Food Twin: Stress Testing the U.S. Food System

Published in

Earth Genome

8 min readNov 16, 2023

Today the Plotline, the food climate and data community organized by Earth Genome, is launching Food Twin, a proof of concept digital twin of the United States food system. Food Twin is based on a custom model, built in collaboration with food system expert Zia Mehrabi, that maps production of grown foods in the United States to where these foods are consumed. This model reveals a brittle and centralized food system that is at a high risk to be impacted by climate change economic shocks.

Why Build a Food Twin?

Did you grow the food you ate for breakfast this morning? Do you know who did? I certainly didn’t and don’t know where it even came from. In one view, our society’s ability to abstract away activities like hunting and gathering is what defines progress; no longer are we spending our days worrying about where our next meal is coming from or storing food so we don’t go hungry over the winter. However, this abstraction also comes with real risks. The food system isn’t without cracks, but most of us don’t have a clear line of sight into where these are, who they will impact, or what will make them worse. As the impacts of climate change become more evident, it’s concerning that such a fundamental resource is so opaque.

The Food Twin developed by Zia Mehrabi and The Plotline in collaboration with Development Seed is a proof of concept digital twin of the United States food system designed to model and illustrate our food system from production through consumption. Recent events such as the COVID-19 pandemic and Russia’s invasion of Ukraine have revealed inherent gaps in the way food systems are modeled. Particularly, these events have exposed how shocks cascade through food systems and impact food security at an individual and community level. Digital twins of food systems hold massive potential to support decision making during and prior to food crises, including those caused or exacerbated by climate change.

The application visualizes a custom built model designed to predict the connection between where food is grown and where it is consumed. Each colored point moving on the map correlates to an amount of calories from that food group. The consumer view on the map display how the food consumed in a county travels to that region; in the producer view it shows where food that grows in that county is consumed and how it gets to that area.

Climate change is destabilizing our food systems across the world, and the US is not immune to this stressor. A missing piece of the puzzle is how extreme weather shocks, which are becoming more common, experienced in one location, are felt by consumers in other locations. In The Food Twin we model climate impacts for all major grown crops in the US. We couple these predictions with our food system model, to identify communities at risk at the other end of the supply chain when drought and heat wave events hit. The scenarios we represent are illustrative in nature, but highlight how digital twins can be used to evaluate how risks spread through food systems, affecting commodities and consumers across the nation.

Our Takeaways

The Food Twin project, although still just a proof of concept, offers several insights into the current state and future of the U.S. food system.

Our current system is brittle and centralized

One of the key takeaways from the Food Twin model is the inherent brittleness of the current food system. In our model, just 5.5% of U.S. counties are responsible for half of the country’s food production. Additionally, this system relies on a limited number of distribution centers and transport routes, further exacerbating its fragility. This centralization, and the just in time contracting environment on which so many consumers rely, creates vulnerabilities, particularly in the face of unexpected events like pandemics or geopolitical conflicts.

Vulnerability to climate change

The model vividly illustrates how weather is already impacting food production. For instance, Kern county, one of the counties producing the highest diversity of crops stands to lose almost 8% of its production in a heat stress event like the one we’ve modeled. With the predicted increases in extreme weather events and shifting climate patterns, these impacts are only expected to grow. This underscores the urgency of integrating climate resilience into food system planning and management. And critically highlights how shocks felt in one distant location propagate through the supply chain to consumers.

Need for diversification and localized solutions

The project points towards the need for more diversified food systems, and proactive policies to ensure consumers relying on brittle or fragile supply chains are buffered from shocks in the future By reducing reliance on a small number of food sources and distribution channels, the system can become more resilient to climate & economic shocks, and the impact felt by consumers buffered from shocks that happen further up in the supply chain

Model Description

For those interested in the technical details, here’s a short summary of how the model is built.

Production

The model that powers this tool uses the USDA’s Cropland Data Layer to generate an estimate of the types of crops that are grown in different regions across the United States. This data is converted from satellite data observations (pixels) to production using USDA Census of Agriculture state outputs from this study on crop stability and diversity. Production to calorie conversions, accounting for waste, as well as losses in conversion to non-plant commodities (such as crops used for animal feed or biofuels) are made using the balance sheets from the Food Availability (Per Capita) Data System. Imported foods are aggregated from the US Census Bureau data; the app shows imported foods as being sourced from counties where the port of entry is located.

Consumption

Consumption per county was calculated using food availability surveys from the USDA and paired with demographic data from the census. Census data used in the model include population, racial demographics, and poverty rates. We used this data to determine the demand for food groups in counties. Population, wealth, and racial demographics influence the types of food groups and amount of calories consumed.

Connecting supply and demand

Consumption and production data were connected via a matrix that took into account possible shipment routes between all counties in the United States. Each crop’s supply and demand was solved in the most efficient way possible based on minimizing travel time along roads, given the central role of trucking in moving food in the nation (trucks move 84% of agricultural freight and 88% of volume and nearly every trip from farm to fork includes at least one trucking step).The routes were calculated using a version of the OSRM algorithm.

Climate impacts

Reduced yield due to climate stress was generated using statistical crop yield models. Specifically, we estimated the impacts of weather (main growing season temperature, precipitation, standardized precipitation index, and days >30C) on 25+ crops and crop groups within the USA over the time series 1983–2016. Statistical crop yield models were fit to state level observations and the yearly impact of weather predicted at the county level. Predicted yields are compared for a given crop and county for a given year and weather observation to the predicted yield under the mean climate observed in that county, to identify the variation in yield due to weather. Illustrative drought and heat wave impacts were identified by drawing on the spatial distribution of drought and heat waves from 2012 and 2016, respectively, and enhancing the magnitude by 25%, to simulate what might be expected for consumers under a more extreme climate in the future.

More information

The Future of Food Twin

Although this model and application offer insights into the U.S food system, our next step at the Plotline is to build a global model. Food systems are interconnected; analyzing only a single country misses out on many of the rippling effects shocks can have. We’re actively convening experts, researchers, and enthusiasts to join this effort. Whether you’re a data scientist, a climate expert, or just someone passionate about food security, we’d be interested to hear how you would use a digital twin model like this.

Animal food products are the other major piece to this model that we want to explore in the future. A large percentage of land area and grown foods go towards meat and dairy products. Animal based proteins are not only some of the highest contributing factors to climate change, but also vulnerable food sources. Integrating non-plant commodities explicitly in our representation is certainly a next step, as is increased resolution on different modes of transport and representation of the supply chain surrounding particular products.

Beyond the model itself, we at Earth Genome believe in the power of open data and community involvement. By making our data and findings accessible, we hope to foster a broader conversation about food security and sustainability. We encourage you to share this information within your networks and start dialogues about food sourcing and food system solutions. If you want to get involved here are a few ways we’d love to collaborate:

Engage with the data: Explore the data we’ve compiled. Use it to inform your understanding and start conversations about food systems in your community.
Spread the word: Share information about Food Twin and its insights. The more people are aware of how food systems function, the more we can be insightful as a society as a whole to build resilience.
Support your local community: Where does your food come from? How can you build more resilient food systems for your friends and family? Start asking these questions and exploring ways you can strengthen your local food system against shocks in future.

The Food Twin project is more than just a digital model; it’s a step towards a more resilient, sustainable, and equitable food future. Join us in this crucial effort to reshape how we understand and interact with our food systems. Here’s to a future where we know where our breakfast came from and can have confidence it will be there tomorrow.