The Importance of Highways to U.S. Agriculture

APPENDIX A

Economic Significance of 

Surface Transportation 

to U.S. Agriculture

Read the full report:

http://dx.doi.org/10.9752/TS295.12-2020
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-1

Contents

List of Figures..................................................................................................................... A-2

List of Tables ....................................................................................................................... A-3

List of Abbreviations........................................................................................................... A-4

Executive Summary ............................................................................................................ A-5

1. Introduction ................................................................................................................ A-8

2. Freight Transportation............................................................................................... A-10

2.1 Agricultural Freight ................................................................................................................ A-16

3. Economic Importance ................................................................................................ A-20

3.1 Macroeconomic Impacts........................................................................................................ A-20

3.2 Logistics and Supply Chain..................................................................................................... A-22

3.3 Food Price Impacts................................................................................................................. A-25

3.4 International Competitiveness............................................................................................... A-27

3.4.1 Case Study: Brazil...................................................................................................... A-29

4. Agricultural Projections ............................................................................................. A-32

4.1 Specific Commodities............................................................................................................. A-33

4.1.1 Soybeans................................................................................................................... A-33

4.1.2 Corn........................................................................................................................... A-35

4.1.3 Wheat........................................................................................................................ A-37

4.1.4 Beef........................................................................................................................... A-39

4.1.5 Pork ........................................................................................................................... A-41

4.2 Overall Trends and Expected Impacts.................................................................................... A-42

5. Conclusion................................................................................................................. A-44

6. Appendix................................................................................................................... A-46

6.1 Commodity Projections Development................................................................................... A-46

6.2 Projection Identities............................................................................................................... A-49

References ....................................................................................................................... A-53
 Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-2

List of Figures

Figure 1. Value Added to GDP by Agriculture and Related Industries, 2007-2017 .................................. A-7

Figure 2. Value of Goods Shipped in the United States 2015-2045.......................................................... A-9

Figure 3. Value of Shipments by Transportation Mode in 2018, as Defined by BTS .............................. A-10

Figure 4. Major Truck Routes on the National Highway System in 2011 ............................................... A-14

Figure 5. Agricultural and total freight moving on U.S. interstate system, 2015 ................................... A-17

Figure 6. U.S. Agricultural Supply Chain.................................................................................................. A-21

Figure 7. 2013 Corn Production by County, Ethanol Plants, Grain Consuming Animal Units by State, and 

Export Port Regions (Denicoff, Prater, & Bahizi, 2014a) ........................................................... A-22

Figure 8: Food Flows in the U.S. (Lin, Ruess, Marston, & Konar, 2019).................................................. A-23

Figure 9. Transportation’s Component of the Domestic Food Dollar (USDA, 2020a) ............................ A-24

Figure 10. Modal Share of Inland Transportation Costs ($/mt) for Soybeans, Average from 2010 to 2018 

(USDA, no date).......................................................................................................................... A-25

Figure 11. Cost-To-Export Indicator........................................................................................................ A-26

Figure 12. U.S. and Brazil Soybean Total Transportation Costs to Shanghai, China (USDA, no date).... A-28

Figure 13. U.S. and Brazil Soybean Total Inland Transportation Costs to Shanghai, China (USDA, no date)

................................................................................................................................................... A-29

Figure 14. Soybean Total Supply and Components................................................................................ A-31

Figure 15. Soybean Total Demand and Components ............................................................................. A-32

Figure 16. Corn Total Supply and Components ...................................................................................... A-33

Figure 17. Corn Total Demand and Components.................................................................................... A-34

Figure 18. Wheat Total Supply and Components................................................................................... A-35

Figure 19. Wheat Total Demand and Components................................................................................ A-36

Figure 20. Beef Total Supply and Components....................................................................................... A-37
 Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-3

Figure 21. Beef Total Demand and Components.................................................................................... A-38

Figure 22. Pork Total Supply and Components....................................................................................... A-39

Figure 23. Pork Total Demand and Components.................................................................................... A-40

List of Tables 

Table 1. Freight Intermodal Connectors on the National Highway System by State: 2018 ................... A-11

Table 2. Percent of U.S. Roadways in Poor Condition (IRI>170) by Functional System and Year (BTS, 

2019a) ........................................................................................................................................ A-12

Table 3. Transportation Characteristics of Commodities, 2017 ............................................................. A-15

Table 4. Modal Transportation Characteristics of Agricultural Commodities by Volume, 2017 ............ A-16

Table 5. Studies Examining the Impact of Transportation Infrastructure .............................................. A-19

Table 6. Cost to Export and Key Agricultural Exports by Country........................................................... A-27
 Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-4

List of Abbreviations

Abbreviation Term

ATRI American Transportation Research Institute

CFS Commodity Flow Survey

BTS Bureau of Transportation Statistics

ERS Economic Research Service

FAF Freight Analysis Framework

FHWA Federal Highway Administration

GDP gross domestic product

IRI International Roughness Indicator

NHS National Highway System

SCTG Standard Classification of Transported Goods

USDA U.S. Department of Agriculture

U.S. DOT U.S. Department of Transportation

VMT vehicle miles traveled
 Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-5

Executive Summary

This report covers the findings of a research project carried out by the Volpe Center on behalf of the U.S. 

Department of Agriculture (USDA) Agricultural Marketing Service. The focus of this research effort was 

to determine the economic impacts of surface transportation on the U.S. agricultural sector, which is an 

important contributor to the U.S. economy. 

The majority of all freight in the United States is shipped via truck, and the majority of truck movements 

occur on the National Highway System (NHS). The NHS is a federally designated system of roadways that 

are considered to be important to the U.S. economy, defense, and mobility—the purpose of the NHS is 

to identify high-priority roadways, and to encourage States to spend Federal money on NHS roads. 

Although congestion is concentrated on a small percentage of NHS roads, the NHS accounts for most 

congestion in the United States. Both congestion and the condition of the roads are growing concerns. 

Deteriorating roads impose higher costs of freight transportation in the form of vehicle operating costs, 

while congestion imposes costs through delays and additional requirements for labor and fuel. These 

cost increases affect ultimate prices for goods, reducing the competitiveness of the U.S. highway system 

in comparison to other countries. 

Transportation costs help shape the overall economy and have even more profound effects on 

agriculture. A large segment of the transportation market is supported by the need to move agricultural 

goods, and in fact, the largest single user of freight services is agriculture. Agriculture does not make up 

a majority of freight movements, but no other single sector uses freight services more than agriculture 

does (USDA, 2010). According to data from the Commodity Flow Survey (CFS), in 2017, around a third 

(by ton-miles) of all goods shipped across all modes in the United States were agricultural products. By 

value, agricultural products were 17 percent of all freight (CFS, 2017). 

Truck movements are particularly important for agriculture, especially for shorter, domestic shipments. 

They are also essential to international trade and have direct implications on international 

competitiveness. Export-bound agricultural products travel more often by rail or barge, but a truck is 

essential to getting the product off the farm and to a railroad or an inland waterway. Trucking competes 

with and complements other modes of service, such as rail and barge shipping. Thus, trucking 

competition moderates freight rates. A highly competitive trucking industry benefits agriculture by 

keeping costs down and expanding markets domestically and abroad. When combined, these 

phenomena enhance competition, lowering freight rates for shippers. 

Although limited research is available on how improvements in surface transportation infrastructure 

would affect the U.S. agricultural sector, two studies on this topic were found. Both studies indicated 

positive results for agriculture, with Tong et al. (2013) finding that a 1-percent increase in the 

investment and maintenance of roads in a State increases agricultural output within the same State by 

between 0.02 and 0.03 percent. Additionally, Onofri and Fulginiti (2008) found that investments in 

public infrastructure gave, on average, a rate of return of 1 percent in real terms. 
 Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-6

This report also looks at the international competitiveness of the United States agricultural exports and 

the role that U.S. transportation plays in keeping agricultural commodities competitive. Generally, 

transportation costs have been one of the key ways that the United States remains competitive against 

other countries—particularly, Brazil and Argentina. Although labor and land may be cheaper elsewhere, 

the United States has had, historically, the most efficient transportation networks. However, in the past 

several years, Brazil—the United States’ strongest competitor for soybeans—has made vast 

improvements to its infrastructure and overtaken the United States in soybean exports. This

development shows how the efficiency of surface transportation systems can affect global 

marketplaces. 

As this report shows, the impacts of infrastructure improvements on the agriculture sector are vast, 

encompassing agricultural productivity, transportation costs, food prices, and international 

competitiveness. All of these areas are instrumental in analyzing where the greatest return on 

investment could be for agricultural freight projects. This report informs the chief task of this project: 

addressing the roles of various levels of government, including an assessment of the impacts of different 

investment levels and needs for future research.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-7

1. Introduction

This report covers the findings of a research project carried out by the U.S. Department of 

Transportation (U.S. DOT) Volpe Center on behalf of the U.S. Department of Agriculture (USDA) 

Agricultural Marketing Service. The research aimed to determine the economic impacts of surface 

transportation on American agriculture. These included trucking’s impacts on economic output,

1

domestic food prices, and the international competitiveness of U.S. agriculture. This research effort also 

involved projecting agricultural production 25 years into the future to understand how changes in the 

agricultural industry may affect surface transportation.

