UAV Phenotyping and Genomic Prediction of Ground Cover can Accelerate Organic Spring Cereal Breeding

UAV Phenotyping and Genomic Prediction of Ground Cover can Accelerate Organic Spring Cereal Breeding | 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 UAV Phenotyping and Genomic Prediction of Ground Cover can Accelerate Organic Spring Cereal Breeding Khalid Mahmood, Lukas Oertelt, Jihad Orabi, Janni Hedensvang Jørgensen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8552708/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Ground cover is a key trait in organic cereal production, contributing to weed suppression, soil protection, and yield stability. Assessing canopy development manually is labor-intensive, whereas unmanned aerial vehicle (UAV) phenotyping offers a high-throughput and objective alternative. In this study, we evaluated the potential of UAV-derived ground cover measurements combined with genomic prediction to support breeding for organic spring oat ( Avena sativa L.) and wheat ( Triticum aestivum L.). A diverse panel of 461 oat genotypes and 218 spring wheat genotypes were phenotyped using UAVs in May and June. Ground cover exhibited substantial variation in May (oat: mean = 0.43, SD = 0.31; wheat: mean = 0.36, SD = 0.35) and approached to maturity in June for Oat. Narrow-sense heritabilities were intermediate for both species and growth stages (0.30–0.50), indicating potential for breeding for increased ground cover. Genomic prediction models showed higher accuracy for early-season ground cover (May: 0.45) compared with later stages (June: 0.35), consistent with greater phenotypic variation at early growth. Positive correlations were observed between genetic values for early ground cover and grain yield suggesting that selection for early canopy development can be very useful for organic farming. These results demonstrate that UAV-based phenotyping, integrated with genomic prediction, provides an efficient strategy for selecting competitive, high-yielding cultivars in organic spring cereals, particularly through early-season canopy traits. UAV phenotyping genomic prediction ground cover spring oat spring wheat organic breeding early canopy development Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers invited by journal 05 Feb, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 08 Jan, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8552708","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587340587,"identity":"f7de9b5b-cffe-425c-80c8-09229f549e90","order_by":0,"name":"Khalid Mahmood","email":"","orcid":"","institution":"Nordic Seed A/S , Denmark","correspondingAuthor":false,"prefix":"","firstName":"Khalid","middleName":"","lastName":"Mahmood","suffix":""},{"id":587340589,"identity":"efbd251b-c867-455d-bc8d-ee2ab788c403","order_by":1,"name":"Lukas Oertelt","email":"","orcid":"","institution":"Nordic Seed 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Assessing canopy development manually is labor-intensive, whereas unmanned aerial vehicle (UAV) phenotyping offers a high-throughput and objective alternative. In this study, we evaluated the potential of UAV-derived ground cover measurements combined with genomic prediction to support breeding for organic spring oat (\u003cem\u003eAvena sativa\u003c/em\u003eL.) and wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). A diverse panel of 461 oat genotypes and 218 spring wheat genotypes were phenotyped using UAVs in May and June. Ground cover exhibited substantial variation in May (oat: mean\u0026thinsp;=\u0026thinsp;0.43, SD\u0026thinsp;=\u0026thinsp;0.31; wheat: mean\u0026thinsp;=\u0026thinsp;0.36, SD\u0026thinsp;=\u0026thinsp;0.35) and approached to maturity in June for Oat. Narrow-sense heritabilities were intermediate for both species and growth stages (0.30\u0026ndash;0.50), indicating potential for breeding for increased ground cover. Genomic prediction models showed higher accuracy for early-season ground cover (May: 0.45) compared with later stages (June: 0.35), consistent with greater phenotypic variation at early growth. Positive correlations were observed between genetic values for early ground cover and grain yield suggesting that selection for early canopy development can be very useful for organic farming. These results demonstrate that UAV-based phenotyping, integrated with genomic prediction, provides an efficient strategy for selecting competitive, high-yielding cultivars in organic spring cereals, particularly through early-season canopy traits.\u003c/p\u003e","manuscriptTitle":"UAV Phenotyping and Genomic Prediction of Ground Cover can Accelerate Organic Spring Cereal Breeding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 18:46:12","doi":"10.21203/rs.3.rs-8552708/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-04T07:06:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115640959517087159530591307323021788608","date":"2026-02-06T00:46:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111955966850041077084925416185369745797","date":"2026-02-05T14:30:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T13:05:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T10:44:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-12T10:40:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Organic Agriculture","date":"2026-01-08T14:09:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"organic-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"orga","sideBox":"Learn more about [Organic Agriculture](http://link.springer.com/journal/13165)","snPcode":"13165","submissionUrl":"https://submission.nature.com/new-submission/13165/3","title":"Organic Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2c874ba1-a98a-4e65-adc7-ebfdfa3f79f9","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T18:46:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 18:46:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8552708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8552708","identity":"rs-8552708","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}