Genetic Divergence Assessment through K-Means Clustering and Principal Component Analysis for Seed Yield, Zinc, Iron and Protein Content in Vigna unguiculata L. Walp

Cowpea [Vigna unculata (L.) Walp.] with chromosome number 2n=24 is an one of the important crops grown in arid and semi-arid regions of the world. Cowpea is a source of high quality protein consumed in many parts of the world. Malnutrition is a global problems needs to be addressed locally. Identification of micronutrient dense genotypes is the major objective of cowpea Biofortification programme. In the present study, 169 cowpea genotypes collected from different sources were analysed for genetic divergence through K-means clustering followed by Principal component analysis (PCA). Considering mean values, genotypes were classified into eight clusters following K-means clustering algorithm. Cluster VII has highest number of genotypes (56 genotypes) followed by cluster III (43 genotypes). Clusters VI and VIII were solitary with single genotype. Results revealed that crosses could be made between genotypes of cluster V & VIII, cluster III & VIII and cluster VII & VIII. According to PCA, Five principal components (PC) with Eigen values greater than one contributed 70.5% of the total variability. Amongst, PC₁ accounted highest proportion of total variance (31.3 %), remaining PCs viz., PC₂, PC₃, PC₄ and PC₅ revealed 14.6%, 10.1%, 7.7% and 6.8 % respectively, revealing pod yield^-1, seed yield^-1, pod length and seeds pod^-1 were major contributors to the total genetic variability and divergence.