1

Agricultural 

Marketing

Service

November 2020

The Impact of Brazil's Infrastructure 

and Transportation Costs on U.S. 

Soybean Market Share: An Updated 

Analysis from 1992-2019
Delmy L. Salin, Agricultural Marketing Service 

Agapi Somwaru, Economic Consulting

U.S. Department of Agriculture

November 19, 2020

For more information: Delmy.Salin@usda.gov

Preferred citation:

Salin, Delmy L. and Agapi Somwaru. November 2020. The Impact of Brazil’s Infrastructure and Transportation 

Costs on U.S. Soybean Market Share: An Updated Analysis From 1992-2019. U.S. Department of Agriculture, 

Agricultural Marketing Service. <http://dx.doi.org/10.9752/TS290.11-2020>

Photo credit: USDA

USDA is an equal opportunity provider, employer, and lender.
i

Acknowledgments

For data, information, and a graphic representation (map) of selected U.S.-South America ocean routes for 

soybeans, the authors would like to thank the following organizations: Escola Superior de Agricultura “Luiz de 

Queiroz”/ Grupo de Pesquisa e Extensão em Logística Agroindustrial; the Soybean Transportation Coalition; 

USDA, Foreign Agricultural Service (FAS), Global Market Analysis; the FAS Office of Agricultural Affairs, Brasilia; 

and the USDA, Economic Research Service (ERS). The authors also appreciate Surajudeen Olowolayemo, 

USDA, Agricultural Marketing Service (AMS), for providing data. The authors are also grateful for comments 

and critiques by Keith Menzie, Joanna Hitchner, and Shawn Arita, USDA, Office of the Chief Economist; Mark 

Ash, ERS; and Oliver Flake, FAS, Brasilia. The authors thank the following AMS employees: Maria Williams, for 

editing; Jessica Ladd, for layout, and Kranti Mulik, for reviewing.

Abstract 

This paper follows up on an analysis published by the authors in 2018 titled, “The Impact of Infrastructure 

and Transportation Costs on U.S. Soybean Market Share: An Updated Analysis from 1992-2019.” The updated 

data and analysis take into account the new transportation routes that have emerged in Brazil, as well as 

possible future developments. The United States has lost market share to Brazil but remains the secondlargest producer and exporter of soybeans. Using a dynamic econometric model and multivariate sensitivity 

analysis, the authors retrospectively estimate the economic attributes of changes in export market shares. 

This study quantifies the impact resulting from changes in the export market shares of soybeans under the 

continued development of Brazil’s transportation infrastructure. The results suggest the U.S. world market 

share could decline an additional 3-6 percentage points, if U.S. farm-to-port transportation infrastructure 

does not significantly improve. A decline of 1 percent in the U.S. soybean market share amounts to just over 

half a billion dollars in lost export sales—based on a world soybean trade volume of 166 million metric tons 

and today’s price of soybeans. Over the back casted study period, 1992-2019, soybean market shares globally 

converge to an equilibrium, indicating the stability of the market. 
ii
iii

Contents

Executive Summary ������������������������������������������������������������������������������������������������������������������������������������������������ 1

Objectives and Organization�����������������������������������������������������������������������������������������������������������������������������������3

Market Shares in the World Soybean Market ��������������������������������������������������������������������������������������������������������3

The United States−South America Ocean Freight Spread ��������������������������������������������������������������������������������������7

Analyzing World Soybean Market Shares: Model and Results�����������������������������������������������������������������������������10

Results��������������������������������������������������������������������������������������������������������������������������������������������������������������� 11

Sensitivity Analysis – Brazil’s Transportation Infrastructure Improvements ��������������������������������������������������������11

Conclusions and Further Research �����������������������������������������������������������������������������������������������������������������������18

Appendix: Methodology��������������������������������������������������������������������������������������������������������������������������������������20

Accounting for Ocean Freight Changes ������������������������������������������������������������������������������������������������������������ 22

Sensitivity Analysis – Brazil’s Transportation Infrastructure Improvement������������������������������������������������������ 22

References������������������������������������������������������������������������������������������������������������������������������������������������������������ 25
1

Executive Summary

Since the 1990s, the United States has lost market share to Brazil.1 U.S. market share declined from 66 percent 

in 1992 to 32 percent in 2019. U.S. competitiveness, relative to Brazil, declined during a period of strong global 

growth in soybean demand. However, the United States remains the second-largest exporter, followed by 

Argentina, Paraguay, Canada, Ukraine, and Uruguay. China drives global soybean trade, accounting for more 

than half of soybean imports worldwide. Brazil has lowered its transportation costs by continually improving 

its transportation infrastructure, making the country more competitive in the world market. Brazil’s position 

has also benefited from low production costs, increases in planted area, high productivity, a weak national 

currency, and recently, the U.S.-China trade dispute. At the farm level, in the main producing areas of the U.S. 

Midwest, per-bushel total production costs are higher than in Brazil and Argentina. Despite having higher 

production costs than South American soybeans, U.S. soybeans are still globally competitive because U.S. total 

costs (including transportation costs, from point of production to the destination in Asia) are generally lower 

than for South American soybeans. Nonetheless, Brazil’s infrastructure has improved, narrowing the cost 

difference between shipping to Shanghai, China, from Sorriso, Mato Grosso, and from Davenport, Iowa. 

As one of the largest producers in the world soybean market, the United States faces stiff competition from 

other major soybean-producing countries. The major competing countries improved infrastructure capacity 

and reduced transportation costs increased their competitiveness. When differences in transportation costs 

make South American soybean exports more profitable than U.S. soybean exports, then trade can be diverted 

from the United States to Brazil or Argentina at key junctures of the most lucrative marketing periods. Since 

2007, the Brazilian government began a comprehensive strategy of infrastructure improvement, involving 

multiple transportation modes, with major institutional and regulatory changes to facilitate agricultural 

exports. In 2011, the Brazilian government introduced a law requiring railroads to sell other railroads the 

rights to use idle capacity if they do not use their rail tracks at full capacity. Two years later, an intermodal 

grain terminal was built facilitating the flow of grain from Rondonópolis, Mato Grosso, to the southern port of 

Santos. In 2014, Brazil’s agribusiness sector created a new export route from Miritituba to Barcarena (Vila do 

Conde), adding a new northern gateway for grain exports from Mato Grosso to China, Europe, the Middle East, 

and Mexico. After the BR-163 highway to connect Sorriso, Mato Grosso, to Itaituba/Miritituba, Pará, was fully 

paved in late 2019, the “first leg” of grain exports journey was reduced from several days, or even weeks, to 

about 35 hours. Twelve years of strategic infrastructure and regulatory overhauls set the stage for a balanced 

Brazilian transportation system that includes all major modes (truck, barge, and ocean vessel). Similar to the 

U.S. Gulf export route, Brazil’s grain-export routes now offer efficient, cost-effective transport options from 

farms to major foreign markets. The road ahead for U.S. soybean competitiveness will be challenging. In 2018, 

when demand for U.S. soybeans declined after China raised its duty on U.S. soybeans by 25 percent, Brazil 

became not only the world largest producer, but also the largest exporter.

This study quantifies the changes of the U.S. market share over time in the world soybean market, using a 

dynamic econometric model.2 The study also examines the effects of ocean freight spreads, and evaluates the 

possible impact of Brazil’s infrastructure development on the U.S. position in the soybean global market by 

1 Until 2013, the United States was the dominant country in the world soybean market in terms of market share/power, including 

production volume and exports. Although, in terms of exports, the United States has lost market share since 2013, the nation 

remains one of the largest soybean producers in the world market. For more information, see Salin et al. (2018) and Salin and 

Somwaru (2015).

