Vol. 11, Issue 8 (2022)

Author(s):
Vivek Singh, Alok Kumar Maurya, Rajesh Kumar Singh, Anil Kumar Singh, Mohammad Nisar, Runa Singh and Gaurav Sharma

Abstract:
The present investigation conducted on 80 accessions of wheat during 2018-19 at Main Experiment Station (MES) of A.N.D. University of Agriculture and Technology, Kumarganj, Ayodhya (U.P.) to evaluate genetic divergence based on cluster and principal component analyses for yield and its contributing characters for the identification of most diverse and promising genotypes. The experiment was laid out in Augmented Block Design. The observations were recorded on various phenotypic characters. The varieties of wheat were grouped into 08 distinct clusters by using K mean and Euclidean cluster analysis. Maximum inter cluster distance was found between Cluster VII-IV (7.2631) followed by Cluster VIII-IV (6.0618), Cluster IV-II (5.5329), and Cluster VIII-III (5.02072). Cluster II & I (DFF), cluster III & IV (FLA), cluster II & V (PH), cluster II & V (DM) and cluster IV & VII (TP), were found more diverse clusters based on cluster mean for traits given in parenthesis. Remaining traits had minimum or no contribution in divergence. The result of Principal Component Analysis (PCA) revealed that all five principal components (PC1 to PC5) are extracted from the original data and having latent roots greater than one, accounting nearly 85.8% of the total variation. The maximum Eigen root value (4.105) was recorded for 1st PC, which explained 37.3% variation. The rest four PCs (2nd, 3rd, 4rd & 5th) explained 17.5%, 14.7%, 11.1% & 5.1% individual variation.