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Vol. 11, Issue 2 (2022)

Estimation of genetic divergence for agro morphological characters in mungbean [Vigna radiata (L.) Wilczek]

Lekha Ram and Ashok Kumar Saxena
The genetic diversity in germplasm provides a good opportunity for plant breeders to create new and better genotypes with beneficial traits, which include both farmer-preferred traits (yield potential etc.) and breeders preferred traits (stress resistance / tolerance etc.). The present investigation was conducted to ascertain the information about the magnitude and extent of genetic diversity of agro-morphological characters in mungbean. A total of sixty genotypes of mungbean were evaluated during Kharif season 2018 with three replications adopting randomized compete block design. The observations of each genotype under each replication for phenological and yield attributing traits were recorded on the basis of per plot and five randomly selected plants, respectively. Based on the D2 values, the sixty genotypes were divided into ten clusters. A maximum of 18 genotypes belongs to cluster I which evolved as the largest cluster, followed by cluster III with 16 genotypes, while cluster VII and IX were solitary. The highest intra-cluster distance was observed for cluster IX (17.20) and the lowest was observed for cluster V, VIII, X (0.0), while the maximum inter-cluster distance was observed between cluster IX and X (35.6). Within the twelve characters studied, the seed yield per plant contributed most to the divergence followed by the 100-seed weight. On the basis the results of this study, it was suggested that a successful hybridization programme could be performed between the genotypes of clusters viz. IX and V, VIII, X that exhibit maximum inter-cluster distance to produce new recombinants with desired traits.
Pages: 561-565  |  302 Views  111 Downloads

The Pharma Innovation Journal
How to cite this article:
Lekha Ram, Ashok Kumar Saxena. Estimation of genetic divergence for agro morphological characters in mungbean [Vigna radiata (L.) Wilczek]. Pharma Innovation 2022;11(2):561-565.

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