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.

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.

Secondary metabolites are among the major contributors of host-plant resistance. Cowpea produces secondary metabolites that are known to enhance resistance to insect pests including flower bud thrips. However, environmental conditions tend to affect the production of secondary metabolites, thereby affecting the response of the host plants to insect pest. The objective of this study was to determine the effect of the genotype × environment interaction on the production of secondary metabolites and flower bud thrips resistance in cowpea. Six cowpea genotypes were evaluated for flower bud thrips damage and the contents of flavonoids, antioxidants, phenolics, proteins, lignin, tannins and reducing sugars in four environments with varying temperatures and rainfall patterns. The data collected were subjected to the analysis of variance and genotype and genotype × environment (GGE) analysis. Flower thrips damage, and the contents of flavonoids, antioxidants, lignin, tannins and reducing sugars varied significantly (p < 0.001) among genotypes. Genotype Sanzi produced high levels of antioxidants, while TVU-9820 led in phenolic concentrations respectively. Metabolite contents were significantly (p < 0.001) different among environments, with the long rain season of field experiments led to increased production of flavonoids, proteins, lignin and tannins. A resistant genotype, TVU-3804, produced relatively stable contents of flavonoids, antioxidants, phenolics, proteins and reducing sugars across environments. In this study, the environment influenced the concentration of the metabolites, which in turn affected the cowpea’s resistance to flower bud thrips.