Vol. 10, Issue 9 (2021)

Author(s):
Vaishali Ladumore, Dela G Jumabhai and SN Patel

Abstract:
Principal component analysis is an important statistical tool through which we can easily assesses important polygenic characters which are of great importance in a plant breeding programme. It’s utilized to examine variation and to estimate relative contribution of various characters for total variability. The experiment was conducted during Kharif, 2019-20 with 32 germplasm accessions of little 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 nine morphological traits were recorded and principal component analysis was done. Principal component analysis indicates that three principal components PC-1, PC-2, PC-3 and PC-4 explains 58.40%, 43.53%, 5.21% and 1.21%, respectively of the total variation. The first principal component had showed positive loading for PT (productive tillers per plant) and HI (harvest index). The second principal component had positive loading for days to 50% flowering (DF), days to maturity (DM), productive tillers per plant (PT), grain yield per plant (GY), straw yield (SY) and harvest index (HI). The third principal component had positive loading for DM, PT, GY, SY and HI. While, the fourth principal component had positive loading for PH, PT, GY and HI. The results of principal component analysis utilized in the study have revealed the high level of genetic variation and the characters contributing for the variation was identified. Hence, the genotypes of this population can be utilized for trait improvement in little millet breeding programs using the traits contributing for major variation.