Character association and principal component analysis for seed yield and its contributing characters in greengram (Vigna radiata (L.) Wilczek)

Abstract

principal component analysis for determining the pattern of genetic diversity. Eight quantitative parameters viz.,plant height, number of primary branches, number of clusters per plant, number of pods per cluster, number ofpods per plant, pod length, number of seeds per pod and single plant yield were measured. The largest variationwas observed for plant height with a coefficient of variation (CV) of 58.98% followed by the number of pods perplant (35.16). The number of pods per cluster has shown the least variation with a  CV of 0.50 %. Estimates ofcorrelation coefficient showed a positive significant association of single yield with the number of pods per clusterand the number of pods per plant. The principal component analysis was used to assess the genetic diversityamong the 74 germplasm collections. The results of PCA revealed that the cumulative variance of 79.90% by thefirst four axes with an Eigenvalue of 1.0 indicates that the identified traits within the axes exhibited greaterinfluence on the phenotype. Amongst the first four PCs, PC1  accounted for a high proportion of total variance(32.60%) and the remaining three principal components viz., PC2, PC3, and PC4 revealed 20.70, 14.30 and12.30% of the total variance, respectively. Hence, it is suggested that the traits such as pod length, number ofseeds per pod (PC 2), number of pods per cluster (PC 3) and single plant yield (PC 4) in the first four principalcomponents contributed to  major variation among germplasm collections. Hence, these traits are considered askey traits for selection criteria for developing high yielding cultivars of green gram.