Optimization of Sparse Phenotyping Strategies in Multi-Environmental Trials in Maize

Optimization of Sparse Phenotyping Strategies in Multi-Environmental Trials in Maize | 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 Optimization of Sparse Phenotyping Strategies in Multi-Environmental Trials in Maize Srinivasa Reddy Mothukuri, Yoseph Beyene, Mehmet Gültas, Juan Burgueno, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4765310/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted 5 You are reading this latest preprint version Abstract The phenotyping needs to be optimized and aims to achieve desired precision at low costs because selection decisions are mainly based on multi-environmental trials. Optimization of sparse phenotyping is possible in plant breeding by applying relationship measurements and genomic prediction. Our research utilized genomic data and relationship measurements between the training (full testing genotypes) and testing set (sparse testing genotypes) to optimize the allocation of genotypes to subsets in sparse testing. Different sparse phenotyping designs were mimicked based on the percentage (%) of lines in the full set, the number of partially tested lines, the number of tested environments, balanced and unbalanced methods for allocating the lines among the environments. The eight relationship measurements were utilized to calculate the relatedness between full and sparse set genotypes. The results demonstrate that balanced and allocating 50% of lines to the full set designs have shown a higher Pearson correlation in terms of accuracy measurements than assigning the 30% of lines to the full set and balanced sparse methods. By reducing untested environments per sparse set, results enhance the accuracy of measurements. The relationship measurements exhibit a low significant Pearson correlation ranging from 0.20 to 0.31 using the accuracy measurements in sparse phenotyping experiments. The positive Pearson correlation shows that the maximization of the accuracy measurements can be helpful to the optimization of the line allocation on sparse phenotyping designs. Maize breeding sparse phenotyping multi-environmental trials line allocation application of genomic prediction relationship measurements and accuracy measurements Full Text Supplementary Files 2SupplementaryInformation.pdf Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2025 Read the published version in Theoretical and Applied Genetics → Version 1 posted Editorial decision: Major revisions 25 Aug, 2024 Reviewers agreed at journal 25 Jul, 2024 Reviewers invited by journal 25 Jul, 2024 Editor assigned by journal 19 Jul, 2024 First submitted to journal 18 Jul, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4765310","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331955374,"identity":"db0a6793-6db4-4371-8940-01bd506b16fe","order_by":0,"name":"Srinivasa Reddy Mothukuri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYLACxgYGAyDZ+ADMO0CMloMQLc0GpGphYJMgSot8A3fi4487Dhsb3D7cVs1TxiDHdyOB8TMPHi0GB3g3Gxw8c9jM4Fxi222ecwzGkjcSmKXxamHg3SZxsO2wjcEZxrbbvG0MiRtuJDDg1SLfgKSlGKilHqiF+Tc+LQwHIFrMQFqYgVoSDG4ksOF32GGgX862pRtLnmFslpxzTsJw5pmHbZZz8DmsvXfjg8o2a8O+M+wPP7wps5HnO558+MYbfA5jRuGBowaYGEgAbKQoHgWjYBSMgpECAG/CTnji37VgAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2232-6100","institution":"The University of Queensland - 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