatabase.NADPNTNstationdatadonothaveassociateduncertaintydataavailable,howeverregionaluncertaintywasanalyzedinapresentationbyDennisetal.(2011).


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8.1.3.11 ApproachforIncorporatingLargeDatasetUncertainty

AmongthelargedatasetstobeusedfortheGHGquantificationmethods,onlyGTR‐NE‐343hassomequantifieduncertaintyinformationforuseinaMonteCarloassessmentofnetGHGemissions.Becauseconfidenceintervalsforonlytwostockvolumesareavailable,onlyalinearrelationshipcanbemodeledwithGTR‐NE‐343information,andnodeparturesfromlinearitycanbeassessed.Furtheranalysisofcarbondensityatothergrowingstockvolumesrequirescomputationofadditionalconfidenceintervals.

Giventhelackofuncertaintyinformationformostoftherelevantlargedatasets,estimatingthissourceofuncertaintyisnotfeasible.Instead,relianceofthemethodsonthelargedatasetsisexplicitlyacknowledgedandreadilyavailableinformationonuncertaintyissummarizedasprovidedabove.

Somelarge“wall‐to‐wall”datasetsareformedviainterpolationofexistingdatafromafixedsetofmeasurementlocations.Forsuchdatasets,apotentialnear‐termnextstepmightbetoincorporateuncertaintybyimputingmeasurementsfromrandomly‐selectedmeasurementlocations.Thisrandomselectioncoulduseprobabilitiesinverselyproportionaltothedistancebetweenthemeasurementlocationsandtheentity.Ifmostlocationsarefarfromtheentity,thentheimputationsareincreasinglyuncertain.

Inthelongerterm,bothnewresearchandsynthesisofexistingresearchwillberequiredtoquantifylargedatasetuncertainty.Methodsfromgeostatistics,forexample,mightbeusedtodescribeanuncertainlargedatasetobtainedbyinterpolation.

8.1.4 ModelUncertainty

Inthecaseoftheexternalmodels,itishardtoappropriatelyaccountforapproximationerrorandoftenonlyonemodelexiststorepresentorestimateemissions(orremovals)fromaspecificactivityorprocess.Sincecomparablemodelsdonotexist,itisalmostimpossibletoestimatetheuncertaintyassociatedwithusingoneparticularmodelversusanother.Whilethisreportspecifiestheuseofseveralexternalmodels—DAYCENT,DNDC,FVS,i‐TreeCanopy,i‐TreeEco,FirstOrderFireEffectsModel(FOFEM)—giventheaboveconsiderations,limitedpublisheddatawasfoundonexternalmodeluncertaintyinherentwiththesemodels.

Thissectionincludesadescriptionoftheexternalmodelsusedforestimatingcarbonstocksandstockchangesfromthecroplandsandgrazinglands,wetlands,andforestrysectors,uncertaintyinformationobtainedfromthemodeldevelopers.Thesemodelshelpprovideaquantitativeandgeographicalviewintotheemissionsassociatedwithavarietyoffactorsfromagriculturalandforestrysystems.Forexample,giveninputssuchasarea,treediameter,treeheight,species,soiltype,andgeography,thesuiteofforestrymodelscanprovideemissionestimatesfromfiredisturbances,approximatechangesinforestcarbonstocks,orprovideurbanforestcarbonstockdata.Table8‐2belowsummarizestheuncertaintyinformationobtainedfromthemodeldevelopersforeachofthemodelsusedtoestimatenetGHGemissions.Giventhelackofquantitativeinformationonmodeluncertainty,thiscomponentofuncertaintywillnotbepartoftheMonteCarlouncertaintyassessment.

Table8‐2:UncertaintyInformationforProcess‐basedModels

ModelAvailabilityofUncertainty

DocumentationOccurrenceofUncertaintyBiases

DAYCENT Ogleetal.,2010BiasesbypracticearequantifiedinOgleetal.(2010).

DNDCa Inputuncertainty:Lietal.(2002) andZhangetal.(2009).Therehavebeen

AMonteCarloapproachorMostSensitiveFactoranalysiscanberunoncertaininputparameters


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ModelAvailabilityofUncertainty

DocumentationOccurrenceofUncertaintyBiases

nopapersfocusedonquantificationofDNDCmodelstructuraluncertainty.

(i.e.,soilmeasurements)toassessthevariabilityoftheparameters(includesexcerptsfromC_AGGwhitepaperbySalasetal.,2012).

ForestVegetationSimulator

Nopublishedquantificationofmodeluncertaintywasfound.

Existsbutnotquantifiableaccordingtoexperts.

i‐TreeCanopy(AerialDataMethod)

Nopublishedquantificationofmodeluncertaintyfound.

Modelbiasislikelylow,accordingtomodeldeveloper.

i‐TreeEco(FieldDataMethod)


Valuesarestandardized,biasisminimized.Unknownbiasfornationaldensityestimates.

FirstOrderFireEffectsModel


Regionalbiases(NorthRockyMountains,PacificNorthwestregions).

aDNDCdoesnotprovideuncertaintyparameterizationofoutputsatthesitelevel,however,theregionalmodelprovidesanoptionforassessinguncertaintyduetoinputuncertainty.

8.1.4.1 DAYCENTModel

TheDAYCENTmodelhasinherentuncertaintyassociatedwithpredictingsoilorganiccarbon(SOC)stockchanges(Ogleetal.,2010;U.S.EPA,2013).Theuncertaintyisassociatedwithimperfectsimulationoftheplantandsoilprocessesassociatedwiththealgorithmsandparameters.Toaddressthisuncertainty,thesimulatedmodelpredictionsofSOCstocksneedtobecomparedtomeasurements.Thecomparisonleveragesthescalabilityoftheprocess‐basedmodeltothewiderangeofconditionsthatexistinagriculturallands,whilehavinganunderlyingmeasurementbasistosupportthereporting(Conantetal.,2011).

ThedifferencesbetweenmeasurementsandsimulatedSOCstocksandstockchangeshavebeenanalyzedusinganempiricallybasedapproachinwhichastatisticalmodelwasdevelopedthatquantifiestheaccuracyandprecisioninthesimulatedpredictions(Ogleetal.,2007).Thelinearmixed‐effectmodelingapproachwasusedforthisanalysis,andvariousenvironmentalconditions(e.g.,climateandsoilcharacteristics)andmanagementpracticeswereevaluatedtodetermineifthemodelismoreaccurateorpreciseforparticularconditionsormanagementsystems.TheapproachreliedonmeasurementsofSOCstocksfromanetworkofsitesacrosstheU.S.agriculturallands.AnetworkiscurrentlybeingexpandedbytheUSDANRCSthatisexpectedtoprovideadditionalmeasurementssupportingtheentity‐scalemethodsforestimatingSOCstockchanges.ThisuncertaintyanalysiswillbeupdatedasnewmeasurementsbecomeavailablefromthenetworkandwillbeincorporatedintoaMonteCarloassessment.

8.1.4.2 DNDCModel

Structuraluncertaintyisrelatedtotheinherentuncertaintyofamodelthatremainsevenifnoneoftheinputdatahadanyvariability.Estimatingmodelstructuraluncertaintyrequirestheuseofindependentvalidationdata(i.e.,fieldmeasurementdatathatwerenotusedtodevelopthemodelalgorithms).Thisapproachrequiresnotonlyaccesstosufficientindependentfielddata,butalsothatthedataincludealltheinputdatathatDNDCrequires.AnumberofvalidationtestswithindependentfielddatahavebeenpublishedalthoughsummarystudiesarecurrentlynotavailabletoquantifyDNDCstructuraluncertainty.


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8.1.4.3 ForestVegetationSimulator

Aspreviouslydescribed,asourceofuncertaintyfortheFVSmodelissamplinguncertaintyassociatedwiththetreelist(themainuserinput).Theadditionaluncertaintyassociatedwiththemodeluncertaintyisdifficulttoquantify.

IntheFVSmodel,diametergrowthistheonlyvariablethatisconsideredstochastic.Forthediametergrowthmodule,arandomseedisusedforprojectionsofchangesinforeststandsratherthanusingthemeandiametervaluetoavoidunderestimatinggrowth.Thisprocessincreaseserrorpropagationbecausetheresultsofthediametergrowthmoduleareusedtomakefurtherestimatesinthemodel,e.g.,usinggrowthandyieldequations(i.e.,Jenkinsequations).However,thestochasticityofdiametergrowthisnotthemaindriverofmodeluncertainty.UncertaintyassociatedwiththeFVSmodeliscomplexbecauseitisderivedfrom20differentregionallyspecificmodelvariantsthatweredevelopedindependently.Eachmodelrunoranalysishastobecalibratedtoaccountforlocaltreevarietyandgrowthrates,introducinganotherlevelofcomplexity(VanDyck,2012).Additionally,errorsmaypropagatefromthebiasinregionalfactors,adjustingtolocalgeographies,climates,theuseoffielddata,andsamplinguncertainty.Giventheoverallcomplexityinherentinthemodel,FVSdoesnotincorporateuncertaintyintheoutputorpost‐analysisofmodelrunsandadditionalresearchisrequiredtoquantifymodeluncertainty.

