Assessing the Genetic Diversity for Yield Attributing Traits in Aromatic Rice Landraces Using Multivariate Analysis

Abstract

The present investigation was carried out in kharif 2020 (July to November) to explore the genetic diversity for yield and its attributing traits. Genetic diversity can directly provide information on germplasm richness and the extent of their genetic amelioration. Multivariate statistical techniques analyze multiple measurements on each individual under study and are widely used in the analysis of genetic diversity. Here, multivariate analysis techniques like principal component analysis was used to assess genetic diversity in 90 aromatic rice genotypes along with six checks for 24 yield attributing traits. Analysis of variance revealed significant and ample amount of variation for all the studied traits. High magnitude of coefficient of variation was observed for number of effective tillers plant-1, panicle weight, number of filled grains panicle-1, total grains panicle-1, grain yield plant-1 (g), 1000 grain weight and biological yield plant-1 (g) indicating the existence of significant variability among the yield traits and offers the opportunities of improvement through desirable selection techniques. Results of principle component analysis revealed that out of twenty-four traits studied, only seven principal components (PCs) exhibited more than 1.00 eigen value and showed about 77.39% cumulative variability among the traits and are given due importance in this study. Here PC1 had the highest variability followed by PC2, PC3 and so on. Top ranking accessions identified in PC1 are Sukra Phool, Tendu Phool, Banspatri and Chinnour. Consequently, the results of the current study can be taken into account while developing new rice varieties.