Uncertainty in agriculture

1. Uncertainty in agriculture Adrian Leip Joint Research Centre, Institute for Environment and Sustainability, Climate Change Unit JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

2. Quantitative Tier 1 uncertainty estimates % of total emissions JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

3. JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

4. EC uncertainty in agriculture Overall uncertainty estimates 5 % to 8 %. JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

5. Contribution of emission estimated with country-specific information to the total EC emission estimates for key sources in the agriculture sector. JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

6. EU-uncertainty for agricultural soils[% of emissions, combined AD-EF] JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

7. Uncertainty for N2O emissions from soils AD uncertainty small compared with EF uncertainty • How can bias be estimated (representativeness) • How does temporal variability translate into uncertainty? • How large is the impact of correlations • EF uncertainty: Spatial variability • high, driven by • climate, soil and morphological variations • cropping patterns, fertilizer mix bias • EF uncertainty: Temporal variability • high, driven by • weather conditions • fluctuations in management, fertilizer mix, cropping changes JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

8. Reducing uncertainty: • STRATIFICATION • Climate regions (freeze-thaw events/rewetting of dry soils) • Effect of soil type (organic carbon, wetness) • Effect of type of N applied (mineral / organic) • Effect of crop type (classes) Jungkunst 2005 JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

9. Mulligan, 2005: DNDC-Italy … mean emission factor for mineral N fertiliser derived from the linear regression of the emission estimates plotted against non-volatilised N fertiliser: 0.83% applied manure emission factor: 1% Butterbach- Bahl and Werner, 2005: DNDC-Germany … Fertilizer induced emissions, were only approx. 50% of total N2O emissions. If the latter figure is used, our estimates are approx. 1/3 lower than estimates based on the IPCC approach for Germany Brown et al., 2002, 2005: DNDC-UK DNDC-UK IPCC Fertiliser 1 1.25 FYM 0.6 1.25 Slurry 1.7 1.25 Grazing 0.5 2 PROCESS-BASED MODELS JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

10. CALCULATION OF N2O EMISSIONS FROM SEPARATE EFs FOR SYNTHETIC FERTILIZER AND ANIMAL WASTES ADDITIVITY of fertilizers • in 40% of the cases the synchronous addition of synthetic fertilizer and animal wastes lead to higher N2O emissions by >10% than the sum of single EFs would suggest • in 10% of the cases the effect is >40% • only 12% of the caseslead to an over-estimation of N2Oemissions by >10% JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

11. FAO / IFA, 2001; Bouwman et al., 2002: • Global estimates of gaseous emissions of • NH3, NO and N2O from agricultural land • Factor class value for fertilizer type • Ammonium bicarbonate, ammonium chloride, ammonium sulphate 0.6 • Calcium nitrate, potassium nitrate, sodium nitrate 2.6 • Calcium ammonium nitrate and combinations of AN and CaCO3 2.3 • Ammonium nitrate 3.0 • Urea and urine 1.9 • Urea-ammonium phosphate 3.2 • Mix of various fertilizers 3.4 • Ammonium phosphate and other NP fertilizers 3.8 • Anhydrous ammonia including aqueous ammonia 4.4 • Organic fertilizers 4.7 • Combinations of organic and synthetic fertilizers 5.9 JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

12. Correlations ADs are regarded as generally uncorrelated in time EFs are regarded as generally correlated in time • dependencies, even if they exist, may not be important to the assessment of uncertainties • When dependencies among inputs are judged to be of importance • modelling the dependence explicitly; • stratifying or aggregating the source categories; • simulating correlation using restricted pairing methods; • use of resampling techniques in cases where multivariate datasets are available; • considering bounding or sensitivity cases. JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

13. Correlation: Disaggregation • The compensation effect reduces uncertainty when adding source categories / countries of similar magnitude SUM OF CATEGORIES EF1 assumed correlated - lack of evidence to provide different default values for various forms of N input does not imply that the error is the same for all nitrogen input! JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

14. Correlation in time • In time uncorrelated sources result in highly uncertain trends • If higher-Tier approaches • (models) are used: • - How should temporal variability be treated? • - Use response to ‘climate’ rather than ‘weather’ for process-based models JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

15. Spatial variability Reported total emissions relative to fertilizer input. Organic soils are adjusted by 8 kg N2O-N ha-1 FAO / IFA, 2001; Bouwman et al., 2002: “… emissions induced by fertilizers amount to 0.9 Mt or approximately 0.8 % of current nitrogen fertilizer input.” JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

16. * based on log-normal distribution; ±2 SD JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

17. HYPOTHESIS 1:Is the compensation effect in the uncertainty assessment appropriately considered? JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

18. HYPOTHESIS 2: As measurements programs alone will not suffice to obtain stratified emission factors, future N2O inventories must rely on models to reduce level uncertainty. JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

19. HYPOTHESIS 3:Temporal variability of N2O emissions from soils leads to high trend uncertainty. Care must be taken how to treat it. JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

20. HYPOTHESIS 4:Direct EFs for emissions from synthetic fertilizer, manure, crop residues should be treated as uncorrelated in the uncertainty assessment. JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005

21. THANK YOU FOR YOUR ATTENTION ! JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005