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Optimal allocation of tomato supply to minimize greenhouse gas emissions in major U.S. metropolitan markets

https://doi.org/10.1016/j.resconrec.2022.106660Get rights and content

Abstract

Our food system is very resource and emissions intensive and contributes to a broad range of environmental impacts. We have developed cradle-to-market greenhouse gas emissions estimates of supplying fresh tomatoes to 10 of the largest metropolitan areas in the United States and applied a linear optimization algorithm to determine the optimal tomato distribution scheme that will minimize tomato-related greenhouse gas emissions across all 10 areas. Monte Carlo simulation was performed to assess the uncertainties in the data. Results indicate that the current tomato distribution scheme is suboptimal. Reallocation of the fresh tomato supply across the 10 areas could decrease transportation-related emissions by 34% and overall tomato-related greenhouse gas emissions by 13%—from 277,000 metric tons of CO2e to 242,000 metric tons of CO2e. Production practices and geographic conditions (such as soil and climate) are more significant for GHG emissions than the supply allocation or the seasonality of supply.

Introduction

Our food system places high demands on natural resource use and emissions, being responsible, for example, for the emissions of approximately 2.6 metric tons of CO2e (tonCO2e) per person per year, or 8.4 kg CO2e per person per day (Weber and Matthews, 2008) in the United States, or roughly 10% of overall greenhouse gas (GHG) emissions (Weber and Matthews, 2008, US EPA. Inventory of U.S 2016). It demands 140 MJ of energy per person per day—four times the global average—and 1,200 liters (330 gallons) of water per person per day (Canning, 2010, UNESCO 2014), accounting for approximately 14% of national energy consumption and half of water withdrawals.

As the global population continues to grow and the middle class expands, demand for food, and different kinds of food—in particular, high-value products such as vegetables, fruits, and meat—will increase. The United Nations estimates that global food production must increase by 70% by 2050 in order to satisfy demand (United Nations 2011). If this expansion in production is to occur in a sustainable manner, care must be taken to minimize the environmental impact of the agricultural systems at regional, national, and global levels. (Bell and Horvath, 2020, Dorr et al., 2021)

In this study, we build a linear optimization model to estimate the cradle-to-market GHG emissions associated with fresh tomatoes supplied to 10 of the 12 most populous metropolitan areas in the United States (Table 1) based on 6 unique geographic production regions and four tomato growing practices (United States Census Bureau 2016). (The U.S. Department of Agriculture's Agricultural Marketing Service has not compiled data for Houston and Phoenix.) The 10 metropolitan statistical areas account for roughly one quarter of the U.S. population and 26% of tomato consumption.

Tomatoes were chosen as the focus of this study for a few reasons. First, tomatoes are one of the most popular “specialty commodities” in the United States. Roughly 9 kilograms (21 pounds) of fresh tomatoes and 30 kilograms (66 pounds) of processed tomatoes are consumed annually per person (USDA 2020). Second, tomatoes are grown using a variety of production methods, including indoors. In 2012, greenhouse tomatoes were a $400 million industry with over 1,000 acres of greenhouse tomatoes under production (USDA 2020). Tomatoes account for more than half of all greenhouse production of fruits and vegetables by area and nearly two-thirds of all greenhouse production by economic value (USDA 2020). Although indoor tomato production often requires more energy relative to conventional production, transportation distances to the consumer are typically shorter. Third, tomato production in the United States is diffuse; in 2019, 10 states reported over 1000 acres harvested (USDA 2020) .

Section snippets

Background

Environmental assessments of tomatoes are numerous in the literature, but not for the United States. Table 2 presents 48 cradle-to-farm-gate GHG intensity values collected from 30 peer-reviewed journal articles. The values represent a variety of growing practices and geographic regions, but only four were calculated for a United States region. The data in Table 2 reflect only the farm stage; processing, transportation, storage, and other stages beyond the farm gate are not included. (In some

Methods

We calculate the GHG emissions associated with fresh tomatoes supplied to each of the metropolitan areas during each week of a year. Next, we implement a linear optimization algorithm to compute the optimal tomato distribution scheme for the 10 metropolitan areas that minimizes total GHG emissions. Last, we comment on whether the presence of an omnipresent national-level agricultural “social planner” could potentially mitigate food-related GHG emissions, or whether the current scheme—whereby

Results

Under the current (i.e., baseline) scenario, supplying the 10 metropolitan areas with fresh tomatoes releases roughly 277,000 tonCO2e per year. Fig. 1 was created by summing the environmental impact of fresh tomatoes across all 10 destination cities. Optimization can save roughly 35,000 tonCO2e per year—a 13% improvement. Our model assumes fixed supply and demand, thus the only opportunity for improvement is in reducing transportation-related emissions by varying the supply portfolios of the 10

Discussion

Out of 10 major metropolitan statistical areas in the United States, Dallas has the lowest-impact tomatoes—0.61 kgCO2e per kg on average—due to its relatively close proximity to Mexican agriculture. Boston has the highest impact at 0.87 kgCO2e per kg on average, an increase of roughly 40%. More significant is the tomato production origin: open-field tomatoes supplied to Philadelphia from Virginia were found to have emissions of 0.38 kgCO2e per kg, whereas controlled-environment tomatoes

Supplementary material

See the attached separate file.

CRediT authorship contribution statement

Eric Bell: Methodology, Data curation, Formal analysis, Software, Writing – original draft. Yuwei Qin: Investigation, Validation, Visualization, Writing – review & editing. Arpad Horvath: Conceptualization, Funding acquisition, Project administration, Resources, Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This material is based upon work supported by the National Science Foundation under Grant No. 1739676. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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