INSEA

• Integrated economic and environmental assessment of climate change mitigation options (LULUCF)

• Integration of farm-level and forest plot-level models with regional and national models

Integrated Sink Enhancement Assessment

INSEA

Economic approach

• to estimate abatement costs:– how much does it cost to farmers to reduce emissions? – Total and marginal costs

• to assess the potential of mitigation policies– how much emissions can be expected from the use of policy

instruments (emission tax, input tax, quotas,…)?

• to capture the heterogeneity of abatement costs– where (who) will abatement occur for a given level of incentive?

• to determine (spatially, trend) emissions and sinks• to link GHG emissions and C sequestration to agricultural and forestry activities

Ecological approach

link

SCENARIOS

SUSTAINABLE IMPACTASSESSMENT

COST-EFFECTIVENESSANALYSIS

COST BENEFIT ANALYSIS

INTEGRATED FRAMEWORKSANALYSIS• databases• models/tools for simulation/foresightsPARTICIPATORY APPROACHES

S.D. STRATEGIES FORSENSITIVE REGIONS

MULTIFONCTIONAL ASPECTSLandscapeRural developmentLand use (infrastructures)Environmental protectionAgriculture/Forests

EXTERNALITIES &THRESHOLDS of SUSTAINABILITY

LAND USE AND SUSTAINABLE DEVELOPMENT

INSEA INSEA

INSEA

Strategy of the Commission (1)

Strategy of the Commission (2)

FARMING SYSTEMS CHARACTERIST. and BENCHMARKING (SD aspects)

•Environment technologies

•BEHAVIOURAL CHANGES

• EXTERNALITIES

STRATEGIES

•LAND USE STRAT.•RURAL DEVELOPM.•PUBLIC GOODS STRAT.

•INTERNATIONAL COOPERATION DIM.

MULTIFUNCT.• DEFINITION• MEASURING• TRADE OFF

Micro Macro

BOTTOM

UP

TOP

DOWN

AGRICULTURE AND SUSTAINABLE DEVELOPMENT

SUSTAINABILITY IMPACTASSESSMENT

andGOVERNANCE

INSEA

INSEA

INSEA

INSEA

Monitoring of Negotiations

Database and Database Strategy

Bio-physical Model

Cost Model

Validation

and

Assessment

Policy implications

Scenario Model

Approach INSEA (1)

WP 3000 Problems to solve

• integrate socioeconomic & biophysical data, spatial & tabular information

• create an ecosystem-based GIS

• match different scales

• maintain thematic and spatial consistency

• develop interface with models

• build common metadatabase

WP 3000 structureWP No WP title DB Topic Source and Scale

3000 Data and Database

3100 GIS coverages Biophysical andinfrastructural data

Vegetation (pre-industrial) CORINE/Bohn-Map: Pot. Nat. Vegetation

Land Cover (LC) CORINE

Soils ESGDB 1:1 Mio

Soil degradation ESGDB

Runoff 3-D, GTOPO30 Precipitation

Inner water bodies HYDRO1K Europe

Climate UK CU/MARSTopography (Geomorphology) 3-D, GTOPO30 Infrastructure 3-D, GTOPO30

3200 Agricultural Area Statistics/ socio-economic statistics

Compile metadata and defaults as model input

Forest Ownership/ownership structure

EUROSTAT, other EU projects

farm types EUROSTAT, Capri, MARS3300 GHG Data/

Management levelCompile metadata and defaults as model input

see table xx Network organisation and centralised data delivery: CarboEurope (CarboInvent, CarboAge, FORCAST, Greengrass, CarboData), domestic/regional cropland research, COST E21, COST 629, other domestic C sequestration projects

3400 Spatial Data Processing/Maps

Combination/merging of spatial LULUCFdata,and spatial connection with aggregated GHGand socio-economic information

3500 Public Data Portal web-map server with results, connected to CarboData or independent

WP 3100 GIS coverages

• overview: see CarboData (CORINE, SGDB, Topography, Climate, water catchments, etc.)

