Description: This dataset accompanies the publication "Archetypes of agri-environmental potential: a multi-scale typology for spatial stratification and upscaling in Europe" by Michael Beckmann, Gregor Didenko, James M. Bullock, Anna F. Cord, Anne Paulus, Guy Ziv and Tomáš Václavík. Developing spatially-targeted policies for farmland in the European Union (EU) requires synthesized, spatially-explicit knowledge of agricultural systems and their environmental conditions. Such synthesis needs to be flexible and scalable in a way that allows the generalization of European landscapes and their agricultural potential into spatial units that are informative at any given resolution and extent. In recent years, typologies of agricultural lands have been substantially improved, however, agriculturally relevant aspects have yet to be included. We here provide a spatial classification approach for identifying archetypal patterns of agri-environmental potential in Europe based on machine-learning clustering of 17 variables on bioclimatic conditions, soil characteristics and topographical parameters. We improve existing typologies by (1) including more recent biophysical data (e.g. agriculturally-important soil parameters), (2) employing a fully data-driven approach that reduces subjectivity in identifying archetypal patterns, and (3) providing a scalable approach suitable both for the entire European continent as well as smaller geographical extents. We demonstrate the utility and scalability of our typology by comparing the archetypes with independent data on cropland cover and field size at the European scale and in three regional case studies in Germany, Czechia and Spain. The resulting archetypes can be used to support spatial stratification, upscaling and designation of more spatially-targeted agricultural policies, such as those in the context of the EU’s Common Agricultural Policy post-2020. Continental application - SOM k20 The identified archetypes of agri-environmental potential showed a relatively even geographical distribution and their coverage ranged from 1.0% (Cluster 20 with 62,000 km²) to 10.1% (Cluster 10 with 640,000 km²) of European land. The largest clusters, 4 (542,000 km²) and 10 (640,000 km²), were in Northern Finland and Russia, suggesting that there is a relatively homogenous space of environmental conditions over a large area, although much of it with low agricultural potential. The highest quantization error was found in clusters 19 and 20, located along the coast of Norway and the northern UK, and also at the coast of Spain, Portugal and the Alpine region. These archetypes were the most heterogeneous, clustering agri-environmental potential with a wide range of conditions, especially elevation and precipitation.
Types:
Origin: /Wissenschaft/Helmholtz-Gemeinschaft/Helmholtz-Zentrum für Umweltforschung - UFZ/Metadatenkatalog
Tags: Norwegen ? Agrarpolitik ? Finnland ? Portugal ? Russland ? Tschechische Republik ? EU-Agrarpolitik ? Bodenverbesserung ? Europäische Union ? Daten ? Fallstudie ? Alpen ? Europa ? Klassifikation ? Küste ? Niederschlag ? Landschaft ? Landwirtschaftliche Fläche ? World ?
Bounding box: -23.76682852° .. 54.63160898° x 32.78974361° .. 71.50494045°
License: unbekannt
Language: Englisch/English
Modified: 2024-03-11
Time ranges: 2022-10-18 - 2022-10-18
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