Richard Volpe, Edward Roeger, and Ephraim Leibtag

Economic

Research

Service

Economic 

Research

Report 

Number 160

November 2013

United States Department of Agriculture

How Transportation Costs Affect

Fresh Fruit and Vegetable Prices
Economic Research Service 

www.ers.usda.gov

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on 

the basis of race, color, national origin, age, disability, and, where applicable, sex, marital status, familial 

status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because 

all or a part of an individual’s income is derived from any public assistance program. (Not all prohibited 

bases apply to all programs.) Persons with disabilities who require alternative means for communication of 

program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 

720-2600 (voice and TDD). 

To file a complaint of discrimination write to USDA, Director, Office of Civil Rights, 1400 Independence 

Avenue, S.W., Washington, D.C. 20250-9410 or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA 

is an equal opportunity provider and employer.

United States Department of Agriculture

Visit our website for more information on this topic:

www.ers.usda.gov/topics/food-markets-prices/processing-marketing.aspx 

Access this report online:

www.ers.usda.gov/publications/err-economic-research-report/err160.aspx 

Download the charts contained in this report:

• Go to the report’s index page www.ers.usda.gov/publications/

 err-economic-research-report/err160.aspx 

• Click on the bulleted item “Download ERR160.zip”

• Open the chart you want, then save it to your computer

Recommended citation format for this publication:

Volpe, Richard, Edward Roeger, and Ephraim Leibtag. How Transportation Costs Affect 

Fresh Fruit and Vegetable Prices, ERR-160, U.S. Department of Agriculture, Economic 

Research Service, Noverber 2013.

Cover photo credit: Shutterstock.
United States Department of Agriculture

Economic 

Research 

Service

Economic 

Research

Report

Number 160

November 2013

Abstract

This report examines the ways that fuel prices are transmitted to wholesale produce 

prices via transportation costs. Specifically, it focuses on marketing costs for asparagus, 

cantaloupes, table grapes, oranges, bell peppers, and tomatoes. Results of the study indicate that transportation costs significantly increase the costs of marketing these produce 

items and therefore their wholesale price. The impact of fuel prices on produce prices 

depends on a number of factors, including the distance between wholesale markets and 

the source of the produce, the method of transportation, the importance and timing of 

imports, and commodity-specific factors such as perishability. Overall, as fuel prices rise, 

so do wholesale produce prices and the margins between farm and wholesale prices. 

Keywords: Price determination, transportation costs, wholesale price margins, fruits and 

vegetables, trade

Acknowledgments

The authors thank Aylin Kumcu, Abby Okrent, Timothy Park, Hayden Stewart, Abebayehu 

Tegene, Jessica Todd, and Michele Ver Ploeg, USDA, Economic Research Service (ERS), 

for their review. We also thank Azzedine Azzam, University of Nebraska; Jesse Tack, 

Mississippi State University; and Marina Denicoff, Adam Sparger, and April Taylor, USDA 

Agricultural Marketing Service, for their comments. Thanks also to editor Courtney Knauth 

and designer Wynnice Pointer-Napper, ERS, for their assistance.

Richard Volpe, rvolpe@ers.usda.gov

Edward Roeger

Ephraim Leibtag, eleibtag@ers.usda.gov

How Transportation Costs Affect

Fresh Fruit and Vegetable Prices
ii

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Produce Sources: Across the Nation, Around the Globe. . . . . . . . . . . . . . . . . . . . . . . . 4

Wholesale Produce Prices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

How Fuel Prices Affect Produce Prices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Trucking Rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Wholesale Price Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Prices From Multiple Origins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Wholesale Price Responses to Oil Price Increases. . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Appendix: Regression model sample statistics for wholesale price analyses. . . . . . . 31
A report summary from the Economic Research Service

ERS is a primary source 

of economic research and 

analysis from the U.S. 

Department of Agriculture, 

providing timely information on economic and policy 

issues related to agriculture, 

food, the environment,and 

rural America. www.ers.usda.gov

United States Department of Agriculture

November 2013

Find the full report 

at www.ers.usda.

gov/publications/erreconomic-researchreport/err-160.aspx

What Is the Issue?

Fuel prices, understood to be a major driver of food prices in the United States, can have 

significant impacts on food price levels and volatility. Since consumer demand for fruits and 

vegetables has been proven to be price-sensitive, fuel price surges could significantly reduce 

U.S. consumption of fresh produce, at least in the short term. There has been little empirical 

research to calculate the magnitude of fuel price changes on food prices and how this effect 

might vary across markets, based on transportation distance or commodity attributes. This 

study analyzes the relationship between fuel prices and wholesale produce prices using data for 

2000-2009. Fresh produce provides particularly clear insight into the effects of transportation 

costs on food prices, because fruits and vegetables typically have few processing or other nontransport expenses and they have clear geographic origins. 

What Did the Study Find?

The study affirmed that, in general, as the distance from the produce source increases, the impact 

on wholesale prices of higher fuel prices also increases. However, the extent of that impact 

depends on the transportation method, importation and seasonality issues, and perishability.

• Fuel prices are a statistically significant factor in determining the difference between 

domestic wholesale and farm-level produce prices. For produce shipped by truck, these price 

margins are increasingly sensitive to fuel prices as the distance traveled from the growing 

source lengthens. 

• Fuel price volatility can lead to substantial geographic variation in produce prices within the 

United States. 

• Truck transport rates, which increase with fuel prices, are the most likely conduit of fuel 

price effects on produce prices. In general, the sensitivity of truck rates to fuel prices 

increases linearly with distance traveled. For example, an increase in national fuel prices 

widens the margin for California-grown produce considerably more in Boston than in Los 

Angeles. This distance/price relationship has been taken for granted, and there has been 

little attempt to establish it empirically.

• However, truck transportation rates do not necessarily vary only with route length. For 

example, the distance from the San Joaquin Valley to Dallas is less than half that to Boston, 

but the average truck rate is more than half.

Richard Volpe, Edward Roeger, and Ephraim Leibtag

Economic

Research

Service

Economic 

Research

Report 

Number 160

November 2013

United States Department of Agriculture

How Transportation Costs Affect

Fresh Fruit and Vegetable Prices

Richard Volpe, Edward Roeger, and Ephraim Leibtag

How Transportation Costs Affect Fresh

Fruit and Vegetable Prices
• A 100-percent increase in diesel prices would lead to a short-term wholesale produce price increase of 20 

to 28 percent, on average. However, this could differ across shipping routes and commodities. The impacts 

on imported produce would likely be much smaller.

• The effect of fuel price increases on wholesale produce prices varies by commodity characteristics: 

 For commodities with multiple growing sources, robust imports, and weak seasonality (e.g., grapes 

and asparagus), fuel price effects are significant but vary across geographic markets and seasons.

 For commodities with clear growing seasons and few major import sources (e.g., cantaloupes 

and oranges), fuel price effects are more constant, with a discernible relationship between price 

increases, seasonality, and transportation distance.

• The prices of commodities with larger import shares generally exhibit less fuel price sensitivity. 

This is likely because air and ship transport are considerably less energy-intensive per mile than 

truck transport.

How Was the Study Conducted?

This report uses wholesale fresh fruit and vegetable prices collected by the USDA Agricultural Marketing 

Service (AMS) for 13 wholesale (terminal) market locations. These large markets were selected to span the 

United States, providing transportation routes of varying lengths for both domestic and imported produce. 

Fuel prices and truck transportation rates were also available from AMS. The data cover 2000-2009. The 

study relies on regression analysis to identify the impact of fuel prices on transportation costs, margins, and 

wholesale prices.

www.ers.usda.gov
1

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

How Transportation Costs Affect Fresh Fruit 

and Vegetable Prices

Richard Volpe, Edward Roeger, and Ephraim Leibtag

Introduction

Over the past 40 years in the United States, the distance between where foods originate and where 

they are consumed has grown considerably. Little is known about how this long-term change has 

affected the overall food supply chain. Consumers have benefited from increased year-round food 

availability of items grown farther away, but they are also subject to price effects as produce travels 

longer distances. A better understanding of this price-distance relationship is increasingly relevant 

as U.S. consumers become more accustomed to foods that cannot be grown continuously in their 

region—if at all—and as energy prices continue to be far more volatile than food prices.1

Within the food marketing chain, fresh produce is a useful area of study for the effects of energy 

costs. The movement of produce from source to market provides a straightforward observation 

of large variations in transport distance, given that many fruits and vegetables are both produced 

domestically and imported. In addition, fresh produce markets are characterized by a fixed-proportion relationship among farm inputs, wholesale products, and final retail products, as well as an 

inelastic short-run supply owing to perishability and the timing of growing seasons. As a result, the 

margin between wholesale and farm prices can be interpreted as the major cost of produce at the 

wholesale level (Wohlgenant, 2001). Given that fresh produce undergoes minimal processing and 

packaging, the most important component of that cost is likely to be fuel used in transportation. 

This report explores how much fuel prices have affected wholesale produce prices (by way of 

transportation costs) as the distance to market has increased, both across the United States and for 

imports from both neighboring and distant countries. For a selection of fruits and vegetables, the 

authors analyze recent (2000-2009) changes in supply patterns and estimate the extent to which fuel 

prices determine wholesale prices. 

All commodities, regardless of origin, have fuel as a cost. Other sectors of food production and 

marketing may use more energy than transportation does, but only the transportation sector remains 

almost exclusively dependent upon fuels derived from oil (Hendrickson, 1994). Fresh produce is 

particularly sensitive to changes in fuel price because of the need for refrigeration during transport. 

Although fuel prices were relatively stable over 1990-2004 (fig. 1), since then world oil markets have 

been more volatile and prices have grown steeply. In transportation costs, both efficiency of the particular mode of transportation and distance traveled are important. Weber and Matthews (2008) rank 

international air transport as more than 3-1/2 times as energy-intensive as trucking, which itself was 

found to be more than 13 times as energy-intensive as international ocean transport (table 1). 

Recent changes in produce sourcing have brought more imports into the United States to satisfy 

year-round demand, supplement domestic supply in times of fluctuating weather and planting, and 

1According to Consumer Price Index data from the Bureau of Labor Statistics, motor fuel prices changed (either up or 

down) by an average of 11.5 percent per year between 1990 and 2011, while overall food prices increased by 2.7 percent 

per year.
2

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

capitalize on lower production costs elsewhere in the world (Calvin et al., 2001; Pollack, 2001). The 

import share of fresh fruit consumption grew from 1.4 percent in 1970-71 to 30.4 percent in 1989-90, 

reaching 45.3 percent in 2007-08. The import share for vegetables grew from 3.9 percent in 1970-71 

to 8.0 percent in 1989-90, reaching 17.2 percent in 2007-08 (USDA, 2009). Thus, we are able to 

examine the effect of fuel prices on wholesale commodity prices for products that travel a wide 

range of distances via different methods of transport. 

Interest in how fuel prices may affect fruits and vegetable costs spans several areas of policyoriented economic research. First, questions abound as to why retail food prices have become 

considerably more volatile since 2006 (Tegene, 2009). Fuel prices, as contributors to food price 

fluctuations, are one area of research (Gilbert, 2010). Further, with health advocates recommending 

increased consumption of fruits and vegetables, researchers and policymakers are investigating the 

factors influencing their consumption, particularly with respect to price (Wendt and Todd, 2011). 

