Comparative evaluation of the performance of nine Machine Learning models for predicting corn yield based on uncalibrated empirical data in Cameroon

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Comparative evaluation of the performance of nine Machine Learning models for predicting corn yield based on uncalibrated empirical data in Cameroon | 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 Comparative evaluation of the performance of nine Machine Learning models for predicting corn yield based on uncalibrated empirical data in Cameroon Sopkoutie Kengni Nerlus Gautier, Bidias Aboh Francis, Deffo Tchouan Gilchrist, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8046361/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: Predicting maize yields is a major challenge for food security and the optimization of agricultural systems in Sub-Saharan Africa. While climate and spectral data are often prioritized, the predictive power of socio-economic factors remains less explored. This study aimed to evaluate the performance of diverse Machine Learning (ML) in predicting maize yield using farmer survey data. Methodology : We assessed the performance of seven ML models and two ensemble methods (Stacking, AdaBoost)(multiple linear regression (MLR), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost, alongside Stacking and AdaBoost models) using survey data collected from 354 maize farmers in Vina, Cameroon. Variable importance analysis, model training, and robust cross-validation ( k = 5 ) were conducted. Results: Variable importance analysis revealed that profit per hectare was the dominant predictor (score = 0.895; r = 0.85 with yield), followed by fertilizer use and agricultural expenditure. Modeling results demonstrated the superiority of ensemble methods. XGBoost achieved the best performance (R 2 = 0.98; RMSE = 0.128), closely followed by Random Forest and Stacking, while linear models and K-Nearest Neighbors (KNN) exhibited insufficient accuracy. Cross-validation confirmed the robustness and generalization ability of ensemble models, with XGBoost, Random Forest, and Stacking all maintaining high accuracy (R 2 ≈ 0.93–0.94) and low variance. Conclusion: Our findings demonstrate that socio-economic data, when analyzed by robust ensemble algorithms, can provide reliable and accurate maize yield predictions. This approach offers a cost-effective and robust alternative to traditional climate- or spectral-based modeling, presenting new opportunities for agricultural planning and precision agriculture in resource-limited areas of Sub-Saharan Africa. Maize yield Machine Learning Prediction Socio-economic data Cameroon Full Text Additional Declarations No competing interests reported. Supplementary Files Dataofmaizefarmers.xlsx SURVEYSHEET.pdf 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-8046361","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576364017,"identity":"9102d873-5c1e-4cb5-bd96-b76bd14a6dba","order_by":0,"name":"Sopkoutie Kengni Nerlus 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Cameroon","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Maize yield, Machine Learning, Prediction, Socio-economic data, Cameroon","lastPublishedDoi":"10.21203/rs.3.rs-8046361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8046361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePredicting maize yields is a major challenge for food security and the optimization of agricultural systems in Sub-Saharan Africa. While climate and spectral data are often prioritized, the predictive power of socio-economic factors remains less explored. This study aimed to evaluate the performance of diverse Machine Learning (ML) in predicting maize yield using farmer survey data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e: We assessed the performance of seven ML models and two ensemble methods (Stacking, AdaBoost)(multiple linear regression (MLR), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost, alongside Stacking and AdaBoost models) using survey data collected from 354 maize farmers in Vina, Cameroon. Variable importance analysis, model training, and robust cross-validation (\u003cem\u003ek = 5\u003c/em\u003e) were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eVariable importance analysis revealed that profit per hectare was the dominant predictor (score = 0.895; r = 0.85 with yield), followed by fertilizer use and agricultural expenditure. Modeling results demonstrated the superiority of ensemble methods. XGBoost achieved the best performance (R\u003csup\u003e2\u003c/sup\u003e = 0.98; RMSE = 0.128), closely followed by Random Forest and Stacking, while linear models and K-Nearest Neighbors (KNN) exhibited insufficient accuracy. Cross-validation confirmed the robustness and generalization ability of ensemble models, with XGBoost, Random Forest, and Stacking all maintaining high accuracy (R\u003csup\u003e2\u003c/sup\u003e ≈ 0.93–0.94) and low variance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur findings demonstrate that socio-economic data, when analyzed by robust ensemble algorithms, can provide reliable and accurate maize yield predictions. This approach offers a cost-effective and robust alternative to traditional climate- or spectral-based modeling, presenting new opportunities for agricultural planning and precision agriculture in resource-limited areas of Sub-Saharan Africa.\u003c/p\u003e","manuscriptTitle":"Comparative evaluation of the performance of nine Machine Learning models for predicting corn yield based on uncalibrated empirical data in Cameroon","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:24:04","doi":"10.21203/rs.3.rs-8046361/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2f75e1ee-e984-45b5-a206-3ad9941de4d7","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T10:26:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:24:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8046361","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8046361","identity":"rs-8046361","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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