Multivariate analysis for assessing genetic diversity in different genotypes of okra (Abelmoschus esculentus L. Moench) for varietal improvement
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https://orcid.org/0000-0003-4780-1641
R. Ebenezer Babu Rajan
C. Praveen Sampath Kumar
J. Sam Ruban
Abstract
Accurate assessment of genetic diversity facilitates the strategic identification of superior genotypes, enabling the development of high-yielding, climate-resilient cultivars and promoting effective crop improvement strategies. The present study was carried out with 48 different genotypes of Okra (Abelmoschus esculentus (L.) Moench) during (Jan-April 2022) to investigate their genetic diversity. The analysis of genetic divergence using D2 statistics revealed substantial variation among genotypes for the twelve traits studied. The 48 genotypes were grouped into nine clusters, with cluster IV having the highest representation of 24 genotypes, followed by cluster II with 12 genotypes, cluster I with 4 genotypes, cluster III with 3 genotypes, and the remaining clusters with one genotype each. The intra and inter-cluster D2 values ranged from 0 to 95.29 and 103.00 to 588.71, respectively. The highest intra-cluster distance was observed in Cluster III (95.29), and highest inter-cluster distance was observed between cluster V and IX (588.71). This range clearly demonstrated that the inter-cluster distance was greater than the intra-cluster distance indicating wide diversity across the groups. Cluster VII showed a high mean for traits plant height (112.53), peduncle length (3.14), fruit length (20.03), and number of locules (7.87). Cluster V showed the highest mean for the number of fruits (39.33) and yield per plant (1.18). The analysis of all characters' relative contributions showed that the number of fruits per plant and the number of locules per fruit contributed most to genetic divergence. High heterotic effects and desired transgressive segregants are anticipated when different genotypes from clusters with the greatest inter-cluster distance are used for hybridization.
Article Details
Article Details
Clusters, Genetic Diversity, Intra and Inter-cluster distance, Multivariate analysis
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