World oil prices (nominal dollars), 1990–2009

$barrel

Source: U.S. Department of Agriculture, Economic Research Service calculations based on U.S. Department 

of Energy (USDOE), Energy Information Administration (EIA) data.

Figure 1

0

20

40

60

80

100

120

140

160

World spot crude oil prices

Jan. 90 Jan. 92 Jan. 94 Jan. 96 Jan. 968 Jan. 2000 Jan. 02 Jan. 04 Jan. 06 Jan. 08

Table 1

Comparison of energy efficiency of different transport methods

Energy use by transportation mode

Mode MJ/t-km

Rail 0.3

Truck 2.7

International air 10.0

International water container 0.2

Note: MJ/t-km represents energy used per ton mile.

Source: Weber and Matthews (2008), p. 3509.
3

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Finally, the economics of price transmission in U.S. food systems and the implications for consumers 

and other supply chain participants is a rich vein of potential research (Sands et al., 2011). 

This report uses wholesale fruit and vegetable price data from 2000 to 2009 for a number of markets 

throughout the United States and examines prices for both domestic and imported produce. It investigates the role of fuel prices on wholesale produce prices through two lines of analyses: (1) the 

effect of fuel costs on the difference between shipping-point and wholesale produce prices across the 

United States for commodities grown in California, and (2) a regression analysis of the sensitivity 

to fuel prices for a more complete set of produce origins, specific to the geographic location of the 

market. Market-specific regressions are necessary to account for the different shipping distances and 

growing conditions associated with produce sources, even among like commodities. These analyses 

may lead to a better understanding of how increased volatility in energy prices affects consumer 

prices and consumption patterns.
4

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Produce Sources: Across the Nation, Around the Globe

The six fresh produce items in this study are asparagus, cantaloupes, table grapes, oranges, bell 

peppers, and tomatoes. While these items represent a small share of fresh fruits and vegetables, 

they account for over 21 percent of U.S. fresh fruit consumption and more than 15 percent of fresh 

vegetable consumption (table 2).2 The items were chosen for analysis based on a number of criteria, 

some of which apply to all of them and some in which they intentionally differ. Imports constitute a 

portion of the domestic supply for all of these produce items, but the overall import share (2007-08 

average, calculated by weight) varies widely among them, from a low of 8.5 percent of total supply 

(oranges) to a high of 81.7 percent (asparagus). Figures 2a-2f illustrate these relative import levels, 

as well as import trends for each produce item over the last 20 years. The dominant exporters to the 

United States (Central America, Chile, Peru, Mexico/Canada) also vary among the produce items, 

presenting different transportation modes and distances. 

The produce items also differ in the seasonality of imports (figs. 3a-f). This is generally determined by an item’s domestic seasonal availability, amount, and price. Grapes and cantaloupes, for 

example, are generally imported to provide a steady supply when domestic production is not possible 

or economical. Tomatoes and bell peppers present a different import scenario: domestic production occurs year-round (alternating mainly between California and Florida), while Mexican imports 

continuously supplement the supplies (most heavily in the winter when domestic production is 

lowest) and Canadian imports arrive throughout the summer months. Finally, asparagus and oranges 

provide further import contrasts. Asparagus is imported throughout the year (mostly from Peru 

and Mexico), with a brief domestic season. Oranges are produced domestically throughout the year 

(mainly in California and Florida), with relatively little outside supply. 

Broad similarities also exist among the selected commodities and facilitate our analysis. All the 

commodities are at least moderately perishable, eliminating the need for models that account for 

storage stocks and easing supply tracking. First, the perishability of the items—which results in a 

2Numbers are based on the 2007-08 average of USDA Economic Research Service (ERS) Food Availability Data 

System (FADS) farm weights and consider cantaloupes in the fresh fruit total.

Table 2

Shares of total disappearance and imports, by commodity

Fresh produce item characteristics, based on 2007/2008 average

Share of total 

disappearance1 Import share2 Primary exporting countries to U.S.

Asparagus 0.6 81.8 Peru, Mexico

Bell peppers 4.9 48.9 Mexico, Canada

Cantaloupes 7.5 34.7 Guatemala, Honduras, Costa Rica

Grapes 6.7 52.2 Chile, Mexico

Oranges 7.0 8.5 Australia, South Africa

Tomatoes 9.6 42.2 Mexico, Canada

1Relative share (%) of the overall fresh fruit or vegetable disappearance in the United States.

2Calculated by weight.

Source: U.S. Department of Agriculture (USDA), Economic Research Service calculations based on data from 

USDA Foreign Agricultural Service.
5

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

2a–Cantaloupe domestic supply origins by year

Million lbs.

20,000

15,000

10,000

5,000

0

1990 95 2000 05 10

Central America

Mexico

Domestic

Dominican Republic

Total imports

Figure 2

Annual U.S. fresh produce supplies by origin

2b–Grapes domestic supply origins by year

Million lbs.

15,000

10,000

5,000

0

1990 95 2000 05 10

Mexico

Domestic

Chile

Total imports

2e–Tomatoes domestic supply origins by year

Million lbs.

30,000

40,000

20,000

10,000

0

1990 95 2000 05 10

Mexico

Domestic

Canada

Total imports

2c–Asparagus domestic supply origins by year

Million lbs.

3,000

4,000

2,000

1,000

0

1990 95 2000 05 10

Mexico

Domestic

Peru

Total imports

2f–Peppers domestic supply origins by year

Million lbs.

15,000

10,000

5,000

0

1990 95 2000 05 10

Other imports

Mexico

Domestic

Canada

Total imports

Source: U.S. Department of Agriculture (USDA), Economic Research Service (ERS) calculations based on data from USDA agencies ERS, 

Foreign Agricultural Service, and National Agricultural Statistics Service.

2d–Oranges domestic supply origins by year

Million lbs.

30,000

40,000

20,000

10,000

0

1990 95 2000 05 10

Other imports

Australia

Domestic

South Africa

Total imports
6

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

3a–U.S. monthly cantaloupe supply from top 3 origins,

1998/99 avg. and 2008/09 avg.

Thousand lbs.

4,000

3,000

1,000

2,000

0

1 2 3 4 5 6 7 8 9 10 11 12

Mexico

Domestic Central America

Red = 1998/99 Black = 2008/09

Figure 3

Seasonal supply patterns across years

3b–U.S. monthly grape supply from top 3 origins,

1998/99 avg. and 2008/09 avg.

Thousand lbs.

3,000

2,000

1,000

0

1 2 3 4 5 6 7 8 9 10 11 12

Mexico

Domestic Chile

Red = 1998/99 Black = 2008/09

3e–U.S. monthly tomato supply from top 3 origins,

1998/99 avg. and 2008/09 avg.

Thousand lbs.

3,000

2,000

0

1 2 3 4 5 6 7 8 9 10 11 12

Canada

Domestic Mexico

Red = 1998/99 Black = 2008/09

1,000

3c–U.S. monthly asparagus supply from top 3 origins,

1998/99 avg. and 2008/09 avg.

Thousand lbs.

400

300

200

0

1 2 3 4 5 6 7 8 9 10 11 12

Mexico

Domestic Peru

Red = 1998/99 Black = 2008/09

100

3f–U.S. monthly pepper supply from top 3 origins,

1998/99 avg. and 2008/09 avg.

Thousand lbs.

2,000

1,500

0

1 2 3 4 5 6 7 8 9 10 11 12

Canada

Domestic Mexico

Red = 1998/99 Black = 2008/09

1,000

500

Source: U.S. Department of Agriculture (USDA), Economic Research Service (ERS) calculations based on data from USDA agencies ERS, 

Foreign Agricultural Service, and National Agricultural Statistics Service.

3d–U.S. monthly orange supply from top 3 origins,

1998/99 avg. and 2008/09 avg.

Thousand lbs.

8,000

6,000

2,000

4,000

0

1 2 3 4 5 6 7 8 9 10 11 12

South Africia

Domestic Australia

Red = 1998/99 Black = 2008/09

10,000
7

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

fixed proportion among inputs, wholesale products, and retail products and an inelastic short-run 

supply—makes it plausible for major costs at the wholesale level (such as transportation) to constitute the farm-wholesale price margin (Wohlgenant, 2001). Second, for all six commodities, farms in 

the continental United States are able to produce the fruit or vegetable in commercial quantities, and 

this enables an analysis involving domestic shipping-point prices. Finally, all six items now have a 

significant year-round presence in U.S. markets, providing continuous time-series data and information on different sourcing patterns across the commodities. 

The different import patterns of the produce items determine the typical modes of transport from 

field to market, likely affecting the sensitivity of the imports to fuel prices. While domestic shipments for these commodities are almost exclusively by truck, produce is delivered into the United 

States via ships, airplanes, or trucks, depending upon the origin. In general, produce items from a 

specific origin have a common method of transport to the United States border (table 3).3 Growing 

sources closest to the continental United States tend to use truck transportation, while more distant 

locations tend to rely on ocean transport or—for commodities like asparagus that are of high value 

and highly perishable—air transport. 

Produce transportation via modes other than trucking is on the rise, concomitant with the strong 

growth in imports. Imports made up about half the growth in U.S. fruit consumption and a quarter 

of the growth in vegetable consumption from 1984 to 2004 (Huang and Huang, 2007), and transportation distances have increased proportionately. As Lucier et al. (2006) noted, “Increasingly, fresh 

fruit imports have been rising during the primary U.S. growing seasons.” 

In addition, domestic growing locations have moved across the country to more productive areas. 

As of 2010, California and Florida accounted for one-half and one-quarter, respectively, of all U.S. 

fruit acreage. Additionally, California was home to more than 90 percent of all U.S. tree nut acreage. 

Accordingly, from 1981 to 1998, the average distance for fresh fruits and vegetables to reach the 

Chicago wholesale terminal market increased by 22 percent, from 1,245 to 1,518 miles (Pirog et al., 

2001). Produce shipments have spread beyond the typical North American Free Trade Agreement 

(NAFTA) trading partners of Canada and Mexico, fresh fruit imports in 2004-06 were primarily 

(67.9 percent) from South and Central America, Australia, New Zealand, and South Africa. Between 

2000 and 2008, NAFTA countries accounted for 69 percent of total U.S. fresh vegetable imports. In 

both cases, there is significant variation in source locations throughout the calendar year.

Many technological advances over the last 50 years have enabled perishable items to be shipped 

from previously impractical locations. The advent of containerized transport, and then of mobile 

refrigerated containers, in the 1950s and 1960s greatly increased the efficiency and viability of 

ocean transport (Ballenger et al., 1999). Controlled-atmosphere technologies in containers for the 

transport of perishable fruits and vegetables have become even more sophisticated in recent years. 

For easily degraded items of high value, such as asparagus and raspberries, air transport has also 

been used in recent years to ensure quick delivery to distant markets. 

