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Research Article Open access CC BY 4.0

Comprehensive Genetic Analysis of Yield and Yield-Related Traits in Soybean Germplasms for Enhanced Crop Improvement

Bhumika Singh lodhi, Pramod Kumar, Monika Chouhan, Alok Rajpoot, Amit Jha

International Journal of Plant & Soil Science · pp. 9–17 · Published 15 Nov 2023

10.9734/ijpss/2023/v35i224109

Abstract

In the context of escalating global food demands, this study provides a comprehensive genetic analysis of soybean (Glycine max (L.) Merrill), focusing on 13 distinct traits that contribute to yield and quality. We investigated four phenological traits marking critical growth phases and nine quantitative traits, utilizing an analysis of variance to highlight the significant genetic influences on these variables. The study reveals that phenotypic and genotypic variances, when measured through the coefficient of variability, present a deeper understanding of soybean variability than variance analysis alone. This is underscored by our findings where phenotypic coefficient of variance (PCV) values consistently exceeded genotypic coefficient of variance (GCV) across all traits, indicating the substantial effect of genotype-environment interactions. The highest GCV was observed in seed yield and biological yield, indicating these traits' potential for genetic improvement. Heritability studies showed days to physiological maturity as the trait with the highest inheritability, suggesting its stability across environmental conditions. Moreover, the significant genetic advance as a percentage of the mean for grain yield and biological yield underscores the potential for considerable gains through selective breeding. By identifying traits governed by additive genes, such as the weight of a hundred seeds and grain yield, our study indicates promising avenues for future soybean breeding programs to enhance these characteristics.

Glycine max (L.) merrill Genetic variability heritability yield traits genotypic coefficient of variance phenotypic coefficient of variance

Cited by 1

Image-based GWAS identifies the genetic architecture of seed-related traits in a soybean mutant population

Jung Min Kim, Jeong Woo Lee, Dae June Kim · Molecular Breeding · 2025

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