Unraveling the Hidden Patterns of Genetic Diversity in Oat (Avena sativa L.) Accessions: Insights from Novel Hybrid Hierarchical K-means Clustering

Abstract

Avena sativa is primarily grown as a food crop but also serves as a valuable feed for livestock. Oat is a versatile and nutritious grain with a range of health benefits, making it a popular choice for consumers and a valuable crop for agricultural production. In pursuit of crop improvement, cluster analysis is an analytical approach that allows researchers to uncover underlying patterns and structures within a dataset. Eventually, this aids in identifying exemplary genotypes for improved yield attributes. The present study involved a meticulous examination of phenotypic data of 62 oat genotypes taken during Rabi 2019-20 at CCS Haryana Agricultural University, Hisar, Haryana. A novel and superior technique, hybrid hierarchical K-means, was employed for enhanced outcomes in comparison to traditional hierarchical and K-means clustering methods. Among the four distinct clusters formed, cluster I exhibited the highest count of genotypes. Additionally, cluster I had the lowest intra-cluster distance and the highest group mean, implying the presence of additive gene action and favorable combinations of alleles for yield-related traits, respectively. Clustering offers breeders the advantage of focusing their efforts on representative genotypes within each cluster, thereby accentuating the efficiency of resource allocation and the management of breeding materials. Promising genotypes from the most diverse clusters can act as parents for hybridization programs. The research will enhance our understanding of oat genetics and facilitate the development of superior cultivars. Ultimately, this will help optimize yields and meet the growing demand for this nutritious crop.