A multivariate approach to assess the genetic diversity in finger millet [Eleusine coracana (L.) Gaertn.] germplasm accessions Nagaraja T. E.*, Bhat Sujata, Nandini C., Saritha H. S., Parveen S. Gazala ICAR-AICRP on Small Millets, ZARS, University of Agricultural Sciences, GKVK, Bengaluru, India, 560065 *E-Mail: tenagaraja@gmail.com
Online Published on 22 January, 2024. Abstract Finger millet [Eleusine coracana (L.) Gaertn.], a staple cereal amongst the rural population of large parts of Africa and South Asia. It is well-known for its nutritional status, especially abundant quantities of iron and calcium. Despite these advantages, finger millet is far from extensive conventional crop improvement programs. To popularize finger millet production and increase its productivity, primarily, the characterization of its germplasm is crucial. In the present study, 587 finger millet genotypes collected from different sources were analyzed for various estimates of genetic variability parameters, genetic divergence through K-means clustering followed by principal component analysis (PCA). The analysis of variance revealed substantial amount of genetic variability for the seven quantitative traits among 587 finger millet germplasm accessions. The genotypes were classified into two clusters following K-means clustering algorithm. The cluster I had higher number of genotypes (343) than cluster II (244). Thus, the grouping of accessions shall be of practical value to finger millet researchers to select and tap the genetic potential of elite accessions from different clusters as donor parentsin crossing programs. Based on PCA, three principal components (PC) with eigen values greater than one contributed 72.55% of the total variability. The PC1 accounted highest proportion of total variance (40.82 %), followed by PC2, PC3, PC4, PC5, PC6 and PC7that revealed 16.74 %, 14.98 %, 11.06 %, 9.19 %, 7.12% and 0.06 % variation, respectively. Top Keywords Finger millet, Summary statistics, Phenotypic Correlation, Principal Component Analysis, K-means cluster. Top |