{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:55:50Z","timestamp":1771844150773,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T00:00:00Z","timestamp":1725753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Considering that investing in the production of corn and soybeans is conditioned by production costs and several risks, the objective of this research work was to develop a simulation model for the prediction of the production costs of these commodities, considering the variability and correlation of key variables. The descriptive analysis of the data focused on measures such as mean, standard deviation, and coefficient of variation. To evaluate the relationship between commodity and input prices, Spearman\u2019s demonstration coefficient and the coefficient of determination (R2) were used. A Monte Carlo simulation (MCS) was used to evaluate the variation in production costs and net revenues. The Predictor tool was used to make predictions based on historical data and time series models. This study was made for the period between 2018 and 2022 based on data provided by fifty companies from the state of S\u00e3o Paulo, Brazil. The results showed that the production cost\/ha of corn faces a high-cost risk, particularly when production and market conditions are characterized by high levels of volatility, uncertainty, complexity, and ambiguity. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops.<\/jats:p>","DOI":"10.3390\/app14178030","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T05:06:06Z","timestamp":1725858366000},"page":"8030","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1618-6316","authenticated-orcid":false,"given":"Fernando Rodrigues de","family":"Amorim","sequence":"first","affiliation":[{"name":"FATEC Sert\u00e3ozinho, Paula Souza State Center for Technological Education, Sert\u00e3ozinho 14170-120, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6907-7829","authenticated-orcid":false,"given":"Camila Carla","family":"Guimar\u00e3es","sequence":"additional","affiliation":[{"name":"FATEC Taquaritinga, Paula Souza State Center for Technological Education, Taquaritinga 15900-000, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3882-2491","authenticated-orcid":false,"given":"Paulo","family":"Afonso","sequence":"additional","affiliation":[{"name":"ALGORITMI, Department of Production and Systems, University of Minho, 4804-533 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0888-0685","authenticated-orcid":false,"given":"Maisa Sales Gama","family":"Tobias","sequence":"additional","affiliation":[{"name":"Institute of Technology, Federal University of Par\u00e1, Bel\u00e9m 66075-110, PA, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,8]]},"reference":[{"key":"ref_1","unstructured":"Arag\u00e3o, A., and Contini, E. 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