Principal Component Analysis for Yield and its Attributing Traits in Aromatic Landraces of Rice (Oryza sativa L.)

Abstract

The present investigation was carried out in Kharif 2019 (July to November) to estimate the relative contribution of various traits for total genetic variability present in aromatic landraces by Principal Component Analysis. Here 90 aromatic rice landraces along with six check varieties were evaluated for 13 quantitative characters by Principal Component Analysis. Principal Component Analysis showed that, out of 13 quantitative characters studied, only five principal components (PCs) exhibited more than 1.00 eigen value and showed about 81.62% cumulative variability among the traits studied. Out of the five principal components exhibiting more than 1.00 eigen value PC1 had the highest variability (25.12%) followed by PC2 (21.8%). The first principal component PC1 was positively contributed mainly by two characters viz., Grain Length and 1000 grain weight. The second principal component PC2 was contributed mostly by three characters like grain yield plant-1, panicle weight and spikelet fertility percentage. The third principal component PC3 is positively associated with panicle weight, grain yield plant-1 and spikelet fertility percentage. The fourth principal component PC4 is positively associated with spikelet fertility percentage, Grain Length/ Breadth ratio and fertile grains panicle-1. The fifth principal component PC5 is positively associated with total grains per panicle-1, grain width and 1000 grain weight. All the principal components were showing positive contribution for yield and its attributing traits. These variations can be exploited in crop improvement programme for developing high yielding varieties.