8.1.4.4 i‐TreeModel

i‐Tree(formerlytheUrbanForestEffectsmodel)isanurbanforestryanalysismodeldevelopedbyDavidJ.Nowak(USDAFS),DanielE.Crane(NRS),andPatrickMcHale(SUNYCollegeofEnvironmentalScienceandForestry).Thei‐Treemodelhelpsquantifythestructureofcommunitytreesandtheenvironmentalservicesthattheyprovide.Itprovidessixanalyticaltoolsincluding:

i‐TreeEco:Providesafullpictureoftheentireurbanforest(usedintheFieldDataMethod)

i‐TreeStreets:Quantifiesbenefitsfromamunicipalitiesstreetleveltrees

i‐TreeHydro:Modelstheeffectsoftreesonwatershedstreamflowandwaterquality

i‐TreeVue:UsesNLCDsatelliteimagerytoassesstreecanopy

i‐TreeDesign:Assessesmultipletreesatparcellevel

i‐TreeCanopy:Providesaquantifiableestimateoftreecoverandotherlandcovertypes(usingintheAerialDataMethod)

i‐TreeEcoandi‐TreeCanopyarerecommendedinthisreportforusebyanentitytoestimatethechangeincarbonstocksintheirurbanforests.

i‐TreeEcoUncertaintyInformation:Thei‐TreeEcomodelproducesuncertaintyestimatesbasedonsamplingerror,butitdoesnotcalculateamodelestimationerror.Accordingtoi‐Treedevelopers,estimationerrorisbasedontheuncertaintyinherentinthebiomassconversionequationsandemissionfactors.Thedevelopersalsonotethatmodelbiasislikelylowgiventhattheinputassumesagivenrandomsampleoftrees,andtreespeciesequationsareselectedbasedonstandheight.Ifaparticularspeciesequationisnotavailablethemodelusestheaverageofavailableequationsfromtheclosestgenera(Nowak,2012).AMonteCarloanalysistoolcouldusethestandarderroroftheestimatedcarbonvaluetoevaluatetheuncertaintyassociatedwithanentity’stotalnetGHGemissions.

i‐TreeCanopyUncertaintyInformation:Thei‐TreeCanopymodelproducesastatisticalestimateofthestandarderrorofthepercenttreecoverestimatebasedontheratioofsamplepoints


8-19

classifiedastreestototalsamplepoints.Ini‐TreeCanopytheuserimportsashapefile,samplespoints,andclassifiesthemaseithertreesornon‐trees.Ananalysisofthetreepointtototalpointratioisusedtoestimatethestandarderrorassociatedwiththepercenttreecoverestimate,asdescribedinthei‐TreeCanopytechnicalnotes,18andshowninEquation8‐1below.

Table8‐3showsestimatesofthestandarderrorasrelatedtotheratiooftreepointstototalsamplepoints(pvalue),wherethetotalnumberofsampledpoints(N)equals1,000.

Basedonthestandarderrorformula,standarderrorisgreatestwhenpequals0.5,andisleastwhenpisverysmallorverylarge(seeTable8‐3).AMonteCarloanalysistoolcouldusethestandarderroroftheestimatedpercenttreecovervaluetoevaluatetheuncertaintyassociatedwithanentity’stotalnetGHGemissions.

8.1.4.5 FirstOrderFireEffectsModel

FOFEMisacomputationalmodelforpredictingtreemortality,fuelconsumption,smokeproduction,andsoilheatingcausedbyeitherprescribedfireorwildfire.FOFEMwasdevelopedbytheIntermountainFireSciencesLaboratoryinMissoula,MT,oftheUSDAForestService.FirstorderfireeffectsarethosecharacterizedwiththedirectimmediateconsequencesofafireincludingGHGemissionestimates.FOFEMisdividedintofournationalregions:PacificWest,InteriorWest,NorthEast,andSouthEast.Themodelincludesseveralforestcovertypestoprovideanadditionallevelofdetailresolution.Thequantitativeoutputcanbeusedinassessmentsafterfiredamage,inanalyzingprescribedfireimpacts,andmodelingvulnerabilitiesinregionalforestgroups.

FOFEMhasaregionalbiasgiventhattheempiricalrelationshipsandassumptionsarebasedonforestedsystemsintheNorthRockyMountainsandthePacificNorthwest.However,theseuncertaintiesarenotquantifiedoradjustedforuseindifferentregions.Forinstance,SoutheastfiresburnwellathumiditylevelsthatwouldnotsupportthemintheWest.Thisphenomenonisnotaccountedforinthemodelandthereisnouncertaintyquantificationaroundtheoutput.Therearealsomaterialdifferencessuchaslitterbulkdensitythatinfluencesconsumptionandemissionwhichcanvaryconsiderablyregiontoregion(Lutes,2012).

18I‐TreeCanopyTechnicalNotes:http://www.itreetools.org/canopy/resources/iTree_Canopy_Methodology.pdf

Table8‐3:EstimatesofStandardError(SE)(N=1,000)ofPercentTreeCoverfromi‐TreeCanopywithVaryingpValues

p SE0.01 0.00310.1 0.00950.3 0.01450.5 0.01580.7 0.01450.9 0.00950.99 0.0031

Equation8‐1:EstimatingStandardErrorofPercentTreeCoverfromi‐TreeCanopy

/ (e.g., 0.33 0.67/1000=0.0149)

Where:

N=Totalnumberofsampledpoints(e.g.,1,000)

n=Totalnumberofpointsclassifiedasatree(e.g.,330)

p=n/N(e.g.,330/1,000=0.33)

q=1− p(e.g.,1− 0.33=0.67)


8-20

8.1.4.6 ApproachforIncorporatingModelUncertainty

Giventhelackofuncertaintyinformationformostoftherelevantexternalmodels,itisnotcurrentlyfeasiblefortheGHGquantificationmethodstoquantifythissourceofuncertainty.Instead,relianceofthemethodsonthemodelswillbeexplicitlyacknowledged.Thepotentialimpactsofuncertainmodelsontheaccuracyandprecisionoftheresultingestimatesisdescribedqualitativelyintheprevioussections.

Itmaybepossibleintheneartermtoelicitexpertjudgmentsonthelevelofmodeluncertaintyattheentitylevel.ModelsusedintheGHGquantificationmethodsaretypicallyconstructedatscalesnosmallerthantheentitylevel.Itisexpectedthatthemodeluncertaintyattheentitylevelwouldbenosmallerthanthemodeluncertaintyatthemodel’sscale,andpossiblylargerduetoadditionalerrorfromdownscalingtotheentitylevel.

Inthelongerterm,moreresearchisneededtoevaluatemodelpredictionswithindependentdata,notusedinthedevelopmentofthemodel.Thedifferencesbetweenmodelpredictionsandindependentdataarethebestpossiblesourceofinformationregardingmodeluncertainty.

ResearchGaps8.2

ThereadilyavailableinformationonparameteruncertaintyisprovidedinthetablesinAppendix8‐B.Asindicated,muchoftheinformationtocharacterizetheuncertaintyisnotavailableandthedatathatareprovidedaremostlydefaultvaluesfromtheliteratureandassumedprobabilitydensityfunctions.ToconductaMonteCarloanalysisforuncertaintyestimation,itisimportanttoobtainprobabilitydensityfunctionsorsummarystatisticsforalluncertainvariables.SignificantresearchisneededtoobtainnewdataandtosynthesizeexistingandnewdatainordertotrulyassessuncertaintyassociatedwitharangeoffactorscausinguncertaintyintheGHGestimatesdevelopedusingtherecommendedmethodsdescribedinthisreport.Inparticular,moreresearchisneededtoassessparameter,sampling,largedatasets,andmodeluncertainties.


8-21

Appendix8‐A:ExampleOutputFilefromFVSSamplingUncertaintyBootstrappingApplicationFVSBoot(asprovidedinGreggandHummel,2002)

ThefollowingtableillustratesstandarddeviationsurroundingthesamplingerroroftheBasalAreaoutputs.FVSBootcanbeconfiguredtodeterminestandarddeviationofthesamplingerrorforanyFVSoutput.

Table8‐A‐1:ExampleOutputFilefromFVSSamplingUncertaintyBootstrappingApplicationFVSBoot(asprovidedinGreggandHummel,2002)

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Appendix8‐B:UncertaintyTables

Thissectionpresentsreadilyavailabledataontheuncertaintyassociatedwithactivityandemissionfactordata.Table8‐B‐1liststhedata

elem

entsthatareprovidedinthesubsequenttablesforeachagriculturesystem.Inparticular,readilyavailableuncertaintyinform

ationis

providedinthefollowingtables:

Table8‐B‐2:CroplandUncertaintyTem

plate

Table8‐B‐3:AnimalPopulationUncertaintyTem

plate

Table8‐B‐4:EntericFermentationandHousingUncertaintyTem

plate

Table8‐B‐5:M

anureManagem

entUncertaintyTable

Table8‐B‐6:ForestryUncertaintyTable

Table8‐B‐1:DataElem

entsProvided

ColumnLabel

Description

DataElem

entNam

eThenam

eofthevariable

Abbreviation/Symbol

Theshorthandrepresentationusedinthereport

EmissionType

Emissionsestimatesthatdependonthedataelement(CH

4,N2O,NH3,CO

2)DataInputUnit

Unitassociatedwiththedataelement

InputSource

Entityentry,defaultentry,m

odeloutput,orfrom

adatabase

Statistic

Availablestatisticfortheparameter

TypeofStatistic

Mean,median,ormode

ProbabilityDistributionType

Theprobabilitydistributionfunctionofthedataelem

ent(normal,lognormal,uniform,triangular,beta)

RelativeUncertainty

Rangeofvaluesaroundthemostlikelyvalue,expressedasapercentofthemostlikelyvalue

ConfidenceLevel

Theprobabilitythattheconfidencerangecapturesthetruevalueofthedataelem

entgivenadistributionof

samples.

