Cowpea is an important legume crop widely grown in sub‐Saharan Africa for food and feed. However, it is largely challenged by bruchid, a serious storage pest resulting in losses in quantity and quality of grains. Therefore, this research was designed to contribute to the breeding of cowpea resistance to bruchid through the identification of candidate genes associated with resistance to bruchid. A total of 217 mini‐core cowpea accessions were genotyped and phenotyped for their reactions to bruchid. To determine the genomic regions linked with bruchid resistance, 41,948 polymorphic SNP markers were used. Genome‐wide association study identified 11 SNPs linked to the average number of eggs, holes, insect emergence and development period and Dobie susceptibility index. Gene search via Phytozome identified six candidate genes (Vigun08g132300, Vigun08g158000, Vigun06g053700, Vigun02g131000, Vigun01g234900 and Vigun01g201900) associated with the resistance traits. These candidate genes could be incorporated into the farmers preferred but susceptible cowpea varieties to bruchid. The SNP markers associated with the resistance traits can be used in marker‐assisted breeding for accurate and rapid screening of cowpea resistant genotypes to bruchid.

The yield and protein performance in a soybean genotype result from its interaction with the prevailing environmental conditions. This makes selecting the best genotypes under varied target production environments more complex. This study’s objectives were to determine protein content and protein stability of 30 elite soybean genotypes in major soybean-growing areas of Uganda, assess the yield performance and stability in soybeans and determine the relationship between the protein content and grain yield in soybeans. The genotypes were planted in a randomized complete block design of three replications for six seasons across eight locations in Uganda. Genotype and genotype-by-environment (GGE) biplot analyses classified the test locations into three mega-environments for soybean protein and grain yields. Genotype NII X GC 20.3 had the highest mean protein content of 43.0%, and BSPS 48A-9-2 and BSPS 48A-28 were superior for the mean grain yield (1207 kg ha⁻¹). Bulindi was the most discriminating and representative test environment for soybean yield. A weak and negative correlation (r = −0.1**, d.f. = 29) was detected between the protein content (%) and yield (kg ha⁻¹). The highest-yielding genotypes BSPS 48A-9-2, BSPS 48A-31, and Nam II × GC 44.2 are recommended for further evaluation under farmers’ production conditions for selection and release as new soybean varieties in Uganda.

Soybean (Glycine max) is a vital crop for food, animal feed, and industrial products. However, its yield performance is significantly affected by genotype-by-environment interaction (GEI), which complicates the selection of high-yielding, stable varieties. This study aimed to evaluate the yield performance and stability of 12 elite soybean varieties across five major production areas in Uganda using GGE biplot and path analysis. The varieties were planted in a randomized complete block design with three replications over two consecutive seasons. Results revealed significant differences in grain yield among the varieties, locations, and their interactions (p < 0.001). The highest-yielding varieties were Maksoy 5N (979 kg ha⁻¹), Maksoy 4N (978 kg ha⁻¹), Maksoy 3N (930 kg ha⁻¹), and Signal (930 kg ha⁻¹). GGE biplot analysis grouped the locations into two mega-environments, with the Maksoy varieties exhibiting greater yield stability compared to Seed Co. varieties. Path analysis showed that traits such as the number of lower internodes, central internode length, and filled pods had the highest positive direct effects on grain yield. This study provides insights into soybean breeding in tropical environments, highlighting traits that can be targeted to improve yield and stability. The findings offer a framework for breeding programs in Uganda and similar agro-ecological regions, promoting more resilient and productive soybean varieties. This study also illustrated the potential advantages of employing more complex mathematical techniques like path analysis to uncover yield and yield-related traits in soybean breeding programs.

Nutritional insecurity has become a major concern for the ever-growing world population. Millets, the small-seeded cereal crops, are predominantly cultivated in Asia and Africa and are an excellent alternative to major staple foods because of minimal water requirement, stress tolerance, adaptation to marginal lands, and nutritional superiority compared to other cereal crops. Marker-assisted breeding with the next-generation sequencing (NGS) approaches can speed up the genomic selection for the germplasm resource analysis, allele mining, QTL mapping, genome-wide marker-trait association, gene tagging, and fine mapping for the millet improvement. There is a need to focus on advanced omics and genomics studies in millets to understand the molecular regulation of important traits and their transfer to other staple cereals for improvement. Efficient transformation and genomic resources will have to gain momentum for successful genome editing in millets. This chapter provides an outlook on the use of marker-assisted selection, genome editing, and omics methods in minor millets for nutrient fortification and the creation of climate-resilient millets with improved nutritional benefits.