A collation of ammonia research Identifying significant gaps and uncertainties in UK ammonia EF J...

A collation of ammonia research

Identifying significant gaps and uncertainties in UK ammonia EF

J Webb (ADAS), TH Misselbrook (IGER), Prof. U. Dämmgen, B Eurich-Menden (FAL), D. Starmans (WUR), RW Sneath (SRI) and R Harrison (Ex-ADAS, now Lincoln University, NZ).

Background

• To develop effective policies to reduce gaseous emissions, it is essential to prepare accurate inventories of emission sources and their size

• two major sources of UK NH3 are livestock buildings and following land spreading of manures, which each account for c. 35% of livestock emissions

Background

• However, while emissions following land spreading of slurry were characterized by c. 25 datasets, there were no data from emissions from some types of housing, e.g. beef suckler cows

• therefore a need to identify and review gaps in emissions data used to compile the UKAEI and NARSES

Background

• Data needed for all significant sources

• obtained over the full range of activity of each source

• taken under a representative range of environmental conditions

• abatement techniques need to have been tested under the range of conditions over which they may be applied.

Objectives

• Identify sources for which we have no data

• assess the accuracy of our estimate of NH3

emissions from all sources

• assess whether data obtained in other European countries can be used to fill gaps

• estimate the likely cost of any further studies

1 Itemize inventory sources

• Data used to calculate EFs for both UKAEI and NARSES identified and itemised

• NARSES housing emissions calculated for each livestock class in the June Census (22)

• for only 10 of these categories have NH3 emissions been measured– for others an EF was derived from a similar

class of livestock

UK census data

No Examples Cattle 9 Dairy cows and heifers; cattle >2 yr: bulls >2 yr; calves Pigs 5 Dry sows; Sows plus litters; boars; finishing pigs; weaners Poultry 7 Layers; broilers; ducks Sheep 8 Ewes; lambs; goats; deer

1 Itemize inventory sources

Emission source Category used Number ofstudies

1. Buildingshousing dairycows & heiferson slurry

Common EFused for allbuildingshousing cattle onslurry

3

57. Outdoor pigs 2

2 Collate data used to create EF for each source

• The emission derived from each UKAEI EF was totaled

• for each EF derived from more than 1 value, a standard deviation, coefficient of variation (CV), and standard error (SE) were derived.

• the SE was expressed as a percentage of the mean for standardized comparison

2 Collate data used to create EF for each source

Emission factor

Value cv% SE as % mean

Cattle FYM spreading

81 % TAN

34 5

Beef cattle grazing

3.34 g lu-1 d-1

243 57

Fatteners >20kg straw

2.84 g lu-1 h-1

71 41

3 Identify gaps where no data exist

• These NARSES categories, each estimated to emit > 2.0 x 103 t NH3-N per year:

– Buildings housing beef suckler cows and heifers on straw (5.41 x 103 t)

– Spreading sheep FYM (2.34 x 103 t)– Buildings housing male turkeys (2.22 x 103 t;

3.40 x 103 t including female turkeys)– Upland sheep grazing (2.05 x 103 t)

3 Assess significance of gaps in EFs - prioritise filling those gaps

• Record range and SE of data and hence range of emissions

• Estimate data needed for an emission estimate of ±20%

• Prioritise areas of either new or additional research

Generating confidence intervals for EFs

Largest 10 sources:

EF Total emission

t x 103

95% confidence interval t x 103

CI as % mean

Cattle FYM spreading 40.0 53.2 133 (23)

Cattle housing FYM 23.6 36.8 156 (17)

Cattle housing slurry 23.3 22.3 96 (29)

Poultry manure spreading 16.5 25.4 154 (18)

Cattle slurry spreading >8%DM 14.2 4.9 35 (31)

Cattle slurry storage 10.9 40.3 368 (5)

Dairy cow feeding yard 10.3 12.7 124 (25)

Cattle slurry spreading 4-8%DM 10.2 2.9 28 (32)

Lowland sheep grazing 9.8 23.5 240 (11)

Generating confidence intervals for EFs

Worst 5 CI as % mean:

EF Total emission

t x 103

95% confidence interval t x 103

CI as % mean

Layer manure ‘break-out’ 0.01 0.03 487 (1)

Broiler litter ‘break-out’ 0.01 0.03 431 (2)

Broiler litter storage 0.18 0.67 375 (3)

Beef cattle grazing 3.13 11.63 372 (4)

Cattle slurry storage 10.93 40.25 368 (5)

4 Prioritise areas of either new or additional research

• Expressing the CI as a % of the mean emission may be misleading when attempting to assess priorities– may over-emphasize importance of small

sources

• simple ‘uncertainty’ ranking (UR) was used based on the size of emission, % SE and % CI

