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University Micrdnlms International 300 N. ZEEB ROAD, ANN ARBOR, Ml 48106 18 BEDFORD ROW, LONDON WC1R 4EJ, ENGLAND 7906144 | I FELDENS, ARAY MIGUEL A transportation .s t o r a g e n e t w o r k a n a l y s i s o f WHEAT ANO SOYBEANS i n Rio GRANDE DO SUL, I BRAZIL. THE OHIO STATE UNIVERSITY, PH.D., i i I I I

International 300 n . z e e b r o a d , a n n a r b o r , mi 4s io g

(£) Copyright by

Aray Miguel Feldens

1978 A TRANSPORTATION-STORAGE NETWORK ANALYSIS

OP WHEAT AND SOYBEANS IN RIO GRANDE

DO SUL, BRAZIL

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Aray Miguel Peldens, B.S., M.S.

# * * # *

The Ohio State University

1978

Reading Committee: Approved By

Donald W. Larson

ii ACKNOWLEDGMENTS

Anyone who has completed a doctoral program is fully aware that many people have contributed to its development and completion. I wish to express my gratitude to some whose support was made clear throughout my graduate studies at OSU.

I wish to express deep appreciation to Dr. Donald W.

Larson, who served as my major adviser. His guidance, encouragement, and friendly way of always being available were crucial and more than vital in the development of this dissertation.

The suggestions and assistance gave by Drs. Norman Rask and Richard Meyer made a positive contribution to this research report.

Many professors influenced the development of this investigation; among them I would like to thank Dr. Francis

E. Walker for the valuable suggestions and comments about the methodological aspects of this work.

This investigation was supported by many Brazilian people and institutions. Special gratitude is expressed to the Centro de Estudos e Pesquisas Economicas (IEPE) at the Universidade Federal do Rio Grande do Sul (UFRGS). This institution made possible my doctoral studies and

iii gave great support to the data collection phase of this investigation. Special thanks are extended to Professor

Eli de Moraes Souza, director of IEPE, and Dr. Humberto

V. Richter, coordinator of the Programa de Ensino Agricola

Superior (PEAS).

Finally, I thank my family who have encouraged my educational efforts and especially to my wife, Maria das

Gracas, for accepting my "absence" for such a long time and for sharing with me the rejoice of completing this proj ect.

My doctoral program and much of the work reported herein were made possible by a fellowship from the Agency of International Development (AID). The Agency’s support is gratefully acknowledged.

To the many unnamed, a very special thanks.

iv VITA

December 16, 1 9 ^ ...... Born - Cruzeiro do Sul, Rio Grande do Sul, Brazil.

1968...... B.S., Paculdade de Agronomia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.

1972...... M.S., Centro de Estudos e Pesquisas Economicas, Univer­ sidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.

1973-197 8 ...... Assistant Professor, Departa- mento de Ciencias Economicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.

1974-197 8 ...... •...... A.I.D. Fellow

FIELDS OF STUDY

Major Field: Agricultural Economics

Studies in Economic Theory. Professors Edward J. Ray, Tetsunori Koizumi, William G. Dewald and Edward J . Kane

Studies in Quantitative Methods. Professors Steven C. Reimer, Wilford L. L'Esperance and~Francis E. Walker

Studies in International Trade. Professor Edward J. Ray

Studies in Agricultural Marketing. Professors Donald W. Larson and Dennis R. Henderson

v TABLE OP CONTENTS Page

DEDICATION...... 11

ACKNOWLEDGMENTS...... iii

VITA ...... v

LIST OP T A B L E S ...... viii

LIST OP FIGURES...... x

CHAPTER

I. INTRODUCTION...... 1

Problem and Justification ...... 2 Organization of the Dissertation. . . . 7

II. SOYBEAN AND WHEAT PRODUCTION, MARKETING TRANSPORTATION IN RIO GRANDE DO SUL . . 9

Production Regions of Wheat and Soybeans...... 11 The Wheat Industry...... 17 The Soybean Industry...... 21 Grain Storage in the State of Rio Grande do S u l ...... 24 The Transportation Network in the State of Rio Grande do Sul...... 30 The Present Highway System...... 30 The Railroad Network...... 32 The Waterway Network...... 34 The Highway/Railroad/Waterway Complex . 34

III. METHODOLOGY ...... 3 6

Theoretical Considerations...... 36 Review of Literature...... 44 The Analytical Instrument ...... 49

IV. THE RIO GRANDE DO SUL WHEAT, SOYBEAN NETWORK AND A FRAMEWORK FOR PRICING TRANSPORT SERVICES...... 57

The Time Periods. 57 The Network Model 61 Estreia Port and Terminal Operations. . The Highway Network ...... The Railroad Network...... The Waterway Network...... A Framework for Pricing Transport Services......

RESULTS AND ANALYSIS......

The Wheat Model ...... The Soybean/Soymeal Model ...... Derivation of the Demand Curve and Elasticities for the Estreia Port . . . 1 The Cross Elasticities...... 1

VI. SUMMARY, CONCLUSIONS AND IMPLICATIONS . 1

Introduction...... 1 Summary of the Wheat Model...... 1 Summary of the Soybean/Soymeal Model. . 1 Conclusions ...... 1 Implications for Transportation and Storage Policy...... 1 Implications for Farmers...... - Implications for the Wheat and Soybean Industry...... 1 Future Research and Limitations of the Investisation ...... -

APPENDICES

Municipios Included in the Study by Region ......

3 The Estimated Fixed and Variable 4- 4- I: 4- OJ C O U J lo ro lv i to I--* I—1 VO CO CO I - J Cost of the Estreia Port. . . . 1 co o ‘-o men over o —i i- e>\ vo tr to to u\ ut h uj t

Railroad Revenue, Costs and Rate Estimations...... 151

BIBLIOGRAPHY ...... 1?"

vi i LIST OP TABLES

TABLE Page

1. Production of Soybeans and Wheat in the State of Rio Grande do Sul, Brasil, 1975-76. . 14

2. Number of Wheat Milling Plants, Total Capacity and Amount of Wheat Processed by Region, Rio Grande do Sul, Brasil, 1976 . . 19

3. Number of Soybean Processing Plants, Total Processing Capacity and Amount of Soybeans Processed by Region, Rio Grande do Sul, Brazil, 1976 23

4. Nominal and Net Storage Capacity by Class of Storage Facility in the State of Rio Grade do Sul, Brasil, 1976 ...... 26

5. Nominal and Net Storage Capacity for Grains by Region in the State of Rio Grande do Sul, 1976...... 28

6. Time Periods for the Soybean/Soymeal Network Model, Rio Grande do Sul, Brazil, 1976 .... 59

7. Time Periods of the Wheat Network Model, Rio Grande do Sul, Brazil, 1976...... 59

8. Wheat Flows, Exports and Industry Demands for Each Period, Rio Grande do Sul, Brazil, September 1975 to August 1976 ...... 33

9. Optimal Solution for 1976 Basic Wheat Network Model and Simulations of the Basic Model . . . 85

10. Flows of Wheat through Alternative Modes of Transportation to Estreia, P. Alegre and Rio Grande in the Basic Model and Simulated M o d e l s ...... 36

11. Soybean Flows, Exports and Industry Demands for Each Period, Rio Grande do Sul, Brazil, March 1976 to February 1977...... 96

viii TABLE Page

12. Basic Solution of 1976 Soybean Network Model and Simulation of Improvements in Waterways, Railroads and Storage Capacities, Rio Grande do Sul, Brazil, 1976 ...... 97

13. Plows of Soybeans through Alternative Modes of Transportation to Estreia, Porto Alegre and Rio Grande in the Basic Soybean Model and Simulated Models, Rio Grande do Sul, Brazil, 1976 ...... 99

14. Partial Listing of the Storage Arcs in the 1976 Basic Network Solution of the Soybeans Model, Rio Grande do Sul, Brazil, 1976 .... 103

15. Storage Capacity, CBAR Value and Additional Storage Capacity Required at Several Regions in Rio Grande do Sul, Brazil, 1976 ...... 112

16. Plow of Product through the Estreia Port at Different Costs of Service, Rio Grande do Sul, Brazil, 1976...... 11°

17. Cross Elasticities Among Alternative Modes of Transportation for Soybeans and Soymeal for Shipments to the Rio Grande Export Port, Rio Grande do Sul, Brazil, 1976...... 120

18. Shipments of Soybean and Soymeal to the .Estreia,Porto Alegre and Rio Grande Port, Rid Grande do Sul, Brazil, 1976...... 122

ix LIST OP FIGURES

FIGURE Page

1. The Main Producing Areas of Soybeans and Wheat and the Port Locations, Rio Grande do Sul, B r a z i l ...... 10

2. The 55 Wheat and Soybean Production Regions of the State of Rio Grande do Sul, Brazil. . 13

3. The Highway and Waterway Network in the State of Rio Grande do Sul, B r a z i l ...... 31

4. The Railroad Network in the State of Rio Grande do Sul, Brazil...... 33

5. The Trade Between Two Regions as a Result of Differences in Supply and Demand Functions...... 38

6. The Effects of Transfer Cost (t) on Prices and Trade ...... 38

7. Equilibrium Prices and Trade Illustrated by Using Differences Between Excess Supply Curves...... 41

8. Two-Period Equilibrium with Storage Introduced ...... 4l

9* Network Flow with Two Origins and Three Destination Points ...... 52

10. Network Flow, Origin Points, Intermediate Points and Final Destination Points in a Multi-Period Case with Storage ...... 5^

11. A Network Model of a Single Region with Four Time Periods, Storage, Processing and Transfer Arcs, Rio Grande do Sul, Brazil, 1976 ...... 62

12. Estreia Transshipment Node in Network Model, Rio Grande do Sul, Brazil, 1976 . . . 66

x FIGURE Page

13* The Highway Network for Soybeans, Soyindal and Wheat in the State of Rio Grande do Sul, Brazil, 1976 ...... 69

14. A Diagram of the Railroad Network Including the Simulated Lines in Rio Grande do Sul, Brazil, 1976...... 72

15* A Diagram of the Waterway Network in Rio Grande do Sul, Brazil, 1976 ...... 74

16. Marginal-Versus Average Cost Pricing when Marginal Cost is Less than Average Cost and Value of Service Pr i c i n g ...... 78

xi CHAPTER I

INTRODUCTION

The agricultural sector in Brazil is an important source of domestic food supply and export revenues. The rapid expansion of agricultural production in the southern states of Brazil in recent years has increased the share of these products in the export revenues. However, this rapid increase in production (mainly soybeans) is causing concern about the ability to efficiently handle this ad- ditional production. Some feel that the present transfer- storage network can transfer additional grains only at in­ creasing cost because of capacity constraints. Thus, ef­ ficiency will be obtained only by increasing the capacity of the existing transfer-storage network and/or,by adding alternative modes of transportation.

In this regard, new railroads and waterway facilities are under construction for transporting grain. This in­ vestigation focuses on an evaluation of the planned modi­ fications of the transportation-storage network in the southernmost State of Brazil.

The State of Rio Grande do Sul is the largest producer of soybeans and wheat in Brazil. In 1970, the state

1 2 production of soybeans was 1,024 thousand tons, represent­ ing 70 percent of the entire national production. In

1976, soybean production increased to 5*107 thousand tons, representing, then 46 percent of the national production.

The increase of soybean production in the State of Rio

Grande do Sul was nearly 400 percent in these' six years.

The state is also the country's largest exporter of soy­ beans, soyoil and soymeal to foreign markets. Additionally, the double-cropping of soybeans and wheat makes the state the largest wheat producer in Brazil. In 1970, wheat production in the State of Rio Grande do Sul consisted of

1,424 thousand tons, and in 1976, production increased to

1,792 thousand tons, an increase of almost 26 percent in six years. In that year, the state's wheat production represented 56 percent of the total national production

(24). Presently, Brazil is the second largest exporter of soybeans (in beans), after the U.S., and exported almost the same quantity of soybean meal as the U.S. did in

1976 (57).

PROBLEM AND JUSTIFICATION

The expansion of agricultural products, mainly cash grains, in southern Brazil in the last few years has sur­ passed the transportation and storage capacities. The in­ crease in demand for these services, in a short period of 3 time, has caused an increase in transportation and storage costs. Naturally, an increase in the transportation costs affects the price of agricultural products in several ways.

If the demand is elastic, the larger share of a cost in­ crease would be transferred to the farmers, in the form of lower prices and output, whereas, if the demand is in­ elastic, the cost increase would be transferred to consumers in the form of higher prices of the product. The final effect on the prices will depend upon the relative elasti­ city of the demand and supply curves.

In the agricultural processing industry, a change in transportation costs can affect the "least-cost" location of any industry, which can be influenced by a change in relationship among costs that brings advantages and dis­ advantages to it. Shipment costs of raw products to firms, storage and handling costs, and shipment costs of processed products to the final markets could also affect the location of the industry. This is the case of the

Brazilian soybean and wheat industry which is composed of cooperatives, processing plants, and export firms.

The increase of soybean production resulting from expansion in acreage and production per hectare created new supply points for the industry. Consequently, both the existing supply network and the "optimal" location of the soybean processing plants were modified. The expansion 4

in exports also changed the relative importance of market demand points and, consequently, it affected the transpor­ tation and storage network.

Another effect of the increased soybean production in Brazil was the greater demand for storage, transporta­ tion and other market facilities, already in operation.

The shortage of storage and even the lack of storage facil­ ities at the production points put pressure on the trans­ portation system, resulting in a high seasonal demand for trucks and trains during harvest time.

The greater seasonal demand caused the already high transportation costs to increase even more during the m harvest season. These seasonal demand problems occur at? the cooperatives, terminal points and at the export markets.

A comparison of soybean production costs between

Brazil and the United States shows that Brazil has a cost of $130.45 per ton while the United States has a cost of

$149.11 per ton. The cost difference between the two countries is $18.66 which gives Brazil a relative advan­ tage in production costs. However, a comparison of the marketing margins between the two countries favors the U.S. producers. The marketing margins for soybeans from the production points to the export points in Brazil are

$56.25 per ton while these same marketing margins for the 5

U.S. producers are $11.99 per ton (8 ). The reasons for the higher margins in Brazil, compared to the U.S., are because most soybean shipments in Brazil are made through roads and railroads, while in the U.S., the bulk of the soybeans are shipped through waterways.

Another reason for higher marketing margins in Brazil is due to direct and indirect taxes (13-75 percent of market price in 1975) and other transportation and loading costs which are nonexistent for the U.S. producers (8).

Consequently, the lower production cost advantage in

Brazil, compared to the U.S., is more than offset by higher marketing margins in Brazil.

The FOB cost of soybeans in Brazil is $186.71 per . ton, while in the U.S. it is $161.12. The marketing mar­ gins in Brazil are 30.2 percent of the cost of soybeans, while in .the U.S. it represents only 7.5 percent of FOB cost. The reduction of these marketing margins for soy­ beans in Brazil is a major objective of producers, cooper­ atives, processing plant owners, and state government, in order to increase their returns and, also, to increase com­ petition in the international markets.

Federal and state government, private business firms, such as the agricultural processing industry, and coopera­ tives want to increase the efficiency of the marketing system - mainly the transportation-network in Brazil. 6

These agencies, especially the public agencies and coop­ eratives, are making large investments in infrastructure at the ports, elevators, rail lines and roads.

The State of Rio Grande do Sul which produced 46 and 56 percent of the nation's soybean and wheat production in 1976 is building a transportation-storage facility for grain. This facility is expected to be completed b'< \ > end of 1978 and will allow shipments of grain through waterways to export points. This complex is located at an intermediate point between the main production regions and thfe export market. It will combine three different modes of transportation: roads, railroads, and waterways.

The Empresa de Portos do Brasil S.A. (PORTOBRAS), a federal agency, is building.this new transportation- storage facility, part of which was used for the 1977 har­ vest season. Use of this new marketing facility complex will change the relationship of the transportation costs from the production regions to the processing plants and to the export markets. Also, it will increase the compe­ tition among alternative ways of shipping grains to terminal points, and consequently, increase the efficiency of grain transfer.

The major concern of this study focuses on the per­ formance of alternative transportation-storage networks and their relationship to current governmental policies in the State of Rio Grande do Sul. 7

Specifically, the objectives of this investigation

are:

1. T.o determine the optimum flow of soybeans,

soymeal and wheat for a given level of output

that minimizes the transportation and storage

costs between production points, processing

plants and export points.

2. To determine the effects .on the cost and flow

of soybeans, soymeal and wheat as alternative

modes of transportation and changes in trans­

portation rates are introduced into the trans­

portation-storage network.

3. To identify and locate bottlenecks’ and asso- .

ciated costs in the existing transfer system

as the new port facility is included in the

model.

Organization of the Dissertation

The second chapter contains a description of the region

studied, the production levels of soybeans, wheat and soy­ meal of each region and the data used in this investigation.

The chapter also presents the highway and waterway system

and planned Improvements in the transport network.

Chapter III contains a review of the literature relevant

to the investigation, describes a theory of transportation 8 and storage and presents an algorithm capable of solving problems of minimum cost flow under capacity constraints.

In Chapter IV the construction of specific models for wheat and soybean/soymeal, the storage nodes, intermediate and terminal operations are discussed. Chapter V presents the results and analysis of the models while Chapter VI presents the summary and conclusions of this•investigation. CHAPTER II

SOYBEAN AND WHEAT PRODUCTION, MARKETING AND TRANSPORTATION IN RIO GRANDE DO SUL

This chapter presents a description and location of the soybean and wheat production regions in the State of

Rio Grande do Sul. It also includes: a) the location of the processing plants for soybeans and wheat; b) the trans­ fer network of grains in the state and; c) the proposed improvements in transfer network for grains.

The southern states of Brazil are the major producers of grains. This region consists of the States of Sao Paulo,

Parana, Santa Catarina and Rio Grande do Sul. The major producer of soybeans and wheat is the State of Rio Grande do Sul followed by the State of Parana.

Rio Grande do Sul is the southernmost state of Brazil.

It is bordered on the west by Uruguay and Argentina, on the north by the State of Santa Catarina and on the east by the

Atlantic Ocean (Figure 1).

The major producing areas of soybeans and wheat in the

State of Rio Grande do Sul are located on the north and west sides of the state whereas the bulk of the production must be shipped to the processing plants and export points NDO

mu

'ESTRELA

.PORTO 'A LEGR

:0 GRANDE

Main producing areas of soybean and wheat

Figure 1. The Main Producing Areas of Soybeans and Wheat and the Port Locations, Rio Grande do Sul, Brazil. 11

of Porto Alegre and Rio Grande which are located in the

southeast of the state. Soybeans and wheat that are

produced at regions such as Ijui and Passo Pundo are

transported to Porto Alegre and to Rio Grande export

points by railroads and highways or shipped to Estreia,

an intermediate point, and from there shipped by water­

ways to the export points (Figure 1).

The average highway distance between the producing

areas (such as Ijui and Passo Pundo) to Porto Alegre and

Rio Grande is 351 and 657 kilometers, respectively, thus,

a large network of transportation services and storage

facilities is needed to move these grains to their final

destinations.

