Abstract
Accurately assessing crop suitability is essential for ensuring food security and guiding agricultural adaptation to climate change. However, existing suitability analyses are mainly based on empirical rules and coarse-resolution predictors, which limits their representativeness and prevents them from capturing contemporary global agricultural patterns with sufficient spatial detail. Here we propose a transferable novel framework that integrates crop samples from remote sensing-based crop layers and high-resolution environmental predictors to model the global suitability of 17 major crops, using the random forest model under present (2024) and future climate scenarios with a 1 km × 1 km resolution. The models achieve an overall accuracy exceeding 0.9 based on the independent test set. Compared with the Global Agro-edaphic suitability dataset from Global Agro-Ecological Zones-Model (GAEZ), our present suitability results show an improved accuracy, with a global pixel-level correlation coefficient (r) of 0.57, and strong agreement in classified suitability area comparisons (r = 0.99, R² = 0.97). This study offers valuable support for optimizing cropping patterns, enhancing agricultural resilience, and informing climate adaptation strategies.
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Data availability
The crop suitability dataset produced in this study is available on Zenodo via the following https://doi.org/10.5281/zenodo.1766672258.
Code availability
The Python code for generating the crop suitability datasets is available for download on Zenodo (https://zenodo.org/records/17853276).
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Acknowledgements
This research acknowledges support from the National Natural Science Foundation of China (42271375, 42461144212, 42525108, and 72221002), and the Youth Interdisciplinary Team Project of the Chinese Academy of Sciences (JCTD-2021-04).
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Tiantian Wang designed the experiments, developed the code, conducted result validation and analysis, and drafted the original manuscript. Jinwei Dong provided overall supervision of the study and revised the manuscript critically for intellectual content. Both authors read and approved the final manuscript.
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Wang, T., Dong, J. Global crop suitability datasets for 17 crops under present (2024) and future climate scenarios (2041–2100). Sci Data (2026). https://doi.org/10.1038/s41597-026-06688-4
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DOI: https://doi.org/10.1038/s41597-026-06688-4
