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

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 Quantitative Tier 1 uncertainty estimates CATEGORY COUNTRY Year TOTAL 4A CH4 4B CH4 4B N2O 4D N2O 4D1 N2O 4D2 N2O 4D3 N2O Austria Belgium Denmark Finland France Greece Ireland Italy Spain Sweden Netherlands UK % of total emissions

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 TOTAL4A-CH44B-CH44B-N2O4D-N2O United Kingdom %100% 19% France %102%101%36% Ireland %102%101%38% Spain %98%100%38% Sweden6.9103%101% 56% Belgium8.1102%101%100%59% Netherlands6103%100% 86% Greece %100% 89% Finland %100%101%94% Italy2.598%99%95%96% Austria5.5103%100% 102% Denmark6.8104%99%100%105%

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 EC uncertainty in agriculture Overall uncertainty estimates 5 % to 8 %. Source category GasEmissions 2003 Share of EC emissions used EC uncertainty 4ACH %12% 4BCH %17% 4CCH %38% 4DCH %127% 4FCH %54% 4B N2ON2O %93% 4D N2ON2O %84% - 195% 4F N2ON2O 3694%54% Total agriculture all %44 – 83%

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 Contribution of emission estimated with country-specific information to the total EC emission estimates for key sources in the agriculture sector. Category Emissions [Gg CO2-eq] Percentage estimated with country-specific approach 4.A.1: Cattle (CH4) % 4.A.3: Sheep (CH4) % 4.B.1: Cattle (CH4) % 4.B.8: Swine (CH4) % 4.B.12: Solid storage and dry lot (N2O) % 4.D.1: Direct soil emissions (N2O) % 4.D.2: Animal production (N2O)285661% 4.D.3: Indirect emissions (N2O) % TOTAL %

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 EU-uncertainty for agricultural soils [% of emissions, combined AD-EF] totaldirectanimalindirect Austria 24 Belgium 252 Denmark Finland France200 Germany Greece Ireland 105 Italy Netherlands Spain Sweden 87 United Kingdom424

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

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 Jungkunst 2005 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)

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 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-UKIPCC Fertiliser11.25 FYM Slurry Grazing0.52 PROCESS-BASED MODELS

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 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 cases lead to an over- estimation of N2O emissions by >10% CALCULATION OF N2O EMISSIONS FROM SEPARATE EFs FOR SYNTHETIC FERTILIZER AND ANIMAL WASTES

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 FAO / IFA, 2001; Bouwman et al., 2002: Global estimates of gaseous emissions of NH 3, NO and N 2 O 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 Correlations dependencies, even if they exist, may not be important to the assessment of uncertainties When dependencies among inputs are judged to be of importance (i)modelling the dependence explicitly; (ii)stratifying or aggregating the source categories; (iii)simulating correlation using restricted pairing methods; (iv)use of resampling techniques in cases where multivariate datasets are available; (v)considering bounding or sensitivity cases. ADs are regarded as generally uncorrelated in time EFs are regarded as generally correlated in time

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 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 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 Spatial variability 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.” Reported total emissions relative to fertilizer input. Organic soils are adjusted by 8 kg N2O-N ha-1

JRC/AL – Uncertainty Workshop, Helsinki 06/09/2005 lognormal mean # of estimates included Range*ratio high/low Global0.9% % %379 Regions of the world America1.2% % %239 Europe0.9% % %505 Asia0.3%690.02% %130 Other1.3%340.26% %28 Individual countries Germany1.5% % %537 UK0.6% % %844 Canada2.1%270.29% %66 * based on log-normal distribution; ±2 SD

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

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