Production Regions of Wheat and Soybeans

The state is divided into 232 municipios which are

the political units of local government. The state is also

divided into 24 micro-regions by the Brazilian Institute

of Geography and Statistics (IBGE) for statistical data

collection. The state is divided into 55 regions for the purposes of this investigation. The criteria developed

for this division are: a) the production levels of soy­ beans and wheat; and b) the existence of alternative modes of transportation for grain. The minimum region size was at least one single municipio because the available sta­ tistical data was collected at the municipio level. The 12 regions on the north and west side of the state are smaller in size because they are the major production areas of soybeans and wheat (Figure 2). Appendix A pre­ sents the 55 regions with the name of each municipio that is included in each region. All the municipios of the state are included in the analysis as a producing unit and as a consumption unit. Not all the regions have processing plants for wheat and soybeans. Some re­ gions are net importers of wheat and soybeans (local pro­ duction is less than industry demand within the region).

All the descriptive and analytical sections of this inves­ tigation are based on the above 55 regions.

The net production of 4,828 thousand tons of soybeans and 1,161 thousand tons of wheat plus the beginning inven­ tories are the basic data for the transfer-storage network developed in this investigation. The state production of soybeans and wheat was 5*107 and 1,234 thousand tons, respectively, in the late 1975-76 crop year. About 6 per­ cent of the soybean production and 10 percent of the wheat production was discounted to account for seed use and losses at the origin points. The production level of soy­ beans and wheat for each region is presented in Table 1.

Regions 45, 50, 51, 52, and 54 are the most significant soybean producing areas with 31 percent of the state's production and regions 26, 45, 46, 47, and 50 are the major 13

FIGURE 2. The 55 Wheat and Soybean Production Regions of the State of Rio Grande do Sul, Brazil 14

TABLE 1. Production of Soybeans and Wheat in the State of Rio Grande do Sul, Brazil, 1975-76

Percent Percent Region Soybean Wheat of of Production Production Total Total Production Production (Tons) (%) (Tons) (%) 1 2,256 .04 1,741 .15 2 3,807 .07 945 .08 3 0 .00 16 .00 4 7,896 .17 15,471 1.34 5 14,777 .30 7,369 • .63 6 11,041 .23 9,500 .83 7 1,776 .03 4 .00 8 114,492 2.38 23,591 2.03 9 20,304 .42 9,720 .34 10 27,260 .56 2,556 .23 11 16,920 .35 9,900 .85 12 41,454 .86 13,824 1.19 13 57,863 1.19 9,556 .83 14 28,200 .53 27,518 2.37 15 68,047 1.41 60,686 5.. 24 16 104,237 2.15 32,358 2.78 17 80,737 1.67 9,940 .87 18 41,510 .87* 27,000 2.32 19 44,148 .91 7,860 .68 20 19,694 .42 1,153 .00 21 9,851 .20 463 .00 22 87,119 1.80 6,311 ...... 55...... 2 3 94,178 1.95 18,165 1.56 24 45,120 .94 14,994 1.29 25 39,480 .82 22,050 1.90 15

TABLE 1 (continued)

Percent Percent Region Soybean Wheat of of Production Production Total Total Production Production

(Tons) O/O (Tons) 00 26 106,805 2.21 70,560 6.08 27 101,520 2.10 19,800 1.70 28 62,901 1.30 7,092 .62 29 80,397 1.66 13,945 1.20 30 7,492 .17 12,060 1.04 31 9,268 .20 2,286 .20 32 12,318 .25 5,868 .50 33 8,272 .17 10,368 .90 34 11,336 .24 7,531 .65- 35 109,360 2.27 13,412 1.15 36 64,394 1.33 10,627 .91 37 63,450 1.31 8,100 .70 38 217,140 4.50 31,660 2.73 39 150,400 3.12 25,200 2.18 40 130,848 2.71 24,120 2.08 41 76,704 1.59 19,440 1.68 42 39,480 .83 33,075 2.85 43 118,440 2.45 25,200 2.18 44 73,207 1.52 16,929 1.48 45 244,663 5.08 67,122 5.78 46 205,578 4.26 69,300 5.98 47 177,124 3.67 75,980 6.54 48 121,486 2.52 13,536 1.17 49 102,648 2.12 34,992 3.01 50 382,314 7.92 69,446 5.98 51 395,533 8.19 61,514 5.30 16

TABLE 1 (continued)

Percent Percent Region Soybean Wheat of of Production Production Total Total Production Production

(Tons) (%> (Tons) (%) 52 265,494 5.50 33,669 2.90 53 144,422 2.99 11,617. 1.00 54 220,947 4.58 17,280 1.49 55 142,339 2.95 16,922 1.46

Total 4,828,447 100 1,161,342 100.00

Source: Fundaijao Estadual de Estatdistica (FEE). 17 wheat producing areas with 30 percent of the state's production. All these regions are located at the north­ ern area of the state. The export points of Porto Alegre and Rio Grande, regions 20 and 7, respectively, are lo­ cated on the east coast of the state. The Estrela port is located in region 22 between the main producing areas of soybeans and wheat and the Porto Alegre and Rio Grande ports.

The Wheat Industry

Wheat transport, storage and milling in Brazil is controlled by the National Wheat Marketing Commission

(CTRIN), a government agency coordinated by the Bank of

Brazil. This commission determines where the wheat should be stored and where each region should ship grain. After the harvest and delivery of the grain to the local coop­ erative or elevator, all the storage and transportation costs from this point forward are paid by this commission.

The price paid to farmers and the price that the milling industry pays is stipulated by the federal govern­ ment. The flour price for the consumer is also controlled by a government agency (SUNAB).1/ Thus, the marginsof

1/ SUNAB signifies Superintendencia Nacional do Abasteci- mento, a federal agency that controls food price policy in Brazil. 18 the milling plants are determined by the difference between the industry purchase price for wheat and the price received for flour. Also, the number, size and location of the flour mills is coordinated by the govern­ ment .

The milling capacity in the state of Rio Grande do

Sul was 2,278 thousand tons in 1976. This is two times the state’s wheat production. This milling capacity is based on a 24-hour operation throughout the year and rep­ resents around 13 percent of the national capacity. The number of plants, milling capacity and the amount of wheat milled by each region is presented in Table 2.

The utilization index of the wheat milling plants

(wheat milled divided by capacity), the same for all re­ gions, is 21. This means that the quota allocated to each plant is based on the plant milling capacity. The utili­ zation index of 21 shows a high idle capacity when the milling capacity is based on a 24-hour operation for all the year according to the SUNAB data. Using a more adequate measure for the milling plants, the capacity was recalcu­ lated on a ten hour/day operation during 300 days a year.

The total capacity would be reduced to 780 thousand tons and the utilization index would increase to 63 percent which is still low. 19

TABLE 2. Number of Wheat Milling Plants, Total Capacity and Amount of Wheat Processed by Region, Rio Grande do Sul, Brazil, 1976

Percent Nuirber Annual of Total of Milling Wheat Wheat Region Plants Capacity Milled Milled (Tons) (Tons) (55)

7 1 48,093 10,464 2.10

8 2 66,338 14,430 2.90

13 1 1,801 396 .00

17 4 125,922 27,396 5.52

18 1 16,852 3,666 .74

19 1 8,639 1,878 .37

20 13 1,253,578 272,753 55.02

22 5 116,236 25,290 5.10

23 2 3,926 852 .16

24 1 2,922 636 .12

26 1 1,378 300 .05

28 2 • 16,889 3,672 .73

29 8 58,269 12,678 2.57

30 10 240,104 52,236 10.54

32 5 64,325 13,992 2.81

33 2 34,130 7,422 1.47

35 2 22,602 4,920 .98

36 1 37,604 8,814 I .65

37 2 9.741 2,118 .42 20

TABLE 2 (continued)

Percent Nuntoer Annual of Total of Milling Wheat Wheat ReffLon Plants Capacity Milled Milled (Tons) (Tons) («

38 3 25,642 5,580 1.12 i i i ( CO 40 1 3,587 o .15

42 1 1,699 372 .07

44 1 19,728 4,290 .85

45 1 19,728 4,290 .85

46 2 12,105 2,634 .52

48 1 4,111 900 .18

49 1 2,971 648 .12

50 1 1,906 414 .08

51 3 7,763 1,692 .33

52 2 3,198 696 .14

53 2 12,501 2,718 .54

55 _4 38,982 8,484 1.70

Total 89 2,278,445 495,737 100

Source: Superintendencia Nacional do Abastecimento (SUNAB). 21

Region 20 has 55 percent of the state’s milling capacity and because of the quota allocation procedure, this region milled 55 percent of the wheat into flour.

Other important milling regions are 30, 17, and 22 in order of importance. None of these regions are major wheat producers but are significant flour consumption centers. Region 20 is the Porto Alegre area (state capital) where almost 40 percent of the state population lives (2.8 million inhabitants). This suggests a govern­ ment policy of shipping wheat to the consumer center where it is transformed into flour. The total wheat trans­ formed into flour (495 thousand tons) represents 42 percent of the state’s wheat production while the remaining wheat is stored and exported to other states. Rio Grande do

Sul is the only state that produces enough water for its consumption and exports the excess to other states.

The Soybean Industry

The soybean industry, contrary to the wheat industry, is less regulated. The domestic market price for. soybeans reflects the supply and demand conditions of the interna­ tional market. The growth of the soybean processing in­ dustry accompanied the increase in production of soybeans.

During the period of 1969-1972, the crushing capacity in 22

the state increased from 590 thousand tons to 1,700

thousand tons which is an increase of 188 percent. In

this same period, soybean production increased from

432 thousand tons to 1,392 thousand tons, an increase of

222 percent. The soybean crushing capacity in the state was 4,068 thousand tons in 1976. During the period

March 1, 1976 to February 28, 1977, 2,615 thousand tons were processed which gives a utilization index of 64

(Table 3)• The average plant size is almost 120 thousand tons a year or 10,000 tons per month. Despite the large number of plants, only three companies processed 1,557 thousand tons of soybeans which corresponds to 59 percent of the total soybeans crushed in the period. The major crushing capacity for soybeans is located in regions 20

(44 percent of the total capacity), 8, 22 and 45 in order of importance. These regions together crushed 77 percent of the soybeans in 1976. Regions 20 and 8 are the export points of Porto Alegre and Rio Grande, respectively.

Region 22 is the Estrela region where the new waterway port is located while region 45 is the Ijui region where the largest soybean and wheat cooperative is located.

Ownership of the processing plants is distributed between cooperatives and private firms. The cooperatives processed ten percent of the soybeans and the remaining

90 percent was processed by private firms in 1976. The ‘23

TABLE 3. Number of Soybean Processing Plants, Total Processing Capacity and Amount of Soybeans Processed*by Region, Rio Grande do Sul Brazil, 1976

Percent Utili­ Region Number Processing Soybeans of Total zation of Capacity Processed Soybeans Index Plants Processed

(Tons) (Tons) (%)

8 2 465,000 391,029 14.95 84 18 1 31,400 2,946 .11 9 20 5 1,384,200 1,166,904 44.62 84 22 3 • 362,000 270,862 10.36 75 23 2 27,970 16,535 .63 59 ' 26 ■1 180,000 114,744 4.39 64 32 1 670,000 123,390 4.72 18 36 1 30,000 3,170 .12 11 38 4 181,200 104,993 4.01 58 44 1 5,005 5,005 .19 100 45 3 302,000 195,554 7.48 65 46 1 59,400 22,995 .88 39 47 1 43,400 7,450 .28 17 48 2 25,200 13,346 .51 53 49 1 21,600 19,215 .73 89 50 2 58,240 26,597 1.02 46 51 1 72,000 52,279 2.00 73 53 1 60,000 49,600 1.90 83 55 1 90,000 28,575 1.10 32

TOTAL 34 4,068,615 2,615,139 100 64

/ f Source: Sindicato das Industrias de Oleos Vegetais do Rio Grande do Sul. 24

private firms such as Olvebra S.A., Sociedade Moinhos

Rio Grandense S.A. (SAMRIG) and Anderson and Clayton are all foreign firms operating in Brazil.

Soybean processing yields two products: 75 percent of the weight is soymeal, 18 percent is oil and the remain­ ing 7 percent is waste. During the year of 1976, 2,447 million tons of soybeans were processed which yielded

441 thousand tons of oil and 1,859 million tons of soymeal.

About 53 percent of the oil was for domestic consumption and 47 percent was exported. Prom the soymeal obtained,

88 percent was exported and 12 percent was for domestic consumption (8).

GRAIN STORAGE IN THE STATE OP RIO GRANDE DO SUL

Storage capacity in the state of Rio Grande do Sul has increased considerably in recent years. The large expansion of soybean and wheat production and other agri­ cultural goods demanded more space and for longer periods of time, especially for soybeans that are processed through out the year.

In 1970, the total storage capacity for agricultural products was 2,900 thousand tons. In that same year, soybean production (1,024 thousand tons) and wheat produc­ tion (1,464 thousand tons) equalled 2,488 thousand tons.

The. storage capacity for agricultural products in the state 25

was 9,676 million tons in 1976 (Table 4). Soybean and

wheat production was 5,107 and 1,234 thousand tons, re­

spectively, for a total of 6,341 thousand tons in that

same year.

About 90 percent of the total storage capacity for

agricultural products in the state of Rio Grande do Sul

is composed of concrete silos, bulk flat storage, pole

buildings (adapted for bulk storage) and bag storage

buildings. According to CESA about 24 percent of the

total storage capacity cannot be used due to lack of tech­

nical conditions.—^ Only 7,375 thousand tons of storage

capacity is technically adequate. The net storage capa­

city, 5,162 thousand tons, is only 53 percent of the

state’s total nominal storage capacity (Table 4). The

difference between nominal and net storage capacity, 30 percent of the total capacity, is due to three reasons

identified by CESA: a) the impossibility of filling the

top corners of the storage facilities; b) internal subdi­

visions that reduce the storage capacity; and c) the test weight of the product.

If one considered all the grains produced in the state, not all the net storage capacity would be available

2/ CESA stands for Companhia Estadual de Silos e Armazens, a state agency that provides storage for farmers, coop­ eratives and other firms at a charge. TABLE 4. Nominal and Met Storage Capacity by Class of Storage Facility in the State of Rio Grande do Sul, Brazil, 1976

Class of With With No Total Storage Technical Technical Storage Facility Capacity Conditions Conditions Capacity

Concrete Nominal 579,404 29,533 608,937 Silo Net 405,583 20,673 426,256

Bulk Nominal 3,854,760 352,940 4,207,700 Flat Net 2,698,332 247,058 2,945,390 Storage

Pole Nominal 382,265 27,200 409,465 Building Net 267,586 19,040 286,626 (Bulk or Bag)

Bag Nominal 2,558,755 790,457 3,349,212 Storage Net 1,191,129 553,320 2,344,449

Deposit Nominal — 1,066,021 1,066,021 Met 746,215 746,215

Steel Nominal 35,178 35,178 Bins Net 24,624 24,624

Total Nominal 7,375,184 2,801,329 9,676,5133/ State Net 5,162,630 1,610,930 6,773,560

Source; Companhia Estadual de Silos e Armazens (CESA), Porto Alegre, RGS, Brazil, 1976. a/ This total does not include the storage capacity at the ports of Porto Alegre and Rio Grande. 27

for soybeans and wheat. However, a large percentage is

available since the major production areas have the largest percentage of storage capacity and they are specialized

in soybean and wheat production. The other grains pro­

duced in these regions are mainly for on-farm consumption.

The double-cropping system of soybeans and wheat does not permit storage for more than one year. When

soybeans are harvested (March-April), most of the wheat has to be shipped to other markets because soybeans will need the storage facilities. The same process occurs when wheat is harvested (October-December) and the soybeans that were stored are shipped to other places.t The stor- age capacity at the port of Rio Grande is 459 thousand tons, while the Porto Alegre port has a storage capacity of only 25 thousand tons. Consequently, the bulk of the production is stored at the production points.

The storage facilities of the state are concentrated in the soybean and wheat producing regions (Table 5)-

Region 8 and 20, major industrial processing regions for soybeans and wheat, have around 17 percent of the total storage capacity. Regions 50, 26, 38, and 46, the produc­ tion areas with the most significant storage capacity, com­ bined have 20 percent of the state's storage capacity for grains. TA B LE 5: Nominal and Net Storage Capacity for Crains by Region in the State of Rio Crarde dc Sul, 1978

Percent of Region Nominal Net Total Net Capacity Capacity Capacity — tons— {%)

1 106,155 25,025 .49 2 18,200 12,600 .24 3 2,520 1,050 .02 4 74,030 55,230 1.07 5 56,490 52,640 1.02 6 103,893 18,900 • 37 7 54,985 . 29,400 .57 8 674,585 458,696 6.89 9 74,690 53,130 1.03 10 47,040 25,410 .49 11 99,050 97,090 1.38 12 71,769 64,209 1.-24 13 211,981 160,125 3.10 14 133,273 120,281 2.33 15 160,958 108,920 2.10 16 171,571 139,674 2.70 17 120,866 72,310 1.42 18 152,509 132,930 2.57 19 65,504 42,910 .83 20 511,196 424.34S s .2 3 21 3,710 50 .00 22 130,175 105,375 2.05 2"? 31,445 47,545 • 93 24 ‘ - 36,715 36,540 .71

25 25,040 26,040 ' „ 4. 25 239,630 234,360 4.p4 TABLE 5 (Cont.)

Percent of Region Ncnlnal Net Total Net Capacity Capacity Capacity — tons------(5) 27 97,374 81,760 1.59 28 41,157 26,236 .51 29 12,011 5,460 .11 30 22,918 1,862 .04 31 5,342 3,360 .07 32 37,534 10,500 .21 33 30,310 23,940 .47 34 44,030 38,990 .76 35 . 64,306 55,440 1.08 36 40,334 34,580 .67 37 34,426 29,134 .56 38 293,286 233,940 4.53 39 141,383 124,712 2.42 40 94,731 94,395 1.89 41 79,100 63,140 1.22 42 80,185 77,770 1.50 43 78,498 59,920 1.16 44 53,025 51,560 1.00 45 219,415 187,390 3.53 46 252,753 197,536 3.82 47 372,470 183,330 3-55 48 96,600 7c,150 1.47 49 104,664 53,390 1.13 50 431,676 378,028 7.32 51 152,280 174,224 3.37 52 200,690 173,330 3-37 53 57,344 40,-04 .78

. 54 117,045 C C j . / z ■ • ■ - 1. o p 32,029 37,330 1 • 11 ■» ,** Total 9,675,513 3,162,5:0

Source: Corcpanhia Sstadual de Silos e Arrazchs (CESA) 30

THE TRANSPORTATION NETWORK IN THE STATE OP RIO GRANDE DO SUL

This section provides a description of the transfer network in the state of Rio Grande do Sul. The infra­ structure of roads, railroads, waterways and major im­ provements that are underway and are expected to be used in the near future are described.

THE PRESENT HIGHWAY SYSTEM

The highway network of the state of Rio Grande do Sul is presented in Figure 3. The major producing areas are located in the northern and western part of the state and the main highways that connect these areas with the Porto

Alegre and Rio Grande ports are: BR386 originating at the municipio of Sarandi (region 54) and extending to

Porto Alegre, a major crushing center; BR116, BR392 and

BR293 are the major highways that connect the port of

Rio Grande to the producing areas through the highways des- 3/ cribed above.— Another major highway, BR285, links the producing areas in the northern part of the state and runs in the west-east direction. All these highways are asphalt paved two-lane highways.

