Modeling maize yield and agronomic efficiency using machine learning models: A comparative analysis

Abstract Machine learning (ML) is increasingly being used to enhance yield predictions and optimize agronomic practices in sub‐Saharan Africa. Yet, understanding how these models generalize across heterogenous ecological context remains unresolved. This study, conducted in Ghana, evaluates the predictive performance of four ML models, namely, random forest (RF), support vector machine (SVM), k ‐nearest neighbors (KNN), and extreme gradient boosting (XGBoost) for predicting maize yield and agronomic efficiency—defined as the increase in yield per unit of nutrient applied. It also compares variable importances identified by these models and how they influence yield and agronomic efficiency. The analysis used 4496 georeferenced maize trial datasets from various agroecological zones across Ghana, incorporating 35 variables related to soil properties, climate, topography, crop management, and fertilizer application. Model performance was assessed using three cross‐validation techniques: leave‐one‐out, leave‐site‐out, and leave‐agroecological‐zone‐out. Accuracy was measured using mean error, root mean square error (RMSE), and model efficiency coefficient. When evaluated under leave‐one‐out cross‐validation, XGBoost consistently achieved the highest predictive accuracy with the lowest RMSE for yield (639.5 kg ha −1 ) and for agronomic efficiency of nitrogen (11.6 kg kg −1 ), which is moderate given the high variability in on‐farm nutrient response. RF also performed well, while KNN and SVM showed poor extrapolation under stringent validation. Nitrogen application rate, rainfall, and crop genotype were consistently identified as the most influential explanatory variables across all models, providing insight into key drivers of productivity. These findings demonstrate the power of ML techniques in supporting agricultural planning and improving maize production in sub‐Saharan Africa.