Several species of herbivores feed on maize in field and storage setups, making the development of multiple insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to fall armyworm (FAW), whilst bulked grains were subjected to a maize weevil (MW) bioassay and genotyped with Diversity Array Technology’s single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance levels of 0.05 and 0.01, respectively, and located within or close to multiple insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple traits, of which, six were associated with resistance to both FAW and MW, suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10–30 kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of the five of the six combined resistance QTNs, thus reinforcing the pleiotropy hypothesis. In addition, through in silico co-functional network inferences, an additional 107 network-based CGs (NbCGs), biologically connected to the 64 GbCGs, and differentially expressed under biotic or abiotic stress, were revealed within MIRGRs. The provided multiple insect resistance physical map should contribute to the development of combined insect resistance in maize.

High populations of species in the whitefly complex Bemisia tabaci Gennadius (Hemiptera: Aleyrodidae) were reported to cause severe damage to cassava in East and Central Africa. However, reasons for B. tabaci population increases are not well understood. We investigated the effect of cassava morphological traits, temperature, rainfall and relative humidity (RH) on the abundance of B. tabaci. Five cassava genotypes with varying levels of resistance to cassava mosaic disease, cassava brown streak disease, and B. tabaci infestation were planted in three Ugandan agro-ecological zones. The experiment was conducted in 2016 and 2017 in a randomized complete block design. Across all locations, the tallest genotype Alado alado supported the lowest number of B. tabaci adults. In areas with high B. tabaci prevalence, leaf area, leaf lobe width, and leaf lobe number exhibited significant positive effects (p < 0.001) on B. tabaci adult count. Positive effects of relative humidity and negative effects of temperature and rainfall on B. tabaci adult and nymph counts were observed in 2016 and 2017, resulting in low populations in Lira. Evidently, temperatures of 28–30 °C, rainfall of 30–150 mm and RH of 55–70%, and deployment of cassava genotypes of low plant height, large leaf area, and lobe width significantly enhanced B. tabaci population growth.

Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.

Often, yam cultivars grown in different agro-ecologies show differential responses across production environments, a term known as genotype-by-environment interaction. Such genotype-by-environment interaction makes selection of the best genotypes under varied production environments more complex. This study evaluated twenty yam genotypes in six test environments to assess genotype, environment, and their interaction effects on tuber yield, response to yam mosaic virus, and dry matter content. The experiments were conducted over two seasons across three locations in Uganda, using a randomized complete block design with three replications. There were significant effects (p ≤ 0.001) for genotype (G), environment (E), and genotype-by-environment interaction for all key traits assessed. Serere (2021) and Namulonge (2021) were identified as the most discriminating and representative environments for testing responses to yam mosaic virus, respectively. Serere (2021) was recognized as the most discriminating environment, whereas Arua (2021) emerged closest to an ideal environment for assessing yam tuber yield. The tested genotypes also exhibited resistance to yam mosaic virus disease, had high tuber yields and dry matter content. Genotypes UGY16020, UGY16034, UGY16042, and UGY16080 demonstrated highest resistance to yam mosaic virus disease, along with high yield and dry matter content, and are thus potential parents for yam genetic improvement. Further evaluation of the four genotypes should be carried out within farmers’ production systems for selection, improvement and release as new yam varieties for Uganda.

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.

Diseases are the most important factors reducing groundnut yields worldwide. In East Africa, late leaf spot (LLS) and groundnut rosette disease (GRD) are the most destructive diseases of groundnut. Limited resistance is available in pure pedigree cultivated groundnut lines and novel sources of resistance are required to produce resistant new varieties. In this work, 376 interspecific lines from 3 different populations derived from crosses with the wild species A. duranensis, A. ipaënsis, A. batizocoi and A. valida were phenotyped for 2 seasons and across 2 locations, Serere and Nakabango, in Uganda. Several genotypes showed a higher yield, a larger seed, an earlier flowering, and similar resistance to the local cultivar checks. Genotypic data was used to construct a linkage map for the AB-QTL population involving the cross between Fleur11 and [A. batizocoi x A. duranensis]^4x. This linkage map, together with the phenotypic data was used to identify quantitative trait loci controlling disease resistance. These lines will be useful in combining good agronomic traits and stacking disease resistance to improve the groundnut crop in sub-Saharan Africa.