The Role of Spatial Data in EU Agricultural Policy Analysis

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1 The Role of Spatial Data in EU Agricultural Policy Analysis Wolfgang Britz Institute for Food and Resource Economics, University Bonn Geospatial Open Source Hosting of Agriculture, Resource and Environmental Data (GEOSHARE) Kickoff Workshop for the Pilot Project

2 Content Background Overview on spatial data layer for EU Two examples: Impact of environmental set-aside on bio-diversity impacts Improved quantification of GHG emissions from agriculture Conclusions Britz: Spatial data in impact assesssment of agricultural policies

3 Background Increasing role of public goods and externalities of agricultural production in policy design: emissions to soil and water, mitigation of global warming, impact on bio-diversity loss, landscape maintenance, cultural heritage.. Spatial heterogeneity is key: Resources - soil, slope, surrounding land cover, irrigation water availability, climate and economic and institutional factors drive farming decisions crop choice, stocking density, irrigation, input use and determine agriculture s interaction with the environment

4 Background Computing power, storage place, modeling tools, inter-operability between tools and data base all dramatically improved data availability major impediment to improve impact assessment Currently, detailed integrated modeling of agriculture in best case possible for sub-national administrative units for selected regions of the world, e.g. FASOM for the US CAPRI for the EU Need and usefulness for high-resolution spatial data will be illustrated picking two recent examples from own work for Europe where such spatial data bases are at least in parts already available but before doing, some words on the underlying data base and how it was constructed

5 The data base Spatial downscaling of existing agricultural data (crop shares, stocking densities, yields, fertilizers and other input use) from administrative units (~280 for EU) to ~ x1 km pixel clusters Original effort (collection of basic data, data processing, development of methodology, estimation..) financed by EU research project Growing applications of resulting spatial data base in policy relevant assessments and for further research (new indicators, input for bio-physical modeling )

6 Spatial processing unit Administrative regions Land cover Soil Slope Processing units Cluster of 1x1 km pixels Large Scale Spatial Dis-Aggregation of Economic Model Results Wolfgang Britz

7 Crop shares Data base on actual land cover per crop : Point and crop specific crop share distribution function statistically estimated from Pan- European data sample (LUCAS) with 10 observations at each 16x16 km raster point Bayesian estimator downscales simultaneously all crops: statistics ex-post or baseline/economic model simulations ex-ante Example: Soft wheat share France, Large Scale Spatial Dis-Aggregation of Economic Model Results Wolfgang Britz

8 Yields and irrigation shares Available data: Water and non-water limited potential yield at soil polygon level for some major crops from crop-growth model hosted at JRC/Ispra (MARS-Project) Agricultural irrigation shares FAO irrigation maps Regional irrigated area, where it matters for major crops, available at Eurostat Statistical estimator consolidates data, i.e. ensures that average yields are recovered Example: Spain, irrigation share in % Large Scale Spatial Dis-Aggregation of Economic Model Results Wolfgang Britz

9 Animal Stocking Densities Example: Ruminants [livestock units per ha fodder area] A priori distribution based on regressions from regional data so far no access to European high resolution data Validation for France with out-ofsample data for > communes showed rather good fit for total stocking density, which is the major driver for the environmental pressure France, available for 13 animal categories Large Scale Spatial Dis-Aggregation of Economic Model Results Wolfgang Britz

10 Example 1: Assessing bio-diversity Background: Continuing loss of species, specific measures are already implemented in the EU s Common Agricultural Policy Bio-diversity loss both Under intensively managed agricultural system, but also possible if agricultural land is abandoned and natural land cover returns Operational, robust indicator needed to characterize impact of changes in farming on bio-diversity, e.g. in policy impact assessment and that is only feasible with data at an appropriate spatial resolution! Paracchini M.K. and Britz W.: Quantifying effects of changed farm practise on Biodiversity in policy impact asessment - an application of CAPRI-Spat Paper presented at the OECD Workshop: Agri-environmental Indicators: Lessons Learned and Future Directions, Tuesday 23 March - Friday 26 March, 2010, Leysin, Switzerland

11 Example 1: Assessing bio-diversity Data on Farming practice (fertilizer, Stocking density) Data on crop areas Necessary data needed at an appropriate spatial resolution, in our example at 1x1 km

12 Example 1: Assessing bio-diversity Policy scenario: farmers are coerced to ensure at least 10% land into environmental friendly set-aside: Application of PE model for Europe Downscaling + indicator calculation

13 Example 1: Assessing bio-diversity Lessons learnt: Surprise: environmental quality drops in certain areas? Reduced supply let prices increase and triggers intensification, negative overall effect in marginal areas: Little or no change in farming necessary to comply with required 10% rate of extensively managed farm area only intensification effect => bad for bio-diversity! Differentiated impact can only be captured with data at appropriate resolution, including farming practice!

14 Example 2: GHG emissions Background: GHG emissions from agriculture depend on farm management (e.g. fertilizer application rates), but also e.g. soil and climate Spatial data base delivers inputs to bio-physical model DNDC (DeNitrification-DeComposition) DNDC simulates the nitrogen and carbon cycle in the crop-root-soil system, with a focus on NOx estimates important for global warming Example: N2O emission [per ha of agricultural land] Estimated with DNDC response surface, Italy, Britz W, Leip A Development of marginal emission factors for N losses from agricultural soils with the DNDC CAPRI meta-model. Agriculture, Ecosystems & Environment 133(3-4): doi: /j.agee

15 Example 2: GHG emissions Lessons learnt: Key GHG emissions such as N2O differ highly even for the same crop under similar management depending on soil and climate => default emission factors from IPCC can be misguiding High resolution and consistent data are key crop shares, yields, organic and mineral fertilizing rates must match

16 Conclusions Spatial data: allow for a so-far unknown richness and accuracy in quantifying interactions between agriculture and the environment specifically provide the spatial resolution necessary for bio-physical modeling are key to locate and analyze impacts on hotspots, such as bio-diversity rich habitats ground water resources water bodies areas threatened by land abandonment areas threatened by erosion or other forms of land degradation

17 Conclusions Serious effort necessary to ensure that spatial data are consistent across scales comparable across regions Un-coordinated country/regional projects not suitable Product and other definitions will not match No global coverage Quality assurance, meta data handling, spatial resolution, formats etc. not harmonized

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