Genetic Diversity Assessment of Wheat Genotypes in Bundelkhand Region Using Principal Component Analysis

To study the principal component analysis in eighty two wheat genotype including with three check varieties namely HD1544, DBW110 and GW322 in Augmented block design at research farm, Rani Lakshmi Bai Central Agricultural University, Jhansi, During Rabi season 2019-20. Principal component analysis is a procedure describes by Banfield (1978). PCA mostly performed on two types of data matrices viz., variance- covariance matrix and correlation matrix.The percent variance accounted for each principal component (PC) is expressed as the Eigen value divided by the sum of Eigen values.Out of fifteen, seven principal components accounted more than one eigen value viz., PC1 (2.69), PC2 (2.35), PC3 (1.76), PC4 (1.39), PC5 (1.22) and PC6 (1.14) with showed about 70.48 % variability within the traits observed for each genotype. Eigen value and percent of variance associated with every principal, slowly reduced and stopped at 7.63.The first (PC) which accounted maximum variability was more related to the traits viz., grain yield per plant, biological yield per plant and days to maturity so it must be considered. The second one (PC2) included the traits peduncle length, plant height and flag leaf length. The third principal component (PC3) accounted positive effects for 1000 grain weight, flag leaf width and grain yield per plant.The fourth principal component (PC4) was more related to the traits spike length, canopy temperature, 1000 grain weight and days to heading. The fifth principal component (PC5) accounted positive effects for chlorophyll content, canopy temperature, harvest index and spike length, whereas, the sixth principal component (PC6) were more related to flag leaf width, days to maturity, flag leaf length and biological yield per plant.