A global clustering of terrestrial food production systems

Abstract

Food production is at the heart of global sustainability challenges, with unsustainable practices being a major driver of biodiversity loss, emissions and land degradation. The concept of foodscapes, defined as the characteristics of food production along biophysical and socio-economic gradients, could be a way addressing those challenges. By identifying homologues foodscapes classes possible interventions and leverage points for more sustainable agriculture could be identified. Here we provide a globally consistent approximation of the world’s foodscape classes. We integrate global data on biophysical and socio-economic factors to identify a minimum set of emergent clusters and evaluate their characteristics, vulnerabilities and risks with regards to global change factors. Overall, we find food production globally to be highly concentrated in a few areas. Worryingly, we find particularly intensively cultivated or irrigated foodscape classes to be under considerable climatic and degradation risks. Our work can serve as baseline for global-scale zoning and gap analyses, while also revealing homologous areas for possible agricultural interventions.

Exploring the potential for nitrogen fertilizer use mitigation with bundles of management interventions

“…To estimate attainable rates of manure cycling to cropland, we combined gridded estimates of baseline manure N cycling to cropland [38] (figure S1) with a global classification of agricultural systems, i.e. landscapes with structurally similar agricultural production systems [46]. The latter have been derived using spatially explicit data on agro-environmental characteristics (e.g.…”

Groundwaterscapes: A global classification and mapping of groundwater’s large-scale socioeconomic, ecological, and Earth system functions

“…Nodes with similar codebook vectors are located closer to each other in this low dimensional grid and dissimilar codebook vectors further apart. SOMs are thus a particularly powerful method for data exploration and visualization as the low-dimensional grid of nodes preserve the topology of the input data and as so have been widely used to address clustering problems (Flexer, 2001;Kohonen, 2013;Vesanto & Alhoniemi, 2000), including the classification of social-ecological systems (Beckmann et al, 2022;Jung et al, 2024;Levers et al, 2018;Václavík et al, 2013;van der Zanden et al, 2016). SOMs are further advantageous for clustering applications as they are less prone to identifying local optima relative to other approaches (Bação et al, 2005).…”

“…mutually exclusive) classification provided by this methodology (where each grid cell is associated with a single node in the selected first stage SOM, and each of these first stage SOM nodes is associated with a single node in the selected second stage SOM) enables a simple classification of geospatial grid cells to their respective groundwaterscape. FollowingJung et al (2024), we apply a modal filter with a 3x3 grid cell moving window to reduce minor speckling noise in the final groundwaterscape map.…”