Multivariate analysis of rice genotypes for seeding traits characterization and evaluation

Abstract

Multivariate analysis has been rarely used for seedling traits characterization of Chinese basmati hybrids. Twenty-two seedling attributes of Basmati and coarse rice germplasm of China and Pakistan origin were analysed using multivariate techniques of Single linkage analysis (SLCA) and Principle component analysis (PCA) and index score method (ISM). Variation among the seedling attributes were recorded by ISM regarding number of genotypes, formation of clusters and superimposition of genotypes in every cluster. Rice genotypes were possibly grouped in six clusters identified through ISM. Highest index scores 68 and 146 were allocated to genotypes of group-I and group-II on morphological basis of seedling attributes. However, the three PCs contributed 87.1 % of the variability among the genotypes and germination rate index, germination %, mean daily germination (MDG), shoot length and root length exhibited maximum positive response in PC1, PC2 and PC3 respectively. PCA ordination on axis I and II were agreed with SLCA. Our study revealed that the agreement in the SLCA and PCA analysis effectively cluster the genotypes for twelve seedlings attribute might be used for the identification of viable genetic material to improve the rice yield potential.