The agricultural sector of the United States plays an important role in the overall economy. Agriculture is 

approximately 5.5 percent of the U.S. gross domestic product (GDP), contributing over $1 trillion in 2017 

(Economic Research Service (ERS), 2018), as can be seen in Figure 1. Although this could be viewed as a 

relatively small component of national GDP, 5.5 percent is still significant in a multi-trillion dollar 

economy. Of this, around $132.8 billion was from the output of America’s farms (ERS, 2018), and 

agricultural exports account for around 10 to 11 percent of all U.S. exports (Cooke, Melton, and Ramos, 

2017). Additionally, agriculture and related industries accounted for 11 percent of U.S. employment in 

2017, equivalent to 21.6 million jobs, and around 2.6 million of these jobs were from direct on-farm 

employment (ERS, 2018). Furthermore, the products from the agriculture sector are essential to the 

overall economy, providing consumers with fresh, high-quality food that allows them to be healthy and 

productive in other sectors. 

Figure 1. Value Added to GDP by Agriculture and Related Industries, 2007-2017

1 Measured by Gross Domestic Output (GDP) – https://www.bea.gov/resources/learning-center/what-to-know-gdp
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-8

This report begins by discussing freight transportation generally in Section 2, providing background 

information on commodity movements across modes and infrastructure conditions. Section 2 also 

provides some background knowledge on agricultural freight specifically. Section 3 provides a discussion 

on the economic relationship of freight trucking to agriculture and the U.S. economy. It discusses 

economic impacts, linkage impacts, food prices, and the international competitiveness of U.S. 

agriculture in the context of surface transportation. Section 4 presents the results of the Volpe Center’s 

25-year commodity projections, which are based on USDA’s 10-year projections. Section 5 concludes the

report with a summary of key findings.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-9

2. Freight Transportation

In the United States, freight movements, facilitated by a variety of networks (road, air, rail, and 

waterways), have steadily increased and are projected to continue to grow. Nearly 18.1 billion tons of 

goods worth about $19.2 trillion moved on the Nation’s transportation network in 2015, based on the 

current Freight Analysis Framework 4 (FAF4) estimates (Bureau of Transportation Statistics (BTS), 2017). 

Every day, 49 million tons of goods valued at more than $53 billion are shipped throughout the country 

on all transportation modes. Figure 2 below shows U.S. DOT projections for the future value of goods 

shipped in the United States. The high estimate shows the value of shipped goods doubling by about 

2040, and even the low estimate shows a large increase.

Figure 2. Value of Goods Shipped in the United States 2015-2045

Source: U.S. DOT, BTS and

Federal Highway Administration (FHWA), FAF, version 4.1, 2016.

All combined, goods moved by the modes of rail, pipelines, air, and river (barge) and ocean vessels total

approximately half the dollar value of what trucks move. Mode shares by value are shown in Figure 3. 

Considering a different measurement, such as total tons moved, would provide a slightly different 

breakdown, but trucks always move the overwhelming majority of goods.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-10

Figure 3. Value of Shipments by Transportation Mode in 2018, as Defined by BTS

Truck

71%

Rail

3%

Pipeline

9%

Air

2%

Water

3%

Multiple Modes or 

Other/Unknown

12%

Trucks move on a variety of road networks throughout the country, but one set of roadways that is vital 

to truck movement is the National Highway System (NHS). The NHS consists of roadways that are 

integral to the U.S. economy, defense, and mobility. It includes subsystems of roadways such as the 

Interstate System, other principal arterials, and intermodal connectors (FHWA, 2017). One of the NHS’s

advantages is it encourages States to focus on high-priority roads, and to focus Federal-aid funds on 

these roads, although Federal-aid dollars still can be used for other roads (Slater, 1996). 

The NHS accounts for only around 5 percent of the country’s roads (by center-line mileage), according to 

2018 data from the Federal Highway Administration (FHWA); however, it accounted for 54.6 percent of 

all vehicle miles traveled (VMT) (FHWA, 2019a). For freight truck traffic specifically, the NHS accounted 

for around 311 million vehicle-miles per day of this traffic in 2015, with projections estimating that it 

could be as much as 488 million vehicle-miles per day in 2045 (BTS, 2019b). Understanding the NHS 

provides insight into truck movements, simply because the NHS’s high volumes of traffic.

Intermodal connectors are also key components of the freight transportation network (see

Table 1). They provide access between major intermodal facilities, such as ports and truck/pipeline 

terminals, and the NHS. Although freight intermodal connectors account for less than 1 percent of total 

NHS mileage (1,604 miles in 2016), they are vital for truck movement. Texas has the highest number of 

freight intermodal connectors (104), followed by Ohio (64).
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-11

Table 1. Freight Intermodal Connectors on the National Highway System by State: 2018 

State

Port 

terminal

Truck/rail 

facility Airport

Truck/pipeline 

terminal

Total 322 276 271 72

Alabama 5 4 4 1

Alaska 8 0 7 0

Arizona 0 2 5 0

Arkansas 3 7 3 3

California 16 15 14 3

Colorado 0 5 6 4

Connecticut 3 0 1 0

Delaware 1 0 1 0

District of Columbia 0 0 0 0

Florida 14 12 25 0

Georgia 5 13 4 7

Hawaii 10 0 5 0

Idaho 1 0 2 1

Illinois 9 43 4 0

Indiana 8 2 5 0

Iowa 6 1 3 3

Kansas 0 4 1 2

Kentucky 4 7 3 3

Louisiana 8 5 8 0

Maine 3 4 5 0

Maryland 8 3 1 3

Massachusetts 5 10 12 0

Michigan 15 8 11 0

Minnesota 1 1 3 0

Mississippi 22 2 3 0

Missouri 4 8 4 0

Montana 0 0 1 0

Nebraska 0 2 1 1

Nevada 0 0 2 0

New Hampshire 1 0 4 0

New Jersey 5 5 2 0

New Mexico 0 0 1 0

New York 8 16 17 0

North Carolina 2 4 9 5

North Dakota 0 0 2 0

Ohio 23 29 8 4

Oklahoma 4 1 2 1

Oregon 15 5 6 1

Pennsylvania 8 8 5 4

Puerto Rico 5 0 4 0

Rhode Island 2 0 1 0

South Carolina 4 2 4 0

South Dakota 0 2 3 0
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-12

Tennessee 5 8 4 2

Texas 43 20 23 18

Utah 0 3 2 6

Vermont 0 2 2 0

Virginia 6 3 7 0

Washington 11 6 14 0

SOURCE: U.S. DOT, FHWA, Office of Planning, Environment, and Realty, Intermodal Connectors, 

available at https://www.bts.gov/freight-intermodal-connectors-national-highway-system-state 2018. 

The condition of roads will naturally deteriorate over time without maintenance or intervention because 

of general wear and tear. This implies that investment must match or exceed the rate of deterioration to 

keep roads in good condition. FHWA reports the International Roughness Index (IRI) for U.S. highways as 

a measure of highway condition. The IRI measures the smoothness of pavement, and a rating of greater 

than 170 inches per mile is generally considered to be “poor” (BTS, 2018). 

As seen in Table 2, collector roads are in the worst condition, both in rural and urban settings. Almost 

half of all urban collectors have a high IRI, indicating that they are in a poor condition. Rural interstates 

have the best condition, with only around 2% having poor condition over the last few years. Rural minor 

arterials have had an increased share of mileage in poor condition, while urban minor arterials had a 

decrease. Poor road conditions contribute to higher freight costs, as rough pavement raises costs for 

vehicle maintenance. Internal estimates from U.S. DOT’S Highway Economic Requirements System

suggest that going from an IRI of 95 to an IRI of 170 increases vehicle operating costs per mile by 13 

percent for a combination truck traveling at 60 mph. Additionally, in extreme cases, poor conditions 

force vehicles to take alternative, longer routes to reach their destinations.

