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University Microfilms Internationa! 300 North Zeeb Road Ann Arbor, Michigan 48106 USA St. John's Road, Tyler's Green High Wycombe, Bucks, England HP10 8HR I 77-32,009

WRIGHT, Charles Leslie, 1945- THE ECONOMICS OF GRAIN TRANSPORTATION AND STORAGE: A BRAZILIAN CASE STUDY.

The Ohio State University, Ph.D., 1977 Economics, agricultural

University Microfilms International, Ann Arbor, Michigan 48106

© Copyright by

Charles Leslie Wright

1977 THE ECONOMICS OF GRAIN TRANSPORTATION AND STORAGE:

A BRAZILIAN CASE STUDY

DISSERATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Charles Leslie Wright, A.B., M.S.

■«■ •»■ * * #

The Ohio State University

1977

Reading Committee: Approved By

Richard L . Meyer

Francis E. Walker

Clark A. Mount-Campbell Department of Agricultural Economics and Rural Sociology Dedicated with affection to

Glorinha, Marcelo and Denison ACKNOWLEDGEMENTS

I wish to express my gratitude to the many individuals and organizations who assisted me during my graduate program.

I extend my sincere appreciation to my adviser, Dr. Richard

L. Meyer, for much time and effort expended on my behalf and much useful advice and encouragement during my graduate studies. I wish to thank Dr. Francis E. Walker for his contributions to my education at OSU; Dr. Clark A. Mount-Campbell for serving on my guidance committee and for valuable suggestions and comments;

Dr. Howard L. Gauthier for permitting the use of his version of the Fulkerson algorithm and comments on earlier drafts of sections of this dissertation; Drs. R. Keith Semple, Donald W. Larson,

Zoltan A. Nemeth and Slobodan Mitrie for comments and assistance during this research.

Various organizations provided financial support during my

Ph.D. program. For the stages of course work and the initial dissertation research, this assistance was composed of a research associateship in the Department of Agricultural Economics and

Rural Sociology, and a University Fellowship. Funds for data collection in Brazil, along with other expenses, were provided by the Midwest Universities Consortium for International Activities.

iii The writing of the dissertation, its duplication and associated expenses were financed jointly by the Fundacao de Amparo a

Pesquisa do Estado de Sao Paulo (FAPESP) and the Ford Foundation.

I am much obliged to these organizations for enabling me to under­ take my graduate program and complete this study.

During five months of data collection in Brazil, I received enthusiastic support from many individuals and organizations. I am indebted to them for such tangible items as office space, data and library facilities, and for equally important intangibles such as extensive interviews, encouragement and helpful comments.

At the risk of omitting some names, I would like to express special

thanks to the following:

Dr. Rafael Iatauro of the Tribunal de Gontas of Parana, for

encouragement and assistance on numerous occasions; Drs. Lycurgo

do Rego Barros Almeida, Vernon Turner Walmsley, Wande Lage Magalhaes,

Rodolfo Kuhn and Edna Teixeira Arantes (GEIPOT); Drs. Rui Neves

Ribas, Luir, Gezar Loureiro de Azevedo, Sergio Abramo Pies and the

technical staff of CIBRAZI$M; Dr. Hagop Kayayan (SUPLAN); Dr.

Cesere Giorgi (RFFSA); Drs. Osiris Stenghel Guimaraes, Gastao

Luiz Mendes Lima, Theodoro Venetikides and other individuals of

the Secretaria dos Transportes of Parana; Drs. Paulo Cameiro,

Joaquim Severino, Eugenio Stefanelo and the staff of the Secre­

taria da Agricultura of Parana; the AGARPA staff; Dr. Jose Car­

valho da Silva, Consultant; Drs. Luiz Gonzaga Pinto, Paulo

Roberto Godoy, Clemente Simiao Junior, Antonio Carlos Banzzatto

and Joao Batista Gongalves of the Secretaria da Industria e do

iv Comercio of Parana; Dr. Joao Paulo Koslovski (OGEPAR); Dr. Luiz

Antonio Amatuzzi de Pinho (APPA); Dr. Alfredo Jorge Budant

(COTRIGUAQU); Dr. Americo Gonrado Meinicke ('Secretaria Municipal de Economia, Ponta Grossa); Dr. Rafael Stresser, IBG; Dr. Marco

Antonio Fiori (Universidade Estadual de Londrina); Dr. Elpidio

Trevizan; Dr. Wilson Carlos Khon, former Mayor of Toledo; Dr. S.

Euclides Anshau1; Dr. Claudio Luchesa; Dr. Joaquim Jose de l Camargo Engler (ESALQ); Drs. Paulo Gidade de Araujo and Iby

Arvatti Pedroso (lEA-SP); Drs. Dusan Paulo Volk and Kuanzi

Kodama (DER-SP); Dr. Luiz Antonio Falavinha and other members of

RFFSA-Curitiba; Dr. Helmut W. Kottel (RICASSILO); Drs. Eduardo

Massignan and Sergio Luiz Panceri (GOAMO); Dr. Leo A.W. Curtis

(NTC); the staffs of the CAMIG, GAMAS, VALGOOP, GOROL, GOPAGRIL,

GOAMO, Guarani, Ponta Grossa, Toledo and Cascavel cooperatives.

I would like to thank several individuals who have con­

tributed in diverse ways to my graduate program and this research:

Dr. David H. Boyne, Head of the Department of Agricultural Economics and Rural Sociology; Dr. Mervin G. Smith, Assistant Dean of the

College of Agriculture and Home Economics; Dr. George P. Crepeau,

Associate Provost; and Dr. Alberto Carvalho da Silva, Consultant.

For much patience, understanding and assistance during a graduate program in two countries, I wish to thank my wife,

Glorinha, and both of our families, Arden and Genevieve Wright, and Antonio and Diva Miotto. Finally, I am indebted to Sherry Levy

for efficient typing of drafts of several chapters, and to Jay

and Betty Wright for assistance in proofreading the manuscript.

v The responsibility for the opinions expressed and any remaining errors rests exclusively with the author.

vi VITA

November 1A, 19^-5.... B o m - Three Rivers, Michigan

1968 ...... A.B., The University of Michigan, Ann Arbor, Michigan

1968-1972 ...... Peace Corps Volunteer, Rural Education and Community Development, Sergipe, Brazil

1973 ...... M.S., Escola Superior de Agricultura "Luiz de Queiroz", branch campus of The University of Sao Paulo, Piracicaba, S^o Paulo, Brazil

1973-1975 ...... Research Associate and University Fellow, Department of Agricultural Economics and Rural Sociology, The Ohio State University

PUBLICATIONS

"A Note on the Decision Rules of Public Regulatory Agencies." Forth­ coming Public Choice.

"Modelling the Economics of Rail Terminal Operations in Grain Transpor­ tation: A Brazilian Case Study." Contributed Paper, 1977 Summer Meetings "of the American Agricultural Economics Association, San Diego, California. Coauthors: Richard L. Meyer and Francis E. Walker.

"Income Inequality and Economic Growth: Examining the Evidence." 1977 Occasional Paper N2 398, Department of Agricultural Economics and Rural Sociology, The Ohio State University, Columbus, Ohio.

"Examining the Evidence Concerning the Relationship of the Rate of Growth to the Level of Development." 1977 Occasional Paper N2 Department of Agricultural Economics and Rural Sociology, The Ohio State University. "Modeling Transportation and Storage Systems in Developing Areas as Capacitated Networks." Contributed Paper, 1976 Summer Meetings of the American Agricultural Economics Association, Pennsylvania State University. Coauthors Richard L. Meyer.

"A Least-Cost Capacitated Transport-Storage Model for Agricultural Commodities in Sao Paulo and Parana, Brazil." 1976 Dissertation Prospectus, Department of Agricultural Economics and Rural Sociology, The Ohio State University.

"Fertilizer Response for Annual Crops in Brazil." 197^+ Occasional Paper N2 210, Department of Agricultural Economics and Rural Sociology, The Ohio State University. Coauthors: Richard L. Meyer and Joaquim Jose de Camargo Engler.

"Fertilizer Prices and Brazilian Agricultural Development." Contri­ buted Paper, 197^ Summer Meetings of the American Agricultural Economics Association, Te:cas A&M University. Coauthor: Richard L. Meyer.

"Analise Economica de Adubagao em Culturas Anuais na Regiao de Ribeirao Preto (SP), Ano Agricola 1971/72." 3973> Departamento de Ciencias Sociais Aplicadas, ESALQ./USP (Piracicaba, Sao Paulo, Published Master's Thesis).

"Aspectos Economicos da Agricultura na Regiao de Ribeirao Preto (SP), Ano Agrxcola 1971/72." 1973 > Departamento de Ciencias Sociais Aplicadas, ESALQ/u SP (Piracicaba, Sao Paulo, Brazil). Three coauthors.

FIELDS OF STUDY

Major Field: Agricultural Economics

Studies in Economic Theory. Professors J. Edward Ray, Dagobert Brito and Ernst Baltensperger

Studies in Economic Statistics. Professors Steven C. Reimer, Michael L. Lichstein, George F. Rhodes and Francis E. Walker

Studies in Development Economics. Professors Inderjit Singh, George J. Demko, Dale W. Adams, Howard L. Gauthier and Douglas H. Graham

Studies in Transportation. Professors Edward J. Taaffe, Howard L. Gauthier, Zoltan A. Nemeth and Slobodan Mitric

Studies in International Trade. Professors J. Edward Ray and Thomas A. Wolf Studies in Resource Economics. Professor Frederick J. Hitzhusen

PROFESSIONAL INTERESTS

Research and teaching. Areas: economic development, transportation, microeconomics and resource economics

HONORS, AWARDS AND FELLOWSHIPS

Midwest Universities Consortium for International Activities, Funda- cao de Amparo a Pesquisa do Estado de Sao Paulo, Ford Foundation (during field research and dissertation preparation); University Fellow and Research Associate, The Ohio State University (1973-1975); Ford Foundation (1972-1973); Regents Alumni Scholarship, The University of Michigan (1964-1968); A.B. with Distinction, Honors in History, Phi Beta Kappa, Robert B. Angell Scholar, Honorable Mention in Woodrow Wilson Competition (all at The University of Michigan); Sigma Chi Delta Agricultural Honorary and Phi Kappa Phi Academic Honor Society (Ohio State Chapters).

FOREIGN LANGUAGES ,

Portuguese: Short courses in Experiment in International Living (1967 ), Peace Corps (1968 )} Napoleao Mendes de Almeida correspondence course in Portuguese grammar (diploma, 197-1)» 5 l/2 years resi­ dence in Brazil. Proficiency: approximates level of educated native speaker.

Spanish: Six semesters at The University of Michigan* 90 hours instruc­ tion and diploma, Instituto Mexicano-Norteamericano de Relaciones Culturales (1966 ). Proficiency: Reading and comphrehension good, spe'aking and writing fair, good with practice.

GRADE POINT AVERAGES

Undergraduate (The University of Michigan): 3*6 on 4.0 scale

Master's Program (ESALQ/University of Sao Paulo): 4.0 on 4.0 scale

Ph.D. Program (The Ohio State University): 3*9 on 4.0 scale

WRITTEN EXAMINATIONS

Economic theory, agricultural economics (with distinction), agricul­ tural development TABLE OF CONTENTS

Page DEDICATION...... ii

ACKNOWLEDGEMENTS...... H i

VITA...... vii

LIST OF TABLES...... xiv

LIST OF FIGURES...... xvi

LIST OF TERMS...... xviii

Chapter

ONE. Introduction...... 1

TWO. Grain Transportation and Storage Problems in the Parana Region of Brazil...... 9

2.0 Introduction...... 9

2.1 Producing regions...... 10

2.2 Estimates of future grain production...... 16

2-3 The transfer infrastructure...... 22

2.3.0 Introduction...... 22 2.3.1 The present highway system...... 22 2.3.2 The present railroad system...... 26 2.3.3 The storage system...... 28 2.3 A Soybean crushing facilities...... 30 2.3.5 Wheat storage and transport...... 31

2 A Transportation improvements...... 32

2.^.1 Highway projects...... 32 2 A . 2 Railroad projects...... 32

2.5 Other improvements...... 33

x Chapter Page

THREE. A Theory of Transportation and Storage Under Con­ ditions of Restricted Capacity...... 35

3.0 Introduction...... 35

3.1 Von Thunen: the effects of transfer costs on agricultural location and production 35

3.2 Economic aspects of commodity transfer over space and time...... 4-1

3.2.0 Introduction...... 4-1 3.2.1 A theoretical basis for interregional | trade ...... 4-1 3.2.2 ' A theoretical basis for storage...... 44- 3.2.3 ; Conditions for commodity transfer...... 4-7

