Genetic Component Analysis and Determination of Optimum Number of Clusters Based on Morpho-Physiological Traits in Wheat

Sourik Poddar; Sahanob Nath; Rupsanatan Mandal; Suvendu Kumar Roy; Saikat Das

doi:10.23910/1.2022.3212

Authors

Sourik Poddar Dept of Genetics and Plant Breeding, Uttar Banga Krishi Vishwavidyalaya, Coochbehar, West Bengal (736 165), India
Sahanob Nath Dept of Genetics and Plant Breeding, Uttar Banga Krishi Vishwavidyalaya, Coochbehar, West Bengal (736 165), India
Rupsanatan Mandal Dept of Genetics and Plant Breeding, Uttar Banga Krishi Vishwavidyalaya, Coochbehar, West Bengal (736 165), India
Suvendu Kumar Roy Dept of Genetics and Plant Breeding, Uttar Banga Krishi Vishwavidyalaya, Coochbehar, West Bengal (736 165), India
Saikat Das Dept of Genetics and Plant Breeding, Uttar Banga Krishi Vishwavidyalaya, Coochbehar, West Bengal (736 165), India https://orcid.org/0000-0003-1277-8664

DOI:

https://doi.org/10.23910/1.2022.3212

Keywords:

Wheat, genetic divergence, K mean clustering, PCA

Abstract

The present study was conducted at the instructional farm, Uttar Banga Krishi Vishwavidyalaya, Pundibari, Cooch Behar, West Bengal, India during the rabi season (November–March) of 2020–2021 aimed at to evaluate the performance of CIMMYT nursery (19^th HTWYT) under Terai zone of West Bengal to assess genetic diversity and clustering them into optimum number of clusters using 12 morpho phenetic traits along with 02 physiological traits and also against spot blotch disease. ANOVA showed non-significant variation among the genotypes for all the 15 quantitative traits under study. The genotypes were also being screened against spot blotch disease and 29 were found highly susceptible, 14 were susceptible to highly susceptible and 06 were susceptible category whereas only the local check DBW 187 was found moderately susceptible to susceptible. The optimum number of clusters was determined by using K mean clustering algorithm which revealed optimum number of cluster of two. Cluster I consisted of 24 wheat genotypes and Cluster II consisted 26 wheat lines. Among the two clusters, higher diversity was present in cluster I (276.67) than cluster II (249.684). Principal component analysis (PCA) for all the 15 traits revealed only five components having Eigen value >1.00. Among them PC 1 and PC 2 accounted for 36.53% and 12.05% variance respectively. Grain yield was found to be positively associated with 08 traits such as awn length, biological and grain yield, grain per spike, harvest index while negatively correlated with tiller m^-1, mean canopy temperature depression, AUDPC%.