Diversity Analysis of Rice (Oryza sativa L.) Genotypes using the Principal Component Analysis and Clustering Method

The investigation was conducted for the diversity analysis using the principal component analysis and clustering method for seventy-one rice genotypes for nine quantitative traits at the Agriculture Farm, Institute of Agriculture, Visva-Bharati, Sriniketan, West Bengal, India during the kharif season from June to November 2021. The principal component analysis (PCA) revealed that the first three principal components (PCs) exhibited more than 1.0 eigenvalues and accounted for 66.46% of the total variations among the characters studied. PC1, PC2, and PC3 contributed 36.53, 15.47, and 14.47% of the total variations, respectively. The number of filled grains, spikelet fertility, panicle length, and the test weight in different principal components were the most important traits influencing the variability among the genotypes. The cluster analysis divided the genotypes into five clusters viz., I, II, III, IV, and V Cluster III was the largest containing 37 lines followed by Cluster II with 22 lines and Cluster I with 10 lines. The remaining clusters, Cluster IV and Cluster V, contained one line each. The clusters, I and IV had the desirable mean values for important characters. The genotypes belonging to these clusters can be utilized for further breeding programs.