3An exception to this is Peruvian asparagus, which transitioned from being mostly transported by boat (85.8 percent) 

in 1999 to being almost completely shipped by air (93.9 percent) in 2003 and then back to a mixture of ocean and air 

transport as fuel prices increased from 2004 through 2009 (air transport share was 86.3 percent in 2004 and 57.8 percent 

in 2008). 
8

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Table 3

Transport modes by origin

Average annual transportation shares (% of lbs shipped), 2000–2009

Origin/commodity Truck Boat Air Rail Unknown

Peru

Asparagus 31 68

Chile

Grapes 100

Central America

Cantaloupes 100

Mexico

Asparagus 100

Grapes 100

Cantaloupes 100

Bell peppers 100

Tomatoes 100

Canada

Bell peppers 74 26

Tomatoes 100

Domestic

Asparagus 94 6

Grapes 98 2

Cantaloupes 97 3

Bell peppers 99 1

Oranges 71 29

Tomatoes 99 1

Source: U.S. Department of Agriculture (USDA), ERS calculations based on USDA Agricultural Marketing Service selected 

shipment data.
9

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Wholesale Produce Prices

This report uses wholesale fresh fruit and vegetable prices collected by USDA’s Agricultural 

Marketing Service (AMS) for wholesale (terminal) market locations throughout the United States. 

Thirteen markets—Boston, New York City, Philadelphia, Pittsburgh, Baltimore, Columbia (SC), 

Atlanta, Miami, Detroit, Chicago, St. Louis, San Francisco, and Los Angeles—represent a variety 

of geographic regions across the country. These terminal markets facilitate large-quantity sales of 

wholesale fruits and vegetables from importers and domestic shippers to other wholesalers, retail 

outlets, and the foodservice industry. 

Terminal markets have traditionally handled a large portion of the fresh produce trade in the United 

States, but since the 1950s they have waned as shippers deal directly with retailers. However, these 

markets are still important distribution channels, handling an estimated 30 percent of national 

produce flows (by weight). Terminal markets continue to be important in the marketing of imports 

and in the disposition of residual fresh produce supply and demand (Cook, 2002). For example, even 

with the decline of the terminal markets over the last 60 years, the New York market still claims to 

be the largest produce market in the world, reporting 2.7 billion pounds of fresh fruits and vegetables 

sold annually (as of 2011). 

The wholesale produce pricing data used in our study covers January 2000 through December 2009 

and includes daily price observations specified as high/low range, with prices at a given market 

differentiated by product origin and a number of product specification variables. This detail provides 

a large number of total price observations per commodity, ranging from over 245,000 for asparagus 

to over 670,000 for oranges. In our analyses, the data are simplified in several ways to produce a 

more uniform set of prices with fewer outlying price observations. First, we use a median price 

instead of the provided high/low range, and we convert the sale prices into dollars per pound from 

dollars per package. We also aggregate the daily data to weekly data, with groupings of price observations dependent on item size and variety. We then exclude all prices in which recorded comments 

indicate that a portion of the quantity is “unsalable” or for items for which the condition, quality, 

or appearance is listed as “poor,” “ordinary,” 4 “fair,” or “holdover.” Finally, price observations for 

organic produce are also dropped from the analyses.5

Figures 4a–4f track wholesale produce prices from 1998 to 2010. As the figures show, wholesale 

prices typically have strong intrayear fluctuations, mirroring seasonal supply shifts. The prices for 

a marketing year, in dollars per pound, are generally centered around a given range, with 2008/09 

average prices ranging from $0.38 for cantaloupes to $1.93 for asparagus (1.93). Common to all six 

produce items across the 12-year span is an increasing price trend, beginning in early 2004. While 

there may be many reasons behind this, it does roughly coincide with an upward shift in oil prices 

(fig. 1). 

In addition to fluctuations in national produce prices, patterns are apparent at the local market level 

too. Price movements are common across vastly separated markets (e.g., in January for grapes, 

cantaloupes, and asparagus) in some months and diverge at other times (figs. 5a-f). Raper et al. 

4“Ordinary” in this sense is defined by AMS as “having a heavy percentage of defects as compared with “good.”

5Organic produce is dropped because price observations of this type are generally not uniformly distributed across 

years or markets and not present in large numbers. When analyzed, organic certification exhibits a very large positive and 

statistically significant effect on prices.
10

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Figure 4

Seasonal price patterns in recent data

4b–Weekly average wholesale grape prices

Pounds

0.50

1.00

1.50

2.00

2.50

3.00

1990w1 2000w1 1990w1 2000w1 1990w1 2000w1 2000w1

4e–Weekly average wholesale pepper prices

Pounds

0.50

1.00

1.50

2.00

1990w1 2000w1 1990w1 2000w1 1990w1 2000w1 2000w1

4c–Weekly average wholesale asparagus prices

Pounds

1.00

2.00

3.00

4.00

1990w1 2000w1 1990w1 2000w1 1990w1 2000w1 2000w1

4f–Weekly average wholesale tomato prices

Pounds

0.50

1.00

1.50

2.00

2.50

1990w1 2000w1 1990w1 2000w1 1990w1 2000w1 2000w1

Source: U.S. Department of Agriculture (USDA), Economic Research Service (ERS) calculations based on data from USDA Agricultural 

Marketing Service.

4d–Weekly average wholesale orange prices

Pounds

0.20

0.30

0.40

0.50

0.60

0.70

1990w1 2000w1 1990w1 2000w1 1990w1 2000w1 2000w1

4a–Weekly average wholesale cantaloupe prices

Pounds

0.20

0.30

0.40

0.50

0.60

0.70

1990w1 2000w1 1990w1 2000w1 1990w1 2000w1 2000w1
11

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Figure 5

Within-year seasonal price patterns by market 

5b–Weekly average wholesale grape prices, 2008/09

Dollars/lbs.

1.8

1.6

1.0

1.2

1.4

1 5 9 13 17 21 25 29 33 37 41 45 49 53

New York

Chicago Los Angeles

Boston

5e–Weekly average wholesale pepper prices, 2008/09

Dollars/lbs.

1.80

1.20

0.80

1.40

1.00

1.60

1 5 9 13 17 21 25 29 33 37 41 45 49 53

New York

Chicago Los Angeles

Boston

5c–Weekly average wholesale asparagus prices, 2008/09

Dollars/lbs.

3.0

2.5

1.0

1.5

2.0

1 5 9 13 17 21 25 29 33 37 41 45 49 53

New York

Chicago Los Angeles

Boston

5f–Weekly average wholesale tomato prices, 2008/09

Dollars/lbs.

1.60

1.00

0.60

1.20

0.80

1.40

1 5 9 13 17 21 25 29 33 37 41 45 49 53

New York

Chicago Los Angeles

Boston

Source: U.S. Department of Agriculture (USDA), Economic Research Service (ERS) calculations based on data from USDA Agricultural 

Marketing Service.

5d–Weekly average wholesale orange prices, 2008/09

Dollars/lbs.

0.80

0.70

0.30

0.40

0.50

0.60

1 5 9 13 17 21 25 29 33 37 41 45 49 53

New York

Chicago Los Angeles

Boston

5a–Weekly average wholesale cantaloupe prices, 2008/09

Dollars/lbs.

0.8

0.6

0.2

0.4

1 5 9 13 17 21 25 29 33 37 41 45 49 53

New York

Chicago Los Angeles

Boston
12

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

(2009), in their study of the wholesale peach market, find that a greater variety of local supply 

origins for markets in the domestic growing season leads to greater differences in prices among 

markets, while prices across markets vary less during the import season when sources are fewer. 

These market-level price movements have a geographic element that becomes more evident in the 

2008-09 set of season-average prices (table 4). For produce items grown predominantly in California 

(asparagus, grapes, cantaloupes, and oranges), prices increase moving west to east in the domestic 

season. Transportation costs increase with distance, and the increase in wholesale prices as produce 

items move from west to east calls for further investigation.6 In the import season, this trend does 

not hold and prices are more similar across the examined markets. 

6For asparagus and oranges, this trend is weaker in Chicago than in New York, likely due to the Chicago market having less California-grown produce.
13

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Table 4

Terminal market commodity origins during the domestic season

Seasonal marketing statistics (2008/09 averages)

Domestic season offerings shares3

Seasonal average prices1 Domestically produced

Commodity/city Domestic

Nondomestic2 California

Not 

California Import

——————— Percent ———————

Grapes

Los Angeles 1.06 1.18 95.2 0.0 4.8

Chicago 1.12 1.17 87.3 1.1 11.6

New York 1.23 1.28 90.8 1.2 8.0

Boston 1.37 1.38 87.5 1.1 11.4

Asparagus

Los Angeles 1.69 1.91 40.9 11.3 47.7

Chicago 1.92 1.85 6.4 22.5 71.1

New York 1.80 1.85 18.7 30.2 51.1

Boston 1.98 2.06 24.4 10.5 65.2

Cantaloupes

Los Angeles 0.24 0.36 56.5 36.0 7.5

Chicago 0.35 0.34 71.0 23.3 5.6

New York 0.38 0.33 64.7 31.2 4.0

Boston 0.46 0.39 66.8 31.8 1.3

Oranges

Los Angeles 0.42 NA 79.7 11.6 8.7

Chicago 0.52 NA 55.5 34.2 10.3

New York 0.44 NA 62.7 35.5 1.8

Boston 0.54 NA 58.3 32.4 9.3

Peppers

Los Angeles 1.29 NA 32.4 0.0 67.6

Chicago 1.33 NA 15.3 20.8 63.9

New York 1.12 NA 11.5 34.1 54.4

Boston 1.24 NA 4.5 36.3 59.2

Tomatoes

Los Angeles 0.80 NA 21.2 2.8 76.0

Chicago 1.04 NA 8.5 34.1 57.4

New York 0.95 NA 7.9 44.0 48.1

Boston 1.00 NA 3.5 51.1 45.4

1Dollars per pound; the domestic season is defined as the weekly range where 80 percent of domestic offerings occur. 

2For oranges, peppers, and tomatoes, significant domestic production occurs throughout the calendar year, and so for 

these commodities, numbers are representative of the entire year.

3Shares of sale price observations

Source: U.S. Department of Agriculture (USDA), ERS calculations based on USDA Agricultural Marketing Service

selected shipment data.
14

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

How Fuel Prices Affect Produce Prices

Our investigation of the ways in which fuel prices affect fresh produce prices involves three 

steps, each analyzing a sector of relevant data: truck rates, wholesale margins, and prices across 

multiple origins.

Trucking Rates

A data series of transportation costs from USDA’s Agricultural Marketing Service (AMS) provides 

detailed weekly rates for full-load produce truck transport between California (specifically, San 

Joaquin Valley) and a number of U.S. cities for 2005-2009.7 While the data do not cover the transportation costs for all routes and items arriving in the wholesale markets, they do provide information for routes common in fresh produce marketing. In the 2008/09 terminal market price data, the 

share of total price observations that show California as the origin ranges from a low of 11.3 percent 

for tomatoes up to 64.5 percent for oranges. 

We estimate how rates for routes of varying distances may be affected differently by changing fuel 

prices.8 AMS calculates the transportation cost, in dollars per pound, from the weekly route price—

the median of the listed weekly high/low total cost for the route— under the assumption that a full 

load is 38,000 pounds.9 Plotting a selection of the route prices, in dollars per pound, illustrates a 

strong seasonal component, a high correlation of price lows and highs among routes, and a spike in 

2008 corresponding closely to the spike in diesel prices (fig. 6). 