• thematic maps: biomass (see ALTERRA report), soil (see JRC map on soil C)

• thematic Maps to be produced in the project (such as litter fall, soil fertility index, N2O emissions – may be the product of 3300 depending on data availability)? or imported from related projects (e.g. CAPRI DynaSpat)

WP 3200 Auxiliary Data

• Farm management/activity data: area statistics/ proportions (farm types, practices, crop production, etc.)

• Additional data needed to define farm types with respect to emission factors: animal density, proximity to market, etc

• parameter identification• definition of what is

“bottom”: management unit• access FADN (Farm

Accounting Data Network), LUCAS, INVECOS

Requirements

WP 3300 Auxiliary Data

• LULUCF data:C sequestration rates, CO2, CH4 and N2O emission factors (most likely non-representative)

• Model input data

• List of practices• Access IPCC emission

factor data base (public) – check for completeness using national reporting

• Results from ongoing research (see ECCP and TWG SOM task 5)

• feed EPIC/DNDC

Requirements

WP 3400 Integration

• create the links between data compilation and data utilization

• Application of upscaling techniques

• model-parameters/farm types need to sooner or later relate to soil+climate

• spatially link activity data with auxiliary data (statistics) and LULUCF data

• Data harmonisation (INSPIRE standards)

• Results from 3100-3300• ongoing data needs from the

models• connect spatially at the

common (smallest) denominator: EU grid (50x50 km)

Requirements

WP 3500 Web Portal

• Example: CarboDat

WP 3000 Work Package Structure

P r a c t ic e s G ISL a n d u se s ta tis t ic s

R esea rchD a ta

S o il M o d e lD a ta

E co n o m ic M o d e lD a ta

resea rch da ta (partly pub lished)

A c t iv ity D a ta

“ L U L U C FD a ta ”

IP C C -lik e e m is s io n fa c to r s E P IC /D N D C e m is iso n fa c to r s

C t/y e a r

E m is s io n s /re m o v a ls p e r a re a /re g io n

C ro s s-v a lid a tio nf ro m b o tto m -u p /to p -d o w n c o m p a ris o n

E m is s io n s /re m o v a ls p e r fa rm ty p e

+ pheno logy+ com lex m aps like the clim atic w ater ba lance+ ecoreions e tc .)

EU R O STAT N U TS II/N U T SIIIIN V E C O SLU C A SFA D N

N 2 O /y e a r

C O 2 /y e a r

C H 4 /y e a r

c o s t/re v e n u e s /y e a r

IP C C d e fa u lt v a lu e sR e se a rc h P ro je c ts (F o re s t R e se a rc h : C a rb o D a t)

+ la n d c o n v e rs io n (fo c u s A R D )

techn ica l da talabor, em ission fac tors,

c ro pg ra ssfo re s t

defin esc a le a n d fa rmty p e /p ra c tic e s

re la te toc lim a te , s o il ,

to p o g ra p h y, c o n -su m e r/p ro c e s s in g

m a rk e tin ha /reg ion

d a ta e n tr y

- s ca le- d a ta typ e s- u n d err ep re sen te d a re a

Model Overview (1)

Model Overview (2)

meso

micro

macro

microEFEM – Economic Farm Emission Model

Feeding module

Grassland farming

Animal husbandry

N-cycle-N-yield model

Manure module

INP

UT

: M

ean

s o

f p

rod

uct

ion

, em

issi

on

s

OU

TP

UT

: P

rod

uct

s, e

mis

sio

ns

Political background, economical data, farm structures

MechanisierungsverfahrenMechanisation techniques

Arable farming

1

micro

Rhe

in/B

oden

see

Sch

war

zwal

d

Alb/Baar

Allgäu

Oberland/Donau

Albvorland/Schwäbischer Wald

Unterland/Gäue

Bauland/HohenloheMap of homogenous regions in Baden-Würrtemberg

EFEM – Economic Farm Emission Model 2

microEFEM – Economic Farm Emission Model 3

Regional capacities,

factoral capacities and extrapolation

factors of farm types

(VGG2= Rhein/Bodens

ee)