3.3 Costs, constraints and government policy...... 54-

3.4- Commodity flow models...... 55

3.4-. 1 Summary of the commodity transfer problem 55 3 .4-.2 A single commodity without storage...... 56 3.4-.3 A single commodity with storage...... 58 3.4-.4- Multiproduct flow with storage...... 60 3.4-.5 The multiregion case...... 63 3.4-.6 Backhauls...... 64 3.4-.7 Convex costs (increases in per unit costs with increases in volume)...... 65 3.4-.8 Modifications in the network...... 66

,3.5 A brief review of transport models...... 66

3 .6 Operationalizing the capacitated network model... 70

3.7 The Fulkerson algorithm...... 73

FOUR. A Network Representation of the Parana Transfer Problem...... 78

4-.0 Introduction...... 78

4-.1 The time periods...... 79

4-.2 The network model...... 80

4-. 2.0 Introduction...... 80 4-.2.1 Storage, processing, and terminal operations...... 81 4-.2.2 The highway network...... 92

xi Chapter Page

4.2.3 The railroad network...... 94

4.3 Transport cost parameters...... 99

FIVE. Results and Analysis...... 103

5.0 Introduction...... 103

5.1 Analysis of 1976 grain transfers...... 104

5.1.0 Introduction: statement of problem and procedures ...... 104 5.1.1 Storage in 1976...... 107 5.1.2 Transportation in 1976...... 112 5.1.3 Simulations of short term rail and storage improvements ...... 117 5.1.4 Summary of 1976 results and simulated short term improvements...... 127

5.2 Long run improvements in transportation and storage facilities...... 130

5.2.0 Introduction...... 130 5.2.1 Assumptions...... 131 5.2.2 Simulations of long range improvements under intermediate production levels 132 5.2.3 Simulations of long range improvements under high production levels...... 148 5.2.4 Summary of results for intermediate and high, production levels...... 152

SIX. Summary, Conclusions and Implications...... 154

6.1 Summary...... 154

6.1.1 The economic problem...... 154 6.1.2 Methodology...... 155 6.1.3 Simulation of short term improvements 156 6.1.4 Simulation of long term improvements 158

6.2 Conclusions...... l6 l

xii Page

6.3 Implications...... 162

6.3.0 Introduction...... 162 6.3.1 Limitations of the study...... 163 6.3.2 Implications for transportation and storage policy...... 1^5 6.3.3 Future research...... 170

APPENDIX

A. Municipios Included in the Study hy Microregion 173

B. Estimates of Intermediate and High Production Levels for Soybeans and C o m ...... 177

C. Transportation Costs and Capacities...... 18^

D. The Storage Sector...... 205

E. Partial 1976 Basic Solution...... 210

SELECTED BIBLIOGRAPHY...... 22^ LIST OF TABLES

Table Page

1. Estimates of mid-1980's grain volumes for Parana under two alternative crop patterns...... 21

2. Simple averages of U.S. monthly farm prices for c o m and soybeans in two periods ($/bu)...... 50

3. Kilter states and kilter numbers...... 75

4. Time periods for Parana grain transfers 81 \

5. Optimal solutions for 1976 basic transfer problem and sequential simulations of improvements in rail transportation and storage...... 10 6

6. Partial listing of storage arcs in optimal solution to basic 1976 transfer problem...... 108

7. Partial listing of rail arcs for 1976 transfer problems, with modal split indicated...... 113

8. Selected interior rail terminal arcs for optimal solution to 1976 basic transfer problem...... 116

9. Total transfer costs for simulated transportation and storage improvements under intermediate and high levels of future production...... 133

10. Modal split for grain arriving in Paranagua in in mid-1980's under intermediate and high levels of future production...... 135

11. Rail lines which operate at capacity under simu­ lated 1980's conditions, time periods and net arc costs of capacity constraints...... 137

12. Potential increase in hectares of soybeans by microregion...... 181

xiv Table Page

13. Identification of origin and destination nodes for computer printout of 1976 basic solution...... 210

14, Computer printout of optimal solution to 1976 basic transfer problem, omitting node prices and kilter numbers...... 211

xv LIST OF FIGURES

Figure Page

1. Southern Brazil...... 10

2. The microregions and boundaries of the state of Parana...... ^

3. Paved highways in Parana...... 23

‘4-. Parana's rail lines with possible improvements...... 27

5. A schematic representation of economic distance and cropping patterns...... 3&

6. Net farm price as a function of proximity to market...... 33

7. Equilibrium of imports and exports...... j ^2

8. Equivalent spatial equilibrium for two regions...... ^3

9. Equilibrium with storage costs for two periods...... ^5

10. Monthly prices and sales with uniform demand...... ^8

11. Network diagram for a good without storage...... 57

12. Network diagram for a good with storage at three locations...... 59

13. Network with two origins and two destinations...... &J-

1^-. Network representation of convex costs...... 65

15 . Complementary slackness conditions for network arcs, an extension of a diagram by Potts and Oliver ...... 75

xvi Figure Page

16. Network representation of production and storage...... 83

17. Processing and terminal operations...... 87

18. A diagram of highway linkages and options omitting dummy nodes...... 95

19. A schematic representation of the railroad network...... 9&

20. Distance determines economical modes when terminal costs are fixed...... 185

21. Modal shift with decreased turn-around costs...... 18 5

xvii LIST OF TERMS

AGARPA. Associagao Ae Credito e Assistencia Rural do Parana. Parana Rural Extension Service.

AGEF. Armazens Gerais Ferroviarios. S. A. General Railroad Storage (RFFSA does net administer rail storage directly).

APPA. Administragao dos Portos de Paranagua e Antonina. Administration of the Ports of Paranagua and Antonina.

GENTREINAR. Centro Nacional de Treinamento em Armazenagem. National Center for Training in Storage, Vigosa, Minas Gerais.

GESA. Comoanhia Estadual de Silos e Armazens. State Company of Silos and Warehouses, Rio Grande do Sul.

GIBRAZ^M. Companhia Brasileira de Armazenamento. Brazilian Storage Company.

CIP. Comissao Interministerial de Pregos. Interministerial Price Commission.

COAMO. Cooperativa Agropecuaria Mouraoense, Ltda. Agricultural Cooperative of Campo Mourao, Parana.

COPASA. Companhia Paranaense de Silos e Armazens. Parana State Company of Silos and Warehouses.

COTRIGUAQU. Cooperativa Central Regional Iguagu. The regional cooperative federation in western Parana.

Cr$. Cruzeiro (s'). The Brazilian unit of currency. In mid-September, 1976 , the official exchange rate was Cr$ll.l = U.S.$1.00.

GREMOS. Grupo Executivo de Movimentagao de Safras. Executive Group for Movement of Harvests.

CTRIN. Comissao de Gomnra de Trigo Nacional. The Brazilian Wheat Purchasing Commission.

xviii FRN. Fundo Rodovario Nacional. National Road Fund.

GEIPOT. Grupo Executivo de Integragao de Planejamento dos Trans- portes. Executive Group for Integration of Transportation Planning, currently the Empresa Brasileira dos Transportes, or Brazilian Transportation Corporation, the research branch of the Ministry of Transportation.

IBG. Instituto Brasileiro do Cafe. Brazilian Coffee Institute.

IBGE. Instituto Brasileiro de Geografia e Estatistica. Brazilian Institute of Geography and Statistics.

Imposto tJnico. Imposto rfnico sobre Lubrificantes e Combustiveis Liquidas e Gasosas (IULCLG). A fuel and lubricant tax.

INCRA. Instituto Nacional de Colonizacao e Reforma Agraria. National Institute for Colonization and Agrarian Reform.

Municipio. The local unit of government in Brazil. Areas of administration include the urban center(s) contained within the municipio boundaries.

OCEPAR. Organizagao das Cooperativas do Estado do Parana. Parana State Organization of Cooperatives.

RFFSA. Rede Ferroviaria Federal, S.A. Federal Railroad Corporation.

RICASSILO, S.A. The RICASSILO storage company in Rolandia, Parana.

Serra. Mountain range. In this study, the escarpment between Curitiba and Paranagua. Chapter One

Introduction

The grain sector of southern Brazil contributes a substantial proportion of the country's domestic food supplies and foreign ex­ change earnings, as well as a significant amount of the world's protein supplies through its soybean exports. Continued increases in grain output, however, are dependent on the development of an efficient transportation and storage system. Such a system must be able to handle much larger volumes at lower costs if the grain sector is to augment domestic food supplies, remain competitive in international markets and reduce inflationary problems.

The focus of agricultural development is now concentrated in the state of Parana and the Dourados region of Mato Grosso.

The state of Parana has already achieved the status of a major contributor to world protein supplies, accounting for k0% of

Brazil's burgeoning soybean production. Both Parana and Mato

Grosso can still double their soybean acreage, while the former leader, Rio Grande do Sul, has largely exhausted its capacity for expanding grain production. Parana's production alone may reach 8 million metric tons within the next 5~l0 years, equiva­ lent to 2/3 of Brazil's current soybean output and 8k% of the estimated 1976 harvest for China, the world's third leading pro­ ducer. 2

The transfer system, however, will he unable to handle these increased volumes unless it is drastically upgraded. The highway and rail system in Parana serves the entire "export corridor", including the Dourados region of Mato Grosso, the south of Sao Paulo state and the Republic of Paraguay. Rio Grande do Sul and Santa Catarina also export small amounts through the state port of Paranagua,

The rapid increases in regional grain production have overloaded

the export corridor. In 1976 the grain traffic saturated the rail

system of the state, while highway and terminal congestion increased

the trucking costs 60 to 80% above the base rate for grain shipments.

The lack of storage and uncoordinated truck shipments at one peak

period created a 26 kilometer line of trucks waiting to unload in

the port area. Lines of 6 to 12 kilometers were common. Typical

waiting times for truck unloading at the port ranged from 1 to 3

days. Waiting periods of 24 hours were common at the processing

firms in the interior of the state.

The grain traffic on Parana's two-lane highways caused satura­

tion of the major arteries from April through September and sub­

stantially increased congestion during other months of 1976 .

Producers were unable or unwilling to delay shipments of grain be­

yond October due to lack of adequate storage space, expected price

declines with the upcoming American grain harvest, and uncertainty

about future road conditions in areas not served by paved highways.

Highway saturation resulted in considerable delay costs and expense to other highway users. These additional costs for the movement of people, other agricultural products and industrial goods were probably as great as the costs of export grain movement by truck borne by pro­ ducers .

The storage situation is equally critical. The lack of sufficient bulk storage space results in crop losses due to delayed harvests and the use of inadequate and more expensive facilities, such as sheds, warehouses, inflatable shelters and even storage on the ground under plastic covering. Spoilage can result very quickly with such con­ ditions in Parana's semi-tropical climate. Efforts to avoid pro­ blems created by lack of adequate storage facilities lead to addi­ tional pressure on the transportation system, as producers attempt to use trucks and rail cars as surrogate storage.

Thus the state of Parana and the surrounding region cannot be expected to realize substantial increases in grain production unless the physical constraints are removed and they are able to continue marketing their output. Furthermore, cost reductions are needed to enhance the social benefits from grain production and to guarantee the export corridor's future competitive position on international markets. Its competitive position may become critical if production increases and world grain reserves have a significant downward effect on world prices.

At present, the export corridor also needs to diversify its agriculture on lands currently planted to continuous soybeans in order to reduce crop and export risk and lower the use of pesticides and herbicides. The main alternative to soybeans appears to be c o m under modem cultivation practices. The higher yields of com, however, would intensify the pressure on the transfer system by increasing the tonnage of grain to be handled. Since c o m has a much lower price/weight ratio than soybeans, low cost transfer facilities are needed to encourage its production.

In sum, the state's storage and transportation facilities must be expanded at reasonable costs if the export corridor region is to realize its potential of an increase in exports of 2 or 3 times within a decade, while diversifying its agriculture and increasing its contribution to domestic supplies of grains and other agricul­ tural products. Unless present facilities are enlarged, the physical constraints will limit the quantities that can be effectively marketed, and unless costs are kept down the competitive position of the area in world markets will be endangered.

Transportation and storage facilities, however, involve sizable expenditures for facilities with few alternative uses. Therefore another aspect of the Parana commodity transfer problem is the lack of knowledge of the effect of different investment projects on the overall capacity, operational costs and allocation of grain tonnage between highways and railroads. The railroad system presently does not reach many important producing areas, nor can it handle the volumes available for shipment from the areas it does serve. There is a need to determine: (1) if the rail capacity limitations are due to a lack of rail cars, inefficient terminals, or rail line capacity; and (2) what the desirable sequence of future improvements should be.

Similarly, the port and processing centers are congested. It is necessary to determine which areas are most deficient in storage facili­ ties, if large amounts of additional facilities are needed and how storage needs will change as production increases. There is also the need to examine the effect of storage quantities and location on the overall transfer problem, as the storage and transportation systems are interrelated in the task of delivering grain to the processing centers and demand points at the proper time.

Finally, the effect of planned extensions of the paved highway system on the overall grain transportation picture needs to be analyzed and compared with alternative improvements in the rail system.

