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<p>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<i> = </i>0.99, <i>R</i>² = 0.97). This study offers valuable support for optimizing cropping patterns, enhancing agricultural resilience, and informing climate adaptation strategies.</p>

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Global crop suitability datasets for 17 crops under present (2024) and future climate scenarios (2041–2100)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Tiantian Wang,
  • Jinwei Dong

摘要

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.