Food Systems Supply-chain Sustainability tool

powered by NiSE at IonE, University of Minnesota


FoodS3 is a data platform and a multi-stage supply chain model that estimates upstream sustainability performance of “high impact” food supply chains in the U.S. Currently, the tool estimates how corn and soy meal feed travels through the farm-to-feed-to-food pipeline in the U.S.

In other words, FoodS3 reveals previously hidden relationships within US agricultural supply chains and helps to identify where opportunities may exist to improve sustainability performance. The tool, today, focuses on animal protein supply chains, linking downstream processing locations to upstream animal production locations and their associated corn, soy and distillers grains feed sheds. At each stage, locationally explicit sustainability indicators are calculated and aggregated to reflect firm-specific supply networks.

Use the FoodS3 Demo

FoodS3 Demo Available for Trial

The University of Minnesota’s Northstar Initiative for Sustainable Enterprise, along with partners at the Environmental Defense Fund, are making significant portions of the FoodS3 tool available to demonstrate its capabilities, encourage its use and further its development.

Follow the link below to ‘demo’ the tool, and don’t hesitate to contact us with questions or suggestions.

Dashboard Dashboard (mobile)

How FoodS3 works

Advancing Commodity Mobility Modeling

A number of approaches to estimate sub-national commodity mobility have been developed through the FoodS3 platform. While international trade data is readily available and increasingly linked to sustainability indicators, traceability across highly variable production-consumption systems within countries is not easily achieved.

Researchers at the University of Minnesota’s Northstar Initiative for Sustainable Enterprise (NiSE) have developed a set of spatial optimization models, leveraging the best available science, which estimate likely mobility patterns from where commodities are grown, raised and processed to where they are consumed.

Developing New Spatial-Temporal Sustainability Indicators across Supply Networks

Broad scientific and practitioner agreement exists around the impact of food systems on local and global sustainability. Existing approaches to measure these impacts, however, tend to rely on broad industry-level assumptions, highly aggregated national data, and highly variable inputs associated with specific case-based processes. These limitations are particularly amplified for systems that are subject to highly heterogeneous inputs and/or outputs across agricultural supply landscapes.

Policy-makers and non-profit advocacy organizations are increasingly looking to engage actors across food supply chains to encourage conservation and environmental impact reduction. Unfortunately, traceability across complex, heterogeneous supply has hindered these efforts. FoodS3 is unique in that it links spatially explicit sustainability indicators of production to specific demand locations. In drilling down to sub-national levels of environmental and social impacts that occur over heterogeneous areas and aggregating these landscape impacts across supply networks, targeted opportunities for improvements can be identified.

Leveraging Comprehensive Public Data

All data used and presented in this demonstration of FoodS3 are from public sources or readily attainable information from a combination of industry reports, corporate reporting and satellite imaging.

More specifically, the supply of, and demand for, U.S. corn and soy currently developed in FoodS3 is estimated at the county level for years 2007 and 2012, based on available data in the two most recent Census of Agriculture (COA) reports from the U.S. Department of Agriculture. To ensure internal consistency between supply and demand, FoodS3 uses a single national dataset of feed production and consumption from the Economic Research Service (ERS) of the USDA. COA and the USDA Federal Grain Inspection Service. Supply and demand associated with for other key categories were allocated with secondary processing facilities also leverages publically accessible data from industry, company and satellite imaging sources.

While the current demonstration presents estimates of annual baseline production-consumption systems based on the most current publicly available data, we anticipate that FoodS3 will be able to increasingly incorporate private user data to explore impacts of managerial or policy decisions.


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