Abstract Background : Rice ( Oryza sativa ) yield improvement is challenging due to its complex, polygenic nature; therefore, robust multi-trait selection frameworks are required to assess trait interactions and enhance breeding accuracy. Methods : This study evaluated ten quantitative traits in 187 advanced rice genotypes using an augmented block design to identify superior candidates. Genetic variability and trait interactions were assessed through ANOVA, correlation analysis, principal component analysis (PCA), and hierarchical clustering, while the Multi-Trait Genotype-Ideotype Distance Index (MGIDI) was applied to determine ideotype excellence. Results : Filled grains per panicle exhibited the highest genotypic (28.59%) and phenotypic (34.74%) coefficients of variation. High heritability coupled with high genetic advance was observed for filled grains per panicle, tiller number, and panicle number per hill, indicating predominant additive gene action. Grain yield showed significant positive correlations with plant height (0.34***), days to 50% flowering (0.33***), and days to 80% maturity (0.31***). PCA revealed that the first five components explained 83.7% of the total variation. Hierarchical clustering categorized genotypes into three distinct groups, and MGIDI analysis successfully identified 28 promising genotypes, with G135, G3, G131, G187, and G120 emerging as the most superior. Conclusion : These findings demonstrate that integrating multivariate analysis with MGIDI enhances selection efficiency for accelerated genetic improvement in rice breeding.
Islam et al. (Thu,) studied this question.
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