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Assessing Current Local Capacity for Agrifood Production to meet Household Demand: Analyzing Select Food Commodities across 377 US Metropolitan Areas Peter Nixon, and Anu Ramaswami Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06462 • Publication Date (Web): 20 Jul 2018 Downloaded from http://pubs.acs.org on August 26, 2018

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Environmental Science & Technology

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Assessing Current Local Capacity for Agrifood

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Production to meet Household Demand: Analyzing

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Select Food Commodities across 377 US

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Metropolitan Areas

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Peter A. Nixon*, Anu Ramaswami.

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Science, Technology, and Environmental Policy, Humphrey School of Public Affairs, University

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of Minnesota – Twin Cities, Minneapolis, Minnesota, 55455

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Department of Bioproducts and Biosystems Engineering, College of Farming, Agriculture, and

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Natural Sciences, University of Minnesota – Twin Cities, St. Paul, Minnesota, 55108

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KEYWORDS

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Local Food, Localization, Current Local Capacity, Urban Food Systems

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ACS Paragon Plus Environment

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ABSTRACT

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Increasing food production in local urban and peri-urban areas is articulated as a potential way

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for local governments to

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human health). However, scientific judgements on localization are difficult to make because the

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degree of current food localization has not been systematically measured or defined across large

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numbers of cities.

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production to meet total household agrifood demand, harmonizing bottom-up and top-down

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approaches to assess direct-plus-embodied agrifood demand of both fresh and processed foods.

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We find unique patterns of localization for different agrifoods, with 21% of US metropolitan

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areas (MSAs)

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embodied in diet, versus only 12% and 16% of MSAs self-sufficient in fruits and vegetables

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respectively. Focusing only on the direct fresh food demands, we find increased current local

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capacity (e.g. 45% MSAs self-sufficient in direct fluid milk demand). Finally, multivariable

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analysis finds that state policies that promote urban agriculture may influence greater

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localization, which, interestingly, is independent of population density. Such spatial demand-

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production analysis is the first step in informing sustainable city/ or regional food policies, and

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envisioning spatial food supply chains to urban areas.

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TOC Art

achieve multiple sustainability outcomes (environmental, social, and

We develop new methods to quantify current local capacity for food

currently capable of local self-sufficiency for eggs- and milk-equivalents


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INTRODUCTION

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Food systems1 encompass the linkages between food production and consumption, the

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institutions and people that govern them, and the associated multiple sustainability outcomes

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(e.g. environmental, economy, equity, human health and wellbeing as noted in the UN

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Sustainability Goals Framework2). Food systems are critical to the wellbeing of people and the

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planet, as they seek to provide essential nutrition to the 7.3 billion people living around the

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world3. At the same time, globally, food systems presently contribute an estimated 24% of total

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GHG emissions4, and affect regional water quality5, water withdrawal6, nutrient flows7, and land

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use change8, thus contributing to numerous environmental impacts. The global food system also

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contributes to an annual 36 million diet-related premature deaths globally either in the form of

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malnutrition or poor diets9. In the USA, agriculture consumes one third of all withdrawn water10,

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while the US food system as a whole uses 15.7% of national energy consumption11. Health risks

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from poor diet are estimated to contribute to over 500,000 annual premature deaths in USA 12. .

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With 80% of the US population living in urban areas13, cities play an important role in

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food systems as they affect human health and environmental sustainability14 - a fact not lost on

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urban governments, with city collectives launching major urban food focused initiatives15–17.

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Recently, 138 major cities (as of the time of this paper) have signed onto the Milan Urban Food

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Policy Pact (2015), which includes (among other actions) a focus on more local food production

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in urban and peri-urban areas. Such localization is articulated by several cities as a potential way

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to achieve multiple sustainability outcomes, including local food security, environmental benefits

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such as GHG mitigation from reduced food transport, local self-reliance and city-resilience18,19.


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However, scientific judgements on the potential benefits of localization are difficult to

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make because localization has not been systematically measured or defined across a large

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number of cities, and urban areas. The environmental and sustainability benefits of local food

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production are also contested and varied.

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estimated to be relatively small (of the order of 10%20), although this can vary by food item.

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More broadly, data on supply-chains and life cycle assessments are needed to assess

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environmental benefits of locally grown produce21,22versus conventionally-sourced foods. For

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example, increasing local food production in already water-stressed cities can increase local

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water stress23,24, and may result in further water pollution in urban areas25. On the other hand,

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urban agriculture may improve local access to fresh food to the underserved in urban areas26,27,

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potentially mitigate urban heat island and flooding risks28, improve subjective wellbeing29, and

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enhance economic development of local food industries30. Evaluating many of these potential

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benefits requires knowledge of the current extent of local food production versus household food

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demand.

