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However, this optimization problem is inherently complex due to the large number of agricultural sites 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 relies on an established multi-objective evolutionary algorithm, and employs a machine learning model able to predict crop production from weather and irrigation conditions, trained on historical data, making it possible to tackle allocation problems of large size. The proposed approach is compared to a quadratic programming algorithm tailored to the target problem. A case study focusing on the allocation of soybean crops in the European continent for the years 2000\u20132023 shows that the proposed methodology is able to identify informative tradeoffs between the three conflicting objectives considered, and identify realistic and meaningful crop allocations for supporting stakeholders\u2019 decisions.<\/jats:p>","DOI":"10.1145\/3744254","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T07:55:37Z","timestamp":1749542137000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Comparison of Optimization Techniques for Large-scale Allocation of Soybean Crops"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5982-2143","authenticated-orcid":false,"given":"Mathilde","family":"Chen","sequence":"first","affiliation":[{"name":"UMR 518 MIA-PS, INRAE, Universit\u00e9 Paris-Saclay, Palaiseau, France, CIRAD, UMR PHIM, Montpellier, France and PHIM, Universit\u00e9 Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3727-6698","authenticated-orcid":false,"given":"George","family":"Katsirelos","sequence":"additional","affiliation":[{"name":"UMR 518 MIA-PS, INRAE, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6385-3703","authenticated-orcid":false,"given":"David","family":"Makowski","sequence":"additional","affiliation":[{"name":"UMR 518 MIA-PS, INRAE, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-4809","authenticated-orcid":false,"given":"Alberto","family":"Tonda","sequence":"additional","affiliation":[{"name":"UMR 518 MIA-PS, INRAE, Universit\u00e9 Paris-Saclay, Palaiseau, France and Institut des Syst\u00e8mes Complexes, Paris-Ile-de-France, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.econmod.2020.05.006"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09569-8"},{"issue":"9","key":"e_1_3_2_4_1","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1002\/jsfa.11713","article-title":"Machine learning algorithms for soybean yield forecasting in the Brazilian cerrado","volume":"102","author":"Santos Valter Barbosa dos","year":"2022","unstructured":"Valter Barbosa dos Santos, Aline Moreno Ferreira dos Santos, Jos\u00e9 Reinaldo da Silva Cabral de Moraes, Igor Cristian de Oliveira Vieira, and Glauco de Souza Rolim. 2022. 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