Journal of Innovative Agriculture
Peer Reviewed Open Access Journal
ISSN: 2394-5389 NAAS: 4.05
Submit Manuscript
Peer Reviewed Open Access Journal
ISSN: 2394-5389 NAAS: 4.05
Submit ManuscriptBackground: A diverse rice genotypes was evaluated for fifteen morphological/growth, reproductive/yield-related, physiological, and phenological traits to uncover their genetic variability and trait associations that are critical for developing stress-resilient high-yielding genotypes.
Methods: Descriptive statistics, four-way Venn diagram, PCA biplot, correlation network, and Composite Performance Index (CPI) from standardized PC1 (grain yield-driven) with k-means clustering were employed to dissect variability and rank genotypes.
Results: Descriptive statistics revealed extensive phenotypic variation, particularly in grain yield per plant (4.95–45.5 g), plant height (PH) (68.1–168.0 cm), and total tillers /productive tillers, while intrinsic water use efficiency (iWUE) showed broad adaptation potential. A four-way Venn diagram of top-performing genotypes across source, sink, growth, and phenology categories highlighted limited functional overlap, emphasizing trait-specific excellence. Principal component analysis (PCA) biplot explained 39.9% of variation (PC1: 23.5%, PC2: 16.4%) and delineated three genotype clusters viz., yield-oriented genotypes processing high total tillers, productive tillers, and spikelet fertility. Physiologically efficient genotypes having enhanced photosynthetic rate (PNET) and iWUE. Genotypes with large flag leaf area (FLA) and PH contributing for tall and biomass accumulation with lower yield efficiency. Correlation network analysis identified three interconnected clusters reproductive/yield-related, morphological/growth, and physiological. Plant height trait interconnects all these clusters in the correlation network. The Composite Performance Index (CPI), derived from standardized PC1 with grain yield as the directional driver, ranked genotypes continuously with k-mean clustering, discriminate the genotypes in to three clusters (Low, medium and high performing). Genotypes viz., IRIS_313-10260, IRIS_313-9160, and IRIS_313-10609 identified as best performing genotypes.
Conclusion: Established a multivariate pipeline that integrates the morphological/growth, reproductive/yield-related, physiological, and phenological traits into an ideotype framework. No genotype excels across all modules, but top CPI performers (IRIS_313-10260, IRIS_313-9160, IRIS_313-10609) integrate complementary strengths, making them ideal core donors.
rice, genetic diversity, PCA biplot, correlation network, composite performance index, traits
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