Principal component analysis is a multivariate statistical technique used to observe the variance and to assess the relative influence of various traits for aggregate variability. In this study, ten advance uniform rice lines were grown for two consecutive years and morphometric data on eight yield and yield related attributes were collected. The principle component analysis was performed on the means of two years’ agronomic data to identify the patterns of variation and to determine the selection criteria. The first five principle components accounted for 97.9% of total variation, with the first three components explaining the cumulative variability of 82.7%. The individual contribution of PC1, PC2 and PC3 were 46.8, 20.7 and 15.2%, respectively. The PC1 and PC2 projected more towards yield contributing traits such as panicle length, number of grains per panicle and 100 grain weight. Days to 50% flowering and days to maturity were grouped under PC3 which regarded as earliness component, as it had the traits which allowed for developing early and late maturing varieties. Panicle length & 100 grain weight showed negative direction to each other, however, both traits grouped under PC2 and genotype under this component were good for further yield improvement. Thus, the prominent traits grouped together in different principal components were causal to describe the variability and breeding consideration. The results of this study will be greatly helpful for the development of early maturing and higher yielding varieties in future breeding programs.
Key words: Oryza sativa, Principal Component Analysis (PCA), rotated component matrix, Biplot, Loading plot.
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