FFAR Grant Uses Supply-chain Modeling to Reduce Environmental Impacts in Dairy Industry

  • Advanced Animal Systems

WASHINGTON (November 13, 2019)— Dairy farming is an important component of the overall agricultural economy, however, both feed and milk production also release greenhouse gas emissions (GHG) and impact local water bodies. Dairy’s environmental impacts differ locally across the country. Models that combine distribution networks and environmental data can help users of dairy products identify opportunities for achieving sustainability goals. The Foundation for Food & Agriculture Research (FFAR) awarded World Wildlife Fund a $65,000 grant to develop localized sustainability data for the dairy value chain in collaboration with the University of Minnesota’s NorthStar Initiative for Sustainable Enterprise (NorthStar). Matching funding was provided by the Walton Family Foundation and the McDonald’s Corporation for a $130,000 total award.

Currently, research models have provided insights into environmental impacts of the dairy industry at the national level. These data may include information about the environmental impacts of feed production, dairy processing and distribution of products. While national datasets are helpful in determining opportunities for improvement, they do not reflect significant regional/geographic differences across the United States. To identify local solutions that improve efficiency and environmental sustainability, information on environmental impacts at a local level are needed.

Localized environmental data provides dairy stakeholders and companies with information to help them prioritize sustainability solutions. In addition, the Food System Supply-chain Sustainability model (FoodS3) allows companies to understand the overlap in supply chains and tackle environmental impacts of dairy production together in prioritized geographic regions. Sally Rockey, Ph.D.
Executive Director Emeritus

World Wildlife Fund and University of Minnesota’s NorthStar are developing a spatial model that links dairy feed, milk and associated environmental impacts at the county-level. To achieve this, researchers are expanding the FoodS3 model, a technology that estimates subnational commodity flows  of crops like corn, wheat and soy, to include dairy production. The model provides an estimated environmental impact of production for each county and enables companies that use dairy inputs to prioritize and focus solutions to reduce their environmental footprint.

“The FoodS3 model will provide opportunities for collaboration across dairy supply chains amongst companies, co-ops and other stakeholders to reduce GHG emissions and water impacts at scale,” said Ms. Sandra Vijn, the principal investigator of this project. “It will also help map the environmental impacts of dairy feed production, which will support the dairy and wider agricultural community with implementation of solutions to conserve natural resources such as soils, biodiversity and water.”

This research will allow the dairy industry to develop targeted interventions to reduce environmental impacts across supply chains and milk-producing regions. This project assesses GHG emissions and irrigated water use in US dairy supply chains. Dairy industry stakeholders will be able to use the FoodS3 model and the published data, to set baselines, prioritize areas for GHG emission and water reduction efforts and implement solutions.

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Foundation for Food & Agriculture Research

The Foundation for Food & Agriculture Research (FFAR) builds public-private partnerships to fund bold research addressing big food and agriculture challenges. FFAR was established in the 2014 Farm Bill to increase public agriculture research investments, fill knowledge gaps and complement the U.S. Department of Agriculture’s research agenda. FFAR’s model matches federal funding from Congress with private funding, delivering a powerful return on taxpayer investment. Through collaboration and partnerships, FFAR advances actionable science benefiting farmers, consumers and the environment.

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