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.

Land-use intensity depiction of grasslands in Germany by using optical satellite data (Sentinel-2) and machine learning methods (see Lange et al 2021). Land-use intensity product includes: (1) Livestock density (livestock units per day and ha); (2) Mowing frequency count; (3) Fertilisation (boolean: 0=no, 1=yes); (4) Field usage (1=mown pasture, 2=pasture, 3=fertilized meadow, 4=meadow, 5=others); (5) Land-use intensity index (see Bluethgen et al 2012); (6) Land-use classes derived from ATKIS data (see BKG 2015); References: [Bluethgen 2012]: Bluethgen, N., Dormann, C.F., Prati, D., Klaus, V.H., Kleinebecker, T., Hoelzel, N., Alt, F., Boch, S., Gockel, S., Hemp, A., Mueller, J., Nieschulze, J., Renner, S.C., Schoening, I., Schumacher, U., Socher, S.A., Wells, K., Birkhofer, K., Buscot, F., Oelmann, Y., Rothenwoehrer, C., Scherber, C., Tscharntke, T., Weiner, C.N., Fischer, M., Kalko, E.K.V., Linsenmair, K.E., Schulze, E., Weisser, W.W., 2012. A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Basic and Applied Ecology 13, 207–2020. doi:10.1016/j.baae.2012.04.001; [BKG 2015]: Bundesamt für Kartographie und Geodaesie (2015): Digitales Basis-Landschaftsmodell (AAA-Modellierung). GeoBasis-DE. Geodaten der deutschen Landesvermessung. Bundesamt für Kartographie und Geodaesie [Lange 2021]: Lange M, Doktor D (2021): Mapping land-use intensities of grasslands in Germany with machine learning algorithms using Copernicus Sentinel-2 optical satellite data. Remote Sensing of Environment. In preparation;

Contour data for LEGATO region VN_1 based on SRTM satellite data (resolution 100m)

SPOT 5 DIMAP image from investigation area VN 2 (LEGATO), Geometric Processing Level: PRECISION 2A, Resolution: 2.5m, Produced from 2 panchromatic channel (2.5 x 2.5m) and 1 multispectral (near infrared) channel (5.0m x 5.0m)

Land use classification based on SPOT5 satellite image

NDVI based on SPOT5 DIMAP image from LEGATO region VN_3

NDVI based on SPOT5 DIMAP image from LEGATO region PH_3

SPOT 5 DIMAP image from investigation area PH 2 (LEGATO), Geometric Processing Level: PRECISION 2A, Resolution: 2.5m, Produced from 2 panchromatic channel (2.5 x 2.5m) and 1 multispectral (near infrared) channel (5.0m x 5.0m)

SRTM (Shuttle Radar Topography Mission) CGIAR-CSI (Consultative Group on International Agricultural Research - Consortium for Spatial Information) 90m DEM version 4; LEGATO Sites: VN_4 Projection: Geographic (Lat/Long) projection, with the WGS84 horizontal datum and the EGM96 vertical datum

Results data of Publication