Statistical Atlas of European Agriculture: Gridded Data from the Agricultural Census 2020 and the Spatial Distribution of CAP Contextual Indicators

Abstract. International organizations have voiced the need to integrate geographical information from agricultural holdings into official statistics to gain a better understanding of the spatial dynamics of the European agricultural sector. This paper presents a set of thematic maps based on the European 2020 agricultural census to explore the major structural differences between regions and countries. To comply with the confidentiality requirements associated with the census data, we applied a multi-resolution gridded approach by varying the resolution of the grid cells as a function of the density, dominance, and quality of individual observations. The data sets contain a mixture of grid resolutions ranging from 1 km to 40 km, preserving a hierarchical structure where higher resolution grids are aggregated into lower levels until the statistical disclosure requirements are met. The variables presented here correspond to the Contextual Indicators of the Performance Monitoring and Evaluation Framework of the Common Agricultural Policy and are divided into three broad categories: structural components (i.e., agricultural holdings, land use, livestock patterns, and labor input); the demographics of farmers (i.e., age, gender, and skills); and agricultural production methods (i.e., irrigation and organic farming). Our exploratory analysis indicates that high farm density occurs in plains and lowlands, as well as fertile soil areas in valleys, high shares of organic farming tend to be concentrated in certain areas with high proportions of grassland, and agricultural holdings managed by young farmers are located in a belt stretching from France through to Switzerland, Austria, Czechia, Slovakia, and Poland. These novel data sets are highly versatile, allowing not only policies to evaluate funding schemes at more local levels, but they also offer researchers new opportunities to draw causal spatial inference from the multi-resolution gridded data. The IFSGRID dataset is the first attempt to create an unprecedented harmonized view of European agriculture with high spatial resolution, and it is available at https://doi.org/10.5281/zenodo.14852709 (Eurostat, 2025).