EffectiveLowerLimit

Minimum

valuefordataelement(excludingoutliers)

EffectiveUpperLimit

Maximum

valuefordataelement(excludingoutliers)

DataSource

Referenceforinformationrelatedtothedataelementandassociateduncertainty

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Table8‐B‐2:CroplandUncertaintyTem

plate

CroplandSub‐Source

Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic


RelativeuncertaintyLow(%)

RelativeuncertaintyHigh(%)

ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Croplands–MultipleSub‐

sources

Area

A

CH4,N2O,

CO2

Hectares

Entity

Entry


sources

CropYield

YCH

4,N2O,

CO2

Metrictonsdry

mattercrop

yield/year

Entity

Entry


sources

Meatyieldper

parcelofland

Meat

CO2

kgcarcass

yield

Entity

Entry


sources

Milkproduction

perparcelofland

MilkProd.

CO2

kgfluidmilk

yield

Entity

Entry

BiomassCarbonStock

Changes

Meanannual

woodybiomass

(t=currentyear's

stocks)

Wt

CO2

Metrictons

CO2‐eqyear‐1

Model

Output

DAYCEN

T

model

simulations

andgrow

th

functions

foragro‐

forestry

BiomassCarbonStock

Changes

Meanannual

woodybiomass(t‐

1=Previousyear's

stocks)

Wt‐1

CO2

Metrictons

CO2‐eqyear‐1

Model

Output

DAYCEN

T

model

simulations

andgrow

th

functions

foragro‐

forestry

BiomassCarbonStock

Changes

Meanannual

herbaceous

biom

ass

(t=currentyear's

stocks)

Ht

CO2

Metrictons

CO2‐eqyear‐1

Entity

Entry

BiomassCarbonStock

Changes

Meanannual

herbaceous

biom

ass(t‐

1=Previousyear's

stocks)

Ht‐1

CO2

Metrictons

CO2‐eqyear‐1

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

BiomassCarbonStock

Changes

ForageYield

Forageyield

forgrazing

lands

CO2

Metrictonsdry

matterper

hectare

Entity

Entry

BiomassCarbonStock

Changes

Num

beroftrees

byageofdiameter

classforeach

agroforestry

practice

Num

berof

Trees

CO2

Num

ber

Entity

Entry

BiomassCarbonStock

Changes

Diameteratbreast

heightfora

subsam

pleoftrees

DBH

CO2

Meters

Entity

Entry

BiomassCarbonStock

Changes

RoottoShoot

Ratio

R:S

CO2

Ratio

Default

Entry

Westetal.

(2010)

BiomassCarbonStock

Changes

Drymatter

contentof

harvestedcrop

biom

assorforage

DM

CO2

Dimensionless

Entity

Entry

BiomassCarbonStock

Changes

HarvestIndex

HI

CO2

Fraction

Default

Entry

Westetal.

(2010)

BiomassCarbonStock

Changes

Cropharvestor

forageyield,

correctedfor

moisturecontent

Y dm

CO2

Metrictons

biom

assha

‐1

Entity

Entry

BiomassCarbonStock

Changes

Approximate

fractionof

calendaryear

representingthe

grow

ingseason

Y f

CO2

Fraction

Entity

Entry

BiomassCarbonStock

Changes

Carbonfractionof

aboveground

biom

ass

CCO

2Fraction

Default

Entry

0.45

Normal

11.0

11.0

IPCC(1997)

CO2fromLiming

Annualapplication

oflime

M

CO2

Metrictons/

year

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

CO2fromLiming

MetrictonsCO2

emissionsper

metrictonsoflime

EF

CO2

Metrictons

C/metrictons

lime

Default

Entry

‐0.04

46.0

46.0

Westand

McBride

(2005)

CO2fromUreaFertilizer

Application

Annualamountof

ureafertilization

M

CO2

Metrictons

urea/year

Entity

Entry

CO2fromUreaFertilizer

Application

ProportionofCin

urea

EF

CO2

Metrictons

C/metrictons

urea

Default

Entry

0.2

deKleinet

al.(2006)

DirectN

2OEmissions

Areaoforganic

soils(histosols)

drainedona

parcelofland(ha)

Aos

N2O

ha

Entity

Entry

DirectN

2OEmissions

Prior‐yearcrop

type

N2O

MetrictonsN

year

‐1 ha‐1

Entity

Entry

DirectN

2OEmissions

Emissionrate

modeledat0level

ofNinput(Nt=0)

ER0

N2O

Metrictons

N2O‐Nha‐1 year‐

1

Model

Output

DirectN

2OEmissions

Emissionfactor

forthetypical

fertilizationrate

EFtypical

N2O

Metrictons

N2O‐Nmetric

tons

‐1N

Model

Output

DirectN

2OEmissions

TypicalNfertilizer

rate

Ntf

N2O

MetrictonsN

ha‐1year‐1

Database

DirectN

2OEmissions

Emissionratefor

thetypicalcase

modeled

ERtypical

N2O

MetrictonsN

ha‐1year‐1

Model

Output

DirectN

2OEmissions

ActualNfertilizer

rate,including

syntheticand

organic

Nf

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DirectN

2OEmissions

BaseEFscalarfor

ΔNf>zeroand

non‐grassland

crops

S EF

N2O

(metrictons

N2O‐N(metric

tonsN)‐2 )ha

year

Default

Entry

0.0274

Appendix3‐A

DirectN

2OEmissions

BaseEFscalarfor

ΔN=>zeroand

grassland

S EF

N2O

(metrictons

N2O‐N(metric

tonsN)‐2 )ha

year

Default

Entry

0.117

Appendix3‐A

DirectN

2OEmissions

BaseEFscalarfor

ΔNf<zero

S EF

N2O

(metrictons

N2O‐N(metric

tonsN)‐2 )ha

year

Default

Entry

0

Appendix3‐A

DirectN

2OEmissions

Drymatter

contentof

harvestedbiom

ass

DM

N2O

Entity

Entry

DirectN

2OEmissions

Residue:yield

ratios

N2O

Ratio

Entity

Entry

DirectN

2OEmissions

Amountofresidue

harvested,burned

orgrazed

Rr

N2O

Entity

Entry

DirectN

2OEmissions

Fractionoflive

biom

assremoved

bygrazing

F r

N2O

Entity

Entry

DirectN

2OEmissions

Nmineralization

from

manure

Nman

N2O

Entity

Entryand

Model

Output

DirectN

2OEmissions

Nmineralization

from

com

post

Ncomp

N2O

Entity

Entryand

Model

Output

DirectN

2OEmissions

Totaldrymatter

yieldofcrop

N2O

Metrictonsdry

matteryear

‐1

Entity

Entry

DirectN

2OEmissions

Stockingratesand

methods

N2O

Head/acre

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DirectN

2OEmissions

Scalingfactorfor

slow

‐release

fertilizers,0where

noeffect

S sr

N2O

Dimensionless

Default

Entry

‐0.21

Normal

‐0.3

‐0.12Meta‐analysis

DirectN

2OEmissions

Scalingfactor

nitrification

inhibitors–sem

i‐arid/aridclimate

S inh

N2O

Dimensionless

Default

Entry

‐0.38

Normal

‐0.51


DirectN

2OEmissions

Scalingfactor

nitrification

inhibitors–mesic

climate

S inh

N2O

Dimensionless

Default

Entry

‐0.4

Normal

‐0.52


DirectN

2OEmissions

Scalingfactorfor

notill,sem

i‐arid/aridclimate,

<10years

followingno‐till

adoption

S till

N2O

Dimensionless

Default

Entry

0.38

0.04

0.72

vanKesselet

al.(2012);Six

etal.(2004)

DirectN

2OEmissions

Scalingfactorfor

notill,sem

i‐arid/aridclimate,

≥10years

followingno‐till

adoption

S till

N2O

Dimensionless

Default

Entry

‐0.33

‐0.5

‐0.16

vanKesselet

al.(2012);Six

etal.(2004)

DirectN

2OEmissions

Scalingfactorfor

notill,mesic/w

et

climate,<10years

followingno‐till

adoption

S till

N2O

Dimensionless

Default

Entry

‐0.015

‐0.16

0.16

vanKesselet

al.(2012);Six

etal.(2004)

DirectN

2OEmissions

Scalingfactorfor

notill,mesic/w

et

climate,≥10years

followingno‐till

adoption

S till

N2O

Dimensionless

Default

Entry

‐0.09

‐0.19

0.01

vanKesselet

al.(2012);Six

etal.(2004)

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DirectN

2OEmissions

Ninslow‐release

Nfertilizerapplied

totheparcelof

land

Nsr

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

DirectN

2OEmissions

Ninmanure

amendm

ents(and

sewagesludge)

addedtothe

parcel

Nman

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

DirectN

2OEmissions

Nexcretedby

cattle,poultryand

swinedirectlyon

theparcelofland

(metrictonsN

year‐1ha‐1)