GHG benefits of reducing freight transport are

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Yet, methods to estimate local household demand for food, covering both direct agrifood

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demand (e.g. eggs eaten as scrambled eggs) as well as agrifoods embodied in processed foods

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(e.g. eggs consumed in bread or pasta) are not available at the subnational level, in ways that are

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consistently aligned with national total consumption and production data.

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often lack food-item granularity (e.g. Consumer Expenditure Surveys31; Food Availability

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Dataset32), while more granular datasets (National Health and Nutrition Examination and

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Survey33) have not been harmonized with national apparent food use. Furthermore, most local

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governments do not know the specifics of what crops are being grown in the county they reside

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in or how best to quantify localization. The focus of this paper is to quantify localization for


Available datasets

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select agrifood items in a consistent and systematic manner across all US metropolitan areas to

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better inform local food policy initiatives. and the design of future sustainable urban food

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systems.

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Prior efforts to study localization of agriculture in urban areas have taken two broad

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approaches: foodshed analysis and local capacity assessment. Foodshed analysis connects local

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demands for agrifoods through supply chains to production regions; they do not assess other

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local production (for export) occurring within a boundary which may be of interest to local

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policymakers. Foodshed analysis requires robust supply chain data, frequently lacking at the

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subnational level. In contrast, local capacity assessments seek to quantify the degree to which

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local production can satisfy local demand, focusing on the capacity to meet, rather than actually

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meeting, local demand.

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Local capacity studies have been conducted in many different ways (Table S1). Some

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studies focus on current local production34–36, while others use land-use models to predict

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maximum hypothetical potential for local production to meet local demand37,38 referred to by

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those authors as the “local potential”. Additionally, local demand is computed in different

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ways39 (e.g. households only, or household plus industrial use). Local household agrifood

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demand can also be computed in different ways, including direct only (i.e. agrifoods that are

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consumed “fresh” or with minimal processing) and direct-plus-embodied agrifood demand.

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Finally, the production boundaries to assess local capacity vary from within the administrative

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boundary of urban areas to 100-mile radius around cities, to regional approaches ranging from

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clusters of counties to multiple states. For example, US federal legislation defines local as

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grown within 400 miles to the point of consumption or within the same state, while consumers

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generally consider local to be defined as food grown within 100 miles or less40. Table S1


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clarifies the varying assumptions seen across several foodshed and local capacity studies

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undertaken in recent years.

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Table S1 shows that only 10-20 studies have conducted local capacity assessments at the

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urban-scale (defined by administrative boundaries) in the US and Canada, often using different

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approaches to quantify the localization of food, and with varying definitions of local production

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and local demand. Only one study, Zumkehr 201537, has addressed future local potential of US

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metropolitan areas, optimizing future local production around urban areas to maximize self-

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sufficiency across broad food categories (grains, eggs, fruits, meats, etc.)

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This paper contributes to the emerging discourse on localization of food production to

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meet urban demand by focusing on current local capacity (CLC) (as opposed to future local

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potential37 ), and its sensitivity to the different methodologies of estimating local production and

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local demand. We quantify the CLC of all 377 US metropolitan areas across the conterminous

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US for four major food items, by developing a novel methodology that provides customized data

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to individual urban areas, consistent with national production-consumption totals. The method

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also quantifies direct and embodied agrifood demand, allowing for a more realistic and nuanced

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analysis of current local food capacity. We study four select agrifood commodities that represent

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a diverse swathe of the average American diet, i.e., fruits, vegetables, dairy (in raw milk-

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equivalents), and eggs, and are often the focus of urban food localization efforts. We also explore

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potential correlations of CLC with various urban sociodemographic, environmental, and policy

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parameters.

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The data and analysis in this paper enables visualizing and quantifying spatial demand-

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supply capacities for these select agrifood items, assessing the similarities and differences in


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current local capacity among the different items across urban areas in the nation. It also provides

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a large sample size required for testing various hypotheses about localization, such as – are larger

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cities (by area or population) more self-reliant on these items? Are denser cities less self-reliant?

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Do local and state policies or demographic parameters in the different metropolitan areas

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significantly impact current localization capacity? Most important, this dataset is public,

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providing local level policy makers with standardized data about demand and supply in and

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around their MSA, upon which the impacts of initiatives such as expanding urban agriculture or

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backyard gardens can be evaluated.

