Clustering and Principal Component Analysis-based Diversity Assessment of Restorer Lines of Pearl Millet under Semi-Arid Region of Gujarat in Hot Summer for Yield and Quality Traits

Abstract

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a climate-smart C₄ cereal that supports food and nutritional security in arid and semi-arid regions. This study assessed genetic diversity among 30 pearl millet restorer lines for eleven quantitative traits under the hot-summer, semi-arid conditions of Gujarat. The lines were evaluated during summer 2024 in a randomised block design with three replications. Genetic divergence was examined using Mahalanobis D² statistics and Tocher clustering, while principal component analysis was used to describe the major sources of variation. Significant differences were observed among the restorer lines for all evaluated traits. Tocher’s method grouped the lines into eight clusters, with Clusters I and II containing the largest numbers of genotypes. The greatest inter-cluster distance occurred between Clusters I and VIII (D² = 1223.33), indicating substantial divergence between these groups. Grain yield per plant (36.10%), zinc content (23.45%) and iron content (18.85%) were the leading contributors to total divergence. The first four principal components explained 71.51% of the total variation. PC1 was mainly associated with grain yield per plant, 1000-grain weight, ear head length and ear head girth. ICMR 20265 and ICMR 20778 were positioned favourably for yield-related traits in the PCA biplot. These results identify genetically diverse restorer lines that may be considered for further hybridisation and heterosis evaluation.