Gauging the GxE interaction and assessing the stability of some soybean genotypes across six major locations in Ethiopia

Gauging the GxE interaction and assessing the stability of some soybean genotypes across six major locations in 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 Gauging the GxE interaction and assessing the stability of some soybean genotypes across six major locations in Ethiopia Tsegaye Mulalem, Garome Shifaraw, Abush Tesfaye, Sentayehu Alamerew, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8142414/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In Ethiopia, soybean is highly important among oilseed crops, yet its production faces several constraints, particularly the insufficiency of stable genotypes. Therefore, the current study aimed to assess the effects of the genotype × environment interaction (GEI) on yield and yield attributes to identify superior, well-adapted genotypes. Thirty diverse soybean genotypes, including two check lines, were laid out in an alpha lattice design with three replications across six major ecological locations, viz., Jimma, Mettu, Teppi, Bako, Assosa, and Pawe, during the 2020 cropping season. The results revealed considerable effects of the GEI on grain yield, phenology, and growth across the six locations. ANOVA revealed highly significant differences (p < 0.01, p < 0.05) among the genotypes and locations. The mean grain yield across locations varied, with Bako having the highest yield (4.05 t ha⁻¹) and Jimma having the lowest yield (0.39 t ha⁻¹). The ScStatus, S1079/6/7, and Pawe-3 genotypes surpassed the others. Combined variance analysis revealed a significant GEI for key traits. The location led to more than 50% of the variation in terms of days to flowering, maturity, number of seeds 1, and number of pods plant-1 and 25--30% in terms of grain yield, plant height, and 100-seed weight. The genotype explained less than 20% of the variation in all the traits except plant height, whereas the GEI contributed to more than 20% of the variation in 100-seed weight and grain yield. Wricke’s Ecovalence and Shukla’s Stability Variance identified TGX2014-16FM and TGX2002-3DM as stable. According to the Cultivar Superiority Measure and Eberhart and Russell analysis, ScStatus, S1079/6/7, S1150/5/22 and TGX2001-8DM were found to be stable. However, ScStatus and S1079/6/7 appear to be more stable and adaptive, as they were the overall highest yielders. However, there is a need to conduct further trials for an additional season to confirm the consistent performance and stability of top-performing genotypes before making a final revelation. Soybean genotypes GxE interaction stability analysis yield and yield attributes Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Therefore, the current study aimed to assess the effects of the genotype × environment interaction (GEI) on yield and yield attributes to identify superior, well-adapted genotypes. Thirty diverse soybean genotypes, including two check lines, were laid out in an alpha lattice design with three replications across six major ecological locations, viz., Jimma, Mettu, Teppi, Bako, Assosa, and Pawe, during the 2020 cropping season. The results revealed considerable effects of the GEI on grain yield, phenology, and growth across the six locations. ANOVA revealed highly significant differences (p \u003c 0.01, p \u003c 0.05) among the genotypes and locations. The mean grain yield across locations varied, with Bako having the highest yield (4.05 t ha⁻¹) and Jimma having the lowest yield (0.39 t ha⁻¹). The ScStatus, S1079/6/7, and Pawe-3 genotypes surpassed the others. Combined variance analysis revealed a significant GEI for key traits. The location led to more than 50% of the variation in terms of days to flowering, maturity, number of seeds 1, and number of pods plant-1 and 25--30% in terms of grain yield, plant height, and 100-seed weight. The genotype explained less than 20% of the variation in all the traits except plant height, whereas the GEI contributed to more than 20% of the variation in 100-seed weight and grain yield. Wricke’s Ecovalence and Shukla’s Stability Variance identified TGX2014-16FM and TGX2002-3DM as stable. According to the Cultivar Superiority Measure and Eberhart and Russell analysis, ScStatus, S1079/6/7, S1150/5/22 and TGX2001-8DM were found to be stable. However, ScStatus and S1079/6/7 appear to be more stable and adaptive, as they were the overall highest yielders. 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