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METHODS

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We define agrifood as outputs of the agriculture and animal production sectors (of the

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NAICS industry classification41) that are subsequently consumed either directly or processed and

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embodied in various food items eaten. Current Local Capacity (CLC) for a certain agrifood item

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is calculated by dividing the total “local” production of that agrifood item (within the area

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designated local) by the local demand. We focus on current local capacity – as it is in the present

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time.

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() =

!"

(Eq. 1)

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CLC thus represents the balance between current local production and current local

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demand, both represented in agri-food equivalent units; however, local production may not feed

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current local demand with today’s supply chain configurations, hence the terminology of current

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local capacity. The steps in computing CLC are described below.

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First, the region to be analyzed is clearly defined (i.e. cities, counties, states, etc.). This study

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focused on major metropolitan statistical areas (MSAs) of the conterminous US (Census), which

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are aggregated counties containing a total population of 50,000 or more, centered around at least

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one large urban core. To reflect previous foodshed and local capacity efforts, only household

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demand was considered for CLC calculations in Equation 1, consistent with the goal of

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nutritional self-sufficiency. We address four select agrifood commodities that are often the focus

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of local production efforts, i.e., fruits, vegetables, dairy (in raw milk-equivalents), and eggs.


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Second, we estimate household agrifood demand (denominator of Equation 1) for which

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five methodologies are available, but none address all-together: varied diets, granular food item

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detail, delineations between direct and embodied agrifood demand, as well as pre-consumer

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losses. The five methods are: (1) Individual consumer-level food intake surveyed by the Center

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for Disease Control’s National Health and Nutrition Examination and Survey (NHANES)33,42.

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The food items eaten recorded by NHANES reflect real-world dietary habits by different

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sociodemographic groups and can be converted to raw agrifood commodity weights (i.e. bushels

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of corn harvested instead of ounces of canned corn eaten) using the Environmental Protection

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Agency’s (EPA) Food Commodity Intake Database (FCID)43.

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Database (FAD)44 estimates national household food demand through mass balance by totaling

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production, net withdrawals from long-term storage, and net imports. The FAD is only available

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at the national scale; it focuses on total available food for human intake, but may not represent

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actual dietary intake with detail on food items eaten as seen in NHANES. FAD contains useful

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statistics on processing losses and post-consumer waste, by food item. (3) Subnational economic

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input-output (IO) tables track expenditures by industries and households at smaller geographic

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scales, and model imports to urban areas (e.g., IMPLAN used in Chavez and Ramaswami

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201345) but such modelled datasets are expensive to acquire. (4) Complete-Diet Modeling46,47

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can effectively model ideal/conceptual household demand but does not represent actual US diets,

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and hence was not included in the analysis. Last, (5) the Bureau of Labor Statistic’s Consumer

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Expenditure data31 can provide basic food demand at the household level by tracking monetary

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expenses, but the data are frequently aggregated in public datasets– either geographically or by

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food groups.


(2) The Food Availability

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After considering all the above approaches, this paper innovated upon the NHANES

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approach by first delineating food items eaten into two categories: a) food eaten directly (“fresh”

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or minimally processed agrifoods) , and, b) food items generated from complex processing or

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complex supply chains (with significant embodied agrifoods) , the sum of which yields total

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direct-plus-embodied agri-food demand of an average US diet. See Figure S1 and Table S2 for

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method details. The food items eaten (assessed from NHANES33) were converted to raw agri-

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food equivalents using EPA FCID43, upon which various Food loss factors (pre-harvest and post-

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harvest, including post-consumer ) are also incorporated32,48 to assess overall agrifood demand

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(direct-plus-embodied-plus-loss). The annual mean consumption of each food item in NHANES

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was tabulated using the two-day survey results (food consumed within and outside the home),

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using statistical weighting to extrapolate individual level consumption to a national per capita

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average49.

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The delineation of food eaten into the two categories of direct/potentially direct, and,

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processed food with complex supply chains (see Table S2) provided a novel approach to

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compare the NHANES-derived methodology with other surveys to assess survey errors50,51, and,

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to also align the derived agrifood demand with national apparent consumption data.

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The direct intake of agrifood items estimated from the NHANES-derived methodology

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shown in Figure S1 incorporating post-consumer loss factors matched well with another

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independent bottom-up data survey on food purchased by household (i.e., consumer expenditure

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surveys (CEX31), when surveys had sufficient granularity for “apple-to-apple” comparisons).

