The global food system is increasingly interconnected and under pressure to support growing demand. At the same time, crop production is facing new and uncertain impacts from climate change. To date, understanding how downstream supply chain actors, such as commodity traders, are exposed to climate change risks has been difficult due to a lack of high-resolution climate and trade data.

However, the recent availability of supply chain data linking subnational production to downstream actors, and gridded projections of crop yield under climate change, allows us to assess individual commodity trader exposure to long-term climate change risk.

Such an analysis is applied to soy production in Brazil, the world’s largest soy exporter. Whilst uncertainty across crop models’ yield projections means it remains difficult to accurately predict how production across the region will be affected by climate change,  the authors demonstrate that the risk exposure of trading actors differs substantially due to the heterogeneity in their sourcing.

This study offers a first attempt to analyze subnational climate risk to individual trading actors operating across an entire production landscape, leading to more precise risk exposure analysis. With sufficient subnational data, this method can be applied to any crop and country combination, and in the context of wider food security issues, it will be pertinent to apply these methods across other production systems and downstream actors in the food system.