4 Uncertainty ranking

Rank SE CI NH3 emission(kg * 106)

1 <10 <100 0-3

2 10-19 100-199 4-7

3 20-29 200-299 8-13

4 30-39 >300 14-20

5 40-49 >20

6 >49

4 Greatest uncertainties

• Fattening pigs housed on straw (45) • dairy slurry lagoons (32)• beef cattle grazing (24)• lowland sheep grazing (18)

– beef slurry lagoons (16) – dairy slurry storage in tanks (16) – dairy cows and heifers housed on straw (12)– upland sheep grazing (12)

5 Priorities for research

• Based on uncertainties in EFs and gaps in data these are:

• buildings housing fattening pigs and dairy cows and heifers on straw

• cattle slurry lagoons– a project is due to report measurements of these

• grazing by beef cattle, upland and lowland sheep

6 Assess usefulness of data obtained in other countries

• 6.1 Examine the EU IPPC Reference (BREF) Notes for information on emissions for the pig and poultry sector

• 6.2 Collate non-UK data available in English-language publications

Usefulness of BREF documentation

• Large list of pig/poultry housing types with EF or expected reductions

• No indication of robustness of EFs

• References cited difficult to follow/obtain

• EF for ‘reference’ systems differ from UK EFs (kg per bird place per year):

BREF UKlayers in cages deep-pit 0.386 0.290 layer in cages, belt removal 0.035 0.117broilers, deep litter 0.080 0.043

• Therefore, difficult to ‘read-across’ for alternative housing systems

• Source data most likely covered in review in this project (Appendix 3)

• Useful source of potential abatement strategies for scenario testing, but would want to use UK-specific data

6 Collate non-UK data available in English-language publications

• Most data are for sources for which the UK EFs are reasonably robust

• in most cases little information available on the environmental or management conditions– difficult to assess the transferability to UK

conditions

6 Collate non-UK data available in English-language publications

• Little or no work from outside the UK on the priorities – straw-based housing systems, pastures grazed

by beef cattle or sheep or from slurry lagoons– results were available of assessment of the

abatement potential of reduced-emissions slurry applicators and rapid incorporation of slurries to arable land

7 Assess usefulness of data obtained in other countries

• Collate non-UK data available in German and Dutch.

• record farming practices and environmental conditions under which data collected.

• filter out data not applicable to UK.

• evaluate usefulness of remainder

7 Collate non-UK data available in German and Dutch

• Again, little information on most areas of uncertainty

• data from Germany on pigs housed on FYM• very little background information with respect

to – N excretion by the livestock, animal age or weight,

temperature or time of year when the measurements were made or of the litter characteristics

7 Collate non-UK data - comparison of Inventory EFs

• EFs for buildings housing livestock on slurry similar– unlike UK, the EF for cattle housed on straw is

the same or greater than that for cattle on slurry• some big differences in storage EFs

– especially for FYM, which we may be underestimating

• UK and German EFs following manure spreading are similar

8 Abatement

• Among the most cost-effective abatement techniques identified for UK conditions are application of slurry by reduced-emission applicators – trailing hose (TH) – trailing shoe (TS) – open-slot injection (SlI)

Trailing hose - % abatement

Grassland Arable

UK 35 30

All 39 21

Field scale 34 35

Trailing shoe - % abatement

Grassland Arable

UK 60 46

All 60 45

Field scale 64 36

Slot Injection - % abatement

Grassland Arable

UK 64 0

All 79 36

Field-scale 79 78

Rapid incorporation of slurry into arable land by tillage

• Very little UK data

• work from NL– only one result for ploughing (more for disc

and tine)– only March, April and September

• Around 22% of cattle and 54% of pig slurry are applied to arable land, mainly in late summer to stubbles prior to cultivation

Priorities for work

• Abatement % appear robust for TH and TS

• work needed at field-scale for slot injection

• rapid incorporation of slurries into arable land a potentially cost-effective means of reducing NH3 emissions.

• data needed from experiments comparing several incorporation techniques in the UK

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0 2 4 6 8 10

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Probability

EF value

Generating confidence intervals for EFs

Propagate range in raw data?

Monte Carlo sampling

@RISK simulations – Latin hypercube sampling

Generating confidence intervals for EFs

0

0.2

0.4

0.6

0.8

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0 2 4 6 8 10

Cumulative probability

EF value

Latin hypercube sampling

3,000 iterations

Uncertainties within inventory total (NARSES)

(excluding fertilizers)

8181.485.6Spreading

UK_InvNARSES

CI as % mean

Total emission

t x 103

Manure management stage

7170.270.4Buildings

8211.911.6Storage

5220.318.9Hard standings

9222.423.3Grazing/outdoor

41206.2209.8TOTAL

2001 activity data