1. Workshop on the Criteria to establish projections scenarios Sectoral projection guidance: Agriculture Mario Contaldi, TASK-GHG Ankara, 15-17 March 2016

2. Outline Source description – Emission from Livestock – Activity Data Projections – Emission Factor Projections – Source specific QA/QC Emissions from Crops/Soils – Activity Data Projections – Emission Factor Projections – Source specific QA/QC Discussion 2

3. Source description The Agriculture Sector 4A Enteric Fermentation 4B Manure management 4C Rice Cultivation (Do it occur in Turkey?) 4D N2O Emissions from Managed Soils 4F Field Burning of Agricultural Residues 4G Other 5B C from Agricultural Soils. 3

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5. Emissions from Livestock: Activity data projections – information needed Projected livestock numbers This provides a simple approach to estimating projections. Economic and Political Influence However, the reliability of the above approach depends on how the livestock numbers have been derived. A simple extrapolation based on current trends does not take into the future impact of existing economic and political influences. So a better method is to estimate the future levels of agricultural produce, by using economic modeling. Combining projected produce and yield data allows projected activity data (and emission factors) to be determined. 5

6. Emissions from Livestock: Emission factor projections – information needed Energy intake (determined from feed properties) N excretion rates (determined from feed properties) Manure management practices – With information on the use of abatement Yield data to link produce with emission factors Extrapolating current trends It would be unrealistic to simply extrapolate the current trends for some parameters e.g. milk yield. 6

7. Emissions from Livestock: Tiers 1, 2 Tier 1 Uses simple projections of livestock numbers (possibly based on extrapolation of current data). Uses current EFs, or EFs that take into account some existing trends. Projections estimated using a “traditional” approach (Activity x EF = Emission). Tier 2 Uses a similar approach to Tier 1, but includes more detail (especially for EFs) – NEX, yields, abatement etc. not held constant across the time series – Livestock numbers and other activity data, not necessarily based solely on current trends – Results may take into account the restriction of production by economic policies, an shifts in consumer demand patterns. 7

8. Emissions from Livestock: Tiers 1, 2 and 3 Tier 3 Uses economic modeling to determine different produce levels. This is combined with yield data to give activity data (e.g. livestock numbers). EFs can then also be determined from this information. Knowledge of country specific farming practices (e.g. manure management systems) then allows projected emissions to be estimated. 8

9. Source specific QA/QC Livestock numbers Consistency between enteric fermentation and manure management emissions, and consistency with other national and international projections. Consistency of census methodologies across the time series. Emission Factors Realistic yield projections (that recognize current trends cannot necessarily be simply extrapolated). All country specific EFs should be compared with default values from the literature. N Balance An N- balance should be maintained such that the total NEX should be completely accounted for by: emissions air, application to land, anaerobic digestion, burning for fuel etc. 9

10. Emissions from Livestock: Ensuring Consistency The main consistency and accuracy problems arise when countries use overly simple methodologies (Tier 1) that can’t account for the impact of policies and measures. Use of higher Tiers should be strongly encouraged and supported – A lack of country specific data is no excuse! – Typical input data values are readily available from other countries, and… Default EFs – European level modeling could be a reference providing default projected EFs for different policies and measures. – International co-ordination, collaboration and bi-laterals should occur by itself, but improved co-ordination/ promotion would be beneficial. 10

11. N2O Emissions from Managed Soils: Activity data projections – information needed Quantification of all N inputs and outputs Synthetic fertiliser application The amount of organic manure applied (including grazing), with N content. Crop residues and N fixation Mineralisation of soil organic matter N deposition, leeching and run-off Requires projected areas of all major crop types (and projected yields) Also requires assumptions regarding the amounts of N applied (and hence the N excess). Synthetic fertiliser vs organic fertiliser – Dependent on many factors- price of synthetic (variable!), supply of organic etc. 11

12. N2O Emissions from Managed Soils: Tiers 1, 2, 3 Tier 1 Uses simple projections of crop areas, by extrapolating historic data (or sourcing data from readily available national datasets). Emission factors are held constant with time (probably IPCC default values). Tier 2 More detailed calculations for future crop areas and sources of N input Crop areas take into account future economic and environmental policies, rather than being simple extrapolations of historic data N inputs account for the availability of animal manure, and hence the organic/synthetic N applied. The impact of current policies on excess N application may also be considered. EFs may be country specific, but are unlikely to differ from the historic inventory. Tier 3 May use detailed process modeling (such as DNDC) Likely to include economic considerations, both in terms of influencing crop types, and the amounts produced. 12

13. Source specific QA/QC Crop Areas Projected crop areas should be consistent with other (inter)nationally available datasets... and not sum to more than the area of the country! Maintaining the N-balance Organic N inputs should be the sum of NEX from grazing animals and that from animal housing applied to land. Country specific data Whilst it is always preferable to use country specific data, these should be checked for consistency with the international defaults, and neighbouring countries for consistency. 13

14. Discussion points Characterising Manure Management Systems Often a limiting factor for improving historic inventories- even more challenging for projections. How well is this really understood? 3. N Cycle and Abatement Increasingly strong control of N being driven by environmental measures (e.g. Nitrates Directive and national level policies). Will these measures actually change farming practices enough to deliver the predicted reduction in N emissions? 4. Yield Data What is the potential for increasing yields? Not the same as recent/historic trends! Do we have confidence in the estimates of projected yields? 14