Cluster Analysis-based Evaluation of Genetic Diversity in Mungbean (Vigna radiata (L.) Wilczek) for Efficient Parent Selection
Amit Sharma *
Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar - 125 004, India.
Pawan Kumar
Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar - 125 004, India.
Ravika Sheoran
Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar - 125 004, India.
Kavita Dhaka
Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar - 125 004, India.
Deepak Kaushik
Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar - 125 004, India.
*Author to whom correspondence should be addressed.
Abstract
Background: Mungbean (Vigna radiata (L.) Wilczek) is a nutritionally rich, short-duration pulse crop widely cultivated in tropical and subtropical regions, contributing significantly to food security and sustainable agriculture through its high protein content and biological nitrogen fixation. However, its productivity remains constrained by limited genetic variability and stress susceptibility, necessitating the systematic evaluation of genetic diversity using multivariate approaches to enhance breeding efficiency and develop improved cultivars.
Aims: The study aims to assess genetic diversity among mungbean (Vigna radiata (L.) Wilczek) genotypes using multivariate cluster analysis and to identify promising parents for yield improvement and breeding programmes.
Study Design: The field experiment laid out in a randomized block design with three replications.
Place and Duration of Study: The study was conducted at CCS Haryana Agricultural University, Hisar during Kharif 2024.
Methodology: A total of 21 mungbean genotypes were evaluated for yield and yield-attributing traits. Genetic divergence was analyzed using Ward’s minimum variance method. Clustering pattern, intra- and inter-cluster distances and cluster mean values were computed to assess variability and identify superior genotypes.
Results: The genotypes were grouped into six distinct clusters, indicating substantial genetic variability. Cluster II and Cluster III contained the highest number of genotypes, while Cluster VI was monogenetic, reflecting unique genetic constitution. The highest intra-cluster distance was recorded in Cluster II (280.21), indicating greater variability within the cluster. The maximum inter-cluster distance was observed between Cluster IV and Cluster VI (545.00), suggesting wide genetic divergence. Cluster mean analysis revealed that Cluster IV recorded the highest mean yield per plot, followed by Cluster V and Cluster III. Cluster V exhibited superior performance for key yield-contributing traits such as number of branches and pods per plant, whereas Cluster II showed relatively lower mean values for most traits.
Conclusion: The study demonstrates the presence of significant genetic diversity among mungbean genotypes. Genotypes from clusters showing high inter-cluster distance and superior mean performance, particularly Clusters IV, V, and VI, can be effectively utilized in hybridization programmes to exploit heterosis and develop high-yielding varieties. Integrating genetic divergence with cluster mean analysis provides a reliable approach for efficient parent selection in mungbean breeding.
Keywords: Mungbean, genetic diversity, cluster analysis, yield traits, genetic divergence