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 NH 3 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 NH 3 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

1Itemize 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 NH 3 emissions been measured –for others an EF was derived from a similar class of livestock

UK census data

1Itemize inventory sources

2Collate 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

2Collate data used to create EF for each source

3Identify gaps where no data exist These NARSES categories, each estimated to emit > 2.0 x 10 3 t NH 3 -N per year: –Buildings housing beef suckler cows and heifers on straw (5.41 x 10 3 t) –Spreading sheep FYM (2.34 x 10 3 t) –Buildings housing male turkeys (2.22 x 10 3 t; 3.40 x 10 3 t including female turkeys) –Upland sheep grazing (2.05 x 10 3 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: EFTotal emission t x % confidence interval t x 10 3 CI as % mean Cattle FYM spreading (23) Cattle housing FYM (17) Cattle housing slurry (29) Poultry manure spreading (18) Cattle slurry spreading >8%DM (31) Cattle slurry storage (5) Dairy cow feeding yard (25) Cattle slurry spreading 4-8%DM (32) Lowland sheep grazing (11)

Generating confidence intervals for EFs Worst 5 CI as % mean: EFTotal emission t x % confidence interval t x 10 3 CI as % mean Layer manure ‘break-out’ (1) Broiler litter ‘break-out’ (2) Broiler litter storage (3) Beef cattle grazing (4) Cattle slurry storage (5)

4Prioritise 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

4Greatest 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)

5Priorities 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

6Assess usefulness of data obtained in other countries 6.1Examine the EU IPPC Reference (BREF) Notes for information on emissions for the pig and poultry sector 6.2Collate 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): BREFUK layers in cages deep-pit layer in cages, belt removal broilers, deep litter 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

6Collate 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

6Collate 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

7Assess 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

7Collate 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

7Collate 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

Trailing shoe - % abatement

Slot Injection - % abatement

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 NH 3 emissions. data needed from experiments comparing several incorporation techniques in the UK

Probability EF value Generating confidence intervals for EFs Propagate range in raw data? Monte Carlo simulations – Latin hypercube sampling

Generating confidence intervals for EFs Cumulative probability EF value Latin hypercube sampling 3,000 iterations

Uncertainties within inventory total (NARSES) (excluding fertilizers) Spreading UK_InvNARSES CI as % mean Total emission t x 10 3 Manure management stage Buildings Storage Hard standings Grazing/outdoor TOTAL 2001 activity data