Nprp

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

DirectandIndirectN

2O

Emissions

Ninsynthetic

fertilizerapplied

toaparcelofland

Nsfert

N2O

MetrictonsN

year

‐1ha‐1

Entity

Entry

DirectN

2OEmissions

Nfrom

achangein

soilorganicmatter

mineralizationdue

toLUCortillage

changeappliedto

aparcelofland

Nmin

N2O

MetrictonsN

year

‐1

Entity

Entry

DAYCEN

T

model

derived

DirectN

2OEmissions

Nfractionof

aboveground

biom

assforthe

croporforage

Na

N2O

Dimensionless

Entity

Entry

DirectN

2OEmissions

Nfractionof

belowground

biom

assforthe

croporforage

Nb

N2O

Dimensionless

Entity

Entry

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

IndirectN

2OEmissions

Emissionratefor

croppedhistosols,

EROS

N2O

Metrictons

N2O‐Nha‐1

year

‐1

Default

Entry

0.008

Uniform

0.002

0.02 4

IPCC(2006)

IndirectN

2OEmissions

Nfertilizerapplied

oforganicorigin

includingmanure,

sewagesludge,

compostandother

organic

amendm

ents

F ON

N2O

MetrictonsN

year

‐1

Entity

Entry

IndirectN

2OEmissions

FractionofNSN

thatvolatizesas

NH3andNOx

FRSN

N2O

kgNkg‐1 N

sfert

Default

Entry

0.1

Uniform

0.03

0.3

IPCC(2006)

IndirectN

2OEmissions

Fractionor

proportionofF

ON

thatvolatizesas

NH3andNOx

FRON

N2O

kgNkg‐1 N

ON

Default

Entry

0.2

Uniform

0.05

0.5

IPCC(2006)

IndirectN

2OEmissions

Emissionfactor

forvolatilizedNor

proportionofN

volatilizedasNH3

andNOxthatis

transformedto

N2Oinreceiving

ecosystem

EFvol

N2O

kgN

2O‐N

kg‐1N

Default

Entry

0.01

Uniform

0.002

0.05

IPCC(2006)

IndirectN

2OEmissions

Fractionor

proportionofNt

thatleachesor

runsoff

FRLeach

N2O

kgNkg‐1 N

Default

Entry

0.3

Uniform

0.1

0.8

IPCC(2006)

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Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

IndirectN

2OEmissions

Emissionfactor

forleachedand

runoffNor

proportionof

leachedandrunoff

Nthatis

transformedto

N2Oinreceiving

ecosystem

EFLeach

N2O

kgN

2O‐N

kg‐1N

Default

Entry

0.0075

Uniform

0.0005

0.02 5

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Cultivationperiod

forriceunderi,j,

andkconditions

t ijk

CH4

Days

Entity

Entry

Methanefrom

WetlandRice

Cultivation

Annualharvested

areaofricefori,j,

andkconditions

Aijk

CH4

Hectares/year

Entity

Entry

Methanefrom

WetlandRice

Cultivation

Applicationrateof

organic

amendm

ent(s)

ROAi

CH4

Metrictons/

hectare

Entity

Entry

Methanefrom

WetlandRice

Cultivation

Baselineem

ission

factorfor

continuously

floodedfields

withoutorganic

amendm

ents

EFc

CH4

kgCH4/ha/

day

Default

Entry

1.3

Uniform

0.8

2.2

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

duringthe

cultivationperiod

–ScalingFactor

SFwfor

continuously

flooded

CH4

ScalingFactor

from

IPCC

Default

Entry

1

Uniform

0.79

1.26

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

duringthe

cultivationperiod

–ScalingFactor

SFwforsingle

aeration

CH4

ScalingFactor

from

IPCC

Default

Entry

0.6

Uniform

0.46

0.8

IPCC(2006)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-31

8-31


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Methanefrom

WetlandRice

Cultivation

Waterregime

duringthe

cultivationperiod

–ScalingFactor

SFwfor

multiple

aerations

CH4

ScalingFactor

from

IPCC

Default

Entry

0.52

Uniform

0.41

0.66

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

beforethe

cultivationperiod

–Scalingfactor

SFpfornon‐

floodedpre‐

season<180

days

CH4

ScalingFactor

from

IPCC

Default

Entry

1

Uniform

0.88

1.14

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

beforethe

cultivationperiod

–Scalingfactor

SFpfornon‐

floodedpre‐

season>180

days

CH4

ScalingFactor

from

IPCC

Default

Entry

0.68

Uniform

0.58

0.8

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Waterregime

beforethe

cultivationperiod

–Scalingfactor

SFpforflooded

pre‐season>

30days

CH4

ScalingFactor

from

IPCC

Default

Entry

1.9

Uniform

1.65

2.18

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Organic

amendm

entused

–scalingfactor

SFo

CH4

ScalingFactor

from

IPCC

Default

Entry

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAifor

straw

incorporation

lessthan30

daysbefore

cultivation

CH4

Conversion

factorfrom

IPCC

Default

Entry

1

Uniform

0.97

1.04

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAifor

straw

incorporation

morethan30

daysbefore

cultivation

CH4

Conversion

factorfrom

IPCC

Default

Entry

0.29

Uniform

0.2

0.4

IPCC(2006)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-32


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAifor

compost

CH4

Conversion

factorfrom

IPCC

Default

Entry

0.05

Uniform

0.01

0.08

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAiforfarm

yardmanure

CH4

Conversion

factorfrom

IPCC

Default

Entry

0.14

Uniform

0.07

0.2

IPCC(2006)

Methanefrom

WetlandRice

Cultivation

Organic

amendm

ent

conversionfactor

CFOAifor

greenmanure

CH4

Conversion

factorfrom

IPCC

Default

Entry

0.5

Uniform

0.3

0.6

IPCC(2006)

MethaneUptakebySoils

PotentialCH4

Oxidationinsoils

PCH4for

grassland

CH4

kgCH4ha‐1

year

‐1

Default

Entry

3.2

Normal

0

6.9

DelGrossoet

al.(2000)


PotentialCH4

Oxidationinsoils

PCH4for

coniferous

forest

CH4

kgCH4ha‐1

year

‐1

Default

Entry

2.8

Normal

0.1

5.5

DelGrossoet

al.(2000)


PotentialCH4

Oxidationinsoils

PCH4for

deciduous

forest

CH4

kgCH4ha‐1

year

‐1

Default

Entry

11.8

Normal

1.9

21.6

DelGrossoet

al.(2000)


CH4oxidation

attenuationfactor:

croplandincluding

set‐aside(CRP)

grassland,grazing

land,andfertilized

orrecently

harvestedforests

AF

CH4

N/A

Default

Entry

0.30

Normal

0.07

1Sm

ithetal.

(2000)


CH4oxidation

attenuationfactor:

naturalvegetation,

0‐100yearsafter

abandonm

entof

agricultural

productionor

timberharvest

AF

CH4

N/A

Default

Entry

0.3+(0.007

×years

since

abandonm

ent)

Normal

0.07+

(0.007

×years

since

abando

nment)

1Sm

ithetal.

(2000)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-33

8-33


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


CH4oxidation

attenuation

factor:>100years

post‐managem

ent

orneverusedfor

agricultural

managem

entor

timberharvest

AF

CH4

N/A

Default

Entry

1

Normal

0.07

1Sm

ithetal.

(2000)

N2Ofrom

WetlandRice

TotalNinputs

from

all

agronomic

sources:mineral

fertilizer,organic

amendm

ents,

residues,and

additional

mineralization

from

LUCortillage

change(metric

tonsNyear‐1 )

Nt

N2O

MetrictonsN

year

‐1

Entity

Entry

N2Ofrom

WetlandRice

FertilizerN

managem

ent

N2O

Rate

Entity

Entry

N2Ofrom

WetlandRice

Organicfertilizer

N

N2O

%N

Entity

Entry

N2Ofrom

WetlandRice

CropresidueN

N2O

%N

Entity

Entry

N2Ofrom

WetlandRice

Emissionfactoror

proportionofN

ttransformedto

N2O

EF

N2O

kgN

2O‐N

(kgN)‐1

Default

Entry

0.0022

Normal

0.2%

0.2%

Akiyamaet

al.(2005)

N2Ofrom

WetlandRice

Scalingfactorto

accountfor

drainageeffects

SFDfor

continuously

flooded

system

s

N2O

Dimensionless

Default

Entry

0

Akiyamaet

al.(2005)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-34


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

N2Ofrom

WetlandRice

Scalingfactorto

accountfor

drainageeffects

SFDforaerated

system

sN2O

Dimensionless

Default

Entry

0.59

Normal

0.4%

0.4%

Akiyamaet

al.(2005)

NonCO2Emissions

BiomassBurn

BorealForest(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.34

Normal102%

102%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Wildfire

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.4

Normal340%

340%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Crow

nfire

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.43

Normal104%

104%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Surfacefire

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.15

Normal

96%

96%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Postloggingslash

burn

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.33

Normal130%

130%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tem

perateForest

(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.45

Normal

51%

51%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Postloggingslash

burn

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.62

Normal264%

264%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Shrublands(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.72

