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Celosia (Celosia argentea), is an important tropical vegetable for households in sub-Saharan Africa. Despite the multifaceted usefulness, available genotypes are low-yielding, and the vegetable faces dangers of genetic erosion due to poor research attention. The magnitude and pattern of variability will guide the choice of breeding methods for improvement. Twenty-one celosia accessions were evaluated in 2018 and 2019 to determine study genetic variability and heterotic patterns among clusters. Accessions and clusters differed significantly (p ≤0.05/0.01) for plant height, number of leaves/plant, stem weight, harvest index and dry matter content. Genotypic coefficients of variation; ranging from 37.89 to 0.12, were lower than phenotypic coefficients of variation which ranged from 114.55 to 0.12, both for number of leaves/plant and harvest index respectively, indicating the importance of environment in the variability. Discriminant analysis indicated low (8.12%) classification error rate, indicating the possibility of heterotic patterns among clusters. Principal component (PC) analysis controlled 73% of the observed variability among accessions and identified all measured traits as important contributors with loadings ranging from 0.30 (in PC 1) to 0.63 (in PC 2) for harvest index and stem weight respectively. Useful levels of association were also observed among measured traits. The study concluded that there was sufficient genetic variability for effective selection. Discriminant and principal component analyses identified plant height, number of leaves/plant and dry matter content as major contributors to variation among accessions. Weight of edible parts of Celosia can be simultaneously improved with plant height and number of leaves/plants.

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.

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.