To look at the relationship of transportation costs with fuel prices more closely, we constructed the 

following basic regression model and applied it to the different route price series:

( ) ( ) ( ) ( ) ( ) 2

i,d 1 2 diesel 3 4 seas sup k (1) = + t _rate ln P trend trend + ’ t _ season ’ t _ supp ββ β β β + + + β ly ,

where t_ratei, d is the dollar-per-pound in week i median cost of transport from the San Joaquin 

Valley to destination city d; t_season is a vector of seasonal dummy variables;10 t_supply is a vector 

of qualitative supply dummy variables k;

11 and Pdiesel is the average of the previous 5 weeks of U.S. 

7Fruit and Vegetables Programs, Market News Branch.

8Truck transport is commonly cited as the dominant mode of domestic transportation for fresh fruits and vegetables; 

94 percent of such transport was by truck in 2002 (Denicoff et al., 2010).

9The authors do not feel the assumption of a 38,000-pound load to be significantly strong because, if applied to each 

observation, the assumption would affect the magnitudes of the results but not the relationships within the results. Dollars 

per pound is used here instead of other price measures (i.e., total route cost, dollars per mile) because it enables direct 

comparisons to produce prices, which are also expressed in dollars per pound.

10The seasonal dummy variables divide the calendar year into quarters. They correspond to January through March, 

April through June, July through September, and October through December.

11These variables vary slightly by commodity. Using conventions drawn from and assigned by AMS, they describe 

the quality, appearance, condition, and, when applicable, the size and color of the produce being shipped. The full list of 

descriptors is available from the authors upon request. 
15

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

on-highway average diesel prices.12 We also included a linear and quadratic time trend to account 

for technological growth and other time-specific factors.13

In the results of this regression (table 5), several basic patterns arise relating the transport cost to the 

distance traveled.14 First, in all routes, save for the short route from San Joaquin Valley to Los Angeles, 

diesel prices were found to have a statistically significant effect on transport prices. As the distance of 

the route increased, fuel prices increased the $/lb shipping cost, but the relationship between truck rates 

and mileage was not linear across all cities.15 For example, the distance traveled from San Joaquin 

Valley to Dallas is less than half that traveled to Boston, but the average truck rate for the Dallas route 

is over 60 percent that of the Boston route. Further, the average truck rates for routes to Chicago and 

Dallas are approximately equal, although the Chicago route is 600 miles longer. 

12We tested each time series value for the presence of a unit root using the Augmented Dickey-Fuller test. Unit roots 

present problems because they indicate that regression results may be spurious. While the inclusion of a time trend typically attenuates concerns in this regard, the truck rate series for Dallas and Los Angeles strongly indicated the presence 

of unit roots. As a result, we estimate the equations for these cities on first-differenced data.

13We also experimented with a yearly fixed-effects estimation. This approach yielded a number of counterintuitive 

results, likely stemming from the fact that several of these commodities undergo growing seasons or peak distribution 

periods that overlap calendar years. Given that it is not clear how to most accurately delineate years on a per commodity 

basis, any approach in this respect would necessarily be ad hoc. 

14Due to the large number of routes in this study and the primary focus on fuel price effects, only coefficients for the 

fuel price variables will be listed in the results tables. Results for the other variables are available upon request.

15The reason for the nonlinear relationships likely has to do with different demands for the different routes and differing possibilities for back-hauling from the destination cities. 

Selected produce truck rates (San Joaquin Valley origin) and diesel prices, 2005–2009

$/lbs.

Note: Diesel Price refers to the on-highway U.S. average price; truck rates were converted to dollars per pound using the 

assumed load of 38,000 pounds.

Source: U.S. Department of Agriculture, Economic Research calculations based on data from USDA Agricultural Marketing 

Service and U.S. Department of Energy, Energy Information Administration.

Figure 6

2005w1 2006w1 2007w1 2008w1 2009w1 2010w1

$/gallon

0

0.05

0.10

0.15

0.20

0.25

2.00

3.00

4.00

5.00

Boston

Atlanta

Dallas

Diesel Price

New York

Chicago

Los Angeles
16

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Fuel-price sensitivity, however, generally increases with distance traveled. The relationship holds across 

nearly all routes. The coefficient on diesel prices is insignificant for the Los Angeles route, and that 

estimation has very poor explanatory power. The Boston, New York, Miami, and Philadelphia routes 

are the longest, each closely comparable in length and with statistically identical fuel price coefficients 

that increase slightly with distance. The coefficient for Atlanta is about 80 percent that for New York, 

and the route is 80 percent as long. Only Dallas deviates somewhat from this linear relationship, with a 

coefficient that is one-third New York’s for half the traveling distance.16

The nonlinearity in the relationship between truck rates and mileage suggests an investigation of the 

importance of diesel costs in determining overall shipping costs. Given that our regression model 

is estimated with truck rates in $US diesel prices in natural log form, it is possible to calculate 

the estimated impact that a doubling of diesel prices would have on truck rates. In our case, that 

would be simply the coefficient on diesel prices divided by the average truck rate, by route. If diesel 

prices were to double, our results suggest that average truck rates for Boston, New York, Miami, 

Philadelphia, Atlanta, and Chicago would all increase by 46 to 48 percent. Dallas truck rates would 

rise about 25 percent, and those for Los Angeles would increase the least, by about 11 percent.17

Overall, this first step of our analysis suggests that fuel prices affect fresh produce prices through 

transport costs and that there is a logical pattern of increasing marketing costs as the distance from 

origin to market grows.

16Using the reported standard errors, it is possible to construct 95-percent confidence intervals for the diesel price coefficients. The estimated Boston, New York, Philadelphia, Miami, Atlanta, and Chicago coefficients are not statistically 

different from one another. The Dallas coefficient is statistically smaller than those for Boston, New York, Philadelphia, 

and Miami.

17Although this effect would not be expected, in practice, given that the coefficient on diesel prices is not significant.

Table 5

Regression results for the truck rate analysis, California-sourced produce

Boston New York Miami Philadelphia Atlanta Chicago Dallas Los Angeles

Pdiesel

1 0.072*** 0.071*** 0.070*** 0.067*** 0.057*** 0.048*** 0.023* 0.003

(0.007) (0.007) (0.006) (0.007) (0.005) (0.006) (0.011) (0.048)

R2 67.29 65.53 70.20 66.83 69.62 57.98 14.55 0.30

N 239 242 236 242 239 240 232 237

Avg. t_rate 0.154 0.148 0.149 0.145 0.118 0.104 0.091 0.026

Distance 3,151 2,930 2,882 2,856 2,313 2,156 1,572 222

% Increase 

in t_rate 46.75 47.97 46.98 46.21 48.31 46.16 25.27 11.54

(*) denotes significance at least at the 10-percent level, (**) denotes significance at least at the 5-percent level,

(***) denotes significance at least at the 1-percent level.

Robust standard errors for the estimated diesel price coefficients are in parentheses.

1For the sake of brevity, the only reported coefficient is for the key variable of interest, Pdiesel. The remaining independent variables included in this 

model are t_season, t_supply, and linear and quadratic time trends. The full regression results are available from the authors upon request.

 “Distance” refers to the approximate distance (in road miles) from San Joaquin, CA to the destination city.

“% Increase in t_rate” is the estimated percentage impact on truck rates that would occur given a doubling of diesel prices. It is calculated as the 

coefficient on Pdiesel divided by the average market-level truck rate.
17

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Wholesale Price Margin 

The second step of the analysis is to examine the price margin between shipping-point prices (a 

proxy for farm-level prices) and wholesale produce prices. We use price observations for Californiagrown produce because the shipping-point price data are exclusive of imports and because 

California is a significant supplier of all six commodities to wholesale terminal markets. Although 

this limits the scope of the analysis in terms of supply origins, a farm-level price proxy does enable 

us to distinguish fuel price effects that are more precisely related to transportation costs. 

This measure of the margin between wholesale prices and approximate farm prices is constructed 

from wholesale terminal market prices and free-on-board shipping-point prices for each commodity, 

provided by AMS. These shipping-point prices are very similar (e.g., daily frequency, item size and 

variety information, and price specified as high or low range) to the terminal market price data and 

thus are treated in a similar manner. AMS does not track all shipping-point prices, so the number of 

price observations over 2000-2009 varies considerably by commodity. In this analysis, the number 

of observations depends on the significance of California as a supplier of the commodity.

To create a variable for the wholesale margin, we decided to match terminal market prices with 

shipping-point prices lagged by 1 week, since most markets are likely to be over 2,000 miles from 

California. In addition, we wanted to avoid the possibility (which could arise in weekly aggregation) 

of shipping-point prices leading terminal market prices. Item size and variety are used to match 

prices to obtain a more consistent measure.18 Thus, shipping points of produce items matched to 

wholesale prices provide approximate differences between farm and wholesale prices that can be 

tracked over time. 

With this estimated measure, we focus on the relationship between fuel prices (specifically, U.S. diesel 

prices, as the shipments being analyzed are all domestic) and the wholesale margin. To isolate the fuel 

price impact, we construct a simple regression model with the following form (for each commodity):

( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

( ) ( ) ( )

,, ,, 1 2 3 4 ,

5 ,6 7 ,8 9

2

10

– ln ln _ ln _

 _ _ _ ln

’ ’

wd j wd j m w w d

w d w w d diesel

spec p seas s

 TM SP supply total off city off

 share off sp dem share light P trend 

 trend spec season

β β β β

β β β ββ

ββ β

(2) = + + +

+ + + ++

++ + ,

where TMw,d,j (or SPw,d,j) is the terminal market (or shipping point) price for week w, market d, 

and commodity j (asparagus, tomatoes, etc.); season is a vector of dummy variables to capture 

seasonality; specp is commodity fixed-effects p (dummy variables for particular varieties and 

sizes); supplym is the estimated overall monthly U.S. supply of the fresh produce item for month 

m; total_offw is the total number of weekly observed prices in all markets for items originating 

from California; city_offw,d is the total weekly number of observed prices at a terminal market; 

share_offw,d is the share of the week’s prices at a terminal market that are from California; sp_demw

is the share of the week’s shipping-point observations where demand was listed as at least “good”; 

share_lightw,d is the share of the week’s terminal market price observations where the offerings 

18For grapes, prices are not matched on size due to the large number of size and variety combinations possible in the 

markets and the consequent lower number of price matches when both dimensions are used.
18

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

were described as “light”; and Pdiesel is the average of the previous 5 weeks’ on-highway average 

diesel price. 19, 20

As with the regression model (1), we include linear and quadratic time trends. We estimate the 

diesel price impact for all commodities and cities analyzed (table 6), with model statistics listed in 

appendix table A1. Recall (from interpreting the coefficients) that a 100-percent increase in Pdiesel

will be associated with an increase of the price margin by ββ8 because Pdiesel is in natural log form 

and the dependent variable is in levels (dollars per pound). The coefficients in table 6 can be directly 

compared across markets and commodities to gain a sense of expected changes in the wholesale 

price margin, expressed in dollars per pound. 

