Mapping Michigan’s Foodshed


Recent analysis of USDA data shows that agriculture covers 1/5 of US land. But the land used to grow food takes up an even smaller proportion–just 20% of all cropland is used to grow the food that we eat.

                                                                            Image source: Bloomberg, 2018

In most states in the Midwest, a typical farm produces inputs–corn, soybeans, and hogs–for industrial production.

US agricultural diversity clusters

                                                                  Source: Columbia University – Urban Design Lab: Link here 

Nationally, more than 30% of the fresh fruits and vegetables Americans consume come from other countries. In 2013, Michigan’s agricultural sector exported over $425 million fruit and vegetable commodity crops, not including other grain crops or livestock [i].

Regionalizing the food system is one way to lower the use of fossil fuels known to contribute to climate change. Furthermore, selling produce locally could strengthen local economies and has the potential to support food access. For years, Ken Meter’s work has demonstrated the ways in which commodity farming extracts wealth from rural communities, who must then spend heavily on imported foods. Alternatively, Meter has demonstrated through multiple case studies how developing local markets could improve local economies, particularly in midwestern farm-states [ii].

Given the diversity of food products Michigan already produces, the negative impact of transportation on our environment, the need to increase statewide employment, as well as the ongoing challenge of food insecurity in the state, the FAIM Project team wondered, “Could Michigan feed itself?”


What is a Foodshed?

To begin to answer that question, the FAIM Project team defined the state of Michigan as the foodshed we would focus on. A foodshed describes the flow of food from seed to table across a specific geographic region. It is commonly thought of as the delineation of the area of land needed to produce food for a certain population [iii]. For instance, a foodshed for an individual person is the land required to produce food for that person to eat. Generally speaking, we want to know whether a particular foodshed can feed a larger population – a city, a region, a state. In order to understand if this is possible, we identified the population that needs to be fed, the dietary requirements of all individuals in that population, and determined the growing capacity of the available agricultural land.

In our study we asked whether the landscape of Michigan can produce enough food for an adequate diet for every Michigander for an entire year.

Previous Work

A number of other researchers have also been asking similar questions: 

– Andrew Zumkehr and J. Elliott Campbell (2015) considered the potential for local croplands to meet US food demand. In their study examining the potential local foodsheds to feed the US population, they found that 90% of Americans could be fed entirely by food grown or raised within 100 miles of their homes, and 100% of the population in the Midwest could be fed from within local-regional foodsheds [iv]. They note that the limitations of a foodshed are largely not agronomic capacity, but instead due to a lack of political support needed to increase the capacity of localization efforts.   

– Researchers at the University of Chicago (Schuble, Bowen & Martin, 2011) examined the footprint required to feed cities like Chicago in their Feeding the City project. Using a model that only considered vegetable and grain production and already cultivated land, they found that Chicago could adequately feed itself within an 80-mile buffer [v].    

Percent of population that could be fed within a 50 mile foodshed, by population center 

Source: Zumkehr & Campbell, 2015: Link here 

     Feeding the city: Chicago foodshed analysis

                           Source: Schuble et al, 2011: Link here 

Additionally, Christian Peters has conducted seminal foodshed analyses, developing methods for mapping potential foodsheds and the land areas that could theoretically feed urban centers. His analysis of New York State (2009) found that it could provide 34% of its total food needs within an average distance of just 30.4 miles. However, because the model did not allocate production potential evenly, most cities could have the majority of their food needs sourced in-state, except for the greater New York City area, which must draw on more distant food-producing regions [vi, vii].


FAIM Foodshed Analysis 


To answer the question – “Can Michigan feed itself?” – the FAIM Project’s spatial analysts turned to quantitative methods in the literature, which is largely dominated by the work of researcher Christian Peters at Tufts University. Over the course of 15 years, Peters’ methodology has created sound estimates of production and consumption, elaborately constructed from best available datasets, average American diet requirements, and calibrated by actual crop yield data for the state of New York.

FAIM Project spatial researchers sought to build off of Peters’ methodology, but explicitly focused on the best available data for Michigan. To create a model of Michigan’s foodshed capabilities, we needed to:

(1) Spatially estimate Michigan’s population and the dietary requirements to feed it
(2) Spatially estimate the growing capacity of Michigan’s agricultural land
(3) Compare these estimates to consider whether the land has the capacity to feed the population

Model of foodshed analysis methodology

How much food is needed to feed the population of Michigan? In order to estimate the food needed to feed Michigan’s population we need two pieces of information: a population estimate, and an estimate of the amount of food consumed by one person for a year. We used the LandScan population estimate from Oak Ridge National Laboratory to create a population estimate of where people are located across the state on an average day. To estimate consumption needs, we used an average diet that someone from the Midwest/Northeast of the United States would follow to meet the USDA recommended food pyramid, which assumes the consumption of fruit, vegetables, grains, meat, and dairy products. With this information we estimated the average Michigander’s consumption needs over a one year time span. The units of this estimate are called Human Nutritional Equivalent (HNE), with 1 HNE representing a weight of food calories that 1 Michigander would need over a year. Finally, within the model the HNE needed per person is inflated by 25% to account for food waste that occurs on the path from farm to table.

