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.

Background and aim : Soybean seeds inoculation with effective rhizobia (Rh) strains and phosphorus (P) application are agricultural best practices that enhance grain yield. However, in Northern Ghana, where these practices are progressively under adoption, unpredictable yield, and poor understanding of factors of yield variation often limit its potential. We assessed the influencing factors to soybean yield variability from biophysical and managed input variables (Rh inoculants, P rates, and sources). Methods On-station and on-farm soybean plots were inoculated with three Rh inoculants (Rh1, Rh2, and Rh3), treated with two P rates (0 and 30 kg P ha-1), and two P sources [rock phosphate and Triple superphosphate forms]. Yield data was predicted using the random forest (RF) model, and factors of yield variability were assessed using the linear mixed models and the forward redundancy analysis (rda). Results The yield prediction accuracy was greater for the on-station experiment compared to the on-farm dataset with a trained coefficient of determination (R2) of 0.77 and 0.66, respectively. The top variables of yield prediction were the Rh × P fertilizer, P sources, Rh strains, and exchangeable soil Mg2+ concentrations. The Rh × P treatment increased soybean grain yield by 3.0 and 3.9 folds for the on-farm and on-station trials respectively, compared to the control. Conclusion The RF model and the forward rda unearthed a significant contribution of the soil exchangeable Mg2+ to the yield variation. The mechanisms underlying the role of Mg on soybean growth deserve further research investigations to increase soybean production in Ghana sustainably.