Principal component analysis in finger millet (Eleusine coracana L.) genotypes for diversity studies

Using multivariate analysis we can easily assesses important polygenic characters which are of great importance in a plant breeding programme. The experiment was conducted during kharif, 2018 with 60 germplasm accessions of finger millet to study genetic diversity for yield and yield contributing traits at Hill Millet Research Station, Waghai, Dangs, Gujarat in a randomize block design. The observations for eight morphological characters were recorded and the multivariate technique, principal component analysis estimated. Principal component analysis indicates that three principal components PC-1, PC-2, PC-3, PC-4, PC-5, PC-6 and PC-7 explains 52.15%, 33.37%, 6.51%, 4.69%, 1.48%, 1.25% and 0.52% respectively of the total variation. The first principal component had showed positive loading for days to 50% flowering (DF), days to maturity (DM), finger length (FL), fingers per earhead (FE) and negative loading for plant height (PH), productive tillers per plant (PT), test weight (TW), grain yield (GY), straw yield (SY) and harvest index (HI). The second principal component had positive loading for DF, DM, PH, FL, TW, GY and SY while negative loadings for PT, FE and HI. The third principal component had positive loading values for DF, PT, FL, FE, GY and SY. The fourth principal component had positive loading for DM, PT, TW and SY while negative loadings for DF, PH, FL, FE, GY and HI. The fifth principal component had positive loading for DF, DM, PT, GY, SY and HI while negative loadings forPH, FL, FE and TW. The sixth principal component had positive loading for DF and PH while negative loadings for DM, FL, FE, TW, PT, GY, SY, and HI. The seventh principal component had positive loading for DF, DM, PT, FL, GY and HI. While negative loadings for PH, FE and SY. The results of this study have revealed the high level of genetic variation and the traits contributing for the variation was identified. Hence the genotypes of this population can be utilized for trait improvement in breeding programs using those characters which contributing for major variation.