The general objective of the present research is to provide a framework of economic analysis useful in understanding Parana's present transportation-storage problem and the probable gains associated with alternative strategies for improving the infrastructure. This framework can provide valuable information for later cost-benefit studies of individual projects. The specific objectives are to:

(1) locate bottlenecks and associated costs in:

(a) the 1976 transfer system (the present system);

(b) the present system with simulated short term improve­

ments ; and

(c) possible future systems under projected increases in

grain production; and

(2) evaluate alternative strategies for improving the infra­

structure on the patterns and costs of grain transfers. 6

In order to achieve these objectives, the regional transfer prob­ lem is examined by using a capacitated network model. This approach allocates flows along minimum cost paths subject to capacity constraints.

It thus permits representation of a cost-minimizing system subject to the type of capacity limitations evident in the Parana transpor­ tation and storage network. Individual highway and rail facilities, terminal and storage capacities and costs are represented by para­ meters on the "arcs" of the network model. Simulation of changes in costs, increases in capacities and the addition of new or extended facilities is undertaken by changing the cost and/or capacity para­ meters or by addition of new arcs. The model which emerges is a cost minimizing network of a multimodal grain transfer system, with multi­ period production, storage and processing, and with separable terminal operations. As such, the model provides a significant empirical ex­ tension of transportation analysis and network modeling applications.

In the text, the model is first employed to represent the trans­ fer system and levels of production in 197&. The various bottlenecks

(or binding capacity constraints) are located and their costs to the transfer system quantified by obtaining and interpreting the least cost network solution. This solution then serves as a starting point for simulations of short run improvements. These include:

(1) increased storage capacity; (2) improved rail terminal operations; and (3) increases in the present rail line capacity on the serra. or mountain stretch leading to the export terminal in the port of 7

Paranagua.

The subsequent use of the model involves simulation of major modifi­ cations in the infrastructure based on an "intermediate" and a

"high" estimate of grain production for the mid-1980's. The simu­ lated intermediate and long run changes include: (1) highway im­ provements; (2) new rail lines; and (3) increases in storage capacity.

The text is organized in the following manner. Chapter 2 provides a brief introduction to the geographical region and describes in more detail the problems faced by grain producers and shippers. It presents the estimates of the quantities of grain to be handled in the next 5~10 years, considering both intermediate and high levels of production. The alternative improvements for the future transfer system are presented as a basis for the model simulations of later chapters, Chapter 3 then develops a theory of storage and transportation under conditions of restricted capacities, including the underlying spatial and temporal equilibrium models, the generic capacitated network model and the Fulkerson solution for such networks. Specific modeling for the region's transfer system is presented in Chapter k. The quantitative analysis using the

Fulkerson solution follows in Chapter 5. An economic interpretation of the subproblems is given in this section, and the implications are discussed with respect to the overall strategy for transpor­ tation and storage of grain. Chapter 6 summarizes the findings, presents the conclusions and explores some of their implications. Several appendices are included to provide the reader details on geographic factors, data and estimation procedures. Since

Brazilian organizations are commonly identified by their acronyms, these are treated as capitalized proper names in the text and an

English translation is given the first time an acronym is cited.

The same practice applies for a few Portuguese terms without good English equivalents. Both terms and acronyms are explained and thereafter used without underlining or repeated explanations.

For the reader's convenience, a list of all such items is given on pages xviii and xix. Chapter Two

Grain Transportation and Storage Problems in the Parana Region of Brazil

"Here, the intermediate run is tomorrow, and the long run is the day after."

Dr. Wilson Carlos Khon Toledo-PR;6 October 1977

2.0 Introduction

The main center of grain production in Brazil consists of the states of Sao Paulo, Parana, Santa Catarina, Rio Grande do Sul and the Dourados region of Mato Grosso (Figure l). The grains of major importance are soybeans and corn, along with wheat in selected areas.

Soybeans in particular complicate the transportation and storage picture due to large increases in production since 1972. In Parana, soybeans also stimulate the expansion of wheat on lands where double cropping is possible. Similarly, improved storage and handling facilities constructed for soybeans also enable producers to sell and export corn in greater quantities, although c o m is presently confined to land generally inappropriate for soybeans.

As recently as 1972, Brazilian farmers harvested less than 3.5 million metric tons of soybeans, with the state of Parana contributing

9 10

Venezuela

Colombia

BRAZIL

Mato Grosso Peru

Bolivia

Dourados Sao Paulo Paraguay

Atlantic Ocean

lantal Argentina . Catjirlna Rio Grande do Sul

Brazil

State

International

Figure 1. Southern Brazil 11 only 20$ of the total [Estado do Parana, 1976a: 8-9]."*" By 1976, however, the total for Brazil had grown to 11.5 million tons, with

Parana contributing nearly h0% of the total. This represents a 333$ increase for Brazil and a 673$ gain for Parana, a phenomenon perhaps unmatched in scope and intensity in the history of agriculture.

Brazil now ranks second only to the United States (having passed

China) among countries producing and exporting soybeans. Within

Brazil, Parana is expected to surpass Rio Grande do Sul as the leading soybean producer during the 1977 harvest.

The area devoted to soybeans (frequently with wheat as a second crop in the same calendar year) comes from areas previously occupied by pasture, forests and coffee, and to a lesser extent, beans, cotton, corn and other crops [Estado do Parana, 1976a: 17]. Currently, corn still accounts for the largest volume of grains harvested, U.7 million tons. Some kjfo of this amount is retained on the farms ! i where it is grown [ACARPA, 1976a: 1; 1976b]. There is, however, a tendency toward greater commercialization and export of corn using the infrastructure recently provided for soybeans. In 1976, corn exports through the port of Paranagua (the state's only important ocean grain terminal) amounted to over 1 million tons, nearly double those of 1975. Corn exports thus totaled 76$ of soybean exports and

51$ of meal exports (1.35 and 2.0l* million tons, respectively)

^Hereafter, metric tons are referred to simply as tons. A metric ton is 1,000 kilograms or 2,20^.6 lbs., or about 10$ greater than the standard U.S. ton of 2,000 lbs. and approximately equal to a long ton of 2,2^0 lbs. 12

[Executive Group for Movement of Harvests (GREMOS): ^7-67].

C o m and soybeans are usually stored and transported in bulk when commercialized and exported. The grain volumes handled are large and there are only a few specific destinations for processing of soybeans and only one export destination for soybeans, meal and corn.^ These characteristics warrant the separate consideration for these products given in the present study. Other crops such as rice and beans are produced in diverse areas throughout the state

✓ and may be consumed locally or shipped to other areas in Parana or Brazil. With some very limited exceptions, these products are stored in bags in warehouses (hereafter referred to as conven­ tional storage). There is presently an excess of conventional storage capacity in Parana. This results from increases in bulk storage for corn, soybeans and wheat, the emptying of the

Brazilian Coffee Institute (iBC) warehouses, and the displacement of other crops by soybeans. Coffee is always stored in bags due to the need for separation of numerous grades and qualities.

In sum, the interstate and export transfers of corn, soybeans and meal are characterized by very large volumes and few destin­ ations, while transfer of other agricultural products is character­ ized by diverse origins and destinations, small volumes and con­ ventional handling and storage. They present no special problems for either transportation or storage and are therefore not included

^Some small tonnages of exports are made through the Sao Paulo port of Santos. 13 in this research.

2.1 Producing regions

The state of Parana is bounded on the north by the state of

Sao Paulo, on the vest by Mato Grosso and Paraguay and on the south by Argentina and the state of Santa Catarina (Figure 2). With the exception of Argentina, all these areas export grains through the port of Paranagua. The maximum distances within the state are 168 km north-south and 67! km east-west. Total area is 201,203 km^, an area slightly larger (kkk km^) than the combined areas of

Ohio and Indiana.

The state is subdivided into 289 municipios or local govern­ mental units roughly corresponding to counties. The municipios are grouped into 2k "homogeneous microregions" for statistical pur­ poses by the Brazilian Institute of Geography and Statistics (IBGE)

[IBGE, 1968]. This classification is based on broad criteria including physiography, economics and transportation. The iiiGli microregions constitute the basic unit of analysis in the present study, with subdivisions made where transportation or production characteristics warrant additional detail.

There are three major reasons for choosing the microregions as the unit of analysis rather than the municipios:

(l) They are more stable units of analysis, since municipios are often dismembered and new ones created (the latest, Nova Santa

^Exports from Santa Catarina and Rio Grande do Sul are cur­ rently very limited and should cease entirely with improvements in their respective ports of Sao Francisco do Sul and Rio Grande. Mato Grosso Sao Paulo 16 (283) 14 (281) Londrina 12 q (279) 13) Bandeirantes 18.3 5 . O, O 18.2 (285) GianorteV^^Sa^ 0 . Assfex Umuarama V f W . 2 (284) 80 Ibaxti O

o (286 ) r° ^ ^ z(5^ 3 Sl0 Campo 17.1 ^ (277) 7 0(274-) §1.1 Arapoti 50 km Guaira Mourao Tibagi Gampi Pi Iretam O La 9.4/__ ,. Ressrva 20 (287 (273) 21.2 O (288) Pitanga 30(270 O 6.1 Centro Azul jerras . . O ddU 23.2 (290) 9 (27b) Ponta ossa Irati 1 (268) O o Curitiba 0 22. 22.2 ^ 2 *3 (289 ( 2 7 # 5.1 o Pato O Q'-^272)i Francisco^(pranCQ O 4. Sul 24.ZL 24.2 (291) Argentina Beltrao , Maneueirinha IBCviij Ixicroregion subdivision Santa Catarina

Figure 2. The microregions and boundaries of the state of Parana 15 Rosa, is not yet listed, on most maps of the state);

(2) Often data is available at the level of microregions but not for the component municipios. Thus data for municipios can always be aggregated for the respective microregions, while dis­ aggregation is very arbitrary and leads to considerable error; and

(3) The municipios are not a significant unit of analysis for grain storage and transportation. Only a small percentage of * the municipios have drying and storage facilities. Grain produced in one municipio is thus dried, cleaned and stored in another.

Only in a few special cases, however, do such transfers occur outside of the microregions. These cases will be handled separ­ ately (see Chapter U).

The microregions and the subdivisions used in the present study are shown in Figure 2. One city in each region is selected as an "origin" for shipments from that region for purposes of distance and cost calculations in later chapters. These cities are generally the principal urban centers in the respective micro­ regions and subdivisions, or are otherwise important in terms of transportation, location and agricultural production. Much of

Parana's grain is actually dried and stored in facilities located within the principal cities identified in the figure.

The 2k IBGE microregions are numbered consecutively from 1 to

2h by most Parana state agencies. This is the simplest procedure and is therefore employed in the present study. The National Institute of Colonization and Agrarian Reform (INCRA) numbers them from 701 16 to 72b, and the IBGE itself uses the numbers 268 to 291- The

INCRA classification may be obtained by adding a "7" to the current

1-2U enumeration, and the IBGE numbers are obtained by the addition of 267. For reference, a complete listing of each municipio, microregion and subdivision included in the present research is given in Appendix A.

The subdivisions are indicated in Figure 2 by a decimal (e.g. , regions 17.1 and 17.2 are the subdivisions of region 17 or IBGE region 28^). Several regions are not considered as producing regions in the study. They may be included, however, as nodes

(centers or points) in the transfer system modeled in Chapter

These are regions 1, 2, 3, k, 5*1 and 0, comprised by the area of the state capital of Curitiba and several contiguous regions.

None of these regions account for a significant proportion of * Parana’s grain production. They are also close to processing and export points and are well served by exisiting transportation facilities.

2.2 Estimates of future grain production

The current levels of production of grain place severe strain on the transfer system in the state of Parana. The lack of ade­

quate bulk storage space results in losses from delayed harvests

and extra costs from storage in alternative facilities, such as

conventional warehouses, inflatable structures and sheds. The

intensity of grain transport by truck causes congestion and delays

for other goods and passenger traffic as well as for grain. Since 17 the 1976 volumes of production are known, they can he used to model the effects of short run changes in transportation and storage facilities on current levels of production.

The transfer problem continues to worsen yearly as production increases. Considerable amounts of good agricultural land currently in forests, pasture and other crops are being incorporated into grain production. Relative output and prices make grains - espe­ cially soybeans - the most promising production option now open to many farmers. Thus continued growth of grain production should occur if the transfer system can accommodate increasing volumes at reasonable costs.

