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.

Time to maturity (TTM) is an important trait in soybean breeding programs. However, soybeans are a relatively new crop in Africa. As such, TTM information for soybeans is not yet as well defined as in other major producing areas. Multi-environment trials (METs) allow breeders to analyze crop performance across diverse conditions, but also pose statistical challenges (e.g., unbalanced data). Modern statistical methods, e.g., generalized additive models (GAMs), can flexibly smooth a range of responses while retaining observations that could be lost under other approaches. We leveraged 5 years of data from an MET breeding program in Africa to identify the best geographical and seasonal variables to explain site and genotypic differences in soybean TTM. Using soybean cycle features (e.g., minimum temperature, daylength) along with trial geolocation (longitude, latitude), a GAM predicted soybean TTM within 10 days of the average observed TTM (RMSE = 10.3; x = 109 days post-planting). Furthermore, we found significant differences between cultivars (p < 0.05) in TTM sensitivity to minimum temperature and daylength. Our results show potential to advance the design of maturity systems that enhance soybean planting and breeding decisions in Africa.

Formal public sector soybean breeding in Africa spans over four decades, and it was initiated by the International Institute of Tropical Agricultural (IITA). As the demand of soybean continues to outstrip production, strategic projects such the Tropical Legume (TL) were initiated, in which the main goal was to enhance the productivity of soybean in the farmers’ fields in Sub‐Saharan Africa. One of the strategies to enhance the productivity of soybean in the farmers’ fields is through developing and deploying improved soybean varieties in the target countries. Through the TL I and TL II projects, a number of varieties were released in the target countries, Kenya, Nigeria, Malawi and Mozambique by employing participatory variety selection (PVS). This review provides highlights of the achievements made by IITA breeding programme and insights of what needs to be done to enhance yield improvement for soybean in Africa using demand‐driven breeding approaches.

Background/Objectives: Understanding the genetic diversity of soybean genotypes can provide valuable information that guides parental selection and the design of an effective hybridization strategy in a soybean breeding program. In order to identify genetically diverse, complementary, and prospective parental lines for breeding, this study set out to ascertain the genetic diversity, relationships, and population structure among 35 soybean genotypes based on agro-morphological traits and Single Nucleotide Polymorphic (SNP) marker data. Methods/Results: Cluster analysis, based on agro-morphological traits, grouped the studied genotypes into four clusters. The first two principal components accounted for 62.8% of the total phenotypic variation, where days to 50% flowering, days to 95% maturity, grain yield, shattering score, and lodging score had high and positive contributions to the total variation. Using the SNP marker information, mean values of 0.16, 0.19, 0.067, and 0.227 were obtained for minor allele frequency (MAF), polymorphic information content (PIC), observed heterozygosity (Ho), and expected heterozygosity (He), respectively. Using different clustering approaches (admixture population structure, principal component scatter plot, and hierarchical clustering), the studied genotypes were grouped into four major clusters. Conclusions:The agro-morphological and molecular analysis results indicated the existence of moderate genetic diversity among the studied genotypes. The traits identified to be significantly related to yield provide valuable information for the genetic improvement of soybeans for yield.

Soybean [Glycine max (L.) Merrill] is an important oilseed crop worldwide and it has recently become the crop of interest in Ghana. In this study, 142 soybean accessions were genotyped with 34 SSR markers and concurrently evaluated for five quantitative and two qualitative phenotypic traits. Twenty‐nine of the SSR markers were polymorphic with mean allele number of 5.3, polymorphic information content (PIC) of 0.51 and gene diversity of 0.55. Molecular analysis based on unweighted paired group arithmetic mean (UPGMA) clustering and principal coordinate analysis (PCoA) was similar in explaining the extent of diversity within the accessions. Structure analysis placed most of the accessions into two subpopulations with 18 (12.7%) as admixtures. Principal component analysis (PCA) based on phenotypic traits revealed two clusters. Both UPGMA clustering‐based SSR data and PCA from phenotypic data showed similar results. The assembled germplasm is genetically diverse with high variation in flowering and maturity period, and key yield components which could be exploited in developing superior varieties well adapted to Ghana and West Africa.

Soybean production has expanded worldwide including countries in sub-Saharan Africa. Several national and international agencies and research groups have partnered to improve overall performance of soybean breeding stocks and have introduced new germplasm from Brazil and the United States with the goal of developing new high-yielding cultivars. Part of this effort has been to test improved soybean lines/cultivars accumulated from private and public sources in multilocational trials in sub-Saharan Africa. These trials are known as the Pan-African Soybean Variety Trials, and the entries come from both private and public breeding programs. The objective of this research was to evaluate entries in the trials that include commercial cultivars or advanced experimental lines for the incidence and severity of foliar diseases. All trials were planted in December 2018 with six located in Zambia and one in Malawi. Plants were evaluated during the reproductive growth stages using a visual pretransformed severity rating scale. Foliar disease ratings were recorded for three bacterial diseases, six fungal diseases, one oomycete, and viruses. The overall occurrence of most of the diseases was high except for soybean rust and target spot, which were only found at two and one location, respectively. However, disease severity was generally low, although there were differences in disease severity ratings among the entries at some of the locations for brown spot, downy mildew, frogeye leaf spot, red leaf blotch, and soybean rust.