Normal147%

147%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Callunahealth

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.71

Normal121%

121%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Fynbos

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.61

Normal195%

195%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Savanna

woodlands(early

dryseasonburns)

(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.4

Normal

93%

93%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Savanna

woodlands

(mid/latedry

seasonburns)(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.74

Normal

99%

99%

IPCC(2006)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-35

8-35


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

NonCO2Emissions

BiomassBurn

Savannawoodland

(mid/late)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.72

Normal270%

270%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tropicalsavanna

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.73

Normal598%

598%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Othersavanna

woodlands

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.68

Normal931%

931%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Savanna

grasslands(early

dryseasonburns)

(all)

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.74

Normal183%

183%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tropical/sub‐

tropicalgrassland

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.74

Normal270%

270%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tropical/sub‐

tropicalgrassland

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.92

Normal151%

151%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Tropicalpasture

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.35

Normal427%

427%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Savanna

Combustion

Efficiency(C)

CH4/N2O

Default

Entry

0.86

Normal

85%

85%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Emissionfactor

EFfor

grassland

CH4

gGHG/kg

burned

biom

ass

Default

Entry

2.3

Mean

Normal

8.0%

8.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Emissionfactor

EFforcrop

residue

CH4

gGHG/kg

burned

biom

ass

Default

Entry

2.7

Mean

Normal50.0%

50.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Emissionfactor

EFfor

grassland

N2O

gGHG/kg

burned

biom

ass

Default

Entry

0.21

Mean

Normal93.0%

93.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Emissionfactor

EFforcrop

residue

N2O

gGHG/kg

burned

biom

ass

Default

Entry

0.07

Mean

Normal50.0%

50.0%

IPCC(2006)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-36


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cfor

shrublands

CH4/N2O

%Burned

Default

Entry

0.72

Mean

Normal68.0%

68.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cfor

grasslands

withearly

seasonburns

CH4/N2O

%Burned

Default

Entry

0.74

Mean

Normal50.0%

50.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cfor

grasslands

withmidto

lateseason

burns

CH4/N2O

%Burned

Default

Entry

0.77

Mean

Normal66.0%

66.0%

IPCC(2006)

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cforsm

all

grains

CH4/N2O

%Burned

Default

Entry

0.9

Mean

Normal50.0%

50.0%

Expert

Assessm

ent

NonCO2Emissions

BiomassBurn

Combustion

efficiency

Cforlarge

grainandother

cropresidues

CH4/N2O

%Burned

Default

Entry

0.8

Mean

Normal50.0%

50.0%

Expert

Assessm

ent

NonCO2Emissions

BiomassBurn

Moisturecontent

ofresiduesand

forage

CH

4/N2O

%moisture

Default

Entry

NonCO2Emissions

BiomassBurn

Residuetoyield

ratioofcrop

R:Y

CH4/N2O

Metrictons

residue/

metrictonsdry

matteryield

Default

Entry

SOCChangeMineralSoils

SoilorganicC

stockattheendof

theyear

SOC t

CO2

MetrictonsC

ha‐1

Model

Output

DAYCEN

T

Model

derived

Ogleetal.

(2007);EPA

(2013)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-37

8-37


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


SoilorganicC

stockatthe

beginningofthe

year

SOC t‐1

CO2

MetrictonsC

ha‐1

Model

Output

DAYCEN

T

Model

derived

Ogleetal.

(2007);EPA

(2013)


Cropselectionand

RotationSequence

CO

2Managem

ent

ListDeveloped

byExperts

Entity

Entry


Irrigation

applicationrate

CO

2Gallonsper

minute

Entity

Entry


MineralFertilizer

applicationRate

CO

2lbs/squarefoot

Entity

Entry


LimeAmendm

ent

applicationRate

CO

2lbs/squarefoot

Entity

Entry


Organic

Amendm

ent

applicationRate

CO

2lbs/squarefoot

Entity

Entry


Num

berofpasses

ineachoperation

CO

2Num

ber

Entity

Entry


Depthofdrainage

CO

2Meters

Entity

Entry


Lengthoffield

CO

2Meters

Entity

Entry


HistoricalW

eather

Patterns

CO

2PRISM

WeatherData

Model

Output


Physicaland

Chem

ical

PropertiesofSoil

CO

2NRCSSURRGO

database

Model

Output

SOCChangeMineralSoils–

GrazingLand

AnimalSizeused

forgrazing

CO

2lbs

Entity

Entry

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-38


Category

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


GrazingLand

StockingRate

CO

2Head/acre

Entity

Entry


GrazingLand

Irrigation

applicationrate

CO

2Gallonsper

minute

Entity

Entry


GrazingLand

MineralFertilizer

applicationRate

CO

2lbs/squarefoot

Entity

Entry


GrazingLand

Depthofdrainage

CO

2Meters

Entity

Entry

SOCChangeOrganicSoils

Emissionfactor

EFforcropland

incool

temperate

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

11

Mean

Normal

45.0

45.0

Ogleetal.

(2003)


Emissionfactor

EFforcropland

inwarm

temperate

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

14

Mean

Normal

35.0

35.0

Ogleetal.

(2003)


EmissionfactorEFforcropland

insubtropical

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

14

Mean

Normal

46.0

46.0

Ogleetal.

(2003)


Emissionfactor

EFforgrazing

landincool

temperate

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

2.8

Mean

Normal

45.0

45.0

Ogleetal.

(2003)


Emissionfactor

EFforgrazing

landinwarm

temperate

regions

CO2

MetrictonsC

ha‐1 year‐1

Default

Entry

3.5

Mean

Normal

35.0

35.0

Ogleetal.

(2003)


Emissionfactor

EFforgrazing

landin

subtropical

regions

CO2

MetrictonsC

ha‐1year‐1

Default

Entry

3.5

Mean

Normal

46.0

46.0

Ogleetal.

(2003)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-39

8-39

Table8‐B‐3:AnimalPopulationUncertaintyTem

plate

AnimalPopulationData

Elem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic


RelativeUncertaintyLow(%)


ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

NumberofAnimals

Beefreplacementheifers

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Dairyreplacementheifers

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Maturebeefcow

sN

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Steers(>500lbs)

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Bulls

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Stockers(All)

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Cattleonfeed

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Dairycow

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Cattle

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Americanbison

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

SheepNOF

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Feedlotsheep

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Goats

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Horses

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Mules/burros/asses

N

CH4,N2O

EntityEntry

‐1.0%

1.0%

ExpertAssessm

ent

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-40

Table8‐B‐4:EntericFerm

entationandHousingUncertaintyTem

plate

DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

DailyMilkProduction

Milk

CH4

kgmilk/animal/day

EntityEntry

3%

5%

Expert

Assessm

ent

Daysinmilk

DIM

CH4

Days

EntityEntry

Drymatterintake

DMI

CH4

kg/animal/day

EntityEntry

DailyWorkDonebyAnimal

Work

CH4

Hours/day

EntityEntry

Averagelivebodyweight–lactating

beefcow

sBW

CH4

kg

EntityEntry

BeefCow

MatureWeight

MW

CH4

lbs

EntityEntry

SteerDailyWeightGainto24months

WG

CH4

lbs/day

EntityEntry

BeefSteerMatureWeight

MW

CH4

lbs

EntityEntry

BeefHeiferMatureWeight

MW

CH4

lbs

EntityEntry

Netenergyrequiredbytheanimalfor

maintenance

NE m

CH4

MJday

‐1

EntityEntry

MilkFatContent

Fat

CH4

Percent

EntityEntry

StarchContentofDiet(DairyCow

s)

Starch

CH5

kg/animal/day

EntityEntry

AcidDetergentFiberContentofDiet

ADF

CH4

kg/head/day

EntityEntry

DE–EachFeedType

DE

CH4

Percentofgrossenergy

EntityEntry

NeutralDetergentFiberinDiet(Dairy

Cows)

NDF

CH4

Percent

EntityEntry

CrudeProteininDiet

CP

CH4

Percent

EntityEntry

AcidDetergentFiberContentofDiet

(DairyCow

s)

ADF

CH4

Percent

EntityEntry

NeutralDetergentFiberinDiet

NDF

CH4

Percent

EntityEntry

SupplementalFat(feedlot)

S.Fat

CH4

Percent

EntityEntry

3%

Mean

24

Expert

Assessm

ent

DietaryForage%

CH

4Percent

EntityEntry

TotalDigestibleNutrients(DairyCow

s)

TDN

CH4

kg

EntityEntry

YmFeedlot–AllRegions

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmBeefCattleNotonFeed(stocker)

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

Ym

BeefCattleNotonFeed(allforaging

animalsexceptdairy)

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

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8-41

DataElem

entNam

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Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

YmDairyRepl.Heif.–California

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–West

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

Ym

DairyRepl.Heif.–NorthernGreat

Plains

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–Southcentral

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–Northeast

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–Midwest

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

YmDairyRepl.Heif.–Southeast

Ym

CH4

%GEconvertedtoCH4

DefaultEntry

Maximum

dailyemissionsfordairy

cows

E max

CH4

MJ/head

DefaultEntry

45.98

Millsetal.

