Phenotypic Characterization for Okra Hybrids Developed by 7x7 Half Diallel Mating Design
Balagoni Maruthi, Sibsankar Das, Arup Chattopadhayay, Umesh Thapa, Anirban Maji
Journal of Advances in Biology & Biotechnology · pp. 1657–1665 · Published 28 Nov 2025
10.9734/jabb/2025/v28i113351Abstract
Aims: The aim of this study is to assess phenotypic variation and genetic diversity among 21 okra hybrids derived from a 7×7 half-diallel design. By evaluating key traits and calculating the Shannon–Weaver Diversity Index (H’), the work identifies superior hybrids and supports selection of promising parental lines for okra improvement. Study Design: The study used a Randomized Block Design with 3 replications. Place and Duration of Study: The existing experiment was worked out at Teaching Farm, College of Agriculture under BCKV, Burdwan Sadar during Rainy season, 2022. Methodology: Key morphological traits were recorded following standard descriptors. Phenotypic characterization was performed using trait-wise observations. Genetic diversity among hybrids was quantified using the Shannon–Weaver Diversity Index (H’). Data were analyzed statistically to determine variability for phenotypic traits. Results: In the study of 21 F1 hybrids and check (NBH-45) H’ with an overall mean of 0.364 was obtained, H’ value varied from 0 to 0.916. The character fruit length at tender marketable stage showed maximum diversity (0.916) followed by fruit shape of the apex (0.859), fruit pubescence (0.819), and fruit surface between ridges (0.676). Seed colour, stem colour, vein colour, fruit locule number, fruit length (at physiological mature stage) and serration of margin of leaf blade did not show any diversity among the 22 hybrids studied. Conclusion: The study revealed moderate overall diversity (H’ = 0.364) among the 22 okra genotypes. Highest variability occurred in fruit-related traits, especially tender fruit length and apex shape, indicating their usefulness for selection. Several traits showed no diversity, suggesting genetic uniformity. These findings support targeted trait-based selection in okra improvement programs.
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