Integrating Taguchi Design and Machine Learning Models for Trait Stability and Predictive Modeling of Forage Quality in Grass pea (Lathyrus spp.)

Integrating Taguchi Design and Machine Learning Models for Trait Stability and Predictive Modeling of Forage Quality in Grass pea (Lathyrus spp.) | 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 Integrating Taguchi Design and Machine Learning Models for Trait Stability and Predictive Modeling of Forage Quality in Grass pea (Lathyrus spp.) Muhammed İkbal Çatal, Cengiz Sancak, Seyid Amjad Ali, Muhammad Aasim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7074016/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Grass pea ( Lathyrus sativus L.) is a resilient legume traditionally used for both human and animal consumption, valued for its drought tolerance and adaptability to marginal soils. However, its utilization is limited due to the neurotoxin β-ODAP, emphasizing the need for selecting genotypes with improved nutritional profiles and reduced toxicity. This study evaluated the forage quality of four naturally occurring Lathyrus species, including one endemic, in the Rize province of Turkey, using a combination of statistical and machine learning approaches. Results regarding different forage traits of Lathyrus species for two years were analyzed by Taguchi Design of experiment. Results revealed L. pratensis as the most robust genotype for critical traits such as crude ash ratio, crude protein ratio, and K/(Ca+Mg) based on signal-to-noise ratio. Application of machine learning models like Random Forest and Light Gradient Boosting Machine models were also used tp predict forage traits. Results illustrated high predictive accuracy for mineral and digestibility-related traits (R² > 0.97). In general, random forest model was superior than light gradient boosting machine model for fiber traits. However, both models failed to predict dry matter intake. The integration of Taguchi design and ML models exhibited most efficient genotype based on forage traits prediction. This dual-framework approach highlights the potential of combining traditional experimental design with modern artificial intelligence tools to support data-driven breeding, optimize forage quality, and promote sustainable livestock production in diverse environments. Taguchi Design Machine Learning Lathyrus Forage traits Prediction Mineral Composition Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviews received at journal 30 Jan, 2026 Reviews received at journal 24 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 06 Nov, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 30 Jul, 2025 Editor invited by journal 30 Jul, 2025 Submission checks completed at journal 29 Jul, 2025 First submitted to journal 29 Jul, 2025 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. 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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-7074016","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495808773,"identity":"db31b8df-9665-4a89-8fe1-3f25ce7cc056","order_by":0,"name":"Muhammed İkbal Çatal","email":"","orcid":"","institution":"University of Recep Tayyip Erdogan","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"İkbal","lastName":"Çatal","suffix":""},{"id":495808774,"identity":"13f390ae-2be2-41a0-a19f-50819879d4ff","order_by":1,"name":"Cengiz Sancak","email":"","orcid":"","institution":"Ankara University","correspondingAuthor":false,"prefix":"","firstName":"Cengiz","middleName":"","lastName":"Sancak","suffix":""},{"id":495808775,"identity":"4086f3fe-e904-4307-b1f7-3d20e0e00cea","order_by":2,"name":"Seyid Amjad Ali","email":"","orcid":"","institution":"Bilkent University","correspondingAuthor":false,"prefix":"","firstName":"Seyid","middleName":"Amjad","lastName":"Ali","suffix":""},{"id":495808776,"identity":"865d1e58-087c-420c-8300-b13e5e6bb502","order_by":3,"name":"Muhammad Aasim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYLCCBAYbBjYIk5loLWlIWtiI03QYxiBCi2774ccfHuact+eTSH78gaHCOrFBvvcBXi1mZ9LMJBK33U5skwAyGM6kJzawsRvg13IgwYwBqCWBjeeAGQNj22GgFgIuMzv//POHxG3n7Nl4jn/+wPiPGC03cgyADjvA2MbeYyDB2ECUljdlQC3JiUAtZRIJx9KN29jSCDksffPHn9vs7OWb2Td/+FBjLdvPfAy/FlSQwEB0TI6CUTAKRsEowAcAs+NCgS+0YWYAAAAASUVORK5CYII=","orcid":"","institution":"Sivas University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Aasim","suffix":""}],"badges":[],"createdAt":"2025-07-08 10:53:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7074016/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7074016/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88398016,"identity":"b87b0121-e4a2-487d-9ae2-99e7737831cd","added_by":"auto","created_at":"2025-08-06 06:36:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1230298,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7074016/v1_covered_215c91dc-81fa-48f2-a209-7758d4eb8fde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Taguchi Design and Machine Learning Models for Trait Stability and Predictive Modeling of Forage Quality in Grass pea (Lathyrus spp.)","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Taguchi Design, Machine Learning, Lathyrus, Forage traits Prediction, Mineral Composition","lastPublishedDoi":"10.21203/rs.3.rs-7074016/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7074016/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGrass pea (\u003cem\u003eLathyrus sativus\u003c/em\u003e L.) is a resilient legume traditionally used for both human and animal consumption, valued for its drought tolerance and adaptability to marginal soils. However, its utilization is limited due to the neurotoxin β-ODAP, emphasizing the need for selecting genotypes with improved nutritional profiles and reduced toxicity. This study evaluated the forage quality of four naturally occurring Lathyrus species, including one endemic, in the Rize province of Turkey, using a combination of statistical and machine learning approaches. Results regarding different forage traits of \u003cem\u003eLathyrus\u003c/em\u003e species for two years were analyzed by Taguchi Design of experiment. Results revealed \u003cem\u003eL. pratensis\u003c/em\u003e as the most robust genotype for critical traits such as crude ash ratio, crude protein ratio, and K/(Ca+Mg) based on signal-to-noise ratio. Application of machine learning models like Random Forest and Light Gradient Boosting Machine models were also used tp predict forage traits. Results illustrated high predictive accuracy for mineral and digestibility-related traits (R² \u0026gt; 0.97). In general, random forest model was superior than light gradient boosting machine model for fiber traits. However, both models failed to predict dry matter intake. The integration of Taguchi design and ML models exhibited most efficient genotype based on forage traits prediction. This dual-framework approach highlights the potential of combining traditional experimental design with modern artificial intelligence tools to support data-driven breeding, optimize forage quality, and promote sustainable livestock production in diverse environments.\u003c/p\u003e","manuscriptTitle":"Integrating Taguchi Design and Machine Learning Models for Trait Stability and Predictive Modeling of Forage Quality in Grass pea (Lathyrus spp.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 06:11:50","doi":"10.21203/rs.3.rs-7074016/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-09T09:18:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T09:49:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-30T09:32:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T11:41:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193251212154763566351208955063643024087","date":"2026-01-16T18:05:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237091597671965465777345103261682788023","date":"2026-01-16T14:45:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298955227036865067031752305774144773848","date":"2026-01-16T04:06:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316452330439952395308151132257921621129","date":"2026-01-15T11:41:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260120335857986814861447326819292881458","date":"2026-01-15T10:55:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92694574252496728326034028547140420932","date":"2025-11-06T07:55:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104921104557160179822864632247714658136","date":"2025-10-31T12:15:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T11:00:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T04:45:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-30T04:43:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-29T14:35:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-07-29T14:31:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ce32ff1-82a4-421e-9ecc-2dce68e6c7d6","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T11:25:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 06:11:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7074016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7074016","identity":"rs-7074016","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}