Path and PCA for Trait Selection in Advanced Generation Aromatic Rice Genotypes with the Goal of Increasing Grain Yield

Abstract

The study was conducted during kharif (June–November, 2024) at the Agriculture Farm of Palli-Siksha Bhavana (Institute of Agriculture), Visva-Bharati, Sriniketan, Birbhum, West Bengal, India to select suitable traits with high PC scores by Path and Principal Component Analysis (PCA) in thirty-five advance generation aromatic rice genotypes for enhanced grain yield. Thirty-day old single seedling per hill was transplanted in randomized complete block design (RCBD) with three replications. According to path analysis plant height, flag leaf breadth, panicle length, number of secondary branches panicle^-1 and test weight have positive relation with grain yield plant^-1 and could be directly selected. In terms of principal component analysis, maximum variability was found in PC1 (35.2%) and four PCs had eigen value greater than one. In PC1 the traits grain yield plant^-1, number of primary branches panicle^-1, Spikelet fertility percentage, test weight and number of secondary branches panicle^-1 contributed most towards variability. Days to 50% flowering which made the most contribution to the total diversity, followed by flag leaf length, grain yield plant^-1, number of panicles plant^-1, number of primary branches panicle^-1, plant height, Spikelet fertility percentage and test weight displayed the longest vector length. On the basis of biplot analysis genotypes, namely 6(MED×PST-5-6), 18(MED×Pakbas-2), 19 (MED×Pakbas-3), 21 (MED×T-Basmati), 22 (Baskota×Gopalbhog-1) and 29 (Baskota×Gobindobhog) identified as the most divergent types for the yield-attributing traits of aromatic rice. Hence these genotypes can be utilized for trait improvement in breeding programs for the traits contributing for major variation.