EU wide analysis of the common agricultural policy using spatially disaggregated data

Recent reforms of the Common Agricultural Policy shifted the emphasis towards competitiveness of the agricultural sector, rural development and environmentally sound farming approaches, acknowledging the considerable role agriculture plays in protecting nature and landscape. Significant progress in the evaluation of policy reform scenarios can be made if it is be possible to link existing economic and environmental models. An important methodological problem in this context is bridgingʺ the scales: whereas most bio-physical models work on field scale, comprehensive EU wide economic models generally work with large administrative regions. The research aims at improving integrated assessment of European policy options by developing methodologies that deliver spatially explicit agricultural data regarding crop shares and farming systems. First a procedure for estimating agricultural land use choices is developed bringing together high resolution information on crops and land cover as well as aggregate information from administrative regions. Combining a binary choice model with a Bayesian highest posterior density estimator, a statistical approach to break down land use choices from European administrative regions to about 100.000, so called Homogeneous Spatial Mapping Units is developed. The applied Bayesian method fully and transparently accounts for the prior information mean and variance of land use shares obtained from binary choice models when searching for consistency between the different scales. Next, an approach for the spatial allocation of farm information is developed. European wide farm information is so far only available at a rather aggregated administrative level. The suggested allocation approach adds a spatial dimension to all sample farms making it possible to aggregate farm types both to natural and to lower scale administrative regions. The allocation approach is implemented as a constrained optimization model searching for an optimal match between farm attributes and spatial characteristics subject to consistency constraints. The objective functions are derived from a Bayesian highest posterior density framework. Finally an approach to integrate spatially explicit farm information in an agricultural sector model in the context of a study on the abolition of the EU milk quota is presented. It presents an economic and environmental impact analysis using the CAPRI model, which has been updated with econometric estimates of milk quota rents from sample farms. Aggregated at EU level for the year 2020, production may increase by 5% while the price drop for raw milk is about 10%. Regions are identified where economic or environmental changes substantially exceed those at the Member State level. While regional nitrate leaching problems could be exacerbated, there is only weak evidence of an increased risk of land abandonment in marginal areas.