Vol. 7, Issue 2 (2018)
Principal component analysis and character association for yield components in greengram [Vigna radiata (L.) Wilczek] genotypes
Author(s): Naveen Kumar Jakhar and Arjun Kumar
Abstract: The present investigation was carried out to determine the relationship and genetic diversity among thirty greengram germplasm accessions using principal component analysis for various quantitative traits. Principal component analysis (PCA) depicted that four components (PC1 to PC4) accounted for about more than 90% of the total variation for different traits. Out of total principal components retained PC1, PC2, PC3 and PC4 with values of 44.15%, 24.23%, 13.82% and 9.285 respectively. Biological yield (29%), seed index (28%) and plant height (17%) showed maximum percent contribution towards total genetic divergence. PCA based clustering showed that genotypes fall in to six different groups/clusters and their inter and intracluster distance showed genetic diversity between different genotypes. The Genotype G-04 which represents the mono genotypic cluster signifies that it could be the most diverse from other genotypes and it would be the suitable candidate for hybridization with genotypes present in other clusters to tailor the agriculturally important traits and ultimately to enhance the seed yield in green gram. Thus the results of principal component analysis revealed, wide genetic variability exists in this greengram germplasm accessions.
How to cite this article:
Naveen Kumar Jakhar, Arjun Kumar. Principal component analysis and character association for yield components in greengram [Vigna radiata (L.) Wilczek] genotypes. J Pharmacogn Phytochem 2018;7(2):3665-3669.