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Vol. 12, Issue 2 (2023)

Genetic variability studies in early generation lines of Soyabean (Glycine max (L.) Merrill.) for yield and its contributing traits

Author(s):
Arvind Chauhan, Kamendra Singh, Nimita Kandwal, Prabhat Singh and Basu Sudhakar Reddy
Abstract:
Eleven line from F4 generation of crosses and their subsequent F5 generation of soybean (Glycine max (L.) Merrill.) were evaluated in split plot design with two replications for variability, heritability and genetic advance during Kharif 2017 and 2018. The analysis of variance for all nine traits unveiled presence of highly significant variations among the treatments in both F4 and F5 generations, which showed presence of variability among the material used for the experiment. The variability estimates calculated showed higher PCV values over GCV in both the generations. The highest PCV value was recorded for seed yield in F4 generation, whereas highest value for PCV in F5 generation was observed for seed yield efficiency, suggesting good amount of variability. High PCV coupled with high GCV indicate the presence of wider adaptability for these traits in the genotypes studied, suggested the less influence of environment in the expression of characters. All the traits under investigation were found to be highly heritable, as majority of them showed high values of heritability (broad sense) estimates. In F4 generation, highest value of heritability estimate was recorded for seed yield per plant, whereas in F5 generation highest value was recorded for dry matter weight per plant. High heritability coupled with high genetic advance as percent of mean indicating operation of additive gene action and the ample scope for improvement in these traits through simple selection.
Pages: 168-172  |  346 Views  188 Downloads


The Pharma Innovation Journal
How to cite this article:
Arvind Chauhan, Kamendra Singh, Nimita Kandwal, Prabhat Singh, Basu Sudhakar Reddy. Genetic variability studies in early generation lines of Soyabean (Glycine max (L.) Merrill.) for yield and its contributing traits. Pharma Innovation 2023;12(2):168-172. DOI: 10.22271/tpi.2023.v12.i2c.18426

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