2 The dynamic model accounts for the interactions of each of the major soybean producer/exporter countries in the world market, 

estimating the behavior and stability of the market shares over time. The market shares converge to an equilibrium, implying that 

opposing market forces are balanced. As a result, the global soybean market is stable and converges. Consequently, the results 

presented are the estimates of the converged models.
2

using multivariate sensitivity analysis.3,4 The analysis considers scenarios, reducing Brazil freight rates to two 

different levels. Assuming Brazil’s infrastructure improvements began to reduce costs in 1992, these scenarios 

attempt to simulate the progressive effects of a generic reduction in Brazil’s domestic transportation costs. The 

assumed reductions by $18/metric ton (mt) and by $28/mt equaled estimates of reductions from the recent 

completions of BR-163 highway and Ferrogrão Railroad (EF-170), respectively. Due to data availability, the base 

estimated model uses ocean freight rates and trade data for the period between 1992 and 2019. 

The United States is one of the largest soybean-producing countries. Brazil and Argentina are major U.S. 

competitors in the world export soybean market. Other competitors are Paraguay, Canada, Ukraine and 

Uruguay. The dynamic model’s analysis shows the export market shares converged over the period of the 

study. This implies world market shares are stable when the U.S. is one of the largest soybeans producers, 

despite the variability of ocean freight rates over the study period. 

The results also suggest, under the base model (current conditions), the world soybean market is in a steady 

equilibrium and the U.S. market share will become smaller over time, as the overall market grows. The initial 

position of the United States erodes, and the market shares of the competing countries grow faster than the 

leading producer country’s share. Similar results were obtained when changes in ocean freight rates over the 

estimated period were considered as the model converged to an equilibrium. The model’s conversion indicates 

the market’s stability. With the overall increasing global soybean production and rising export market demand, 

Brazil attains a larger market share than the United States.

Under scenarios 1 and 2, the findings indicate the U.S. world soybean market share in 2019 could decline 

by 3 and 6 percentage points, respectively. U.S. market share could decline without significant farm-toport upgrades in U.S. transportation infrastructure that would reduce U.S. costs, in line with Brazil’s cost 

reductions.5 In scenario 2, the analysis shows Brazil’s global export market shares for 2019 increase 9 

percentage points from 50 percent to 59 percent, primarily a result of potential structural improvements in 

Brazil. Assuming the world soybean trade is 166 million metric tons (mmt) (World Agricultural Supply and 

Demand Estimates (WASDE) and FAS September 2020), a 1-percentage-point decline in the U.S. soybean 

market share equals just over half a billion dollars lost in export sales (1.7 mmt × $332/mt). 

According to the sensitivity analysis, market penetration depends on the underlying technology and 

infrastructure for transporting grain and soybeans from farm to port. Thus, infrastructure improvements are 

critical to maintaining U.S. competitiveness and global market position, over the long term. Other things equal, 

improved U.S. infrastructure would result in higher income for farmers.

3 Ocean freight spread is the cost difference between two vessel routes to the same destination.

4 Multivariate sensitivity analysis is an economic modeling tool to analyze probable events by considering alternative possible 

outcomes. In this case, it is uncertain how much Brazil’s infrastructure will improve within the next 10 years and by how much freight 

rates will be reduced over time. Unlike scenario analysis, which assesses one uncertain condition at a time, sensitivity analysis can 

assess changes of several uncertain conditions at the same time to evaluate an outcome. This analysis simulates, retrospectively, 

Brazil’s infrastructure and transportation improvements from 1992 to 2019 from the farm to the port and develops new estimated 

market shares for Brazil, while the shares of the remaining countries in the global soybean market adjust. The new market shares are 

then used to re-estimate the model and compare the models’ outcomes to the results when the actual data are used.

5 Note that scenario 2 assumes the completion of Ferrogrão Railroad (EF-170), connecting Sinop, Mato Grosso, to Miritituba, Pará. 

It also assumes the southern ports of Santos, Paranaguá, Rio Grande, and Sao Francisco do Sul modernize their facilities to compete 

for cargo with the Northern Arc ports. Of the two scenarios, scenario 2 captures the largest change in export market shares.
3

Objectives and Organization

This study: (1) quantifies the dynamic changes of U.S. market shares in the world soybean market; (2) 

examines the effects of ocean freight spreads rates on the global soybean export market; and (3) analyzes the 

impact of Brazil’s potential infrastructure development on the world soybean market on the underlying market 

structures where the United States initially operates as a dynamic dominant firm/country model.6 

The study begins with an analysis of the U.S.-South American market shares in the world soybean market 

structure. Second, it examines the characteristics of U.S. and South American ocean freight spreads. Third, a 

dynamic model analyzes the behavior of the underlying market interactions in a world soybean export market. 

The model performs a sensitivity analysis of the potential impact of Brazil’s infrastructure development on the 

soybean global market. The final section contains conclusions and recommendations for further research. 

Market Shares in the World Soybean Market

For decades, the United States had the dominant market share of international soybean trade, with Argentina 

and Brazil as smaller competitors. However, since the late 1990s, Brazil’s market share has grown. In 2019, 

Argentina and Brazil accounted for 56 percent of the world’s soybean market, and the United States accounted 

for 32 percent.7 From 1992 to 2017, the U.S. soybean export market share declined from 66 percent to 40 

percent. Following China’s retaliatory tariffs on U.S. soybeans, that share dropped further to 32 percent in 2019 

(fig. 1 and table 8). 

6 Model specification, Salin and Somwaru (2015).

7 Other major competing countries include Paraguay, Canada, Ukraine, and Uruguay (USDA/FAS, 2020).

Figure 1. The United States is one of the world’s largest soybean producer, but has lost market 

share to Brazil since the late 1990s 

Source: USDA, Foreign Agricultural Service’s Production Supply and Distribution (PSD Online).

0

10

20

30

40

50

60

70

80

1992 1997 2002 2007 2012 2017

Market shares (%)

United States

Brazil

Argentina Other
4

From the late 1990s to 2012, nominal prices for soybeans in the global market increased, but then declined to 

$372/mt in 2019, as measured by CIF Rotterdam prices (fig. 2).8 The United States has lost its cost advantage 

over South America, but still retains a significant share of global soybean exports (see fig. 1). From 2005 to 

2019, the world soybean trade volume more than doubled, from 64.8 mmt to 148.3 mmt (FAS, 2020). With 

production and transport costs competitive with U.S. costs, Argentina’s and Brazil’s soybean exports are also 

competitive in the world market (Salin and Somwaru, 2018; Meade et al., 2016; USITC, 2012; Schnepf et al., 

2001; and Dohlman, 2000). The exports of both countries have been rising. 

At the farm level, per-bushel total production costs of the main U.S. producing areas are higher than Brazilian 

and Argentinian costs (Salin and Somwaru, 2018). Although variable costs in the United States are lower than 

in Mato Grosso and Paraná, fixed costs are much higher because of land values and capital costs (Meade 

et al., 2016). Even though U.S. production costs are higher than those of South American soybeans, U.S. 

soybeans can compete because their landed costs (including transportation costs, from point of production 

to the destination in Asia) are generally lower. However, developments in Brazil are lowering transportation 

costs, making the country more competitive in the world market (Salin, 2013, 2017a and b, 2018b, and 

2020a). Transportation costs can, at times, give South American soybean exports a competitive edge over U.S. 

soybeans (fig. 3).9

8 The cost of the goods, insurance, and freight delivered to Rotterdam.

9 For more information about U.S.-South American ocean freight spreads, see Salin (2020b) and O’Neil (2015).

Figure 2. CIF* Rotterdam price for soybeans declined 37 percent in 2019 from the peak of 2012

*The cost of the goods, insurance, and freight delivered to Rotterdam.

Source: Oil World.

