Predicting staple crop yields under climate variability using multiple regression techniques

Abstrak

Global food systems rely on staple crops—rice, wheat, maize, potato, soybean, and sugarcane, which are vital in Asia, where production is high. However, climate change threatens crop yields, potentially increasing hunger and malnutrition. Yield variability due to climate factors like rainfall and temperature underscores the need for accurate crop yield predictions. This paper analyzed the relationships between staple crop yields, climate variables, and pesticide usage. It aimed to develop a predictive model for crop yields in Asia using multiple regression techniques in Google Colab. The model was evaluated using a hybrid set of metrics like mean absolute error (MAE), root mean squared error (RMSE), and R² score. Findings revealed that reliable yield predictions are achievable despite weak linear relationships among variables. The extreme gradient boosting (XGBoost) achieves the highest R² score of 0.958367, which indicates superior predictive performance for staple crop yield forecasting due to its lower overall error rates and greater consistency in performance. This highlights the effectiveness of ensemble methods like XGBoost in capturing complex crop yield patterns. Despite newer machine learning (ML) techniques, these models remain recommended for similar tasks due to their robust performance.