3/ The prefix BR indicates a federal highway. 31

l.'UI BR 285

BR116] rBR 156 SAfiri M~KU { C V t o $>.. 290

BR 392

RIO GRANDE

Highways

Waterways

Figure 3. The Highway and Waterway Network in the state of Rio Grande do Sul, Brazil. PLEASE NOTE:

Dissertation contains small and indistinct print. Filmed as received.

UNIVERSITY MICROFILMS. 31

IJUI BR 285

BRII61 'BR 156 S'.fJtA UAbU i C V l <0 Si.. 290

BR 392

RIO GRANDE

Hi ghways

Waterways

Estrela Port Facility O

Figure 3. The Highway and Waterway Network in the state of Rio Grande do Sul, Brazil. 32

THE RAILROAD NETWORK

The present railroad network of the state of Rio

Grande do Sul has an extension of 3,620 kilometers of single lanes and-14 kilometers of double lanes. In 1976, the railroad provided transportation for 1,081 thousand tons of soybeans and 571 thousand tons of wheat; this corresponds to 29.4 percent and 18.7 percent of the total ton/kilometers shipped by the railroad. The main delivery points of both products were the ports of Porto Alegre and Rio Grande. Figure 4 presents the railroad network and the new rail line that is under construction in the

State of Rio Grande do Sul.

The railroad corporation had 2,585 railroad cars for transporting grains with a capacity of 121,844 tons or a dynamic capacity of 258,978 tons at an average of 2.7 loads per car/month in 1976. Only 3 percent of the above capacity cannot be used for bulk grain transportation.

The most significant change in the rail network will be the new railroad line Passo Fundo-Estrela-Porto Alegre that is expected to be completed by the end of 1978. This new railroad line has a distance of 293 kilometers and will reduce the distance between Passo Fundo and Porto

Alegre by 360 kilometers (Figure 4). This railroad line will transport soybeans and wheat from the northern area of the state (such as the Passo Fundo region and surrounding 33

SANTA ROSA- GET VARGAS. ILAJES.

PASSO FUNDO CATUIPE

MCARl S.OGRJA

SANTIAGO JABOTICABA , y ITACUl' P.OCA SALES Lg-ssOCAXIAS DO S U L x^ [CARLOS OAR CCS A

A. DORNELES R. DOS SINOS // CANABARRO S. LC0°0''.D0 /' R. PAROC ___O.A.PESTANA // GAL. LUi prOsTANDARD / / j

■ ( (P Porto Alegre/ / S. GABRIEL

ARMOR

BAGE

PEtOTAS

OAZILIO IANOE OUlNTj

Existing rail lines

Projected rail line

Figure 4. The Railroad Network in the State of Rio Grande do Sul, Brazil. 34

tT production areas) to Estrela and the Porto Alegre port.

Product that is shipped to Estrela will be transferred to barges and then shipped by waterways to the Rio Grande port.

THE WATERWAY NETWORK

The most important inland waterway is the Patos Lake.

This lake connects the port of Porto Alegre (through the

Guaiba River) with the port of Rio Grande (Figure 3).

The Jacui and Taquari Rivers are going to be used for the new port facilities built in Estrela. The Taquari River is navigable to the Estrela port; further navigation re- quires construction of additional locks. Between the

Estrela port and the ports of Porto Alegre and Rio Grande, there is on lock that is 'located around 30 kilometers below the port of Estrela at the Taquari River. This river is connected to the Jacui River and the Jacui River runs into the Guaiba River.

THE HIGHWAY/RAILROAD/WATERWAY COMPLEX

The new port facility built in Estrela on the Taquari

River is the major improvement in the transportation net­ work for grains in the State of Rio Grande do Sul (region

22). The construction of the port facilities was completed in 1977 but the railroad that will link the port to the production regions of soybeans and wheat is not expected to be completed until the end of 1978. A major highway

(BR386) which connects the port with the production region originates at the municipio of Sarandi (region 5*0 passes by Estrela and terminates at Porto Alegre.

This port is the most important construction for integrating the waterways in the export corridor program established by the Department of Transportation. It will be under the administration of Portobras S.A., a public company that administers all the Brazilian ports.

The port handling capacity is expected to be one million tons of grain a year. It has two storage facil— ities for grains: a concrete silo with 40 thousand tons capacity for soybeans and wheat and a flat bulk storage with 15 thousand tons capacity for soymeal. The receiv­ ing capacity at the port is 300 tons per hour (by rail and truck) and a transfer capacity to barges of 400 tons per hour.

In closing, the major two improvements of the grain transfer network in the state are the new railroad line

Passo Pundo-Estrela-Porto Alegre and the new waterway port for using internal waterways. 3oth changes in the transfer system are analyzed in Chapter 5 and are intro­ duced in the model in order to evaluate the effect of each on the total transport cost for grain in the State of Rio Grande do Sul. CHAPTER III

METHODOLOGY

This chapter is divided into three parts. The first part presents a summary of the theoretical considerations of the effects of transportation and storage costs on interregional trade in a two-period case. The second part presents a review of the literature related to the generalized transportation network model employed in this investigation. The mathematical representation of the capacitated network model is presented. Finally, examples of the network model are described and features such as the time dimension (storage) are incorporated into the model.

Theoretical Considerations

Not all products produced in a region are consumed locally nor are all products consumed in a region produced there. This consideration led to the concept of inter­ regional trade. Some products - mostly agricultural - can be produced only in specific regions because of cer­ tain necessary conditions such as appropriate soils, 37

climate and cost of inputs. These specific conditions

create production cost advantages for some regions over

others. These cost advantages favor specialization'in production. Bressler and King (10) analyze the equili­ brium price and quantity for a single product in two

regions, considering first no trade and, secondly, with

interregional trade.

Consider the case of a single product produced and

consumed in both regions. In the absence of trade, the

equilibrium of price and quantity is established indepen­

dently by the intersection of the demand and supply

curves in each region. Figure 5 shows the equilibrium

of price and quantity in Regions A and B.

In Region A, the equilibrium price is PA and in

Region B the equilibrium price is Pg. Comparing the two graphs in Figure 5, it is possible to observe a difference in prices between the two regions. If trade is permitted, ignoring the transfer cost, traders can buy the product at the lower price in Region B and then sell it in Region A, making some profit. The flow of products from Region B to

Region A will increase the price in Region B, while decreas­ ing the price in Region A. The combined graph shown in

Figure 5 is the horizontal summation of the demand and supply curves of Regions A and B. The intersection of 38

A+B

A+B

A+B Region A Region B Combined

FIGURE 5. The Trade Between Two Regions as a Result of Differences in Supply and Demand Functions

P' B

PB

0

I I 0 a b 0 Region A Region B

FIGURE 6. The Effects of Transfer Cost (t) on Prices and Trade 39 these curves determines the equilibrium price and quantity traded. The equilibrium price P is the same as and » Pg in Regions A and 3. The equilibrium price obtained in the combined graph does not consider transfer costs.

In the real world transfer costs exist and, in some cases, are so high as to cause a barrier to trade between regions.

Consider now a model including the transfer cost be­ tween two regions. In order to facilitate interpretation, the graph of Region B is displaced upward in Figure 6 by the amount of the transfer cost.

Region A has a higher equilibrium price than does

Regipn B, even with the addition of the transfer cost to the equilibrium price for Region B. As long as the'dif­ ference in prices between the two regions is larger than the transfer cost between them, there will be a movement of products from Region B to Region A. In the absence of trade, the equilibrium price in Region A is PA and the quantity demanded is 0a. In Region B, the equilibrium price is Pg and the quantity demanded is 0<}. With trade, the price in Region A will decrease from PA to PA and the quantity demanded will increase from 0a to Ob by import­ ing the quantity ab from Region B. On the other hand, after trade, the price in Region B will increase from Pg i to Pg while the quantity consumed will decrease from 0^ to 0C by exporting the amount cd to Region A. The 40

difference in price between Regions A and B, after trade, represents the transfer cost "t" which is shown in the graph of Region B in Figure 6 by the distance O'O.

Katzman (32), using a Von Thunen model, analyzed the effects of a reduction in transportation cost on the agricultural sector. Such a reduction in cost will ex­ pand the agricultural frontier, increase the use of inputs by farmers, increase farm income and lower prices to con­ sumers. Similar effects will result from a reduction in storage cost but this case is not discussed explicitly by Katzman.

Consider now the effect of a reduction in transfer cost. This reduction will decrease the value of "t" and, consequently, the upward displacement of the graph of

Region B. The result will be that Region B will increase ’ exports to Region A and the price, in Region B, will in­ crease. Consequently, in Region A there will be more im­ ports and a larger decrease in price. The relationship between transfer cost and trade movements can be verified by examining the excess supply curves of Regions A and B in a "back-to-back" diagram, as shown in Figure 7.

Esa and ESfo represent the excess supply curves of

Regions A and B, respectively. The curve Esa - Esb reP“ resents the vertical difference between Esa and ESb . If the unit transfer cost is represented by the distance 0a , 41

p

Region A Region B

sa

sa

FIGURE 7. Equilibrium Prices and Trade Illustrated by Using Differences Between Excess Supply Curves

ES ES

FIGURE 8. Two-Period Equilibrium with Storage Introduced 42 the quantity exported by Region B to Region A is Oh.

The equilibrium price in Region A is and in Region B, it is Pg. The total transfer cost between the two is represented by the area of the rectangle Oabh. A reduc­ tion in transfer cost will increase the exports made by

Region B from Oh to Oh1 and the price will increase from

PB to Pg in Region B and decrease from P^ to P^ in

Region A.

Prom the analysis above it is evident that a reduc­ tion in transfer cost by an increase in efficiency of the transport network will increase interregional trade and i will affect prices in both regions. The size of the changes in price and quantity will depend on the elasti­ cities of the supply and demand curves in those regions.

A similar back-to-back diagram, used to explain the two region spatial equilibrium model, is used to illustrate a two-period equilibrium model. Three cases are considered: a) no storage, b) storage without storage costs, and c) storage with storage costs. All three cases are illus­ trated in Figure 8. The supply curve is given in the first period by the vertical line S while the demand curves for the first and second period are given by and D2D2, respectively.

In the absence of storage, the quantity consumed in the first period is Oa and the equilibrium price level is P. The excess supply curve for the first period is given by the curve ES]_. At the price P all the product is consumed in the first period and the excess supply is zero. The excess supply curve for the second period is given by the line ES2. This excess supply curve represents the amount by which the available supply, in this case, zero, exceeds the quantity demanded at each price. The curve ES2 - ES^ represents the vertical difference between ES2 and ESi.

With storage but no storage costs, the equilibrium price Ps is given by the intersection of the excess supply curves ESi and ES2 . The quantity consumed in the first period is Od while in the second period is da (= oe).

Finally, the case of storage with storage cost is illustrated. Supposing that the unit storage cost is the vertical distance Os; then a horizontal line drawn from point s to the ES2 - ES]_ curve identifies point c. Read­ ing vertically from point c, this quantity intersects

ESi at price Pi and ES2 at price P2 for the first and second periods, respectively. The quantity consumed in the first period is od" while in the second period is o d ’ which is equal to oe* by construction.

The effect of a reduction in storage cost would cause an increase in the price and reduce consumption in the first period while decreasing price and increasing con­ sumption in the second period. Hk

REVIEW OP LITERATURE

The purpose of this section is to present a review

of some previous studies using transportation models. An

overview of selected optimizing models and a review of

research done in Brazil specifically in network models

are described here.

Wright (63) provides a review of specific transpor­

tation models and their assumptions and applications in

the case of single and multi-commodity cases over time.

The optimal allocation of goods and services from a

"surplus'' region to a "deficit" region can be solved by

using one of the four general types of models: a) the

simple transportation model; b) the transshipment model;

c) the spatial equilibrium model; and d) the capacitated * network model. The selection of any particular model de­ pends on the problem to be studied, on the assumptions to be made, and on the availability of data.

The spatial equilibrium model incorporates the supply and demand schedule in the model, while In the remaining three models the quantity supplied and the quantity demanded are given to the model. The single commodity and multi­ commodity case can be handled by the models presented above.

Incorporation of the time dimension in the model requires a multi-period dimension with goods transferred from one period to the following. 45

Weinschenk, Henrichsmeir and Aldinger (6l) present a survey in the field of spatial equilibrium theory, particularly with respect to its application to the loca­ tion of agricultural production. They considered the transportation model as a special case of the spatial equilibrium model with completely price inelastic demand and supply functions (fixed amounts of demand and supply for the regions) and interdependencies did not exist. In this case the spatial equilibrium problem is reduced to the well-known transportation problem [Hitchcock (29);

Keeopmans and Reiter (35); and Orden (43)].

A transportation model for solving a minimum cost flow allocation can be formulated in a linear programming

(LP) format or as a network problem.

Wright (62) developed a transshipment model in an

LP format for evaluating the effect of changes in the transportation rates on the least-cost location of the

U.S. flour milling industry and the consequent potential impact on different sectors and regions of the wheat-flour economy. Leath and Blakley (37) in a multi-commodity, interregional analysis of the U.S. grain marketing industry usedan LP program. Five primary products, two processed products, forty two domestic regions and thirteen export regions were used in the model. Similarly, Martin and

Hedley (38) in an LP format analyzed the feed milling sector. 46

The objective function of the model was to find the com­ bination of plant sizes and locations which minimizes the total cost of grain assembly, feed mixing and feed distribution.

Most recently, Puller, Randolph and Klingmann (23), after formulating a plant location problem for a cotton ginning industry in an LP format, came to the conclusion that a network model for solving minimum cost-flow prob­ lems is 100 to 150 times faster than the best general purpose commercial linear programming computer codes; thus, they formulated the problem as a network problem.

Durbin and Kroenke (18), Bennington <9), Phillips and

Jensen (44), Walker (60), Wright and Meyer (64), and

Taaffe and Gauthier (54) describe the basic formulation of the transportation network model.

The standard transportation model becomes a trans­ shipment model when intermediary points between the supply and demand points are allowed. It becomes a capacitated network model when lower and/or upper constraints on the amount of flow between points are added. King, Casseti and Kissling (33) were concerned with the determination of optimal transportation patterns for particular commod­ ities on networks having capacity constraints. They em­ ployed the out-of-kilter algorithm (OKA) in the determina­ tion of optimal least-cost flow patterns involving the 47 transportation patterns of bituminous coal throughout the Great Lakes, and in another study, the movement of certain perishable fruits throughout New Zealand. The

OKA can be used for a variety of network problems besides those presented above, such as caterer problems, employ­ ment schedules, tanker schedules, and others.

To our knowledge, the first major study in transpor­ tation models applied to a Brazilian problem was done by Gauthier (25). The OKA was used to evaluate the pro­ gram of transportation construction and improvement under­ taken by the State of Sao Paulo in Brazil.

Figueiredo, Silva and Neves (22) studied the ration­ alization of alcohol production in the State of Sao Paulo by minimizing transportation cost. They used a linear programming model to minimize the total shipping cost.

The results show the optimum quantities to be moved from each sugar refinery to each mixing center, the incremental cost of alternative routes, and the transportation network that would tend to reduce existing costs.

Vilas (58) conducted an interregional analysis for the Brazilian rice economy using a spatial equilibrium model such as that suggested by Samuelson and Takayaraa and Judge. Partial analyses investigating the consequences of changes in the transportation cost matrix, changes in the conditions of demand and supply, and the impact of 48

storage programs In both deficit and surplus regions were performed. Changes in population and income were also

analyzed, as well as the consequences of changes in area planted or an increase in yields.

Tyner and Campos (56) were concerned with the effi­

cient operation and location of seed processing facilities and their distribution in northeast Brazil. A transship­ ment model in a linear programming format was used to ac­ complish their objectives. They determined the optimum shipping patterns from production to processing to con­ sumption based on the existing processing facilities. No references were made to location of storage facilities.

C. Wright (63) conducted a major study using the OKA technique for a least-cost transport model for agricultural commodities in Parana, Brazil. It was a multi-commodity study which evaluated the network system including storage facilities and export ports. He analyzed the optimal flow of corn, soybeans and wheat for the production-transfer conditions of 1976. In addition, he simulated production- increases for the mid-1980's and used these results as a basis for analyzing future transport-storage problems and possible solutions. The major conclusions of his research suggest improvements in railroad transportation for some specific rail-lines and the construction of new rail lines.

For the purposes of this research study of soybeans and wheat transportation in Rio Grande do Sul, the capacitated 49 transportation model is the most appropriate. The features of the present transfer-storage network can be incorpor­ ated in the model and changes in transfer costs and addi­ tional arcs can be evaluated efficiently.

THE ANALYTICAL INSTRUMENT

Mathematically, a constrained network flow problem can be represented as a linear program [developed by

Fulkerson and presented by Phillips (44)] given by:

min TC = ZZCijXij

subject to the following constraints

a ) -^ij £ xij i. uij b) ZX - ZX = 0 J Ji J ij where

cij = transfer cost per uni-t from i to j

Xij = amount shipped from i to j

Ij, j = lower limit of amount shipped

uij = upper limit of amount shipped

Equation (b) is the conservation flow principle which states what goes into a node must go out.

The OKA determines endogeneously the net arc cost

(Cij = C3AR), the amount shipped through each node and the kilter number. CBAR is defined as ^ - ttj where tm is the value (relative price) of product at node 50

i, Cij is defined as before and ttj is the value (relative price of product at each node j .

c) if Cij < 0, then Xij = u ^

d) if C^ = 0, then lij < Xij < u ^

e) if Cij > 0, then Xij = lij

If Cij < 0, it would be possible to ship more units through this arc and reduce cost but it is restricted by an upper limit (Xij = ^j). Similarly, if > 0, less units should be shipped through this arc but it is restricted by the lower limit (Xjj = lij)* If an arc satisfies one of the conditions (c), (d), and (e) it is in kilter and would be an optimal solution. An arc X ^ which does not satisfy any of the restrictions (c), (d), and (e) is out-of-kilter.

A feasible solution but not anoptimal is obtained if:

C±j < 0 ana Xu < u±J

°ij- > 0 and xij > lij

An infeasible solution occurs if any of the following con­ ditions Drevails:

cij > 0 and X y < lij

Cij = 0 and X±J < ly

Cij = 0 and X^j > u^ j

Cij < 0 and Xij > U i j 51

The kilter number in the OKA output will be non-negative

for all arcs and will be positive for all out-of-kilter

arcs in a feasible solution.

The analytical instrument presented above is based

on the following assumptions:

(1) the product studied is homogeneous;

(2) regions may be represented by points;

(3) the quantity supplied and demanded at each

point is known;

(4) perfect competition; and

(5) transportation costs are independent of the

quantity of units shipped.

A network consists of a number of points or junction points, each joined to some or all of the others by arcs.

Essentially if one can draw a diagram (node/arc chart) of the problem, it can easily be solved by the OKA technique.

The OKA will yield an optimal solution to the minimal cost circulation problem.

