A Comparison of Optimization Techniques for Large-scale Allocation of Soybean Crops

A Comparison of Optimization Techniques for Large-scale Allocation of Soybean Crops | 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 A Comparison of Optimization Techniques for Large-scale Allocation of Soybean Crops Mathilde CHEN, George KATSIRELOS, David MAKOWSKI, Alberto TONDA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5648771/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 The optimal allocation of crops to different parcels of land is a problem of paramount practical importance, not only to improve food and feed production, but also to address the challenges posed by climate change. However, this optimization problem is inherently complex due to the large number of agricultural parcels available which generates a vast search space that renders traditional optimization techniques impractical. Moreover, as maximizing average production may generate solutions characterized by high year-by-year instability and lead to large and unrealistic cultivated areas, it is necessary to optimize crop allocation considering several objectives at the same time. In order to tackle this complex optimization problem, we propose a multi-objective approach, simultaneously maximizing the average production, minimizing the year-on-year production variance, and minimizing the total cultivated surface. The approach exploits an established multiobjective evolutionary algorithm, and employs a machine learning model able to predict crop production from weather and soil conditions, trained on historical data, making it possible to tackle allocation problems of large size. As a reference, we also present a comparison with a quadratic programming algorithm specifically tailored to the target problem. A case study focusing on the allocation of soybean crops in the European continent for the years 2000-2023 shows that the proposed methodology is able to identify informative trade-offs between the conflicting objectives, and identify realistic and meaningful crop allocations for supporting stakeholders’ decisions. Artificial Intelligence and Machine Learning Agronomy Crop allocation Crop yield forecasting Machine learning Multi-objective optimization 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. 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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-5648771","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":390504748,"identity":"2906f105-3488-4031-ba75-639f887ca418","order_by":0,"name":"Mathilde CHEN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHACNoaEAmYgzQPEFWDSAEjjA8xALQYwLWegWs4Q0sIA08LYBhbCr0W3/fyxBw8MrOUY2M8efFw4z07GvP3wBoaDe3BrMTuTzG6QYJBuzMCTl2w8c1syj8yZtAKGA8/waDmQzCaRYHA4sUGCx0yad9sBHgmGHAPmDwfwaDn/GKylHqjF/DfvHKAW/jcGDAfwabkBsSWBAWgLM28DUItEDiEtj82AWtIN23hyjKV5jiUDtTwrOIBXy/nEZ5I/Kqzl+dnPGH7mqbGzl+BP3vgAnxY4YEPmEKNhFIyCUTAKRgEeAABjeEoHHhqUEwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5982-2143","institution":"(1) UMR 518 MIA-PS, INRAE, Université Paris-Saclay, France; 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