Understanding the Genetic Basis of Yield-related Traits in Little Millet (Panicum sumatrense Roth. ex. Roem. and Schultz.) Germplasm through Association and Diversity Analysis Amaravel M.1, Nirmalakumari A.1,*, Geetha S.2, Sathya K.1, Renuka R.3 1Centre of Excellence in Millets, Tamil Nadu Agricultural University, Tiruvannamalai-606 603, Tamil Nadu, India 2Department of Pulses, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India 3Department of Plant Breeding and Genetics, Agricultural College and Research Institute, Tamil Nadu Agricultural University, Madurai-625 104, Tamil Nadu, India *Corresponding Author: A. Nirmalakumari, Centre of Excellence in Millets, Tamil Nadu Agricultural University, Tiruvannamalai-606 603, Tamil Nadu, India, Email: amaravelbscagri@gmail.com
Online Published on 05 January, 2024. Abstract Background Little millet is an important crop grown by tribal farmers in India. Genetic variability can be exploited to develop new varieties with higher yield. However, yield is complex and depends on multiple interconnected component characters. Diversity analyses, such as D2 analysis, are used to evaluate the diversity among genotypes and determine the traits that contribute the most diversity in a given population. These analyses are crucial for achieving the goal of developing new varieties with increased yield. Methods In this study, 323 little millet genotypes were evaluated using an Augmented RCBD, focusing on ten quantitative traits. The experiment was conducted during the rabi season of 2020-2021 and good agronomic practices were followed. D2 cluster analysis and path analysis were used to analyze the data, with the “R” tool and the “biotools” and “agricolae” packages, respectively. Result In this study, 323 little millet genotypes classified into thirteen distinct clusters based on Mahalanobis’s D2 statistics, reflecting differences in their phenotypic characteristics. The largest cluster (cluster I) included 243 genotypes, while the smallest clusters (Cluster IX, Cluster X, Cluster XI, Cluster XII and Cluster XIII) had only 1 genotype. The inter-cluster distance varied, with the largest value (577.7) between cluster V and XII. This analysis can be useful for identifying desirable genotypes and understanding the population’s genetic diversity and structure. Top Keywords Augmented RCBD, Mahalanobis’s D2, Path analysis. Top |