Assessment of Genetic Divergence in Mungbean Genotypes (Vigna radiata) Using Multivariate Analysis for Crop Improvement
Soumya Patel
*
Genetics and Plant Breeding, College of Agriculture, JNKVV, Jabalpur, M.P., India.
Stuti Sharma
Genetics and Plant Breeding, College of Agriculture, JNKVV, Jabalpur, M.P., India.
Shikha Upadhyay
Genetics and Plant Breeding, College of Agriculture, JNKVV, Jabalpur, M.P., India.
Prashant Namdeo
Genetics and Plant Breeding, College of Agriculture, JNKVV, Jabalpur, M.P., India.
Radheshyam Sharma
Biotechnology Centre, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, M.P., India.
Ayushi Soni
Genetics and Plant Breeding, College of Agriculture, JNKVV, Jabalpur, M.P., India.
*Author to whom correspondence should be addressed.
Abstract
Aim: Mungbean (Vigna radiata (L.) Wilczek) is an essential short-duration, self-pollinated, annual herbaceous bean primarily planted for nutritional quality, adaptability and role in sustainable agriculture. However, low genetic variety within cultivated germplasm prevents progress toward yield improvement. Assessing genetic diversity between genotypes is thus essential for identifying diverse parents for successful breeding programs.
Study Design: The study used three replications in randomized complete block design (RCBD).
Place and Duration: The experiment was conducted during kharif, 2023, at the Breeder Seed Production Unit, Department of Genetics and Plant Breeding, College of Agriculture, JNKVV, Jabalpur, Madhya Pradesh.
Methodology: In the present study, 35 mungbean genotypes were analysed for genetic diversity using Mahalanobis's D2 statistics and principal component analysis (PCA). 13 key quantitative traits were recorded and genotypes were grouped into 14 clusters based on genetic distance using tocher’s procedure.
Results: Genotypes from clusters XII and XIV had the greatest inter-cluster distance, whereas Cluster V had the greatest intra-cluster distance. The traits with the highest percentage contribution to the overall variance were determined to be the number of pod clusters per plant (22.35%). PCA analysis found that, of the 13 principal components, four had eigenvalues of more than 1.30, accounting for 71.17% of total variance. The first principal component accounted for 31.36% of the variability and was mainly associated with yield-related traits. Using multivariate analysis, it was found that MH-2-15, IC 103821, and IC 373199 were genetically varied genotypes that were utilized to promote genetic recombination and to create a more resilient hybrid.
Conclusion: The study demonstrated considerable genetic diversity among the mungbean genotypes, highlighting the effectiveness of D² statistics and PCA in parental selection. The identified diverse and high-performing genotypes can be strategically utilized in hybridization programmes to enhance genetic recombination and improve yield potential in mungbean.
Keywords: Clusters, D2 statistics, genetic diversity, mungbean, principal components