Compositional nutrient diagnosis and associated yield predictions in maize: A case study in the northern Guinea savanna of Nigeria

Developing optimal strategies for nutrient management of soils and crops at a larger scale requires an understanding of nutrient limitations and imbalances. The availability of extensive data (n = 1,781) from 2-yr nutrient omission trials in the most suitable agroecological zone for maize (Zea mays L.) in Nigeria (i.e., the northern Guinea savanna) provides an opportunity to assess nutrient limitations and imbalances using the concept of multi-ratio compositional nutrient diagnosis (CND). We also compared and contrasted the use of linear regression models and bootstrap forest machine learning to predict maize yield based on nutrient concentration in ear leaves. The results showed that 35% of the experimental plots had low yields due to nutrient imbalances (hereafter referred to as low yield imbalanced [LYI]). These experimental plots were dominated by control plots (without any nutrients applied), plots without N fertilization, and plots without P fertilization. Using the control plot as the ultimate indicator of nutrient imbalance, the significantly limiting nutrients in order of decreasing frequency of deficiency were N, P, S, Ca > Cu, and B. Both linear regression and bootstrap forest machine learning models fairly predicted maize grain yield based on nutrient concentration in ear leaves only in the LYI group and when examining all data with an independent validation dataset. These results suggest that nutrient management strategies, especially through the site-specific management approach, should consider S, Ca, Cu, and B in addition to the existing nutrients N, P, and K to improve nutrient balance and maize yield in the study area.