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See left side of Table 1. The total direct-plus-embodied agrifood requirements computed in our

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methodology from individual-level food recall surveys, upon which pre- and post-consumer

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losses were applied, were also then compared with national apparent consumption (determined


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as the sum of total production and net imports on a per capita basis reported by the USDA and

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FAS to assess coherence (see Table 1 – right side). The analysis (in Table 1) allows for an

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internally consistent set of both agri-food consumption and production data across all US

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counties, on the basis of which we are able to assess the production-consumption dynamics

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across all US counties and MPOs. This is a key methodological contribution.

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As with any survey, CEX and NHANES suffer from recall errors and under-reporting of

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guilty foods. We estimated a rough magnitude of these errors by reviewing errors in the CEX as

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reported in the GHG consumption-based footprinting literature52. Researchers have found that

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CEX (consumer expenditure surveys) under-report compared to personal consumption estimates

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(PCE; derived largely through retailer sales accounts) by ~30% when considering all food

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purchased for human consumption – including “guilty food” items like alcohol, which show the

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greatest discrepancy53.

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vegetables”53, and even lower (+3%) for reporting of “fruits” (when comparing CEX and PCE)

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(personal communication and spreadsheet provided by Taylor J Wilson, BLS). These

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comparisons suggest that survey recall errors with NHANES may also be of similar magnitudes,

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ranging from 3% (for healthy foods) to 30% overall. Nevertheless, the ranges of direct food

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purchases estimated from our NHANES-derived method are consistent with the PCE-corrected

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consumer expenditure data, and, the direct-plus-embodied agrifood demand computations are

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also consistent with national production-import data. These findings (in Table 1) demonstrates

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that our methodology is internally consistent across estimates of consumption and estimates of

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production plus net imports, and aligned with national datasets, with a likely errors ranging from

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3% to ~20% in agrifood demand estimates for the four items studied in this paper.

The differences are less (20%) for the expenditures on “fruits and


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The nominal CLC was computed for both total direct-plus-embodied agrifood equivalent

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demand (CLCDirect+Embodied), and also estimated for only the direct part of the demand (CLCDirect):

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! #$"% = ( ! #$"% &' ( !") ( )*& ,-,-

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! = ! &' ( !" ( )*& ,-,-

&' ( )*& +

&' ( )*& +

(Eq. 2a)

(Eq. 2b)

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The last step to compute CLC, requires agrifood production data at the county level (for

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the numerator in Equation 2), provided by the USDA Census of Agriculture and other USDA

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surveys (accessed from the USDA Quickstat Database54), aggregated to obtain local production

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for select crop and crop categories within the MSA boundary. The census of Agriculture

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catalogues all agricultural operations that produce more than a $1,000 worth of agricultural

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product. Smaller commercial urban farming operations, community gardens, and household

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gardens are often excluded in the Ag Census; the potential impact of these smaller local

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production operations is evaluated briefly in the results section. The USDA sometimes does not

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disclose production for specific crops for counties which contain only a small number of

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producers, for privacy concerns. The undisclosed data was modelled using simple mass balance

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technique (similar to the way GDP is modelled in Sherwood et al. 201755); first allocating the

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unaccounted-for production at the state level using total production data at the state level (when

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available), and then allocating the remaining unaccounted-for production at the national level to

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the counties where state and county data was withheld or unavailable. Overall, greater than 95%

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of total production was accounted for at the state level for all agrifood categories studied. At the

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county-level , 11% of the total fruit, 25% of the total vegetable, 40% of total egg, and 12% of

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total milk production, nationally,were undisclosed and modeled. The nominal current local

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capacity at the MSA-level (CLCDirect+Embodied, MSA) was computed from Equation 2a, by dividing


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local production by the local total agrifood demand (direct plus embodied) for the same

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administrative unit, i.e., the MSA.

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We also explored the sensitivity of CLCDirect+Embodied to expanding boundaries of local

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production at varying radii of 25-, 50-, 75-, and 100-miles around the population-weighted center

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of the metropolitan areas, based on various definitions of local from people’s perception of

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local56,57, popular culture58,59, distance traveled by farmers to farmers’ markets60, and previous

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studies conducted by cities to analyze their local food capacity (see Table S1). Two scenarios

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were created to accomplish this. A non-competitive scenario allocated all food produced in the

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surrounding counties to the demanding-MSA, after subtracting the household food demand needs

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of the surrounding counties. The non-competitive scenario informs the draw that single MSAs

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can have on their surroundings.

