Formerly International Journal of Basic and Applied Agricultural Research

D2 and principal component analysis for variability studies in Vigna and Phaseolus species

Pantnagar Journal of Research, Volume - 18, Issue - 3 ( September-December, 2020)

Published: 2020-12-31

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Eight Vigna and one Phaseolus species were used in the present study to estimate the contribution of component traits to the total variation. The genotypes included nine each from black gram and mungbean, three wild relatives of black gram and one mungbean, three genotypes of rice bean, five genotypes of cowpea and one genotype of french bean. The contribution of different morphological traits has been evaluated by using D2 and principal component analysis, which has led to the recognition of significant phenotypic variability. The relative contribution of root dry weight (9.952), shoot to root dry weight ratio (6.817), P content in seed (6.320), 100 seed weight (5.695), total P uptake at maturity (5.382) and seed yield/plant (5.248) was maximum towards the genetic divergence by D2 method. The seven principal components PC1, PC2, PC3, PC4, PC5, PC6 and PC7 with eigen roots of 8.721, 5.048, 3.268, 1.941, 1.155, 1.005 and 0.812, respectively have accounted for 91.46% of total variation of which first three principal components accounted for 70.98 per cent variation. PCA analysis revealed the maximum contribution of root dry weight (0.269) followed by total biological yield/plant (0.253) in PC1, harvest index (0.369) followed by 100 seed weight (0.294) in PC2 and seed yield/plant (0.384) followed by plant height (0.268) in PC3. The eigen root of first principal component accounted for 36.338 per cent of total variation followed by second to seventh principal components, which accounted for 21.035, 13.615, 8.089, 4.813, 4.189 and 3.382 per cent of total variations present in the genotypes, respectively. These results confirmed the presence of considerable genetic diversity for use in Vigna and Phaseolus genotypes improvement program. The study revealed that principal component analysis was more effective in partitioning variation than D2 analysis.

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