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Geographical downscaling of outputs provided by an economic farm model calibrated at the regional level
Authors:P Cantelaube  PA Jayet  F Carré  C BampsP Zakharov
Institution:a INRA, UMR Economie Publique, 78750 Grignon, France
b Envi-Care, France
c European Commission, DG Joint Research Centre, IES, Italy
d MinesParisTech, Centre de Géosciences, 35 rue Saint Honoré, 77305 Fontainebleau, France
e INERIS, Parc Technologique ALATA, BP 2, 60550 Verneuil-en-Halatte, France
Abstract:There is a strong need for accurate and spatially referenced information regarding policy making and model linkage. This need has been expressed by land users, and policy and decision makers in order to estimate both spatially and locally the impacts of European policy (like the Common Agricultural Policy) and/or global changes on farm-groups. These entities are defined according to variables such as altitude, economic size and type of farming (referring to land uses). European farm-groups are provided through the Farm Accountancy Data Network (FADN) as statistical information delivered at regional level. The aim of the study is to map locally farm-group probabilities within each region. The mapping of the farm-groups is done in two steps: (1) by mapping locally the co-variables associated to the farm-groups, i.e. altitude and land uses; (2) by using regional FADN data as a priori knowledge for transforming land uses and altitude information into farm-groups location probabilities within each region. The downscaling process focuses on the land use mapping since land use data are originally point information located every 18 km. Interpolation of land use data is done at 100 m by using co-variables like land cover, altitude, climate and soil data which are continuous layers usually provided at fine resolution. Once the farm-groups are mapped, European Policy and global changes scenarios are run through an agro-economic model for assessing environmental impacts locally.
Keywords:Downscaling  Land use  Spatial statistics  Farm-groups  Farm Accountancy Data Network
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