Inte

nsi

ve li

vest

ock

(po

ult

ry)

Inte

nsi

ve li

vest

ock

(pig

s)

Fo

rag

e g

row

ing

(cat

tle)

Fo

rag

e g

row

ing

(sh

eep

)

Per

man

ent

Cro

ps

Reg

ion

al

Cap

acit

ies

VG

G2

Crop land ha 80,0 80,0 21,0 - 3,0 123.539

Permanent pasture ha - - 45,4 78,0 - 48.554

Orchards ha - - 0,0 - 5 15.985

Vineyards ha - - 0,0 - 3,6 11.596

Sugar beet ha 1,3 1,3 - - 1.512

Potatoes ha - - - - 0,5 1.658

Male cattle Cap. - - 19,0 - 20.289

Dairy cows Cap. - - 30,0 - 32.109

Suckler cows Cap. - - 6,2 - 6.594

Sheep Cap. - - - 700,0 36.100

Pigs for fattening Cap. - 150,0 - - 53.884

Breeding pigs Cap. - 35,2 - - 12.660

Laying hens Cap. 396,3 - - - 310.731

Broiler Cap. 13,0 - - - 10.230

Extrapolation factor Value 784,2 359,2 1070,3 51,6 3197,0

Database : FADN

Database : agricultural census data

microEFEM – Economic Farm Emission Model 4

EFEMEFEM DNDCDNDC

soil mapsoil map land use mapland use map climate dataN-depositionclimate dataN-deposition

GISdatabase

GISdatabase

crop areafertilizer intensityC + N of manure

crop areafertilizer intensityC + N of manure

C-balanceSOC, C-pools

emissions (soil)N2O, CH4, CO2

leachingNO3, DOC

C-balanceSOC, C-pools

C-balanceSOC, C-pools

emissions (soil)N2O, CH4, CO2

emissions (soil)N2O, CH4, CO2

leachingNO3, DOCleaching

NO3, DOC

farm emissionsN2O, CH4, CO2, NH3

return ratesshadow prices

mitigation costseconomic indicators

farm emissionsN2O, CH4, CO2, NH3

farm emissionsN2O, CH4, CO2, NH3

return ratesshadow prices

mitigation costseconomic indicators

return ratesshadow prices

mitigation costseconomic indicators

EFEM-DNDC managementphenology

managementphenology

farmstructures

farmstructures

politicalenvironment

politicalenvironment

economicindicatorseconomicindicators

emissionfactors

emissionfactors

meso

Data resolution :FADN regionAdministrativeregions

AROPAj model

Estimation of GHG abatement andcarbon sequestration costs from agriculture

Animal « block »- cattle demographic balance

- capital adjustment- feeding

Crop « block »- yields

-fertilizers (N org. & min.)- use (market / on-farm)

Manure - CH4

- organic N

GHG- CH4- N2O

- NO, O3 ?+ C

C sequestration- soils (change in practice, land use)

- upper biomass (trees)

Yieldsfunctions

Climatechange

adaptation

Modular structure

1

meso 2

Area Animal Feeding

Animal numbers

N2O agricultural soils (synthetic fertilizers)

X (N use)

N2O agricultural soils (crop residues and N-fixing crops)

X (N use)

N2O agricultural soils (manure applied to soils)

X

N2O agricultural soils (animal production)

X

N2O manure management X

CH4 manure management X

CH4 enteric fermentation X (X)

CH4 rice cultivation X

Carbon sequestration (X)

AROPAj model

meso 3

Data(FADN)