Table 2. Percent of U.S. Roadways in Poor Condition (IRI>170) by Functional System and Year (BTS, 2019a)

Road Type 2014 2015 2016 2017 2018

Rural Interstate 2.1% 1.8% 2.0% 2.0% 2.0%

Rural Other Principal Arterials 3.8% 4.4% 4.4% 4.1% 4.1%

Rural Minor Arterials 7.0% 7.9% 8.0% 8.1% 8.1%

Rural Collectors 20.2% 21.5% 22.0% 22.1% 20.4%

Urban Interstates 5.2% 5.0% 5.2% 5.2% 5.2%

Urban Other Freeways and Expressways 8.3% 8.2% 8.5% 7.8% 7.4%

Urban Other Principal Arterials 26.1% 27.7% 26.8% 26.7% 26.7%

Urban Minor Arterials 35.6% 38.3% 36.1% 36.9% 34.5%

Urban Collectors 49.5% 52.2% 50.8% 49.7% 47.7%

Bridge conditions have improved over the last couple decades, with the number of deficient bridges 

declining by approximately 40 percent since 2000. However, 7.6 percent of all bridges are still 

categorized as structurally deficient by FHWA, and an additional 11.1 percent of all bridges have weight 

restrictions to reduce stress on the structures (ARTBA, 2019). Further investment in U.S. infrastructure 

will likely be needed to help improve the state of surface transportation, which will benefit multiple 

industries and individuals, as well as the agricultural sector.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-13

Although there is less data on local, rural roads, these roads are also often in poor condition (Cohen, 

2020). Agricultural commodities may travel primarily on routes on the NHS and other large highway 

systems, but local roads are crucial to the beginning of every journey. Simply put, goods have to make it 

off of the farm even to reach a highway, rail head, or inland waterway. In these rural areas, 

transportation needs may often be underfunded, particularly because no State or Federal assistance can 

be used to help with smaller, local roads (Cohen, 2020). 

Additionally, farm equipment often has to traverse these rural roads as the farmers take care of their 

crops or livestock, making the roads vital to every stage of the farming process, even before goods are 

ready to be shipped off of the farm. Heavy trucks and farm equipment wear down roads faster than a 

light-duty vehicle would. Some estimates indicate a legally loaded semi-trailer truck produces as much 

as 10,000 times the road damage of a car (Cohen, 2020). Because of limited funding and the high rate of 

road deterioration, the condition of rural, local roads is a key issue for rural communities and farmers.

In addition to the condition of the infrastructure, congestion on the roads is also a growing concern.

Congestion imposes higher costs of freight transportation in the form of additional labor, fuel, and other 

vehicle-related costs. Congestion also has an opportunity cost in terms of time—more congestion means 

more hours lost. Lost hours can translate into other types of costs, such as higher inventory costs, for 

businesses. These cost increases affect ultimate prices for goods, reducing the competitiveness of the 

U.S. highway system compared to other countries. A congestion measurement from Texas A&M 

Transportation Institute’s 2019 Urban Mobility Report put the congestion cost for trucks at 

approximately $20 billion in 2017 (Schrank, Eisele, and Lomax, 2019). The report also stated that the 

truck share of congestion costs is higher than the truck share of traffic (Schrank, Eisele, and Lomax, 

2019). The American Transportation Research Institute’s (ATRI) estimate of congestion is significantly 

higher, at $74.5 billion in 2016 (Hooper, 2018).

FHWA data show that congestion is not equally distributed across the country. For example, based on 

2005 data, the top 10 highway-interchange bottlenecks cause an average of 1.5 million annual truck 

hours of delay each, compared to less than 250,000 annual hours of truck delay for other truck 

bottlenecks (FHWA, 2005). These bottlenecks were identified using FHWA’s Highway Performance 

Monitoring System, and truck volumes were determined using FAF data. The calculation considers things 

such as volumes, highway capacity, and temporal distributions, and uses available data to extrapolate 

key variables of interest, such as speeds and delay (FHWA, 2005). ATRI also produces a list of the top 

100 truck bottlenecks every year, serving as another source of congestion data (ATRI, no date). These 

areas of significant congestion occur where there are large concentrations of people and, therefore, 

high demand for commodities, and are also often home to a major port where goods are exported 

overseas.

Figure 4 shows the major truck routes on the NHS in 2011. As the map shows, many areas of the NHS 

regularly experience high volumes of truck traffic. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-14

Figure 4. Major Truck Routes on the National Highway System in 2011 

The map shows all freight movements, not limited to agricultural freight, and the highest truck volumes 

are concentrated in a number of corridors in the Northeast, Midwest, Texas, and California. These areas 

can experience even higher congestion levels during peak seasons and periods. Nonetheless, even areas 

with lower annual truck volumes could still potentially experience congestion during peak periods. 

2.1 Agricultural Freight

Across sectors, agriculture is the largest user of freight services (USDA, 2010). The Commodity Flow 

Survey (CFS), conducted every 5 years, provides information on how much and where goods flow across 

the country. The 2017 CFS data indicate that agriculture represented just under 25 percent of all tons 

and about 33 percent of all ton-miles moved by the U.S. transportation system. Table 3 summarizes the 

volumes and value of agricultural commodity movements in 2017.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-15

Table 3. Transportation Characteristics of Commodities, 2017

Commodity 

Group

Value ($ millions) Tons (millions) Ton-Miles 

(millions)

Average Miles 

Per Shipment

All Commodities 14,517,812 12,468.9 3,116,876 662

Agricultural 

Commodities

- - - -

Animals and Fish 

(Live)

10,777 4.6 1,570 866

Cereal Grains 

(includes seed)

108,561 707.1 230,498 158

Other Agricultural 

Products

236,965 315.2 145,852 524

Animal Feed and 

Products

134,423 325.3 78,947 514

Meat, Poultry, Fish, 

Seafood

356,421 93.9 46,001 183

Milled Grain and 

Bakery Products

198,051 132.8 56,404 187

Other Prepared; 

Fats and Oils

606,280 530.2 203,307 373

Alcoholic 

Beverages

226,894 111.9 27,798 195

Tobacco Products 79,806 4.7 877 1,014

Fertilizers 57,886 172.5 40,112 102

Wood Products 220,916 322.8 88,755 350

Pulp, Paper 127,501 139.6 83,375 267

Paper or 

Paperboard Articles

148,420 80.9 26,305 577

Agricultural 

Products Sub-Total

2,512,901 2,941.5 1,029,801 -

% of All 

Commodities

17% 24% 33% -

Source: U.S. DOT, BTS and U.S. Census Bureau, Commodity Flow Survey, 2017. 

The CFS also shows shipments by commodity type and mode. Table 4 shows this data from 2017, 

breaking out the commodities by mode as a percent of all million tons shipped. These mode shares are 

across all shipments of the specific commodities, but the mode used can vary significantly between 

domestic and exported shipments. As a specific, older example, using data from USDA, 32 percent of all 

domestic wheat (in tons) was shipped via truck in 2011, while only 4 percent of exported wheat was 

shipped via truck (Denicoff, Prater, & Bahizi, 2014c). This difference was due to the use of barges, which 

moved only 1 percent of domestic shipments versus 29 percent of exported shipments. Rail use was 

relatively constant, at 67 percent for both domestic and exported wheat (Denicoff, Prater, & Bahizi, 

2014c). 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-16

Table 4. Modal Transportation Characteristics of Agricultural Commodities by Volume, 2017 

Commodity Group Truck % of All 

Modes

Rail % of All 

Modes

Water % of All 

Modes

All Modes 

(Million Tons)

Animals and Fish (Live) 99.4% 0% 0% 4.6

Cereal Grains (includes seed) 55% 22.4% 16.9% 707.1

Other Agricultural Products 62.7% 11% 20.1% 315.2

Animal Feed and Products 85.7% 3.9% 1.5% 325.3

Meat, Poultry, Fish, Seafood 94.5% 0.3% 1.2% 93.9

Milled Grain and Bakery 

Products

82.8% 5.1% 0% 132.8

Other Prepared; Fats and 

Oils

86.4% 5.3% 0.9% 530.2

Alcoholic Beverages/ 

Denatured Alcohol

93.8% 0.8% 0.3% 111.9

Tobacco Products 94.1% 0% 0% 4.7

Fertilizers 76.5% 6.7% 6.5% 172.5

Wood Products 88.3% 4.1% 0% 322.8

Pulp, Paper 70.3% 8.6% 0% 139.6

Paper or Paperboard Articles 91% 1.4% 0.3% 80.9

Source: U.S. DOT, BTS, and U.S. Census Bureau, Commodity Flow Survey, 2017 

The CFS also provides information on where trucks are traveling, as well as the types of commodities 

they carry. Figure 5 shows total U.S. freight movements on the U.S. interstate system, as well as

agricultural freight as a subset of those movements. The thicker lines represent heavier freight traveling 

along that route. There is a high volume of freight movements, and particularly agricultural freight 

movements, in the Midwest region, as well as in the Northeast and in California. Gulf Coast States see 

somewhat lower levels of agricultural freight movements by truck, likely because much of that freight 

travels along the Mississippi River. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-17

Figure 5. Agricultural and total freight moving on U.S. interstate system, 2015 

Source: U.S. Department of Transportation, Bureau of Transportation Statistics and Federal Highway 

Administration, Freight Analysis Framework, version 4.3, 2018. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-18

3. Economic Importance

This section discusses the economic importance of surface 

transportation both to the economy generally and to agriculture

specifically. The section is further divided into four subsections: the 

first few sections cover the general macroeconomic impacts of the 

trucking industry, the importance of trucking to logistics and the 

agricultural supply chain, and the implications for food prices. The 

fourth and final subsection looks at surface transportation’s role in

maintaining international competitiveness in the agriculture sector, 

while also providing a case study using Brazil.

