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Tracing Nitrogen Flows Associated with Beef Supply Chains: A Consumption-Based Assessment.

Anaís OstroskiOleg A ProkopyevVikas Khanna
Published in: Environmental science & technology (2024)
While highly connected food chains provide numerous benefits, they lack traceability and transparency. As such, understanding the spatial heterogeneity in their environmental burdens is critical for targeted interventions. This is especially important for nutrient-related impacts such as nitrogen since the release of reactive nitrogen has been linked to loss of biodiversity and decrease in water quality in different parts of the world. Animal feed production is heavily dependent on synthetic fertilizers, and the consumption of beef products, in particular, is associated with high nitrogen footprints. Although there is a rich body of work on nutrient footprints of beef production, there is a gap in understanding the spatial distribution of the nutrient releases throughout the beef supply chain in the U.S. We present an optimization-based framework to trace supply chain networks of beef products at the county level. Using publicly available data, we construct a weighted network of nutrient flows based on mass balance, including synthetic fertilizers, manure production, and crop uptake and residues. The results show that beef consumption in a county can be associated with nitrogen losses in hundreds of counties. One year worth of beef consumption in the United States released approximately 1.33 teragrams (Tg) of N to the environment, and most of it as diffuse pollution during the feed production phase. Analysis also revealed the huge disparity between consumption-based and production-based impacts of beef and the need for considering consumption-based accounting in discourse around the environmental sustainability of food systems.
Keyphrases
  • human health
  • water quality
  • heavy metals
  • physical activity
  • single cell
  • magnetic resonance imaging
  • drug delivery
  • wastewater treatment
  • electronic health record
  • artificial intelligence
  • health risk assessment