0

100

200

300

400

500

600

700

1992 1997 2002 2007 2012 2017

US$ per metric ton
5

Figure 3. Weekly ocean freight rates to Shanghai, China, were higher from the U.S. Gulf 

than from Santos, Brazil, and Bahia Blanca, Argentina, in 2019

Source: O’Neil Commodity Consulting.

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

U.S.$ per metric ton

U.S. Gulf

Brazil

Argentina

China soybean imports represent 61 percent of global soybean trade (USDA/FAS, 2020), up from 23 percent 

in 2000. For the last 19 years, China is the top source of growth in global soybean trade. In 2017, China was 

the largest U.S. soybean export destination, accounting for 57 percent of total U.S. soybean exports, valued 

at $12.2 billion (USDA, FAS, 2020). Starting in 2018 and continuing through 2019, China imposed a 25-percent 

duty on U.S. soybeans, reducing U.S. world market share from 40 percent in 2017 to 38 percent in 2018, and to 

32 percent in 2019. Even though U.S. soybean exports to China increased to $8 billion in 2019 over 2018, total 

soybean export value in 2019 remained at 65 percent of 2017 values.10

In 2019, transportation costs accounted for about 22-25 percent of the total landed cost of shipping U.S. 

soybeans to Shanghai, China, from the U.S. Gulf and 24 percent of total landed costs from the Pacific 

Northwest (PNW) (Olowolayemo, 2020).11 Transportation costs of shipping Brazilian soybeans to Shanghai, 

China, represented 16-28 percent of the total landed costs from the Southern ports and 16-23 percent of total 

landed costs from the northern and northeastern ports (Salin 2020). 

South American and U.S. soybeans are comparable. The United States, Brazil, and Argentina all export 

genetically modified (GM) soybeans.12 U.S. soybean production is supported by a transportation system that 

includes all major modes of transportation (truck, rail, barge, and ocean vessel), from farm to major 

10 On January 15, 2020, China and the United States signed a Phase One economic and trade agreement. China agreed to purchase 

and import, on average, at least $40 billion of U.S. food, agricultural, and seafood products annually for a total of at least $80 billion 

over the next 2 years. Products will cover the full range of U.S. food, agricultural, and seafood products (USTR, 2020).

11 The landed cost is the total cost of goods to a buyer, including the cost of transportation without handling costs.

12 The Brazilian soybeans peak export season, March through July, complements the U.S. peak shipping season, October through 

December. This complementarity may mitigate the competition. 
6

export markets. As a result, the production-cost advantages of Brazil and Argentina have not yet undermined 

the U.S. position in the world soybean market. However, as developments in Brazilian infrastructure lower 

Brazil’s transportation costs, the country becomes more competitive in the world soybean market.

The future U.S. position in the world soybean market hinges on U.S. and competing countries’ ability to 

improve their infrastructure capacity and reduce their transportation cost. Small differences in transportation 

costs can make South America soybean exports more or less expensive than U.S. soybeans, diverting soybean 

trade from Brazil or Argentina to the United States, or vice versa. 

Overall, Brazil’s infrastructure is improving, narrowing the cost difference between shipping to Shanghai, China, 

from Mato Grosso and from Iowa. In 2010, when soybeans shipped to Shanghai from Sorriso, Mato Grosso, 

using trucks for the first leg, the rates were about $73-80/mt more than for U.S. soybeans shipped through the 

U.S. Gulf and PNW routes. Four years later, for the same route, Sorriso soybean shippers paid only $42-$44/

mt more than U.S. exporters. In 2019, shipping soybeans to Shanghai by truck for the first leg, the route from 

Sorriso, Mato Grosso, cost about $22/mt more than the routes from the U.S. Gulf and PNW. However, the cost 

advantage to U.S. shippers narrowed to $10/mt when Mato Grosso soybeans were shipped by rail to Santos for 

the first leg and to $9/mt when shipped by barge to Barcarena for the first leg (Salin, 2020c). 

During the 2019 peak harvest season, loading delays and vessel backups were similar in Brazilian ports and the 

U.S. Gulf, averaging 3-10 days—narrowing the time spread between the regions. Brazil’s Northern Arc ports 

had fewer loading delays and vessel backups than the southern ports of Santos and Paranaguá. Barcarena had 

vessel loading delays of only 3-4 days, which nearly offset the roughly 3-day-longer voyage time to Shanghai 

from Barcarena, compared with voyage time from the ports of Santos and Paranaguá. In 2019, the ocean 

freight spread was about $1-$2/mt between routes to Shanghai from the northeastern ports of Barcarena 

($34.96/mt) and São Luís ($34.81/mt), as one option, and from the Port of Santos ($33.65), as another option 

(Salin, 2020a and c).

The United States exports about 25 percent of its grain13. Grain and oilseeds (including soybeans) are mostly 

exported through ports in the U.S. Gulf (55 percent) and PNW (28 percent) (fig. 4) (USDA/AMS, 2020). The 

major grain ports in the U.S. Gulf are New Orleans, Baton Rouge, Houston, Beaumont, and Galveston.14 The 

PNW grain ports are Portland, Seattle, Tacoma, and Kalama. Brazil’s largest ports for soybean exports are 

Santos, Paranaguá, and Rio Grande. Argentina’s ports are Bahia Blanca and Rosario River. China’s main entry 

gateways for U.S. grain are the ports of Shanghai, Qingdao, Nanjing, Nanning, Tianjin, Dalian, Huangpu, 

Xiamen, Fuzhou, and Guangzhou. 

13 Grain: in this study grain refers to grain and soybeans.

14 The U.S. Gulf includes the East Gulf, Mississippi River, and North and South Texas.
7

The United States−South America Ocean Freight Spread

Ocean freight spread is the cost difference between two vessel routes to the same destination, such as the 

U.S. Gulf and PNW versus South America to Asia (China and Japan), or the U.S. Gulf versus South America to 

Europe and China (fig. 4 and table 1). Despite longer voyages than from the U.S. Gulf, the ocean freight rates 

for grain cargos from South America to Asia are often less expensive because of dry-bulk-vessel route patterns, 

lower cost port charges, no Panama Canal tolls or delays, and less burdensome navigation restrictions (O’Neil, 

2015; Salin, 2020b). South America shipments provide some natural competitive advantages for Brazilian and 

Argentinean grains and oilseeds by sailing around Cape of Good Hope in large vessels. Thus, these shipments 

can gain economies of scale and avoid the Panama Canal (and its fees and delays) when the need exists. 

Brazilian ports also provide less expensive berthing (dockage) costs for vessels than U.S. ports. However, 

recently, Panamax and Post-Panamax soybean vessels from the U.S. Gulf to China have also gained economies 

of scale by going around the Cape of Good Hope, bypassing the Panama Canal to avoid fees, waiting times, 

Figure 4. U.S.-South America selected ocean routes for soybeans

Source: USDA, Agricultural Marketing Service and USDA, Foreign Agricultural Service.
8

and the uncertainty of getting a slot to transit the new expanded Locks.15 U.S. Gulf grain transits the Panama 

Canal mostly in Handymax, Supramax and Ultramax vessels— and some Panamax vessels with a cargo capacity 

of under 69,000 mt.16 Unlike container, auto, and liquefied natural gas (LNG) vessels, dry-bulk vessels are not 

allowed to preschedule their lock times in transiting the Neo Panama Canal locks. Grain vessels must wait for 

an opening if they wish to go through the new locks.

Currently, loading delays and vessel backups in South America resemble those in the United States. The cost 

of any resulting vessel demurrage, however, significantly affects the value of the free on board (FOB) cargo 

and the price received by South American producers.17 For example, “FOB Santos” shows that the Brazilian 

seller will pay for transporting the grain to the Port of Santos and the cost of loading the grain onto the ship, 

including inland haulage, customs clearance, origin documentation charges, and demurrage. Once all the grain 

is on board, the buyer pays for all costs beyond that point. The ocean rates from PNW and the U.S. Gulf to 

Japan are higher than rates to China because of higher Japanese port fees and berth restrictions that limit the 

size of the receiving vessels (fig. 5).18

15 Post-Panamax are vessels with a capacity of 85,000-100,000 deadweight tonnage (dwt); Panamax vessels have a capacity of 

75,000-78,000 dwt; Handymax vessels have a capacity of 40,000-65,000 dwt. New vessels can load more cargo on lower drafts. Dwt 

carrying capacity is the weight that a cargo ship can carry when immersed to the appropriate load line, expressed in tons, including 

total weight of cargo, fuel, fresh water, stores, and crew.

16 Supramax are vessels with a capacity of 48,000-60,000 dwt; Ultramax vessels have a capacity of 60,000- 65,000 dwt. It should 

be noted that, by the Panama Canal ́s size classifications, Supramax and Ultramax vessels are considered Panamax because they may 

register a 32-meter beam size.

17 Demurrage costs are the charge levied when a shipment is not loaded or unloaded within the allowed time. FOB Origin indicates 

that the sale is considered complete at the seller’s shipping dock, and thus, the buyer is responsible for freight costs/liability.

18 Ocean rates from PNW and the U.S. Gulf to China are not available from 1992-2006. For that reason, ocean rates from PNW and 

the U.S. Gulf to Japan were used.
9

Table 1. Vessel costs to Shanghai, China, from the U.S. Gulf versus from Argentina and Brazil, 

October 15 to November 15, 2019

U.S. Gulf U.S. Gulf U.S. Gulf Rosario, 

Argentina

Bahia 

Blanca, 

Argentina

Santos, 

Brazil

São Luís/

Itaqui, 

Brazil

São Luís/

Itaqui, 

Brazil

Cargo mean 

quantity 58,000 mt1 68,000 mt 68,000 mt 55,000 mt 60,000 mt 66,000 mt 65,000 mt 65,000 mt

Vessel type Panamax PostPanamax

PostPanamax No Top-Off2 2 Port With 

Top-Off Varied Varied Varied

Route via Panama 

Canal Neo-Canal1 Cape of Good Hope

Cape of 

Good Hope

Cape of 

Good Hope

Cape of 

Good Hope

Panama 

Canal

Cape of 

Good Hope

Nautical miles 10,013 10,013 14,973 11,541 11,610 11,056 11,087 11,708

Voyage days 

(at 12 knots) 35 35 52 40 40 38 38.5 40.5

Panama Canal wait 

time 2 4 0 0 0 0 2 0

Laytime both ends 15 15 15 20 26 20 20 20

Total voyage 

duration days 52 54 67 60 66 58 60.5 60.5

U.S. Gulf daily hire 

rate ($18,000 X 

total voyage days)

 $936,000 $972,000 $1,206,000 - - - - -

Brazil and 

Argentina daily 

hire rate ($16,500 X 

total voyage days)

- - - $990,000 $1,089,000 $957,000 $998,250 $998,250 

Ballast Bonus3 $800,000 $800,000 $800,000 $650,000 $650,000 $650,000 $650,000 $650,000 

Singapore Bunkers4 

($12,500/day) $437,500 $437,500 $650,000 $500,000 $500,000 $475,000 $481,250 $506,250 

Port Fees $232,025 $280,300 $280,300 $260,000 $434,000 $56,000 $80,000 $80,000

Panama Canal Fees 

(one way) $198,783 $239,783 - - - - $239,783

Total vessel costs $2,604,308 $2,729,583 $2,936,300 $2,400,000 $2,673,000 $2,138,000 $2,449,283 $2,234,500 

Freight rate per mt: $44.90 $40.14 $43.18 $43.64 $44.55 $32.39 $37.68 $34.38

1 Metric tons. Neo-Canal = new Panama Canal locks.

2

 No top-off: the port of Rosario channel draft is not deep enough to load full Panamax and Post-Panamax vessels. Sellers have to decide to 

load up to 55,000 mt of cargo at Rosario (No top-off); or two ports with top-off—i.e., to load 45,000-50,000 mt at Rosario and finish loading 

(top-off) an additional 10,000-15,000 mt at Bahia Blanca.

3

Ballast Bonus is a special payment above the chartering price when a ship has to sail a long way on ballast to reach the loading port. 4

The fuel cost estimates include the costs of the global Sulphur limit of 0.50 percent mass by mass (m/m) to comply with the International 

Maritime Organization (IMO) regulation of 0.50 percent m/m, called IMO 2020, effective January 1, 2020. Vessel fuel cost is an estimate for a 

laden Panamax vessel steaming at 12 knots and consuming 25 tons of intermediate fuel oil (IFO) per day.

Note: the above estimates assume that a vessel is simply waiting for a transit slot and not paying extra for a reserved schedule to transit 

through the Neo-Panama locks.

Source: O’Neil Commodity Consulting.
10

Analyzing World Soybean Market Shares: 

Model and Results

To better understand the underlying forces of the world soybean market, we performed an extensive 

econometric analysis using a dynamic approach. A dynamic model best addressed the purpose of this 

study—to evaluate changes in market shares over time. We specified and empirically estimated a difference 

equation system and made use of techniques in the specialized field of economics called “econometrics.” 

This empirically estimated system enables analysis of how the underlying world soybean market shares 

behave, over time. The model developed for this paper includes the theoretical specification, model layout, 

and empirical framework used for capturing the dynamic changes of the world soybean market (Salin and 

Somwaru, 2015). The base model uses ocean freight rates and trade data from 1992 to 2019. The Appendix 

presents detailed results on the empirical estimation of the export market shares, estimation of ocean freight 

rates’ effects on market shares, and performance of the sensitivity analysis. Several statistical tests were 

performed to analyze and validate the behavior of world soybean market shares and gain insights into how 

Brazil’s infrastructural improvement affected U.S. competitiveness, keeping the U.S. infrastructure constant. 

Brazil is the leading producer in the world soybean market followed by United States and Argentina. Other 

competing countries are Paraguay, Canada, Ukraine, and Uruguay.

Figure 5. Monthly freight rates from the U.S. Gulf and Pacific Northwest to Japan, 1996-2019

Source: O’Neil Commodity Consulting, The Baltic Exchange, and Drewry Maritime Research.

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

U.S.$ per metric ton

U.S. Gulf-Japan

PNW-Japan
11

Results

On the demand side, the model validated that China is the major driver of the world soybean trade. On the 

supply side, the United States is one of the world’s leading producing countries, and Argentina and Brazil are 

modeled as the two main competing countries (Appendix). The model results suggest that, under current 

conditions, the U.S. market share could be stable as the overall market grows (Appendix table A-1). The 

estimated parameters of this dynamic model of the world soybean market are widely known as β converge and 

σ converge. Considering changes in ocean rates over time, the model converges to an equilibrium and leaves 

the ranking of the countries’ market shares unchanged (Appendix table A-2). 

This model’s outcomes suggest the stability of the market and validates (indirectly) the export market shares 

analysis (see Appendix table A-2 and Salin and Somwaru (2015)). In the future, as the competing countries 

of Argentina and Brazil acquire larger market shares, whenever any change in price or supply management 

is initiated solely by one top producer, the change will likely become less effective and costlier to administer 

over time. In sum, the dynamic analysis indicates that the market shares converge (see β converge estimates in 

the Appendix and Salin and Somwaru (2015)) to a so-called global market equilibrium, although the interplay 

between the United States, Argentina, and Brazil in the world soybean market is very important.

Sensitivity Analysis – 

Brazil’s Transportation Infrastructure Improvements 

To estimate the long-term U.S. position in the world soybean market and provide insights into the impact 

of Brazil’s infrastructure improvements, we performed a sensitivity analysis (see Appendix for details). The 

sensitivity analysis simulates two scenarios because Brazil is intensifying efforts to improve transportation 

infrastructure and has gained soybean market share in the world market.