3.1 Macroeconomic Impacts

Transportation-related industries employ a large portion of the population—over 13.3 million people, or 

approximately 9 percent of the labor force, in 2017. Of that share, 1,748,140 were truck drivers for 

heavy trucks or tractor-trailers; 877,670 were truck drivers for light or delivery services; and another 

570,300 were industrial truck and tractor operators.

Over 95 percent of trucking companies are small businesses with fewer than 20 trucks, and 84 percent 

have 6 or fewer trucks (OOIDA, 2020). Despite this prevalence of hundreds of thousands of small 

businesses, the 50 largest trucking companies represent 38 percent of the trucking industry (Cassidy, 

2019). Most long-haul drivers are paid by the mile, by a flat fee, or portion of the gross revenue, not by 

the hour. The estimated average driver’s pay is $0.60 per mile or $23.50 per hour (Murray and

Glidewell, 2019). These truck drivers are vital to the U.S. economy as a whole, as well as to agriculture 

specifically. 

Because of agriculture’s reliance on trucking, the availability of drivers, especially during critical times 

such as planting and harvest, is key to farmers’ profitability. Although the trucking industry has 

identified a driver shortage as a top industry issue (Miller, 2018; Truckload Carriers Association, no 

date), researchers in the field are skeptical that a labor shortage could persist, given that the barrier to 

entering the occupation is quite low. Increases in wages should easily incentivize new workers to enter 

the market (Burks and Monaco, 2016). Regardless, recruiting and retaining drivers in the long-haul 

trucking sector is an acknowledged problem, though it is largely outside the scope of this research 

effort. Nonetheless, it is worth remembering that infrastructure investment is not the only factor that 

could affect agricultural transportation.

Economic theory suggests multiple reasons infrastructure improvements could benefit the economy. 

Improved roads could reduce travel times, effectively reducing the cost of transportation and allowing 

producers to shift funds previously spent on transportation to other inputs, allowing them to increase 

Transportation's importance to the 

U.S. economy is underscored by the 

fact that more than $1 out of every 

$10 produced in U.S. GDP is related 

to transportation activity. This 

includes all aspects of 

transportation, including the 

movement of goods and people, as 

well as the purchase of 

transportation-related products 

and services.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-19

their output. Lower costs for delivered products could also translate to lower costs and higher utility for 

consumers. Additionally, improved worker commute times could have a positive effective on the 

productivity of a worker, also benefiting production (Cohen, 2007). 

Many studies attempt to quantify how infrastructure improvements could affect the U.S. economy, but 

they are generally not specific to agriculture. Most studies evaluating the output and productivity 

impact of transportation infrastructure in the agricultural sector have been outside the United States. 

(Antle, 1983; Craig et al., 1997; Felloni et al., 2001; Benin et al., 2009; Zhang and Fan, 2004). This review 

only found two studies—summarized in Table 5—that investigated the economic effects of 

infrastructure on U.S. agriculture. 

Table 5. Studies Examining the Impact of Transportation Infrastructure

Study Public Capital 

Measure

Outcome 

Measures

Findings

Onofri, A., and Fulginiti, L. E. 

(2008). Public inputs and 

dynamic producer behavior: 

endogenous growth in U.S. 

agriculture.

Public capital 

stocks are values 

of Federal, State, 

and local 

structures. 

Output is an index 

of all crops and 

livestock products.

On average, 

investments in public 

infrastructure give a 

rate of return of 1%.

Tong, T., Yu, T. H. E., Cho, S. H., 

Jensen, K., and Ugarte, D. D. L. T. 

(2013). Evaluating the spatial 

spillover effects of transportation 

infrastructure on agricultural 

output across the United States 

State road 

disbursements 

Agricultural output 

data generated by 

physical quantities 

and market prices 

of crops and 

livestock.

A 1% increase in the 

investment and 

maintenance in roads 

in one State increases

agricultural output 

within the same State 

by 0.02-0.03%. 

Onofri and Fulginiti (2008) studied how investments in public transportation infrastructure affected the 

performance of U.S. agriculture. The authors tested two datasets, one from USDA covering 1948-1994 

and one from an independent set of researchers covering 1926-1990. The authors used an endogenous 

growth model to test their various hypotheses. One finding was that an average dollar spent on public 

research and development stocks will reduce costs by 6.5 percent, indicating a rate of return of 190 

percent, while the same investment in public infrastructure gave a rate of return of only 1 percent. 

However, the authors caution that there are modeling issues that they did not consider, and that their 

two datasets gave different results even though both datasets measured the U.S. agricultural sector

(Onofri and Fulginiti, 2008). Given the uncertainty surrounding the study’s results, caution should be 

used in interpreting the findings. Still, the findings are presented here because this study is one of the 

few that considers how investment affects U.S. agriculture. 

Tong et al. (2013) looked at the direct effect and spatial spillover effect of transportation infrastructure 

on agricultural output. Their data covered 44 States from 1981 to 2004, and the authors used a spatial 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-20

Durbin panel data model for their analysis. Their analysis had several findings relevant for this report. 

First, the authors found that a 1-percent increase in road disbursement in a given State increases 

agricultural output in all other States by an average of 0.27 percent (Tong et al., 2013). 

Additionally, Tong et al. (2013) further refined their finding based on the location of the State—border 

and coastal States had a much smaller spillover effect in their model than central States, due to simple 

geography. A State with only a few direct neighbors (potential examples could include Rhode Island, 

Florida, and Washington) had smaller spillover effects simply because there were fewer States nearby. 

However, Missouri has eight direct neighbors, and those States have 16 additional neighbors, which 

have 10 additional neighbors—spillover effects from Missouri would affect the majority of the 

continental U.S. In their model, a 1-percent increase in road disbursement in Missouri has a spillover 

effect in the range of 0.40 to 0.45 percent, while the same increase in Washington has a spillover effect 

only in the range of 0.10 to 0.15 percent (Tong et al., 2013). This finding indicates that road 

improvements in central States, which generally also are regions of major agricultural production, will 

have larger benefits on agriculture than road disbursement increases in other States (Tong et al., 2013).

3.2 Logistics and Supply Chain

Because of the nature of the agriculture supply chain, trucking is the sector’s most-used mode of 

transportation. In a supply chain that stretches from the farm to the consumer, trucking provides the 

first miles, the last miles, and sometimes all the transportation miles. Trucks are generally more efficient 

than rail for trips under 500 miles (although this is only a rough estimate, and there are a variety of 

factors that affect the efficiency of trucks and rail), making trucks particularly crucial for short, domestic 

movements (Association of American Railroads, 2017; Zietlow et al., 2017). This is as true for agriculture 

as it is for other industries. Flexibility, timeliness, and efficient service are vital to shippers who handle 

perishable agricultural products. Figure 6 illustrates the intermodal agricultural supply chain for raw and 

processed products in the United States. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-21

Figure 6. U.S. Agricultural Supply Chain

Courtesy of USDA, "U.S. Agricultural Supply Chain for Raw and Processed Products," p. 2, Study of Rural 

Transportation Issues, USDA, 2010

Thus, trucking provides a critical link for the national economy. It links farmers, ranchers, manufacturers, 

and service industries to packinghouses, grain elevators, ethanol plants, processors, feedlots, and rail, 

barge, and port terminals. The linkage with rail, barge, and port facilities is especially important because 

of the complementary and competitive relationship among modes of transport. Dependence on trucking 

has increased in rural areas over the past several years—particularly due to the lack of, decline of, or 

withdrawal of rail service, restrictions on access and routings to competing railroads, and rail rate 

increases (USDA, 2010). 

Trucking competes with and complements other modes of service, such as air, rail, intermodal, and 

barge shipping. As a result, trucking competition moderates freight rates. Disruptions in barge traffic and 

sharp increases in barge rates divert cargo to trucks and rail (USDA, 2010). Diversions of business are

particularly common in the winter, when parts of the Mississippi River close to make repairs and to 

avoid winter freezes (USDA, 2019). Combined, these phenomena enhance competition, thus lowering 

freight rates for shippers. A highly competitive trucking industry benefits agriculture by keeping costs 

down and expanding markets domestically and abroad. Despite wide variations, the average ratio of 

operating cost to operating revenue is a tight 95 percent in over-the-road long-haul truckloads, 

demonstrating that the sector is highly competitive, approaching what economists call atomistic or 

perfect competition. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-22

To illustrate an example with a particular commodity, Figure 7 shows locations vital to the corn industry. 

Counties are highlighted by their level of corn production, showing where corn shipments are 

originating. Ethanol plants and the number of grain consuming animal units by state are also marked, 

indicating two major sets of domestic locations for corn. As the map shows, ethanol plants are densely 

concentrated in corn-producing areas, minimizing the distance that corn needs to travel to reach a 

plant. Similarly, States with high numbers of grain-consuming animal units also tend to produce, or be 

near other States that produce, a lot of corn, although this relationship is weaker than the one between 

ethanol plants and corn. Because ethanol plants and animals are generally close to corn farms, corn 

shipments usually travel by trucks to reach these domestic locations. However, if corn, or ethanol, is 

exported, those shipments will typically travel by rail or barge, since the port locations are relatively far 

from corn farms and ethanol plants.

