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 k400 The regional application clustered European land into 400 smaller and more homogeneous agri-environmental archetypes than in the case of SOM k20. The sizes of clusters ranged from 2,230 km² (0.04% of the study area) for cluster 381 to 34,000 km² (0.5% of the study area) for cluster 184, with a median of 15,068 km², which is close to 1/400 of the total study area. Smaller clusters tended to be more heterogeneous (lower QE), but the overall cluster quality was uniformly distributed across Europe and higher than in the case of k20. A correlation of input variables with the clusters’ mean QE showed that QE was positively associated with annual precipitation, soil coarse fragments, terrain ruggedness and elevation. Therefore, agri-environmental potential with high values of these variables, located along the coast of Norway, Northern UK and the Alpine region, were also more heterogeneous and thus less likely to form homogeneous archetypes.

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This service visualizes land system archetypes data. Land use is a key driver of global environmental change. Unless major shifts in consumptive behaviours occur, land-based production will have to increase drastically to meet future demands for food and other commodities. To better understand the drivers and impacts of agricultural intensification, identifying global, archetypical patterns of land systems is needed. However, current approaches focus on broad-scale representations of dominant land cover with limited consideration of land-use intensity. In this study, we derived a new global representation of land systems based on more than 30 high-resolution datasets on land-use intensity, environmental conditions and socioeconomic indicators. Using a self-organizing map algorithm, we identified and mapped twelve archetypes of land systems for the year 2005. Our analysis reveals unexpected similarities in land systems across the globe but the diverse pattern at sub-national scales implies that there are no one-size-fits-all solutions to sustainable land management. Our results help to identify generic patterns of land pressures and environmental threats and provide means to target regionalized strategies to cope with the challenges of global change. Mapping global archetypes of land systems represents a first step towards better understanding the driving forces and environmental and social outcomes of land system dynamics.

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.