Soybean productivity in sub-Saharan Africa (SSA) is less than half of the global average yield. To plug the productivity gap, further improvement in grain yield must be attained by enhancing the genetic potential of new cultivars that depends on the genetic diversity of the parents. Hence, our aim was to assess genetic diversity and population structure of elite soybean genotypes, mainly released cultivars and advanced selections in SSA. In this study, a set of 165 lines was genotyped with high-throughput single nucleotide polymorphism (SNP) markers covering the complete genome of soybean. The genetic diversity (0.414) was high considering the bi-allelic nature of SNP markers. The polymorphic information content (PIC) varied from 0.079 to 0.375, with an average of 0.324 and about 49% of the markers had a PIC value above 0.350. Cluster analysis grouped all the genotypes into three major clusters. The model-based STRUCTURE and discriminant analysis of principal components (DAPC) exhibited high consistency in the allocation of lines in subpopulations or groups. Nonetheless, they presented some discrepancy and identified the presence of six and five subpopulations or groups, respectively. Principal coordinate analysis revealed more consistency with subgroups suggested by DAPC analysis. Our results clearly revealed the broad genetic base of TGx (Tropical Glycine max) lines that soybean breeders may select parents for crossing, testing and selection of future cultivars with desirable traits for SSA.

Soybean rust (SBR), caused by Phakopsora spp., is a major global concern for soybean producers. SBR causing fungi are polycyclic and obligate biotrophs, rendering the study of their biology particularly tedious. Over the past four decades, substantial progress has been made towards understanding the epidemiology of the disease, the identification of sources of resistance, and the mapping of soybean loci conferring resistance to P. pachyrhizi (Rpp genes), since this species is particularly well established and widespread in many soybean growing areas. Although host-plant resistance is generally considered as the most desirable solution from an environmental, economic, and social perspective, other disease control approaches such as agronomic practices and chemical application are also important, and influence rust epidemiology as well as the durability of host plant resistance. This review focusses primarily on genetic aspects of SBR management and summarizes the research in the following areas: SBR symptoms, aetiology, pathogenic variation and population structure of Phakopsora populations, expression of soybean resistance to Phakopsora infection, genetics and molecular diagnostics of host resistance to pathogen, and resistance gene deployment approaches. Finally, the role of multidisciplinary strategies is discussed for achieving higher durability of SBR resistance in soybean.

Background: The success of hybridization programme depends on the combining ability of parental lines. Methods: Seven soybean genotypes and all their partial diallel crosses in the F2 generation were evaluated in a randomized complete block design at two locations in Nigeria, during the 2017-2018 growing season. Result: Analysis of variance showed that both environments and genotypes were significantly different for all measured traits. The genotype TGx 1988-5F was the best general combiner for earliness in flowering and poding, while TGx 1448-2E was the best general combiner for number of pods/plant and seed yield/plant. Crosses having significant and positive specific combining ability effect for number of pods/plant and seed yield/plant were TGx 1485-1D × TGx 1448-2E and TGx 1988-5F × TGx 1989-19F, respectively. Genotypes TGx 1988-5F and TGx 1448-2E exhibiting good general combining ability for earliness and seed yield/plant, are thus, promising for utilization in the future hybridization programme for soybean improvement.

Genotype by environment (GE) interaction has a large impact on selecting adapted and predictable genotypes. Therefore, it is necessary to evaluate maize genotypes across different environments, seasons or locations for a successful selection. Twelve 3-way cross maize hybrids obtained from the International Institute of Tropical Agriculture (IITA) were evaluated on the field of the Federal University of Agriculture, Abeokuta, Nigeria (latitude 7? 15? N and longitude 3? 25? E) across three growing seasons of 2021 and 2022. The experiment was laid out with three replicates. Additive main effect and multiplicative interaction (AMMI), genotype (G) plus GE (GGE) biplot and joint regression techniques were used to identify stable and high-yielding genotypes. The AMMI analysis showed that the total variances in the yield of the three-way maize hybrids accounted for by G, environment (E) and GE interaction were 30.6%, 44.19% and 25.31%, respectively. Based on the AMMI biplot, the genotypes LW1701-10 and OBA SUPER-9, which combined high yield with stability, were the most desirable. The GGE biplot showed that hybrids LW1701-10, OBA SUPER-9 and LW1701-6 were the most stable and desirable genotypes. The joint regression technique showed that the performance of the genotypes could not be revealed in a linear manner as the deviation component variance accounted for 81.05% and identified LW1701-6, LW1701-16, LW1701-12, LW1701-21, LW1701-4 as stable and desirable genotypes. The study revealed that the GGE and AMMI models were more effective than the joint regression technique in examining yield stability of maize hybrids. The study deals with the comparison of AMMI, GGE biplot and joint regression techniques.

Genetic Assessment of Combining Ability for Seed-Yield and Its Related Traits in Soybean [Glycine max (L.) Merrill]