Figure 7. 2013 Corn Production by County, Ethanol Plants, Grain Consuming Animal Units by State, and 

Export Port Regions (Denicoff, Prater, & Bahizi, 2014a)

On the other end of the supply chain, large quantities of goods travel to West Coast and Gulf Coast 

ports. Figure 8 shows food commodity flows in the United States, but does not show details on the exact 

path being traveled. Instead, it pulls data from the FAF to show origin-destination pairs at the county 

level. A little less food travels around the East Coast—but still a sizeable amount. The area of the 

country with the most food flows is in the Midwest or Central U.S., particularly around the Mississippi 

River and locations with farmland. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-23

Figure 8: Food Flows in the U.S. (Lin, Ruess, Marston, & Konar, 2019)

3.3 Food Price Impacts

The USDA calculates a “food dollar” to provide an answer to the question “for what do our food dollars 

pay”? According to this data, the total transportation cost tends to make up around 3-4 percent of the 

food dollar (Canning, 2011). For exported goods, this ratio may be different, given the ocean journey, 

but for domestic goods, transportation is a small part of the total cost of food. That share has generally

declined over the past several years, though the total dollar value has held roughly steady (Canning, 

2011). Incorporating all aspects of preparing the food, from farm to fork, USDA’s food dollar includes 

industries such as foodservices and retail trade that end up outweighing the transportation cost. 

Transportation’s estimated share of the food dollar, in both cents per dollar and the total value in 

millions of dollars, can be seen in Figure 9. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-24

$20,000

$22,000

$24,000

$26,000

$28,000

$30,000

$32,000

$34,000

$36,000

$38,000

3

3.5

4

4.5

19931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018

Million 2009 Dollars

Cents per Food Dollar

Year

Transportation: Cents per Domestic Real Food Dollar Transportation: Million 2009 Dollars

Figure 9. Transportation’s Component of the Domestic Food Dollar (USDA, 2020a) 

Although agricultural commodities also move on inland waterways and on rail, trucking is generally the 

most expensive of the three inland modes in terms of dollars per ton-mile and makes the most sense for 

the majority of shipments as it is both the quickest mode, and provides the connections for rail, barge, 

and ocean transportation. At only 3-4 cents per food dollar, transportation is not the largest component 

affecting domestic food costs. However, profit margins in agriculture tend to be very tight, and in 2013, 

USDA reported that almost 70% of all farms were in the “critical zone,” meaning that their operating 

profit margin (the ratio of operating profit to gross cash farm income) was less than 10% (Hoppe, 2015). 

Small changes in costs, therefore, affect profit margins for farmers, particularly for small farms that have 

tighter margins than large farms (Hoppe, 2015). 

For exported goods, the transportation cost share of food prices can be larger, as ocean freight rates 

and detention and demurrage charges due to port congestion become factors. Total transportation 

costs for soybeans from Minnesota to Japan have often been close to $100/metric ton, and costs 

increase if the goods go through the Gulf Coast instead of the Pacific Northwest, likely because of the 

longer ocean journey with expensive ocean freight rates (Denicoff, Prater, and Bahizi, 2014b). However, 

the truck share of that journey typically costs only around $10 to $15 per metric ton, with most of the 

inland cost coming from barge or rail freight rates. Figure 10 shows the percentage of the inland 

transportation cost that comes from the different modes for two routes of soybean exports.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-25

Figure 10. Modal Share of Inland Transportation Costs ($/mt) for Soybeans, Average from 2010 to 2018

(USDA, no date) 

Truck

25%

Rail

13%

Barge

62%

From Minneapolis, MN, through 

the Gulf Coast

Truck

17%

Rail

83%

From Fargo, ND, through the Pacific 

Northwest

3.4 International Competitiveness

The efficiency of the transportation system also plays a key role in determining how competitive U.S. 

agricultural products are on a global scale. The United States faces strong competition from other 

countries for various commodities, and different countries have their own transportation costs and ways 

in which they attempt to increase their shares of the export market. 

Adamopoulos (2011) used a general equilibrium framework to show how a large disparity in 

transportation infrastructure density translates to agricultural productivity differences across different 

countries. In other words, high transport costs distort allocation of resources not only across 

geographically dispersed production units within the agriculture sector but also across different sectors 

in the economy.

The efficiency and flexibility of trucking enables the United States to be competitive in the global 

marketplace for agricultural products. Disruptions in the multimodal supply chain—in rural and urban 

areas as well as at ports and inland terminals—affect agricultural markets and prices paid to farmers.

The World Bank presents data on the cost to export by country. This cost index measures the total cost 

of importing or exporting a typical 20-foot ocean freight container in U.S. dollars. This cost covers all the 

fees associated with completing the procedures to export or import the goods, including documents, 

administrative fees for customs clearance and technical control, customs broker fees, terminal handling 

charges and inland transport. In Figure 11, the darker the shade of the country, the higher the value of 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-26

its cost-to-export indicator is. The country with the highest value in the world (9,050) is Tajikistan, and 

the country with the lowest value in the world (410) is Timor-Leste.2

Figure 11. Cost-To-Export Indicator

 (https://www.indexmundi.com/facts/indicators/IC.EXP.COST.CD) 

Source: World Bank, Doing Business project (http://www.doingbusiness.org/).

Data from the World Bank were compared across key U.S. agricultural competitors. The cost-to-export 

indicator, as well as the main agricultural exports in which the country is competitive, appear in Table 6. 

The table does not include all agricultural commodities nor all countries that produce commodities, but 

rather presents the United States and its top competitors for select commodities, including soybeans, 

corn, sorghum, wheat, and cotton. As the table shows, while not the lowest, the United States does 

have a relatively low cost to export, particularly compared to Brazil and Argentina which are some of the 

United States’ biggest competitors. Additionally, the cost-to-export indicator from the World Bank does 

not include refrigeration costs. Therefore, commodities that would require refrigeration (such as beef, 

pork, and chicken) are not included here.

2 Definition: Cost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with 

completing the procedures to export or import the goods are included. These include costs for documents, 

administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges 

and inland transport. The cost measure does not include tariffs or trade taxes. Only official costs are recorded. 

Several assumptions are made for the business surveyed: that it has 60 or more employees; is located in the 

country's most populous city; and is a private, limited liability company. Also assumed is that the business does not 

operate within an export processing zone or an industrial estate with special export or import privileges; is 

domestically owned with no foreign ownership; and exports more than 10% of its sales. It is assumed that each

traded product travels in a dry-cargo, 20-foot, full container load. Also assumed is that the product is not 

hazardous or include military items; does not require refrigeration or any other special environment; and does not 

require any special phytosanitary or environmental safety standards other than accepted international standards. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-27

Table 6. Cost to Export and Key Agricultural Exports by Country

Country Cost to Export Indicator Top Agricultural Exports1,2

United States 1,224 Soybeans, Corn, Sorghum, Cotton, Wheat

Argentina 1,770 Soybeans, Corn, Sorghum

Australia 1,200 Sorghum, Wheat

Brazil 2,323 Soybeans, Corn

Canada 1,680 Soybeans, Wheat

China 823 Cotton

India 1,332 Cotton

Russia 2,401 Wheat 1

These are the top agricultural exports from a country that are competitive with the U.S. in the world export 

market. A country’s top agricultural export may not be listed in this table if it is only a small percent of the entire 

export market for that product.

2

An italicized commodity means that the country is the largest exporter of that commodity.

3.4.1 Case Study: Brazil

Brazil is one of the biggest competitors for U.S. agriculture. The largest U.S. agricultural exports are 

soybeans and corn, both of which Brazil also produces in large quantities. The United States used to be 

the largest exporter of soybeans. However, Brazil overtook the United States in soybean exports in 2013

and has continued to be the largest exporter since then. Brazil has a lower cost of production than the 

United States because of lower costs for land and labor (Salin and Somwaru, 2018). Part of what allows 

the United States to remain competitive is transportation efficiency, but Brazil has been making strides 

in recent years in improving its road network to lower transportation costs, increasing their 

competitiveness in the export market.

At the end of 2019, Brazil finished a project to pave and improve BR-163, which connects Brazil’s largest 

grain producing region to Mirituba, allowing products to reach northern ports in Brazil (Salin and

Somwaru, 2018). This was only the most recent step in Brazil’s efforts to improve its infrastructure to 

allow grains to make it to ports more quickly and more cheaply than in the past. These improvements to 

Brazil’s infrastructure will result in less expensive and more competitive grain and soybeans to the end 

user. Plus, at the margin, Brazil’s improvements will lead to more demand for Brazilian corn and 

soybeans, and less demand for U.S. corn and soybeans, all other things being equal. 

