A discrete number of studies have been conducted on the effects of rhizobia (Rhz) inoculants, phosphorus (P) management, and combined application of Rhz and P fertilizer on the enhancement of grain legume yield across soils of Ghana and elsewhere. However, the extent to which the various inoculated Rhz strains, P application, and combined application of Rhz + P studies contribute to improving yield, performed on a comprehensive analysis approach, and profit farmers are yet to be understood. This study reviewed different experimental studies conducted on soybean (Glycine max (L.) Merr.), cowpea (Vigna unguiculata [L.] Walp), and groundnut (Arachis hypogaea [L.]) to which Rhz inoculants, P supplements, or Rhz + P combination were applied to improve the yield in Ghana. Multiple-step search combinations of published articles and multivariate analysis computing approaches were used to assess the effects of Rhz inoculation, P application, or both application of Rhz and P on yield variation. The random forest (RF) regression model was further employed to quantify the relative importance of various predictor variables on yield. The meta-analysis results showed that cowpea exhibited the highest (61.7%) and groundnut (19.8%) the lowest average yield change. The RF regression model revealed that the combined application of Rhz and P fertilizer (10.5%) and Rhz inoculation alone (7.8%) were the highest explanatory variables to predict yield variation in soybean. The Rhz + P combination, Rhz inoculation, and genotype wang-Kae explained 11.6, 10.02, and 8.04% of yield variability for cowpea, respectively. The yield in the inoculated plants increased by 1.48-, 1.26-, and 1.16-fold when compared to that in the non-inoculated cowpea plants following inoculation with BR 3299, KNUST 1002, and KNUST 1006 strains, respectively. KNUST 1006 strain exhibited the highest yield increase ratio (1.3-fold) in groundnut plants. Inoculants formulation with a viable concentration of 109 cells g−1 and a minimum inoculum rate of 1.0 × 106 cells seed−1 achieved the highest average yield change for soybean but not for cowpea and groundnut. The meta-analysis calls for prospective studies to investigate the minimum rate of bacterial cells required for optimum inoculation responses in cowpea and groundnut.

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28Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide 29 the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect 30 of most modelling exercises because it requires expensive and time-consuming field experiments. 31GSPs could also be estimated using multi-year and multi locational data from breeder evaluation 32 experiments. This research was set up with the following objectives: i) to determine GSPs of 10 33 newly released maize varieties for the Nigerian Savannas using data from both calibration 34 experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the 35 accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-36 Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different 37 experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. 38 Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were 39 evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 40 120kg N ha -1 ). For the model calibration using experimental data, calculated model efficiency (EF) 41 values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. 42 Calibration of time-series data produced nRMSE below 7% while all prediction deviations were 43 below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges 44 were lower. Prediction deviations were below 17% of the means for all measured variables. Model 45 evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high 46 EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen 47 contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from 48 detailed experiments. If unavailable, data from breeder experimental trials collected from many 49 locations and planting dates can be used with lower but acceptable accuracy.

Absence of site-specific nutrient recommendation and high spatial variability of soil fertility are major factors affecting maize response to applied nutrients in Nigeria. In this study, we assessed maize response to applied nutrients and nutrient use efficiency in different management zones, for designing site-specific nutrient management recommendations for maize in the maize belt of Nigeria. The maize belt in Nigeria was earlier delineated into four management zones (MZ1 to MZ4) based on soil properties. In the current study, data from two different trials, nutrient omission trials (NOTs, N = 293) and fertilizer response trial (FRT, N = 705), conducted in the years 2015 to 2017, were extracted for MZ1 to MZ3; to analyze maize yield responses to application of N, P and K, and secondary and micro-nutrients (SMNs). Maize yield response to K application was only positive in MZ1. Responses to N and P application were positive for all MZs. However, the magnitude of maize response to P varied between the MZs, indicating a differentiation in the degree to which P is limiting maize production in the study area. Average nitrogen requirement was higher for MZ3 (138 kg ha-1), than for MZ2 and MZ1 (121 and 83 kg ha-1, respectively). Average P requirement was higher for MZ3 (45 kg ha-1) than for the other zones. Potassium requirement was 26 and 28% higher in MZ2 and MZ3 compared with MZ1 (~15 kg ha-1). The use of the specific nutrient rates for the MZs may reduce risks and uncertainties in crop production. The delineated MZs of the maize belt of Nigeria that incorporates spatial variability in soil fertility conditions are useful for nutrient management for larger areas.

This paper takes a critical look at the 20-hectares research/demonstration farm at Bayero University Kano’s Centre for Dryland Agriculture (CDA) in Kano, Nigeria. The paper examines how knowledge-based mode farm driven by scientific, ethical, and technological innovations contributes to ensuring some level of food security during the COVID-19 pandemic. The main research question driving the current study is: in what ways can universities demonstration farms support urban food security during pandemics? The study circumvented lockdown restriction challenges by deploying elevator pitch approach, walk-in interviews, document analysis, and covert observation to elicit the needed data for the study. The CDA farm produced several tons of variety of vegetables using its clean energy and locally recyclable water sources to secure food needs of some urban households during the lockdown. The ability of the farm to effectively embrace sustainable farming system suggests that transitioning to bioeconomy based food security is achievable and affordable in developing countries. Essentially, the paper recommends the need for universities to take a leading role and responsibility in promoting the principles of bioeconomy in agriculture through engagement and collaborations with municipalities and planned and unplanned urban communities in Africa’s fast urbanising cities.