Major transportation projects, however, require time consuming engineering studies and often have gestation periods of several years. They also tend to have large indivisibilities. For example, capacity on a saturated two-lane highway may be marginally in­ creased by adding passing bays or lanes, but beyond a certain congestion level, it becomes necessary to increase the number of lanes. This in effect implies conversion of a two-lane highway into a four-lane freeway.^ This represents a very large investment, and results in increasing the capacity four times rather than marginally. Similarly, rail capacity can be augmented by improving the physical efficiency of terminals, acquiring additional rolling stock and making sidings larger and more frequent. Nonetheless,

^The construction of a third lane would in itself represent a large indivisibility and is in any case a generally unsatisfactory solution [Highway Research Board: 282-318]. 18 an additional line must be built when these possibilities are exhausted with the same type of large indivisibilities as for converting a highway into a freeway. Where no rail line exists presently, the decision to construct or not involves the provision of an annual rail service capacity of zero or literally millions of tons, rather than a marginal increase in the level of service. 1

Thus it is imperative to have some estimates of the quantities of grain to be transported and stored during the next decade, even though precise quantities of production for a given year cannot be specified. Much can be learned about the economic performance of a projected system or modification of an existing facility by modeling the relevant features and evaluating performance under different production levels.

For example, it is possible to examine:

(1) if a system or facility constructed for a given volume of production or traffic is actually capable of handling a much larger amount;

(2) the effect of new highways or rail lines on operating costs and on the allocation of cargo between these two modes;

(3) if increases in traffic above a predicted level could be handled by marginal improvements in infrastructure or if new facil­ ities are needed; and

(U) the increases in bulk storage facilities necessary to meet a seasonal demand structure. 19

In this study, two levels of future grain production are estimated for interstate shipments and export through the port of Paranagua.

The two levels are:

(1) intermediate production with soybean expansion. This level derives from the assumption that soybeans will expand into 50% °f "the remaining land suitable for mechanized agriculture.

(2) high production with a soybean/com rotation. This level is derived from the same soybean expansion as option 1, but assumes an annual rotation of 1/3 of the total acreage in soybeans with c o m under modern cultivation practices and increased productivity.

The soybean expansion option represents a continuation of present trends and leads to an approximate doubling of current grain tonnage for the port of Paranagua. The soybean/corn option increases production volumes for transfer about three times. Al­ though it is not representative of current trends, such a pattern presents some desirable features for Brazil: (l) reduced crop risk through the implied diversification; (2) reduced use of pes­ ticides and herbicides; and (3) diversification of exports.5

The time perspective for these two options is 5-10 years, given the time lags associated with cropping shifts, tenure adjust­ ments and land clearing. The grain "surpluses" in each Parana

^Export diversification would imply world higher soybean prices as well as reduced price risk. Brazil's corn exports are not suf­ ficiently large for increases in its export level to substantially lower world corn prices. However, continued increases in soybean exports will exert downward pressure on world prices due to Brazil's importance on the world soybean market (see Chapter 3). 20 microregion under the two options are given in Table 1. Details on the data and estimation procedures are provided for the interested reader in Appendix B.

It is of note that although the two production levels are de­ rived from very specific assumptions, they could be approximated by

a variety of conditions. The intermediate level, for example, could be achieved by: (l) soybean expansion into less than 50$ of the available area suitable for mechanized cultivation but with

increased productivity; (2) soybeans produced in a rotation with

corn under modern cultivation but with total grain expansion into less than 50$ of the mechanizable area; or (3) greater than 50$

expansion of soybeans and lower productivity than at present. Simi­ larly, the high production level could be achieved under: (1) soybean

expansion into less than 100$ of the remaining mechanizable area but with increased productivity; or (2) reduced levels of expansion

of soybeans but a greater percentage of rotation with corn.

The soybean expansion option is considered an "intermediate"

level of future production, since it represents a continuation of

present trends and makes allowances for possible deficiencies in new

lands and for competition from other crops. The soybean/corn option

represents a possible production level, but is considered as a

"high" estimate: only an exceptional combination of grain expansion,

productivity and percentage rotation of corn could produce volumes

of grain substantially in excess of this level. 21

Table 1. Estimates of mid-J^O's grain volumes for Parana under two alternative crop patterns

Intermediate a High'-1 Region Tons fo Tons %

05.2 33,943 0.1+ 45,323 0.3 06.1 250,100 2.6 330,327 2.4 06.2 176,688 1.8 266,074 1.9 07. 0 57,556 0.6 77,469 0.6 09.0 77,753 0.8 9 6 ,62b 0.7 10.0 96,178 1.0 129,655 0.9 11.0 368,727 3.8 512,444 3.7 12.0 611,655 6.3 804,022 5.8 13.0 168,224 1.7 230,097 1.7 14.0 961,684 9.9 1,161,619 8.4 15.0 1+38,81*5 4.5 603,031 4.4 16.0 27,189 0.3 34,086 0.2 17.1 361+,111 3.8 477,342 3.5 17.2 163,808 1.7 235,674 1.7 18.1 222,1+55 2.3 347,908 2.5 18.2 179,778 1.9 255,326 1.8 18.3 195,858 2.0 238,689 1.7 19.1 161,243 1.7 212,859 1.5 19.2 ll+i+,62U 1.5 191,000 1.4 19-3 830,032 8.6 1,190,700 8.6 19.4 49,869 0.5 66,595 0.5 20.0 168,1+36 1.7 233,783 1.7 21.1 1+81,81+1+ 5.0 622,105 4.5 21.2 1,755,515 18.1 2,848,938 20.6 22.1 231,554 2.4 349,294 2.5 22.2 1+01,171 4.1 617,045 4.5 22.3 251,1+31 2.6 374,661 2.7 23.1 1+1,035 0.4 55,558 0.4 23.2 605,481+ 6.2 915,180 6.6 24.1 87,783 0.9 157,934 l.l 2b.2 88,010 0.9 142,694 1.0

Total 9,692,583 100.0 13,824,056 99.8

a Assumes soybean expansion into 50% of remaining mechanizable land, b Assumes intermediate acreage with 1/3 rotation of com. 22

The intermediate and high estimates of 9.7 and 13.8 million tons represent approximately two and threefold increases over 1976 grain and meal exports. The present transfer system would he -unable to handle such volumes since it was ;near saturation in 1976. Conversely, the lack of a suitable infrastructure would prevent farmers from effectively marketing their grain and consequently prevent such increases in production. The most realistic options for increasing the transfer capacity are analyzed in the following section.

2.3 The transfer infrastructure

2.3.0 Introduction

This section provides a description of the major problems and deficiencies in the highway, rail, port and storage sectors. It also outlines the alternative improvements in the system which will be modeled and simulated in Chapters 3-5. Details on estimation procedures and representation in models are deferred to those chap­ ters and several appendices.

2.3.1 The present highway system

Parana's 1976 paved highway network did not reach many of the production centers in the state (Figure 3). The main producing areas are located in the northern and western sections of the state, and are connected to Ponta Grossa (the major crushing center) and

Paranagua by only two highways: BP-376, connecting Ponta Grossa with the Londrina-Apucarana highways, and BR-277, which extends Sao Paulo

Maring£

Cianorti Umuarama

Campo Mourao

Campina da Lagoa

Ponta Grossa tatc Ocean Atlantic

O Mangueirinha

Planned

Figure 3- Paved highways in Parana Zb westward into the Cascavel area.^

The areas not served by paved highways are frequently cut off from processing centers and the port by rains, as the dirt roads become impassable and macadam roads often cannot be used by larger 7 trucks. Producers and shippers are thus often forced to ship at times of high costs and congestion to avoid the possibility of being cut off from these centers while trying to fulfill contracts for grain delivery. Macadam roadways add approximately 50% and dirt roads 100% to the per kilometer line haul costs of trucking over costs on asphalt.

The paved roads, however, are only two-lane highways. The numbers of heavy trucks loaded with grain saturate the main highways from April through September. During the remaining months, they contribute to congestion although volumes do not induce saturation.

The calculation of highway costs, capacities and levels of service is developed in Appendix C. The relevant conclusions, however, may be stated here:

(1) the grain traffic saturates roadways which are not heavily traveled in terms of numbers of vehicles;

(2) the need for conversion of two-lane highways into freeways is caused by truck traffic rather than by automobiles and buses;

^The prefix "BR" indicates a federal highway (the prefix "PR" refers to a Parana’state highway).

"^Warned after its Scottish inventor, John L. MacAdam (1756- 1836), a macadam road has successive layers of small broken stones on the earthen roadbed and is generally passable even under rainy conditions. (3) the grain traffic, like other heavy diesel truck traffic, is subsidized (the tax on diesel fuel does not correspond to the ton-kilometer share of traffic and the tax itself is insufficient for either maintenance or construction of highways); and

(U) shipping charges by truckers underestimate the economic costs of grain transportation due to (3) and to the congestion costs that truck traffic imposes on other highway users, i In addition to highway congestion, trucks face a serious ter­ minal congestion problem. The congestion is critical only at the unloading terminals, although it occurs to some extent at loading facilities as well. Congestion is much lower at the latter terminals since trucks are contracted only when grain is actually available for loading. The loading itself takes only ^-5 minutes with gravity

(flood) loading. The necessity of weighing each truck empty and full, along with the required documentation and the number of trucks involved, can create a moderate level of congestion and waiting times of .one to several hours.

The receiving terminals, in contrast, are tremendously con­ gested. The diverse origins and shippers limit terminal officials' ability to coordinate the ^ arrivals of truck shipments. The ter­ minal authorities must weigh, document and send each truck to the proper location within the terminal. Waiting time for unloading in Ponta Grossa for processing is often 2b hours or more, and for

Paranagua, three or more days during peak seasons. At certain times truckers have refused to transport grain due to the tremendous 26 congestion in the terminal areas. With combined line haul and ter­ minal congestion, freight rates typically increase from 60-80% during the peak seasons.

It should be emphasized that these increased rates represent increases in costs rather than spurious profits to the truck owners.

These costs are in turn derived from the disadvantages inherent in truck transportation. Just as trucks have a comparative advantage in shipments from diverse destinations over diverse routes to diverse destinations, they have a disadvantage when converging to the same destination over the same arteries. This disadvantage is increased when the commodity has a high weight/value ratio as in the case of grains. Within terminals, a volume equivalent to one 80- car train requires 2U0 trucks, given Parana averages for the two modes. This requires 2^0 sets of documentation and instructions on the part of terminal authorities, along with 1+80 weighings.

2.3.2 The present railroad system

The railroad problem is equally serious. First, the rail net­ work does not serve the west and southwest of the state at all, since the western branch ends at Guarapuava (Figure U). Much of the rest of the system does not warrant consideration in the present study as it leads out of the state (to Sao Paulo and Santa Catarina) or has no facilities for bulk handling of grain.

The permanent way presents numerous disadvantages: all track is single track, the guage is narrow (l meter), sharp curves are frequent (minimum radius of 90 meters on some stretches), steep 27

Ourinhos

Londrina

Itapeva Clanorto

Ponta Groasa Caacavol

Fo b de -o-- Iguagu

EngS Gutierrez

Sao Francisco do Sul

In study

not in study

possible or projected

Figure Parana's rail lines with possible improvements 28 grades (up to 3%), light rails and limited siding and station capa­ city. All these factors imply light cars, short trains, low speeds and increased line haul expense. The serra subsection of the Eng°

Bley-Paranagua line and the Guarapuava - Ponta Grossa railway have severe rail line capacity constraints.

Other problems include a rail car shortage caused by poor ter­ minal facilities for loading in the interior and unloading in the port. The processing facilities in Ponta Grossa also lack proper receiving terminals for unprocessed soybeans from the interior of the state. Delays in the interior terminals are typically 2k hours or more, and average up to 72 hours under non-peak conditions in

Paranagua.

Additional information on rail capacities and costs is contained in Chapters 3 and U and in Appendix C.

2.3.3 The storage system

Inadequate storage space forces shipment out of producing areas even at .times of high transport costs and/or low selling prices. Thus peak demands on the transportation system are intensified. Terminal congestion is also increased by improper handling facilities. This problem is especially serious in Paranagua since the port lacks sufficient receiving and storage facilities designed for separation of the three products exported (com, soybeans and meal).

The state's storage facilities serve three basic purposes:

(l) cleaning and drying of grains; 29

(2) storage for brief time periods (less than 6 months) or

longer time periods (more than 6 months); and

(3) collection for transshipment.

For purposes of this research, the primary concern is with the adequacy of bulk storage facilities in relation to fulfilling seasonal

demands, the effect of storage availability on t-ransportation costs, and

the additional costs incurred when alternative storage is used. These

include losses due to delayed harvest (quite common with com) and the

extra terminal operations associated with "emergency" type storage

facilities (including rubberized inflatable warehouses, sheds

and other available facilities, such as "conventional" areas for

storage in bags).

Most of the costs incurred in storage facilities are incurred

in the cleaning, drying and handling operations. Only the costs of

the handling operations are included in the present research. The

alternate storage facilities involve, at a minimum, an additional

handling .cost, and this is included in the models as a cost of

alternative storage as opposed to bulk storage. The costs of stor­

age over time in a given facility are not included in the model. In

the first instance, for the time periods considered, these costs

are small in relation to handling and transfer costs. Secondly,

these costs (interest, minimal product loss, etc.) are expected to

be offset by price increases over time, in accordance vith the

theories developed in Chapter 3. The seasonal nature of the demand

structure constrains certain volumes to Sao Paulo and export through 30

Paranagua to given time periods. Thus, the seasonal demands are built

into the analytical model of latter chapters, while the cost of the time element of storage and the offsetting product price increases are not.