Fuel prices are a statistically significant factor in determining the difference between domestic 

wholesale and farm-level produce prices: the large majority of diesel price coefficients are positive 

and significant (table 6). The estimated magnitudes vary considerably by commodity and terminal 

market,21 but a 100-percent increase in the price of diesel fuel can be expected to increase the 

produce marketing margins from between 3 to 19 cents per pound. 

By region, the regression results generally show a pattern of increasing sensitivity to fuel prices 

with distance, with the diesel coefficients for the Northeast and Southeast markets generally larger 

than those for the Midwest, which are in turn larger in most cases than those for California (table 

6). This is most clear when examining the population-weighted average coefficients by region and 

commodity. For asparagus, the average coefficient for California is higher than that for the Midwest, 

but for the most part, marketing margins are more sensitive to fuel prices with distance.

Examining the coefficient of variation (CV) across commodities but within markets helps clarify 

the extent to which the impact of fuel prices varies.22 The CV for the average regional coefficients 

is highest in California at 1.5 and decreases with distance, reaching a minimum of 0.5 for the 

Northeast markets. This suggests that transportation costs may serve to mitigate wholesale price 

volatility across commodities and possibly within commodity markets across time. As the fuel 

share of wholesale prices increases, the potential for volatility deriving from alternative supply-side 

sources, such as labor or financing costs, decreases.

It is interesting to note how the fuel price effect varies with mileage and how this variation differs 

across commodities. For example, the effect for asparagus—an item for which the California 

19This monthly aggregate supply variable estimates a basic accounting of the total U.S. supply for a given fresh produce item by combining monthly import and export data from the USDA Foreign Agricultural Service (FAS) with annual 

U.S. production data from the USDA-ERS’s Food Availability Data System, which has been has been scaled to monthly 

numbers using domestic movement data from AMS.

20Many empirical approaches to studying marketing margins have employed structural models, examining supply 

and demand simultaneously in order to identify key economic relationships (Wohlgenant, 2001). Our markets of interest 

benefit from highly inelastic supply in the short term, but nevertheless, in this reduced-form setting the shifters for seasonality must be thought of as proxies for changes in demand. In practice, a wide number of factors other than price can 

influence demand.

21The markets are generally categorized by region in a geographic manner, with assignments as follows: California—Los Angeles and San Francisco; Midwest—Detroit, Chicago, and St. Louis; Southeast—Atlanta, Columbia, and 

Baltimore; Northeast—Boston, New York, Philadelphia, and Pittsburgh. Geographically, Baltimore may fit more closely 

in the Northeast, but the results for this market are much closer to the Atlanta market in both produce price analyses.

22The CV is the sample standard deviation of a variable divided by the sample mean. It is a unitless measure of dispersion that can be interpreted as a percentage and is ideal for comparing dispersion across samples with different units or 

means. 
19

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

marketing season is relatively short and for which there is a continuous, significant supply of imports—

differs fundamentally from that for cantaloupes and oranges. Estimates of the fuel price effects for 

asparagus are generally much higher than for the other commodities, and there is no clear pattern 

across geographic markets of their magnitudes. However, California is the dominant supplier to all 

terminal markets for much of the year for cantaloupes and oranges. For these two items, there is a welldefined pattern of increasing sensitivity to fuel prices as the distance from California increases, and 

within a given market, the coefficients are very similar for both cantaloupes and oranges.

For the three lowest priced produce items (cantaloupes, oranges, and tomatoes), the average wholesale price margins themselves (appendix table A1) follow a logical pattern of increasing west to east, 

indicating that transport costs are a particularly significant factor in determining wholesale prices. 

Peppers also follow this pattern to some extent, while asparagus and grapes, which both have noticeably higher average margins in our data, have similar margins across markets. A comparison of the 

diesel price coefficients with the results in the appendix suggests that, in general, a large portion of 

the wholesale margin is related to fuel costs for transport, given that both sensitivity to fuel prices 

and the margins themselves increase with distance for four of the six commodities. 

Table 6

Regression results for the diesel price coefficient, ln(Pdiesel), in the wholesale price margin analysis

Asparagus Grapes Cantaloupes Oranges Tomatoes Peppers

Coefficient of 

Variation1

Northeast

Boston 0.111*** 0.187*** 0.089*** 0.075*** 0.115*** 0.116*** 0.361

New York 0.175*** 0.051*** 0.066*** 0.061*** 0.048*** -0.003 0.885

Philadelphia 0.094*** 0.099*** 0.076*** 0.085*** 0.073*** -0.011 0.586

Pittsburgh 0.048 0.131*** 0.055*** 0.091*** 0.047*** 0.048*** 0.490

Average2 0.141 0.085 0.070 0.070 0.062 0.016 0.544 

Southeast

Atlanta 0.333*** 0.106*** 0.079*** 0.102*** 0.137*** 0.051*** 0.752

Columbia N/A 0.026 0.075*** 0.082*** 0.067*** -0.224*** 24.992

Baltimore 0.169*** 0.156*** 0.092*** 0.110*** 0.056*** 0.090*** 0.383

Average 0.253 0.114 0.083 0.103 0.106 0.039 0.621

Midwest

Detroit 0.041 0.121*** 0.076*** 0.032*** 0.050*** 0.036*** 0.574

Chicago 0.023 0.070*** 0.032*** 0.044*** -0.039*** 0.060*** 1.223

St. Louis -0.046 0.162*** 0.058*** 0.059*** 0.064*** 0.060*** 1.106

Average 0.016 0.099 0.048 0.043 0.002 0.054 0.775

California

Los Angeles 0.124*** -0.022 -0.002 0.029*** 0.000 0.052*** 1.751

San Francisco 0.160*** 0.105*** 0.018*** 0.006** -0.029*** 0.050*** 1.350

Average 0.133 0.010 0.003 0.023 -0.007 0.051 1.458

 (*) denotes significance at least at the 10-percent level, (**) denotes significance at least at the 5-percent level, (***) denotes significance at 

least at the 1-percent level.

1The coefficient of variation is a measure of volatility that is calculated as the standard deviation of the estimated regression coefficients, across 

commodities but within markets, divided by their mean.

2The average coefficients, by region, are weighted by population as provided by the 2010 Census.

“NA” signifies that this model was not used due to a small sample size.

The wholesale price margin analysis features a number of independent variables not reported in the table. These are season, supply, total_off, 

city_off, share_off, sp_dem, share_light, and linear and quadratic time trends. The complete regression results are available from the authors 

upon request.
20

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Prices From Multiple Origins 

As the last step in analyzing the relationship between fuel prices and wholesale price margins, we 

look at data on produce shipments from multiple sources. In these estimates, we omit a farm-price 

measure due to data limitations. The objective is to explore the impact of fuel costs on fresh produce 

prices by focusing on the entire calendar year and a more varied set of transport routes.

We construct a model very similar to the wholesale-price-margin model (2) in the previous section, 

one that can be applied to a greater variety of shipping routes. These routes are again organized by 

market and origin and commodity, using the major origins shown in figures 2a-f along with a few 

others that are significant in a limited number of markets.23 Our wholesale price model is:

( ) ( ) ( )

( ) ( ) ( ) ( )

( ) ( ) ( )

wd jo m p p w o

wd po 5 wdop w d oil

seas s

 TM supply spec total off 

 city off + share off share light P

 trend trend season

, , , 1 2 3, 3 ,

4 ,,, ,,, 6 , 7

2

9 10

ln ln _

ln _ _ _ ln

’ ,

β β β β

β β ββ

ββ β

(3) = + + +

+ + +

++ +

where the representations other than those given for model (2) are TMw,d,j,o for the terminal market 

price for week w, market d, commodity j, and origin o; total_offw,o as the total number of weekly 

observed prices in all markets from origin o; city_offw,d,p,o as the total weekly number of observed 

prices of specification p from origin o at a terminal market; share_offw,d,p,o as the share of the 

week’s prices at a terminal market that were from origin o; and Poil for the average of the previous 5 

weeks’ world crude oil spot price.24

The supply origins in these markets can vary both by commodity and geographic location of the 

market (table 4). With commodities such as grapes and cantaloupes, there is a specific domestic 

origins-only season with a single region (California, for both items) accounting for the vast majority 

of price observations in the markets, complemented by an off-season when a single exporting region 

supplies most terminal markets. This implies that for these two cases, at most points in the year, 

there are only a few supply routes per market at a given time.25 This is in contrast to tomatoes and 

bell peppers, where for most of the year there are several different supply regions producing concurrently. For these two commodities, production sourcing is less concentrated, and the produce sold at 

a given time in the terminal markets covers a vast array of transport distances. For example, data for 

the New York City market in the summer months include a significant number of price observations 

for tomatoes from California, Mexico, Canada, and regional sources, all occurring concurrently. 

When multiple origins supply an identical (or nearly so) commodity to a given market, arbitrage 

within terminal markets may limit the capacity for agents to adjust produce prices quickly and 

completely in light of fuel price fluctuations. This possibility should be kept in mind when interpreting the results.

23The routes are organized by origin in this analysis because of both the clear need to account for origin-specific 

fixed effects and fuel price coefficients and for the greater flexibility of allowing the season and supply variables to vary 

significantly based on the origin.

24This more general fuel price is used here rather than a diesel price (even though diesel is a common fuel in truck and 

ocean transport) because of the greater variety of origins (and thus different fuel types) represented in this analysis.

25Oranges also fit closely with this last statement, since imports generally make up a small share of offerings in all 

markets and domestic supply is primarily from Florida or California, with offerings from Florida tending to show up 

more in East Coast markets.
21

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Applying our wholesale price model to the different commodities, markets, and origins reconfirms the statistical significance of fuel prices in determining wholesale fresh produce prices 

and suggests a number of underlying patterns. Correlation coefficients with Poil—the average of 

the previous 5 weeks’ world crude oil spot price from model (3)—are listed in table 7 for each 

commodity, market, and origin series (with sample statistics listed in appendix table A2). There 

is a general similarity among coefficient magnitudes within regions and by origin that, along with 

the statistical significance of the estimates, implies that fuel prices are an important determinant 

of wholesale fresh produce prices and that the fuel price effect is geographically different.26 The 

overarching theme of the results remains that the impact of variations in the fuel price increases 

with distance to market; however, with more complicated routes and more origins for comparison 

purposes, there are caveats to this statement. The six commodities analyzed lend themselves to 

a convenient organization for discussion due to differences in their sourcing and the patterns 

observed in their fuel price coefficients.

Cantaloupes and Oranges

Cantaloupes come largely from California and Central America, enabling a clear comparison of 

fuel price effects on domestic and imported wholesale prices. The coefficients corresponding to 

California as the product origin correlate with distance from the origin, as they did in the wholesale 

price margin analysis. However, the same relationship does not apply to cantaloupes from Central 

America,27 which show very little sensitivity to changes in fuel prices. 

A comparison with the California origin (table 6) for cantaloupes shows lower coefficients for 

Central America, even when the marketing distance is much longer. Although many factors may 

be contributing to this difference in impacts, a few immediately stand out. Foremost is the method 

of transportation. Cantaloupes are trucked across the United States, but all tracked shipments from 

Central America arrived by ship, and ocean transport is only about 10 percent as energy-intensive 

as truck transport. The dispersion in coefficients could also be due to each of these originators being 

dominant suppliers when they appear in the markets, with their time periods typically distinct within 

the calendar year. 

