Multivariate analysis of superior Helianthus annuus L. genotypes related to metric traits

Abstract

To increase seed yield and oil contents, variability in breeding material is a pre-requisite. Plant material was comprised of forty-nine sunflower genotypes to investigate the variability and identification of superior genotypes by multivariate analysis. The data were recorded for ten quantitative traits; days to maturity (DM), plant height (PH), stem diameter (SD), head diameter (HD), number of leaves (NOL), achene per head (APH), achene yield per plant (AYP), 100- achene weight (100AW), filled achene percentage (FA) and oil contents (OC). The genotypes showed significant variation for all traits except OC. A highly significant association of achene yield was observed with 100AW. Principal component analysis (PCA) separated into four components (PC-I, II, III, IV) with Eigenvalue greater than one accounting for 62.63% of the total variation. Total variance percentage was maximum in PC-I (24.4%) followed by PC-II (14.70%). Cluster analysis further classified the sunflower genotypes in three clusters based on seed yield and its related traits. A maximum number of genotypes were included in cluster I (26 genotypes) followed by cluster III (11 genotypes) contributing 65.30%, 24.48%, respectively of total genotypic strength. In addition, maximum number of traits were included in cluster III followed by cluster II. PH and NOL were closest of all the ten traits suggesting their strong correlation. Taken together, these results can be useful for breeders to develop high yielding sunflower hybrids.