Figure 12 and Figure 13 compare U.S. soybean transportation costs to Brazilian ones. U.S. costs have 

generally been lower historically, shipping either through the U.S. Gulf Coast or the Pacific Northwest. 

The Brazilian port of Santos has tracked closely with U.S. costs, and Paranaguá has had significantly 

higher costs than the United States (USDA, no date).3 However, prices began to change around 2014. 

The costs of shipping through Paranaguá plummeted, due to decreased costs for trucking in Brazil. The 

3 U.S. Gulf costs are from Minneapolis, MN, through the Gulf Coast; the U.S. Pacific Northwest costs come from 

Fargo, ND; the Santos costs are from South Goiás, Brazil, through the port of Santos; and Paranaguá comes from 

North Mato Grosso, Brazil.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-28

costs for Santos also dropped. They remain consistently below the costs of both the U.S. Gulf Coast and 

the Pacific Northwest through 2016 and 2017, and 2018 data is unavailable for the Santos route. The 

costs of shipping from Fargo, ND, through the Pacific Northwest remain relatively constant throughout 

the entire period analyzed in the graph (2010-2018), while the U.S. Gulf Coast route tends to have more 

variation because of rail and barge prices.

Figure 12. U.S. and Brazil Soybean Total Transportation Costs to Shanghai, China (USDA, no date) 

U.S. Gulf: From Minneapolis, MN, through the U.S. Gulf Coast; U.S. Pacific Northwest: From Fargo, ND, through the 

Pacific Northwest; Santos: From South Goiás, Brazil, data not available for 2018; Paranaguá: From North Mato 

Grosso, Brazil

40

60

80

100

120

140

160

180

200

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

2010 2011 2012 2013 2014 2015 2016 2017 2018

U.S. $/metric ton

Year

Santos Paranagua U.S. Gulf U.S. Pacific Northwest
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-29

Figure 13. U.S. and Brazil Soybean Total Inland Transportation Costs to Shanghai, China (USDA, no date) 

U.S. Gulf: From Minneapolis, MN, through the U.S. Gulf Coast; U.S. Pacific Northwest: From Fargo, ND, through the 

Pacific Northwest; Santos: From South Goiás, Brazil, data not available for 2018; Paranaguá: From North Mato 

Grosso, Brazil

20

40

60

80

100

120

140

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

2010 2011 2012 2013 2014 2015 2016 2017 2018

U.S. $/metric ton

Year

Santos Paranagua U.S. Gulf U.S. Pacific Northwest

Breaking out the inland transportation costs further shows that, for these routes, the U.S. truck cost is 

often around $10/metric ton, although it does vary, with the majority of the cost coming from the rail or 

barge portion of the journey (USDA, no date). Because the rail and barge components make up more of 

the overall cost for exported soybeans, it is possible that improvements to railroads and inland 

waterways could have a larger effect than improvements to roads on the U.S. cost to export. However, 

improvements to surface transportation are needed, as trucks provide the connections to railroads and 

waterways. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-30

4. Agricultural Projections

Given the enormous role of agriculture in the economy, it is essential to use agricultural data to inform 

transportation planning. In particular, the future size of the agricultural industry is relevant for 

understanding the importance of surface transportation. Industry growth will increase congestion and 

require investment to mitigate the impacts of increased truck traffic. Additionally, transportation 

projects often have a long timeframe from planning to implementation. Freight projections are, 

therefore, particularly important to help strategically plan transportation projects. To help understand 

the future of agriculture, this project created projections for key agricultural commodities 25 years into 

the future.

USDA has its own 10-year projections of several commodities, which USDA updates every year. At the 

time this report was produced, the most current USDA forecast was from February 2020 and covered 

the period to 2029 (USDA, 2020b). (Some early-release tables from the February 2021 forecasts are now 

available, but were not used in this analysis.) These 10-year projections were used as a basis to develop 

25-year projections of the same commodities that USDA projected. The approach leverages the longrun

supply-demand equilibriums that exist for agricultural products for purposes of developing a long-term

outlook. This relationship can be observed in time series data, whereby in the long run, there is a stable

and consistent relationship between the two series, even though the two series may drift away from

each other in the short run (in time series analysis, this is called a co-integrating relationship).4 This

relationship can be used to guide the development of a projection extension and ensure it is consistent

with the USDA projection and historical trends. The process also recognizes and maintains the

relationships between the stock and flow concepts by establishing identities used to produce total

supply and use projections.

The detailed results of the 25-year projections will be presented for soybeans, corn, wheat, beef, and 

pork, and a description of the methodology behind the projections can be found in Section 6 of this 

Appendix. This section will provide an overview of the expected future trends for a few commodities, 

and discuss how these trends may affect traffic, infrastructure needs, and international competitiveness.

There is always uncertainty around any projection, and this is particularly true for projections that cover 

many years, such as these 25-year projections. Thus, these projections are intended to serve only as a 

rough estimate of trends. 

4

 For a more detailed discussion of co-integration, see Co-Integration and Error Correction: Representation, 

Estimation and Testing, R. Engle and C. Granger, Econometrica, March 1987. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-31

4.1 Specific Commodities

The projection work covered all commodities for which USDA has 10-year projections. However, certain 

commodities represent a small share of the overall industry, and while those commodities are still 

critical to the agricultural industry, they do represent a smaller share of freight movements and 

agricultural exports. Accordingly, this section will highlight some of the largest commodities by volume 

and value. For crops, these large commodities include soybeans, corn, and wheat, and for livestock 

products, they include beef and pork.

4.1.1 Soybeans

Although there have been some declines in soybean supply in previous years, soybean projections 

indicate that the market should recover and continue to grow in the next 25 years. The projections

include the export market—soybean exports should continue to rise, although the growth rate is

consistent with the overall growth rate of demand of around 1.2% per year. Figure 14 and Figure 15

show the soybean projections.

Figure 14. Soybean Total Supply and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029.

0

1000

2000

3000

4000

5000

6000

7000

8000

1990 2000 2010 2020 2030 2040 2050

Soybean 

Total Supply and Components

Year

Total supply; Million bushels Production; Million bushels

Imports; Million bushels Beginning stocks; Million bushels

2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-32

Figure 15. Soybean Total Demand and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029. 

0

1000

2000

3000

4000

5000

6000

7000

8000

1990 2000 2010 2020 2030 2040 2050

Soybean 

Total Demand and Components

Year

Total Use Million; bushels Seed and residual; Million bushels

Crush; Million bushels Exports; Million bushels

2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-33

4.1.2 Corn

Similar to the soybean market, the supply and demand for corn are both expected to continue to 

increase through the 25-year projection, at a rate similar to the soybean rate. Figure 16 and Figure 17

show the corn projections. 

Figure 16. Corn Total Supply and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029. 

0

5000

10000

15000

20000

25000

30000

1990 2000 2010 2020 2030 2040 2050

Corn

Total Supply and COmponents

year

Total supply Million bushels Production Million bushels

Imports Million bushels Beginning stocks Million bushels

2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-34

Figure 17. Corn Total Demand and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029.

0

5000

10000

15000

20000

25000

1990 2000 2010 2020 2030 2040 2050

Corn

Total Demand and Components

Year

Total use million bushels Domestic use million bushels

Exports million bushels 2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-35

4.1.3 Wheat

At 0.3-0.4% per year, the growth in supply and demand for wheat is much lower than that of corn and

soybeans. Domestic use and exports represent relatively equal shares of wheat consumption, with no 

large changes expected to occur over time. Figure 18 and Figure 19 show the wheat projections. 

Figure 18. Wheat Total Supply and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1990 2000 2010 2020 2030 2040 2050

Wheat

Total Supply and Components

year

Total supply Million bushels Production Million bushels

Imports Million bushels Beginning stocks Million bushels

2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-36

Figure 19. Wheat Total Demand and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029. 

0

500

1000

1500

2000

2500

3000

3500

1990 2000 2010 2020 2030 2040 2050

Wheat

Total Demand and Components

Year

Total use Million bushels Domestic use Million bushels

Exports Million bushels 2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-37

4.1.4 Beef

Supply and demand for beef are projected to grow at more slowly than soybeans and corn. Changes in 

consumer preferences could contribute to a slowing demand for red meat, but consumption is still 

expected to increase overall. The vast majority of beef is consumed in the United States, with only a 

small fraction exported to other countries. Figure 20 and Figure 21 show the beef projections. 

Figure 20. Beef Total Supply and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029.

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1990 2000 2010 2020 2030 2040 2050

Beef

Total Supply and Components

year

Total supply Million pounds Total production Million pounds

Imports Million pounds Beginning stocks Million pounds

2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-38

Figure 21. Beef Total Demand and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1990 2000 2010 2020 2030 2040 2050

Beef

Total Demand and Components

Year

Total Use Million pounds Total disappearance Million pounds

Exports Million pounds 2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-39

4.1.5 Pork

Supply and demand for pork are expected to grow fairly steadily, particularly compared to beef. 