An example of a network is presented in Figure 9. It consists of two origin points (1.1 and 1.2), three destina­ tion points (2.1, 2.2 and 2.3)* one super source (Si) and one super sink (S2 ). Any origin can ship to any of the three destination points given the unit transport cost, and the upper and lower limit for each arc. The amount (o» Ujj. Ijj)

FIGURE 9. Network Flow With Two Origins and Three Destination Points 53 supplied at each origin is given by the upper limit of the arc connecting the nodes (S^, 1.1) and (S]_, 1.2),

•and the amount required at each destination is given by the lower limits of the arcs connecting the nodes (2.1,

S2), (2.2, S2) and (2.3, S2). Each arc connecting the origin nodes to the destinations may have different unit transport cost and different upper and lower capa­ cities. The circulation of flow principle is provided by adding a returns arc which connects the super sink node to the super source node (also called dummy origin). This arc has a zero unit cost, a large number as an upper ca­ pacity (bigger or equal to the sum of the requirements at the destination points) and zero units as lower capacity.

The diagram in Figure 10 represents a multi-period network problem with transshipment points, storage points and final demand points. Node 1.1 represents the origin node in period I. . Product from this origin (harvested in period I) may be shipped to six alternative nodes. First, it may be shipped to storage node for a temporary stor­ age or to storage node S21 for the next period. Node may receive product from storage node Sqi (beginning in­ ventory) . Product that is not left in storage may be shipped to nodes 1.2, 1.3, 1.4 or pll* Nodes 1.2 and 1.3 are transshipment nodes and product from this node can be shipped to processing node P ^ or to node 1.4 for export. Period 0 Period I Period II Period III ;

12j 22

11

"1 Sll

- — -T - * ©

i I

FIGURE 10. Network Flow, Origin Points, Intermediate Points and Final Destination Points in a Multi-Period Case With Storage

VJJ -tr 55

The processing node P^1 and export node E-^ represent final demand points. Additional arcs and nodes could be incorporated in the network model representing alternative modes of transportation (such as railroads, waterways), processing plants, transshipments points and export ports.

In order to incorporate a second time period, the network in period I is duplicated with the same features. The network model is duplicated three times for a three period model and so forth. The restrictions of the number of arcs and nodes depends only on the capacity of the computer program.'

The origin node in the second time period is repre­ sented by node 2.1. The description of the network in period II is the same as for period I. The arc over node

Sgi represents product storage for the third period. An arc cost (Cij) upper limit ( u ^ ) and lower limit (lij) is assigned to each arc. They may be different for each time period. This may be the case at origin nodes or at a processing plant due to seasonality factors. As an example, the processing plant P n in period I may process

100 tons of product while the same processing plant (P22) may process 120 tons in the same period. This condition is represented by an upper and lower limit equal to 100 tons for arc P11-P12 and 150 tons for arc P2l"?22' T^e same may happen for the export arcs 1.4-E]_ and 2.4-E2 when different amounts are exported in each period. Simulations of changes in transfer costs and alter­ native modes of transportation can be performed by incor­ porating these changes in the basic model. The procedure is the following: construct the basic model and compose the results for the flows through each arc at some total cost. After this, include the additional arc in the model and another output is obtained. The results are compared and analyzed. The simulation may or may not have changed the results depending upon the additional effect of this arc to the model. Simulations of changes on the transfer cost, upper and lower limits are conducted in this way. CHAPTER IV

THE RIO GRANDE DO SUL WHEAT, SOYBEAN NETWORK AND A FRAMEWORK FOR PRICING TRANSPORT SERVICES

This chapter presents the network characteristics such as the collection points of the grain, transfer arcs, storage locations, the surplus arcs from each re­ gion, the time periods, the specific highways, railroads, and waterways in the state. A framework for pricing transport services is also developed in this chapter.

The Time Periods

The seasonality in the harvest of some agricultural products, such as wheat and soybeans creates a seasonality of demand for services such as harvesting machines, stor­ age space and transport. Because of the seasonality in the transfer of these products, the capacitated network model was divided in four time periods. The wheat model extends from September 1, 1975 to August 31, 1976 while the soybean/soymeal model runs from March 1, 1976 to

February 28, 1977. The first three periods are each two months long while the fourth period is six months long.

57 58

The first three periods correspond to the harvest and transfer of the product and the fourth period is the off­ season time period.

The soybean/soymeal model periods are shown in

Table 6. The first period for these products runs from

March 1 to April 30. During this period, 31 percent of the soybeans enter the system, 11 percent of the total soybeans processed are crushed in this period, 9 percent of the beans and 11 percent of the soymeal total exports are exported in this period. The second period, from

May 1 to June 30, is considered the peak period for the transfer of this grain from the production points*to the collection points and to other destinations. During this period, 61 percent of the soybeans enter the system.

There is a high demand for storage space and transporta­ tion services. Almost one third of the soybean exports occur in this period, 19 percent are processed and 19 per­ cent of the meal exports are made in this period. The third period begins on July 1 and terminates on August 31.

This is the peak period for soybean exports, 3^ percent of the total exports. Only 7 percent of the beans enter the system in this period. The fourth period begins on

September 1 and extends to February 28 of the following year. This is the off-season period for soybeans and only 59

Table 6 . Time Periods for the Soybeans/Soymeal Network Model, Rio Grande do Sul, Brazil, 1976

Item Time Periods I II III IV March-April May-June Julv-Aug. SeDt.-Feb. * n ~—1 4L.

Enter3 31 61 7 1

Processing 11 19 20 50

Export (beans) 9 30 34 27

Export (meal) 11 19 20 50

aRepresents the percentage of soybeans entering the network model in each period

Table 7. Time Periods of the Wheat Network Model, Rio Grande do Sul, Brazil, 1976

I tern Time Periods I II III IV Sept.-Oct. Nov.-Dec. Jan.-Feb March-Aug.

Entera 3 91 6 0

Milling . 16 16 16 52

Export ...... 4 34 28 34

aRepresents the percentage of wheat entering the network model in each period 60

1 percent of the soybeans moved into the system. In this period, 50 percent of the total soybeans processed are crushed and 50 percent of the soymeal was exported in this period.

The wheat network model has time periods similar to the soybeans/soymeal model and is presented in Table 7.

The difference between the two models is the beginning date of the first period. The first period begins at the harvest season of wheat, September 1, and extends to

October 31. During this period, only 3 percent of the total wheat production moves into the system.- The second period which runs from November 1 to December 31 is the peak period for wheat entering the system. During this period, 91 percent of the wheat enters the system while

34 percent of the total exports to other states are made in this period. The third period starts on January 1 and runs until February 28. These three periods of the wheat model correspond to the fourth period of the soybean/soy­ meal model; the off-season period for the soybeans.

Finally, the fourth time period of the wheat model which runs from March 1 to August 31 is the off-season period for wheat.

This subdivision of the year into four time periods permits the identification of bottlenecks and changes in 61 the demand for services in the transfer of grains which is the subject of this investigation.

The Network Model

Two capacitated network models are built: one for soybeans/soymeal and the other for wheat. Both models use the same origin nodes, transfer and terminal facili­ ties. The soybean/soymeal model includes also the trans­ fer of soymeal from the processing plants while the wheat shipped to the milling plant was considered as a final destination. Soymeal was included in the analysis due to the large amounts of soymeal (1,837 thousand tons) that are shipped to export points and through the water­ way system.

The capacitated network model contains four origin nodes for each one of the 55 regions; each one represents a time period. The arcs connecting the dummy origins to the origin node have upper and lower limits equal to the flow of product entering the system in each time period

(Figure 11). A zero cost was assigned to the arcs connect ing the dummy origin to the origin nodes. From the origin node the product may be shipped to a local processing plant (P) to an intermediate node (I), directly to a ter­ minal point or left in storage at the origin to be (Cij,Uij,Llj) BI

to, DO DD

El DD

FIGURE II. A Network Model of a Single Region with Four Time Periods, Storage, Processing and Transfer Arcs, Rio Grande do Sul, Brazil, 1976

o\ ro 63 transferred to the following period. All four time per­ iods in the model are interconnected by storage arcs with the appropriate capacities and storage cost. The arcs connecting node 1 to node 2, node 2 to node 3 and so forth are the storage arcs. The upper capacity of these arcs represents the storage capacity in metric tons and the arc cost represents the storage cost per ton for a period of two months. The arc connecting node 4 to node El is the ending inventory node and an appropriate storage cost is assigned to this arc which corresponds to the transfer of product to the following period. The arc 1-P is the processing arc which connects the origin node to a local processor in period 1. The upper capacity represents the plant processing capacity for each period and a zero cost is assigned if the processing plant is located within the production region. In the case of shipments to process­ ing plants located in other regions a transfer cost is assigned. Prom the processing node, one fourth of the soybeans processed weight is shipped over the arc P-DD.

This one fourth weight represents the soyoil and waste from the processing of soybeans (in the soybean/soymeal model) which is not included in the analysis. The arc

P-DD represents a dummy destination arc with zero cost.

The remaining 75 percent of the soybeans1 weight which is transformed into soymeal is sent over the arc P-S to node S. From this last node the soymeal may be shipped to the final destination over any alternative transfer node. It is assumed that all the soymeal originated from a processing plant in each period is exported within the same period. This is very realistic according to the data obtained for the soybeans processed and soymeal ex­ ported in 1976.

The major intermediate node in this network model is the Estrela node. It is a transshipment node located on the east bank of the Taquari River in the municipio of Estrela (region 22), hereafter called the Estrela port.

Grain is shipped to this port by truck or railroad and

» then transferred to barges for shipment to the export points of Porto Alegre and Rio Grande.

ESTRELA PORT AND TERMINAL OPERATIONS

The Estrela port facility, which is expected to be completed by the end of 1978, has the capacity to handle one million tons of grain per year. It will be a terminal port for trucks and railroads. Grain and other agricul­ tural products arriving at this node will be transferred to barges that will take these products to processing plants or export ports located in Porto Alegre and Rio

Grande. The arcs and nodes of Estrela, duplicated for the four time periods, are shown for one period in Fig­ ure 12. The soybean/soymeal model contains two receiving 65 nodes: one for receiving soybeans and another for soy- meal. This was necessary because soybeans are shipped to processing plants from the Estrela port and the iden­ tification of the product was required at this point.

Soybeans may be received at the Estrela port through node ITES (shipped by truck) or node IRES (shipped by rail). Soymeal is received at node 5TES (shipped by truck) or at node 5RES (shipped by rail). A zero cost is assigned to arcs 1TES-5TES and 1RES-5RES, a large upper capacity and zero lower limit are assigned to these arcs. The receiving cost at the port is assigned to arcs that connect nodes 5TES and 5RES to node 1ES. The • upper capacities are given the letters A and B which identify the constraint of receiving products by truck or rail and a zero lower capacity is assigned to these arcs. From node 1ES, product may be shipped over the storage arc 1ES-2ES with the appropriate storage cost and upper and lower constraints. If the product is shipped out during the same period it was received, the product flows through node H E to 1LE. The arc connecting these two nodes represents the loading costs on barges (L) and the upper capacity (E) of the port facilities for loading on barges. The nodes 1WPA and 1WRG identify the destina­ tion of the barges as the Porto Alegre or Rio Grande ports. Soybeans/Wheat Soybeans/Wheat Truck Hail

1 TES 1 RES

o o

Soymeal Soymeal 5 RES Rail 5 TES Rail

o

1 WP A 1 WRG

FIGURE 12. Estrela Transshipment Node in Network Model, Rio Grande do Sul, Brazil, 1976 67

The total cost of transferring grain from the trucks or railroads at the Estrela port is Cr$26,00 per ton based on a volume of one million tons of product. The fixed cost is around Cr$21,00 per ton which represents

81 percent of the total cost.ii/ This high fixed cost is due to a large fixed investment in construction of the port facilities.

The receiving and terminal operations at the ports of Porto Alegre and Rio Grande are similar to the nodes and arcs described for the Estrela port. The Porto Alegre and Rio Grande port may receive grain by truck, rail and waterway. The receiving rates for grain are the same for both ports, CR$l4,00, per ton, while the receiving rate by waterways is Cr$l6,00 which is 12 percent higher. The loading rate for shipping grain from Porto Alegre to Rio

Grande is Cr$l6,00. The arcs and nodes for loading ships for export destinations were not included in the model.

Soybeans, soymeal and wheat are exported through the ports of Porto Alegre and Rio Grande in the four time periods.

Soybeans, soymeal and wheat export data.for each port by time period was obtained to set demands at each port which were maintained fixed throughout the analysis.

V Appendix 3 contains a more detailed explanation of the cost and other expenditures of the Estrela port facility. V

68

THE HIGHWAY NETWORK

The wheat and the soybean/soymeal model have the

same highway network. There are 55 origin nodes in both models and all of them are connected by roads to inter­ mediate and terminal nodes. The regions that have pro­ cessing plants are connected to other regions because some are net importers of soybeans and wheat. Each node and arc is duplicated four times along with the inter­ mediate, processing and other terminal points because of the time periods.

Most of the highway network is shown in Figure 13.

Only the most important road sections are presented and the node number identifies the region. Nodes 7 and 20 are the export nodes of Porto Alegre and Rio Grande.

• Node 22 is the Estrela port facility. The most important road sections are those that begin at node 5^ (Sarandi region) and pass through node 39 (Carazinho region) and go to node 22 (Estrela port) and continue until node 20 which is the Porto Alegre terminal node. This road,

BR386, links the production region of soybeans and wheat to Porto Alegre. Product may flow on this highway until node 22 (Estrela port) where it is transferred to barges.

This transshipment to waterways would eliminate the road traffic between nodes 22-20 and 20-7 for product that is Santa Kosa Erechim Vacaria 55 50 S a r a n d i 49 )G ir ua

*>wSao Francisco Santo LIjui Carazinho 45 31/ de Paula Angelo f 4 6 39 Pas so Fundo

Caxias Sao Luiz 26 ) Cruz 40 j Gonzaga Alta Carapo Estrela do Sul Real 22

Santa 1 5 ]Sao Borja Maria *— s. Porto 17 20) Alegre

16 Santiago

Itaqui Rosar io 11

Sao Gabriel 10

io Grande Alegrete Uruguaina L ivramento Bage

FIGURE 13. The Highway Network for Soybeans, Soymeal and Wheat in the State of Rio Grande do Sul, Brazil, 1976 ov YO 70

exported through Rio Grande. Node 45s another important

production region, has two alternative routes to ship

grain to Rio Grande. Grain may be shipped to node 39

and from there to node 22 where it is transferred to barges for shipment to Rio Grande. The other alternative

is shipping the grain directly to Rio Grande through

node 17 to node 7. The flow direction will depend on

the waterway and transfer rates at the Estrela port for

grain produced in the surrounding regions.

Node 20 (Porto Alegre) has about 50 percent of the

soybean processing capacity. In the analysis, 1,166

thousand tons of soybeans were shipped to industry located

there which represented 45 percent of the soybeans crushed

in the four periods.

The transfer charges for truck transportation from the origin nodes to intermediate nodes, processing plants

and export ports were obtained from the wheat and soybeans

cooperative federation (PECOTRIGO), the major marketing

association for soybeans, and from CTRIN, a government

commission that is responsible for wheat distribution to the milling plants and export ports. The PECOTRIGO was responsible for over 50 percent of the soybean exports

In 1976 through a pool organized by its affiliating coop­ eratives. The average rate paid by PECOTRIGO and CTRIN

for truck transportation was used as the transfer rate for 71 soybeans and wheat. It is expected that these rates reflect approximately the average total cost for truck transportation of grain because the market for these services is very competitive and free of rate regulation.

The same rates were used in the four time periods.

As was presented in Chapter II, some regions have more than one municipio. In order to use the appropriate truck rate for each region, the average weighted produc­ tion of soybeans and wheat multiplied by the truck rate of each municipio was used as the rate for the region.

Truck rates to the Estrela port were estimated based on the market rates of 1976.

THE RAILROAD NETWORK

Only 23 of the 55 origin nodes in the network model have railroad transportation to Estrela, Porto Alegre and

Rio Grande. The origin nodes that are connected by this mode of transportation are shown in Figure 14. In the railroad network, a new proposed rail line is also included.

The line between node 38-22 and 22-20 is expected to be completed by the end of 1978. The rail line from node 38 to node 22 is part of the new transportation complex

(highway/railroad/waterway) located at node 22 (Estrela port). This new railroad will reduce the distance between the Passo Fundo region (node 38) and Porto Alegre (node 20) 72

35

50 37

49 39 43

45 46

26 47

25 15 Por to 16 24 Alegre Port

17

14

10 11.

Rio Grande existing Port line simulated line

FIGURE 14. A Diagram of the Railroad Network Including the Simulated Lines in Rio Grande do Sul, Brazil, 1976 73

by 360 kilometers compared to the existing rail line that

passes through the Santa Maria region (node 17). Consid­

ering the existing rail line, grain that priginates at

nodes 5 5 * 3 7 > 3 8 , and 39 has to be shipped through nodes

17 and 10 if the destination is Rio Grande. This route

increases the rail distance and consequently the charges because the railroad corporation charges the transporta­

tion costs based on ton/km.

The railroad rates used in this investigation were

obtained from the Rede Perroviaria Federal S.A., the rail'

road corporation that operates in the State of Rio Grande

do Sul. These rates are 30-33 percent lower than the of-

ficial published rail rates because they are hegotiated between the cooperatives, processing plants and the rail­ road corporation.—^

THE WATERWAY NETWORK

The basic waterway network is shown in Figure 15.

Grain arriving at Estrela port is transferred to barges or small ships and shipped to Porto Alegre or Rio Grande.

The barges or ships go through the Taquari River until this river joins the Jacui River. The Jacui River runs

5/ Appendix C discusses in more detail the ton/km of soy­ beans, soymeal and wheat transported by the railroad corporation and subsidies for transporting these goods. Estrela Port

Porto Alegre Port

Rio Grande Port

FIGURE 15. A Diagram of the Waterway Network in Rio Grande do Sul, Brazil, 1976 75

into the Guaiba River where the Porto Alegre port is

located. The Guaiba River is connected to the Patos

Lake and the lake is linked to the Rio Grande port by the Sao Goncalo River. At the Porto Alegre port, the product may be exported or shipped to Rio Grande. The distance by road from Estrela to the Porto Alegre port and to the Rio Grande port is 105 km, respectively.

The same routes by water are 138 km to Porto Alegre and

448 km to Rio Grande which represents 31 and 5 percent more, respectively, by waterways. A barge takes an average of 17 hours from Estrela to Rio Grande and makes on the average five to six round trips per month. The waterway between Estrela and Porto Alegre does not allow night navigation due to lack of navigational lights on the river. This decreases the number of monthly round trips. The capacity for shipping grains from the Estrela port was estimated at 200,000 tons for each period. This capacity is limited by the capacity of the port facilities in receiving and loading the barges. During rainy days it is not possible to load grain on the barges; that reduces even more the capacity. There is no capacity constraint for shipping grain between Porto Alegre and Rio Grande port. The only restrictions at the port of Porto Alegre are the size of the load on ships because the channel that links the Guaiba River to the Patos Lake is only 76

17 feet deep. Most of the large ships that load in 4 Porto Alegre, get only one-third of their cargo and complete the full load at the Rio Grande port.

