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Range Management and Agroforestry
Year : 2023, Volume : 44, Issue : 2
First page : ( 288) Last page : ( 297)
Print ISSN : 0971-2070. Online ISSN : 2249-5231.
Article DOI : 10.59515/rma.2023.v44.i2.10

Variability, character association and genetic divergence studies in cowpea [Vigna unguiculata (l.) Walp] genotypes

Phyu Phyu Oo, Panchta Ravish*, Nimbal Somveer, Kharor Neeraj, Arya Sataywan, Arya Rajesh Kumar, Langaya Sonu

CCS Haryana Agricultural University, Hisar-125004, India

*Corresponding author e-mail: rpanchta@gmail.com

Online Published on 4 January, 2024.

Abstract

The present experiment comprised of 46 cowpea genotypes to estimate genetic variability, interrelation among 13 quantitative characters and genetic divergence during Kharif 2020. High heritability coupled with high genetic advance as per cent of mean was observed for the characters viz., seed yield per plant, number of clusters per plant, number of pods per plant, plant height, days to 50% flowering and days to maturity. High phenotypic and genotypic coefficient of variation, heritability and high genetic advance as per cent of mean (GAM) were observed in seed yield per plant, number of clusters per plant, number of pods per plant and plant height. Seed yield per plant was found positively and significantly correlated with pods per plant, seeds per pod, pods per cluster, pod length and 100 seed weight. Path analysis revealed that number of seeds per pod, number of pods per plant and 100 seed weight would be selected as important characters towards seed yield as they showed a high positive direct effect on seed yield. D2 analysis revealed that all the genotypes were grouped into seven distinct clusters. Cluster V had the maximum intra-cluster distance. The crossing among the genotypes of clusters V and VII, II and V, III and VI would result in novel recombinants as they showed high inter-cluster distance. Increased contribution to the divergence was found for the characters like plant height, seed yield per plant, number of clusters per plant and days to 50% flowering. Cluster V was found to be best performing for agronomic characters, followed by Cluster VI and Cluster III. Cluster II was best performing for nutritional value and earliness.

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Keywords

Correlation, D2 analysis, GCV, Path coefficient, PCV.

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