Unraveling drivers of maize (Zea mays L.) yield variability in Ghana: A machine learning approach

作者：Anselme K. K. Kouame, G.B.M. Heuvelink, P.S. Bindraban · 发表于：Computers and Electronics in Agriculture · 年份：2025 · DOI：10.1016/j.compag.2025.110647 · 被引用次数：8 · 研究领域：Smart Agriculture and AI

Maize as staple crop is essential for food security in Ghana, yet its yields remain low and highly variable despite increased fertilizer use. Understanding yield variability is crucial for effective agronomic strategies and reducing the risks of inappropriate fertilization strategies. This study quantifies maize yield variability, evaluates predictive model accuracy, and identifies key yield-influencing factors using machine learning (ML) models. A dataset of 5213 maize yield observations from 2000 to 2022 was analyzed using random forest (RF), gradient boosting (GB), and cubist models. Model performance was assessed through nested 20 × 10-fold cross-validation and grid search, using model error, concordance correlation coefficient, model efficiency coefficient (MEC), and mean absolute error. SHapley Additive exPlanations value and H-statistics determined variable importance and first- and second-order effects. Yield coefficient of variation was 55 %, with fertilizer use reducing variability on average by 15 %. ML models demonstrated high predictive accuracy, with RF, GB, and cubist achieving similar mean MCEs of 0.69. All three models identified nitrogen fertilizer (NF) and exchangeable soil magnesium (Mg) as the key drivers of maize yield. NF increased maize yields by an average of 181 kg ha −1 (RF), 287 kg ha −1 (GB), and 293 kg ha −1 (Cubist) across a range of 0–240 kg NF ha −1 . Similarly, Mg contributed to yield increases on average of 161 kg ha −1 (RF), 198 kg ha −1 (G...