Principal component analysis for seven quantitative traits of different rice (Oryza sativa L.) genotypes tested at Pawe, northwestern Ethiopia

Principal component analysis (PCA) is aimed at reducing the dimensionality to find a smaller number of dimensions (usually 2 or 3) that exhibit most of the variations present in the data helping to identify the relative importance of individual traits on the genotypic diversity of the genotypes. The PCA was computed using seven quantitative traits measured from 321 rice genotypes evaluated using augmented RCBD experimental design with a plot area of 2.5m2 involving 4 rows per plot. The seeds were drilled in rows with a seed rate of 60kg per hectare. NPS (Nitrogen-Phosphorus-Sulfur) (124 kg per hectare) and Urea (100 kg per hectare) fertilizers were applied. The quantitative traits such as days to 50% heading, days to 85% maturity, plant height, panicle length, number of filled and unfilled grains per panicle, and 1000 seed weight were collected and subjected to the principal component analysis using XLSTAT 5.03 statistical software so as to determine the importance of the measured quantitative traits for the genetic diversity of the tested rice genotypes. The first three principal components (PC1, PC2 and PC3) were identified with a total cumulative variation of 78.90% showing that the genotypes could be grouped at least into three main varied classes. From the observed distribution plot, the tested genotypes were almost uniformly distributed in four quadrants pointing the presence of genetic diversity among the genotypes.