The shipping rates by waterways were obtained from the Navegacao Lajeado, which is the major company in transporting grains through the waterway system. In

1 9 7 6 , this company had 73 percent of the transportation capacity and transported over 90 percent of the grain shipped by inland waterways. Only four companies are involved in the waterway grain transportation business.

The total shipping capacity of the four companies was

19>776 tons with an estimated five to six monthly round trips resulting in a 100,000 to -120,000 tons capacity per month.

The receiving capacity for grain by waterways at the port of Rio Grande was estimated at 156,000 tons per month while Porto Alegre port had an estimated capacity of 96,000 tons per month.

A FRAMEWORK FOR PRICING TRANSPORT SERVICES

The pricing of transport services that require large amounts of initial investment in infrastructure such as roads, railroads and basic port facilities is crucial in the transportation industry. The price level and changes in it may cause an intermodal shift in the demand for a specific mode of transportation. In many countries, such 77

as Brazil, the government is responsible for building

(and maintaining) roads, railroads and waterways, and

consequently determines the demand by alternative modes

of transportation through pricing policy. The criteria

established for traffic allocation for a specific mode

of transportation may depend more on the objective of the policy makers than on the cost structure for provid­ ing the service.

According to standard economic theory for profit maximization in a competitive environment, the transport service price must be set equal to the marginal cost.

This theoretical criteria for efficient resource alloca­ tion is not very helpful for transport sector analysis unless supplemented by a great deal of additional infor­ mation (2). Because of large fixed costs, the marginal cost for providing transportation services is often below its average cost. If price is equated to marginal cost under these conditions, revenue will not cover expendi­ tures. There are three ways to solve this problem: a) equate price and marginal cost despite the deficit; b) use average cost pricing; and c) base the transport price on the value of the service. These conditions are shown in Figure 16. The marginal cost is below the aver­ age cost in the relevant section of the curves. Equating price to marginal cost, the output would be 0Qm but a loss 73

Cr $ Ton/Km

MC

0 Q Ton/Km

FIGURE 16 Marginal-Versus Average Cost Pricing When Marginal Cost is Less than Average Cost and Value of Service Pricing 79 equal the distance AB would occur. In this case, revenue would not cover expenditures. Equating price to average cost, the output would be reduced to 0Qa . In this case there would be an intermodal shift and loss in output would depend on the demand elasticity. If the demand for transport services is inelastic in the range BC of the demand curve, the average cost pricing would reduce very little the quantity demanded of this mode of transport service. Thus, average cost pricing might be the best policy. Also, it would increase the total revenue with a price increase, as long as the relevant range of the demand curve is inelastic.

The value-of-service pricing avoids a deficit by charging each shipper according to the worth of the ser­ vice to him. This means that tariffs are, in part, based on the value of the commodity rather than on the cost of transporting it. Charging prices like Pm , P]_, and the sum of payments is the area 0P2EFDGBQm in

Figure 16. As long as this area is equal to or greater than the area OPeAQm , this pricing policy would provide enough revenue to cover expenditures. This discriminant pricing policy would be possible only if consumers cannot shift to another mode of transportation, i.e., there is

•some monopoly power or the consumers are prohibited to do so. 80

Some economists argue that, much of the plant cost,

e.g., railroad permanent way and highways, are "sunk

costs," and therefore, should not be included in the cost

structure for pricing transport services. According to

Abouchar, this argument for less-than average cost pric­

ing can be justified under six headings: a) income dis­ tribution, which would be directly frustrated by seeking

full-cost recovery; b) national security; c) declining

average cost of highways, requiring a subsidy from

society; d) the joint nature of the highway facility, requiring disproportionately large contributions from automobile users; e) the inability, caused by prohibitively high costs, to discriminate among users, creating exter­ nalities in land rents which can subsequently be tapped for road contributions; and f) timings— road investment is made today, but the road lasts a long time.

Problems of interpretation arise also, as soon as one attempts to carry out calculations of the actual aver­ age and marginal cost, chiefly on account of the follow­ ing situations: a) the single product firm is exceptional; b) the condition that total cost is a continuous function of output, is consequently inoperative; c) the total cost function does not remain stable over expansion or contrac­ tion of output; and d) the time element is usually ignored, 81 whereas the rate of growth over time of an industry un­ doubtedly affects its cost (42).

This framework developed for pricing transport services is utilized for analyzing the road, railroad and waterway price policy in Chapter V.. Presently, the railroad corporation collects only 73 percent of its total cost thus requiring a subsidy from the public.

Simulations of changes in the rates are presented and demand elasticities as well as cross elasticities among alternative modes of transportation are calculated based on the network models developed in this chapter. CHAPTER V

RESULTS AND ANALYSIS

In this chapter the empirical results and inter­ pretation of the transfer-storage network of wheat and soybean/soymeal are presented. The wheat model results are presented first, followed by the soybean/soymeal model results.

The Wheat Model

The 1975-76 productioh level of wheat was considered as the basic data for the transfer-storage network. The period of analysis which begins September 1, 1975 and runs until August 31, 1976 was divided in four time periods.

The amount of wheat that entered the network, inventories, exports and demand requirements for each time period are shown in Table 8 . The second time period is the peak period for wheat entering the network which corresponds to the months of November and December. During these two months, 73 percent of the wheat enters the system. In the first period only 21 percent of the total wheat enters

82 Table 8. Wheat Flows, Exports and Industry Demands for Each Period, Rio Grande do Sul, Brazil, September 1975 to August 1976

Periods Flow of___ Exports______Industry Storage Wheat P. Alegre R. Grande

Metric Tons

1 304,118a 19,922 26,266 79,317 178,613

2 1,056,821 9,440 10,032 79,317 1,136,645

3 69,681 46,262 5; 525 79,317 1,075,222

4 0 99,525 173,125 257,786 544,796b

Total 1,430,062 175,139 214,948 495,737 2,935,276

269.278 tons of wheat entered in Period 1 in the model as beginning inventory

b544.796 tons of wheat are left in storage for the next period 84 the network model while the remaining six percent flows in the third period. The. largest demand for transport services and storage capacity at the production points occurs during the second time period. All the wheat produced is for domestic consumption in the state or shipped to other Brazilian states. Shipment to port fa­ cilities for export to other states is not significant compared to the soybeans and soymeal exports.

A total of eleven runs are performed with the wheat model. The results of each simulation are presented in

Tables 9 and 10. The first six iterations present the results of simulated changes in the network model while the remaining simulations deal with changes in rail and truck rates for transporting wheat.

The total cost for the OKA basic network solution for the allocation of wheat is CR$l4l.O million (Iterat-r tion .1, Table 9). This basic solution represents the 1976 wheat network in the State of Rio Grande do Sul. The amounts of wheat which flow through the transshipment port of Estrela (old port), Porto Alegre and Rio Grande ports are presented in Table 10. In the basic network model, the Estrela port could receive wheat only by truck while

Porto Alegre and Rio Grande could receive grain by truck, rail and barge. The Estrela port received 76.3 thousand tons by truck and all the wheat was sent to local milling 85

TABUS 9: Optimal Solution for 1976 Basic Wheat Network Model and Simulations of the Basic Model Rio Grande do Sul, Brazil

Percent Total Changes Change Simulation Iteration Cost in Cost in Cost — Millions Cr$-- (55)

Basic Solution 1 141.0 -

Estrela Port 2 141.0 0.0 0.00

Rail to Estrela 3 140.7 -0.3 -0.21

Rail to P. Alegre 4 140.6 -0.1 -0.10

Variable-Cost at Estrela Port 5 140.6 0.0 0.00

No Storage Constraints 6 140.4 -0.2 -0.14

Total Savings -0.6

Increase Rail Rates by

1055 7 143.2 +2.8 +1.99 CVJ o 8 145.4 +2.2 +1.53

30% 9 147.0 +1.6 +1.10

38% 10 147.8 +0.8 + .54

Total Increase in Cost +7.4

Increase Truck Rates by

2055 11 154.5 +6.7 +4.5 TABLE 10: Plows of Wheat through Alternative Modes of Transportation to Estrela, P. Alegre and Rio Grande in the Basic Model and Simulated Models Rio Grande do Sul, Brazil, 1976

Mode of Transportation Location Iteration Total Truck - Rail Water

Tons Percent Tons Percent Tons Percent Tons

Estrela 1 76.3 100 N.A. N.A. 76.3 P. Alegre 1 189.1 60 12*1.3 *10 0 0 313.*! Rio Grande 1 65.4 30 1*19.2 70 0 0 21*1.6

Estrela 2 76.3 100 N.A. , N.A. n—* n—f 76.3 P. Alegre 2 189.1 60 .12*1.3 *10 0 0 313**1 Rio Grande 2 65.4 30 1*19.2 70 0 0 21*1. *1

Estrela 3 5*1.5 71 21.7 29 N.A. - 76.2 P. Alegre 3 189.2 60 12*1.3 70 0 0 313**1 Rio Grande 3 65.4 30 1*19.2 70 0 0 21*1.6

Estrela *1 54.5 71 21.7 29 N.A. ---- 76.2 P. Alegre 1*17.0 *11 166. *1 59 0 0 313. *1 Rio Grande 4 65.*1 30 1*19.2 70 0 0 21*1.6 TABLE 10 (Cont.)

Location Iteration ------Mode of Transportation------Total Truck Rail Water

Tons Percent Tons ' Percent Tons Percent Tbris

Estrela 5 5^.7 71 21.8 29 N.A. — tt 76.5 P. Alegre 5 147.0 41 166.6 59 0 0 313.6 Rio Grande 5 65.4 30 149.2 70 0 0 214.6

Estrela 6 . 54.7 71 21.8 29 N.A. 76.5 P. Alegre 6 144.6 46 168.8 54 0 0 313.4 Rio Grande 6 65.4 30 149.2 70 0 0 214.6

Estrela 7 55.4 73 21.0 27 N.A. - , 76.4 P. Alegre 7 195.9 62 117-5 38 0 0 313.4 Rio Grande 7 78.0 36 136.9 64 0 0 214.9

Estrela 8 76.4 100 0 0 N.A. 76.4 P. Alegre 8 201.1 64 112.7 36 0 0 313.8 Rio Grande 8 101.5 . 47 113.4 53 0 0 214.9

Estrela 9 76.4 100 0 0 N.A. r1M 76.4 P. Alegre 9 202.1 64 111.7 36 0 0 313.8 Rio Grande 9 186.8 87 28.0 13 0 0 214.8 TABLE 10 (Cont.)

Mode of Transportation Location Iteration Truck Rail Water Total

Tons Percent Tons Percent Tons Percent Tons

Estrela 10 76.4 100 0 0 N.A. 76.4 P. Alegre 10 202.1 64 111.7 36 0 0 313.8 Rio Grande 10 207.3 96 7.6 6 0 0 214.9

Estrela 11 68.3 89 8.1 11 N.A. r-r-r-, 76.4 P. Alegre 11 195-9 62 117.5 38 0 0 313.4 Rio Grande 11 86.7 40 128.2 60 0 0 214.9

N.A. is not applicable 89 plants. The Porto Alegre port receive.d 313.4 thousand tons; truck shipments represented 60 percent (189.1 thou­

sand tons) and the remaining 40 percent (124.3 thousand tons) was shipped by rail. At the Rio Grande port, the total shipment in the four periods was 214.6 thousand

tons; rail shipments accounted for 70 percent (149.2 thousand tons) and 30 percent was shipped by truck (65.4 thousand tons). No waterway transportation entered the optimal solution.

The only relevant constraint in this basic solution was storage capacity at region 29 in the time period

2-3 (CBAR = -30) and at region 30 in time period 2-3

(CBAR = -128). Arc 2-3 represents the storage arc from period two to period three (Nov./Dec.-Jan./Feb.). The negative CBAR values at region 29 and 30 indicate the

savings that would occur by a one unit increase in stor­ age capacity. By increasing the storage capacity one ton at region 30 from period 2 to period 3 * the total

OKA cost would decrease by CR$128.

Iteration 2 simulates the new Estrela port facility.

This new transshipment point introduces waterway shipment

for grain from Estrela to Porto Alegre and to Rio Grande.

The total OKA cost of the simulated model in iteration 2 is Cr$l4l.O million. This new waterway arc did not affect any flow or cost when compared to the basic network model 90 of iteration 1. Region 29 and 30 still showed a negative

CBAR value for storage capacity from period 2 to 3.

Iteration 3 introduces the. new railroad from Passo

Fundo (region 38, see Chapter II) to the Estrela port.

This new rail line links the existing rail line to the port. By introducing this rail line, the Estrela port receives 29 percent of its wheat shipment by rail at a savings of Cr$0.3 million in the total OKA cost compared to the basic model. This small reduction in the total cost does not justify the railroad to the Estrela port considering only the wheat transfer. Shipments by truck and rail to Porto Alegre and Rio Grande were not affected in this simulation.

Iteration 4 links the Porto Alegre port to the Estrela port by railroad and thus the distance between the muni- cipio of Passo Fundo (region 38) and Porto Alegre by railroad is reduced by 360 kilometers. This modification in the network model causes an intermodal shift from truck to rail at the Porto Alegre terminal and consequently re­ duces by Cr$0.1 million the total transfer cost compared to the cost of iteration 3 *

Shipments by truck to Porto Alegre are reduced by

41 percent (42.2 thousand tons) while rail shipments are increased by 34 percent (42.4 thousand tons). 91

In the first four iterations the waterway arc did

not have any flow in the optimal solution. By introduc­

ing the new port facilities in iteration 2 , a transfer

charge of Cr$26 was included in the model. This cost

represents the costs of capital, labor and energy for

transferring one ton of grain from truck or rail to barge.

This cost represents the full fixed and variable cost of

the services provided by the Estrela port. The fixed

cost represents 8 l percent (Cr$21) while the variable

cost is 19 percent (Cr$5)* According to the theoretical

framework for pricing transport services described in

Chapter IV, a reduction in the price for transport ser­

vices would increase the demand for such services.

In Iteration 5 S only the variable cost of Cr$5 was

charged for transferring wheat to barges at the Estrela port. The result was that the waterway arcs (Estrela-

Porto Alegre and Estrela-Rio Grande) do not appear in the

optimal flow of wheat in the State of Rio Grande do Sul even though only the variable cost of the Estrela port

services is charged. The new Estrela port (excluding rail to Estrela) has no effect on the optimal allocation of wheat in the state.

The storage bottleneck discussed in iteration 1 is analyzed in iteration 6 . In this iteration, the storage

capacity in region 29 and 30 is increased to a large 92 upper limit (1 million tons) in order to find out the additional-required storage capacity for these regions.

Region 29 needs an increase of 5>117 tons or a 93 percent increase of the actual 5 >460 tons capacity while region

30 needs an increase of 16,149 tons which is almost ten times the present capacity of 1,862 tons. The total cost of this iteration is Cr$l40.6 million which is a reduction of Cr$0.2 million. This is the savings that would be ob­ tained by increasing the storage capacity of these two regions (29 and 30). However, this reduction in cost is not obtained solely by increasing the storage capacity but also by the intermodal shift from truck to rail ship­ ments to Porto Alegre that resulted from the change in storage capacity. This fact can be observed by the in­ crease in railroad shipments by 2.2 thousand tons to Porto

Alegre (Table 10, iteration 6 ).

The next five iterations of the wheat model deal with changes in transfer rates. The railroad corporation in the State of Rio Grande do Sul in other states of Brazil is subsidized by the Federal government. In the 1976 fis­ cal year, the railroad corporation collected only 73 per­ cent of its total expenditures (47). This means that the railroad rates have to increase by 38 percent for the revenue to equal the expenditures of the railroad corpora­ tion. Similar situations occur for truck shipments. 93

Abouchar1’found that the government’s transport pricing policy has subsidized shippers and passenger travel by common carrier. On the average, the transportation costs f n n nr>nriur>'h

rhich simulates a 10 per

w e shown in Table 9 and

’Cr$2.8 million from the previous iv intermodal shift from rail to truck Rivals at Estrela, Porto Alegre and Rio Grande.p The subsequent iterations 8, 9 and 10

(increase rail rates by 20, 30 and 38 percent) produce a result similar to iteration 7. The railroad loses its competitiveness in the market for transporting grain in the state if a full-user-charge policy is followed. By increasing the rail rates 38 percent, the railroad would transport only 112.2 thousand tons of wheat to the three terminal points considered in the analysis. This is a reduction of 59 percent in the tons of wheat transported from iteration 7 (275-0 thousand tons).

On the other hand, the truck rates should also in­ crease by 17 percent if the same policy as that for rail shipping is adopted. This is accomplished in iteration 11.

The truck rates are increased by 20 percent (3 percent 93

Abouchar found that the government’s transport pricing

policy has subsidized shippers and passenger travel by

common carrier. On the average, the transportation costs

for products shipped by trucks would increase 17 percent

and the railroads rates would increase 30-35 percent if

a full-cost user-charge policy were adopted (1).

The results of iteration 7 which simulates a 10 per­

cent increase in the rail rates are shown in Table 9 and

10. First, total costs increase Cr$2.8 million from the previous iteration and second an intermodal shift from rail to truck occurs for arrivals at Estrela, Porto Alegre and Rio Grande^ The subsequent iterations 8, 9 and 10

(increase rail rates by 20, 30 and 38 percent) produce a result similar to iteration 7. The railroad loses its competitiveness in the market for transporting grain in the state if a full-user-charge policy is followed. By increasing the rail rates 38 percent, the railroad would transport only 112.2 thousand tons of wheat to the three terminal points considered in the analysis. This is a reduction of 59 percent in the tons of wheat transported from iteration 7 (275.0 thousand tons).

On the other hand, the truck rates should also in­ crease by 17 percent if the same policy as that for rail shipping is adopted. This is accomplished in iteration 11.

The truck rates are increased by 20 percent (3 percent 94

over the Abouchar findings) because the rail rates were

also increased by 3 percent over the 35 percent estimated.

Truck shipments predominate for Estrela (89 percent)

and Porto Alegre (62 percent) while to Rio Grande, 60 per­

cent of the wheat shipments are made by rail. The increase

in total cost was Cr$13.5 million for an increase of 9.5 percent over the basic model of iteration 1. In other

words, if a full cost pricing policy is observed, on the

average, the transfer rates (mix of rail and truck) would

increase by almost 10 percent in the present transfer

network model for wheat in the State of Rio Grande do Sul.

The Soybean/Soymeal Model

The analysis for the soybean/soymeal network model

is similar to the wheat network model. First, the results

of the soybean model are presented and secondly the soymeal

transfer activities are added to the soybean network model.

A demand curve for the Estrele port is derived and the

own price elasticity of demand of the port and cross elas­

ticities between waterway, truck and rail are calculated

using the soybean/soymeal model.

The amount of soybeans that entered the network model

is almost five times the quantity of wheat that entered.

Including the soymeal in the model, the quantity of prod­ uct flow is even larger. 95

The quantity of soybeans entering the system, export

and industry demands for each period are presented in

Table 11. Periods 2 and 3 are the peak periods for the

processing industry and exports. Prom a production of

4,828 million tons of soybeans, 2,615 million tons are

processed and 2,226 million tons are exported during the

period of March 1, 1976 to February 28, 1977. .The main

terminal points for soybeans are Rio Grande, Porto Alegre

and Estrela in decreasing order of importance.

