Spatial disaggregation in CAPRI Adrian Leip1, Renate Koeble1 European Commission, Joint Research Centre (JRC) E-mail: adrian.leip@jrc.ec.europa.eu

Outline CAPRI modelling framework Disaggregation Generating a priori Land use maps Homogeneous Spatial Units Spatial data FADN spatial allocation CAPRI-disaggregation

The CAPRI modeling framework Data Base Tools Projection GLOBAL CAPTRD EuroStat FaoStat ... Scenario CoCo CAPMOD CAPREG Supply Market Disaggreg. Impact Assessment Environment Welfare FADN spatial allocation CAPRI-disaggregation

Integrated economic and agri-environment modelling: why Disaggregation? 1. For several indicators, environmental impact is non-linear or asymmetric 2. Data on the distribution of the environmental impact in space more relevant than the average value (hot or cold spots, shares, thresholds, …) 3. Interpretation of environmental indicators at administrative boundaries if often meaningless for policy decisions (protected areas, areas delimited by natural borders (e.g. watersheds), areas with natural constraints, …) FADN spatial allocation CAPRI-disaggregation

Integrated economic and agri-environment modelling: why CAPRI? Provides input data to bio-physical models or to indicators (e.g crop shares, stocking densities, yields, input use (N,P,K; energy; feed ...) which are based on EU official data sources (FSS, FADN …) consistent across scales: farm type / region / MS / EU / trade blocks used in economic modules and passed to environmental modules CAPRI is in line with DG-AGRI projections, provides benchmark for counterfactual scenarios Therefore consistent modelling chain for economic (farm income, trade flows …) and environmental assessment is ensured FADN spatial allocation CAPRI-disaggregation

Spatial data: CAPRI as spatial impact assessment tool Agri-environmental indicator at the country and province scale Aggregation rules Agri-environmental indicator at the pixel scale FADN spatial allocation CAPRI-disaggregation

Sequential disaggregation Altitude Slope T-sum Veg-period Rain Fodder Regression HPD Distances Sequential disaggregation A priori land shares NUTS3 crop areas HPD 1. Crop shares 3. Livestock density 5. Indicator calculation Irrigat. shares Pot.+ rainfed yields Irrigated areas HPD 2. Yield/ irrigation 4. N inputs Ndep MITERRA coeff Soil data Rain classes Distances Sequential estimation of N input (manure, crop residues, mineral fert.) FADN spatial allocation CAPRI-disaggregation

[1] http://ec.europa.eu/agriculture/rurdev/eval/guidance/note_f_en.pdf   CMEF OECD Eurostat Greenhouse gas emissions Baseline 26 IV. Environ. Impacts of agriculture-4 AEI 19 - Greenhouse gas emissions (pressure) Nutrient surplus Baseline 20 III. Use of farm inputs and natural resources-1. AEI 15 - Gross nitrogen balance (pressure) Risk of soil erosion by water Baseline 22 IV. Environ. Impacts of agriculture-1 AEI 21 - Soil erosion (pressure) Agricultural landscape -  IV. Environ. Impacts of agriculture-1  Proxy for: AEI 28 - Landscape – state and diversity (state) Biodiversity friendly farming practices Baseline 18: Biodiversity: HNV farmland and forestry’ IV. Environ. Impacts of agriculture- 6 Wildlife Habitats AEI 23 - High nature value farmland (state) Environmental compensation zones - [1] http://ec.europa.eu/agriculture/rurdev/eval/guidance/note_f_en.pdf [2] http://www.oecd.org/agriculture/sustainable-agriculture/1890235.htm [3] http://ec.europa.eu/eurostat/web/agri-environmental-indicators/indicators-overview FADN spatial allocation CAPRI-disaggregation

Locally Weighted Max. Likelihood + + + LUCAS SWHE 30% Sand 680mm Rain ….. GRAS 25% Sand 700mm Rain SWHE 30% Sand 680mm Rain ….. GRAS 25% Sand 700mm Rain Corine Class 1 + + + + + + + + di Corine Class 2 Weighting Function: wi=f{di} @ di < d + d example: LUCAS points L, model for soft wheat x: Environmental attributes at the LUCAS points (e.g. annual rainfall, vegetation period, sand content, …) BETA: coefficient measuring the impact of the change of parameter x by one unit LAMBDA: dummy variable expressing the probability that wheat is grown at the LUCAS point. Logit avoid the unboundness of a linear probability model Locally Weighted Logit : Di=exp{b’xi} [1+exp{b’xi}]-1 (= share of land use ) max: ln{L}=Siwi[yi ln{Di} + (1-yi)ln{1-Di}] FADN spatial allocation CAPRI-disaggregation

Estimating a priori land use shares Yest Ycon Y(h,c) pdf{Y(h,c)} 60ha 300m asL 650mm Rain ….. 40 ha 500m asL 700mm Rain HSMU 26% SWHE -> 10.4 ha 72% GRAS -> 28.8 ha 70% SWHE -> 42 ha 35% GRAS -> 21 ha Yest(h,c)=f{bc, xh} ESTIMATION 25% SWHE -> 10 ha 75% GRAS -> 30 ha 76% SWHE -> 45 ha 25% GRAS -> 15 ha 55 ha SWHE 45 ha GRAS Nuts II Data consistent shares Ycon(h, c) for all crops c and HSMU h max: SiSj[Ycon(hicj)] s.t. SiYcon(hicj) Ai=Aj SjYcon(hicj) =1 FADN spatial allocation CAPRI-disaggregation

Quality check of disaggregation on FSS 2000 data Fig. A1. Percentage of misclassified areas in validated NUTS 2 regions after dis-aggregation. The pies show the contribution of different crop groups to the total error in the region (Cereals: soft wheat, durum wheat, barley, rye, oats, maize, other cereal; Fallow: fallow land; Rice and Oil Seeds: rice, sunflower, soya, texture crops, pulses, other crops; Root Crops: potatoes, sugar beet, root crops, rape, nurseries; Per-manent/Industrial Crops: tobacco, other industrial, vegetables, flowers, citrus trees, fruit trees, olive trees, vineyards; Grassland: grassland, fodder production). Note that the size of the pie is related to the area of the NUTS 2 region for visualization purposes only. Leip et al., 2008 FADN spatial allocation CAPRI-disaggregation

Update: Lamboni et al. (in review) Methodological changes Multinomial logit Updated spatial layer (HSU2) Updated explanatory variables Current challenges Problems with non frequent crops Introduction of physical constraints