An Explainable Climate-Aware Generative and Predictive Modelling Framework for Simulation of “What-if” Plausible Climatic Scenarios across Multiple Crops

Abstract. Climate fluctuations influence many aspects of agriculture including crop growth, soil conditions, and the distribution of fertilizer and water resources. These climatic fluctuations thereby pose significant challenges for agricultural productivity worldwide. However, the availability of agricultural datasets to study the impact of various adverse climatic conditions on different crops remains limited. To address this data availability limitation for agro-climatic impact study, this paper introduces an Explainable Climate-Aware Generative AI framework (XCA-GenAI). The framework combines a Conditional Tabular GAN (CTGAN) to generate realistic synthetic datasets, a Random Forest (RF) regressor to predict crop yield and stress-related parameters, and a SHAP-enabled “what-if” simulation module that evaluates and explains crop responses under varying temperature and rainfall conditions. The proposed framework is employed to generate synthetic representations of ten climatic variations ranging from Very Hot–Dry to Very Cool–Wet using the SF24 dataset. Crop-specific predictive models then estimate how change in climatic condition alters crop density, pest pressure, and frost risk. Further, explainability analysis provides interpretable insights of climate impact across multiple crops represented in the dataset. Comprehensively, this work introduces a climate-aware agricultural decision-support framework to aid farmers and agronomists for informed decision making under varying climatic conditions.