Michigan urban area populations*

How much food can Michigan’s agricultural land produce? In order to estimate how much agricultural land is needed to fulfill the dietary needs of the state, we needed to calculate the amount and types of available agricultural land, and the types of crops that land could produce. Potential landscape productivity was based on soil type and was then adjusted based on a suggested crop rotation for that soil (i.e. you shouldn’t grow corn in the same spot every year). Soil productivity was also calibrated based upon actual crop yield data from recent years in the state of Michigan, as well as the effect of statewide annual average rainfall. We then proportioned the food types needed to fulfill the average diet (considered above) into two groups based on how those food types are produced—annual and perennial crops. For example, following Peters’ methodology, fruit was considered an annual crop grown for direct consumption, while hay, grown to feed livestock, was considered a perennial crop. To be able to compare this estimate to our population consumption estimate above, the agricultural land production potential was also expressed in Human Nutritional Equivalent (HNE) units.

Soil data used for yield estimates

Estimating foodshed capacity: Once we had spatial estimates of consumption and production across the Michigan landscape in similar units of Human Nutritional Equivalent, we could then map those estimates and directly compare whether or not the production potential could meet the consumption needs across the state. We modeled this relationship at a statewide scale by summing the values of each across the entire state, as well as regionally. Given that areas of low population will tend to have low consumption needs and more production than they need locally, we can begin to envision regional foodsheds where that surplus is earmarked for a nearby area with a higher consumption demand. 

In our final step, we created a network analysis where we allocated the state’s agricultural land’s production estimate to each urban area’s dietary consumption estimate, in order to assess whether the production potential could fulfill the population’s dietary demand.

Production & Consumption estimates, in HNE

What We Found

Our conservative estimate based on this model shows Michigan theoretically can produce about 1.3 times more food than it needs–and that should we choose to source our food locally– Michigan can definitely feed itself! In fact, depending on how aggressive of a rotation cycle (e.g. maximizing the number of years in a decade that your land produces annual crops), and the amount of additional land that could be put into production (e.g. land in urban or rural areas that currently does not account for any production yields) Michigan has the potential to produce nearly twice its food needs. 



This finding is important as it describes a possibility that we could strive for as a state. However, there are some limitations to keep in mind. In considering the production potential and the consumption needs, we do not take into account how the food that is grown is processed and transported to consumers. We also do not account for transportation infrastructure in this model–a significant limitation given the decades of disinvestment in spaces for processing locally produced foods, and as well as distribution systems for local food. Additionally, the iteration of the model does not account for the sustainability of different growing practices, which is key to consider as we forecast future potential. For instance, research colleagues at the University of Michigan have shown organic practices can yield at least equivalent amounts or more than conventional practices (Badgley et al, 2007) [viii]. And finally, the model also doesn’t address the legacy of discriminatory policies and practices that have created inequities and make it that much more challenging for small farms and farmers of color in the state to get their product to market.


Future Directions

The Michigan foodshed analysis shows us that Michigan has potential to feed itself with food grown in the state. Now, we must grapple with the reality of existing barriers within our current supply chain, as indicated by small farmers and farmers of color across the state, and the small retailers looking to sell locally grown food.


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Interested in exploring your community’s foodshed?  Check out the FAIM Project interactive Foodshed StoryMap


[i] USDA-NASS Great Lakes Region. Michigan Agricultural Statistics 2014-2015. Link:

[ii] Meter, K. Local Farm & Food Economy Studies. Crossroads Resource Center. Retrieved from:

[iii] The Foodprints and Foodsheds Project. Footprints and foodsheds page. Link:

[iv] Zumkehr & Campbell (2015) The potential for local croplands to meet US food demand. Frontiers in Ecology and the Environment, 13(5):244-248.  Link:

[v] Schuble et al (2011) Modeling the relationship between food, energy, and environmental impacts. Link:

[vi] Peters CJ, Bills NL, Lembo AJ, Wilkins JL, & Fick GW (2009). Mapping potential foodsheds in New York State: A spatial model for evaluating the capacity to localize food production. Renewable Agriculture and Food Systems 24(1): 72-84. Link:

[vii] Peters CJ (2013) Capacity for meeting food needs with local and regional production: Tales from the Northeast United States. Presentation at OSU. Link:

[viii]  Badgley C, Moghtader J, et al (2007) Organic agriculture and the global food supply. Renewable Agriculture and Food Systems, 22(2): 86-108. Link: