Modeling and explaining fertilizer effect heterogeneity on maize yield in Ghana using causal and predictive machine learning

Context: Maize is a key staple crop in Ghana, yet yields remain low (20–40 % of potential). Although fertilizer is promoted to enhance productivity, adoption is limited by highly variable yield responses. Objective: This study analyzed spatial and environmental drivers of fertilizer effect heterogeneity using 2854 yield observations from randomized controlled trials and 10,916 pairwise absolute yield response to fertilizer (AR) estimates. Methods: Causal forest (CF) and boosted random forest (BRF) models to estimated fertilizer effects, with BRF performance evaluated via a 10 × 10 nested cross-validation and grid search. SHapley Additive exPlanations and Accumulated Local Effects analyses identified key drivers of fertilizer effect heterogeneity and quantified the magnitude of their influence on fertilizer yield effect. Results and conclusions: Fertilizer effect varied widely (–4.7–8.9 t ha–1), with the Sudan Savannah showing the highest median AR (2.8 t ha–1) and the Forest-Savannah Transition the lowest (0.9 t ha–1). BRF outperformed CF in predicting fertilizer effects (ME: –0.06–0.05 t ha–1 vs. –0.19 t ha–1, RMSE: 1.17–1.23 t ha–1 vs. 1.3 t ha– 1, MEC: 0.32–0.38 vs. 0.24 and CCC: 0.46–0.54 vs. 0.34). Key determinants of fertilizer effect heterogeneity included both climatic variables (Palmer Drought Severity Index [PDSI], vapor pressure deficit, rainfall) and soil properties (silt content, exchangeable aluminum). PDSI emerged as the dominant driver of fertilizer effect heterogeneity in the entire data set. However, the relative importance of soil versus climate varied spatially: soil properties were the main drivers of fertilizer effect in the Semi-Deciduous Forest and the Forest-Savannah Transition, whereas climatic variables played a stronger role in northern zones. Fertilizer yield effect increased by 0.4–1.6 t ha–1 with increasing PDSI, indicating that improved moisture availability enhances fertilizer use efficiency. Overall, optimal moisture conditions (PDSI > –2.0), the use of hybrid seeds, and the application of briquette fertilizer all contributed to higher fertilizer effects, whereas drought conditions substantially reduced them. Furthermore, fertilizer effect decreased by 0.2–1.4 t ha–1 as silt increased from 9 % to 30 %, and by 0.3–0.6 t ha–1 as exchangeable aluminum increased from 36 to 221 mg kg–1. Significance: This study presents the first large-scale, data-driven assessment of fertilizer yield effects heterogeneity in Ghana, integrating causal and predictive machine learning with explainable AI. Findings support tailored fertilizer strategies by agro-ecological zones to reduce farmer risk and promote sustainable intensification.