There are a number of economic issues associated with the financing

of these storage units. They are tangential, however, to the present

research and can be better handled with analytical tools other

than the network models developed in the text. Some of these issues

are mentioned in Appendix D, which also contains descriptive back­

ground information on the storage situation in Parana.

The bulk storage facilities are generally large units (often in

the 10-1*0 thousand ton range) with few or no internal partitions.

The lack of internal partitions reduces the space available for stor­

ing grains when more than one grain must be stored in the same unit.

This means that only about Q0% of total capacity can be used for

corn and soybeans from February through October, and since the

wheat harvest enters at that time, only about k0% of capacity is

available for storage in the producing regions for the remainder of

the year.

2.3.1* Soybean crushing facilities

In 1976 Parana had sufficient crushing capacity to produce slight­

ly over 2 million tons of soybean pellets, of which 1,825,31*2 tons

were exported through the port of Paranagua [GREMOS], The major

centers for processing are Ponta Grossa, with about half of the

state capacity, and the Londrina-Maringa area, with around 25$ of

the state capacity. Major expansion is slated by the Anderson 31

Clayton company in Ponta Grossa by 1980, and by the association of cooperatives in the Cascavel area (COTRIGUAQU),

Processing capacity has two effects on the transfer of soybeans.

The first is a weight loss of about 7%, as soybeans yield about

75% soybean pellets and lQ% oil in the state. The second effect is to prolong the shipping period for exports. Due to the heavy capi­ tal investment, industrial capacity is not left idle for any signif­ icant periods of time besides those required for cleaning and maintenance. This implies a tendency for a relatively uniform amount of crushing and export throughout the year, as revealed in export data [GREMOS].

2.3.5 Wheat storage and transport

The difficulties farmers have experienced with wheat in recent years make it impossible to estimate its future production levels with any accuracy. Total volumes will, however, be much lower than for soybeans, as yields are much lower and plantings are limited to areas double cropped with soybeans not easily subject to frosts during the maturation phase. The principal effect of wheat on the grain transportation system is to reduce the amount of storage space available for corn and soybeans to about h0% of nominal capacity during the November to January period. This effect is modeled in subsequent chapters.

All storage unit to flour mill transportation is undertaken by the national purchasing commission (CTRIN) between wheat harvest and the beginning of the harvest of soybeans and corn. A considerable amount of wheat flows into Sao Paulo under governmental allocation procedures. Storage at the flour mills is used only for wheat and the amount is adequate in most cases. Due to the allocation procedures, the destinations and the reduced amount of grain transported, wheat transfers are not dealt with in the models of later chapters.

2.4 Transportation improvements

2.4.1 Highway projects

Given current plans and Parana's good record in completing its highway projects, the paved highway system is expected to reach almost all the principal production centers by 1980 (shown in Figure 3)•

The chief beneficiaries will be Capanema (22.1), Palmas (24.2),

Guaira (21.1), Iretama (19.4) and Pitanga (20). Other important improvements include: (1) the Irati-Relogio subsection of the

BR-2775 enabling the export traffic from the western part of the state to bypass Ponta Grossa, and (2) an outerbelt around Curitiba, enabling traffic to bypass that center. Some two-lane highways may be converted into four-lane freeways by the early 1980's. Those relevant to grain transportation are the Curitiba-Paranagua and

Curitiba-Campo Largo (towards Ponta Grosso) sections. All these highway improvements are simulated in Chapters 3-5.

2.b.2 Railroad projects

There are a few improvements in railroad operation which are possible before 1980. They include improved terminals, expanded rolling stock (via purchases of hopper cars) and some increases in line capacity through lengthened and more frequent sidings. The new track from Curitiba to Paranagua should be ready about 1980, as 33

substantial progress has already been achieved, despite some delays.

Several other improvements are being considered but cannot be

expected until the mid-1980's. The government is now giving pri­ ority to construction of the "steel railway" linking Minas Gerais

and Sao Paulo. When it is completed, some of the remaining Parana projects may be undertaken, singly or in combination. They

include: (l) extension of the existing line from Guarapuava to

Cascavel (and eventually to Foz do Iguagu); (2) Eng° Bley-Eng°

Gutierrez; (3) Eng° Gutierrez-Guarapuava; (A) Cianorte-Guaira; and

(5) Maringa-Campo Mourao (Figure ^).

The first option is part of an international agreement between

Brazil and Paraguay concerning the Itaipu dam. The second would cut * the distance from Guarapuava to Paranagua by 121 kilometers by eliminating the need to pass through Ponta Grossa. Both the Eng°

Bley-Eng° Gutierrez and Eng° Gutierrez-Guarapuava lines would improve operating characteristics and relieve capacity constraints between the cities they serve. The Cianorte-Guaira section has been planned for forty years [Wicholls, 1969: p. 53]. There is no proj­ ect for the Maringa-Campo Mouraoline, although the cooperative in the latter city has solicited one [COAMO, 1976; p. 2]. It is an inter­ esting option since the terrain permits economic construction and

Campo Mourao is a key producing center.

2.5 Other improvements

As mentioned in section 2.3.U, some major additions to current

soybean crushing capacity are expected in Ponta Grossa and Cascavel by 1980. These are simulated in sections dealing with transfer under increased production. Increases in bulk storage capacity are also simulated in order to examine the interrelationships of storage and transportation and the bulk storage requirements for meeting future production and demand levels. Additional details are de­ ferred until the analytical model is developed in the next two chapters. CHAPTER 3

A Theory of Transportation and Storage Under Conditions of Restricted Capacity

3.0 Introduction

This chapter presents a conceptual framework for economic analysis of storage and transportation. Although the presentation is theoretical, a number of examples and applications to the Parana

situation are included to facilitate the presentation in the

following chapters.

3.1 Von Thunen: the effects of transfer costs on agricultural

location and production

The combined effects of location and transportation on agricul­ tural production were first subjected to systematic analysis by Von

Thunen in his 1826 publication, The Isolated State. Von Thunen's numerous contributions have influenced hundreds of writers in fields ranging from agricultural economics to urban studies. From a methodological standpoint, Von Thunen presented a well developed

economic model, explicitly recognizing the role of simplifying

assumptions and the interplay of theory and empirical investigation

(Von Thunen himself devoted years to collecting data to test his

hypotheses). He developed a theory of economic rents independently

of Ricardo, and is credited with the first systematic presentation

35 36 of the theory of marginal productivity [Roll, 1957: 328-32].

In addition to these contributions, several of Von Thunen's concepts are of relevance to the present research. They include:

1) the agricultural frontier;

2) the interplay of improvements in transportation (or storage) and changes in agricultural production; and

3) the criteria for selecting crops within a given farm or area.

Von Thunen initially assumes a uniform plain with respect to soil, transportation and all other factors, with a single market located in the center. Transportation costs are proportional to distance throughout the plain, so that production of different crops takes place within bands marked by concentric circles (Figure 5a)•

Figure 5. A schematic representation of economic distance and

cropping patterns [Von Thunen] A unique price for each product in the marketplace implies that the effective price received by each farmer equals the market price minus transport costs. The first band (band "a" in Figure 5) is thus occupied by highly perishable items. The next band (b) is occupied by products with high weight/market price ratios (timber in Von Thunen's time). Perishability and high weight/value imply transport costs which increase very rapidly with distance. Further from the market, the crop choice is narrowed to small grains with lower weight/market price ratios (band c). The last band (d) is devoted to production of livestock and perhaps cheese, products whose market prices would offset both production and transportation costs (cheese has a low weight/market price ratio and livestock can be driven to market). The outermost ring then marks the frontier of commerical agriculture. No agricultural crop or domestic animal would have a market price capable of covering both production costs and farm-to-market transportation costs. Settlers in the area beyond would lapse into hunting, trapping or subsistence agriculture.

Von Thunen next relaxes the assumption of uniform transport costs by considering the simple case of a navegable river passing through the market. The effect of the reduced transport costs along the river is to extend the area of cultivation for each pro­ duct and push back the agricultural frontier. The bands are now elipitical (Figure 5^).

The concept of location rents is directly related to that of the bands. Figure 6 shows the relationship of net returns to 38

Net farm price

P

0 f i n proximity to market

Figure 6. Net farm price as a function of proximity to market

proximity to the market. Assuming constant production costs OP, the largest location rent is obtained on field "n” nearest the market.

The sice of the rent is given by the shaded area. For an interme­ diate farm "i", the location rent would be reduced due to an in­

crease in transportation costs and the consequent reduction in effec­ tive prices. The farthest commercial farm "f" has very small rents, with its furthest commercial acreage having a zero rent. This

extremity marks the agricultural frontier, where the effective

price has been reduced to the level of production costs by high

transportation expenses.

Von Thunen’s model thus shows how distance and transportation

improvements influence both crop choice and the level of rents. 39

Katzman recently derived a series of implications from the Von

Thunen paradigm using a Cobb-Douglas production function [197^].

Specific effects of transport cost reductions are:

1) extension of the agricultural frontier;

2) intensification of the use of inputs;

3) an increase in net income to fanners with prices to consumers

stable or declining; and

U) a favorable ceteris paribus regional income effect, as

areas farthest from the market benefit relatively more from the

improvements (areas closer to the markets benefit only from improve­ ments on the market side, not from those leading to the frontier

areas).

Although Katzman does not analyze storage, the effects are

similar. A reduction in storage costs (or capacity increases) can

reduce the combined transfer costs, that is, the total expense of

getting the product to the market at the appropriate time. This is

consistent with Von Thunen*s emphasis on the economic cost of mar­

keting, as opposed to transport distances alone.

The favorable effects of both Von Thunen*s and Katzman*s models

stem from increases in net farm prices. Von Thunen*s analysis re­

laxes the assumption of constant production costs to permit more

reali c,J‘" c representation of the net returns to farms at diverse

locations. Thus, soil fertility and input costs as well as location

are determinants of net farm rents and consequent crop selection

and cultivation practices. 40

In Parana, the agricultural frontier is presently an "internal" frontier, since the commercial cultivation of soybeans and other crops now extends into Mato Grosso and Paraguay. Within the state, areas in timber and pasture continue to be incorporated into agri­ cultural use, chiefly for soybeans. A sizable price increase since 1970 has allowed farmers to extend the cultivation of this product in areas of more difficult access or higher production costs. Net farm prices have also improved due to improved drying, storage, road and port facilities. Soybeans have motivated and financed many of these improvements, which are now used for other crops as well, with corn on export routes being the best example.

The effect of locational advantages on the profit picture for the state's regions, however, must be tempered by other considera­ tions. The most fertile lands lie to the north and western regions of the state, so that the rent advantages of Ricardian (fertility) rents and Von Thunen (locational) rents lie in opposite directions.

The Cascavel and Guaira regions, for example, are poorly located with respect to the export terminal and purchased inputs (ferti­ lizers are generally imported from Paranagua and lime is found most readily in the Curitiba-Ponta Grossa area). On the other hand, both of these regions enjoy considerable advantages due to soil fer­ tility. Transportation improvements in these regions, therefore, do not necessarily improve the regional income distribution in the state. Likewise, a study of tenure would be required to evaluate the effects of transportation improvements on the individual distribution of income among Parana's farmers. 41

3.2 Economic aspects of commodity transfer over space and time

3.2.1 Introduction

Parana produces several million tons of grains yearly beyond those required for local consumption. This "surplus" is available

for consumption in other states and export to foreign nations. The production is highly seasonal: harvest is confined to three months or less while consumption, processing and export occur throughout the year. Storage permits these activities to be extended beyond harvest to meet seasonal demands. It also permits more efficient use of the transportation network by allowing more time to move the harvest. Storage thus has an effect similar to that of an expanded physical capacity of transportation facilities.

This section develops the underlying theory of commodity trans­

fer over space and time, emphasizing the conceptual similarity of transportation and storage.