Additionally, the United States exports a larger share of pork than beef. Projections show the exported 

share of pork staying relatively constant over time. Figure 22 and Figure 23 show the pork projections. 

Figure 22. Pork Total Supply and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029

0

10000

20000

30000

40000

50000

60000

1990 2000 2010 2020 2030 2040 2050

Pork

Total Supply and Components

Year

Total supply Million pounds Total production Million pounds

Imports Million pounds Beginning stocks Million pounds

2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-40

Figure 23. Pork Total Demand and Components

Source: USDA 2020-2029 projection; Volpe projection extends from 2029

4.2 Overall Trends and Expected Impacts

Robust conclusions cannot be drawn from the 25-year projections, given the uncertainty surrounding 

them. However, this section attempts to draw some general themes based on the overall trends in the 

projections. Across the agricultural industry as a whole, projections generally show continued growth, 

meaning the industry will continue to grow throughout the next 25 years. The United States will

continue to be a large, competitive agricultural exporter. 

0

10000

20000

30000

40000

50000

60000

1990 2000 2010 2020 2030 2040 2050

Pork

Total Demand and Components

year

Total Use Million pounds Total disappearance Million pounds

Exports Million pounds 2029
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-41

An increase in the number of commodities produced and shipped across the country will result in a 

larger strain on surface infrastructure and the trucking industry. More goods mean more trucks and 

miles traveled, increasing congestion on already congested roads. Overall, vehicle miles traveled (VMT) 

are expected to grow at a rate of just under 1 percent per year through 2047, and VMT for combination

and single-unit trucks will grow at 1.5 percent and 1.9 percent annually, respectively (FHWA, 2019b). 

Based on the expected growth rates, agriculture’s share of all freight traffic may not shift much. It is 

difficult to draw an accurate comparison as a 1-percent increase in agricultural products does not 

necessarily result in an equivalent 1-percent increase in the truck traffic required to carry those 

products, because it depends also on the actual volume of the increase, how much freight a single truck 

can carry, and changes in freight demand in other sectors. These projections cannot say for certain how 

traffic will be affected by the predicted growth rate, but the 

fact that agricultural production is expected to continue to 

grow indicates that agriculture will continue to be one of the 

most important types of freight shipped in the United States. 

It is also worth noting that the projections do not predict 

substantial changes in the share of agricultural production 

that gets exported. The total volume of exported goods will 

grow proportionally with total production, and the relative 

share of agricultural goods as a whole that are exported is 

expected to continue to follow current trends, although 

there will likely always be a certain amount of variation from 

year to year. If exports were to grow substantially, that

increase could raise pressure on certain parts of the 

transportation system, such as inland waterways, railroads, 

ports, and the intermodal connections to them. It is difficult 

to predict exactly how agricultural flows across the country 

will shift over the next several years. However, the current 

geographic pattern of flows, which reflects the spatial

distribution of producers, consumers, and export markets, 

remains a reasonable estimate of future flow patterns. 

Improving the NHS and the larger roadway system in the 

United States will benefit all road users, but it will certainly 

benefit agriculture freight as well. Agricultural freight should continue to be a substantial portion of the 

overall system of freight for many years into the future. 

Multiple aspects of this analysis have been 

affected by the COVID-19 pandemic, which 

has affected agricultural production, 

processing, and shipments. At the time this 

report was prepared, the most recent USDA 

forecasts were from the February 2020

release, which did not reflect any pandemicrelated impacts. Subsequent updates to 

these forecasts do include estimated impacts 

through October 2020, as well as a revised 

outlook for future years. Although there 

continues to be uncertainty over the near 

term, the 25-year forecasts presented here 

represent a reasonable estimate of future 

conditions over the longer term. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-42

5. Conclusion

U.S. surface transportation plays a large role in the country’s economy. The transportation industry itself 

employs millions of workers, including truck drivers, but also serves to ensure various commodities can 

reach their intended markets for consumption. Agricultural products comprise a large portion of all 

inland truck movements, particularly for domestic shipments. However, U.S. roadways are aging and 

congested, which can increase the time required for a shipment, increase operational costs, reduce 

international competitiveness, and raise the final delivered price. 

Studies have shown that improving transportation infrastructure does positively impact various 

industries, including agricultural. One study found that increasing investment by 1% could increase 

agricultural output in the same State by 0.2-0.3% (Tong et al., 2013). Larger increases in investment 

spending would then also cause larger increases in agricultural output. Investing in infrastructure to 

alleviate congestion could also reduce transportation costs, particularly for domestic goods, which travel 

primarily by truck. Exported goods travel more on rail and barges, limiting the ability to improve the 

movements of exported goods by solely improving trucking. There is, however, always a truck 

component to every journey. While the exact cost per ton mile varies, truck costs are typically higher 

than the costs for rail or barge, such that the truck component of a journey can be a major contributor 

to the overall cost. Higher truck transportation costs can reduce the prices paid to farmers and make 

exports less competitive in the international market.

Other benefits from investment in infrastructure include reducing costs to farmers and food producers 

or to domestic food consumers. Farmers often have small profit margins, and any reduction in 

transportation cost could increase their profit. For domestic consumers, reductions in transportation 

costs can reduce the final cost of the food, saving consumers money and potentially allowing them to 

purchase more commodities. 

The efficiency of the U.S. transportation network helps reduce the cost of moving goods and increases 

U.S. competitiveness in the global agricultural market. Other countries, such as Brazil, are making strides 

toward improving their transportation systems, thereby making their commodities more attractive 

substitutes for American ones. Data have shown that Brazil’s transportation costs used to exceed those 

of the United States, but Brazil’s costs have trended steeply downward in recent years with improved 

infrastructure.

Transportation is vital to every industry, including agriculture. This report highlights the necessity of 

maintaining and improving transportation networks and the benefits these infrastructure projects will 

have for the entire U.S. agricultural sector. Future research on this project will include analyses of costeffective investment patterns, roles of different levels of government, impacts of different levels of 

investment, and future research needs. 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-43

6. Appendix

6.1 Commodity Projections Development 

Introduction

The approach used for developing a 25-year outlook is based on USDA’s current 10-year projection. This 

leverages the existing USDA projections, and underlying trends, and means the new projection will be a 

15-year extension built on the existing 10-year projection that ends in 2029. The full range of the longrun projection will therefore be from 2019-2043—the first 10 years from the current USDA (2020)

outlook, followed by a 15-year extension.

This approach has the advantage of being relatively easy to structure and implement, and implicitly 

incorporates the assumptions imbedded in the USDA projection. In practice, this means leveraging the 

10-year projected growth rates for commodities in the USDA outlook to guide the 15-year extension,

and incorporating the long-term supply-demand equilibriums that exist in these markets. (That is, there

is a co-integrating relationship between supply and demand in these markets that can be used to guide

the projection extension.) Concepts covered for each commodity projection are not homogenous, and

the specifics of the approach will vary from case to case. The process also recognizes and maintains the

relationships between the stock and flow concepts by establishing identities used to produce total

supply and use projections. The projection extension methodology is described below: the three main

steps include data review, projection development, and projection validation.

Data Review

The first step requires documenting and graphing all concepts tracked and projection by USDA in the 

agricultural projections report, covering the key metrics—domestic use and export volume/price—and 

all connected market information. These concepts include all components used to build up total supply 

and total use, such as beginning stock, harvested acres, yield, production, imports, domestic use, 

exports, and ending stock. This step also requires identifying any subcomponents within this structure 

that may require additional or separate modeling (e.g., ethanol production as a subset of corn use, 

which is unmeasured in total corn use). The domestic prices of commodities and the associated value of 

product consumed are also considered. 

Initial Projection Development

The initial approach focuses on identifying and projecting the primary supply and demand components 

for each commodity. In particular, data will be examined to identify the historical relationship between 

commodity supply (without stocks) and total demand (domestic and export). In general, this relationship 

is fairly stable, with periods where demand exceeds supply being followed by an increase in supply to 

meet the higher demand (and, perhaps, a moderation in demand due to higher prices). In time series 

analysis, this is a co-integrating relationship, whereby in the long run, there is a stable and consistent 

relationship between the two series, even though in the short run, the two series may drift away from 
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-44

each other.5 This relationship can then be leveraged to guide the projection extension and ensure it is 

consistent with the USDA projection, and also historical trends. The specific steps for deriving the basis 

of the projection extension are: 

1) Supply Projection: the initial step begins with projecting the commodity supply (with no stock)

and establishes the foundation for the 15-year extension. A moving average process is utilized

for this projection, which grounds the extension trend in the USDA 10-year projection (the

starting point for the extension is a 3-period moving average, but different lag-lengths may be

tested/used for different commodities depending on variation in historical trends).

2) Estimating Longrun Supply/Demand Relationship: This process involves estimating the longterm relationship (average gap) between the supply (with no stock) and total demand (domestic

and export). This relationship is estimated based on examining the USDA projection and

historical data, providing a stable base from which to predict the evolution of both demand and

supply in the projection extension. Importantly, the estimated longrun gap needs to allow for a

smooth transition from the USDA projection and the extension. (That is, there should be no

discernable jumps in the first year of the extension projection, nor should the extension trend

be significantly different from the USDA projection.)

