Exploring the Genetic diversity studies in little millet (Panicum sumatrense) using multivariate tools – Principal component analysis and Cluster analysis

The depletion of biodiversity not only distracts the process of plant development designed for genetic enrichment but also disrupts the ultimate services that the ecosystem offered to humanity. Evaluation of variability is a multidimensional problem. The multivariate statistical tools aids in a comparative evaluation of genetic variability. The availability of access to diverse genetic material is important to be successful in any plant breeding effort. Sixty-three little millet genotypes were evaluated for determining the genetic divergence. Observations were recorded on days to fifty percent flowering, days to maturity, plant height, flag leaf length, flag leaf width, peduncle length, peduncle exertion, length of inflorescence, and grain yield per plant. Analysis of variance imparted significant differences for most of the characters studied. The first three principal components having Eigen value more than one are cumulatively contributing 67.97% to the total variability. PC1 has the contribution from the traits viz., days to fifty percent flowering (0.39), days to maturity (0.39), flag leaf width (0.22), and grain yield (0.33) which accounted for 36.90 % of total variability indicating these traits contributed more to the total variance. Cluster analysis revealed that the little millet genotypes were grouped into four clusters based on hierarchical clustering. Cluster II comprised the highest number of (21) genotypes whereas Cluster III consisted of the lowest number of (10) genotypes. This analysis reveals the presence of wide genetic variance in little millet breeding lines.