MULTIVARIATE ANALYSIS IN UPLAND COTTON (GOSSYPIUM HIRSUTUM L.) USING PRINCIPAL COMPONENT AND CLUSTER ANALYSISK. Mohan Vishnuvardhan, B. Venkata Ravi Prakash Reddy, D. Lakshmikalyani, M. Sivaramakrishna, K. Sudheepthi, K. Amarnath1 and N.C. VenkateswarluAbstract The experiment material comprising 17 cotton genotypes with a view to study of genetic parameters for different yield and yield parameters. The analysis of variance revealed the existence of significant differences among the traits studied. The findings of correlation coefficient studies revealed that seed cotton yield established strong positive correlation with lint yield (0.9827) followed by plant height (0.6405) and number of bolls per plant (0.4717). The results of principal component analysis revealed that, 4 Principal Components (PCs) were established with Eigen value greater than 1.00 which accounted for 83.9 % of the total variation for discriminating the lines. From principal component analysis, PC1 showed highest amount of variance (33.1%) with mostly related to traits like boll weight, seed index, lint index and halo length indicated the importance of these traits in relation to yield enhancement. Cluster analysis classified the genotypes into five clusters among which cluster I was largest with eight genotypes followed by cluster III and cluster IV with four and three genotypes respectively indicating the versatility of the genotypes of these clusters in the exploitation of heterosis. |