Browsing by Author "Gumede, Mbali Thembi."

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Identification of cowpea (vigna unguiculata (L) (walp) genotypes and genetic improvement for enhanced yield and nutritional quality.
(2023) Gumede, Mbali Thembi.; Mabhaudhi, Tafadzwanashe.; Gerrano, Abe Shegro.
Cowpea (Vigna unguiculata (L.) Walp) is a staple legume crop with potential to address food insecurity and malnutrition in the Sub-Saharan Africa (SSA). It is also among the neglected underutilized legume crop species in the region. The crop’s yield production has never met the potential yields of the crop in the SSA. In addition, given its nutritional value, ability to withstand extreme environmental conditions, nitrogen fixation ability and its greater potential to address nutrient deficiencies and food insecurity in the SSA. These attributes make cowpea an ideal crop to sustainable future for the people and environment. There is a need to develop cowpea varieties that are high yielding with high nutritional values to combat food insecurity. Therefore, the objectives of the study were: (1) to assess the genotype by environment interaction effect and select cowpea genotypes with high grain yield and adaptation across selected cowpea growing environments in South Africa, (2) to assess the phenotypic variability and correlation analysis in cowpea based on yield and yield related traits, (3) to evaluate the variations of nutritional content and phytochemical compositions among cowpea genotypes under diverse environments, (4) to assess the genetic diversity among cowpea genotypes using single nucleotide polymorphism (SNP) markers, and to select distinct and complementary genotypes for developing improved cultivars and (5) to determine the combining ability effects and gene action controlling the yield and yield-related traits among selected cowpea parental genotypes and their progenies. The first study assessed the extent of genotype by environment interaction (GEI) of cowpea genotype to the influence of genotype (G), environment (E) and their interaction (GEI) effects on grain yield in cowpea and to assess the stability of cowpea genotypes to identify stable and high-yielding genotypes for broad or narrow adaptation to improve cowpea productivity in South Africa and identify the identical agro-ecologies using analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) and the genotype-by-environment interaction (GGE) biplot analyses. The AMMI ANOVA showed the significant GEI effect which accounted for 57% variation, whereas genotype and environment main effects accounted for 29% and 13% variation, respectively. The AMMI stability values (ASV) analysis identified genotype Acc-Cowp44 as the most stable genotype recording the lowest ASV of 0.03. The biplot depicted eight sectors and environments were clustered into three of the eight sector whereby E4 (Brits 2020/2021), E5 (Loskop 2020/2021), E6 (Mafikeng 2020/2021) and E7 (Polokwane 2020/2021) which formed a mega-environment and the second sector which involved environments E1 (Brits 2019/2020), E2 (Loskop 2019/2020) and E3 (Roodeplaat 2019/2020) formed one mega-environment. The cowpea genotypes Acc-Cowp38, Acc-Cowp2, Acc-Cowp9, Acc-Cowp5 and Acc-Cowp39 were identified as ideal for grain yield, in that order. These genotypes are recommended for production in South Africa or in similar agroecologies, and for incorporation in future breeding programs targeting genetic improvement for grain yield. The second study assessed 50 cowpea genotypes using yield and yield components to determine the phenotypic correlations among them and selection of best performing genotypes among tested genotypes for enhanced cultivar development. The study revealed the significant differences at 5% and 1% level of probability among the assessed grain yield and yield component traits. The study further indicates that number of pods per plant (NPP), pod length (PL), number of seeds per pod (NSP) and hundred seed weight (HSW) had significant and positive correlations with grain yield, therefore these traits can be used as a proxy trait for increased grain yield. Similarly, the principal component analysis (PCA) biplot identified number of branches (NB), number of pods per plant (NPP), pod length (PL), pod width (PW), number of seeds per pod (NSP), and hundred seed weight (HSW) as the important traits in the production of grain yield. Genotypes Acc-Cowp2, 98K_5301, Acc-Cowp4, Acc-Cowp17 and Acc-Cowp9 were grouped together based on their high exhibition of NPP, PW, NSP, PL, HSW and grain yield (GY). The selected genotypes could be considered as potential sources of gene to improve these traits and could serve as parental genotypes in breeding programs targeting enhanced high-yielding varieties. The third study assessed the nutritional and phytochemical traits among the 50 cowpea genotypes to select superior lines with high nutritional compositions for cultivar development for nutritional quality. The study highlighted the significant effects for all nutritional and phytochemical traits for genotype, environment, and genotype by environment interaction evaluated except for flavonoids and fat content. Genotypes Acc-Cowp6, Acc-Cowp17, Acc- Cowp14, 98K_5301, Acc-Cowp9, Acc-Cowp32, Acc-Cowp9, Acc-Cowp4, Acc-Cowp16 and Acc-Cowp21 were selected based on high concentration of Ca, Mg, P, Na and Zn. Genotype Acc-Cowp31 and Acc-Cowp13 were highly associated with protein content while genotype Acc-Cowp39 were in close association with fat content. Further, genotypes Acc-Cowp34, Acc- Cowp18, Acc-Cowp48, Acc-Cowp22, Acc-Cowp26, Acc-Cowp49 and Acc-Cowp28 had low concentrations of total phenolic, flavonoids and condensed tannins. The fourth study used 90 genetically diverse cowpea to assess the magnitude of the genetic diversity and population structure among cowpea genotypes using single nucleotide polymorphisms (SNP) markers. The study revealed that 49% of the selected SNP markers were highly polymorphic and efficiently discriminate the tested cowpea accessions. The low heterozygosity and the high inbreeding coefficients observed among cowpea varieties indicate that the accessions reached an acceptable level of homozygosity. The model-based (structure analysis) and distance-based (UPGM) clustering approaches were used in this study. The model-based analysis revealed the presence of four subpopulations at K = 4 whereas the distance-based cluster analysis classified the cowpea accessions into three distinct clusters. The subpopulations identified exhibited a high level of genetic diversity and were moderately differentiated. This result suggests that the accessions studied are unique and have greater potential to contribute to new varieties for breeding programs in South Africa. The fifth study determined the combining ability effects and gene action controlling the yield and its related traits among 10 selected parental genotypes and 45 crosses using the half diallel mating design. There were significant genotypic, environmental and their interaction effects for almost all traits except leaf length (LL) and number of seeds per pod (NSP) exhibited by both parental genotypes and their progenies. The GCA effects were significant for LW, PL, NSP and HSW whereas the SCA effects were significant for pod width (PW) only. The GCA x environmental interaction effects were highly significant for all traits while the SCA x environmental interaction effects were significant for all the traits except plant height (PH) and LL. The parents Acc-cowp17, Acc-cowp31, Acc-cowp9, Acc-cowp5, Acc-cowp38 and Acccowp19 were identified as good combiners for grain yield and its associated traits productivity. The newly developed progenies Acc-Cowp31 x Acc-Cowp5, Acc-Cowp38 x Acc-Cowp19, Acc-Cowp9 x Acc-Cowp2, Acc-Cowp47 x Acc-Cowp9, Acc-Cowp31 x Acc-Cowp9, Acc- Cowp32 x Acc-Cowp9, Acc-Cowp47 x Acc-Cowp38, and Acc-Cowp17 x Acc-Cowp38 were found to be the best performing due to their desirable SCA effects for enhanced grain yield. The study revealed that trait expression was controlled by both additive and non-additive effect with the additive gene action shown to be the important in controlling traits including NB, LW, NPP, NSP and HSW.