Spatial patterns of global land management intensity are influenced by socioeconomic, biophysical and behavioural factors

Land systems are increasingly influenced not only by land-use change but also by land management intensity. However, persistent data limitations prevent systematic understanding of management intensity and its dependencies on behavioural, socioeconomic, and biophysical factors. We develop a global dataset of land management intensity for 2020 at 0.01° spatial resolution, distinguishing unmanaged, very extensive, extensive, and intensive management across cropland, pasture, and forest systems. Intensive management occupies about 22 % of global managed land, while extensive and very extensive management dominate (78 %). Intensive cropland management accounts for around 44 % of cropland area, but intensive pasture is limited to 10 % of pasture area, and intensive forest systems to only 5 % of forest area, revealing distinct sectoral contrasts. Management intensity is highly heterogeneous, with intensive cropland concentrated in North America, Europe, and South and East Asia, while extensive management dominates in Africa and Latin America. Five countries account for nearly half of global intensive cropland. Income, market access, population density, and aridity influence cropland management intensity, whereas pastures and forests show more complex relationships. Comparison with global land decision-making types shows spatial consistency (68 %), suggesting that land management intensity is influenced by land-user behaviour. This dataset may be useful for various earth system modelling applications in land system science and environmental studies. The dataset can be visualised through global land management intensity dataset explorer: https://ee-ankitasaxena03as.projects.earthengine.app/view/global-land-use-management (last access: 27 August 2026; Saxena et al., 2025), and accessed through https://doi.org/10.5281/zenodo.18249970 (Saxena et al., 2026) for research purposes. The input data for socioeconomic variables (GDP, GINI, market access and working age population) can be accessed through https://doi.org/10.5281/zenodo.17580780 (Perkins et al., 2025).