3.2.2 A theoretical basis for interregional trade

Figure 7 provides a representation of trade flows of a homogen­

eous commodity between two regions. In the absence of trade, prices

and quantities traded are determined by the domestic supply and

demand curves for each region. The equilibrium price in region 1 is

P-p and that in region 2 is Pg + ^125 or P0*1^ where the two

excess supply curves cut the vertical axis. The price in 2 exceeds

that in 1 by more than the transfer cost Wien trade is

allowed, the good flows from region 1 to region 2 until the negative

ES2 intersects the positive ES-j^ at price C, with region 2 importing 1*2

Region 2 Region 1

ES ES

-E'21

Figure 7. Equilibrium of imports and exports [Samuelson:

286-8 8 ]

CB. The "price in region 2 is C + T-^s differing from that in region

1 by the cost of transfer between the regions

The equilibrium conditions are [Samuelson]:

If P2 Pq + 1q2> then Eq2 =0*

If P^_ _> ?2+ ^21’ ®21 = ^*

If Pp < ^1 + ^12 an<^ ^1 < ^2 + ^21’ 'th611 ®12 = ®21 = Figure 8 shows the excess supply curves of Figure 7 without dis­ placing the axes. Equilibrium is now attained where the two excess

supply curves differ by Tqp* ^ we take the vertical difference ^3

Region 2 Region 1

ES

ES

21

W X

Figure 8. Equivalent spatial equilibrium for two regions

[Samuelson: 286-88] 244 between the two excess supply curves and plot it, we obtain the net excess supply curve NN. Transport costs are plotted as YZ for ship­ ping the good from 1 to 2. Equilibrium is thus at F where the net excess supply curve NN intersects the transport cost curve YZ. This figure leads to the concept of a Net Social Payoff (NSP), where:

NSP = (Social Payoff in 1 + Social Payoff in 2) - Transport Costs

The social payoff for a region is equal to the area under the excess demand curve, which is the negative of the area under the excess supply curve. The combined social payoff in both regions is thus the area under ES2 minus that under ESq, or the area GFMO under the net excess supply curve NN. Subtracting out transport costs, we have the Net Social Payoff given by GYF.

Reductions in the transport cost Tqp would increase the price in the exporting region 1, reduce the price in the importing region 2, and increase both the quantity traded and the Net Social Payoff.

3 .2.3 A theoretical basis for storage

The _timirg of production and consumption of grains is an impor­ tant consideration since supply is fixed at harvest time, while demand is spread over the entire year for processing, domestic consumption and export. This situation can be treated conceptually for the two period case using Figure 9 [Bressler and King],

The supply OS is the quantity harvested, and D2 are the demand curves for time period 1 and time period 2, respectively. Without storage, the supply and demand curves SS' and Dq intersect at price

P, and the entire stock OS is consumed in the first period. 24.5

$/ ton

Time 2 Time 1

ES

ES

NES

b d 0 a e c S

Figure 9. Equilibrium with storage costs for two periods

[Bressler and King: 208] ij-6

With storage at a unit cost of Ox = Pg - P^» Oc is consumed in

time 1 at price Pj_ and Od = cS is stored for consumption in time 2

at price P2. If storage costs were reduced to zero, the price in both

time periods would be Ps , with Oa consumed in the first period and Ob

consumed in the second period. A reduction in storage costs thus

raises prices at harvest time, loweis them in the post-harvest per­

iod, decreases quantities consumed at harvest time and increases

consumption throughout the post-harvest period.

It is also possible to define a net excess supply curve WES, where NES = ESq - ES2, with the net social payoff for storage as the area under this curve minus storage costs (Ox)(Oe). Decreasing

storage costs increases the net social payoff, in the same way as a

reduction in transport costs. The effects of transportation and

storage are thus quite similar, justifying their joint denomination

as transfer costs.

Assuming a perfectly competitive environment, price differen­

tials in ,any pair of regions and time periods may be no greater than the combined costs of transport and storage. Clearly, in a real

area such as Parana, price differentials and implied commodity move­ ments may differ considerably from what would be predicted from these models. These differences result from imperfect information, govern­

ment restrictions on exports, elements of monopoly in local or

international markets or other disequilibrium conditions.

In general, the possibility of transfer implies higher effec-

tive prices to Parana producers and lower prices to consumers, ^7 including foreign importers. Reductions in transfer costs imply greater quantities traded in a given year, with higher net farm prices and lower prices to importers. The higher net farm prices have the additional dynamic (or Von Thunen) effects of stimulating production in the following years, through crop shifts, incorporation of additional areas into cultivation and through more intensive use of inputs.

3.2A Conditions for commodity transfer

Storage occurs when the owners of a commodity expect to profit by selling it at a later date. This can occur for two reasons: l) the expected future selling price is higher than the present price; or 2) the costs of transporting it to the market will be less in the future (low cost carriers may be able to carry only a small part of production during the harvest period). The expected total of product price increase and transportation cost reductions must be equal to or greater than the opportunity costs of the factors employed in the storage activity.

Storage may occur at any location if it is motivated by expected price increases, providing the combined costs of storage and transpor­ tation are the same. Assuming a closed economy and no year-to-year storage, a competitive environment would produce a monthly price- quantity relationship such as that shown in Figure 10. The first month (harvest) is associated with the greatest monthly sales (Qq) and lowest price (Pq). In order to compensate for the costs of plac­ ing the commodity in storage, a considerable increase in price to ^ 8

$/ton

P12

P.1

^12 ^ Q3 ^2 ^1 tons/month

Figure 10. Monthly prices and sales with uniform demand [Bressler and King: 213]

Pg must he expected for the next month. At Pg, only Qg will te sold.

Smaller price increases are to he expected in each following month to compensate for the lower variable costs involved in keeping the

product in storage. Subsequent price increases are less than the

difference from P^ to Pg, and the reductions in quantities sold are

also lower than from month 1 to month 2.

For Parana, the price-quantity relationship may be expected to

differ from that of Figure 10 in several important respects. First,

in order for marketing to occur at all, the product must be cleaned ^9

and dried at the storage units. Thus, the price increase from month

1 to month 2 (P2 - Pp) need not he greater than any subsequent differ­

ential Pp - Pp _ Secondly, the time perspective for uniform price

increases is only about six months rather than the entire year due

to three factors: (1) the expected decline in grain prices on the

world market with the coming of the American harvest in October and

November encourages liquidation of Brazilian crops harvested earlier;

(2) many bulk storage units are not suitable for storage beyond 6 months

(Appendix D); and (3) wheat competes for space in the regional

storage facilities starting in October and November. Finally, soy­ bean processing is an important factor in exports of meal. The

processing operation adds value to the product and changes its form.

The processing costs are sufficiently high to overshadow time-related

price fluctuations. Thus equipment is normally idle only for cleaning and maintenance. As in other industrial processes, a reasonably uniform

quantity of output is produced each month, which in effect elimin­

ates the ^incentive for storage given a uniform demand throughout the

year. The GREMOS data indicate that monthly exports of meal are in

fact subject to little variation during the year.

The seasonal price variations for corn and soybeans, however,

have significant implications for the timing of Brazil's exports and

consequently the seasonal demands for transportation and storage in

Parana. Table 2 shows the average monthly U.S. farm prices for corn

and soybeans during a period of considerable world reserves (1960-

69), and during the more recent period of low reserves (1970-76). Table 2, Simple averages of U.S. monthly farm prices for c o m and soybeans in two periods ($/bu)

Month Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec.

1960-69 a C o m 1.07 1.08 1.09 1.10 1.13 1.14 1.14 1.12 1.13 i.07b 1.01 1.06

, a Soybeans 2.48 2.32 2.34 2.57 2.36 2.53 2.54 2.57 2.39^ 2.36 2.40 2.44

1970-76

C o m 1.8 7 1.87 1.84 1.8la 1,86 1.96 2.03 2.18 2.07 2.01b 1.90 2.02

Soybeans 4.12 4.30 4.33a 4.30 4.38 5.01 4.86 5.36 4.8 5° 4.78 4.60 4.74

Source: U.S.D.A.: Agricultural Prices (various issues)

EL Approximate onset of harvest in southern Brazil

^Approximate onset of U.S. harvest. The U.S. averages are reasonable proxies for world prices and thus of prices faced by Parana exporters.

As predicted from the theoretical model of Figure 10, U.S. corn prices decline with the onset of harvest in October, reach the low­ est point at the peak of harvest (November), with a sharp increase in December to compensate for the initial storage operations. There are gradual increases in the following months, with stabilization or even decline in prices immediately before harvest.

g Brazil is a price taker on the world corn market. Thus the

prospect, based on theory and past trends, is that Brazil­ ian producers, harvesting after April, will have no normal price incentive for storage of surplus intended for export beyond the month of October. Since their harvest comes at a time of high world prices, they may instead expect a substantial penalty for interna­ tional sales after October. The GREMOS data reveal that Parana's corn exports were consistent with expectations: 88$ of corn exports were made in the six months from May through October (for Brazil, the figure was 86$).

A similar analysis holds for soybeans despite Brazil's significant share of world exports, amounting to about 20$ of the world total in 1976 [GREMOS; U.S.D.A., 1976].9 Basic price trends for the 1960-69

^Brazilian corn exports in 1975 were about l.U million (metric) tons, compared with approximately ^3 million for the U.S. [GREMOS: U.S.D.A., 1976].

^Brazil exported approximately as much soybean meal as the U.S. in 1976 [GREMOS; U.S.D.A., 1976 (projected)]. and 1970-76 periods indicate price increases from the start of

Parana's soybean harvest (about March).through August, with a price decline after the start of the American harvest in September. The price incentives are therefore for export of unprocessed soybeans by early September.

Theoretically this situation will remain unchanged with increas­ ing relative participation by Brazil in world soybean exports. As

Brazil's share grows relative to that of the United States, the price fluctuations should change from a 12-month cycle as indicated by Figure 10 to two six-month cycles, with low price periods corre­ sponding to the American and Brazilian harvests. Producers and exporters in each country would then have an incentive to store grain only until the eve of the other country's harvest. U.S. producers would see their market period shortened, but Brazilian exporters would still not have an incentive to store soybeans for export be­ yond the U.S. harvest period. The pattern of export incentives could be modified, of course, by the emergence of buffer stocks, embargos by either Brazil or the U.S. or other drastic changes in the international trade environment.

The above tendencies do help explain, however, why Brazilians have not more readily invested in vertical silos and other types of more expensive storage equipment suitable for long term storage.

They have previously not expected to store grain for periods in

excess of 5-6 months. The disadvantages of such a strategy include

reduced flexibility with respect to domestic reserves and buffer 53

stocks and the possibility of short term deterioration and spoilage due to the type of facilities used (Appendix D).

The location of storage, as opposed to the time period, will be affected by the influence of total transfer costs rather than seasonal price fluctuations alone. The processing facilities, for

example, require a fairly constant supply of grain throughout the year. Minimum amounts may be needed during certain months at ter­ minals to avoid delay and demurrage charges.

In summary, commodity transfer between regions and time periods

is expected to occur at any price differential equal to or greater than the costs of effecting the transfer. Actual transfers viewed post hoc naturally will diverge somewhat from the predictions of a least cost model, due to information lags, inaccurate costs (if time, risk and handling charges are underestimated), or monopoly elements.

Furthermore, the model as represented in the spatial and temporal price equilibrium diagrams of Figures 7-9 requires a great deal of data including demand and supply functions for each region and each ✓ time period. For Parana, such data requirements are both unrealis­ tic and unnecessary. Instead, it is possible to use the GREMOS and

ACARPA data as estimates of points on the excess demand and supply

curves, since the form of the total curves is unknown. The data

from these two sources thus furnish point estimates of the "export­

able surpluses" in each region and the seasonal export demands, given the underlying price incentives which were described above. 5^

3.3 Costs, constraints and government policy

The preceding sections developed a theory of commodity trans­ fer and showed that exogeneous estimates are available for quantities to be shipped from different regions in various time periods. They also demonstrated that considerable social benefits may be obtained by minimizing transfer costs. In addition, emphasis was placed on the physical capacity constraints in the highway, rail and storage systems. A number of regions within the state are not served by either rail lines or paved highways. Absolute capacity limitations on rail shipments are present for the areas with rail service. Terminals are generally inadequate and are congested in a number of instances. The quality, availability and capacity of storage facilities is highly variable among regions. Storage capacity in many regions is insufficient to allow efficient use of transporta­ tion facilities.

The highways and railroads in the area are built, maintained and regulated by government agencies. There is extensive government involvement in the storage sector. Thus, public investments are crucial in determining the capacities and costs of the transfer system. A series of alternative improvements planned or under con­ sideration for the rail, highway and storage sectors was discussed in Chapter 2. Such network modifications change the relative isola­ tion and locational advantages of the producing regions and deter­ mine the costs and capacities for commodity transfer. As indicated 55 earlier, agricultural development and cropping shifts in Parana will place changing and intense demands on the system in the coming years.

Failure to solve these problems will lower on-farm prices and farm income, slow agricultural development, reduce exports and contribute to Brazil's inflationary problems.

The remaining sections of this chapter are therefore devoted to developing an economic model capable of incorporating the cost and capacity characteristics of the area's transfer system in order to study the economic consequences of different improvements in the physical infrastructure.

3.^ Commodity flow models

3.^.1 Summary of the commodity transfer problem

The Parana grain transportation and storage problem has the following characteristics:

1) producing regions are spatially separated from processing centers and export facilities;

2) production occurs during a short time period, while processing and export occurs over a much longer period;

3) storage to facilitate transport and to meet future processing and export demands may occur in the producing areas, at processing plants or at terminal facilities; and

U) flows occur over specific rail and highway linkages, and through specific storage facilities, each associated with costs and

capacity constraints.

