Hybrid Machine Learning Modelling for Rice Yield Forecasting under Climatic Variability in South Sudan
DOI:
https://doi.org/10.63746/njtd.v23i2.4454Keywords:
Climate variability, Hybrid ensemble, Machine learning, Random forest, Rice yield, XGBoostAbstract
Reliable agricultural yield forecasting is crucial for strengthening food security planning in South Sudan, where rice yields remain highly sensitive to climatic variability and structural production constraints. This study evaluates classical statistical and ensemble-based machine learning models for forecasting national rice yield using key agronomic and climatic determinants: cultivated land area, historical yield trends, rainfall, temperature, and soil pH. The empirical analysis draws on annual time-series data spanning nearly five decades (1961–2010), with the dataset partitioned chronologically into training (n=38) and testing (n=10) subsets to enable disciplined out-of-sample validation. Four modelling approaches were examined: Multiple Linear Regression (MLR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a Hybrid RF–XGBoost ensemble constructed via prediction aggregation. Model performance was assessed on the reserved test set using RMSE, MAE, R², and MAPE. Within the test sample (n=10), all models exhibited strong correlation (R² > 0.91). MLR achieved R²=0.9128 but exhibited higher RMSE (295.60 kg/ha) and MAPE (31.75%) relative to the ensemble approaches, suggesting limited flexibility in capturing nonlinear yield responses. Random Forest improved performance (R²=0.9647; RMSE=273.39; MAE=227.13; MAPE=28.26%), while XGBoost delivered R²=0.9360 with RMSE=289.25, MAE=182.05, and MAPE=30.59%. The Hybrid model attained the lowest RMSE (259.14 kg/ha), lowest MAE (174.29), lowest MAPE (26.91%), and highest R² (0.9725). Although improvements are moderate in magnitude, the results suggest that hybrid ensemble aggregation can provide incremental gains in prediction accuracy under data-constrained conditions. Given the limited sample size, findings should be interpreted cautiously; nonetheless, this study offers an applied comparative assessment to support evidence-informed agricultural planning in fragile production environments.
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