(2003)

Averagelivebodyweightforlactating

cows

BW

N2O/NH3

kg

EntityEntry

TypicalAmmoniaLossesfrom

Dairy

HousingFacilities–Opendirtlots(cool,

humidregion)

NH3loss

N2O 3

PercentofN

ex

DefaultEntry

15%

30%

Koelshand

Stow

ell

(2005)


Dairy

HousingFacilities–Opendirtlots(hot,

aridregion)

NH3loss

N2O

PercentofN

ex

DefaultEntry

30%

45%

Koelshand

Stow

ell

(2005)


Dairy

HousingFacilities–Roofedfacility

(flushedorscraped)

Roofedfacility(dailyscrapeandhaul)

NH3loss

N2O

PercentofN

ex

DefaultEntry

5%

15%

Koelshand

Stow

ell

(2005)


Dairy


(shallowpitunderfloor)

NH3loss

N2O

PercentofN

ex

DefaultEntry

10%

20%

Koelshand

Stow

ell

(2005)


Dairy


(beddedpack)

NH3loss

N2O

PercentofN

ex

DefaultEntry

20%

40%

Koelshand

Stow

ell

(2005)


Dairy


(deeppitunderfloor,includesstorage

loss)

NH3loss

N2O

PercentofN

ex

DefaultEntry

30%

40%

Koelshand

Stow

ell

(2005)


Beef

HousingFacilities–Opendirtlots(cool,

humidregion)

NH3loss

N2O

PercentofN

ex

DefaultEntry

30%

45%

Koelshand

Stow

ell

(2005)

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DataElem

entNam

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Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


Beef

HousingFacilities–Opendirtlots(hot,

aridregion)

NH3loss

N2O

PercentofN

ex

DefaultEntry

40%

60%

Koelshand

Stow

ell

(2005)


Beef


(beddedpack)

NH3loss

N2O

PercentofN

ex

DefaultEntry

20%

40%

Koelshand

Stow

ell

(2005)


Beef



loss)

NH3loss

N2O

PercentofN

ex

DefaultEntry

30%

40%

Koelshand

Stow

ell

(2005)

N2OEmissionFactorformanurein

housing(drylotsandpitstorage)

EFN2O

N2O

kgN

2O‐N/kgN

DefaultEntry

IPCC(2006)

NitrogenExcretionfrom

BeefCattle—

Daysonfeedforanindividualration

DOF x

N2O,NH3

Days

EntityEntry


BeefCattle—

Livebodyweightatfinishoffeeding

period

BW

FN

2O,NH3

kg

EntityEntry


BeefCattle—

Livebodyweightatthestartoffeeding

period

BW

IN

2O,NH3

kg

EntityEntry


BeefCattle—

Standardreferenceweightforexpected

finalbodyfat

SRW

N2O,NH3

kg

EntityEntry


BeefCattle—

Concentrationofcrudeproteinoftotal

ration

C CP‐x

N2O,NH3

gcrudeprotein/gdry

feed

EntityEntry

MonthlyBeefFeedlotNH3Emissions—

Dietarycrudeprotein

CP

NH3

Percentofdrymatter

EntityEntry

Averagemonthlytemperature

T

N2O,NH3

DegreesKelvin

EntityEntry


Grow‐Finish

Pigs–Averagedailyfeedintakeover

finishingperiod

ADFI

G

N2O,NH3

g/day

EntityEntry


Grow‐Finish

Pigs–Concentrationofcrudeproteinof

total(wet)ration

C CP

N2O,NH3

Percent

EntityEntry

C

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8-43

DataElem

entNam

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Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


Grow‐Finish

Pigs–Daysonfeedtofinishanimal

(grow‐finishphase)

DOF G

N2O,NH3

Days

EntityEntry


Grow‐Finish

Pigs–Final(market)bodyweight

BW

FN2O,NH3

kg

EntityEntry


Grow‐Finish

Pigs–Averagedressingpercent(yield)

atfinalw

eight

DP F

N2O,NH3

Percent

EntityEntry


Grow‐Finish

Pigs–Initialbodyweight

BW

IN2O,NH3

kg

EntityEntry


Grow‐Finish

Pigs–Averagefat‐freeleanpercentage

atfinalw

eight

FFLP

FN2O,NH3

Percent

EntityEntry


WeaningPigs–

Averagedailyfeedintakeoverfinishing

period

ADFI

G

N2O,NH3

g/day

EntityEntry


WeaningPigs–


(wet)ration

C CP

N2O,NH3

Percent

EntityEntry


WeaningPigs–

Daysonfeedtofinishanimal(nursery

phase)

DOF N

N2O,NH3

Days

EntityEntry


WeaningPigs–

Averagefat‐freeleangainfrom

20to

120kg

FFLPG

N2O,NH3

g/day

EntityEntry


WeaningPigs–

Finalbodyweightinnurseryphase

BW

F‐N

N2O,NH3

kg

EntityEntry


WeaningPigs–

Initialbodyweightinnurseryphase

BW

I‐N

N2O,NH3

kg

EntityEntry


GestatingSow

s–Averagedailyfeedintakeduring

gestation

ADFI

SN2O,NH3

g/day

EntityEntry


GestatingSow

s–Concentrationofcrudeprotein

C CP

N2O,NH3

Percent

EntityEntry


GestatingSow

s–Gestationperiodlength

GL

N2O,NH3

Days

EntityEntry


GestatingSow

s–Gestationleantissuegain

GLTG

N2O,NH3

Kg

EntityEntry

Cha

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DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


GestatingSow

s–Num

berofpigsinlitter

LITTERN2O,NH3

Head

EntityEntry


Lactating

Sows—

Averagedailyfeedintakeduring

lactation

ADFI

LACT

N2O,NH3

g/day

EntityEntry


Lactating

Sows—

Concentrationofcrudeprotein

C CP

N2O,NH3

Percent

EntityEntry


Lactating

Sows—

Lactationlength(daysto

weaning)

LL

N2O,NH3

Days

EntityEntry


Lactating

Sows—

Lactationleantissuegain

LLTG

N2O,NH3

Kg

EntityEntry


Lactating

Sows—

Litterweightatw

eaning

L WEAN

N2O,NH3

Kg

EntityEntry


Lactating

Sows—

Litterweightatbirth

LWBIRTHN2O,NH3

kg

EntityEntry


Swine


(flushedorscraped)Roofedfacility

(dailyscrapeandhaul)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

5%

15%

Koelshand

Stow

ell

(2005)


Swine


(shallowpitunderfloor)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

10%

20%

Koelshand

Stow

ell

(2005)


Swine


(beddedpack)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

20%

40%

Koelshand

Stow

ell

(2005)


Swine



loss)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

30%

40%

Koelshand

Stow

ell

(2005)


Broilers,

Turkeys,andDucks—Feedintakeper

phase

FIx

N2O,NH3

gfeed/finishedanimal

EntityEntry


Broilers,

Turkeys,andDucks—Concentrationof

crudeproteinoftotalrationineach

phase

C CP‐X

N2O,NH3

gcrudeprotein/g(wet)

feed

EntityEntry

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8-45

DataElem

entNam

e

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource


Broilers,

Turkeys,andDucks—Retentionfactor

fornitrogen

NRF

N2O,NH3

Fraction

EntityEntry


LayingHens—

Feedintake

FI

N2O,NH3

gfeed/finishedanimal

EntityEntry


LayingHens—


ration

C CP

N2O,NH3

gcrudeprotein/g(wet)

feed

EntityEntry


LayingHens—

Eggweight

Egg w

tN2O,NH3

gEntityEntry


LayingHens—

Fractionofeggsproducedeachday

Egg p

ro

N2O,NH3

Eggs/hen/day

EntityEntry


Poultry

Housing–Roofedfacility(litter)(Meat

Producingbirds)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

25%

50%

Koelshand

Stow

ell

(2005)


Poultry

Housing–Roofedfacility(stacked

manureunderfloor‐,includesstorage

loss)(Egg‐producingbirds)

%NH3

loss

NH3

PercentofN

ex

DefaultEntry

25%

50%

Koelshand

Stow

ell

(2005)

MethaneEmissionsfrom

Goats–

Emissionfactorforgoats

EFG

CH4

kgCH4/head/day

DefaultEntry

0.0137

Mean

IPCC(2006)

MethaneEmissionsfrom

Bison–

Emissionfactorforbison

EFAB

CH4

kgCH4/head/day

DefaultEntry

Cha

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Table8‐B‐5:M

anureManagem

entUncertaintyTem

plate

DataElem

entNam

e

DataElementAbbreviation/Symbol

EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

TotalDryManure–BeefFinishingCattle

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

2.4

Mean

‐20

20

ASABE(2005)

TotalDryManure–BeefCow

(confinem

ent)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

6.6

Mean

‐20

20

ASABE(2005)

TotalDryManure–BeefGrowingcalf

(confinem

ent)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

2.7

Mean

‐20

20

ASABE(2005)

TotalDryManure–DairyLactatingcow

CH4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

8.9

Mean

‐20

20

8.7

11.3

ASABE(2005)

TotalDryManure–DairyDrycow

CH4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

4.9

Mean

‐20

20

8.8

11.2

ASABE(2005)

TotalDryManure–DairyHeifer

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

3.7

Mean

‐20

20

ASABE(2005)

TotalDryManure–DairyVeal118kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.12

Mean

‐20

20

ASABE(2005)

TotalDryManure–HorseSedentary500

kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

3.8

Mean

‐20

20

ASABE(2005)