The results for oranges exhibit, similar to cantaloupes, an increasing fuel price effect with distance. 

Throughout each season of the year, California and Florida account for the most and second-most price 

observations for oranges, respectively, while imports constitute only a small share of U.S. orange supplies. 

In the case of California-grown oranges, the familiar pattern emerges of lower fuel price sensitivity in the 

California markets and higher coefficients as distance increases from the San Joaquin Valley.

For Florida-grown oranges, the story is somewhat different. The fuel price coefficients are lowest 

for the Miami market, but in the other regions there is less distinction based on geography. Notably, 

the estimated fuel price sensitivity in the Northeast markets is the highest (table 7), even though 

the Midwest and California markets are geographically more distant. Thus, with oranges and 

cantaloupes, we are able to identify an increasing influence of fuel prices on wholesale prices for 

commodities that have a relatively small number of geographic sources and that exhibit a clear, yearlong hierarchy of total supply to U.S. consumers.

26There are also a few cases where some dispersion exists with a region and origin group; possible causes for some of 

these instances will be discussed later. 

27Central America in this study refers to the countries of Costa Rica, Guatemala, and Honduras.
22

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Table 7

Regression results for oil price coefficient, ln(Poil), in the multiple origin price analysis

Asparagus Cantaloupes Peppers

California Mexico Peru California

Central 

America California Florida Mexico Canada

Northeast

Boston 0.350*** 0.373*** 0.225*** 0.085*** 0.031*** 0.251*** 0.166*** 0.333*** 0.207***

New York 0.565*** 0.225*** 0.220*** 0.047*** -0.013* 0.159*** 0.147*** 0.324*** 0.288***

Philadelphia 0.380*** 0.367*** 0.248*** 0.066*** 0.028*** 0.142** 0.111*** 0.391*** 0.356***

Pittsburgh 0.446*** 0.303*** 0.351*** 0.058*** 0.028*** 0.186*** 0.157*** 0.355*** 0.171***

Average 0.491 0.297 0.236 0.057 0.004 0.171 0.144 0.340 0.280

Florida

Miami 0.634*** 0.261*** 0.330*** 0.080*** 0.021*** 0.063 -0.080*** 0.420*** 0.086

Southeast

Atlanta 0.398*** 0.357*** 0.401*** 0.093*** 0.052*** 0.148*** 0.148*** 0.407*** 0.205***

Columbia 0.161 0.200*** 0.247*** 0.097*** 0.023*** -0.012 0.206*** 0.168*** 0.292***

Baltimore 0.316*** 0.238*** 0.162*** 0.087*** 0.042*** 0.254*** 0.129*** 0.288*** 0.281***

Average 0.352 0.306 0.313 0.091 0.046 0.167 0.147 0.349 0.236

Midwest

Detroit 0.293*** 0.373*** 0.257*** 0.063*** 0.023*** 0.149*** 0.129*** 0.171*** 0.129***

Chicago 0.318*** 0.241*** 0.214*** 0.038*** 0.005 0.177*** 0.100*** 0.045** 0.121***

St. Louis 0.240*** 0.139*** 0.250*** 0.066*** 0.027*** 0.115*** 0.181*** 0.057*** 0.152***

Average 0.298 0.258 0.231 0.049 0.013 0.159 0.121 0.080 0.128

California

Los Angeles 0.341*** 0.363*** 0.187*** 0.005 -0.009 0.131*** N/A 0.132*** N/A

San Francisco 0.434*** 0.226*** 0.269*** 0.035*** 0.021*** 0.212*** N/A 0.170*** 0.466***

Average 0.364 0.328 0.208 0.013 -0.001 0.151 N/A 0.142 0.466

Grapes Oranges Tomatoes

California Chile Mexico California Florida California Florida Mexico

Northeast

Boston 0.299*** 0.268*** 0.147*** 0.102*** 0.096*** 0.242*** 0.255*** 0.041*

New York 0.231*** 0.226*** 0.206*** 0.098*** 0.072*** 0.126*** 0.142*** 0.075***

Philadelphia 0.194*** 0.184*** 0.309*** 0.112*** 0.094*** 0.172*** 0.130*** 0.190***

Pittsburgh 0.299*** 0.227*** 0.176*** 0.125*** 0.107*** 0.130*** 0.161*** 0.102***

Average 0.239 0.224 0.215 0.103 0.082 0.152 0.157 0.094

Florida

Miami 0.232*** 0.099*** 0.172*** 0.110*** 0.025*** 0.099*** 0.054*** 0.174***

Southeast

Atlanta 0.242*** 0.318*** 0.156*** 0.128*** 0.060*** 0.215*** 0.181*** 0.276***

Columbia 0.150*** 0.141*** 0.011 0.118*** 0.059*** 0.221*** 0.224*** 0.330***

Baltimore 0.254*** 0.275*** 0.280*** 0.136*** 0.090*** 0.177*** 0.174*** 0.082***

Average 0.238 0.289 0.182 0.130 0.069 0.204 0.183 0.221

—continued
23

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Tomatoes and Bell Peppers

In table 7, an alternative sourcing picture occurs with tomatoes and bell peppers. Both commodities 

exhibit supply patterns in which the terminal markets alternate sources throughout the year, principally in seasons with supplies from Mexico/Florida and California/Canada, along with some smaller 

regional suppliers in the summer months. The patterns for items originating from California and 

Florida follow a correlation with distance, where markets closest to the production origin show the 

least fuel price impact. However, as with oranges, for bell peppers and tomatoes from California and 

Florida the coefficients within the Midwest and Northeast regions are very similar. In looking at the 

import series, Mexican-sourced items generally have the highest fuel-market price coefficients in those 

regions, while the Canadian-origin series show the lowest oil price sensitivity in the Midwest, where 

markets are closest to the growing regions of central and western Canada (in the case of peppers). 

Grapes and Asparagus 

The last two commodities analyzed, grapes and asparagus, generally have the least recognizable 

patterns in fuel price coefficients, but oil prices are still a significant determinant in their wholesale prices. For both commodities, California is the dominant domestic supplier to the terminal 

markets; however, for asparagus in recent years, the domestic production season has included heavy 

imports. As noted in the wholesale price margin analysis, this may be an underlying cause for a 

lack of geographic correlation with the coefficients across markets for asparagus. Imports during 

the domestic growing season are a further confounding factor in identifying the role of fuel prices, 

resulting in scattered observed effects. 

For grapes (table 7), relatively stronger patterns emerge from the analysis, with coefficient magnitudes typically similar across origins (especially for California and Chile), and with the lowest values 

presenting some interesting relationships. California-grown grapes generally show increasing fuel 

price sensitivity with distance from California, though the pattern is not as clear as it has been for 

other commodity/origin pairings given that the average commodities for the Northeast, Southeast, and 

Midwest are all closely comparable. Chilean-grown grapes also have their lowest coefficients for the 

Table 7

Regression results for oil price coefficient, ln(Poil), in the multiple origin price analysis—Continued

Grapes Oranges Tomatoes

California Chile Mexico California Florida California Florida Mexico

Midwest

Detroit 0.230*** 0.277*** 0.325*** 0.064*** 0.061*** 0.123*** 0.188*** 0.102***

Chicago 0.205*** 0.201*** 0.245*** 0.069*** 0.063*** -0.076*** 0.083*** -0.012

St. Louis 0.293*** 0.307*** 0.325*** 0.088*** 0.082*** 0.162*** 0.241*** 0.164***

Average 0.226 0.239 0.279 0.071 0.066 0.016 0.137 0.047

California

Los Angeles 0.095*** 0.270*** 0.116*** 0.053*** 0.036*** 0.042*** 0.140*** 0.171***

San Francisco 0.211*** 0.272*** 0.266*** 0.052*** 0.026*** 0.078*** 0.111*** 0.134***

Average 0.124 0.271 0.154 0.053 0.033 0.051 0.133 0.162

(*) denotes significance at least at the 10-percent level, (**) denotes significance at least at the 5-percent level, (***) denotes significance at least 

at the 1-percent level.

“NA” signifies that this model was not used due to a small sample size.

The multiple origins analysis features a number of independent variables not reported in the table. These are season, supply, total_off, city_off, 

share_off, spec, share_light, and linear and quadratic time trends. The complete regression results are available from the authors upon request.
24

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Northeast, which has the main ports of entry (New York and Philadelphia) for this commodity and 

origin,28 and for Florida, which has the geographically closest U.S. city (Miami). 

There is increasing interest in the economic impacts and viability of local and regional food systems 

in the United States (Martinez et al., 2010). Economists, policymakers, and agents within U.S. agribusiness are interested in the potential impacts of the expansion of local and regional production 

on food prices and the environment. The difference in the means by which fuel prices influence 

regional versus national food systems is likely to be an important component in the discussion. The 

results for the six commodities studied here suggest that there are likely to be important differences 

in this regard throughout U.S. food supply systems and that the continued growth of imports may 

have an impact in mitigating regional price volatility that is due to fuel price fluctuations.

Wholesale Price Responses to Oil Price Increases

Our discussion to this point has focused on comparing the values of the oil price coefficients 

themselves across origins and markets, with the reasoning that this provides a direct and absolute measure to compare how oil price changes affect market prices of goods traveling routes 

of varying lengths. However, it is also worthwhile to gauge how significant these different coefficients are relative to the typical produce price for these markets and origins. To facilitate such 

comparisons, we divide each estimated coefficient on oil prices by the average wholesale market 

price for the commodity of interest. We then average these ratios, weighting by population, within 

regions (table 8). Given that we measure the fuel-price sensitivity using the natural log of oil 

prices, this ratio can be interpreted by the percentage increase in market prices that would be 

expected given a doubling of oil prices. 

Examining these ratios across commodities and regions, we are able to garner new insights into 

the importance of fuel prices in shaping fresh produce prices. Central American cantaloupe prices 

again had the least overall sensitivity to oil price changes, showing the potential to increase by only 

3 percent given a doubling of oil prices. In most cases, imported commodities show the least sensitivity to oil prices for their respective commodities, likely due to the fact that international transport 

is generally less energy-intensive. On average, for most domestic origins, the results suggest that 

market prices could be expected to increase by approximately 20 to 28 percent if oil prices doubled. 

Given the number of commodities with multiple origins and large import shares, however, these are 

likely to be short-term impacts in many cases. When applicable and all else held constant, import 

shares can be expected to increase given a sharp rise in domestic produce prices.

These percentages corroborate previous work conducted on fuel and energy impacts on food prices. 

Canning (2011) calculated the value added of the energy and transportation industries on the retail 

food dollar to be approximately 10.2 cents. This can be interpreted to mean that a 100-percent 

increase in fuel and transportation costs would result in an upper-bound 10-percent increase in 

consumers’ food expenditures (the type of food costs that Canning estimated in his food dollar 

analysis).29 Given that the figures calculated in table 8 pertain to wholesale prices, which incorporate fewer labor, packaging, and trade costs, it is expected that the relative impact of fuel prices 

calculated for wholesale prices should be somewhat higher than the retail price value added.

28Summary of AMS Movement Reports, 2000 through 2009. Available online at http://www.marketnews.usda.gov/

portal/fv.

