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<p>Soybean farming—providing protein-rich feed for farm animals worldwide—is the third largest driver of tropical deforestation and expanding. Importing economies are considering regulating the trade of soybeans and other deforestation-driving commodities, and trading companies will be required to conduct due diligence to ensure compliance. However, complex supply chains obscure provenance, and origin declarations may be falsified. Here, leveraging Gaussian Process modelling and a georeferenced dataset of isotopic and elemental composition of soybeans from across the main soy growing areas of South America, we identify soybean origin to within 192.52 ( ± 23.51) kilometres from the true harvest location. The average 95% Credible Regions reduces prediction uncertainty to within 3.8% of the area considered for prediction. Our spatially explicit model is a leap forward in commodity traceability, enabling both origin determination and verification of origin claims in true geographical space. Applicable to many commodities, this framework provides transparency regardless of supply-chain complexity, and facilitates effective regulation of commodity supply chains to tackle illegal deforestation.</p>

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High-resolution soybean tracing for deforestation-free supply chains

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月13日

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  • Roi Maor,
  • Jakub Truszkowski,
  • Francesca Ablett,
  • Henry Jennings,
  • Heather Walker,
  • Jessica Dunn,
  • Marigold Norman,
  • Rosario Carrasco,
  • Marysol Jaime-Arteaga,
  • Isabella Miles-Bunch,
  • Lauren Phelan,
  • Lydia Prior,
  • Alexandre Antonelli,
  • Paul Wilkin,
  • Jade Saunders,
  • Victor Deklerck,
  • Caspar C. C. Chater

摘要

Soybean farming—providing protein-rich feed for farm animals worldwide—is the third largest driver of tropical deforestation and expanding. Importing economies are considering regulating the trade of soybeans and other deforestation-driving commodities, and trading companies will be required to conduct due diligence to ensure compliance. However, complex supply chains obscure provenance, and origin declarations may be falsified. Here, leveraging Gaussian Process modelling and a georeferenced dataset of isotopic and elemental composition of soybeans from across the main soy growing areas of South America, we identify soybean origin to within 192.52 ( ± 23.51) kilometres from the true harvest location. The average 95% Credible Regions reduces prediction uncertainty to within 3.8% of the area considered for prediction. Our spatially explicit model is a leap forward in commodity traceability, enabling both origin determination and verification of origin claims in true geographical space. Applicable to many commodities, this framework provides transparency regardless of supply-chain complexity, and facilitates effective regulation of commodity supply chains to tackle illegal deforestation.