The following section develops an analytical model to

represent these characteristics. A subsequent section describes 56 an algorithm capable of providing a least cost solution to the prob­ lem, permitting comparisons of alternatives and furnishing useful information for post-optimal sensitivity analysis.

3.^.2 A single commodity without storage

The remainder of this chapter develops a capacitated network model for the Parana transfer problem. The flexibility and other advantages of this approach are discussed where appropriate. 1C Figure 11 introduces some concepts and notation. It repre­ sents the simple case of a homogeneous commodity flowing from a farming region to terminal facilities in the harvest season with no storage. Arrows indicate the direction in which flows may occur.

An arc consitutes a link between two points in the network known as

"nodes". Each arc is characterized by three numbers: a cost

(c-•) of sending a unit of flow between nodes i and j , and a lower J (l. .) and an upper bound (u-*) on the units of flow which may occur 1 J J between the two nodes. Arcs are identified by their endpoints.

Thus arc'(l,3) begins at node 1 and ends at node 3. Cost is given

in cruzeiros, and capacities in (metric) tons."''''' Node 1 represents the producing region, node 2 an intermediate point and node 3 the terminal. Nodes DO and DD are the dummy origins and destinations,

respectively.^ The arc from DO to node 1 has an upper capacity

■^Notation in network analysis is not uniform. Conventions adopted in this study are similar to those used by Taaffe and Gauthier, King et al. and other authors.

^--*-The cruzeiro is the Brazilian unit of currency. In mid- September, 1976, the official exchange rate was Cr$ll.l = U.S.$1.00. 1 O The DO and DD nodes are created to obtain a solution, as explained below in section 3.7. 57

C\i

12 ' 12 * 12

Figure 11. Network diagram for a good without storage

(u q I) which indicates that the producing region cannot export more than the surplus it has available. Similarly, the arc from node

3 to DD has a lower capacity (lg^) indicating the amount the des­ tination region must import. Underlined values are constraints

emphasized to illustrate concepts developed in the text.

The solid arc(1,3) indicates that the commodity may be sent

directly from the producing region to the terminal at a cost of c^g per ton, until a limit of minCu^-pU^g) is reached. Other available units may be sent via intermediate point 2 at a cost of c + c23

up to a maximum of minlu^pa^g), as long as the total of tonnage on

the direct and indirect paths does not exceed Uq-j_*

Arc(1,3) might represent a direct shipment by truck from node

1 to node 3. The indirect route via node 2 might represent transfer from small truck to large truck or from truck to train (i.e.,

a terminal operation as opposed to a line haul). Each segment of the trip (l,2; 2,3) would have its own cost and capacity parameters.

The dashed line from node 1 to node 3 could be added to repre­

sent an alternative transportation mode such as a railway, a spe­

cialized carrier, or another direct route from 1 to 3. Again, each

arc would have its own cost and capacity parameters.

3.^.3 Single commodity with storage

The first reason storage of agricultural products occurs is the need to ration a quantity produced in the brief period of a harvest

season over a longer period when it is "needed" for consumption.

Stated in a slightly different manner, storage is motivated by

expected price increases. Referring back to Figure 9, the demand-

supply relationships for each period determine the quantities Oc and

Od to be consumed in period 1 and period 2, respectively. In terms

of Figure 12, these quantities are the amounts required in time

periods 1 and 2, that is, the point estimates on the excess demand

and supply curves discussed previously. These requirements are repre­

sented as the lower capacities on the arcs(3.1,DD) and (3.2,DD).

Secondly, differential costs of storage and capacity constraints

on the transportation system in period 1 can motivate storage and

influence its location. The demand constraints require that l^q be

delivered to the destination in period 1 and l^q in period 2. The

location of storage may be the producing regions, the intermediate

point or the final destination. Since nodes 1.1 and 1.2 are the 5 9

DD

O'

3.1

23’ 23’ 23

2.1

12 * ’ U12 *' X1 2 '

1.1 (ci ,'Ul'»1l ,)

Figure 12. Network diagram for a good with storage at three locations

same geographical point (or facility) in two different time periods, storage in the producing region is represented by a flow over arc

(l.l,l.2). The product must then flow over arcs(1.2,2.2) and (2.2,

3.2) in order to arrive at the destination in period 2, represented as node 3.2. Storage costs are represented by the c • * values on the 6 o storage arcs. For example, c^' represents the cost of storage at location 3, in turn represented "by flow over the arc (3.1,3.2).

The transportation arcs may differ among periods. In Figure

12, arc(l.1,3.1) links nodes 1 and 3 directly in period 1. This arc is absent in the second period. The deletion of this arc could represent the suspension of barge service due to freezing or low water levels, or rail abandonment. Similarly, the arc might be left in for the second period but assigned different cost or capacity parameters.

The single commodity case with storage is represented in a

study by King et_ ad for the shipment of coal in the Great Lakes area.

The storage options considered in the study are quite limited, how­

ever, and related to the absence of shipping links in the transpor­ tation network during the winter months. Sinclair and Kissling analyze the least cost placement of fruits in cool stores. Storage is not

actually represented, however. The complete time period considered

is one woek, and no consideration is given to the need to meet demands throughout the year. The authors in fact state that this limitation may result in a non-optimal pattern of allocation in the solution.

3.4.4 Multiproduct flow with storage

The case of multiple products with storage is a logical exten­

sion of the model. There are three possibilities: l) multiple but

heterogeneous products competing for the same transfer facilities;

2) heterogeneous products which do not compete for the same facili­

ties; and 3) products competing for the same facilities, but which 6 1 may be treated as homogeneous products.

Hu develops a method of treating the first case by considering one commodity as a priority good, maximizing its flow and then allocat­ ing the remaining commodity along unsaturated routes [1969]. Hu's logic parallels the preferential treatment by Parana producers for the more valuable commodity (soybeans) in terms of volume occupied, from harvest onward.

An empirical example of the second case is represented by grains and soybean oil. The oil uses special carriers, storage and terminal facilities. Thus there are two separate networks and an optimal solution would be obtained by maximizing each separately and ignoring the minimal competition for space on the highways.

The present study considers only the network representing a major transport problem (grain shipments).

The third possibility, that of considering different products as homogeneous commodities within the network analysis, is the approach taken in the present research. Corn and soybeans in the

storage system and corn, soybeans and pellets on export routes com­ pete for common facilities. Shippers in the actual system will not mix the products. Efficient terminal facilities in the port in

Paranagua would require product separation by creation of three

sets of receiving equipment. Present practice in the port terminal

is to use the facilities for one product at a time. In any case, the costs for handling, storing and transporting corn, soybeans and meal are the same per ton of product. They may be stored in the 62 same units if internal partitions are avaiable, with the exception that meal may not "be stored in vertical silos. Soybeans are avail­ able for crushing in all regions near processing plants so that special allocation procedures are not necessary to insure that soybeans are provided to the plants instead of corn. The weight/ volume ratios are quite similar, so that neither shippers nor those who provide rail or truck service make a distinction with respect to per ton charges.

The commodities are therefore treated as homogeneous with respect to terminal and line haul operations. Since the storage units have large indivisibilities, however, there is an effective reduction in available space (e.g., two 10,000 ton units cannot hold 15,000 tons of soybeans and 55000 tons of corn). This reduc­ tion in effective space is included in the network formulation in

Chapters U and 5-

The sum of the exogeneously estimated demands in each period is equal to the sum of the exportable surpluses. Since the harvests for c o m and soybeans and the seasonal demands largely overlap, there is no reason to separate the grain movements by type in the network, even though it is done by shippers.

Aside from the present study, no empirical application of net' work analysis for multiple agricultural products has been encountered in the literature reviewed. They are encountered, however, in the linear programming approaches, such as the studies by Fedeler, Heady and Koo of the U.S. grain transportation system. 63

3.^.5 The multiregion case

The case of two origins (nodes 1.1 and 1.2) and two destinations

(nodes 3.1 and 3.2) is shown in Figure 13 for a single product with­ out storage. Both origin nodes may supply "both destination nodes directly via arcs (1.2,3.2), (l.2,3.l), (1.1,3.2) and (l.1,3.1), or indirectly by shipping first to node 2 and then to the terminal via arcs (2,3.1) and/or (2,3.2). The arcs from the two destination nodes

(3.1 and 3.2) to the dummy destination DD have lower capacities representing the total demanded at each real destination. Similarly, the arcs from the dummy origin DO to the two origin nodes 1.1 and

1.2 have upper capacities equal to supplies available. Simple modif­ ications of the diagram permit representation of more realistic situations. Shippers in region 1.1 may be able to ship via a node such as 2 1 with which region 1.2 has no connections. Storage may be represented as before by duplicating those nodes where storage may occur and connecting them with arcs whose parameters represent the cost and capacities of storage at the nodes.

Among the early applications of the multiregion case is the ~ 13 Gauthier study of vehicular traffic in the State of Sao Paulo.

Traffic flows, especially in urban areas, have subsequently become a major application of network analysis, due to the relative ease of calculating theoretical highway capacities and costs [Muraco; Potts and Oliver]. Other studies include containerized freight in New

T3phe most accessible reference for the Gauthier study is the 1973 reprint. 64

DD

1.1 1.2

DO

Figure 13. Network with two origins and. two destinations

Zealand, fruit shipments in the same country and coal shipments on the Great Lakes [McCurdy, Reid and Skinner; Sinclair and Kissling;

King et_ al. ].

3.4.6 Backhauls

For non-specialized carriers, backhauls are sometimes possible

and may lead to lower freight costs. In some cases, costs of return

hauls with freight are essentially the same as an empty backhaul

[Casetti; Curtis].

The presence of specialized carriers and substantial turn­

around costs, however, may make the empty backhaul a more economical

alternative, particularly for railroads. For highways, competition among truckers will implicitly reflect the cost reductions due to available backhauls (Appendix C). This implies that the backhauls need not be explicitly represented in the network formulation, even though such representations are possible [C. Wright, 1976a]. Wo empirical problem of a network with backhauls has been found in the literature, although Casetti discusses a case using a linear pro­ gramming format.

3-4.7 Convex costs (increases in per unit costs with increases in

volume)

As described in Chapter 1, highways and terminals become conges­ ted with increases in bulk grain shipments in the Parana region.

Increases in per unit shipping costs are shown in Figure lU. Ship­ ping costs per unit are Cr$3 for the upper arc. This would be the

(4 ,0 ,100)

Figure 14-. Network representation of convex costs 66 first arc chosen in a least cost solution until the upper limit of

100 units is reached. An increase beyond this limit requires the use of more expensive arcs. Up to 100 additional units may be shipped at $U per unit, and up to 100 units beyond those at $5 per unit, for a total of 300 units maximum.

3.^.8 Modifications in the network

The Parana transfer infrastructure will be subject to a series of improvements and alterations in the coming years, as the demand for storage and transportation services increases. Improvements and alternatives in the structure may be simulated by changing the cost and capacity parameters on arcs as well as by the creation of new arcs and nodes. Certain arcs could also be deleted to represent withdrawal of services. Artificial or dummy arcs may be incorpor­ ated to represent constraints and alternatives. These options will be discussed in more detail as appropriate in the text.

The capacitated network approach is therefore a flexible method of representing commodity flows in complex real networks. The next section presents a comparison with alternative approaches, to be followed by a section on operationalizing the approach to provide a least-cost solution.

3.5 A brief review of transport models.

The literature on transportation models contains a number of approaches to the study of the relationship between transportation and development. Of these, the cost-benefit study (project analysis) is perhaps the most frequently used [Adler; Wilson; Fromm; Spriggs]. Although interesting and necessary for project selection, such re­ search neither comprises a sound basis for generalization nor pro­ vides a means of analyzing a transportation network. Other methods include: (l) modeling transportation as one sector of an entire economy [Kresge and Roberts]; and (2) analysis of proper finance and pricing strategies for the transport sector [Abouchar, 1967, 1969a,

1969b; Baer, Kerstenetzky and Simonsenj Howland; Wilson; Stokes;

Cipollari].

These latter two approaches help clarify the role of transport investments in the economy, as well as the proper role of subsidies and user charges for transport services. They are not well adapted, however, to locating bottlenecks in the system or to estimating the quantitative effect of alternative improvements in a transport network. Further, they cannot be used to quantify the impact of different pricing schemes on the intermodal distribution of freight.

Analytical techniques capable of treating these questions are needed to supplement large scale modeling efforts and financial guidelines. Some of the available techniques are discussed below, beginning with the traditional linear programming models.

Researchers in agricultural economics and related disciplines have traditionally used three types of models to study the least-cost allocation of commodities from surplus (producing) regions to defi­ cit (importing) regions: l) the simple transportation model (ST);

2) the transshipment model (TS); and 3) the spatial price equilibrium model, a special case of general linear programming (GLP). These 6 8 models are highly interrelated among themselves, as well as to the capacitated network (CN) model [Dantzig]. Like all models, they derive results and consequences from the assumptions on which they are based. Those which have fewer assumptions may be used to repre­ sent a wider range of real-world problems, but relaxing an assump­ tion may imply increasing difficulties for operationalizing the models and computing a solution, as discussed below.

