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The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis

Author

Listed:
  • Karina Braga Marsola

    (Agroindustrial Logistics and Commercialization Laboratory, School of Agricultural Engineering, University of Campinas, Av. Cândido Rondon, 501, Campinas 13083-875, SP, Brazil)

  • Andréa Leda Ramos de Oliveira

    (Agroindustrial Logistics and Commercialization Laboratory, School of Agricultural Engineering, University of Campinas, Av. Cândido Rondon, 501, Campinas 13083-875, SP, Brazil)

  • Matheus Yasuo Ribeiro Utino

    (Institute of Mathematical and Computer Sciences, University of São Paulo, Av. Trab. São Carlense, 400, São Carlos 13566-590, SP, Brazil)

  • Paulo Mann

    (Institute of Mathematics and Statistics, Rio de Janeiro State University, Av. São Francisco Xavier, 524, Rio de Janeiro 20550-013, RJ, Brazil)

  • Thayane Caroline Oliveira da Conceição

    (Agroindustrial Logistics and Commercialization Laboratory, School of Agricultural Engineering, University of Campinas, Av. Cândido Rondon, 501, Campinas 13083-875, SP, Brazil)

Abstract

Predicting road freight prices is a challenging task influenced by multiple factors. Understanding which variables have the greatest impact is essential for building more accurate models, and consequently for enhancing the competitiveness of Brazilian soybeans in the global market. This study aims to evaluate the influence of different exogenous variables on soybean freight prices and to analyze how this influence varies across different distance ranges. To achieve this, a combination of machine learning techniques was applied to a comprehensive dataset containing information on freight costs, regional characteristics, production, fuel prices, storage, and commercialization. The results indicate that distance is the most significant variable in determining freight costs, directly reflecting operational expenses such as fuel consumption and labor costs. Additionally, macroeconomic factors such as the exchange rate and export volume play a crucial role, highlighting the global context of Brazil’s soybean exports. Stratified analysis by distance ranges reveals distinct patterns; short-distance freight is predominantly related to domestic markets, while medium- and long-distance freight are strongly linked to export logistics.

Suggested Citation

  • Karina Braga Marsola & Andréa Leda Ramos de Oliveira & Matheus Yasuo Ribeiro Utino & Paulo Mann & Thayane Caroline Oliveira da Conceição, 2025. "The Impact of Exogenous Variables on Soybean Freight: A Machine Learning Analysis," Sustainability, MDPI, vol. 17(3), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1067-:d:1578876
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    References listed on IDEAS

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