Phenomic prediction in drought-stressed faba bean across spectral, structural, and fused canopy predictors

Phenomic prediction in drought-stressed faba bean across spectral, structural, and fused canopy predictors | 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 Phenomic prediction in drought-stressed faba bean across spectral, structural, and fused canopy predictors Lennart Scheer, Lennard Roscher-Ehrig, Benjamin Wittkop, Jason Wenzig, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9714994/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 Background Faba bean is an important grain legume in temperate cropping systems because it provides protein-rich seed and contributes biological nitrogen fixation. However, its productivity is highly sensitive to drought, and breeding for improved drought performance is constrained by complex genotype by environment interactions and the difficulty of measuring relevant traits at scale. This study evaluated whether scanner-derived vegetation indices (VI), 3D canopy traits, and their combination can predict key agronomic and physiological traits in drought-stressed faba bean, and how predictive ability changes when information is used from single dates or cumulatively across the season. Results Predictive performance was strongly trait dependent and varied with predictor set and temporal strategy. Combined VI + 3D predictors generally produced the highest and most consistent predictive ability for major traits. Total grain yield reached 0.75 under cumulative VI + 3D prediction at 93 days after sowing (DAS 93), cumulative water uptake peaked at 0.80 at DAS 97, and total straw biomass reached 0.66 at DAS 104. In contrast, some component traits were predicted equally well or better by 3D information alone, including grain number with 0.70 and pod number with 0.55 under cumulative 3D prediction. Useful prediction windows also differed among traits, with broad late-season windows for major agronomic traits but narrower, more stage-specific windows for productive tillers, thousand kernel weight, and water-use efficiency. Conclusion Phenomic prediction under drought in faba bean was strongly shaped by trait type, predictor composition, and temporal design. Combined VI + 3D predictors were most effective for integrative traits, whereas several component traits were predicted equally well or better by 3D information alone. These findings highlight the potential of scanner-based multisensor phenotyping to support drought-related selection in faba bean breeding. Plant Physiology and Morphology high-throughput multispectral 3-dimensional phenotyping faba bean phenomic selection Full Text Additional Declarations The authors declare no competing interests. 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. 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-9714994","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640338350,"identity":"8f10ae97-7c76-4601-bfab-f841b2f5a00f","order_by":0,"name":"Lennart Scheer","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0002-5463-4407","institution":"Department of Plant Breeding, Justus Liebig University Giessen","correspondingAuthor":true,"prefix":"","firstName":"Lennart","middleName":"","lastName":"Scheer","suffix":""},{"id":640338351,"identity":"08840b20-389d-46ca-85bd-9f5c11be338c","order_by":1,"name":"Lennard Roscher-Ehrig","email":"","orcid":"","institution":"Department of Plant Breeding, Justus Liebig University Giessen","correspondingAuthor":false,"prefix":"","firstName":"Lennard","middleName":"","lastName":"Roscher-Ehrig","suffix":""},{"id":640338352,"identity":"71b60d7c-3c16-47f0-b061-46f6bd705cc0","order_by":2,"name":"Benjamin Wittkop","email":"","orcid":"","institution":"Department of Plant Breeding, Justus Liebig University Giessen","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Wittkop","suffix":""},{"id":640338353,"identity":"5498b68f-8d8b-4dc0-8d94-166808a6ecec","order_by":3,"name":"Jason Wenzig","email":"","orcid":"","institution":"Department of Plant Breeding, Justus Liebig University Giessen","correspondingAuthor":false,"prefix":"","firstName":"Jason","middleName":"","lastName":"Wenzig","suffix":""},{"id":640338354,"identity":"6d0015f8-7ebf-4e6a-aa2d-b151411aca24","order_by":4,"name":"Andreas Stahl","email":"","orcid":"","institution":"Institute for Resistance Research and Stress Tolerance, Julius Kühn-Institute","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Stahl","suffix":""},{"id":640338355,"identity":"373bb557-be83-4094-908e-c9f9e5b84ece","order_by":5,"name":"Olaf Sass","email":"","orcid":"","institution":"NPZ Hans-Georg Lembke KG","correspondingAuthor":false,"prefix":"","firstName":"Olaf","middleName":"","lastName":"Sass","suffix":""},{"id":640338356,"identity":"06ed83ec-b1c0-4275-96e9-24abf7bd35f5","order_by":6,"name":"Gregor Welna","email":"","orcid":"","institution":"NPZ Hans-Georg Lembke KG","correspondingAuthor":false,"prefix":"","firstName":"Gregor","middleName":"","lastName":"Welna","suffix":""},{"id":640338357,"identity":"22b4abfa-df1e-4852-a202-bbc7c9fb0eac","order_by":7,"name":"Hanna Tietgen","email":"","orcid":"","institution":"NPZ Innovation GmbH (NPZi)","correspondingAuthor":false,"prefix":"","firstName":"Hanna","middleName":"","lastName":"Tietgen","suffix":""},{"id":640338358,"identity":"e2b7b428-b1c2-4aeb-817f-20db5d42afec","order_by":8,"name":"Rod J. 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However, its productivity is highly sensitive to drought, and breeding for improved drought performance is constrained by complex genotype by environment interactions and the difficulty of measuring relevant traits at scale. This study evaluated whether scanner-derived vegetation indices (VI), 3D canopy traits, and their combination can predict key agronomic and physiological traits in drought-stressed faba bean, and how predictive ability changes when information is used from single dates or cumulatively across the season.\u003c/p\u003e\n\u003cp\u003eResults Predictive performance was strongly trait dependent and varied with predictor set and temporal strategy. Combined VI + 3D predictors generally produced the highest and most consistent predictive ability for major traits. Total grain yield reached 0.75 under cumulative VI + 3D prediction at 93 days after sowing (DAS 93), cumulative water uptake peaked at 0.80 at DAS 97, and total straw biomass reached 0.66 at DAS 104. 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