Genetic Analysis to Estimate Components of Genetic Variances in Indian Mustard (Brassica juncea L.)

The current investigation on Brassica juncea L. genotypes focuses on exploring variations through hybridization to enhance genetic diversity. Additionally, it aims to gather genetic insights into yield and related traits essential for selecting superior varieties in subsequent generations. Using a half diallel design, eight potential genotypes were selected and crossed in every imaginable way. All of the qualities had substantial differences according to analysis of variance; these differences were further examined using Hayman's technique, which revealed that all of the traits are dominated by both additive and dominant gene effects. The findings suggested that dominance and additive genetic variations play a significant role in regulating these features. The magnitude of dominance (H1 and H2) was highly significant higher than additive components (D) for all traits. Positive alleles were not equally distributed among parents (H2/4H1 ≠ 0.25) for all the studied traits. The estimates of environmental variance (E) were significantly positive for all studied traits, except for biological yield per plant suggests that environmental factors exert a notable influence on all examined traits. Narrow sense heritability was less than (0.50) for all traits. The graphical analysis Wr/Vr indicated the importance of over dominance gene effects in controlling all traits. Thus suggesting that selection could be effective in latter generations.

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Publication Details

Journal
Journal of Experimental Agriculture International
Published
2024-09-03
DOI
https://doi.org/10.9734/jeai/2024/v46i92837
Primary Topic
Nitrogen and Sulfur Effects on Brassica
Type
article
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article

Genetic Analysis to Estimate Components of Genetic Variances in Indian Mustard (Brassica juncea L.)

Deepak Gupta, Ashok Kumar Meena, Giradhari Lal Yadav, M.L. Jakhar et al.
Journal of Experimental Agriculture International
Nitrogen and Sulfur Effects on Brassica
article

Genetic Analysis to Estimate Components of Genetic Variances in Indian Mustard (Brassica juncea L.)

Deepak Gupta, Ashok Kumar Meena, Giradhari Lal Yadav, M.L. Jakhar, S. S. Rajput
article en

Abstract

The current investigation on Brassica juncea L. genotypes focuses on exploring variations through hybridization to enhance genetic diversity. Additionally, it aims to gather genetic insights into yield and related traits essential for selecting superior varieties in subsequent generations. Using a half diallel design, eight potential genotypes were selected and crossed in every imaginable way. All of the qualities had substantial differences according to analysis of variance; these differences were further examined using Hayman's technique, which revealed that all of the traits are dominated by both additive and dominant gene effects. The findings suggested that dominance and additive genetic variations play a significant role in regulating these features. The magnitude of dominance (H1 and H2) was highly significant higher than additive components (D) for all traits. Positive alleles were not equally distributed among parents (H2/4H1 ≠ 0.25) for all the studied traits. The estimates of environmental variance (E) were significantly positive for all studied traits, except for biological yield per plant suggests that environmental factors exert a notable influence on all examined traits. Narrow sense heritability was less than (0.50) for all traits. The graphical analysis Wr/Vr indicated the importance of over dominance gene effects in controlling all traits. Thus suggesting that selection could be effective in latter generations.

Journal of Experimental Agriculture InternationalVol. 46(9)
Sri Karan Narendra Agriculture University, Jobner (IN)
Life in Land
Openalex Percentile: Top 87%
Nitrogen and Sulfur Effects on Brassica
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Genetic Analysis to Estimate Components of Genetic Variances in Indian Mustard (Brassica juncea L.) — Deepak Gupta, Ashok Kumar Meena, et al. · Journal of Experimental Agriculture International (2024) | TGRS Research Map | TGRS