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between multiple MSAs drawing on the same hinterland counties must be assessed and was done

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by proportionally allocating excess production in the hinterland counties to the multiple

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demanding MSAs based on their respective demand.

However, as we are analyzing actual capacity – competition

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The sensitivity analyses were repeated to assess the current local capacity for only the

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direct portion of agrifood demand, yielding a CLCDirect, with MSA boundaries as local

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production areas (Equation 2b). While CLCDirect+Embodied assesses the current potential for local

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production to meet the total demand of raw agrifood equivalents in our diet (offering a full mass

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balance perspective), the latter estimate (CLCDirect) addresses local production capacity to meet

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the demand of food items which are typically less processed, presenting a more realistic

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assessment of local capacity from an operational and policy perspective.


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The nominal current local capacity (CLCDirect+Embodied,), computed using MSA boundaries

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for local production and local household demand, was further evaluated in Rstudio (utilizing the

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regression package) to explore statistical correlations with various features of the urban system,

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such as demographic information (total population, percent population foreign born, population

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density, and median income), agrifood inputs (average solar radiation per day, precipitation, and

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water input for irrigation), total land area of MSA, and MSA level economic data (percent of

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total GDP from agricultural sector), and the presence of local and state level policies promoting

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urban agriculture – collated from previous literature reviews26,61–65 and combing state websites66.

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To assess if MSA characteristics by total area, by population size or by population density,

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impact current local capacity, these relevant parameters (sourced from US Census Bureau) were

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also included in the analysis. After filtering for redundantly correlated variables, the remaining

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variables, as seen in Table S2, were assessed to provide added insight on factors that might

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influence the current local capacity across all 377 US MSAs analyzed.

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RESULTS

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Our estimate of direct agrifood intake from NHANES aligns fairly well with the

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Consumer Expenditure Survey for the agrifood items where such comparisons are possible (e.g.

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within 10% for eggs and fluid milk; Table 1). In addition, the total (direct-plus-embodied)

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agrifood equivalents derived from bottom-up household food recall surveys (NHANES) in our

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methodology is also found to match fairly well with national level production plus net imports,

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normalized per capita (with the exception of fruits). See Table 1, where the differences are of

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the order of 1% for milk, 7% for eggs, 8% for vegetables. Direct-plus-embodied demand of

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fruits differed by about (~38%),, largely because of uncertainties in the dilution factor of fruit

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juices, which impact the results.

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The delineation of the agrifood demand into direct/potentially direct and direct-plus-

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embodied demand is also illuminating. For dairy, the direct-plus-embodied demand (615 lbs, of

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raw milk-equivalents) is almost four times the direct fluid milk intake (162lbs). Similar ratios

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are observed for fruits and vegetables while the ratio for eggs is roughly two. See Table 1. This

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motivates the computation of current local capacity with the direct-plus-embodied demand

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(CLCDirect+Embodied) while the direct demand estimation can be utilized to calculate the area’s

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ability to meet “fresh food” demand (CLCDirect). Losses were factored into both estimates of

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current local capacity (CLCDPEDirect+Embodied and CLCDirect).

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The nominal local capacity (CLCDirect+Embodied) analysis (Equation 2a) for the four select

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items across 377 US metropolitan statistical areas (MSAs) varies widely from CLC 1 (net exporters).

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MSAs only produced eggs and fruits in small quantities, producing less than 5% of the local

Detailed in figure 1a, a large portion of the


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direct-plus-embodied household demand, which includes not only direct food items (whole eggs,

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oranges), but also the quantities of food utilized as processed food ingredients (eggs in bread,

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oranges consumed as orange juice, etc.).

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production have the capacity to meet local demand to a higher degree than eggs and fruit, with

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median CLC of 18% and 23% respectively. These numbers are surprising as they indicate

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significant local capacity exists even today – however, local production may presently be

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exported rather than dedicated to local demand.

Dairy (in raw milk-equivalents) and vegetable

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Figure 1b highlights the percentage of self-reliant MSAs (those with CLC >1). Dairy

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and eggs had the highest potential for local production to meet total household demand (direct-

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plus-embodied), with roughly one fifth of all US MSAs capable of being self-sufficient (21%) –

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less so for fruits and vegetables, with 12% and 16% MSAs being capable of self-sufficiency

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respectively. Imposing an additional requirement to be self-sufficient in producing feed for the

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animals needed to produce dairy (milk equivalents) and eggs (see Methods in SI), yielded small

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changes in the results. Of the MSAs self-sufficient in dairy and eggs (both 21% respectively),

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97% and 84% produced enough animal feed inputs (or contained enough pasture acreage) to

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adequately feed all the dairy cows and egg-laying chickens in their metropolitan area

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respectively, thus not substantially changing the results in Figure 1.

