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The origin, supply chain, and deforestation risk of Brazil's beef exports.

Erasmus K H J Zu ErmgassenJavier GodarMichael J LathuillièrePernilla LöfgrenToby GardnerAndré VasconcelosPatrick Meyfroidt
Published in: Proceedings of the National Academy of Sciences of the United States of America (2020)
Though the international trade in agricultural commodities is worth more than $1.6 trillion/year, we still have a poor understanding of the supply chains connecting places of production and consumption and the socioeconomic and environmental impacts of this trade. In this study, we provide a wall-to-wall subnational map of the origin and supply chain of Brazilian meat, offal, and live cattle exports from 2015 to 2017, a trade worth more than $5.4 billion/year. Brazil is the world's largest beef exporter, exporting approximately one-fifth of its production, and the sector has a notable environmental footprint, linked to one-fifth of all commodity-driven deforestation across the tropics. By combining official per-shipment trade records, slaughterhouse export licenses, subnational agricultural statistics, and data on the origin of cattle per slaughterhouse, we mapped the flow of cattle from more than 2,800 municipalities where cattle were raised to 152 exporting slaughterhouses where they were slaughtered, via the 204 exporting and 3,383 importing companies handling that trade, and finally to 152 importing countries. We find stark differences in the subnational origin of the sourcing of different actors and link this supply chain mapping to spatially explicit data on cattle-associated deforestation, to estimate the "deforestation risk" (in hectares/year) of each supply chain actor over time. Our results provide an unprecedented insight into the global trade of a deforestation-risk commodity and demonstrate the potential for improved supply chain transparency based on currently available data.
Keyphrases
  • human health
  • electronic health record
  • big data
  • risk assessment
  • heavy metals
  • high resolution
  • mass spectrometry
  • artificial intelligence
  • high density