29This is assuming no factor substitution in production.
25

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

At the outset of this report, we noted that understanding how fuel prices can affect the levels and 

volatility of fruit and vegetable prices can affect consumer expenditure patterns. There is timely 

motivation for research into this relationship, given the importance of fruits and vegetables to 

consumers’ diets and the breadth of work within USDA and beyond aimed at understanding and 

promoting American’s consumption of fresh produce. Andreyeva et al. (2010) reviewed the empirical 

work on this subject to calculate demand elasticities for fruits and vegetables. Twenty studies of fruit 

demand yielded estimates ranging from 0.16 to 3.02, with an average of 0.70. For vegetables, another 

20 studies demonstrated a demand elasticity range of 0.21 to 1.11, with a mean of 0.58. Therefore, 

fruit and vegetable demand is responsive to price and may well be price elastic for several individual commodities. Quantifying the potential impacts of fuel price surges on demand, even in the 

short term, is beyond the scope of this study. Such an analysis would require the estimated impact 

of fuel price increases on retail produce prices as well as cross-price elasticities among the produce 

commodities, which are likely to exhibit variation in their fuel-price responsiveness. However, the 

Table 8

Comparison of oil price coefficients to average market prices in the multiple origin 

price analysis1

Northeast Florida Southeast Midwest California Average

Asparagus

California 34.0 41.1 21.8 20.5 22.6 27.9

Mexico 16.9 13.1 17.3 15.1 18.1 16.5

Peru 12.3 17.0 15.2 11.4 9.3 12.1

Cantaloupes

California 19.0 24.2 28.7 17.4 5.6 17.2

Central America 1.2 6.3 14.0 3.9 -0.7 3.1

Peppers

California 19.2 8.1 19.1 20.9 21.0 19.2

Florida 30.7 -14.7 20.0 20.5 N/A 22.5

Mexico 28.2 37.3 27.5 7.1 14.2 21.4

Canada 25.6 N/A 13.0 8.7 24.0 20.0

Grapes

California 22.4 23.2 23.9 21.3 11.8 20.1

Chile 20.8 9.2 28.8 21.0 25.0 21.8

Mexico 19.8 14.8 17.4 25.0 14.3 19.1

Oranges

California 23.8 24.4 29.5 16.1 15.5 21.1

Florida 26.9 9.5 24.0 21.4 N/A 23.5

Tomatoes

California 23.5 14.6 29.0 2.9 8.4 16.0

Florida 22.6 10.3 26.9 21.3 26.6 22.8

Mexico 11.1 21.2 26.6 6.1 22.7 15.0

Note: These numbers represent the multiple origin price analysis oil price coefficient divided by the average market price in 

the analysis data (scaled up by 100 percent) and then averaged by region. They provide an estimate of how large the oil 

price coefficients are relative to the overall market prices. They can also be interpreted as the percentage increase in the 

average market price that would be expected, given a doubling of the fuel price.

1The impacts on demand are drawn from estimates of demand elasticities for fruits and vegetables, as reviewed by 

Andreyeva et al. (2010). Any errors are our own. Each reported value gives the absolute value of the estimated impact on 

demand, given a doubling of fuel prices, in percentage terms. These calculations assume the complete and symmetrical 

transmission of wholesale price changes to retail prices.
26

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

potential relationship between fresh produce consumption and fuel price increases warrants further 

investigation, in light of the estimated impacts of fuel price surges on wholesale prices.

Examining the impact of a doubling of fuel prices on wholesale commodity prices shows that the 

magnitudes of the fuel price coefficients do not necessarily tell the complete story. For example, 

California grapes have higher oil price coefficients compared with most other commodities, yet their 

average market prices are also much higher, leading to the overall impact on the wholesale produce 

price to be relatively low. This is also true for asparagus, with its much higher market price than that 

for other commodities. Finally, it is not clear whether produce of domestic or international origins 

have lower average effects of fuel prices on market prices overall. The fuel price share of commodity 

prices is largely dependent on the region considered, with proximity to growing source and port of 

entry of key importance.
27

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Conclusions

This study provides empirical insights into how the sensitivity of wholesale prices for fresh produce 

to transportation costs is related to supply patterns of the produce. Our estimates of this price sensitivity are based on analyses of three aspects of produce marketing. The analysis of produce truck 

transportation rates directly estimates the link between fuel and transport costs across the Nation. 

The wholesale price margin analysis examines the relationship between fuel costs and terminal 

market prices, allowing for changes occurring at the farm level within and across years. Finally, the 

multiple origin analysis gives a comparative picture of the effect on prices from major supply origins 

across the calendar year and shows patterns that are specific to different sources, which can vary 

considerably across commodities.

The procedures for our exploration follow general strategies in the existing literature of wholesale 

produce market pricing models, but these strategies have limitations to be addressed.30 The present 

study consistently finds unidirectional price influence between the two empirical price measures of 

fuel and wholesale market prices, but as all of the terminal markets themselves are unique, much is 

still unknown about local price setting. In addition, certain variables that could provide more precision in the estimation of the pricing models are not available. These variables include exact quantities supplied or total sales to better gauge the supply and demand interaction, as well as upstream 

produce prices (in the case of the multiple origin analysis) or transport costs to measure the exact 

margin for all cases. Finally, we looked closely at only six commodities. Although our results may 

be applicable to other fruits and vegetables with similar supply patterns and pricing characteristics, 

there is potentially much that is not known about items with different supply characteristics, for 

example, fruits and vegetables with greater capacity for storage.

Several questions are inherent in our analyses. First, Do large increases in fuel prices foretell 

significant increases in wholesale fresh produce prices? The answer suggested by the study is yes, 

supported by the consistency of the price-changing effect across geographic regions, specific to the 

supply patterns of each commodity. This is illustrated by a comparison of the results for asparagus 

and cantaloupes or oranges. For high-priced (dollars per pound) produce items such as asparagus, 

with more mixed transport modes and without clearly defined seasons for different origins, an 

increase in fuel prices does lead to statistically significant increases in market prices, but effects on 

particular regions or times of the year are not clearly defined. In contrast, low-priced produce items 

like cantaloupes or oranges, with more defined supply seasons and basic transport routes, have definite impacts from fuel price changes. During the domestic growing season, fuel price impacts (an 

increase in wholesale prices) show a strong correlation with distance from the production origin. For 

imports from a relatively close source (Central America), wholesale price sensitivity to fuel costs 

was generally found to be minimal. With cantaloupes, the fuel price effect has a clear relationship 

with time of year because the supply origins are linked to the domestic growing season. 

Next, Are wholesale fresh produce price changes from fuel price effects likely to be a strong 

determinant of market prices? Again, the answer suggested by the study is, yes, in most cases. 

Aside from Central American cantaloupes, imports were estimated to exhibit an overall average 

impact of a 12- to 21-percent increase in market prices from a doubling in world oil prices. Domestic 

prices were also found to have a significant connection to fuel prices, with an overall average impact 

30See, for example, Goodwin et al. (1988). Additionally, Wohlgenant (2001) provides a review of the literature up to 

that date on the economic analysis of marketing margins.
28

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

of a 16- to 28-percent increase in market prices from a 100-percent increase in world oil prices. For 

the domestic cases, there was considerable mitigation in sensitivity if the produce originated in the 

same State (or, in most cases, adjoining States) where it was marketed. 

Further research to extend this analysis to retail prices would be particularly worthwhile. An empirical understanding of the relationship between wholesale price volatility and retail price volatility 

would be valuable, particularly in the context of how these phenomena may differ according to the 

impact of fuel prices. Our findings also suggest a potential relationship between the length of shipping routes and price volatility, which warrants further investigation due to the potential implications 

for consumer demand and retail price forecasting. Overall, a better understanding of the impact 

of fuel prices on consumer food expenditures and consumption can add to the body of research on 

Americans’ adherence to healthy eating guidelines. 
29

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

References

Andreyeva, T., M. Long, and K. Brownell. 2010. “The Impact of Food Prices on Consumption: A 

Systematic Review of the Research on the Price Elasticity of the Demand for Food,” American 

Journal of Public Health 100(2):216-22.

Ballenger, N., W. Coyle, W. Hall, B. McGregor, and R. Hawkins. 1999. “Transportation Technology 

Eases the Journey for Perishables Going Abroad,” Agricultural Outlook. U.S. Department of 

Agriculture, Economic Research Service. 

Calvin, L., and R. Cook. 2001. U.S. Fresh Fruit and Vegetable Marketing: Emerging Trade 

Practices, Trends, and Issues. Agricultural Economic Report 795, U.S. Department of 

Agriculture, Economic Research Service. 

Canning, P. 2011. A Revised and Expanded Food Dollar Series: A Better Understanding of Our 

Food Costs. Economic Research Report 114., U.S. Department of Agriculture, Economic 

Research Service.

Cook, R. 2002. “The Dynamic U.S. Fresh Produce Industry: An Industry in Transition.” Chapter 

in A. Kader (ed.), Postharvest Technology of Horticultural Crops, Third Edition. University of 

California at Davis, Division of Agriculture and Natural Resources, Publication 3311. 

Denicoff, M., E. Jessup, A. Taylor, and D. Nibarger. 2010. “The Importance of Freight 

Transportation to Agriculture.” Chapter in Study of Rural Transportation Issues, Congressionally 

mandated study produced by the U.S. Department of Agriculture and the U.S. Department of 

Transportation. 

Gilbert, C. 2010. “How to Understand High Food Prices,” Journal of Agricultural Economics

61(2):398-425.

Goodwin, H., S. Fuller, O. Capps, and O. Asgill. 1988. “Factors Affecting Fresh Potato Price in 

Selected Terminal Markets,” Western Journal of Agricultural Economics 13(2):233-43.

Hendrickson, J. 1994. Energy Use in the U.S. Food System: a Summary of Existing Research 

and Analysis. Center for Integrated Agricultural Systems, University of Wisconsin-Madison, 

Madison, WI.

Huang, S., and K. Huang. 2007. Increased U.S. Imports of Fresh Fruit and Vegetables. Outlook FTS 

328-01, U.S. Department of Agriculture, Economic Research Service.

Lucier, G., S. Pollack, M. Ali, and A. Perez. 2006. Fruit and Vegetable Backgrounder. Outlook VGS 

313-01, U.S. Department of Agriculture, Economic Research Service.

Martinez, S., M. Hand, M. Da Pra, S. Pollock, K. Ralston, T. Smith, S. Vogel, S. Clark, L. Lohr, 

S. Low, and C. Newman. 2010. Local Food Systems: Concepts, Impacts, and Issues. Economic 

Research Report 97. U.S. Department of Agriculture, Economic Research Service.
30

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Pirog, R., T. Van Pelt, K. Enshayan, and E. Cook. 2001. Food Fuel, and Freeways: An Iowa 

Perspective on How Far Food Travels, Fuel Usage, and Greenhouse Gas Emissions. Leopold 

Center for Sustainable Agriculture, Ames, IA.

Pollack, S. 2001. “Consumer Demand for Fruit and Vegetables: The U.S. Example.” Chapter in A. 