TotalDryManure–HorseIntense

exercise500kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

3.9

Mean

‐20

20

ASABE(2005)

TotalDryManure–PoultryBroiler

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.03

Mean

‐20

20

ASABE(2005)

TotalDryManure–PoultryTurkey(male)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.07

Mean

‐20

20

ASABE(2005)

TotalDryManure–PoultryTurkey

(fem

ales)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.04

Mean

‐20

20

ASABE(2005)

TotalDryManure–PoultryDuck

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.04

Mean

‐20

20

ASABE(2005)

TotalDryManure–Layer

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.02

Mean

‐20

20

ASABE(2005)

TotalDryManure–Sw

ineNurserypig

(12.5kg)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.13

Mean

‐20

20

ASABE(2005)

TotalDryManure–Sw

ineGrowfinish(70

kg)

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.47

Mean

‐20

20

ASABE(2005)

TotalDryManure–Sw

inegestatingsow

200kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.5

Mean

‐20

20

ASABE(2005)

C

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8-47

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

TotalDryManure–Sw

ineLactatingsow

192kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

1.2

Mean

‐20

20

ASABE(2005)

TotalDryManure–Sw

ineBoar200kg

CH

4,N2O,

NH3

kgdry

manure/animal/day

EntityEntry

0.38

Mean

‐20

20

ASABE(2005)

Volatilesolids–BeefFinishingcattle

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.81

Mean

‐25

25

ASABE(2005)

Volatilesolids–BeefCow(confinem

ent)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.89

Mean

‐25

25

ASABE(2005)

Volatilesolids–BeefGrowingcalf

(confinem

ent)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.85

Mean

‐25

25

ASABE(2005)

Volatilesolids–DairyLactatingcow

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.84

Mean

‐25

25

ASABE(2005)

Volatilesolids–DairyDrycow

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.85

Mean

‐25

25

ASABE(2005)

Volatilesolids–DairyHeifer

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.86

Mean

‐25

25

ASABE(2005)

Volatilesolids–DairyVeal118kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

Mean

‐25

25

ASABE(2005)

Volatilesolids–HorseSedentary500kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.79

Mean

‐25

25

ASABE(2005)

Volatilesolids–HorseIntenseexercise

500kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.79

Mean

‐25

25

ASABE(2005)

Volatilesolids–PoultryBroiler

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.73

Mean

‐25

25

ASABE(2005)

Volatilesolids–PoultryTurkey(male)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.8

Mean

‐25

25

ASABE(2005)

Volatilesolids–PoultryTurkey(fem

ales)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.79

Mean

‐25

25

ASABE(2005)

Volatilesolids–PoultryDuck

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.58

Mean

‐25

25

ASABE(2005)

Volatilesolids–Layer

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.73

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

ineNurserypig(12.5

kg)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.83

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

ineGrowfinish(70kg)

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.8

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

inegestatingsow

200

kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.9

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

ineLactatingsow

192

kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.83

Mean

‐25

25

ASABE(2005)

Volatilesolids–Sw

ineBoar200kg

VS

CH4,N2O

kgVS/kgdrymanure

EntityEntry

0.89

Mean

‐25

25

ASABE(2005)

Storagetemperature

T

CH4

Kelvin

EntityEntry

Manuretemperature

Tmanure

NH3

Kelvin

EntityEntry

Ambientairvelocity

Va

NH3

m/s

DefaultEntry

Height

hN2O

m

EntityEntry

Width

W

NH3

m

EntityEntry

Radius

rNH3

m

EntityEntry

pH

pH

NH3

‐EntityEntry

7.5

6.5

8.5

Expert

Assessm

ent

Cha

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DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Totalnitrogenatagivenday–beef

finishingcattle

N2O

kgN/kgdrymanure

EntityEntry

0.07

Mean

ASABE(2005)

Totalnitrogenatagivenday–beefcow

(confinem

ent)

N2O

kgN/kgdrymanure

EntityEntry

0.03

Mean

ASABE(2005)

Totalnitrogenatagivenday–beef

grow

ingcalf(confinem

ent)

N2O

kgN/kgdrymanure

EntityEntry

0.05

Mean

ASABE(2005)

Totalnitrogenatagivenday–dairy

lactatingcow

N2O

kgN/kgdrymanure

EntityEntry

0.05

Mean

ASABE(2005)

Totalnitrogenatagivenday–dairydry

cow

N2O

kgN/kgdrymanure

EntityEntry

0.05

Mean

ASABE(2005)

Totalnitrogenatagivenday–dairyheifer

N2O

kgN/kgdrymanure

EntityEntry

0.03

Mean

ASABE(2005)

Totalnitrogenatagivenday–dairyveal

118kg

N2O

kgN/kgdrymanure

EntityEntry

0.13

Mean

ASABE(2005)

Totalnitrogenatagivenday–Horse

Sedentary500kg

N2O

kgN/kgdrymanure

EntityEntry

0.02

Mean

ASABE(2005)

Totalnitrogenatagivenday–Horse

IntenseExercise

N2O

kgN/kgdrymanure

EntityEntry

0.04

Mean

ASABE(2005)

Totalnitrogenatagivenday–poultry,

broiler

N2O

kgN/kgdrymanure

EntityEntry

0.04

Mean

ASABE(2005)


turkey(male)

N2O

kgN/kgdrymanure

EntityEntry

0.06

Mean

ASABE(2005)


turkey(females)

N2O

kgN/kgdrymanure

EntityEntry

0.06

Mean

ASABE(2005)


duck

N2O

kgN/kgdrymanure

EntityEntry

0.04

Mean

ASABE(2005)

Totalnitrogenatagivenday–layer

N2O

kgN/kgdrymanure

EntityEntry

0.07

Mean

ASABE(2005)

Totalnitrogenatagivenday–swine

nurserypig(12.5kg)

N2O

kgN/kgdrymanure

EntityEntry

0.09

Mean

ASABE(2005)

Totalnitrogenatagivenday–swinegrow

finish(70kg)

N2O

kgN/kgdrymanure

EntityEntry

0.08

Mean

ASABE(2005)


gestatingsow200kg

N2O

kgN/kgdrymanure

EntityEntry

0.06

Mean

ASABE(2005)


lactatingsow192kg

N2O

kgN/kgdrymanure

EntityEntry

0.07

Mean

ASABE(2005)

Totalnitrogenatagivenday–swineboar

200kg

N2O

kgN/kgdrymanure

EntityEntry

0.07

Mean

ASABE(2005)

Totalammonianitrogeninthemanure–

beefearthenlot

TAN

NH3

kgNH3/m

3 EntityEntry

0.1

Mean

00.02

ASABE(2005)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-49

8-49

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource


poultry,leghornpullets

TAN

NH3

kgNH3/m

3 EntityEntry

0.85

Mean

0.66

1.04

ASABE(2005)


poultry,leghornhen

TAN

NH3

kgNH3/m

3 EntityEntry

0.88

Mean

0.54

1.22

ASABE(2005)


poultry,broiler

TAN

NH3

kgNH3/m

3 EntityEntry

0.75

Mean

ASABE(2005)

Ammoniaconcentrationintheliquid–

dairylagooneffluent

NH3

NH3

kgNH3/m

3 Calculated

0.08

Mean

ASABE(2005)


dairyslurry(liquid)

NH3

NH3

kgNH3/m

3 Calculated

0.14

Mean

ASABE(2005)


SwineFinisher‐Slurrywet‐dryfeeders

NH3

NH3

kgNH3/m

3 Calculated

0.5

Mean

ASABE(2005)


SwineSlurrystorage‐dryfeeders

NH3

NH3

kgNH3/m

3 Calculated

0.34

Mean

0.19

0.49

ASABE(2005)


Swineflushbuilding

NH3

NH3

kgNH3/m

3 Calculated

0.14

Mean

ASABE(2005)


Swineagitatedsolidsandwater

NH3

NH3

kgNH3/m

3 Calculated

0.05

Mean

ASABE(2005)


SwineLagoonsurfacewater

NH3

NH3

kgNH3/m

3 Calculated

0.04

Mean

ASABE(2005)


SwineLagoonsludge

NH3

NH3

kgNH3/m

3 Calculated

0.07

Mean

ASABE(2005)

MethaneConversionFactor(MCF)a–Dairy

Cow

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)

MethaneConversionFactor

a –Cattle

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Buffalo

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Market

Swine

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a–Breeding

Swine

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)

MethaneConversionFactora–Layer(Dry)

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a–Broiler

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a–Turkey

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Duck

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Sheep

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Goat

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Horse

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Mule/Ass

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-50

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource


a –Buffalo

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Invessel

manurecomposting

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a–Staticpile

manurecomposting

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Intensive

windrow

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)


a –Passive

windrow

MCF

CH4

%

DefaultEntry

‐30

30

IPCC(2006)

Maximum

MethaneProducingCapacities–

BeefReplacementHeifers

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐20

20

U.S.EPA

(2011)

Maximum


DairyReplacementHeifers

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.17

‐20

20

U.S.EPA

(2011)

Maximum


MatureBeefCow

sBo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐20

20

U.S.EPA

(2011)

Maximum


Steers(>500lbs)

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐20

20

U.S.EPA

(2011)

Maximum


Stockers(All)

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.17

‐20

20

U.S.EPA

(2011)

Maximum


CattleonFeed

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐20

20

U.S.EPA

(2011)

