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This research aims to assess crop yield variability influenced by climate change using advanced predictive modeling techniques.

February 2, 2026Open Access

A Data-Driven Assessment of Crop Yield Variability and Global Food Security under Climate Change

Key Points

This research aims to assess crop yield variability influenced by climate change using advanced predictive modeling techniques.
Developed an AI-driven predictive modeling framework using historical agricultural and climate data.
Utilized a global crop yield dataset incorporating rainfall, temperature, and pesticide usage.
Compared traditional statistical regression with machine learning approaches such as Random Forest and Gradient Boosting.
Evaluated model performance using standard metrics including RMSE, MAE, and R².
Machine learning models significantly outperformed traditional linear regression in predicting crop yields.
Key contributors to yield fluctuations were identified as rainfall and temperature variations.
Findings suggest that climate change significantly threatens agricultural productivity, emphasizing the need for data-driven solutions.

Abstract

Climate variability poses a significant threat to global agricultural productivity and food security, particularly in regions that are highly dependent on climate-sensitive farming systems. Accurate and timely prediction of crop yields is therefore essential for informed decision-making and policy formulation. This study presents an AI-driven predictive modeling framework for estimating crop yields under varying climatic conditions using historical agricultural and climate data. A publicly available global crop yield dataset incorporating rainfall, temperature, and pesticide usage was utilized to develop and evaluate predictive models. The traditional statistical regression was compared to the machine learning approaches, namely Random Forest and Gradient Boosting regressors, to determine the predictive performance and strength of the models. The standard regression metrics, which included root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²), were used for model evaluation. The findings of the study showed that the models based on machine learning methods completely outclass the traditional linear regression in grasping the nonlinear relations between climate factors and crop yield. The analysis of feature importance also points out specific regional and temporal variations in rainfall and temperature as the two leading contributors to changes in crop yield fluctuations, thus implying the vulnerability of agricultural productivity to the effect of climate change. The results highlight the ability of models predicting crop yield using AI technology to be one of the main supports for climate-sensitive agricultural planning and global food security improvements. This work offers one more example of AI being used in the agricultural sector and the policy area, also as it is an input to making climate change and its effects on crop production data-driven matters, thus increasing the whole AI 'sustainable agriculture' and 'evidence-based policy area.

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