Twelve simulations are performed in the soybean

model. The total OKA cost of the basic soybean network

is Cr$112.7 million (iteration 1, Tables 12 and 13). The

basic iteration includes handling costs for storage fa­

cilities, storage costs, and all line haul transport

charges to industry, intermediate points and export ports.

Truck transportation, the only mode for shipping

soybeans to Estrela, shipped 183-5 thousand tons to this

point. All the beans were shipped to local processing plants.

The Porto Alegre terminal received 1,552.2 thousand

tons; 95 percent was shipped by truck and the remaining

five percent was' shipped by rail. Of this total, 408.7

thousand tons were exported and 1,143-5 thousand tons were for the local processing plants. No shipments by waterways were made to Porto Alegre. The Rio Grande TaLle 11. Soybeans Flows, Exports and. Industry Demands for Each l'eriod, Rio Grande do Sul, brazil, March 1976 to February 1977.

Periods Flov o f ______Exports______Industry Storage Soybeans P. Alegre Rio Grande

Metric Tons

1 1,o 6c3,9(w 3/ 7,878 51,361 281* ,373 1,321* ,857

2 2,397,068 ll* 3,163 792,709 l»95,173 2,790,875

3 337,99J 92,138 525,332 522,1*22 1 ,9 8 8 ,971*

1* ll*l*,85l* 165,066 M 3 , 8iy 1,313,223 206,702 b/

'otal 5 ,ol-8 ,.‘382 l*0o,270 1,818,721 2,615,191 6 ,31 1 ,1*06

,A220J«35 tons of soybeans entered in period 1 in the model as beginning inventory

l'’2 0 6 ,7*12 tons of soybeans are left in storage for the next period

vo O v 97

Table 12. Basic Solution of 1976 Soybeans Network Model and Simulation of Improvements in Waterways, Railroads and Storage Capacities Rio Grande do Sul, Brazil, 1976

Simulation Iteration Total Changes Percent Cost in Cost Changes in cost

Millions Cr.$— Basic Solution 1 112.7

Estrela Port Pa- 2 112.7 0.0 .cilities

Railroad to Es- 3 108.3 -k.k -3.9 trela

Railroad to P. 4 105.7 - 2.6 -2.U Alegre

Variable Cost— 5 10U.8 0.9 - 0.1 Estrela

No Storage con- 6 99. ^ 5.H -5.1 straints

Increase Rail Cap- 7 98.1 1.3 -1.3 acity

Total Savings 14.6 96

Table 12 (continued)

Simulation Iteration Total Changes Percent Cost in Cost Changes in Cost

Millions Cr$ (%) Increase in Rail Rates by

10 percent 8 117.3 19.2 19.5

20 percent 9 131.9 lb. 6 12. k

30 percent 10 140.4 3.5 6.4

38 percent 11 142.7 2.3 1.6

Total Increase in Cost 44.6

Increase in Truck Rates by

20 percent 12 197-5 54.8 36.4 Table 13. Plows of .'ioybeuus Through Alternative Modes of Transportation to Estrela, Porto Alegre and Rio Grande in the basic IJoyLean Model ana Giinululed Models,rRlo Grande do Gul, Brazil, 1976

Location Iteration______Mode of Transportation rti ± 'ruck Rail Water Total

Tons Percent Tons Percent Tons Percent Tons

Estrela 1 103.1; 100 xi .A. N.A. - 183.5 P. Alegre 1 1,1(71.6 55 80.6 5 0 0 1,552.2 Ric Grande 1 1(00.2 21 l,l»7l*. 3 79 0 0 1,871*. 5 A J A I OJ IA Estrela 2 183-5 100 N.A. II .A. — 183.5 P. Alegre 2 1,1*71.5 95 80.6 5 0 0 1,552.2 Rio Grande 2 1(00.2 21 1,1*71*. 3 79 0 0 1,871*. 5

Estrela 3 5-5 3 178.0 97 ;J.A. - 183.5 P . Alegre 3 1,1*71.6 95 80.6 5 0 0 1,552.2 Rio Grande 3 1(69.1* 25 1 ,1*05.1 75 0 0 1,871*.5

Estrela I* 75.7 1*1 107.8 59 ii.A. — 183.5 P. Alegre 1* 1,237.1* 80 3ll» .8 20 0 0 1,552.2 Rio Grande k 1*61.0 26 1,393.5 71* 0 0 1,871*.5

Estrela 5 262.0 66 136.6 3li ii.A. - 398.6 P. Alegre 5 1,202.2 81 289.9 19 0 0 1,552.1 Rio Grande 5 296.6 15 1,389.5 7J* 215.1* 11 1 ,87!*.! Table 13 (continued)

Location Iteration Mode of Transportation______Truck Rail Water Total

Tons Percent Tons Percent Tons Percent Tons

Estrela 6 220.9 67 107.7 33 ii.A. — 318.6 F*. Alegre 6 1,215.6 70 336.6 22 0 0 1,552.2 Rio Grande 6 3l»1.3 18 1,308.5 71* ll*l* .9 8 1,87*1.7

Estrela 7 128.3 5** 107.7 1*6 ii.A. — 236.0 P. Aleere 7 1,216.7 78 335.5 22 0 0 1,552.2 Rio Grande 7 2 8 2 .1* 18 1,539-9 82 52.1* 3 1,871*. 7

Estrela 8 75.9 1*1 107.7 59 ii.A. . — 183.6 P . Alegre 8 1,286.2 32 266.1 18 0 0 1,552.3 Rio Grande 8 1*85.2 25 1,369.5 75 0 ; 0 1,871*. 7

Estrela 9 75.9 1*1 107.7 59 ii.A. : — 183.6 P. Alegre 9 1,1*38.6 92 113.7 8 0 0 1,552.3 Rio Grande 9 902.1 1*8 973.2 52 0 0 1,871*.7

Estrela 10 75-9 1*1 107.7 59 ii.A. — 183.6 P. Alegre 10 1,1*90.0 96 6 2 .1* 1. 0 0 1,552.1* Rio Grande 10 1 ,569.7 81* 305.0 16 0 0 l,87l*.7

Es trela 11 75.9 1*1 107.7 59 ii.A. — 183.6 P. Alegre 11 1 ,1*90.0 96 6 2 .1* 1* 0 0 1,552.1* Rio Grande 11 1,833.0 98 1*1.8 O 0 0 1 ,87!*-. 8 100 Table 13 (continued)

Location Iteration Mode of Transportation Truck Rail Water Total

Tons Percent Tons Percent Tons Percent Tons

iJstrcla .12 110. H 51 107.7 1*9 N.A, 210.1 P. Alegre IP 1,1*36.6 93 113.7 7 0 0 1,552.3 Rio Grande 12 756.2 1*0 1,08H. 0 58 3l*.5 2 1,071*. 7

N.A. is not applicable. 10 2 export port received 1,874.5 thousand tons of soybeans.

The modal split for soybean shipments to Rio Grande is

400.2 thousand tons by truck (21 percent) and 1,474.3 thousand tons by rail (79 percent). Rail shipments are more important for longer hauls in this basic model, which is also similar to the result obtained in the wheat network model.

The most costly transportation bottleneck in the basic network model was the railroad capacity for shipments to the Rio Grande port. A negative CBAR value of -8 and -11 was obtained for period 2 and 3. Another negative CBAR value of -5 was obtained for the handling capacity of grain at the Porto Alegre port in period 2.

Several regions have negative CBAR values for storage capacity. Table 14 presents a partial listing of these bottlenecks, respective CBAR values and actual storage capacity. Twenty-one regions presented storage capacity constraints which demonstrate the importance of these ser­ vices in the transfer network of soybeans. The two regions that had storage capacity constraints in the wheat model

(region 29 and 30) also have storage capacity constraints in this model. The most significant storage bottlenecks in the soybean model are in regions 51 and 52. An increase in storage capacity in these two regions would result in more savings to the total OKA cost than any other regions

/ Table lU. Partial Listings of the Storage Arcs in the 1976 Basic Network Solution of the Soybeans Model, Bio Grande do Sul, Brazil, 1976

Location Facility Arc Cij CBAR Uij Xij (Periods) (Arc Cost) (Upper Limit) (Flow)

Cr$/ton — Thousands of Tons—

Region 22 Storage 2-3 22 -18 1*8.2 1*8.2 Region 23 -torage 2-3 22 -13 1*5.0 1*5.0 Region 27 Storage 2-3 22 -13 75.5 75.5 Region 28 Storage 2-3 22 -5 25.2 25.2 Region 29 Storage 1-2 22 -8 5.*l 5.1* Region 29 Storage 2-3 22 -2l* 5.b Region 30 Storage 1-2 22 -5 1.8 1.8 Region 30 Storage 2-3 22 -22 1.8 1.8 Region 31 Storage 2-3 22 -3 3.1 3.1 Region 32 Storage 2-3 22 -2k 10.5 10.5 Region 35 Storage 2-3 22 -13 55-1* 55.^ Region 36 Storage 2-3 22 -3 3l».5 3l*.5 Region 36 Storage 3-h 22 -3 3k.5 31*. 5 Region 37 Storage 2-3 22 -12 2 6.6 26.6 Region 39 Storage 2-3 22 -12 88.8 88.8 Region 1*0 Storage 2-3 22 -13 9*».3 9k.3 Region 1*1 Storage 2-3 22 -13 56.3 56.3 Region 1*1* Storage 2-3 22 -13 32.9 32.9 Region 1*8 Storage 2-3 22 <-3 60.6 60.6 Region 51 Storage 2-3 22 -13 ll*1.2 ll*1.2 103 Table ll* (continued)

Location Facility Arc Cij CBAR Uij Xij (Periods) (Arc Cost) (Upper Limit) (Flow)

Cr$/ton -- Thousands of Tons--

Region 51 storage 3-1* 22 -23 lUl.2 11*1.2 Region 52 storage 2-3 22 -13 155.9 155.9 Region 53 Storage 2-3 22 -13 38.1* 38.1* Region 53 Storage 3-U 22 -9 38.1* 38.1* Region 5^» Storage 2-3 22 -12 66.9 66.9 Region 51* Storage 3-1* 22 -6 66.9 66.9 t? 01 t? 105 because these regions have the highest negative CBAR values.

The savings that would be obtained by a unit increase in storage capacity at region 51 would be Cr$ (13 + 23) = 36.

The CBAR values are additive since a one unit increase in storage capacity would also be an increase in storage ca­ pacity in the following period. A similar interpretation follows for the other regions with-negativ. CBAR values.

An increase in storage capacity for the soybean model would be beneficial to the wheat model because both regions use the same storage facilities. Thus, the negative CBAR val­ ues for storage capacity in the wheat model for region 29

(CBAR = -30) and region 30 (CBAR = -123) may be added to the CBAR values obtained in region 29 for two periods

(CBAR’s = -8 and -24) and also in region 30 for two periods

(CBAR's = -5 and -22). The total savings for both models would be Cr$62 in region 29 and Cr$l45 in region 30 if the storage capacity is increased by one unit in each region.

The simulated increase in storage capacity is presented in iteration 6.

The results obtained in this basic network solution for the 1976 transfer of soybeans to Porto Alegre and

Rio Grande differ slightly for shipments by-waterways-.-when., compared to the actual flows of 1976. In 1976, shipments through waterways were made to Rio Grande from the old port

(located at the municipio of Taquari in region 22) but no shipments by waterways occurred in the basic minimising 106

cost model. In the basic 1976 network model, regions

such as 38, 39, 40 and 55 that are located north of region 22 (where the Estrela port is located) have a comparative advantage in shipping soybeans to industry

located in regions 20 and 22. The northwest area of the state supplies the port of Rio Grande for the export needs. In this same year (1976), the Wheat and Soybean

Cooperative Federation (FECOTRIGO) organized an export pool for soybeans. Some cooperatives that are located in the northern area of the state (regions 38, 39, 40 and 55) participated in the pool and consequently shipped soybeans to Rio Grande through waterways which is the minimum cost arc for shipping grains to the export port.

Thus, the minimizing cost results in the state differ from actual flows in this respect.

The intermodal split between railroads and trucks for shipments to Porto Alegre and Rio Grande is close to the actual 1976 flows to the ports.

Iteration 2 simulates the new port facilities located in Estrela (region 22). While the transfer charge for grain from truck and railcar to barges at the old port was Cr$12/ton, the new port cost for transferring grain to barges is increased by 116 percent that amount. The operating cost for unloading one ton of grain from truck or rail and loading this amount on a barge is Cr$26. This cost figure is used in iteration 2. The result from this 107

iteration is that no product will flow through the new » port and no changes in the cost occurred. The same flow

pattern as the previous iteration was obtained for ship­

ments to the terminal points of Porto Alegre and Rio

Grande (Table 13).

The third iteration incorporates the new railroad

that links the existing railroad network to the Estrela

port. This new railroad originates at the municipio of

Passo Fundo (region 38) and runs in a north-south direc­

tion. The rate charged for this simulated railroad is

the same as that used for existing lines. The new OKA

total cost is now Cr$108.3 million. A savings of Cr$4.4

million is obtained by introducing this new arc or a 3.9 percent reduction in cost from the previous model.

Some intermodal truck-rail shift occurs at Estrela

and Rio Grande. There is a 178.0 thousand ton transfer

from truck to rail shipments to the Estrela port while at

Rio Grande the railroad loses 69.2 tons to truck shipments.

This modification in the railroad network to the Estrela port still leaves the railroad capacity to Rio Grande with negative CBAR values of -8 for period 1 and 2. The Porto

Alegre port capacity also has a CBAR value of -2 for period 2.

As the new railroad is linked to the Estrela port, it also becomes possible to transfer grain to Porto Alegre 108 on the railroad that originates at Passo Fundo and passes through Estrela. As was described in Chapter II, this new railroad arc will decrease the distance .by railroad between Passo Fundo and Porto Alegre by 360 km. Itera­ tion 4 adds to the network model the railroad from Estrela to Porto Alegre and the total OKA cost is reduced to

Cr$105*7 million, an additional savings of Cr$2.6 million or a 2.4 percent reduction in cost from the previous iter­ ation. The result is almost a three-fold increase in rail shipments to Porto Alegre; from 80.6 thousand tons to

314.8 thousand tons or 290 percent increase. The new railroad arc also affected the shipments to Estrela and

Rio Grande. At both points an intermodal shift from rail to truck occurs. Shipments by truck to Estrela increase by 70.2 thousand tons and rail shipments decrease by the same amount. At Rio Grande there is a transfer of 11.6 thousand tons from rail to trucks. As the new railroad to Porto Alegre is included in the model, the Porto Alegre terminal capacity for grain was increased by 50 thousand tons in each period representing the additional capacity that will be added by the new railroad. The major bottle­ neck in the transfer arcs is still the railroad shipment capacity to Rio Grande with a CBAR of -8 for periods 1 and 2. 109

Of the 21 regions with storage capacity constraints,

18 still presented negative CBAR values (some with dif­

ferent values) while one additional region (55) was added

as a storage bottleneck after the simulated changes in

railroad lines.

In the simulation of the Estrela port, iteration 3S

the waterway arcs did not enter the optimal solution.

In that simulation, the cost charged for transferring

grain from truck or rail to barges was Cr$26/ton. This

cost reflects the full cost of the port services; 81 per­

cent (Cr$21) represents the fixed cost and the remaining

19 percent (Cr$5) is the variable cost. Another simula­

tion is performed with the soybean model by charging

only the variable cost for the Estrela port services which

is similar to that done for the wheat model. The results

are shown in iteration 5 (Tables 12 and 13). The reduction

in total cost is Cr$0.9 million from the previous iteration.

This is a small reduction in cost (less than 0.1 percent) but a substantial intermodal shift among alternative modes of transportation occurs. Shipments to Estrela increase by 215.4 thousand tons. Prom a total of 398.6 thousand tons, 66 percent was shipped by truck and 34 percent was shipped by rail to Estrela. Prom the Estrela port, 215.4 thousand tons were shipped by waterways to Rio Grande.

Truck shipments increase by 24.8 thousand tons and rail 110 shipments decrease the same amount at the Porto Alegre terminal. Truck and rail shipments decrease at the port of Rio Grande but the largest decrease was in truck transportation. Thus, a reduction in the cost of ser­ vices at the Estrela port has a more significant effect on truck transportation. This fact demonstrates that the waterway system may be more competitive with road transportation than rail transportation. In other words, the cross elasticity between road and water transporta­ tion is higher than between rail and water shipments.

In the first five iterations of the soybean model no soybean shipments by waterway were made between Estrela and Porto Alegre. It is cheaper to transfer soybeans from Estrela to Porto Alegre by truck or rail because of the required additional handling charges by waterways.

Another economic reason for this result is that the truck drivers are willing to deliver the product to Porto Alegre for an additional Cr$20 per ton that is less than the waterway rate of Cr$40 per ton between Estrela and Porto

Alegre. This result is similar to that observed in the wheat model presented earlier.

The largest reduction in the OKA cost occurs in iteration 6 which simulates an increase in storage capacity at all regions that presented negative CJBAR values in iteration 5. The savings that result from an increase in Ill

storage capacity is Cr$5.4 million which is a 5.1 percent

reduction in cost from the previous simulated model

(Table 15). Region 51 followed by region 53 are those

which required the largest increase in storage capacity.

A total 515.582 tons of additional storage capacity is

required to minimize the total OKA cost. Some regions

that presented negative CBAR values in iteration 5 did not require additional storage capacity when the storage

capacity of all the regions is increased simultaneously.

Thus, changes in storage constraints in one region affect product flow in other regions.

The increase in storage capacity also has a sizeable effect on the shipments by modes to Estrela, Porto Alegre and Rio Grande. First, it reduces the amount of soybeans shipped to Estrela by truck and by rail because of the

32 percent reduction (70.2 thousand tons) of soybean ship­ ments to Rio Grande by waterways. Secondly, an intermodal shift from truck to rail for arrivals in Porto Alegre occurs. Finally, there is some increase in truck shipments to Rio Grande and an insignificant effect on rail shipments

(iteration 6, Table 13) to that port. The only negative

CBAR remaining in the output of iteration 6 was the rail shipment capacity to Rio Grande (CBAR = -8 for period 2 and 3). 112

TABLE 15: Storage Capacity, CBAR Value and Additional Storage Capacity Required at Several Regions in Rio Grande do Sul, Brazil, 1976

Iteration 5 Iteration 6 n^ ™ Arc Actual Region Period Storage CBAR Product Additional ______Capacity Value Stored Storage

Tons Cr$ Tons Tons

22 2-3 48,296 -14 53,143 4,847 23 2-3 45,042 -10 81,717 63,675 27 2-3 75,545 -14 -- 28 2-3 25,235 - 06 29 2-3 5,460 -28 43,035 37,575 30 2-3 3,137 -6 7,417 5,555 c 31 2-3 3,137 —o 32 2-3 10,500 -28 35 2-3 55,440 -14 86,991 31,551 36 2-3 34,580 -9 43,273 3,693 37 2-3 26,607 -9 58,373 31,766 &0 2-3 94,395 -14 -- 41 2-3 56,369 -14 - - 44 2-3 32,993 _Q 65,859 32,926 51 2-3 141,230 -14 312,824 - 51 "3-4' 141,230 -27 329,926 188,696 52 2-3 155,981 -14 - - 53 ' 2-3 38,477 -9 118,087 - 53 3-4 38,477 -21 118,207 79,310 5.4 2-3 62,954 -8 66,058 3,!°4 54 3-4 62,954 -0 55 2-3 57,330 -8 111,714 54,384

Total Additional Storage 515,582 113

The effect of an increase in rail capacity for ship­ ments to Rio Grande is performed in iteration 7. An

additional savings of Cr$1.3 million is obtained by in­

creasing the rail capacity to the Rio Grande export port.

