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Research Article Open access CC BY 4.0

Characterization of the Genetic Variability in Rice (Oryza sativa L.) and its Assessment Using Principal Component Analysis (PCA)

Mudavath Ramesh Naik, C. P. Chetariya, Jeevan Sai Akkenapally

International Journal of Environment and Climate Change · pp. 1257–1269 · Published 28 Jun 2022

10.9734/ijecc/2022/v12i1030923

Abstract

Present investigation was conducted to estimate genetic parameters such as Genotypic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation (PCV), heritability and Genetic Advance (GA) along with correlations, path coefficients, Genetic divergence and Principal Component Analysis (PCA) from data collected on 21 rice genotypes. The results revealed highly significant mean squares due to genotypes for all traits studied, indicating the existence of sufficient variation among the genotypes and therefore an ample scope for effective selection. High PCV, GCV, heritability and genetic advance as per cent of mean was observed for 1000-grain weight, seed yield per plant, and harvest index, indicating the effectiveness of direct selection for improvement of these traits. Thousand grain weight and harvest index had recorded positive and significant association with seed yield per plant. The results on path analysis also revealed high and positive direct effect for harvest index followed by flag leaf width, days to 50% flowering, days to maturity, plant height, panicle length, number of filled grains/panicle, number of spikelets/panicle and hence, these traits were identified as the most effective selection criteria for improvement of seed yield per plant in rice genotypes. The results on divergence analysis revealed that the genotypes were grouped into five clusters. Cluster I constituted maximum number of (17) genotypes. Maximum differences among the genotypes within the same cluster (intra-cluster) were shown by cluster I (18.33) followed by cluster II, III, IV, V showed zero intra -cluster distance. Cluster III and V (37.41) showed maximum inter cluster distance, suggesting that the genotypes constituted in these clusters may be used as parents for future hybridization programme. Principal Component Analysis (PCA) was utilized to estimate the relative contribution of various traits for total variability. Five components were found to possess eigen value more than 1.00. The PC1, PC2, PC3, PC4 and PC5 contributed 31.2, 14.5, 12.6, 10.5 and 10.1 per cent of variability. Together, they accounted for 78.9% of the variability of the genotypes used in the study has revealed the traits contributing for the variation.

genetic parameters correlation path coefficient seed yield per plant divergence principal component

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