Relationship between solar radiation and meteorological variables in predictive models for crop yields

ABSTRACT Knowledge of the complicated correlation between meteorological variables and crop yield is crucial for food security and agricultural sustainability. This study aimed to investigate how incident solar radiation has affected crop production in the Gadarif region of Sudan over the last 41 years. Using a predictive framework, trends in annual incident solar radiation and temporal variations during sorghum and sesame growing seasons were examined and machine learning (ML) with Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF), and K-Nearest Neighbors (K-NN) was used to predict crop yield. Significant relationships between incident solar radiation indicators and crop yields were identified via detrending approaches and correlation analyses. Results indicate a significant inverse correlation between solar radiation and sorghum yield, and a positive correlation between sesame yield and solar radiation. For both sorghum and sesame yield, K-NN was the most accurate model, demonstrating the significance of incident solar radiation and temperature in predicting crop yield. These findings highlight the potential of ML to improve agricultural forecasting models and inform adaptive agricultural practices in the region. In general, this study provides valuable insights into the dynamic relationship between incident solar radiation and crop yield, emphasizing the importance of considering meteorological factors in agricultural planning and management.