Deciphering the Multidimensional Trait Space of Yield and Quality Attributes in Oat (Avena sativa L.) Using Multivariate Analysis

Since time immemorial, Oat as an important multipurpose cereal crop have been closely associated with humans. In the Indian subcontinent, it is popularly used as food, feed, and fodder. The present investigation was done at the Experimental Dairy Farm, Nagala, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, for the identification and characterization of the most promising oat lines contributing yield and quality traits through genetic diversity analysis. The findings demonstrated that oat genotypes varied widely in terms of genetic diversity. Grain yield, dry fodder yield, green fodder yield, dry matter percentage, and 100-seed weight all showed strong heritability and high genetic advancement, suggesting additive-type gene action and improvement by simple selection. Eight PCs contributed 87.75 percent of the total variation across the genotypes evaluated for sixteen characters, according to principal component analysis (PCA). PC1 contributed the greatest towards the variability (25.8%), followed by PC2 (19.1%) and PC3 (13.1%). Cluster analysis categorized the accessions under six major clusters, which revealed a reasonable relationship of genetic diversity. The highest inter-cluster distance was observed between clusters V and VI (9.01), followed by clusters VI and I (8.61). In order to create high diversity for efficient selection in the segregating generations for the production of high-yielding oat cultivars, intercrossing between members of these diverse clusters would be necessary. Sufficient diversity was identified in the genotypes based on phenotypic and genotypic variance, principal component analysis, and cluster analysis, which may be employed by researchers in future nutri-agricultural crop improvement programs.