Legume Research
Web of Science
  • Year: 2026
  • Volume: 49
  • Issue: 1

Principal Component and Cluster Analysis in Mungbean [Vigna radiata (L.) Wilczek]

  • Author:
  • Sayantan Das1, Ashutosh Sawarkar1*, Samita Saha1, R.B. Raman2, T. Dasgupta1
  • Total Page Count: 10
  • Page Number: 15 to 24

1Division of Genetics and Plant Breeding, IRDM Faculty Centre, Ramakrishna Mission Vivekananda Educational and Research Institute, Narendrapur-700 103, Kolkata, West Bengal, India.

2Nalanda College of Horticulture, Bihar Agricultural University, Noorsarai, Nalanda-803 113, Bihar, India.

*Corresponding Author: Ashutosh Sawarkar, Division of Genetics and Plant Breeding, IRDM Faculty Centre, Ramakrishna Mission Vivekananda Educational and Research Institute, Narendrapur-700 103, Kolkata, India. Email: annu.sawarkar@gmail.com

Abstract

Mungbean is a warm-season crop and its ability to grow quickly, allowing adaptation to multiple cropping systems and crop rotations. Principal component analysis (PCA) and cluster analysis are the useful tool to explain the diversity among the genotypes, identification of superior genotypes and characters forfuture breeding programs.

The experiment was conducted at the Agricultural Experimental Farm, (AEM), Division of Genetics and Plant Breeding, RKMVERI, Narendrapur, West Bengal, for summer, 2021 and 2022 in a randomized block design.

Principal components i.e. PC1 to PC3 which eigen value more than one extracted from the data contributed 67.45% from the total variation. Cluster analysis revealed that the maximum contribution for the total divergence was recorded for number of branches per plant (25.7%), seed yield per plant (14.5%), plant height (13.8%) and number of pods per plant (12%). Genotypes had classified into six clusters and their intra and inter cluster distance showed the genetic diversity between them. The highest genotypes i.e. nine had in the cluster 1 followed by cluster 5 contributed six genotypes, cluster 2, 4 and 6 represented four genotypes each and the cluster 3 had three genotypes. The identified diverse genotypes like AKM 96–2, IPDI-539, PRATIKSHA NEPAL, SUKETI-1, TMB 96–2, SAMRAT, SIKHA and VIRAT showed high contribution to total diversity for number of branches per plant, plant height, number of pods per plant and number of primary branches per plant could be used in future breeding program.

Keywords

Cluster analysis, Greengram, Multivariate analysis, Principal component analysis, Yield