Sensitivity analysis is a way to predict outcomes under different conditions. In this case, the world market 

shares change in response to two scenarios other than the status quo. We assumed that Brazil’s infrastructure 

improves, and its freight rates are reduced by two different amounts. The first scenario has a rate reduction 

of $18/mt. For the second scenario, we assumed Ferrogrão Railroad (EF-170) is built, connecting Sinop, Mato 

Grosso, to Miritituba, Pará. We also assumed the southern ports of Santos, Paranaguá, Rio Grande, and Sao 

Francisco do Sul modernize their facilities to compete for cargo with the Northern Arc ports. These changes 

amount to a reduction of $28/mt for the second scenario. The base estimated model uses ocean freight rates 

and trade data from 1992 and 2019 (see columns 6 and 7 in table 2, and tables 3 and 4).

It is uncertain how much Brazil’s infrastructure will improve within the next 10 years and by how much freight 

rates will be reduced over time. Nevertheless, we know that the completion of paving for the BR-163 highway 

reduced transportation costs and facilitated exports via the Northern Arc ports. Sensitivity analysis, which is 

also known as “what if” analysis, is used to evaluate the model’s outcomes when key factors, under certain 

assumptions, change. 

In this case, using data from 1992 to 2019 and applying the assumptions that Brazil’s infrastructure advances 

reduced its transportation freight rates (at two different levels), the model estimated “new” exports and 

export shares for Brazil and its competitor countries (see columns 6 and 7 in table 2, and tables 3 and 4) to 

conduct the sensitivity analysis. Note that scenario 2 assumed the largest reduction of $28/mt. Using the 

assumed export market shares, the analysis re-estimated the model to capture the effect of scenario 2 on the 

world soybean market (see Appendix tables A-3 and A-4), because scenario 2 captures the largest change on 

the export market shares. Since the analysis assumed more than one change, this sensitivity analysis is called a 

multivariate sensitivity analysis. The sensitivity analysis aims to shed light on the effects to world market shares 

of Brazil’s potential transportation infrastructure improvements and possible competitive ocean freight rates. 
12

In constructing the sensitivity analysis, the following are accounted for: 

1. Improved Transportation Infrastructure - We assumed Brazil’s domestic transportation infrastructure 

(farm to port) greatly improves, increasing Brazil’s ability to export soybeans to China in two scenarios 

(see columns 6 and 7 in table 2). These scenarios attempt to simulate the progressive effects of a 

generic reduction in Brazil’s domestic transportation costs if they had begun in 1992, initiated by Brazil’s 

infrastructure improvement. The assumed reductions by $18/mt and by $28/mt were chosen because 

they are estimates based on the recent completion of BR-163 highway and on the future completion of 

Ferrogrão Railroad (EF-170), respectively. In particular, the scenarios are specified as follows: 

• Scenario 1: Assumes the completed pavement along highway BR-163, connecting Sorriso, Mato 

Grosso, to Miritituba, Pará. This improvement reduces transportation costs by $18/mt. The resulting 

changes are measured in market share percentages (see column 6 in table 2, and table 3). 

• Scenario 2: Assumes Ferrogrão Railroad (EF-170) is built, connecting Sinop, Mato Grosso, to 

Miritituba, Pará, and the southern ports of Santos, Paranaguá, Rio Grande, and Sao Francisco do Sul 

modernize their facilities to compete for cargo with the Northern Arc ports. It is then assumed these 

changes reduce the transportation costs by $28/mt. The resulting changes are measured in market 

share percentages (see column 7, table 2, and table 4).

2. Unchanged Exports with Different Market Shares – The analysis assumes exports from the United 

States, Argentina, and the rest of the world remain the same, but market shares change (see columns 

2, 4, and 5 in tables 3 and 4) because Brazil’s exports under the sensitivity analysis change (column 3 in 

tables 3 and 4).19 

It is worth noting that, instead of designing a sensitivity analysis, where one factor at a time is changed, an 

analysis is performed to allow for changes to all factors at the same time. In this way, the multivariate analysis 

accounts for the compounded impact of all possible improvements of Brazil’s competitiveness in the world 

soybean market (see tables 2, 3, 4, and Appendix for details). 

The sensitivity analysis considered the two scenarios described above and produced the following results: 

Scenario 1: (reduced transportation costs of $18/mt) 

• Resulted in Brazil’s global export market shares for the period 1992 to 2019 increasing from 17 

percent in 1992 to 55 percent in 2019 (see column 6 in table 2, and table 3); and 

• At the same time, the U.S. world market share declined by 2 percentage points in 1992 and by 3 

percentage points in 2019, as a result of infrastructural improvements in Brazil. 

Scenario 2: (reduced transportation costs of $28/mt)

• Resulted in Brazil’s global export market shares for the period 1992 to 2019 increasing from 19 

percent in 1992 to 59 percent in 2019 (see column 7 in table 2, and table 4); and 

• At the same time, the U.S. world market share declined by 4 percentage points in 1992 and by 6 

percentage points in 2019 as a result of structural improvements in Brazil. 

19 Given Brazil’s new market shares, the shares of the United States, Argentina and other countries adjust because we keep the 

total observed export data from 1992-2019.
13

Figure 6. Brazilian soybean main export ports 

1

World Wildlife Fund.

2

Brazilian Institute of Geography and Statistics—Produção Agricola Municipal.

Source: USDA, Agricultural Marketing Service and USDA, Foreign Agricultural Service.

For clarity, using the shares developed for the sensitivity analysis for scenario 2, the analysis took the extra 

step of re-estimating the model and presenting the result of scenario 2. This extra step was taken because this 

scenario accounts for the largest changes in Brazil’s transportation and infrastructure (see Appendix and tables 

A-3 and A-4). The sensitivity results (Appendix tables A-3 and A-4) show the estimated market shares depend 

on the countries’ exporting capacity, which in turn, depends on the underlying technology and infrastructure 

from farm to port, as well as the competitiveness of ocean freight rates in the case of world soybean market. 

Figure 6 shows the Northern Arc ports complex that includes the following: Itacoatiara/Manaus (Amazon 

River), Santarém (Amazon River), Barcarena (Pará River), São Luís (Maranhão, MA), Salvador (Bahia), Porto 

Velho (Madeira River) and Miritituba (Tapajós River). The Madeira and Tapajós River run through the Amazon 

Rainforest and are major tributaries of the Amazon River. The distance by truck from Sorriso, Mato Grosso, 

to Itaituba/Miritituba is 663 miles (1,067 km), via BR-163. Currently, it takes about 35 hours to ship grain to 

Miritituba (ESALQ-LOG, 2020; FAS, 2020).
14

It is worth noting that Brazil’s production costs—particularly in Mato Grosso and Paraná —were below Iowa’s 

costs, because of lower land prices and capital costs. Brazilian and U.S. soybeans directly compete with one 

another because both countries leverage the same technological advances. Brazil can import or develop 

its own technology and increase planted area to increase exports. Brazil expanded its export capacity and 

balanced its transportation system like the United States, including all major modes. Thus, Brazil lowered its 

transportation costs and opened new export gateways, making the country more competitive. 

In 2011, the Brazilian government introduced a law requiring railroads to sell to other railroads the rights to 

use any idle capacity (Salin and Somwaru, 2015). This major step significantly increased railway use. It should 

be noted that U.S. railroads have no obligation to allow other railroads to use their rails. Instead, access is 

negotiated with competing railroads at an agreed-upon price. In 2013, an intermodal grain terminal was built 

in Rondonópolis, Mato Grosso, to facilitate the flow of grains to the southern port of Santos. In 2014, Brazil’s 

agribusiness sector and barge companies created a new export route from Miritituba to Barcarena (Vila do 

Conde), adding a new northern gateway for grain exports from Mato Grosso to China, Europe, the Middle East, 

and Mexico (Salin, 2017a and 2018b).