Regmi (ed.), Changing Structure of Global Food Consumption and Trade. Agriculture and Trade 

Report WRS 01-1. U.S. Department of Agriculture, Economic Research Service.

Raper, K., S. Thornsbury, and C. Aguilar. 2009. “Regional Wholesale Price Relationships in 

the Presence of Counter-Seasonal Imports,” Journal of Agricultural and Applied Economics 

41(1):271-90.

Sands, R., P. Westcott, M. Price, J. Beckman, E. Leibtag, G. Lucier, W. McBride, D. McGranahan, 

M. Morehart, E. Roeger, G. Schaible, and T. Wojan. 2011. Impacts of Higher Energy Prices 

on Agriculture and Rural Economies. Economic Research Report 123, U.S. Department of 

Agriculture, Economic Research Service, Washington, DC.

Tegene, A. 2009. “High and Volatile Commodity Prices: What Do They Mean for Food Prices and 

Consumers: Discussion,” American Journal of Agricultural Economics 91(5):1468-69.

U.S. Department of Agriculture, Agricultural Marketing Service. 2009. Agricultural Refrigerated 

Truck Quarterly. RTQ-4th Quarter. U.S. Department of Agriculture, Washington, DC.

Weber, C., and H. Matthews. 2008. “Food-Miles and the Relative Climate Impacts of Food Choices 

in the United States,” Environmental Science and Technology 42(10):3508-13. 

Wendt, M., and J. Todd. 2011. The Effect of Food and Beverage Prices on Children’s Weights.

Economic Research Report 118. U.S. Department of Agriculture, Economic Research Service, 

Washington, DC.

Wohlgenant, M. 2001. “Marketing Margins: Empirical Analysis.” Chapter in B. Gardner and G. 

Rausser (eds.), Handbook of Agricultural Economics, Elsevier Science.
31

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Appendix: Regression model sample statistics for 

wholesale price analyses

Table A1

Average margins and sample statistics for the wholesale price margin analysis

Asparagus Grapes Cantaloupes

Market E(Y)1 Obs. R2 E(Y) Obs. R2 E(Y) Obs. R2

Boston 0.28 356 0.23 0.29 1,192 0.36 0.15 696 0.61

New York 0.24 299 0.32 0.31 1,176 0.23 0.15 697 0.46

Philadelphia 0.21 271 0.29 0.24 1,196 0.20 0.14 704 0.57

Pittsburgh 0.33 306 0.23 0.24 992 0.34 0.14 664 0.44

Atlanta 0.53 177 0.40 0.25 931 0.23 0.15 723 0.58

Columbia NA NA NA 0.36 682 0.26 0.20 438 0.50

Baltimore 0.31 321 0.37 0.28 1,054 0.38 0.17 727 0.57

Detroit 0.28 284 0.37 0.30 1,207 0.28 0.15 668 0.50

Chicago 0.28 274 0.20 0.28 939 0.11 0.13 757 0.23

St. Louis 0.25 196 0.31 0.27 832 0.31 0.11 634 0.45

Los Angeles 0.26 410 0.36 0.24 1,006 0.17 0.05 628 0.43

San Francisco 0.25 401 0.35 0.29 1,164 0.25 0.07 679 0.33

Oranges Tomatoes Peppers

Market E(Y)1 Obs. R2 E(Y) Obs. R2 E(Y) Obs. R2

Boston 0.18 3,798 0.45 0.26 371 0.35 0.17 332 0.17

New York 0.16 3,510 0.32 0.22 473 0.40 0.16 537 0.23

Philadelphia 0.17 3,305 0.36 0.22 368 0.31 0.16 522 0.15

Pittsburgh 0.20 3,205 0.67 0.23 449 0.39 0.18 475 0.37

Atlanta 0.17 2,772 0.51 0.22 480 0.43 0.18 498 0.21

Columbia 0.17 1,654 0.69 0.21 290 0.16 NA NA NA

Baltimore 0.17 3,604 0.54 0.28 329 0.34 0.22 531 0.38

Detroit 0.17 3,527 0.24 0.17 1,054 0.38 0.15 714 0.07

Chicago 0.19 3,597 0.31 0.18 725 0.20 0.14 891 0.21

St. Louis 0.14 3,623 0.38 0.23 983 0.29 0.14 900 0.21

Los Angeles 0.08 3,762 0.18 0.12 1,302 0.37 0.10 1203 0.15

San Francisco 0.06 3,119 0.07 0.13 1,153 0.29 0.13 713 0.14

Note: “NA” signifies that this model was not used due to a small sample size.

1E(Y) gives the predicted mean of the farm-wholesale margin, by market and commodity.
32

How Transportation Costs Affect Fresh Fruit and Vegetable Prices, ERR-160

Economic Research Service/USDA

Table A2Regression sample sizes and R2 Values for the multiple origin wholesale price analysis

Asparagus Cantaloupes Peppers

California Mexico Peru California Central America California Florida Mexico Canada Regional

Boston 945 (0.5089) 813 (0.3215) 2,896 (0.3624) 1,171 (0.4896) 1,281 (0.2419) 1,139 (0.4004) 2,893 (0.4595) 2,213 (0.553) 2,385 (0.7864) 820 (0.5269)

New York 718 (0.3627) 1,165 (0.4164) 2,659 (0.3572) 1,041 (0.4022) 1,120 (0.2296) 1,459 (0.5145) 3,275 (0.4681) 2,273 (0.5496) 1,225 (0.7807) 955 (0.5482)Philadelphia 618 (0.3968) 1,165 (0.4068) 2,073 (0.2941) 1,062 (0.569) 1,194 (0.3136) 1,091 (0.5185) 2,916 (0.45) 1,673 (0.7003) 479 (0.8115)

Pittsburgh 446 (0.2425) 294 (0.2894) 1,279 (0.2662) 1,032 (0.4063) 987 (0.2695) 926 (0.6412) 2,343 (0.5053) 1,597 (0.7551) 1,047 (0.4122)

Miami 135 (0.5826) 232 (0.6224) 3,114 (0.3053) 1,010 (0.46) 1,139 (0.206) 684 (0.4618) 1,971 (0.5562) 314 (0.5999) NA

Atlanta 339 (0.3739) 489 (0.4596) 2,241 (0.3081) 1,253 (0.4341) 1,315 (0.2678) 1,291 (0.5394) 2,008 (0.4774) 1,559 (0.6525) 1,062 (0.6934)

Columbia NA NA 537 (0.3583) 622 (0.4511) 545 (0.2184) 514 (0.6296) 1,621 (0.6552) 554 (0.7165) 531 (0.8204)

Baltimore 503 (0.4163) 531 (0.495) 1,355 (0.3796) 1,126 (0.5318) 1,026 (0.3076) 881 (0.6746) 2,367 (0.4456) 1,234 (0.6741) 902 (0.828)

Detroit 571 (0.399) 587 (0.3545) 1,644 (0.305) 1,007 (0.4645) 1,127 (0.3862) 1,365 (0.4984) 1,545 (0.7528) 2,658 (0.6363) 2,087 (0.6645) 742 (0.5828)Chicago 522 (0.3491) 1,025 (0.4507) 2,121 (0.3009) 1,214 (0.3739) 1,099 (0.2348) 2,030 (0.618) 1,480 (0.4234) 2,585 (0.6045) 1,399 (0.6786)

St. Louis 303 (0.5469) 500 (0.4507) 1,086 (0.4038) 931 (0.4616) 878 (0.2696) 1,522 (0.6132) 1,274 (0.4902) 3,398 (0.5629) 880 (0.405)

Los Angeles 1,717 (0.6006) 2,104 (0.5699) 1,661 (0.2784) 1,364 (0.5681) 1,123 (0.3855) 2,871 (0.5869) 24 (0.8948) 2,137 (0.4619) NA

San Francisco 1,655 (0.5628) 1,243 (0.5315) 1,040 (0.294) 1,146 (0.5248) 771 (0.3801) 1,595 (0.6394) 27 (0.9197) 2,496 (0.5955) 1,266 (0.3357)

Grapes Oranges Tomatoes

California Chile Mexico California Florida California Florida Mexico Canada Regional

Boston 3,620 (0.5314) 3,110 (0.5056) 711 (0.6077) 4,929 (0.3483) 4,799 (0.5415) 828 (0.6891) 1,891 (0.7897) 2,071 (0.4227) 3,375 (0.66)

New York 4,286 (0.4645) 3,569 (0.5515) 778 (0.5278) 4,330 (0.3277) 4,183 (0.6648) 869 (0.5768) 1,714 (0.6455) 1,728 (0.53) 1,364 (0.3708)

Philadelphia 4,267 (0.5379) 3,641 (0.5619) 726 (0.5259) 3,698 (0.2975) 3,177 (0.6405) 518 (0.5932) 1,771 (0.7877) 1,077 (0.5209) 573 (0.6848)

Pittsburgh 2,065 (0.4792) 1,603 (0.3661) 338 (0.4426) 3,754 (0.4126) 1,710 (0.5659) 675 (0.4265) 1,091 (0.4012) 1,006 (0.4012) 1,097 (0.4782)

Miami 1,846 (0.4126) 1,774 (0.301) 366 (0.3691) 2,672 (0.2711) 2,423 (0.2949) 561 (0.5587) 1,993 (0.3496) 316 (0.1878) NA

Atlanta 2,770 (0.4563) 2,402 (0.4387) 742 (0.4406) 3,708 (0.4304) 6,007 (0.5888) 1,111 (0.7403) 2,979 (0.6016) 953 (0.4902) NA 1,042 (0.3101)

Columbia 793 (0.5753) 453 (0.5298) NA 2,087 (0.516) 3,967 (0.578) 546 (0.4557) 2,558 (0.3634) 586 (0.3546) NA 745 (0.3412)

Baltimore 2,665 (0.5128) 2,321 (0.4629) 668 (0.537) 4,390 (0.34) 4,012 (0.6364) 635 (0.7637) 2,551 (0.4973) 926 (0.5336) NA 994 (0.553)

Detroit 3,742 (0.5577) 2,679 (0.5589) 581 (0.6256) 4,897 (0.404) 1,926 (0.5019) 1,359 (0.3251) 1,348 (0.476) 2,246 (0.4423) 2,211 (0.4721)Chicago 2,233 (0.5125) 2,298 (0.5174) 534 (0.5314) 4,816 (0.3996) 4,631 (0.5638) 1,146 (0.7284) 1,755 (0.7822) 2,181 (0.4467) 1,580 (0.5125)

St. Louis 1,605 (0.4296) 1,729 (0.5168) 431 (0.5522) 4,678 (0.3617) 1,250 (0.4829) 1,484 (0.3714) 1,346 (0.299) 2,955 (0.3448) NA

Los Angeles 2,756 (0.5509) 1,878 (0.4451) 396 (0.6647) 4,686 (0.3961) 216 (0.5188) 1,962 (0.6031) 547 (0.3662) 3,779 (0.5555) NA

San Francisco 2,581 (0.5077) 1,551 (0.616) 241 (0.5222) 4,999 (0.3082) 627 (0.3388) 2,254 (0.6518) 361 (0.2262) 2,991 (0.5037) NA

Sample sizes are listed first, followed by R2 values in parentheses.

 “NA” signifies that this model was not used due to a small sample size.