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Grain Legume Yield Responses to Rhizobia Inoculants and Phosphorus Supplementation Under Ghana Soils: A Meta-Synthesis

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...

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Detalles Bibliográficos
Autores principales: Buernor, Alfred Balenor, Kabiru, Muhammad Rabiu, Bechtaoui, Noura, Jibrin, Jibrin Mohammed, Asante, Michael, Bouraqqadi, Anis, Dahhani, Sara, Ouhdouch, Yedir, Hafidi, Mohamed, Jemo, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261782/
https://www.ncbi.nlm.nih.gov/pubmed/35812914
http://dx.doi.org/10.3389/fpls.2022.877433
Descripción
Sumario: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 10(9) cells g(−1) and a minimum inoculum rate of 1.0 × 10(6) 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.