3) Total Demand Projection: In this step, total demand (domestic and export) is projected using

the co-integrating gap estimated previously (total demand = supply (no stock) − co-integrating

gap).

a. Projection performance can be checked through applying the gap to historical supply

and calculating the mean absolute percent error (MAPE) between the estimated and

actual demand—smaller MAPEs imply a better estimation.

4) Projecting Disaggregated Supply and Demand: Individual components of supply and demand

(e.g., domestic demand, production, etc.) are projected using a share allocation approach.

Historical shares are used to guide this process, with the focus being on recognizing and

maintaining the historical distribution of supply (demand) among the component shares, as well

as recent trends in how these shares may be evolving.6

5) Harvested Acres: Where applicable by commodity, harvested acres are projected using a

moving average methodology.

6) Yield: Projected using identity and appropriate unit conversions, which varied per commodity.

7) Commodity Prices: These are extended from the USDA base using a moving average approach.

Proceeding from projection development, the stock and flow relationships are developed for each 

commodity. For example, in the first year of the projection extension, the ending stocks in the previous 

year become this year’s beginning stocks. Domestic and import supply (projection as noted above) are 

added to beginning stocks to provide total stocks. This process is done using the appropriate identities 

for demand to provide a year-by-year accounting of how the market for each commodity will evolve. 

5 For a more detailed discussion of co-integration, see Co-Integration and Error Correction: Representation, 

Estimation and Testing, R. Engle and C. Granger, Econometrica, March 1987. 

6 Historical average shares are used when the series is stable, but if this is not the case, then more recent data, or 

the USDA projection, are used.
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-45

Projection Validation 

Projection validation requires determining whether the projection extensions produced reasonable, 

longrun trends. For this, projections are checked for internal consistency within each commodity 

market, looking specifically for:

• Reasonable and stable relationship between supply and demand with a projection trend

consistent with the USDA outlook;

• Beginning and ending stocks plausibly bounded by historical ranges;

• Balance between consumption and supply, so that ending/beginning stocks do not decline to

implausible levels (given historical trends);

• Extension projection trends broadly consistent with USDA 10-year outlook;

• Compound annual growth rates consistent with the 10-year projections (e.g. not significantly

deviating from the prior projection);

• Yield and stock/use ratios also reviewed to determine consistency with historical and USDA

projection trends; and

• General measures (e.g., demand per capita) compared to historical trends.

Commodity projections that appear unreasonable (e.g., negative values for stock components) are 

reassessed, with the initial adjustment being made in the supply projection. 
6.2 Projection Identities

Commodity Identities Notes

Corn Production = Harvest Acres X Yield

Total Supply = Beginning Stocks + 

Production + Imports

Beginning Stocks = Ending Stocks (previous 

period) 

Domestic Use = Feed & Residual + Food, 

Seed & Industrial

Total Use = Domestic Use + Exports

Ending Stocks = Total Supply − Total Use

Stocks/use ratio, %

Ethanol and by-products are a 

standalone sub-component and does 

not factor into Domestic Use. 

Price is projected but not part of 

identities.

Sorghum, 

Barley, Oats

Production = Harvest Acres X Yield

Total Supply = Beginning Stocks + 

Production + Imports

Beginning Stocks = Ending Stocks (previous 

period)

Domestic Use = Feed & Residual + Food, 

Seed & Industrial

Total Use = Domestic Use + Exports

Ending Stocks = Total Supply − Total Use

Stocks/use ratio, %

Price is projected but not part of 

identities.

Wheat Total Supply = Beginning Stocks + 

Production + Imports

Beginning Stocks = Ending Stocks (previous 

period)

Domestic Use = Food + Seed + Feed & 

Residual

Total Use = Domestic Use + Exports

Ending Stocks = Total Supply − Total Use

Stocks/use ratio, %

Price is projected but not part of 

identities.

Soybeans Total Supply = Beginning Stocks + 

Production + Imports

Beginning Stocks = Ending Stocks (previous 

period)

Domestic Use = Crush + Seed & Residual

Total Use = Domestic Use + Exports

Ending Stocks = Total Supply − Total Use

Stocks/use ratio, %

Price, including crush margins, are 

projected but not part of identities.

Production of soybean meal and oil are 

identities from soybean (When a 

bushel of soybeans weighing 60 pounds 

is crushed, the typical result is 11 

pounds of soybean oil, 44 pounds of 48 

percent protein soybean meal, 4 

pounds of hulls and 1 pound of waste.)
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-47

Rice Total Supply = Beginning Stocks + 

Production + Imports

Beginning Stocks = Ending Stocks (previous 

period)

Domestic Use & Residual

Total Use = Domestic Use + Exports

Ending Stocks = Total Supply − Total Use

Stocks/use ratio, %

Price is projected but not part of 

identities.

Upland 

Cotton

Total Supply = Beginning Stocks + 

Production + Imports

Beginning Stocks = Ending Stocks (previous 

period)

Domestic Use

Total Use = Domestic Use + Exports

Ending Stocks = Total Supply − Total Use

Stocks/use ratio, %

Price is projected but not part of 

identities.

Beef Total Supply = Beginning Stocks + Total 

Production + Imports

Total Production = Farm Production + 

Commercial Production

Beginning Stocks = Ending Stocks (previous 

period)

Domestic Use = Total Consumption

Total Use = Total Consumption + Exports

Ending Stocks = Total Supply − Total Use

Prices varied across product uses. 

Prices are not part of any identity but 

projected.

Prices: Beef cattle, Calves, Steers (5-

area), Feeder Steelers (Oklahoma City) 

Inventory in different units/measure 

were also projected but is not part of 

any identity.

Total Cow Inventory

Beef Cow Inventory

Cattle Inventory
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-48

Pork Total Supply = Beginning Stocks + Total 

Production + Imports

Total Production = Farm Production + 

Commercial Production

Beginning Stocks = Ending Stocks (previous 

period)

Domestic Use = Total Consumption

Total Use = Total Consumption + Exports

Ending Stocks = Total Supply − Total Use

Price and inventory are projected but 

not part of identities.

Young 

chicken

Total Supply = Beginning Stocks + Total 

Production

Federally Inspected Slaughter

Beginning Stocks = Ending Stocks (previous 

period)

Domestic Use = Total Consumption

Total Use = Total Consumption + Exports

Ending Stocks = Total Supply − Total Use

Prices varied across product uses. 

Prices are not part of any identity but 

projected.

Prices: Broilers (Farm), Broilers 

)National Composite) 

Broilers - A broiler is any chicken that is 

bred and raised specifically for meat 

production.

Turkey Total Supply = Beginning Stocks + Total 

Production

Beginning Stocks = Ending Stocks (previous 

period)

Total Use = Domestic Use + Exports

Ending Stocks = Total Supply − Total Use

Prices varied across product uses. 

Prices are not part of any identity but 

projected.

Prices: Turkey (Farm), Hen Turkeys

(National) 

Egg Total Supply = Beginning Stocks + Total 

Production + Imports

Beginning Stocks = Ending Stocks (previous 

period)

Total Use = Domestic Use + Hatching Use + 

Exports

Ending Stocks = Total Supply − Total Use

Prices varied across product uses. 

Prices are not part of any identity but 

projected.

Prices:

Eggs, Farm

New York, Grade A Large
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-49

Dairy

Milk fat basis: Total Commercial Supply= 

Beginning commercial stock + Marketings 

+ Imports

Milk fat basis: Domestic Commercial Use =

Total Consumption

Milk fat basis: Total Use = Domestic

Commercial Use + Commercial Exports

Milk fat basis: Ending Stocks = Total

Commercial Supply − Total Use

skim solids basis: Total Commercial Supply

= Beginning commercial stock + 

Marketings + Imports

skim solids basis: Domestic Commercial 

Use = Total Consumption

skim solids basis: Total Use = Domestic 

Commercial Use + Commercial Exports + 

CCC donations

skim solids basis: = Total Commercial 

Supply − Total Use

Ending Commercial stocks

The DPDP addresses low margins for 

dairy

operations by using Commodity Credit

Corporation (CCC) funds to purchase 

dairy

products for donation to public and 

private

nonprofit organizations that provide 

nutrition

assistance to low-income populations.

Some series reported in the USDA 

projection but not part of identities.

Number of cows

Milk per cow

Milk production

Farm use

Marketings

Prices:

All milk

Cheese

Butter

Nonfat dry milk

Dry whey
Appendix A: Economic Significance of Surface Transportation to U.S. Agriculture A-50

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U.S. Department of Transportation 

John A. Volpe National Transportation Systems Center

55 Broadway

Cambridge, MA 02142-1093

617-494-2000

www.volpe.dot.gov
Appendix A 

The Importance of Highways to U.S. Agriculture

http://dx.doi.org/10.9752/TS295.12-2020