- Yields- Area- Variable costs- Producing activities- Size of farms- Altitude- …

Other sources

- Emissions coefficients- Soils characteristics- Fertilizer uses and prices- …

Typology15 countries, 101

regions734 farm-types

Model inputs- Prices- Technical parameters- CAP-related parameters

Calibration

734 modelsMaximize gross margin

Subject to :- Technical constraints

- Policy constraints

Model output- Optimal area- Livestock numbers- Animal feeding- Net emissions

Estimation

AROPAj model

meso 4

Farm Type

Country

Region

Crop

Sources

• European Soil Map

(1/10 )6

• MARS Project JRC DataBase

• FADN : AROPAj calibrating procedure

Manure

Irrigation

Sources

• FAO • Eurostat

• Experts

Cultivars

N fertilizer type

Fertilization calendar

Others management

crop data for STICS

DataBase

soil

climat

Fertilizer prices

AropaStix : Client-Server Architecture

SERVER

Oracle, MySql, PostGres, …..

Java ClientJava Client

Java Client

Network

in progressBinta Niang

AROPAj model

macroIMAGE POLESCanada CanadaUSA USA

Mexicorest of Central AmericaBrazilrest of South AmericaEgyptAlgeria & Lybiarest of North Africa

West Africa West AfricaEast Africa East AfricaSouthern Africa Southern Africa

FranceUnited KingdomItalyGermanyAustriaBelgium & LuxemburgDenmarkFinlandIrelandThe NetherlandsSwedenSpainGreecePortugalrest of West EuropeHungary, Poland, Czech and Slovak Republicsrest of Eastern Europe

Former Soviet Union exUSSR TurkeyGulf countriesRest of Middle EastIndiaRest of South AsiaChinaRest of East Asia

Southeast Asia South East AsiaOceania OceaniaJapan Japan

Eastern europe

Middle East

South Asia

East Asia

Central America

South America

North Africa

OECD Europe

Data resolution :IMAGE (17 regions).FAO statistical data (38+2 Poles regions)

Agripol model

– dairy livestock– non-dairy livestock– rice– cereals – pulses and oil seeds– roots and tubers– artificial pastures – biofuel.

8 agricultural activities

soil

Data resolution :field-size area - up to 100 ha

Soil model: EPIC

Possible Non-CO2 GHGabatement in the agricultural sector

Major components• weather simulation• hydrology• erosion-sedimentation• nutrient and carbon cycling• pesticide fate• plant growth and competition• soil temperature• tillage• economics• plant environment control

• crop rotations• tillage operations• irrigation scheduling• drainage• furrow digging• liming• grazing• burning operations• tree pruning• thinning and harvest• manure handling• fertilizer and pesticide application rates and timing.

Management components

1

soil

Hydrological Response UnitHRU

E P IC

R ain , S no w , C hem ica ls

S ubsurface F low

S urface F lo w

B elow R oot Zo ne

E vap oration and

Tran sp ira tion = homogenous combination of soil/topography/climate/management

Soil model: EPIC 2

Data needs EPIC Approaches SSCRI

S oil a lbedo

F ie ld water capacity

S oil pH

etc.Ra

ster

s/ve

cto

rs w

ith

ad

de

d

qu

an

tita

tiv

e va

lue

s fo

r s

oil

inp

ut

pa

ram

eter

s in

to S

GB

1:M

IL

CLUSTER classification (ISO DATA , k-M EANS etc.)VE CTOR ISATIO N

HR

U v

ecto

r u

nit

s fo

r so

il im

pu

ts

Approach I

WP 3400 Work Approach (1)

Data availability• activity data (feasability: see AGRIPOL work plan)• LULUCF data (“external” research, EPIC)

Definitions• scale• farm type/practices• compile frame conditions of each model

Data base• compile model input data• compile model error budgets

WP 3400 Work Approach (2)

Method development• expert matrix to connect data types

identify site factors [soil/climate(topography)] for eachfarm type/practiceif not available: derive (regional) productivity indexfrom land use/EUROSTATS statistics andrelate to mapped site factors

Map production• Input data maps (e.g. N fertilizer input, forest

management types)• Output data maps (e.g. N2O emissions in Europe)

• extrapolate into areas with little data coverage• compare bottom-up/top-down using area statistics• calculate upscaling errors/regional uncertainties