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Plant Science Today (PST)

Research Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Principal component analysis (PCA) and genetic diversity studies for discrimination of genotypes and yield parameters in ICRISAT Pearl Millet [Pennisetum glaucum (L.) R. Br.] accessions

DOI
https://doi.org/10.14719/pst.8243
Submitted
14 March 2025
Published
20-08-2025

Abstract

PCA and cluster analysis were used to assess 50 pearl millet genotypes for 26 attributes to identify associations among individuals as well as their traits and patterns of variation. Among 26 PCs (Principal Components), eight unveiled eigenvalues greater than one, which accounted for 78.29 % of the total variability among the traits. The first component (PC1) had a great maximum variability of about 18.53 % with the highest eigenvalue. It was observed that PC1 revealed maximum variation in comparison with other 25 PCs, whereas PC2 to PC8 showed gradual reduction in variability with values 17.38, 11.71, 8.17, 7.37, 5.63, 5.0 and 4.49 percent respectively. Therefore, selecting lines and traits based on PC1 would be beneficial. The factor loading of PCs exhibited that PC1 accounted for maximum variability for traits like NTrPP (number of tillers per plant), LAI (leaf area index) and GY (grain yield) per plant. PC2 counted for 17.38 % of the total variance and exhibited the greatest variability for number of leaves per plant (NLPP). PC3 showed 11.71 % variability and taken maximum variability for leaf:stem ratio (LSR). PC4 exhibited 8.17 % of variability and possessed maximum variability for plant height (PtH), leaf width (LfW), flag leaf width (FgLW) and stem girth (SG) which pointed out the huge impact in the total variation of the genotypes. Cluster analysis recorded cluster III had desirable mean values for most of the traits studied. The genotype, viz. IP 8327 from cluster III would be used as parent for pearl millet improvement program.

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