Aims: To assess genetic variability and identify key phenotypic traits influencing yield in aromatic rice genotypes using principal component analysis (PCA) for targeted breeding programs. Study Design: Augmented Randomized Block Design. Place and Duration of Study: ICAR-Indian Institute of Rice Research, Hyderabad, India, during the 2023 kharif cropping season. Methodology: Ninety-six rice genotypes, alongside five control varieties (three aromatic are PB 1121, HUR 1309, RNR 15459; two non-aromatic are MTU 1010, Gonthrabhidhan-3), were evaluated for phenotypic traits including DFF (days to 50 % flowering), PL (panical length), PH (plant height), NT (number of tillers), PT (productive tillers), FLL (flag leaf length), FGPP (filled grains per panical), 1000gw (thousand grain weight), SPY (single plant yield). Analysis of variance (ANOVA) was conducted to assess treatment effects, with phenotypic (PCV) and genotypic (GCV) coefficients of variation calculated. Heritability and genetic advance as a percentage of the mean (GAM) were estimated. PCA was applied to identify traits contributing to variance. Results: ANOVA revealed significant treatment effects (P < 0.01) for all traits, indicating high genetic variability. Panicle length showed the highest variability (PCV: 38.61%, GCV: 37.62%) and genetic advance mean (GAM: 76.81%). Heritability ranged from 27% (1000-grain weight) to 99.42% (flag leaf length). PCA identified four components explaining 71.67% of total variance, with flag leaf length, plant height, and productive tillers as major contributors. Significant block effects were observed, suggesting environmental influences. Conclusion: Research on rice revealed significant genetic variation in key phenotypic traits, confirmed by ANOVA showing highly significant treatment effects (P ≤ 0.01) for traits like 1000-grain weight, days to 50% flowering, and panicle length. High heritability (86.27% to 99.42%) and genetic advance (up to 76.81% for panicle length) indicate a strong genetic basis and potential for improvement through phenotypic selection. Minimal PCV-GCV differences suggest limited environmental influence, enhancing selection reliability. PCA showed four components explaining 71.67% of variance, with single plant yield, plant height, and productive tillers as key contributors, supporting targeted rice breeding programs.
Naik et al. (Wed,) studied this question.