A collation of ammonia research

1. 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).

2. 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

3. 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

4. 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.

5. 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

6. 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

7. UK census data

8. 1 Itemize inventory sources

9. 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

10. 2 Collate data used to create EF for each source

11. 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)

12. 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

13. Generating confidence intervals for EFs Largest 10 sources:

14. Generating confidence intervals for EFs Worst 5 CI as % mean:

15. 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

16. 4 Uncertainty ranking

17. 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)

18. 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

19. 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

20. 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 UK • layers in cages deep-pit 0.386 0.290 • layer in cages, belt removal 0.035 0.117 • broilers, 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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)

27. Trailing hose - % abatement

28. Trailing shoe - % abatement

29. Slot Injection - % abatement

30. 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

31. 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

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

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

35. Manure management stage Total emission t x 103 CI as % mean NARSES UK_Inv Buildings 70.4 70.2 71 Hard standings 18.9 20.3 52 Storage 11.6 11.9 82 Spreading 85.6 81.4 81 Grazing/outdoor 23.3 22.4 92 TOTAL 209.8 206.2 41 Uncertainties within inventory total (NARSES) (excluding fertilizers) 2001 activity data