a collation of ammonia research identifying significant gaps and uncertainties in uk ammonia ef j...
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![Page 1: A collation of ammonia research Identifying significant gaps and uncertainties in UK ammonia EF J Webb (ADAS), TH Misselbrook (IGER), Prof. U. Dämmgen,](https://reader035.vdocument.in/reader035/viewer/2022062321/56649e415503460f94b32b60/html5/thumbnails/1.jpg)
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).
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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)
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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
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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)
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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)
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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
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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
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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)
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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
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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
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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
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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
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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
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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
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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
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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
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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)
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Trailing hose - % abatement
Grassland Arable
UK 35 30
All 39 21
Field scale 34 35
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Trailing shoe - % abatement
Grassland Arable
UK 60 46
All 60 45
Field scale 64 36
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Slot Injection - % abatement
Grassland Arable
UK 64 0
All 79 36
Field-scale 79 78
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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
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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
1
2
3
4
5
6
7
8
9
10
0 2 4 6 8 10
.
Probability
EF value
Generating confidence intervals for EFs
Propagate range in raw data?
Monte Carlo sampling
@RISK simulations – Latin hypercube sampling
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Generating confidence intervals for EFs
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
Cumulative probability
EF value
Latin hypercube sampling
3,000 iterations
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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