Multivariate analysis and selection indices to identify superior cultivars and influential yield components in chickpea (Cicer arietinum L.)

Abstract

Genetic diversity is essential for the development of more efficient plant types. In the present study, 529 chickpea accessions were evaluated for their agronomic performance, genetic divergence and identification of promising accessions through the use of a simple lattice design. These accessions varied widely in all agronomic traits. The first three principal components (PCs) explained 78.35% variation. The PC1 and PC2 explained 38.05 and 24.30% of total variations. Three traits namely, branches per plant, pods per plant and seed yield per plant contributed to maximum variation. The hierarchical clustering analysis carried out on PCs grouped the accessions into eight clusters. Among 127 selection indices formulated, higher relative efficiency (422.52%) coupled with high genetic advance (34.31%) was exhibited by the combination involving six characters. Based on the index score of greater than 100, 15 genotypes were promising for improving the grain yield. The results of both PC analysis (PCA) and selection indices suggested that branches per plant, pods per plant and 100-seed test weight traits have to be considered for any genetic yield gains. Both the techniques (PCA and selection indices) identified three genotypes (GAG 0733, IC 268988 and IC 269031) as the best performers, suggesting that the two techniques are equally efficient in the identification of superior germplasm that can be used in chickpea hybridization programmes for yield improvement.