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Okra yields are low in West and Central Africa and factors including narrow genetic base of available germplasm have been implicated. An assessment of genetic variability among germplasm from various sources and knowledge of trait contributions to these variations is crucial to the success of okra breeding program. Eighteen okra accessions were evaluated during the 2020 cropping season in Nigeria to assess the genotypes for genetic diversity, group the accessions into clusters and identify traits that chiefly accounted for the variations among the genotypes. Data obtained were subjected to analysis of variance (ANOVA), metroglyph and principal component analyses (PCA). ANOVA revealed significant accession mean squares for majority of the measured traits. Metroglyph analysis grouped the accessions into four clusters with members of Cluster IV generally high-yielding, early-maturing and short genotypes. NGB00353 and NGB00356 that were among genotypes with high total index scores were members of Cluster IV. The first four principal components (PCs) accounted for 80% of the total observed variation. PC1 with the highest discriminatory power was loaded with days to budding, internode length, pod yield.plot−1 and the number of pods.plant−1. The variation within and between clusters could be explored in okra breeding program.

Knowledge of the pattern of trait variation among accessions, and the trait profiles of the accessions is crucial for improvement of a crop. Twenty-one Amaranthus accessions were evaluated in 2018 and 2019 to investigate the extent of genotypic diversity among the amaranth accessions and their trait profiles. Data were subjected to analysis of variance, and correlation and principal component analyses. Taking stem weight (SWT) as the yield, the accession × yield-trait combination (GYT) biplot was employed to investigate the trait profiles of the accessions. Accession, year, and accession × year mean squares were significant (P≤0.05/0.01) for most of the measured traits. The first three principal components explained 88.18% of observable variation among the accessions and identified plant height (PHT), number of leaves per plant (NOL), and root weight (RWT) as the major contributors. Significant (P≤0.01) correlation was observed in the association of SWT with NOL, TBM, and RWT. Accessions NGB00019 and NGB00061 were associated with the sector containing all the YT combinations considered. However, NGB00019 was identified as the best combiner of yield with other traits. Further studies involving more traits should determine the trait profiles of the remaining accessions.

The possibility of developing deep-sowing tolerant (DST) maize to absorb moisture from subsoil zones is crucial to maize adaptation to water-stressed environments. The function of the mesocotyl in field emergence of seedlings is established in grasses. However, information is scarce on the extent of genetic variability for mesocotyl length (ML) in maize. Sixty-eight maize genotypes were studied using Completely Randomised Design in a laboratory experiment to investigate the extent of genetic variability for ML, and the relationship of seed biochemical components with ML. Ten seeds of each genotype were germinated for 10 days in the dark. Mesocotyl length was determined by placing cut mesocotyl against a flexible measuring tape. Biochemical contents of seeds were determined at a standard diagnostic laboratory. Analysis of variance revealed highly significant (p ≤ 0.01) genotype mean square, indicating sufficient variability for genetic improvement. Broad-sense heritability and genetic advance were high and implied that ML was heritable. Mean ML for genotypes ranged from 0.58 to 9.02 cm, thus, planned crosses can be made for ML improvement. A dendrogram from cluster analysis based on Ward’s minimum variance cluster analysis classified 65 of the genotypes into clusters I, II, and III with ML (mean ± standard deviation) of 0.49 ± 0.18, 4.25 ± 0.96, and 9.16 ± 0.93 cm, respectively. All the measured biochemical parameters, except selenium, showed significant (p ≤ 0.05/0.01) associations with ML. Crosses can be planned involving genotypes from clusters 1 and III, to exploit heterosis for ML in a hybrid program. The results obtained from this study provide a basis for the development of DST maize for drought-prone environments.

Genetic analysis of phenotypic variations of traits under drought stress to identify associated loci will facilitate crop improvement and performance in marginalized drought-prone areas. To this end, we identified genomic regions/loci underlying variations among recombinant inbred lines (RILs) of WAB638-1/BRS-PRIMAVERA rice under drought stress and control environments using diversity array technology (DArT)-based markers in linkage analysis. The linkage map of 94 RILs and 581 DArT-Based markers (295 SNP and 286 SilicoDArT) covers all the 12 rice chromosomes. The total length of the linkage map was 3057.85 cM, with an average of 254.82 cM per chromosome. The average interval between two adjacent markers was 5.82 cM and ranged from 0.56 cM to 39.8 cM, with 429 (73.8%) markers less than 5.0 cM from their nearest neighbours. A total of 28 additive effect QTLs associated with seven agronomic traits across the two environments were localized on chromosomes 1, 2, 3, 8, 10, and 11. The QTLs individually explained 0.96% to 41.6% of the phenotypic variance (PVE), with 11 (40%) major-effect QTLs, each explaining more than 10% PVE. Four major-effect QTLs were mapped to regions consistent with other studies, while seven are newly reported. The consistent QTLs can be deployed for marker-assisted breeding, and the new QTLs should be further characterized and validated. While the QTLs detected for various agronomic traits advances our understanding of phenotypic variability, those associated with drought treatments are significant for improving rice performance in drought-prone areas of the upland ecology.

The success of a hybrid breeding program is dependent on available heterotic patterns for exploitation of grain-yield heterosis. The efficiency of the assignment of germplasm lines into heterotic groups is a prerequisite for obtaining useful heterotic patterns among germplasm lines. A total of 256 maize hybrids, comprising 244 top crosses, six diallel cross hybrids, and six checks, were grown under Striga infestation, drought, and optimal conditions, from 2015 to 2017. The study determined the combining abilities of the parental inbreds, classified the inbreds into heterotic groups, and compared the efficiencies of the following four grouping methods for classifying the inbreds: specific combining ability (SCA) effect of grain yield, general combining ability (GCA) effects of multiple traits (HGCAMT), SCA and GCA (HSGCA) for yield, and single nucleotide polymorphism-based genetic distance (SNP-based genetic distance (GD)). Significant GCA and/or SCA mean squares were revealed for most measured traits in all test environments. Sums of squares (SS) due to GCA were higher than SCA SS for measured traits in all test environments. The HSGCA, SCA, and SNP-based GD methods identified four heterotic groups, whereas the HGCAMT identified three groups, in all environments. The additive gene effect was preponderant in the inheritance of most measured traits. The efficiencies of the grouping methods varied with the test environments. The HSGCA and SCA methods were the most efficient for grouping in all test conditions. For practical breeding purposes, the HGCAMT and HSGCA methods were recommended under Striga infestation and drought, respectively. The heterotic patterns, which were revealed in this study, were effective for planning hybridization schemes for developing high-yielding, Striga-tolerant/resistant, and drought-tolerant maize hybrids for stressful environments.