The waterway mode Estrela-Rio Grande loses 92.5 thousand tons to railroad transportation while the railroad has the highest share (82 percent) for shipments to Rio Grande in this simulated model. It is evident from this result that railroads have an advantage over the alternate modes of transportation for shipments in longer line hauls.

The total savings that was obtained by simulating all the improvements and changes in the basic soybean network model was Cr$l4.6 million or a reduction of 12.8 percent from the cost of the 1976 basic soybean model.

In the next five iterations of the soybean model, simulations of changes in the transfer cost are analyzed.

As was pointed out in the previous section of the wheat model, the minimum cost allocation is affected by the transfer cost that is used in the model. A full-cost transfer policy is also performed with this model to com­ pare the results with the market rates used in the pre­ vious simulations.

The railroad rates are increased by 38 percent in increments of 10 percent in order to follow the effects until a full-cost pricing policy is obtained. After 114

completing the 38 percent increase in rail rates, a

20 percent increase in the truck rates is also included

in the model. The results of these simulations are pre­

sented in Tables 12 and 13 (iterations 8 to 12).

Iteration 8 simulates a 10 percent increase in

rail rates. The total OKA cost is now Cr$117*3 million which is an increase of 19.5 percent from the previous

iteration (Table 12). This increase in the rates caused

an intermodal shift from waterways and rail to truck

transport. The most significant effect is that, the

changes in rail rates eliminate the waterway arc Estrela-

Rio Grande from the optimal solution (Table *13, iteration

8). The elimination of the waterway arc occurs because

the transfer arc rail-water combination is more expensive with the rail rate increase. The simulations of rail rate increases in iteration 9 (20 percent), 10 (30 per­

cent) and 11 (38 percent) have similar effects. The rail­ road loses shipments to the ports of Porto Alegre and

Rio Grande. The rail rate increase between 20-38 percent did not affect shipments to Estrela. The new railroad line has an advantage over truck for shipping to that point but the combination rail-waterway to Rio Grande did not enter the optimal solution.

The overall increase in the total OKA model is

Cr$44.6 million or a 39.9 percent increase in the total 115 transfer-storage cost of soybeans in the period analyzed.

If the railroad corporation were to follow the full-cost charge of a 38 percent increase it would transport only

107.7 thousand tons to Porto Alegre and 41.8 thousand tons to Rio Grande do Sul (Table 13, iteration 11).

The final iteration with the soybean model is to increase the truck rates by 20 percent (Table 12 and 13, iteration 12). The increase in the OKA cost is Cr$54.8 million or a 38.4 percent increase from iteration 11.

This iteration represents full-cost pricing for the

Estrela port facilities, railroad and highway transpor­ tation. The increase in the OKA cost compared to the basic model (iteration 1) is Cr$84.8 million or a 75.2 percent increase in the transfer storage cost.

The split among alternative modes of transportation is the following: to Estrela, 51 percent by truck and

49 percent by rail; to Porto Alegre, 93 percent by truck and 7 percent by rail and; to Rio Grande, 40 percent by truck, 58 percent by rail and 2 percent by waterway. The use of full-cost pricing makes the waterway more compe­ titive. Also, by charging the economic cost a more ef­ ficient market allocation of the transport-storage re­ source is obtained.

The increase in the transfer-storage cost by using the full-cost pricing will be transferred to farmers or 116 consumers depending on the price elasticity of demand and supply for soybeans. Considering Brazil as a price taker for soybeans in the international markets, the in­ crease in transportation cost would be transferred to farmers in the form of lower prices for their product.

On the other hand, those regions that have a reduction in transfer cost would receive the benefit of higher prices for their product.

Derivation of the Demand Curve and Elasticities For the Estrela Port

The results obtained in the wheat and soybean model demonstrate that the new Estrela port facility did not enter the optimal solution in either network model if the current market rate for rail and trucks and a full- cost pricing of the Estrela port services is considered.

However, when full-cost pricing is applied to rail and truck transportation, the Estrela port entered the optimal solution for shipping soybeans to Rio Grande by waterways

(Table 13* iteration 12).

Considering the investment in the port as a "sunk cost" and charging only the variable cost of services, the waterway is used for shipping soybeans to Rio Grande

(Table 13, iteration 5).

To derive the demand curve for the Estrela port, the soymeal transfer activity is added to the soybean model 117

and the results are presented in Table 16. The Estrela port is equipped to handle soymeal in the same way that

other products are transferred to barges. Soybean pro­

cessing plants located at region 22 and north of the port may ship soymeal by waterway to Rio Grande for export.

Seven iterations were performed starting with the

full cost of Cr$26 and decreasing it to Cr$3 which is below the average variable cost. As the cost of the ser­

vice is lowered from Cr$26 to Cr$15, product starts to

flow through the port by waterways to Rio Grande. By

charging only the variable cost (Cr$5) to transfer one ton of grain from truck or rail to barge almost 500 thou­

sand tons flow through the port. If the objective of the port were to maxmize revenue it would charge a rate between Cr$12 and Cr$9 where the price elasticity of de­ mand equals one. In this situation, the port would re­ ceive the variable cost (Cr$5) plus some return (Cr$5 to Cr$7) for the fixed investment.

Changes in the cost of the port services have the same effect as equal changes in the transportation rates of the waterways to Porto Alegre and Rio Grande. If the variation is in the port service rates, the port owner may increase or decrease revenue and affect the barge owners in the same direction depending on the demand elas­ ticity of both services. Thus, either the port owner or 118

TABLE 16: Plow of Product Through the Estrela Port at Different Costs of Service, Rio Grande do Sul, Brazil, 1976

Service Total Demand Iteration Plow Savings Cost Cost Elasticity

Cr$/ton Tons Million Million Cr$ Cr$

26 1 0 686.8 - -

15 2 127,884 685.6 -1.2 1.85

12 3 238,893 685.O -0.6 1.13

9 4 384,497 684.2 -0.8 0.44

7 5 440,297 683.4 -0.8 0.29

f / 6 496,602 682.6 -0.8 0.13

3 7 546,707 681.4 -1.2

Total Savings -5.4

q / — Cr$ 5 is the average variable cost of service of the Estrela port. 119 barge owner is affected if one of them changes its rate

for the service. The barge owners would also maximize their revenue if the Estrela port charged a price where the demand elasticity is unity.

The Cross Elasticities

The cross elasticities between truck/water and rail/water are estimated by using the soybean/soymeal model. In interpreting the results of the cross elasti­ cities it is important to note that all regions in the state have truck transportation but not all regions have rail transportation. The procedure to obtain the cross elasticities was to lower the waterway rate and observe the amount of product which shifts to waterways from truck and rail transportation.

As the service rate of the Estrela port decreased, the flow of product increased (Table 16). The lowering of the waterway transport rate has the same effect on the flow through this mode of transportation.

By lowering the rate of the Estrela port from Cr$26 to Cr$15, product that was shipped by truck and rail to the Rio Grande port now goes by waterway. A change in the Estrela port rate has a more significant effect on truck transportation according to the results of the cross elasticities (Table 17). 120

TABLE 17: Cross Elasticities Among Alternative Modes of Transportation for Soybeans and Soymeal for Shipments to the Rio Grande Export Port, Rio Grande do Sul, Brazil, 1976

Estrela Port Costs Truck/Water Rail/Water Cr$/Ton Elasticities Elasticities

26 - -

15 0.03 0.02

12 0.63 0.37

9 0.20 0.11

7 0.10 0.05 1 2 1

As the rate decreases from Cr$26 go C r$ 1 5 i the cross

elasticity between truck/water is 0.03 and rail/water is

0.02. With a further decrease of the rate charged by the Estrela port to Cr$12, the cross elasticity increases to 0.63 for truck/water and to 0.37 for rail/water. This means that the waterway becomes more competitive with the

alternate modes of transportation. The truck/water cross

elasticity (0.63) is almost the double of the rail/water cross elasticity (0.37) showing that a larger shift from truck shipment to waterway occurs as the service port charge is decreased.

A final simulation increases the truck and rail rates by 20 and 38 percent respectively in the soybean/soymeal model. Also, a full cost of Cr$26 is charged for the

Estrela port service.

The results demonstrate that.the waterway Estrela-

Rio Grande becomes a basic variable in the model is the full-cost price is charged for rail, truck and port ser­ vices. The share by the modes of transportation is shown in Table 18.

Shipments to Estrela are divided between truck (56 percent) and rail (44 percent). Prom the Estrela port

230.1 thousand tons are shipped to Rio Grande by water­ ways and the remainder is shipped to local industry.

Truck shipments to Porto Alegre represent 87 percent of 122

TABLE 18: Shipments of Soybean and Soymeal to the Estrela, Porto Alegre and Rio Grande Port, Brazil, 1976

Location Truck Rail Water Total

Tons Percent Tons Percent Tons Percent

Estrela 220.8 56 172.8 44 N.A. - 393.6

Porto Alegre 1,095.0 87 164.9 13 0 0

Rio Grande 1,573.7 47 1,351.8 40 419.1 13 123

the total while 13 percent was shipped by rail. At the port of Rio Grande, 47 percent of the soybean/soymeal shipments are made by truck, 40 percent by rail and 13 percent by water. It is observed that by including the soymeal transfer in the model, truck shipments increase to Rio Grande. Consequently, truck shipments have a larger share of soymeal shipments than soybeans alone

(Table 13, iteration 12).

Iteration 1 (Table 16) represents the soybean/ soymeal model simulation where the full cost of the port service and the market rates for truck -and rail are used.

The total OKA cost of that simulation is Cr$686.8 million.

Comparing that cost with the OKA cost of 'Cr$802.8 million for the simulation where the full cost pricing policy is also used for rail and truck transportation, an increase of Cr$ll6 million (17 percent) is obtained. In other words, if the full cost for the port and truck and rail rates are used, the total storage-transfer OKA cost of the soybean/soymeal model increases by 17 percent. 1

CHAPTER VI

SUMMARY, CONCLUSIONS AND IMPLICATIONS

Introduction

The expansion of wheat and soybean production in the

State of Rio Grande do Sul in the last few years has de­ manded a large increase in storage and transportation services. At the same time, increases in the processing capacity of soybean plants require larger quantities of soybeans for processing and storage throughout the year.

The double-cropping system of wheat-soybeans further re­ quires that as the harvest of one product begins the other product must be removed from storage leaving space for the next crop.

Exports of soybeans and its subproducts soypieal and soyoil also have increased. The soybean price increases in the international markets produced record soybean ex­ ports by the State of Rio Grande do Sul in 1976. This fact created an increase in the demand for transport ser­ vices from the production points to the export points.

Because of this increase in soybean and wheat production,

124 125 the government and other firms (such as cooperatives) invested in the construction of new storage facilities, railroads, roads and waterways for transferring grains to the export ports of Porto Alegre and Rio Grande.

The major objective of this investigation was to analyze and evaluate alternative transfer-storage networks and their performance in relation to current governmental policies toward alternative modes of transportation in the

State of Rio Grande do Sul.

A capacitated network model using the Out-of-Kilter algorithm was used for accomplishing the objectives of this investigation. The model minimizes the transport- storage costs subject to constraints that were set for the model. The model was divided into four time periods to identify seasonality factors that occurred in the transfer-storage of soybeans, soymeal and wheat.

A total of 11 iterations were performed with the wheat model while 12 iterations with the soybean model were performed. The soymeal transfer activity was included in the soybean model and a demand curve for the Estrela port was derived.

These iterations were:

1. Basic solution

2. Introduction of Estrela port and waterway

3. Introduction of a railroad line Passo- Fundo-Estrela 126

4. Introduction of a railroad line Estrela-Porto Alegre

5. Variable cost pricing at Estrela Dort

6. New storage facilities in production areas

7. Rail rates increase by 10 percent

8. Rail rates increase by 20 percent

9. Rail rates increase by 30 percent

10. Rail rates increase by 38 percent

11. Truck rates increase by 20 percent

Summary of the Wheat Model

The basic transfer-storage model had a OKA cost of

CR$l4l.O million. This cost represents the transfer and storage of 1,430 thousand tons of wheat that entered the wheat network model. After the inclusion of the new waterway system and the new railroad to Estrela and to the Porto Alegre port, the total OKA cost was reduced to

CR$l40.6 million or a reduction of CR$0.4 million (0.2 percent). No waterway shipments of wheat were made even when the variable cost of the Estrela port services was charged.

Two regions presented storage constraints (29 and 30 representing the Nova Prata and Caxias do Sul regions) and by increasing the storage capacity at these two locations the total OKA cost was reduced by an additional CR$0.2 127 million. After all the changes were introduced in the model the total OKA cost was reduced only CR$0.6 million which represents a 0.4 percent reduction in cost from the basic model.

An increase in the rail rates by 38 percent and the truck rates by 20 percent, increased the total transfer-

storage network cost to CR$154.5 million which is almost a 10 percent increase from the cost of the.basic model.

This rate increase for rail and truck represents the full- cost price policy for these modes of transportation.

The waterway arc Estrela-Rio Grande did not enter the optimal solution of the wheat model even when the full cost of transportation is charged for truck and rail trans­ portation.

Summary of the Soybean/Soymeal Model

A total of 5,049 million tons of soybeans and 1,961 million tons of soymeal were considered in the transfer- storage network model. The total OKA cost of the basic transfer-storage network of the soybean model was CR$112.7 million. ,The introduction of the new transfer arcs (rail­ road and waterways) did affect the transfer cost of soy­ beans in the State of Rio Grande do Sul. The new Estrela port including the railroad line Passo Fundo-Estrela re­ duced the total OKA cost by CR$4.4 million, representing a 3.9 percent decrease in cost. However, no waterway 128

shipments through the arc Estrela-Porto Alegre-Rio Grande

occurred. The additional railroad arc Estrela-Porto Alegre

caused another CR$2.6 million decrease in the total cost.

The simulated waterway arc for shipments from Estrela to Rio Grande entered the optimal solution when the trans­

fer charges of the Estrela port were decreased from Cr$26 to Cr$5 per ton. The charge of Cr$5 per ton is the vari­ able cost of transferring one ton of grain from truck or rail to barge.

Twenty one regions presented storage constraints and the most significant occurred in the arc period 2-3 (May/

June-July/August). The reduction in the total cost from the previous model (iteration 5, Table 12) was Cr$5.4 mil­ lion when the storage constraints are removed from the model. An additional 515,582 tons of storage capacity is necessary in order to eliminate all the storage con­ straints in the model (Table 15). By removing the rail capacity constraint to Rio Grande another Cr$1.3 million decrease in the total OKA cost resulted. Summing up all the changes made in the basic model, the total cost reduc­ tion of the soybean model was Cr$l4.6 million or 12.8 per­ cent less than the basic model.

The full-cost pricing policy for truck and rail transportation made the waterways more competitive with the alternative modes of transportation considered in this investigation. By increasing the rail rates 38 percent 129 and the truck rates 20 percent, the waterway arc Estrela-

Rio Grande entered in the optimal solution. The total cost increase was Cr$84.8 million more than the basic transfer-storage network model or a 65.5 percent increase.

To derive the demand curve of the Estrela port, the soymeal transfer activities were included in the soybean model. As the Estrela port transfer charges for grain to barges decrease, the flow of product through the port increases. The port will maximize its revenue (the same is valid for the barge owners) if the port charges a rate between Cr$12 and Cr$9 per/ton for transferring grain to barges where the own demand elasticity varies between 1.13 and 0.44.

The cross elasticities between truck/water were higher than the cross elasticities between rail/water. Consequently, the waterways are more competitive with truck transportation.

The modal shares of soybean and soymeal to the Estrela port were 56 percent by truck and 44 percent by rail, to

Porto Alegre, 87 by truck and 13 percent by rail and to

Rio Grande, 47 percent by truck, and 40 percent by rail and the remaining 13 percent by waterways. 130

Conclusions »

The most significant conclusion derived from this investigation is that introduction of the new waterway/ highway/railroad complex has a small effect on the trans­ portation cost of wheat and soybeans in the State of Rio

Grande do Sul. A reduction of Cr$0.3 million was obtained when the new railroad line, Passo Pundo-Estrela, was added to the basic 1976‘wheat network model. An additional

Cr$0.1 million reduction in the total transfer-storage cost was obtained by introduction of the new railroad line,

Estrela-Porto Alegre. These two new railroad lines will reduce the total transfer-storage cost of wheat by Cr$0.4 million, only a 0.3 percent cost reduction from the basic model.

The waterway arc Estrela-Porto Alegre-Rio Grande did not have any effect on the transfer of wheat for two rea­ sons. First, only 215 thousand tons of wheat were exported through the port of Rio Grande. Secondly, wheat that is exported through the port of Rio Grande is shipped by truck from the production regions located in the southwest region of the state, while the major changes in the trans­ fer network are made in the north-central area of the state.

In the soybean model, the new railroad line, Passo

Fundo-Estrela, reduced the total transfer-storage cost by 131

Cr$4.4 million which represents a 3.9 percent reduction in the total cost from the basic model. The railroad line Estrela-Porto Alegre caused an additional Cr$2.6 million reduction in cost corresponding to a 2.4 percent reduction, compared to the previous iteration of the soybean model.

The introduction of the waterway arc Estrela-Porto

Alegre-Rio Grande in the basic 1976 soybean transfer- storage network had no effect on the soybean shipments nor on the total costs. The use of waterways for trans- / porting soybeans to the export point of Rio Grande did not reduce the transportation-storage costs. The main reason is that it is still necessary to use truck or rail transportation to the Estrela port, which is located half way between the main production areas and the export point of Rio Grande. The lower waterway rates from Estrela to

Rio Grande are partially offset by the additional handling costs at the Estrela transfer point. The regions that would have a reduction in cost for shipping soybeans and wheat by waterways to Rio Grande, such as Passo Pundo ship their production to the local processing plants and/or to the industries located in the Porto Alegre region in order to minimize the total transfer-storage cost in the state. Thus, the waterway reduced the transfer cost between the production regions located to the North of the Estrela port and Rio Grande. However, if the objective is to 132

minimize the total transfer-storage cost within the state,

no soybeans will be shipped to the port of Rio Grande by

waterways.

Waterway shipments between Estrela and Porto Alegre

are not feasible because the short distance by truck

(105 kilometers) offsets the added handling costs and the

lower water rates. The main reason for this is that the truckers are willing to go to Porto Alegre (instead of

unloading at the Estrela port) for a small additional charge because of the higher possibility of getting a back haul from Porto Alegre to the production regions.

When the transfer charge from truck or rail to barges at the Estrela port was decreased to the variable cost (Cr$5 per ton), the waterways entered the optimal solution and

215.4 thousand tons of soybeans were shipped from Estrela to Rio Grande. This result, indicates that with a vari­ able cost price policy for the Estrela port, the waterways would become competitive with rail and truck transporta­ tion at their current rates. Such a policy would require a subsidy of Cr$21 per ton, which represents the fixed cost of transfer charges at the Estrela port.