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Figure 2 highlights the spatial location of MSAs that are net producers relative to local

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direct-plus-embodied household demand (CLC>1). Specific fruits and vegetables, like oranges

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and tomatoes, that are major components of our diet on a mass basis, have large concentrated

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areas of production in Florida and California, respectively. .

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connecting agrifood demand in urban areas with the production regions – whether locally or in


Such data are important in

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terms of production hot spots that serve much of the country (e.g., almost all apples come from

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Washington State, almost all tomatoes for processing come from Central California).

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Three major dairy net export regions, the Inland Northeast and Upper Midwest, and

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Inland California, satiate the majority of US household demand. Of the agrifoods assessed, dairy

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had the largest percentage of MSAs which had no production of that commodity (12%). In

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contrast, eggs, vegetables, and fruits were produced to some degree in nearly all the MSAs of the

328

United States. Vegetables and eggs appear to have less distinct regions of net export with

329

numerous and disparate MSAs being self-sufficient. Vegetables are grown in a wider variety of

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temperate climates than fruits, and egg production facilities have a great deal of geographic

331

independence in where they can operate.

332

Figure 3 displays the results of the CLC analysis utilizing only the direct demand of

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several select agrifoods (fluid milk, eggs, apples, and tomatoes). Local capacity to only meet

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demand for direct agrifood purchases (dietary intake with losses incorporated) was found to be

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higher, with 45% of MSAs be self-sufficient in meeting direct fluid milk demand – in contrast to

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a 21% self-sufficient rate among MSAs when considered all dairy. The self-sufficiency rate for

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eggs changed only slightly. The self-sufficiency rate for apples consumed directly varied only

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slightly from the aggregated direct-plus-embodied fruit category (10% MSAs self-sufficient)

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while the self-sufficiency rate of tomatoes consumed directly (28% MSAs self-sufficient) proved

340

to be higher than the aggregated direct-plus-embodied vegetable demand (16% MSAs self-

341

sufficient).

342

These numbers help contextualize local food production efforts. For example. Ramsey

343

County Minnesota (population of 538,133, home to St. Paul, Minnesota) consumed 98,270


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344

metric tons of vegetables (direct-plus-embodied) and produced 3,851 metric tons of vegetables

345

as estimated in the NHANES-based consumption and USDA-based production methods

346

described in this paper, giving Ramsey County a current local capacity of 0.04 (4%) for direct-

347

plus-embodied vegetables. A survey of home garden mapping undertaken by our research group

348

revealed roughly 6% of households garden.

349

research67, the total vegetable contribution from gardening can be estimated at roughly 800

350

metric tons of vegetables, or enough to boost the local capacity of direct-plus-embodied

351

vegetable demand from 4% to 5%, a 25% increase. A similar analysis can be undertaken looking

352

at only direct tomato use (see Table S2). When analyzing only the supplementation of direct

353

tomato demand, backyard gardening is estimated to contribute roughly 300 metric tons of

354

tomatoes to the local food supply, boosting the current local capacity for direct tomato demand

355

from 4% (without household gardens) to 8% (with household gardens), a doubling of local

356

capacity.

357

sufficiency for typical fruits or vegetables ubiquitous to US gardening.

Based on garden yield estimates of previous

Such contextual analyses are useful to local policymakers and can inform self-

358

The sensitivity of CLCDirect+Embodied to the production radius around the demanding MSAs

359

is explored in Figure 4 As expected, the percentage of MSAs assessed to to be self-sufficient, in

360

the absence of competition, increases dramatically with increasing radial distance of the

361

production regions around the demanding MSA. For example, 90% MSAs could become self-

362

sufficient in eggs and about 60% in both dairy and vegetables, in the case of no competition if

363

production boundaries are extended. But this effect is significantly dampened by inter-city or

364

inter-region competition, which is acknowledged in many of local food assessments undertaken

365

by regional policy initiatives, but the effects of which are rarely quantified. Our results show

366

that Inter-MSA competition dampens the increase in the number of MSAs self-sufficient in an


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367

agrifood as production radii increase. Varying greatly by item, Inter-MSA competition impacts

368

the CLCDirect+Embodied scores of fruits the least (12% difference at 100-mile radius), and vegetables

369

the most (29% difference at 100-mile radius). The effects of competition on the ability of

370

metropolitan areas to be self-sufficient frequently counteracts the increased zone of production –

371

as evident by the decrease in the total number of MSAs self-sufficient in vegetables when going

372

from a 50-mile radius of production to a 75-radius of production.