The agricultural exporters in the Center-West of Brazil gained a major competitive boost from strategic port 

improvements, extended railway miles, and the completion of the pavement along the BR-163 highway, 

connecting Sorriso, Mato Grosso, to Itaituba/Miritituba, Pará, at the end of 2019. The pavement of BR-163 

ended truck drivers’ uncertainty about how long it will take to haul grain to Miritituba during the rainy season 

(Cordonnier, 2020). Now, truck drivers know that the trip’s first leg takes about 35 hours, versus the several 

days or weeks that the same trip took before the road’s completion (ESALQ-LOG, 2020; USDA/FAS, 2020). 

Over the last 5 years, soybean exports from the Port of Barcarena nearly quadrupled, from 1.1 mmt in 2014 to 

5.8 mmt in 2019. Barcarena quickly became the fifth-largest Brazilian soybean exporting port after Santos, Rio 

Grande, Paranaguá, and São Luís. Truck rates from Sorriso, Mato Grosso, to Rondonópolis (rail terminal) and 

to the northern river ports of Santarém and Itaituba/Miritituba (barge terminal) decreased. Industry analysts 

expect transportation costs to drop further, by as much as $6/mt (or R$30/mt) for the route from Sorriso to 

Itaituba (Salin, 2020c).20 Expanded use of the northern ports helped to ease congestion at southern ports, as 

well. Much of the cost savings—from these upgrades, as well as others implemented over the last 12 years—

are passed on to Brazilian farmers as higher prices for their products.

Many improvements in U.S. infrastructure and technology—critical to maintaining U.S. competitiveness in 

the world soybean market—are under consideration or are already underway. Some examples of critical 

infrastructure improvements include lengthening the locks on the Mississippi River and its tributaries to 

permit larger barge tows, enhance maintenance of locks and dams along the inland waterway system, increase 

investment to improve rural bridges that connect farms with the original point of sale, dredging to deepen 

the water channels, and developing a better intermodal system with wider use of containerized shipments 

throughout the country. Improved U.S. infrastructure would result in an increase in market share, a more 

competitive U.S. export sector, and higher income for farmers, just as they have in Brazil.

For example, assuming world soybean trade is 166 million metric tons (mmt) (WASDE and FAS September 

2020), a 1-percent decline in the U.S. soybean market share amounts to just above half a billion dollars in lost 

export sales (1.7 mmt × $332/mt).21

20 Exchange rate of 5.5293 real per U.S. dollar, September 1, 2020.

21 This statement is based on the concept known as “elasticity.” The coefficients are considered elasticities because the estimated 

equation is expressed in double logarithmic form (see Appendix). For example, the coefficient value for the entire estimation period, 

1992-2019, is 0.0236 percent (see Appendix, table A-1). After Brazil’s infrastructural improvements, for the entire estimation period 

sensitivity analysis, the coefficient declines to 0.0114 (Appendix, sensitivity analysis table A-3). This amounts to 1.22-percent declines 

in market shares (0.0236 - 0.0114 = 0.0122).
15

Table 2. Market shares data: actual and for Brazil's sensitivity analysis.1

Actual market shares Brazil market shares, 

sensitivity analysis1

Year United States 

(%)

Brazil 

(%)

Argentina 

(%)

Other2

(%)

Scenario 1 

(%)

Scenario 2 

(%)

1992 66.25 13.78 11.43 8.54 16.62 19.34

1993 71.59 13.84 7.55 7.02 16.70 19

1994 57.72 19.60 10.90 11.78 23.32 26.

1995 71.34 11.13 8.05 9.48 13.51 15.81

1996 73.00 10.92 6.64 9.44 13.54 16.0

1997 66.30 23.17 2.08 8.45 27.99 32.3

1998 60.52 22.28 7.17 10.03 27.92 32.

1999 57.74 23.55 8.07 10.64 28.74 33.35

2000 58.06 24.29 9.02 8.63 30.07 35.1

2001 50.47 28.80 13.60 7.13 34.95 40.21

2002 54.88 27.50 11.30 6.32 32.99 37.7

2003 46.48 32.10 14.10 7.33 39.18 45.03

2004 43.14 36.50 12.05 8.31 43.09 48.5

2005 46.06 31.06 14.76 8.12 36.64 41.47

2006 40.20 40.72 11.39 7.70 48.55 54.6

2007 42.85 33.12 13.48 10.55 40.04 45.7

2008 40.07 32.23 17.58 10.11 37.94 42.8

2009 45.39 39.09 7.29 8.23 45.21 50.3

2010 44.32 31.04 14.22 10.43 36.36 41.00

2011 44.73 32.71 10.05 12.51 38.34 43.

2012 40.52 39.51 8.03 11.95 45.44 50.4

2013 36.00 41.75 7.71 14.54 47.71 52.6

2014 39.56 41.54 6.96 11.94 47.44 52.34

2015 39.73 40.10 8.38 11.79 45.91 50.7

2016 39.88 41.03 7.49 11.61 45.01 48.55

2017 40.10 42.94 4.78 12.18 46.63 49.94

2018 37.94 49.74 1.39 10.93 54.40 58.34

2019 32.07 50.30 6.14 11.49 55.27 59.4

1

Scenario 1: Assumes completion of the pavement along highway BR-163, connecting Sorriso, Mato Grosso, to Miritituba, Pará. 

With the pavement finished, transportation costs will likely drop by $18/metric ton, measured in market shares (percentage).

1

Scenario 2: Assumes Ferrogrão Railroad (EF-170) is built, connecting Sorriso to Miritituba and the southern ports of Santos, 

Paranaguá, Rio Grande, and Sao Francisco do Sul modernize their facilities to compete for cargo with the Northern Arc ports. In this 

case, transportation costs will likely drop by $28/metric ton, measured in market shares (percentage).

2

Other competing countries include Paraguay, Canada, Ukraine, and Uruguay.

Sources: Columns 2 through 5, USDA, Foreign Agricultural Service, 2020; columns 6 and 7, Sensitivity analysis results—calculations 

by USDA, Agricultural Marketing Service and Economic Consulting.
16

Table 3. Scenario 1: New market shares for conducting sensitivity analysis1

Year United States (%) Brazil (%) Argentina (%) Other2 (%)

1992 64.06 16.62 11.06 8.26

1993 69.21 16.70 7.30 6.79

1994 55.05 23.32 10.40 11.2

1995 69.43 13.51 7.84 9.2

1996 70.85 13.54 6.45 9.16

1997 62.14 27.99 1.95 7

1998 56.13 27.92 6.65 9.3

1999 53.82 28.74 7.52 9.

2000 53.62 30.07 8.34 7.9

2001 46.11 34.95 12.43 6.51

2002 50.73 32.99 10.44 5.84

2003 41.63 39.18 12.63 6.56

2004 38.66 43.09 10.80 7.45

2005 42.34 36.64 13.57 7.46

2006 34.88 48.55 9.89 6.68

2007 38.42 40.04 12.09 9.4

2008 36.70 37.94 16.10 9.26

2009 40.83 45.21 6.56 7.40

2010 40.90 36.36 13.12 9.62

2011 40.98 38.34 9.21 11.4

2012 36.54 45.44 7.24 10.7

2013 32.32 47.71 6.92 13.05

2014 35.57 47.44 6.26 10.73

2015 35.88 45.91 7.57 10.6

2016 37.19 45.01 6.98 10.83

2017 37.51 46.63 4.47 11.39

2018 34.42 54.40 1.26 9.9

2019 28.86 55.27 5.52 10.

