Trait-Based Genetic Variability of Linseed Genotypes for Seed Yield and Yield Components in West Shewa, Ethiopia

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Abstract Linseed ( Linum usitatissimum L.) is a significant oilseed crop in the central highlands of Ethiopia, where breeding efforts primarily focus on enhancing seed yield through the exploitation of genotypic diversity. However, progress has been hindered by the limited availability of superior varieties. This study aimed to assess the genetic diversity and trait associations among 35 linseed genotypes for key agronomic traits. The experiment was conducted during the 2023 main cropping season using an alpha lattice design. Analysis of variance (ANOVA) revealed highly significant differences (P ≤ 0.01) among genotypes for all 14 agronomic and yield-related traits, indicating substantial genetic diversity. The results showed wide ranges in traits such as days to maturity, number of capsules per plant, seed yield, and oil content. Estimates of genetic parameters showed high phenotypic and genotypic coefficients of variation (PCV and GCV) for traits such as tiller number, harvest index, seed yield, oil yield, and oil content, indicating potential for effective selection. Broad-sense heritability was high (> 90%) for most traits, indicating strong genetic control and high expected genetic gain. Principal Component Analysis (PCA) revealed that the first five components explained 77% of the total variation, with oil yield, seed yield, biomass, and plant height being major contributors. Cluster analysis grouped genotypes into five distinct clusters, highlighting significant inter-cluster genetic distances, especially between clusters III and V, which could be valuable for hybridization. Correlation analysis indicated significant positive associations among seed yield, oil content, and harvest index, while lodging was strongly correlated with plant height and maturity. The overall results underscore the presence of exploitable genetic variability among the tested linseed genotypes, providing a solid foundation for future breeding efforts to improve yield and adaptability.
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Trait-Based Genetic Variability of Linseed Genotypes for Seed Yield and Yield Components in West Shewa, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trait-Based Genetic Variability of Linseed Genotypes for Seed Yield and Yield Components in West Shewa, Ethiopia Tilahun Mola Tessema, Gudeta Nepir Gurumu, Fikadu Ebiyo Jira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9254061/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Linseed ( Linum usitatissimum L.) is a significant oilseed crop in the central highlands of Ethiopia, where breeding efforts primarily focus on enhancing seed yield through the exploitation of genotypic diversity. However, progress has been hindered by the limited availability of superior varieties. This study aimed to assess the genetic diversity and trait associations among 35 linseed genotypes for key agronomic traits. The experiment was conducted during the 2023 main cropping season using an alpha lattice design. Analysis of variance (ANOVA) revealed highly significant differences (P ≤ 0.01) among genotypes for all 14 agronomic and yield-related traits, indicating substantial genetic diversity. The results showed wide ranges in traits such as days to maturity, number of capsules per plant, seed yield, and oil content. Estimates of genetic parameters showed high phenotypic and genotypic coefficients of variation (PCV and GCV) for traits such as tiller number, harvest index, seed yield, oil yield, and oil content, indicating potential for effective selection. Broad-sense heritability was high (> 90%) for most traits, indicating strong genetic control and high expected genetic gain. Principal Component Analysis (PCA) revealed that the first five components explained 77% of the total variation, with oil yield, seed yield, biomass, and plant height being major contributors. Cluster analysis grouped genotypes into five distinct clusters, highlighting significant inter-cluster genetic distances, especially between clusters III and V, which could be valuable for hybridization. Correlation analysis indicated significant positive associations among seed yield, oil content, and harvest index, while lodging was strongly correlated with plant height and maturity. The overall results underscore the presence of exploitable genetic variability among the tested linseed genotypes, providing a solid foundation for future breeding efforts to improve yield and adaptability. Genotypic coefficient of variation genetic diversity heritability Linseed Phenotypic traits Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 02 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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