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Hindi Principal Component Analysis for Yield and Yield Attributed Traits in Lowland Rice (Oryza sativa L.) Genotypes
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Copyright: © 2021 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Principal component analysis was utilized to determine the variation and to estimate the relative contribution of various characters for total variability. The experiment was laid out using randomized block design with three replications during 2017/2018 main cropping season at Fogera, Ethiopia. The first four principal components axes accounted for 85.3% cumulative variance of the total variability for seventeen agronomic characters. PC1, PC2, PC3 and PC4 explained 44.15%, 19.31%, 14.913% and 6.97% of variation from the total variation, respectively. Thus, maximum variation was found in first PC; therefore, selection for characters under PC1 would be desirable. The variability in PC1 was accounted by flag leaf length, panicle length, days to heading and days to 50% flowering, while PC2 was accounted by harvest index. For future breeding program that employ hybridization, parental material selection should be carried out considering principal components influence to breeders’ interest.
