Deciphering multivariate patterns and diversity analysis of yield and associate traits in okra (Abelmoschus esculentus (L.) Moench) accessions of northwestern India
2024
Keerthana , Sai | Singh, Hardeep | Dubey, N. | Delvadiya, I. R. | Avinashe , H.
Okra is a valuable crop cultivated worldwide for its edible fruits. Understanding genetic variability and the relationship among key traits is essential for improving yield and other agronomic characteristics. This study focused on evaluating 27 okra (Abelmoschus esculentus (L.) Moench) genotypes at research farm of Lovely Professional University, Phagwara, Punjab to explore their genetic diversity and yield-contributing traits. The genetic variability of okra genotype assessed the relationships between yield-contributing traits and identified the traits with the most significant direct and indirect effects on the yield of okra. A Randomized Complete Block Design (RCBD) with three replications was used for traits such as plant height, fruit production, seed weight and other characteristics. Variance analysis, heritability estimation, correlation analysis, path coefficient analysis and cluster analysis were conducted to identify significant relationships and genetic diversity among genotypes. The highest coefficient of variation was observed for fruit weight per plant, the number of main branches and seed weight per plant. Path analysis showed that seed weight per plant had the largest direct positive effect on fruit production per plant (0.878) and total number of main branches (0.845). Negative influences were seen from days to first flowering (-0.164). Cluster analysis revealed the highest genetic distance between cluster II and V (D2 = 356.76). Plant height (23.93%) and biological yield (22.22%) contributed most to genetic divergence. This study identified key traits such as seed weight and fruit production with the highest genetic potential for yield improvement in okra. The findings can be applied in breeding programs to develop high-yielding okra varieties through targeted selection.
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