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Advancements in Soybean Price Forecasting: Impact of AI and Critical Research Gaps in Global Markets

Author

Listed:
  • Fernando Dupin da Cunha Mello

    (Stricto Sensu Department, SENAI CIMATEC, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil)

  • Prashant Kumar

    (Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK)

  • Erick G. Sperandio Nascimento

    (Stricto Sensu Department, SENAI CIMATEC, Av. Orlando Gomes, 1845, Salvador 41650-010, BA, Brazil
    Surrey Institute for People-Centred AI, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK)

Abstract

Soybeans, a vital source of protein for animal feed and an essential industrial raw material, are the most traded agricultural commodity worldwide. Accurate price forecasting is crucial for maintaining a resilient global food supply chain and has significant implications for agricultural economics and policymaking. This review examines over 100 soybean price forecast models published in the last decade, evaluating them based on the specific markets they target—futures or spot—while highlighting how differences between these markets influence critical model design decisions. The models are also classified into AI-powered and traditional categories, with an initial aim to conduct a statistical analysis comparing the performance of these two groups. This process unveiled a fundamental gap in best practices, particularly regarding the use of common benchmarks and standardised performance metrics, which limits the ability to make meaningful cross-study comparisons. Finally, this study underscores another important research gap: the lack of models forecasting soybean futures prices in Brazil, the world’s largest producer and exporter. These insights provide valuable guidance for researchers, market participants, and policymakers in agricultural economics.

Suggested Citation

  • Fernando Dupin da Cunha Mello & Prashant Kumar & Erick G. Sperandio Nascimento, 2024. "Advancements in Soybean Price Forecasting: Impact of AI and Critical Research Gaps in Global Markets," Economies, MDPI, vol. 12(11), pages 1-24, November.
    • Handle: RePEc:gam:jecomi:v:12:y:2024:i:11:p:310-:d:1522019
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    References listed on IDEAS

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    2. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    3. Raghav Goyal & Michael K. Adjemian, 2023. "Information rigidities in USDA crop production forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(5), pages 1405-1425, October.
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