373

can be observed as spreading outward from concentrated areas of production to neighboring

374

MSAs as the zone of local production expands from a 25-mile radius to 100-mile radius, but

375

levels off.

In general, self-sufficiency

376

Last – we evaluated the correlation of CLCDirect+Embodied (computed nominally based on

377

MSA boundaries for production and demand) – with several urban sociodemographic and policy

378

parameters (Table S5). While not implying causation, these correlations provide insights into

379

types of cities, environments, and management practices that may be associated with higher local

380

capacity. Most of the correlations are as expected, but a few significant correlations (and lack or

381

correlations) stand out. As expected, local capacity was positively correlated with parameters

382

that represent high levels of agricultural activity such as the percent economic activity from

383

agricultural sectors, percent foreign born population (likely correlated with fruits and vegetable

384

production), and total water use for irrigation. It was surprising that the average MSA population

385

density has no correlation to current local capacity, indicating that land availability per se is not a

386

limiting factor. Land policies like zoning, enabling community gardens, and supporting direct-

387

to-consumer sales may play a large role. In contrast, it is interesting that the presence of state

388

policies to support urban agriculture appears to be correlated with CLC for some food items,

389

requiring further exploration.


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390

It is also interesting that the relationship with policy and other variables are seen for some

391

food items but not others. The animal products (dairy and eggs) have similar correlation

392

coefficients to the various explanatory variables, signaling the similar market forces that

393

determine where animal product operations are created when compared to other agricultural

394

operations. The crop categories (fruit and vegetables) also had similar correlation coefficients,

395

but frequently differed from the animal product foods. Fruit production had a (relatively) strong

396

and statistical significant correlation with the percent of an MSA’s GDP generated from the

397

agricultural sectors, showing that not only is fruit production the most geographically

398

concentrated of the food items, but that the areas with high fruit production are frequently the

399

most economically dependent on agricultural operations.

400

correlation between fruit CLC and percent GDP generated from agricultural sector is the relative

401

high value of fruit per pound when compared to eggs and dairy. Lastly, egg production appears

402

to be unlike dairy, fruits, and vegetables as its production is not correlated with the amount of

403

irrigation.

404

DISCUSSION

Additionally contributing to the

405

Prior to this study, there were very little data available on the current ability of US urban

406

areas to support household food demand through local agrifood production. Often the popular

407

press has highlighted non-US cities such as Shanghai68 and Havana69 as examples of self-

408

sufficient urban areas, and Singapore’s ability to supply 12% of its fresh vegetable demand from

409

internal production70,71is frequently noted. Our study is the first that has assessed the current

410

local capacity for local and regional agriculture around metropolitan areas to meet MSA-level

411

household demand, addressing all 377 US mainland metropolitan areas in a consistent manner,

412

with consumption-production data aligned with national datasets.


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413

Our results reveal the surprising finding that even with present day farming practices,

414

nominally, 21%, 21%, 12%, 16% of US MSAs have the capacity to be fully (100%) self-

415

sufficient in meeting household demands for dairy (in raw milk-equivalents), eggs, fruits, and

416

vegetables respectively, assuming that supply chains allow local production to serve local

417

demand. This statistic is impressive because the household demand includes not only direct

418

household food demand for milk, eggs, fruits, and vegetables, but also demand for embodied

419

agrifoods (i.e. eggs in bread, apples in apple sauce) and losses up the supply chain. More

420

surprisingly, while US MSA’s with populations 1, to net importers with CLC 1.25) represent self-sufficient MSAs and

701

likely major exporters of the specific agrifood. Red areas are the least self-sufficient.

702


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703

704 705

Figure 3. A histogram (A) and cumulative distribution curve (B) of the results of the current

706

local capacity analysis of the total direct-plus-embodied consumption (denoted as ‘total’) and the

707

consumption of foods consumed directly or with minimal processing (demoted as ‘direct’): total

708

dairy (in raw milk-equivalents) (solid purple), total eggs (solid yellow), direct fluid milk (dotted


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709

purple), direct eggs (dotted yellow), direct apples (red), and direct tomatoes (green) using

710

metropolitan statistical area administrative boundary to define local production of food.

