Diagnostic on-farm nutrient omission trials were conducted over two cropping seasons (2015 and 2016) to assess soil nutrients related constraints to maize yield in the northern Nigerian savanna agro-ecological zone and to quantify their variability. Two sets of trials were conducted side by side, one with an open pollinated maize variety (OPV) and the other one with a hybrid maize variety and each set had six equal treatments laid out in 198 farmers’ fields. The treatments comprised (i) a control, (ii) a PK (‘−N,’ without N), (iii) an NK (‘−P,’ without P), (iv) an NP (‘−K,’ without K), (v) an NPK and (vi) an NPK + S + Ca + Mg + Zn + B (‘+SMM,’ NPK plus secondary macro- and micro-nutrients). Moderate to a large variability in most soil characteristics was observed in the studied fields. Consequently, cluster analysis revealed three distinct yield-nutrient response classes common for the two types of maize varieties. These define classes were fields that have (i) no-response to any nutrient, (ii) a large response to N and P and (iii) a large response to N alone. Although overall yield performance of OPV and hybrid varieties was similar, a distinct fourth class was identified for the hybrid variety, (iv) fields with a large response to N and secondary macro- and micro-nutrients. The results indicate that the large variability in soil nutrients related constraints need to be accounted for to optimize maize yield in the northern Nigerian savanna. The development of field- and area-specific fertilizer recommendations is highly needed, using simple decision support tools that consider variable soil fertility conditions and yield responses as obtained from this study.

• New and better fitting QUEFTS’ prediction equations for indigenous soil N, P, and K supply were developed for the Northern Nigerian Savanna. • A good correlation was observed between the observed and parameterized QUFETS predicted maize grain yield. • The QUEFTS model predicted a balanced N, P, K uptake to linearly increase with grain yield until 50-60% of the potential yield. • The QUEFTS model is a suitable tool for site-specific nutrient recommendations in maize in the Northern Nigerian Savanna.

Site-specific nutrient management can reduce soil degradation and crop production risks related to undesirable timing, amount, and type of fertilizer application. This study was conducted to understand the spatial variability of soil properties and delineate spatially homogenous nutrient management zones (MZs) in the maize belt region of Nigeria. Soil samples (n = 3387) were collected across the area using multistage and random sampling techniques, and samples were analyzed for pH, soil organic carbon (SOC), macronutrients (N, P, K, S, Ca and Mg), micronutrients (S, B, Zn, Mn and Fe) content, and effective cation exchange capacity (ECEC). Spatial distribution and variability of these parameters were assessed using geostatistics and ordinary kriging, while principal component analysis (PCA) and multivariate K-means cluster analysis were used to delineate nutrient management zones. Results show that spatial variation of macronutrients (total N, available P, and K) was largely influenced by intrinsic factors, while that of S, Ca, ECEC, and most micronutrients was influenced by both intrinsic and extrinsic factors with moderate to high spatial variability. Four distinct management zones, namely, MZ1, MZ2, MZ3, and MZ4, were identified and delineated in the area. MZ1 and MZ4 have the highest contents of most soil fertility indicators. MZ4 has a higher content of available P, Zn, and pH than MZ1. MZ2 and MZ3, which constitute the larger part of the area, have smaller contents of the soil fertility indicators. The delineated MZs offer a more feasible option for developing and implementing site-specific nutrient management in the maize belt region 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.

This study investigated the impact of three adjacent land use systems [land under arable cropping (ARL), cattle grazing fallow (GFL) and Jatropha curcas L. Orchard (JCL)] on selected soil physical quality indicators in a Northern Nigeria Savanna Alfisol. GFL had significantly higher organic carbon content (26.2–32.1% higher) and bulk density (4.5–9.2% higher) than JCL and ARL respectively. Higher bulk density (ρ_b) in GFL aided by trampling induced compaction resulted in its high relative field capacity (RFC), permanent wilting point (PWP) and micro-pore spaces (PMIC). Continuous tillage in ARL created loose soil in the plough layer (<20 cm) which turn out to its low bulk density (ρ_b) and high plant-available water capacity (PAWC), total pore spaces (f) and macro-pore spaces (PMAC) over JCL and GFL. Total nitrogen content, dry stability of large macro-aggregate fractions [5–2 mm, (LMag)] and mean weight diameter (MWD) were higher in JCL than GFL and ARL to about 47.2–60.6, 12.5–68.8 and 8.57–44.76%, respectively. This showed some possibilities of JCL in improving the nitrogen content and stability of soil. Yet, long term studies on the impact of J. curcas L. on the soil quality are needed in order to evaluate if such possibilities are sustainable or not.

To close the nutrient-related yield gaps in maize, balanced nutrition using primary and secondary macronutrients as well as micronutrients is recommended. Multi-nutrient diagnostic field trials were conducted in Guinea Savanna of Nigeria to assess the interactive effects of macronutrients and micronutrients on maize grain yields, nutrient uptake as well as N, P and K use efficiencies. The treatments consisted of a control (zero fertilizer applied), an NPK treatment and 10 other treatments in which macronutrient (Mg, S) and/or micronutrients (B, and Zn) were added to the NPK. The experiment was laid out in Randomized Complete Block Design with 3 replications. Data collected were subjected to mixed model with nutrient management strategy as fixed effects while replication nested in location and interaction between location and nutrient management strategy as random effects. The results revealed significant effects (P < 0.05) of nutrient management strategy on maize yield, nutrient uptake and nutrient use efficiencies of N, P and K. The study revealed that yield advantage over the recommended NPK fertilizer as a result of application macronutrients and micronutrients were highest with Mg in Lere (2.4 t ha⁻¹), S + B + Zn in Faskari (2.8 t ha⁻¹), S + B in Doguwa (1.5 t ha⁻¹) and S + Zn in Toro (2.4 t ha⁻¹). Addition of Mg, S and B significantly increases macronutrient uptakes over the recommended NPK only. Agronomic use efficiency, internal utilization efficiency, apparent recovery efficiency and partial factor productivity were significantly increased with the addition of S, Mg, and B but were not improved with Zn application. It was concluded that nutrient limitations to maize in the Guinea Savanna go beyond N, P and K. This study recommends that S, Mg, and B are needed to improve maize productivity and engender improve the use efficiency of NPK fertilizers.