Climate change alters key environmental factors such as temperature, precipitation, and soil characteristics, thereby impacting agricultural land suitability.This project presents an interpretable machine learning-based approach to assess the effects of climate change on agricultural land suitability across Eurasia . By integrating climate data, soil properties, and land-use information, models such as Random Forest and XG Boost are employed to classify land suitability levels. Model interpretability is ensured using SHAP analysis, enabling transparent understanding of feature influence. The proposed approach supports climate-resilient agricultural planning and informed decision-making.
Perumal et al. (Thu,) studied this question.