Maximum


DairyCow

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.24

‐20

20

U.S.EPA

(2011)

Maximum


Cattle

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.19

‐20

20

U.S.EPA

(2011)

Maximum


Buffalo

b Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.1

IPCC(2006)

Maximum


MarketSwine

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.48

‐30

30

IPCC(2006)

Maximum


BreedingSw

ine

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.48

‐30

30

IPCC(2006)

Maximum


Layer(dry)

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.39

‐30

30

IPCC(2006)

Maximum


Layer(wet)

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.39

‐30

30

IPCC(2006)

Maximum


Broiler

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.36

‐30

30

IPCC(2006)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-51

8-51

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Maximum


Turkey

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.36

‐30

30

IPCC(2006)

Maximum


Duck

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.36

‐30

30

IPCC(2006)

Maximum


Sheep

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.19

‐20

20

IPCC(2006)

Maximum


Feedlotsheep

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.36

‐20

20

IPCC(2006)

Maximum


Goat

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.17

‐30

30

IPCC(2006)

Maximum


Horse

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.3

‐30

30

IPCC(2006)

Maximum


Mule/Ass

Bo

CH4

m3 CH

4/kgVS

DefaultEntry

0.33

‐30

30

IPCC(2006)

EmissionfactorforthefractionofCH4

producedthatleaksfrom

theanaerobic

digester–Digesterswithsteelorlined

concreteorfiberglassdigesterswithagas

holdingsystem

(eggshapeddigesters)and

monolithicconstruction

EFCH

4,

leakage

CH4

%

DefaultEntry

2.8

CDM(2012)



theanaerobic

digester–UASBtypedigesterswith

floatinggasholdersandnoexternalw

ater

seal

EFCH

4,

leakage

CH4

%

DefaultEntry

5

CDM(2012)



theanaerobic

digester–Digesterswithunlined

concrete/ferrocement/brickmasonry

archedtypegasholdingsection;

monolithicfixeddomedigesters

EFCH

4,

leakage

CH4

%

DefaultEntry

10

CDM(2012)



theanaerobic

digester–Otherdigesterconfigurations

EFCH

4,

leakage

CH4

%

DefaultEntry

10

CDM(2012)

Tem

porarystorageofliquid/slurry

manure–N

2Oemissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.005

‐50

100

U.S.EPA

(2011)

Long‐termstorageofsolidmanure–N

2O

emissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.002

‐50

100

U.S.EPA

(2011)

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-52

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Long‐termstorageofslurrymanure–N2O

emissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.005

‐50

100

U.S.EPA

(2011)

CattleandSwineDeepBedding(Active

Mix)‐N

2Oemissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.07

IPCC(2006)

CattleandSwineDeepBedding(NoMix)‐

N2Oemissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.01

IPCC(2006)

PitStorageBelow

AnimalConfinem

ents‐

N2Oemissionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.002

IPCC(2006)

Naturalaerationaerobiclagoons–N2O

conversionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.01

‐50

100

IPCC(2006)

Forcedaerationaerobiclagoons–N2O

conversionfactor

c EF

N20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.005

‐50

100

IPCC(2006)

N2Oemissionfactorforliquidstorage–

uncoveredliquidmanurewithacrustc

EFN20

N2O

kgN

2O‐N/kgN

DefaultEntry

0.8

‐50

100

IPCC(2006)


uncoveredliquidmanurewithoutacrustc

EFN20

N2O

kgN

2O‐N/kgN

DefaultEntry

0

‐50

100

IPCC(2006)


coveredliquidmanurec

EFN20

N2O

kgN

2O‐N/kgN

DefaultEntry

0

‐50

100

IPCC(2006)

Composting–Ammoniaemission(loss)

relativetototalnitrogeninmanure

EFNH3

NH3

kgNH3‐N/kgN

DefaultEntry

0.05

Hellebrand

andKalk

(2000)

ManureManagem

ent–MultipleSources–

collectionefficiency,coveredstorage(with

orwithoutcrust)

ηCH

4percentage

DefaultEntry

1

Sommeretal.

(2004)

ManureManagem


collectionefficiency,uncoveredstorage

withcrustformation

ηCH

4percentage

DefaultEntry

0

Sommeretal.

(2004)

ManureManagem


collectionefficiency,uncoveredstorage

withoutcrustform

ation

ηCH

4percentage

DefaultEntry

‐0.40

Sommeretal.

(2004)

ManureManagem


Ratecorrectingfactors(b

1)

b 1

CH4

dimensionless

DefaultEntry

1

Sommeretal.

(2004)

ManureManagem


Ratecorrectingfactors(b

2)

b 2

CH4

dimensionless

DefaultEntry

0.01

Sommeretal.

(2004)

ManureManagem


Arrheniusparam

eter,cattle

A

CH4

gCH

4/kgVS/hr

DefaultEntry

43.33

Sommeretal.

(2004)

ManureManagem


Arrheniusparam

eter,swine

A

CH4

gCH

4/kgVS/hr

DefaultEntry

43.21

Sommeretal.

(2004)

C

hapt

er 8

: Unc

erta

inty

Ass

essm

ent f

or Q

uant

ifyin

g G

reen

hous

e G

as S

ourc

es a

nd S

inks

8-53

8-53

DataElem

entNam

e


EmissionType

DataInputUnit

InputType

EstimatedValue

TypeofEstimate



EffectiveLowerLimit

EffectiveUpperLimit

DataSource

Potentialm

ethaneyieldofthemanure

cattle

E CH4,pot‐

CH4

kgCH4/kgVS

DefaultEntry

0.48

Sommeretal.

(2004)

Potentialm

ethaneyieldofthemanure‐

swine

E CH4,pot

CH4

kgCH4/kgVS

DefaultEntry

0.5

Sommeretal.

(2004)

ManureManagem


Kinem

aticviscosityofaire

vNH3

m2 /s

DefaultEntry

White(1999)

ManureManagem


MassdiffusivityofNH3e

D

NH3

m2 /s

DefaultEntry

Watson

(1966)and

Baker(1969)

Tem

porarystackandlong‐termstockpile–

Resistancetomasstransferthroughthe

manuree

Rs

NH3

s/m

DefaultEntry

Rotzetal.

(2011)

Tem


Resistancetomasstransferthroughthe

covere

Rc

NH3

s/m

DefaultEntry

Rotzetal.

(2011)

Tem


Ratiodegradablevolatilesolidstototal

volatilesolids‐cattleliquidmanure

VS d/VS T

CH4

Unitless

DefaultEntry

0.46

Mølleretal.

(2004)

Tem


Ratiodegradablevolatilesolidstototal

volatilesolids‐swineliquidmanure

VS d/VS T

CH4

Unitless

DefaultEntry

0.89

Mølleretal.

(2004)

Tem


RatioNon‐degradablevolatilesolidsto

totalvolatilesolids‐cattleliquidmanure

VS n

d/VS

T

CH4

Unitless

DefaultEntry

0.54

Mølleretal.

(2004)

Tem


Rationon‐degradablevolatilesolidsto

totalvolatilesolids–swineliquidmanure

VS n

d/VS

T

CH4

Unitless

DefaultEntry

0.11

Mølleretal.

(2004)

Solid‐liquidseparation–Efficiencyof

mechanicalsolid‐liquidseparation

e

CH4,N2O,

NH3

Percent

EntityEntry

Fordand

Flem

ing

(2002)

a Thevaluesformethaneconversionfactor(MCF)varydependingonthetemperatureandthemanuremanagem

entsystem.IPCC(2006)providesestimateduncertainty

rangesfortheseMCFs.

b TherearenodataforNorthAmericaregion;thedatafrom

WesternEuropeareusedtocalculatetheestimation.Thereisnoreporteduncertaintyforthisadaptedvalue.

c IPCC(2006)reportslargeuncertaintieswithdefaultN

2Oemissionfactors.TheN

2OEFvaluesvarydependingontheanimalspeciesandtemperatureofthemanure

managem

entsystem.

d ValuesforN2Oconversionfactorsareavailablefordairycow,cattle,swine,andotheranimalsandcanbefoundinthechapter.

e Defaultvaluesareavailableinthechapter.

Cha

pter

8: U

ncer

tain

ty A

sses

smen

t for

Qua

ntify

ing

Gre

enho

use

Gas

Sou

rces

and

Sin

ks

8-54

Table8‐B‐6:ForestryUncertaintyTable

ForestrySub‐

SourceCategory

DataElementName

Abbreviation/Symbol

EmissionType

DataInputUnit

InputSource

Statistic

TypeofStatistic




ConfidenceLevel(%)

EffectiveLowerLimit

EffectiveUpperLimit

DataSource

UrbanForestry–

AerialDataMethod

TreeCover

Percent

CO

2%

Model

Output

i‐TreeCanopy

UrbanForestry–

AerialDataMethod

UrbanArea

CO

2m

2 Entity

Entry

UrbanForestry–

AerialDataMethod

Average

Annual

Carbon

Sequestration

CO

2kg

C/m

2 /yearDefault

Entry

2.8

mean

14.814.8

Now

aketal.(2013)

UrbanForestry–

AerialDataMethod

Average

Carbon

Storage

CO

2kgC/m

2 Default

Entry

7.69

mean

Now

aketal.(2013)


8-55

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