Determination of genetic diversity in strawberry (Fragaria x ananassa) using the principal component analysis (PCA) and single linkage cluster analysis (SLCA).

Abstract

To assess the nature and magnitude of variability in 22 genotypes, strawberry from diverse eco- geographic origins were evaluated using Principal Component Analysis (PCA) and single linkage cluster analysis (SLCA), assessing the divergence and similarity. The experiment was laid out in randomized complete block design (RCBD) with three replications. The genotypes were classified into five for the determination of variability and four cluster groups for similarity by PCA and SLCA, respectively. The highest inter cluster distance was observed between cluster II and V (129.39), followed by IV and V (114.082) and the lowest between II and IV followed by III and IV. The highest intra- cluster distance was observed for cluster III and the lowest for the cluster VI and V. PCA showed that four of the four principal component axes had Eigen values greater than one and altogether accounted for 77.34% of the total variation. The first two accounted for 57.88% with PCA 1 accounting for 36.31% and PCA 2 accounting for 21.57%. The major contributing traits in PC1 was number of flowers, number of leaves and number of fruit/plant of leaflets per plant; whereas in PC2, fruit length and fruit weight were major contributors for higher yield and quality. Thus, PCA was useful tool for identifying the characters responsible for major variability and to be used for breeding programme for higher yield and yield attributing traits, whereas SCLA proved to be a better tool in multivariate analysis since it provided much clearing information concerning the extent of relationship among the genotypes.