The waterways become competitive with the alternative modes of transportation if the subsidy is eliminated from rail and truck transportation. When the rail and truck rates were increased by 38 percent in the model and the 133

grain transfer cost to barges at the Estrela port was

increased to the full cost of Cr$26 per ton. A total

of 34.5 thousand tons of soybeans were shipped by water­ ways from Estrela to Rio Grande in the model.

A demand curve for the Estrela port was derived

from the soybean/soymeal model. In addition the price

elasticity of demand and the rail/water and truck/water

cross elasticities were calculated from the soybean/soy­ meal model. The Estrela port will maximize its revenue

if a rate between Cr$9 and Cr$12 is charged for its trans­

fer services. The cross elasticities indicated that the waterways are more competitive with truck than with rail transportation.

The transfer cost difference between Brazil and the

United States for shipping soybeans from the production points to the export points will not be reduced by the new waterway/highway/railroad complex if the objective is to minimize the total transfer cost in the State of Rio

Grande do Sul. As it was discussed earlier, some regions will have a reduction in the transfer cost to the export point of Rio Grande by shipping the soybeans to the Estrela port by truck or rail and from there by waterways to Rio

Grande. However, the regions that have this transfer cost advantage ship their production to the processing plants located at region 20 and 22 which processed 55 percent of 134

the total soybeans crushed in 1976 in the State of Rio

Grande do Sul.

According to the results of the optimal flow of

soybeans, the regions located at the northwest area of

the state ship soybeans to the export point of Rio Grande.

These regions, such as 14 to 17 and 47 to 51, can only

transport the soybeans by truck or rail to Rio Grande.

The alternative of shipping the soybeans from these re­

gions through the Estrela port to Rio Grande would increase the total transfer-storage cost with the 1976 rates. Thus, the utilization of the waterway from Estrela to Rio Grande

for shipping soybeans will depend on the pricing policy to be followed by the policy makers.

The inclusion of the soymeal transfer in the soybean model showed that neither soybeans nor soymeal was shipped through the Estrela port when the full cost of the transfer services of the port was charged. However, if the transfer cost of the services was lowered to the variable cost (Cr$5 per ton), 496.6 thousand tons of soybeans and soymeal were shipped to Rio Grande by the waterways. Of this total

215.2 thousand tons were soybeans and 281.2 thousand tons were soymeal. Thus, the waterways will be utilized for shipping soymeal to Rio Grande if only the variable cost of Cr$5 per ton is charged for transferring soymeal to barges. 135

Implications for Transportation Policy

The introduction to this research argued that a major

problem in Rio Grande do Sul was the high marketing costs

for wheat and soybeans which reduce Brazil’s competitive

advantage in world markets. Soybean production costs in

Brazil are lower than in the U.S. but the high marketing

costs result in a higher FOB price for soybeans in Rio

Grande do Sul ports than for soybeans FOB Gulf ports in

the U.S.

Several transport alternatives were analyzed in this

investigation. None of these alternatives will contribute

to substantially reduced marketing costs for wheat and

soybeans in Rio Grande do Sul and therefore will do little to lower the FOB price of soybeans at Brazilian ports.

Transfer-storage cost reductions were obtained in the wheat and soybean models only when the transfer cost of the services at the Estrela port was reduced below the full cost of Cr$21 tons. In other words, only by subsidizing the transfer costs at the port can total transport costs be reduced.

Because of the location of the soybean processing plants, the regions that ship soybeans to the export point of Rio Grande are those located in the West and Northwest production areas of the state. A reduction in the transfer costs between these regions and the port of Rio Grande 136 would have a higher impact on the total cost compared to , the waterways analyzed in this investigation. The changes in the transfer network considered in the model (waterways and railroads) are located between the north, central and southeast areas of the state. Therefore, they do not affect directly the exporting regions that are shipping their product to the port of Rio Grande and Porto Alegre.

The possibility of. construction of a new waterway port at the Jacui River in region 18 may have a more sig­ nificant impact on the transfer cost to the Rio Grande port. That new port would be located halfway between the northwest production regions and the export port of Rio

Grande. The products could be shipped by truck or rail to region 18 and by barges from there to Rio Grande.

Railroad and road transport services already link the northwest regions to region 18 and only construction of a port on the Jacui River is required for the use of waterways to ship grains to Rio Grande.

The substitution of truck and rail transportation for waterway will depend mainly on the pricing policy to be used in the government sector. The share of each mode of transportation (waterway, highway and railroad) for transporting soybeans, soymeal and wheat depends on the transfer costs, the availability of alternative modes of transportation and on the product destination. 137

The utilization of the Estrela-Rio Grande waterway

will depend mainly upon the government subsidies toward

railroad and highway transportation. Elimination of the

present subsidy to railroad and highways would make the

waterways more competitive. This policy is most attrac­

tive since it would improve resource allocation in the

economy, however it would increase marketing costs for

wheat and soybeans.

A larger participation of waterway transport would

have several positive effects. The two that appear most

significant are the reduction of oil consumption (Brazil

imports 80 percent of its needs) and the reduction of

trucks on the highways that will reduce the congestion

costs at peak demand periods.

The consumption of fuel oil for transporting grain

from Estrela to Rio Grande is 2.5 liters per ton by water while by highway it is 7.7 liters per ton. This is an

increase of over 200 percent in oil consumption. A simi­

lar distance by railroad would consume liters per ton.

Including the return trip the reduction in oil consumption would be even higher. Future increases in oil prices would make the waterway more competitive and at the same time reduce oil consumption.

By manipulating the subsidies and taxes on the trans­ portation sector, the government can determine the share 138

of each mode of transportation within the capacity con­

straints of each shipping modes.

The storage sector is interrelated with the trans­

portation sector because a new highway or railroad may

affect the required storage capacity at some specific

region. This fact was observed in the soybean model when

the new railroad Passo Pundo-Estrela was introduced in

the model (iteration 4, Table 12).

Construction and expansion of new storage capacity

has to take into consideration new or projected modes of

transportation as well as increases in production.

Implications for Farmers

The introduction of the new waterway/highway/railroad

complex for transporting wheat and soybeans in the State

of Rio Grande do Sul will reduce the transportation cost to the domestic processing plants. There is no effect on the shipping cost of grains to the export point of Rio

Grande. The soybean processing plants located at regions

20 and 22 with 60 percent of the state’s capacity will re­ ceive soybeans from the production regions located in the

North and northwest area of the state according to the flow results of the OKA model.

The new railroad and waterway are located between the production regions of the northern area of the state and 139 the processing plants in regions 20 and 22. These pro­ duction regions will benefit from a reduction in the trans­ fer cost to the domestic processing industry and the ex­ port point of Rio Grande. However, no soybeans are shipped to Rio Grande from these regions because of the cost ad­ vantage for shipping to the processors.

Processors located in regions 20 and 22 have transport cost advantages over those not located in these regions and also in relation to the export market for soybeans.

Because of this advantage, they can pay higher prices for soybeans in the region the transport cost was lowered

(transfer part of the cost reduction to the farmers).

The farmer also can obtain an additional benefit from the new waterway/highway/railroad through the impact that »• the lower transfer cost would have on the shipment of in­ puts such as fertilizer since most of these inputs are produced in region 20.

Implications for the Wheat and Soybean Industry

The new waterway and railroad will affect the soybean industry more than the wheat industry. The new railroad line will transport wheat from the production points to the milling plants located in the Porto Alegre region.

No wheat will be shipped by waterways to Porto Alegre or 140

Rio Grande under any of the conditions analyzed in this

investigation. On the other hand, the regions that are

linked by the new railroad and waterway will have a re­

duction in the transportation cost to the processing plants as well as to the export ports.

The soybean processing plants located in regions 20,

22, 38 and 39 will benefit from the changes on the 1976

network by using the waterways and railroads for shipping

soymeal to Rio Grande. This assumes that more soybeans will be processed in the future and that larger amounts

of soymeal will be exported compared to the soybeans.

Thusy the additional transfer arcs will also affect the

soymeal transfer by the reduction in the transportation costs to the export points.

The processing plants linked by the new railroad and waterways will become more competitive with other plants. They will also have alternative modes of trans­ portation for receiving and shipping product and thus may increase their bargaining power and pay lower transporta­ tion costs.

Future Research and Limitations of the Investigation

Future research in the transfer-storage of wheat, soybeans and soymeal should be done by including the final demand points for flour, soyoil and soymeal. 141

The wheat milling plants and the export points were the final destinations for wheat while the soybean pro­ cessing plants and the export points were the final de­ mands for soybeans. The soymeal transfer activity was added to the soybean model but soyoil was not considered in the investigation. Further research by including the transfer activities of flour and soyoil to the final des­ tinations may change the results of grain shipments to

Estrela, Porto Alegre and to Rio Grande and consequently change the total cost.

The storage cost for all the regions was the same except at the port facilities of Estrela, Porto Alegre and Rio Grande. Additional regional storage cost analysis of alternative storage facilities and cost differential may affect the optimal allocation of grain.

One of the limitations of this investigation is that the storage and transfer costs are independent of the quantities of product stored or transferred. The existence of economies of scale in storage and transport services were not taken into consideration. The storage and trans­ fer costs used in this investigation were for the year 1976.

Thus, the transfer-storage network model minimized the al­ location of grain subject to the 1976 market conditions. 142

An efficient economic allocation of grains should consider the economic cost of the services (transfer and storage costs). The economic -cost for rail, truck and the Estrela port services was analyzed, however, the economic costs of waterway transportation were not avail­ able for Rio Grande do Sul so the 1976 market rates were used. This may have resulted in an underestimate of the cost of waterway transportation in Rio Grande do Sul. ✓

APPENDIX A

Municipios Included in the Study by Region

143 144

Appendix A

Municipios Included in the Study by Region

R e g i o n 1 Region 12 1. Uruguaina 1. Sao Sepe 2 . Capapava R e g i o n 2 3 . Santana da Boa Vista 1. Livramento Region 13 R e g i o n 3 1. Encruzilhada do Sul 1 . Q u a r a i 2 . D. Feliciano 3 . Camaqua R e g i o n 4 4 . Tapes 1 . B ag e Region 14 R e g i o n 5 1 . Itaqui 1 . Dom P e d r i t o 2. Lavras do Sul Region 15 1 . Sao Borja R e g i o n 6 1. J a g u a r a o Region 16 2. Arroio Grande 1. Santiago 3. H e r v a l 2 . Sao Francisco de Assis 3. Jaguari R e g i o n 7 4 . Mata 1. R io G r a n d e 5 . Sao Vicente 2 . Santa V itoria do Palmar 3. Sao Jose do Norte. Region 17 1. Santa Maria R e g i o n 8 2 . Sao Pedro do Sul 1 . P e l o t a s 3. Nova Palma 2. Pedro Osorio 4 . Faxinal 3. C a n g u p u 5 . Dona Francisca .4. Piratini 6. Restinga Seca 5. Pinheiro Machado 7. Formigueiro 6. Sao Lourenpo Region 18 R e g i o n 9 1 . Cachoeira 1 . A l e g r e t e Region 19 R e g i o n 10 1. Rio Pardo 1. C a c e q u i 2 . Arroio dos Ratos 2 . R o s a r i o 3. Sao Jeronimo 4 . Butia R e g i o n 11 1 . Sao Gabriel 145

1. Porto Alegre 6 . Venancio Aires 2 . M o s t a r d a 7. A gudo 3 . T r a m a n d a i 8. General Camara 4 . O s o r i o 5 . T o r r e s R e g i o n 24 6 . Santo Antonio de 1. Julio de Castilhos P a t r u l h a 7. V ia m a o R e g i o n 25 8 . G r a v a t a i 1 . Tupancireta 9 . A l v o r a d a 1 0 . Cachoerinha R e g i o n 26 1 * 1 . C a n o a s 1. Cruz Alta 1 2 . E s t e i o 1 3 . Sapucaia do Sul R e g i o n 27 1 4 . Sao Leopoldo 1 . E s p u m o s o 1 5 . Novo Hamburgo 1 6 . Estancia Velha R e g i o n 28 1 7 . Campo Bom 1 . S o l e d a d e 1 8 . Sapdranga 2. Barros Cassal 1 9 . G u a i b a 3 . Arvorezinha 2 0 . Barra do Ribeiro 4. Fontoura Xavier 5. Ilopolis R e g i o n 21 6. Putinga 1. Montenegro 7. Anta Gorda 2 . Sao Sebastiao do C a i Region 29 3. P o r t a o 1. Nova Prata 4 . Salvador do Sul 2 . Veranopolis 5. I v o t i 3. Guapore 6. F e l i z 4. Serafina Correa 7. T r i u n f o 5. Nova Bassano 6. Nova Araca R e g i o n 22 7. Parai 1 . E s t r e l a 8. Casca 2. Cruzeiro do Sul 9. David Canabarro 3. Bom R e t l r o do S u l 4 . T a q u a r i Region 30 5. Roca Sales 1. Caxias do Sul 6 . L a j e a d o 2 . Antonio Prado 7. Encantado 3. Flores da Cunha 8. Nova Brascia 4 . Sao Marcos 9. Arroio do Mcio Region^31 R e g io n 23 1. Sao Francisco de Paula 1. Santa Cruz do Sul 2. Bom Jesus 2. Sobradinho 3. Cambara do Sul 3. Arroio do Tigre 4. Rolan te 4. Candelaria 5. Canela 146

Region 31 continued Region 39 6. Gramado 1. Carazinho 7. Tres Coroas 8. Igrejinha Region 40 9. Taquara 1. Campo Real 10. Dois Irmaos 2. Colorado 11. Nova Petropolis 3. Victor Graeff

Region 32 Region 41 1. Bento Goncalves 1. Tapera 2. Farroupilha 2. Selbach 3. Carlos Barbosa 4. Garibaldi Region 42 5. Bento Goncalves 1. Ibiruba 6. Mu9um Region 43 Region 33 1. Santa Barbara 1. Vacaria Region 44 Region 34 1. Panambi 1. Lagoa Vermelha 2. Pej uyara 2. Esmeralda 3. Ibiraiaras Region 45 1. Ijui Region 35 2. Augusto Pestana 1. Sananduva 3. Aj uricab a 2. Tapej ara 4. Catuipe 3. Ibiaca 5. Chiapeta 4. Cirlaco Region 46 Region 36 1. Santo Angelo 1. Marcelino Ramos 2. Barracao Region 47 3. Sao Jose do Ouro 1. Sao Luiz Gonzaga 4. Cacique Doble 2 . Bossoroca 5. Machadinho 3. Santo Antonio das 6. Paim Filho Missoes 7. Maximiliano de 4. Sao Nicolau Almeida 5. Caibate 8. Viadutos 9. Gaurama Region 48 1. Cerro Largo Region 37 2. Roque Gonzales 1. Getulio Vargas 3. Sao Paulo das Missoes 4. Porto Xavier Region 38 5. Guarani das Missoes 1. Passo Fundo 2. Sertao Region 49 3. Marau 1. Girua Region 50 Region 54 1. Santa Rosa 1. Sarandi 2. Candido Godoi 2. Rondinha 3. Campina das Missoes 3. Ronda Alta 4. Porto Lucena 4. Cons tantina 5. Santo Cristo 5. Liberato Salzano 6. Alecrim 6. Nonoal 7. Tuparendi 8. Independencia Regi on 55 9. Tres do Maio 1. Erechim 10. Tucunduva. 2. Jacutinga 11. Horizontina 3. Campinas do Sul 4. Sao Valentim Region 51 5. Itatlba do Sul 1. Santo Augusto 6. Erval Grande 2. Campo Novo 7. Aratiba 3. Sao Martinho 8. Mariano Moro 4. Humaita 9. Severino de Almeid 5. Boa Vista 10. Barao de Cotegipe 6. Crissiumal 7. Tres Fassos 8. Tenente Portela 9. Miraguai 10. Braga 11. Redentora 12. Coronel Bicaco

Region 52 1. Palraeira das Missoes 2. Condor 3. Chapada » Region 53 1. Frederico West- phalen 2. Erval Seco 3. Seberi 4. Rodeio Bonito 5. Planalto 6. Alpestre 7. Irai 8v Palmi tinho 9. Caipara 10. Vicente Dutra APPENDIX B

The Estimated Fixed and Variable Cost of the Estrela Port

148 149

Appendix B

The Estimated Fixed and Variable Cost of the Estrela Port

The investment in the new waterway/highway/railroad

complex is estimated to be around Cr$ 280.060 million.

This total investment cost includes two storage facilities,

the transfer facilities for grains and the administration

buildings.

The investment cost of capital on the port is based

on a 6 percent interest rate and on a flat depreciation

rate over 50 years. This cost is Cr$ 2,240 thousand per year. Estimating a movement of 1,200 thousand tons through

the port per year, the fixed cost per ton would be Cr$ 18.66.

Besides the fixed capital cost, additional fixed labor,

energy and material cost is included because these costs

are independent of the quantity of goods handled by the port. It represents Cr$ 195,400 per month or Cr$ 1.95 per

ton based on 1,200 thousand tons per year. The total fixed

cost (capital and administration) is Cr$ 20.61 per ton.

The variable cost includes the receiving and loading

cost for grain only. Labor is Cr$ 4.00 and energy and fuel

is Cr$ 1.00.

The average cost (fixed and variable) for transferring one ton of grain from truck or rail to barge is Cr$ 26.00 150

(81. percent Is fixed cost and 19 percent is variable cos t) . APPENDIX C

Railroad Revenue, Costs and Rate Estimations

I

151 152

Appendix C

Railroad Revenue, Costs and Rate Estimations

The railroad transported 7,413 million tons of goods during 1976. Coal, soybeans and wheat were the most significant goods transported by the railroad.

These three products together represented 70 percent of total tonnage transported.

The total expenditures of the railroad corporation were Cr$ 706,038 million while the revenues were Cr$ 516,285 million. These revenues represented only 73 percent of the expenditures thus, the railroad deficit was Cr$ 189,753 million. The railroad corporation (Rede Ferroviaria Fed­ eral S.A.) is a public utility company and the Federal government subsidized this difference of Cr$ 189,753 million.

The revenues obtained from the transport of goods were

Cr$ 446,284 million which is 86 percent of the total revenue.

The total ton/km of these goods was 2,560,010 million and the average rail rate was Cr$ 0.174 ton/km.

The ton kilometer of soybeans and wheat transported by the railroad was 1,206,765 million (47 percent of the total ton kilometer) generating a revenue of Cr$ 176,278 million. These revenues accounted for 34 percent of the total revenue. 153

The average rail race charged for soybeans and wheac was Cr$ 0.146 per con/km which Is 16 percent lower than the average rate of Cr$ 0.174 charged In 1976. If the railroad corporation would follow a full-cost pricing policy to eliminate the deficit, the rail rates have to be increased.

Maintaining the 34 percent share of revenues generated by wheat and soybeans and eliminating the deficit for the railroad, the average rail rate for transporting wheat and soybeans has to increase from Cr$ 0.146 to Cr$ 0.199 which is a 38 percent increase. This rate increase for all the goods would eliminate the deficit of the railroad for the • « amounts of goods transported in 1976. BIBLIOGRAPHY

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