1

Scenario 1: Assumes—if the pavement along highway BR-163, connecting Sorriso, Mato Grosso to Miritituba, Pará is finished—

transportation costs will likely drop by $18/metric ton, measured in market shares (percentage).

2

Other competing countries include Paraguay, Canada, Ukraine, and Uruguay.

Source: Sensitivity analysis results—calculations by USDA, Agricultural Marketing Service and Economic Consulting.
17

Table 4. Scenario 2: New market shares for conducting sensitivity analysis1

Year United States (%) Brazil (%) Argentina (%) Other2 (%)

1992 61.98 19.34 10.70 7.

1993 66.95 19.42 7.06 6.57

1994 52.57 26.77 9.93 10.7

1995 67.58 15.81 7.63 8.98

1996 68.79 16.05 6.26 8.89

1997 58.40 32.32 1.83 7.44

1998 52.26 32.89 6.20 8.66

1999 50.33 33.35 7.04 9.28

2000 49.75 35.13 7.73 7.40

2001 42.38 40.21 11.42 5.9

2002 47.09 37.79 9.70 5.4

2003 37.63 45.03 11.42 5.93

2004 34.95 48.55 9.77 6.7

2005 39.11 41.47 12.53 6.89

2006 30.74 54.66 8.71 5.89

2007 34.75 45.76 10.93 8.55

2008 33.79 42.85 14.83 8.53

2009 37.04 50.30 5.95 6.7

2010 37.91 41.00 12.16 8.92

2011 37.76 43.19 8.49 10.56

2012 33.22 50.41 6.58 9.7

2013 29.26 52.66 6.27 11.82

2014 32.26 52.34 5.67 9.73

2015 32.65 50.78 6.88 9.69

2016 34.79 48.55 6.53 10.13

2017 35.18 49.94 4.19 10.68

2018 31.45 58.34 1.15 9.0

2019 26.19 59.41 5.01 9.3

1

Scenario 2: Assumes that BR-163 and Ferrogrão Railroad (EF-170), connecting Sorriso to Miritituba, are built, and the southern 

ports of Santos, Paranaguá, Rio Grande, and Sao Francisco do Sul modernized their facilities to compete for cargo with the 

Northern Arc ports. In this case, transportation costs will likely drop by $28/metric ton, measured in market shares (percentage).

2

Other competing countries include Paraguay, Canada, Ukraine, and Uruguay.

Source: Sensitivity analysis results—calculations by USDA, Agricultural Marketing Service and Economic Consulting.
18

Conclusions and Further Research

The world soybean market is growing, but the U.S. is losing market share. Brazil has surpassed U.S. soybean 

exports, becoming not only the top world soybean exporter, but also the top producer. Twelve years of 

strategic infrastructural developments and major regulatory changes set the stage for a balanced Brazilian 

transportation system, resembling the U.S. Gulf export route from farms to major export markets—a system 

integrating all the major modes (truck, barge, and ocean vessel). Brazil’s position has also benefited from low 

production costs, increases in planted area, high productivity, and a weak national currency. 

From 2012 to 2019, nominal prices declined in the international market as market supplies exceeded demand 

for soybeans. The empirical analysis shows that market shares converged to their so-called “steady-state 

values” or dynamic equilibrium values, even in the late 2000s. Based on the observed data, Argentina and 

Brazil behave as major “competing countries” in the international soybean market. There is no indication that 

Argentina or Brazil limited their production to maintain a stable international market price.

From 2011 to 2019, new policy, infrastructure, and routes revolutionized Brazil’s transportation system. First, 

the government introduced a law extending railways miles and requiring railroads to sell other railroads the 

rights to use their rail tracks’ idle capacity. In the United States, railroads have no such obligation to allow other 

railroads to use their rails. Nonetheless, access is often negotiated with competing railroads at an agreed-upon 

price. Second, an intermodal grain terminal was built facilitating the flow of grains from Rondonópolis, Mato 

Grosso, to the southern port of Santos. Third, the Brazilian’s agribusiness sector created a new export route 

from Miritituba to Barcarena (Vila do Conde), adding a new northern gateway for grain exports from Mato 

Grosso to China, Europe, the Middle East, and Mexico. Finally, the agricultural exporters in the Center-West of 

Brazil gained another competitive boost when the pavement of BR-163, connecting Sorriso, Mato Grosso, to 

Itaituba/Miritituba, Pará, was completed at the end of 2019.

These new upgrades enhanced Brazil’s transportation system from farm to port, including all major modes 

and making Brazil’s soybeans more competitive with U.S. soybeans. Because Brazilian and U.S. producers use 

the same technological advances in production and produce very close soybean substitutes, infrastructure 

improvements are critical to keeping U.S. transportation costs low and keeping U.S. soybeans competitive 

worldwide. 

The sensitivity analysis suggests the U.S. world market shares could show further decline in 2019 by 3 to 6 

percentage points. From the empirical dynamic model’s outcomes, we see a major exporter, even with no cost 

advantage, does not necessarily price itself out of the market (in an expanding market), but instead maintains 

a fairly constant market share over the long run. As long as the major players continue to operate as they have, 

market shares are expected to converge to an equilibrium, despite the variability or fluctuations of the ocean 

freight rates, over time.

According to the multivariate sensitivity analysis results, the market shares of the United States, Brazil, 

and Argentina in the global soybean market depend in the long term on the countries’ exporting capacity. 

That capacity, in turn, depends on the underlying technology and infrastructure from farm to port and the 

competitiveness of ocean freight rates. As the U.S. market share declines, the sensitivity analysis shows the 

rate of convergence to equilibrium (declining values of β converge) gets smaller. This finding indicates the 

global soybean market is in still in equilibrium but converging with decreasing rates. The sensitivity analysis 

also shows U.S. infrastructure improvements are critical for maintaining the Nation’s competitiveness in 

the world soybean market. Improved U.S. infrastructure would result in an increase in market share, more 

competitive U.S. exports, and higher income to farmers. For example, assuming world soybean trade were 

166 million metric tons (mmt) (USDA/WASDE, 2020; USDA/FAS, 2020), a 1-percent decline in the U.S. soybean 

market share would amount to just above half a billion dollars in lost export sales (1.7 mmt × $332/mt). 
19

Further research is needed to understand the underlying forces that move soybeans from the farms to markets 

and to the exporting ports. In this context, the interactions of cash prices with future prices; storage costs with 

transportation costs; and freight rates for truck, barge, rail, and ocean domestically with foreign competitors 

need to be captured and analyzed. Further research should also address the effects on agricultural exports of 

enhancing maintenance of locks and dams along the inland waterway system, increase investment to improve 

rural bridges that connect farms with the original point of sale, and developing a better intermodal system 

with wider use of containerized shipments throughout the country. 
20

Appendix: Methodology

Salin and Somwaru (2015) (pp. 22-25) present extensive theoretical underpinning of the model specification, 

the estimation procedure used, and the statistical method employed. The ocean freight rates data used in the 

empirical estimation of the transitional dynamic model are from O’Neil Commodity Consulting 2020, the Baltic 

Exchange 2007, and Drewry Maritime Research 2006, from 1992 to 2019 (figs. 3 and 5). For the selection of 

the periods and sub-periods (tables A-1 and A-2), see Salin and Somwaru (2015). 

The estimated parameters of this dynamic model of the world soybean market are presented in tables A1- and 

A-2. The model results suggest the U.S. market share could be stable as the overall market grows, but with 

a smaller share over time. In the future, as the competing countries—Argentina and Brazil—acquire larger 

market shares, any price- or supply-management policy initiated solely by one top producer would likely 

become less effective and costlier to administer. Thus, the interplay among the United States, Argentina, and 

Brazil becomes a very important factor to determining the behavior of the world soybean market.

