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The COVID-19 pandemic has been a life threatening and spreads wildly with physical human contact. Physical distancing is recommended by health experts to prevent the spread; thus, agronomic research has to be designed in conformity to this preventive standard during the pandemic. Consequently, this study was designed to evaluate the reliability of using digital tools in nutrient management research amid the COVID-19 pandemic in northern Nigeria. Fifty extension agents (EAs) were selected across 15 LGAs of Kaduna and Kano states. The EAs were trained on how to generate fertilizer recommendation using an android mobile phone-based nutrient expert (NE), to measure farmers' field sizes using UTM Area measure mobile phone app, and open data kit to record, submit and aggregate data during the exercise. Each EA covered 50 farms, where two nutrient management practices—one determined by the farmers: farmer fertilizer practice (FFP), and the other generated using the NE were evaluated. Results show that around 90% of the farmers have an average field size of 1.13 ha. All selected farmers used improved maize varieties for planting, among which 21% been able to use the exact recommended or lower seed rate. Use of inorganic fertilizer was 33% higher than the average recommended NE rate, while average yield of the NE fields was 48% higher than for the FFP. The results of this study indicate that yield can be improved with site-specific nutrient management (SSNM) extension approach. The SSNM using digital tools as the NE seem promising and befits to agronomic research in northern Nigeria amid the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

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. Core Ideas: Thirty‐five percent (n = 625 out of total 1,781) of the experimental plots have low yield due to nutrient imbalances.Nitrogen and P are the most yield limiting nutrients.Nitrogen, P, S, Ca > Cu, and B were the significant deficient nutrients in decreasing order of importance.Most of the obtained CND nutrient sufficiency ranges are comparable to ranges published in literature.Linear regression and bootstrap forest models perform fairly and comparably in yield prediction. [ABSTRACT FROM AUTHOR]

Low nutrient use efficiency in maize as a result of imbalanced nutrition has been reported to drastically reduce yield. We implemented a nutrient omission experiment to assess the effect of nutrient application on maize yield and nutritional balance. Maize ear leaves were analyzed for nutrients, to identify nutrient balance status using the Diagnostic and Recommendation Integrated System (DRIS) approach. Results indicated that omission of N or P resulted in highly imbalanced DRIS indices respectively, and significantly lower grain yield. A strong inverse relationship between K ear leaf content with DRIS index suggests that K application negatively increases K imbalance in many situations. Imbalances of Mg, Ca and Cu were more associated with higher yielding treatments. A Which-Won-Where result show that nutrient imbalances in the diagnosis were systematically frequent when N was omitted. All the diagnosed nutrients were imbalanced even under the highest yielding NPKZn treatment; indicating further opportunity for yield increase with more balanced nutrition. Balanced nutrition of maize in the maize belt of Nigeria should target application of varying rates of N, P, K, Mg, S and Zn, depending on the soil conditions. But, because of complexities of nutrient interactions during uptake, it is hardly possible to realize a balanced nutrition. However, differentiating the application of antagonistic nutrients into foliar or soil-based methods is recommended for a more balanced maize nutrition. [ABSTRACT FROM AUTHOR]

The Crop Environment Resource Synthesis (CERES)-Maize model in Decision Support System for Agricultural Technology Transfer (DSSAT) was calibrated and evaluated with experimental data for simulation of response of two intermediate-maturing maize varieties to different sowing dates in the Nigerian savannas. The calibration experiments involved 14 consecutive field trials conducted in the rainy and dry seasons in Bayero University Kano (BUK), Dambatta, and Zaria between 2014–2019. Two sets of field experiments were conducted simultaneously for model evaluation in Iburu in the southern Guinea savanna zone and Zaria in the northern Guinea savanna zone during 2015 and 2016 cropping seasons. The experiments for calibration had two maize (SAMMAZ-15 and SAMMAZ-16) varieties planted under optimum conditions with no water and nutrients stresses. The trials for model evaluation were conducted using the same varieties under four different nitrogen (N) rates (0, 60, 120 and 180 kg N ha−1). A 30-year (1985–2014) term simulation was performed to determine effect of varying sowing dates on yields of two maize varieties (SAMMAZ-15 and SAMMAZ-16) in the Sudan savanna (SS), northern Guinea savanna (NGS), and southern Guinea savanna (SGS) zones. The calibration results showed that the cultivar coefficients of the two maize varieties resulted in simulated growth and development parameters that were in good agreement with observed parameters. Model evaluation showed a good agreement between simulated and observed data for phenology and growth of maize. This demonstrated the potential of the CERES-Maize model to simulate growth and yield of maize in the Nigeria savannas. Results of 30-year sensitivity analysis with 9 different sowing windows showed that in SS, sowing the intermediate maize varieties from early to mid-June produced the highest grain yields. In NGS, the optimum sowing windows were found between late June and late July for the both varieties. In SGS, the optimum sowing window is from early June to late July for SAMMAZ-15 and mid-June to late July for SAMMAZ-16. These planting windows gave the highest long-term average yields for each variety. The variety SAMMAZ-15 was found to be best performing across the three agro-ecologies. Maize performance was generally higher in NGS than in SGS. SS in the Sudan savanna recorded the lowest yield compared with other locations. [ABSTRACT FROM AUTHOR]