Projections of future agricultural management and crop choice under shared socioeconomic pathways.

Future agricultural landscapes will likely be shaped by the interplay between socioeconomic developments and natural conditions. However, existing theory-driven, process-based models often rely on idealized assumptions, limiting their capacity to capture real-world complexities fully. To complement these methods through an observational, data-driven approach, we developed a novel global dataset utilizing a statistical fixed-effects model. This paper presents a novel global dataset detailing projections of harvested area allocation for ten major crop groups across 197 countries and regions from 2020 to 2100. The dataset was generated using a statistical fixed-effects model calibrated on historical data. It includes annual projections under six distinct SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5). For each scenario, the dataset provides future trajectories for key national agricultural management inputs-including nitrogen application rates, irrigation extents, and mechanization levels-and the resulting projected cropping shares. This dataset is designed to support assessments of food security, trade policy, and environmental impacts by providing a consistent, data-driven set of future agricultural landscape patterns.