Table A-1. Estimation results of the transitional dynamics of the world soybean market, 1992-2019

Year Parameter Estimate Standard 

error

95% Confidence 

limits: lower bound

95% Confidence 

limits: upper bound

Entire period

1992–2019

α

(Intercept) 0.0514 0.0677 -0.2399 0.3426

β

(strength of converge) 0.0236 0.0443 -0.1670 0.2141

σ

(converge—steady state)

(second moment of the distribution)

0.570

First period

1992–2004

α

(Intercept) 0.0804 0.0907 -0.3097 0.470

β

(strength of converge) 0.0293 0.0449 -0.1637 0.2223

σ

(converge—steady state)

(second moment of the distribution)

0.6300

Second period

2005–2008

α

(Intercept) 0.1679 0.0354 0.0157 0.320

β

(strength of converge) 0.0554 0.0143 -0.0062 0.1169

σ

(converge—steady state)

(second moment of the distribution)

18.7700

Third period

2009–2019

α

(Intercept) 0.0227 0.0630 -0.2485 0.293

β

(strength of converge) 0.0076 0.0227 -0.0902 0.105

σ

(converge—steady state)

(second moment of the distribution)

0.120

Source: USDA, Agricultural Marketing Service and Economic Consulting, based on model results.
21

Table A-2. Estimation results of the transitional world soybean market accounting 

for freight rates and time intervals, 1992–2019

Year Parameter Estimate Standard 

error

95% Confidence 

limits: lower bound

95% Confidence 

limits: upper bound

First period

1992–2001

α

(Intercept) 0.0653 0.0965 -0.3501 0.4806

β

(strength of converge) 0.0209 0.0402 -0.1522 0.194

σ

(converge—steady state)

(second moment of the distribution)

0.3300

Second period

2002–2004

α

(Intercept) 0.1970 0.1366 -0.3906 0.784

β

(strength of converge) 0.0626 0.0547 -0.1728 0.297

σ

(converge—steady state)

(second moment of the distribution)

1.5800

Third period

2005–2007

α

(Intercept) 0.1499 0.1170 -0.3535 0.653

β

(strength of converge) 0.0486 0.0439 -0.1401 0.2373

σ

(converge—steady state)

(second moment of the distribution)

1.4200

Fourth period

2008–2012

α

(Intercept) 0.00849 0.0468 -0.1405 0.157

β

(strength of converge) 0.0095 0.0161 -0.0403 0.062

σ

(converge—steady state)

(second moment of the distribution)

0.1930

Fifth period

2013–2019

α

(Intercept) 0.0105 0.0167 -0.0428 0.063

β

(strength of converge) 0.0084 0.0099 -0.0215 0.041

σ

(converge—steady state)

(second moment of the distribution)

0.1355

Source: USDA, Agricultural Marketing Service and Economic Consulting, based on model results.
22

Accounting for Ocean Freight Changes

Transitional dynamics are also applied to the world soybean market while accounting for changes in ocean 

freight rates over the estimated period.22 The estimated dynamics cover the following five sub-periods: 1992–

2001 (first), 2002–04 (second), 2005–07 (third), 2008–12 (fourth), and 2013–19 (fifth). Table A-2 presents the 

parameter estimates, indicating the market shares (when we account for ocean freight rates and fluctuations) 

converge to an equilibrium. This result supports and indirectly validates the export market shares analysis (A-1) 

regarding the stability of the world soybean market. For more details see Salin and Somwaru (2015). 

The empirically estimated model indicates the U.S. market share will be stable as the overall market grows. 

As the competing countries—in this case, Argentina and Brazil—acquire larger market shares, any price- or 

supply-management policy initiated solely by one top producer might become less effective and more costly to 

administer. Please note the United States has never had a support program for soybeans. Because the largest 

players in the world soybean market (United States, Argentina, and Brazil) compete with one another so 

tightly, the ways these countries interact with one another will have a large impact on the market.

Sensitivity Analysis – 

Brazil’s Transportation Infrastructure Improvement

Table A-3 presents the parameter estimates under the sensitivity analysis for scenario 2. Table A-4 presents 

the parameter estimates of freight rates under the sensitivity analysis for scenario 2. The results in table A-4 

indicate—when we account for observed changes or fluctuations in freight rates over time and improvements 

in Brazil's infrastructure—that competing countries tend to converge toward an equilibrium, and the rate of 

convergence is faster under the sensitivity assumptions. 

The sensitivity results show the world soybean market is affected by the underlying technology and 

infrastructure from farm to port, as well as by the competitiveness of ocean freight rates. The analysis also 

concluded that improved U.S. infrastructure would result in an increase in market share, a more competitive 

U.S. export sector, and higher income for farmers.

22 For more information about U.S.-South American Ocean Freight Spreads see Salin (2020b); Salin (2018a); and O’Neil (2015).
23

Year Parameter Estimate Standard 

error

95% Confidence 

limits: lower bound

95% Confidence 

limits: upper bound

Entire period

1992–2019

α

(Intercept) 0.0279 0.0753 -0.2961 0.352

β

(strength of converge) 0.0114 0.0341 -0.1352 0.1580

σ

(converge—steady state)

(second moment of the distribution)

0.0016

First period

1992–2004

α

(Intercept) 0.0673 0.0999 -0.3623 0.497

β

(strength of converge) 0.0243 0.0461 -0.1740 0.222

σ

(converge—steady state)

(second moment of the distribution)

0.0028

Second period

2005–2008

α

(Intercept) 0.1553 0.0420 -0.0253 0.3359

β

(strength of converge) 0.0507 0.0167 -0.0211 0.12

σ

(converge—steady state)

(second moment of the distribution)

0.0004

Third period

2009–2019

α

(Intercept) 0.0006 0.0771 -0.3312 0.332

β

(strength of converge) 0.0004 0.0258 -0.1106 0.111

σ

(converge—steady state)

(second moment of the distribution)

0.002

* Note: The sensitivity analysis assumes Brazil’s improved infrastructure and transportation cost; saving $28/mt.

Source: USDA, Agricultural Marketing Service and Economic Consulting, based on model results.

Table A-3. Estimation results of the transitional dynamics of the world soybean market, 1992–2019*
24

Table A-4. Estimation results of the transitional dynamics of the world soybean market accounting 

for freight rates and time intervals, 1992–2019*

Year Parameter Estimate Standard 

error

95% Confidence 

limits: lower bound

95% Confidence 

limits: upper bound

First period

1992–2001

α

(Intercept) 0.0285 0.1162 -0.4715 0.5285

β

(strength of converge) 0.0092 0.0426 -0.1742 0.19

σ

(converge—steady state)

(second moment of the distribution)

0.050

Second period

2002–2004

α

(Intercept) 0.1192 0.1517 -0.5336 0.7

β

(strength of converge) 0.0387 0.0567 -0.2055 0.2828

σ

(converge—steady state)

(second moment of the distribution)

0.520

Third period

2005–2007

α

(Intercept) 0.0777 0.1297 -0.4802 0.63

β

(strength of converge) 0.0255 0.0458 -0.1714 0.17

σ

(converge—steady state)

(second moment of the distribution)

0.3400

Fourth period

2008–2012

α

(Intercept) 0.0296 0.0409 -0.0377 0.0924

β

(strength of converge) 0.0242 0.0179 -0.0327 0.124

σ

(converge—steady state)

(second moment of the distribution)

0.070

Fifth period

2013–2019

α

(Intercept) 0.0383 0.0228 0.0008 0.0758

β

(strength of converge) 0.0208 0.0081 -0.0049 0.0464

σ

(converge—steady state)

(second moment of the distribution)

0.150

* Note: The sensitivity analysis assumes Brazil’s improved infrastructure and transportation cost; saving $28/mt.

Source: USDA, Agricultural Marketing Service and Economic Consulting, based on model results.
25

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