Multivariate analysis and multi-trait genotype-ideotype index (MGIDI)-based identification of high-performing foxtail millet (Setaria italica L.) genotypes
摘要
A total of 168 foxtail millet accessions were evaluated for grain yield and 12 related agronomic traits to assess genetic diversity and identify superior genotypes for crop improvement. Substantial phenotypic variability was observed among the accessions, with higher Shannon’s diversity index values recorded for grain yield, fodder yield, No. of productive tillers/plant and peduncle exertion. Grain yield and fodder yield showed higher genotypic variance, while, No. of productive tillers/plant and flag leaf width showed lower genotypic variance than environmental variance. Correlation analysis revealed significant positive associations between grain yield and most of the evaluated traits, suggesting their usefulness as indirect selection criteria in breeding programs. Principal component (PC) analysis condensed the 13 traits into four principal components with eigenvalues greater than one, where grain yield and plant architectural traits contributed predominantly to PC1. Cluster analysis classified the accessions into four distinct groups, with cluster II exhibiting superior mean performance for the majority of the studied traits. High predicted selection gains were observed for grain yield, fodder yield and panicle length. Integration of multi-trait genotype-ideotype index (MGIDI), principal component analysis (PCA)-based genetic divergence and cluster analysis enabled the identification of three elite genotypes. Among them, SiA 4266 and SiA 4314 showed a desirable combination of traits, such as superior grain and fodder yields, longer panicles, higher panicle harvest index, enhanced harvest index and ideal plant height. Strategic hybridization among selected genotypes, combined with multi-trait selection indices and multivariate analyses, will accelerate the development of high-yielding and resilient foxtail millet cultivars suitable for changing climate.