711 712


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713 714

Figure 4. The percentage of self-sufficient metropolitan areas, as judged by having a local

715

capacity above one, mapped over a number of scenarios with varying definitions of local and

716

resource allocation scenarios. Results from no resource competition scenarios (dotted line with

717

circles) and resource competition scenarios (solid line with diamonds) are displayed for dairy (in

718

raw milk-equivalents) (purple), eggs (orange), fruits (red), and vegetables (green).


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719

Table 1. Comparison of measures of direct food intake and measures of national total raw commodity intake of select agrifoods. All

720

data are for the year 2012. Approximations of per capita direct food intake of select agrifoods (CEX vs. NHANES)

Quick Description of data source

Summary of method Dairy (in raw milkequivalents ) Eggs Fruits Apples Vegetables Tomatoes

Approximations of total (direct + embodied + loss1) US consumption of select agrifoods per capita (bottom-up NHANES vs. FAD vs. USDA)

Consumer Expenditure Survey (for benchmarking purposes)), adjusted to account for SNAP/WIC spending using CE:PCE ratio8 Consumer Expenditures per capita, converted to mass6, and adjusted for nonprofit spending.

National Health And Nutrition Examination and Survey (NHANES), filtered for direct intake only

NHANES direct-plusembodied food intake data, translated to raw commodity weights, with losses1 included

US Apparent Consumption from the USDA ERS Food Availability Dataset (for benchmarking purposes)

Food items eaten by individuals, estimated from surveys (bottomup): Focus on direct intake of four agrifoods.

Agrifood-equivalent demand exerted by an individual in the US (direct-plusembodied), incorporating losses across the supply chain

US apparent consumption (production + net imports and stock changes) as reported by FAD

USDA-derived apparent per capita consumption of select agrifoods from production and net imports data2 US apparent per capita agrifood consumption (US agrifood production plus net imports)7, estimated from aggregated county production

159 lbs.3 (fluid milk only)

162 – 175 lbs.3,5 (fluid milk only)

615 lbs.4

613 lbs.4

623 lbs.4

16.4 lbs. n/a n/a n/a n/a

16 – 20.9 lbs.5 n/a 15 – 15.5 lbs.5 n/a 11 – 22 lbs.5

35 lbs. 346 lbs. 61 lbs. 403 lbs. 73 lbs.

33 lbs. 244 lbs. 44 lbs. 391 lbs. 87 lbs.

33 lbs. 250 lbs.5 34 lbs.5 439 lbs.5 90 lbs.5


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721

1

Losses include post-consumer waste, inedible portions, retail loss, farm-to-retail loss, processing mass loss, and harvest loss. 2Trade

722

data for milk products detailed by the USDA Production, Supply, and Distribution Database. Trade data for eggs, fruits, and

723

vegetables from FAO Food Balance Sheets data. 3Fluid milk consumption used a proxy for direct consumption of dairy (in raw milk-

724

equivalents) products as fluid milk is less processed than other dairy (in raw milk-equivalents) products. Fluid milk includes beverage

725

milk, buttermilk, and cream. Other dairy (in raw milk-equivalents) includes yogurt, cheese, ice cream, cottage cheese, dry milk, and

726

condensed milk products. 4Considers the raw milk equivalent of milk dedicated to the production of fluid milk products and non-fluid

727

dairy (in raw milk-equivalents) products. 4Direct intake of fluid milk, eggs, apples, and tomatoes was estimated by analyzing the food

728

item description as reported by NHANES. A range is represented due to the uncertainty to the degree of processing involved with

729

certain food products. See Table SI-1 for detailed classification of food items. Consumption of dairy (in raw milk-equivalents)

730

ingredients of food items as recorded as NHANES is normalized by milkfat content. Displayed in table is the raw milk equivalent,

731

which closely mirrors the fluid milk intake. 5FAO import and export data for fruits and vegetables does not translate the weight of

732

juice and other processed food products back to raw commodity equivalents. 6These surveys do not account for food embodied within

733

other processed foods or food consumed outside the home. Post-consumer waste subtracted from total purchased. 7Changes to long-

734

term storage not included. .

735

profits and government institutions (SNAP/WIC), unlike Personal Consumption Expenditure (PCE). A PCE:CEX ratio from a PCE to

736

CEX concordance table was utilized to adjust the consumption to account for this previously unaccounted-for spending53.

8

Consumer Expenditure (CEX) surveys do not account for spending for the household sector by non-


38

Assessing Current Local Capacity for Agrifood ... - ACS Publications

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