Multivariate Cluster and Principal Component Analyses on Yield Traits in Foxtail Millet (Setariaitalica (L.) P. Beauv.) Genotypes

Purpose: The research aims to employ Multivariate Cluster and Principal Component Analyses to examine yield traits in Foxtail Millet genotypes cultivated in Nagaland’s foothills. It addresses the need to identify distinctive groups among millet varieties and discern the principal components influencing yield. This investigation seeks to enhance millet-breeding strategies for improved productivity in the region’s specific agro-climatic conditions.
Research Method: The investigation was carried out from July 2022 to May 2023 for four different dates of sowing with a twenty-five days interval. Two environments were maintained under rain fed conditions and the remaining two environments were maintained under irrigated conditions at seven days intervals. The experiment was conducted in a randomized complete block design with three replications in all environments.
Findings and Values: The Elbow Method identified four clusters, with Cluster-1 (16.6%) having five genotypes, Cluster-2 (20.0%) six, Cluster-3 (43.3%) thirteen, and Cluster-4 (20.0%) six. The maximum inter-cluster distance was between Cluster-2 and Cluster-4 (33.09). Principal component analysis revealed five components (eigenvalues >1) explaining 75.8% of total variance, while correlation analysis showed nine significant positive and three negative associations among yield traits. Ward’s dendrogram grouped the genotypes into five clusters, with Cluster-1 comprising superior performers are G1 (ERP 26), G5 (ESD 3), G18 (FOX 4336), G22 (FOX 4386), and G25 (FOX 4394), that consistently excelled in grain yield and yield-contributing traits.