Principal component and cluster analyses for assessing agro-morphological diversity in rice
Abstract views: 50
Keywords:Rice, correlation, cluster analysis, PCA
Forty-five rice genotypes were evaluated for determining the pattern of variation and relationship among 14 yield contributing traits. Four principal components (PCs) exhibited eigen values >1.0 and explained about 79.5 % of the total phenotypic variability. From rotated component matrix it has been observed that the highest positive eigen vector was taken by secondary branches (0.945), followed by total spikelet number (0.945), fertile spikelet number (0.889), primary branches (0.676) and harvest index (0.632) in PC1, indicating the major effects in the overall variation among the genotypes. Seven groups were formed after cluster analysis. Cluster I had lowest average for days to 50% flowering, Cluster II had highest mean value for harvest index, Cluster III had highest mean for flag leaf area, test weight, and straw and grain yield per plant, and Cluster V had highest mean value for primary branches, total spikelet number, fertile spikelet number and fertility %. So, desirable genotypes fromdifferent cluster can be selected and hybridization programme may be initiated to utilize heterosis in F1 generation and wide spectrum of recombinants in segregating generations for selection of promising segregants.
How to Cite
Copyright (c) 2023 Association of Rice Research Workers
This work is licensed under a Creative Commons Attribution 4.0 International License.