The simple transportation model has very restrictive assumptions.

Its usefulness is thus limited to relatively simple problems such as finding the least-cost solution for shipping a commodity directly from a series of origins (e.g., factories) to a series of destina­ tions (e.g., warehouses). The ST model does provide, however, a method of showing the interrelationships of all four models. The assumptions of the ST model are given below. For comparison, the symbols of the other models are presented in parentheses next to the assumptions which they relax.

1) There are direct links between each origin and destination

(CN,TS,GLP).

2) There are no capacity constraints on the links (CN,GLP).

3) There is no storage (CN,TS,GLP).

M Unit transport costs are independent of the number of units

shipped (CN,GLP).

5) The amounts of "surplus" and "deficit" are known and fixed

in each region (GLP).

6) The product shipped is homogeneous (CN,TS,GLP). 69

T) Regions may be represented by points.

As indicated by parentheses, the transshipment model can be used to relax assumptions 1, 3 and 6. A transshipment point is allowed between each origin-destination (0-D) pair. This permits greater flexibility and analysis of more complex problems such as the deter­ mination of the optimum combination of processing, storage and interregional commodity movement patterns [King and Henry; Kriebel].

Optimal location analysis also is possible [King and Logan; Rhody,

B. Wright; Goldman; Casetti].

The different forms of the GLP model permit relaxation of assump­ tions 1 through 6. More than one transshipment point can also be handled under some formulations. The GLP model is thus the most

"general" of the four, going beyond the capacitated network model by permitting relaxation of the assumption of known surpluses and deficits (6), thereby permitting endogeneous determination of equilibrium demand and supply [Duloy and Norton]. The assumption is instead made that the demand and supply schedules are known, con­ tinuous and linear. It is thus used to project trade flows where statistics do not permit direct mapping of interregional patterns of trade [Morrill and Garrison; King; Takayama and Judge; Walker;

Fedeler, Heady and Koo, 1975a and 1975k]. Judge has also formulated the model as a quadratic program.

As discussed earlier, however, there is no need to make prices

endogeneous in order to study the transfer system for Parana’s

grain. For the case at hand, the capacitated network approach is equally general. It assumes only that regions may be represented by points and that estimates of supply and demand for each region and time period are available exogeneously. The first assumption merely implies that within-region transportation is not considered in the model. Out-shipments are considered as originating in the principal cities selected as the centers of producing regions.

Intermediate locations and terminals are also considered as points.

The next section describes the practical advantages offered by the network approach which make it the analytical instrument most suited to examining the Parana transfer problem.

3.6 Operationalizing the capacitated network model

Bradley attributes the growing popularity of network models to four factors [1975: 222],

1) flexibility (accurate modeling of many situations);

2) ease of use and interpretation (when related to physical net­ works, they may be explained to individuals with "little quantita­ tive background");

3) low cost solutions (100-300 times faster than linear programs for many of the same problems); and

U) ability to solve problems with more variables and constraints than any other optimization method.

The flexibility of the approach has been indicated in section

3.^. Although no description can be exhaustive, some additional situations are described in Chapter k when modeling the Parana trans­ fer system. 71

The ease of use and interpretation provides the researcher with greater flexibility in formulating models. It also facilitates communication of the results to other researchers and individuals with limited specialized training but who are interested in the empirical applications.

The issue of costs and computer capacities is also crucial.

Until recent improvements, computer capabilities could be exhausted

on very small problems formulated as GLP or quadratic programing problems if they contained capacity constraints [Ford and Fulkerson:

93]* Although recent expansion of computer capacities and increased efficiency of programs have relaxed this constraint somewhat, some researchers still find it necessary to use "heuristic" methods,

that is, procedures which stop before reaching an optimum [Ladd and Lifferth ] .

Fuller, Randolph and Klingman provide an impressive example of

the time and implied cost savings on computer use available through network analysis. They present a plant location problem which would require an estimated 800 hours of computer time for an optimal solu­

tion if the most efficient non-network algorithm available were used.

Network solution of the same problem without any changes in assump­

tions required times of only 10.1 to 298,7 seconds, depending on

the number of plant locations considered [1976:^321.

There are now a number of alternative programs available within the class of network algorithms yielding optimal solutions. Until very recently, the "out-of-kilter" (OKA) codes were considered 72 most efficient, with considerable variation existing among individual programs [Bradley: 229] Since that time, a class of advanced dual algorithms and a class of special purpose primal simplex algorithms have been shown to require less solution time than the OKA codes for some problems [Bradley; Hultz, Klingman and Russell]. A con­ siderable amount of operations research is currently underway com­ paring the relative efficiency of algorithms on different types and sizes of problems, as reviewed by Bradley. Computation speed, problem size and storage capacity are the three measures of perfor­ mance. Bradley reports on problems involving over 20,000 nodes and

1*50,000 arcs [229].

The network problem formulated in the present research is too large and complex for considering a solution by non-network methods.

Its absolute size, however, is not great enough to warrant concern over locating the most advanced algorithms currently available, par­ ticularly since many of them are proprietary programs [Bradley].

The computations in this study employ a version of the OKA generously made available by H. L. Gauthier.^

■^The name Fulkerson gave the procedure in his original article, "out-of-kilter," is a misnomer suggesting a heuristic (non-optimal) solution. The rationalization is that at least one of the arcs is "out-of-kilter" (defined as infeasible or non-optimal) until the final solution is reached, if it exists. However, all optimiz­ ing algorithms have non-optimal characteristics until the optimal solution is reached. A more appropriate name would be the "kilter algorithm."

-l-5professor of Geography, The Ohio State University. 73

3.7 The Fulkerson algorithm

The algorithm used for solving the commodity flow problems in this research is a version of the Fulkerson algorithm (OKA)

[Fulkerson; Potts and Oliver; Durbin and Kroenke; Ford and Fulker­ son]. The cost and capacity parameters for all arcs are estimated exogeneously. Supply may be equal to or greater than the sum of the amounts demanded. The parameters are all expressed as integers, with costs in cruzeiros and upper and lower bounds given in (metric) tons. The algorithm determines the maximum set of flows, x .., so as to minimize the total transfer costs including transport, storage and other costs assigned to the arcs. The maximum flow is deter­ mined by the minimal cut-set [Potts and Oliver: U3]. If all the

"supply" can be forced through the network, the supply arcs con­ stitute the cut-set, so that the maximal flow equals the available supply. Thus, the maximal flow is a given flow and will be allo­ cated to the least cost arcs.

The -OKA minimizes

1) Zcj jX.. for all i and j

subject to

2) I-* x- • ^ u-. ^or aH i ant^ J lj and

3) Ex.. - Ex.. = 0 for all i j J1 j 1J where

c -. is the unit cost for shipment from i to j (Cr$/ton) J

x -j is the quantity shipped from i to j (tons) J- J 7^

1.. is the lower limit on shipment from i to j (tons) X J u- • is the upper limit on shipment from i to j (tons)

Condition (3) is the conservation of flow principle that the

total flow into a node must equal the total flow out of it. Thus,

in order to solve a network problem with the OKA, a dummy arc (DD,

DO) must be added to the system connecting the dummy destination

(sink) with the dummy origin (source) to complete the system.

This avoids loss of flow at the source (DO) and gain of flow at the

sink (DD).

In addition to giving the optimal flows x . . and the total cost X J

in the optimum solution, the OKA determines endogeneously the fol­

lowing values: l) net arc costs (c*. or CBAR values); 2) node prices X J (p^); and kilter numbers.

Node prices p^ are calculated at each iteration so that increases

in commodity flow are along the least expensive paths. They are

relative prices and are the network equivalent of Von Thunen's loca­

tion rents (for the optimal solution). They determine, for given

arc costs (c..), the net arc cost:

b) c .. = c .. - (p. - p.) iJ ij J Given these parameters, each arc has a kilter state and kilter

number as defined in Table 3 [Fulkerson: 20-21].

These concepts are illustrated in Figure 15. The difference

in origin and destination prices (p. - p.) is plotted on the vertical 0 axis and the amount of flow (x..) on the horizontal axis. The thick x J line represents the three "in-kilter" states. The net arc cost Table 3. Kilter states and kilter numbers [Fulkerson: 20-21]

Kilter State Kilter Number

> (a) 1 0 0, = 1. . 0 H- C_j. Xij ij (b) = 0 , < X. . < u 0 Ci j 1ij = IJ = ij (c) < 0 , x. = u. . 0 "ij ij ij (ax ) > 0, X. . "ij IJ < 1ij xij " Xij (bx ) = 0 , X. . < 1 "ij IJ ij xij ~ Xi j (c1 ) < 0, X. . < u. . Uij) Gi j IJ ij "ij - ( > 0, X. > 1. . (x. . - &2, ^ Ci j IJ iJ 7ij IJ I1 O ,H

(i>2) •*“3= 0, > u. . - u. . Xi j 1J Xij ij (c2 ) < 0, x. . > U. - u. . Ci j ij Ij Xij IJ

ij

Figure 15. Complementary slackness conditions for network arcs, an extension of a diagram by Potts and Oliver [l9?2; 76] (CBAR) represents the marginal cost of increasing the flow over the arc in question by one unit, with respect to the total cost to the entire network. Thus state (a) represents an arc with a positiv net arc cost but lowest permissible flow = 1^^ ) • Any greater flow would be non-optimal, placing the arc in the (a2) state, and any lower flow would be infeasible, placing it in state (a^). The economic interpretation of state (a) is a route with destination prices (Pj) too low to cover the origin price (p^) and the transfer cost (c-.)» To minimize costs, the amount shipped is equal to the J lowest feasible amount (l. ). ij Arcs with CBAR values of zero and feasible flows are in state b (Table 3 and. Figure 15). Flows over these arcs may be adjusted within these bounds in order to bring other arcs in-kilter and lower total network costs. The states (b^) and (b^) are infeas­ ible. The CBAR values are both zero, but flow over the arcs vio­ lates either the lower bound (state b-^) or the upper bound (state bg). The economic interpretation of the in-kilter state (b) is a destination price exactly equal to the origin price plus transfer costs. The shipper is thus indifferent to quantities transferred along arcs in state (b), since neither gains nor losses are incurred

Arcs with negative net arc costs and maximum flows are in state

(c). The absolute CBAR value is the savings which could be attained by diverting a unit of flow from more expensive arcs onto the respec tive state (c) arc. Economically, these arcs are interpreted as the bottlenecks in the system. Shipments over these arcs cost less 77 than those sent over alternative arcs, hut they are saturated so that the more expensive paths must he used. Any arc with a negative GEAR value hut less than maximum flow is in the non-optimal state

(Tahle 3 and- Figure 15). Flows ahove u ^ are infeasible, placing the arc in state (eg)• For the optimal state (c), destination prices exceed origin prices hy more than the cost of transfer, so that the shipper sends as much as possible over such arcs.

The three states (a), (h) and (c) are feasible and optimal, with zero kilter numbers (Table 3 and Figure 15). The remaining states are out-of-kilter due to infeasibility, non-optimality or both. These states are assigned positive kilter numbers (Table 3)* Each iteration of the algorithm works to lower the positive kilter number asso­ ciated with an out-of-kilter arc without increasing the kilter num­ ber of any other arc. Change(s) on any arc(s) will bring the affected arc(s) toward one of the optimal states (a), (b) or (c). A single iteration frequently lowers the kilter number of many arcs. Thus all changes which occur are toward optimality for every affected arc and for the network as a whole. In addition, the OKA computa­ tional routine may be initiated with any flow (feasible or infeasible) using a primal-dual approach. The optimal solution to a problem furnishes a starting point for post-optimal analysis, permitting rapid solutions to subproblems where arcs are added or assigned different parameters. These factors make the algorithm a rapid and efficient method of solving network problems. Chapter k

A Network Representation of the Parana Transfer Problem

^.0 Introduction

The first two chapters of this study described the Parana trans­ fer problem and the storage and transportation system. Chapter 3 presented a theory of cost minimization under capacity restrictions subject to seasonal production and demand requirements. It also developed a capacitated network model for representing these features and described an algorithm capable of providing a quantita­ tive solution to problems formulated as capacitated networks. The arcs in a network model were used to represent characteristics such as handling, line haul and storage operations for the "exportable surpluses" from each microregion. The data needs for such a formulation were seen to be estimates of available surpluses and demands and the cost and capacity parameters for the arcs in the model.

This chapter integrates these concepts in an economic model of the Parana transfer system. It develops the specific capacitated network model used in the quantitative analysis to follow. The reader is referred to Appendix G for additional information on

78 79

externalities, highway and rail line costs and capacities, as well

as details on data and estimation procedures.

4.1 The time periods

Chapter 3 discussed the seasonality factor in the transfer of

agricultural commodities. For purposes of the present study, the

calendar year is divided into four transportation periods.

Period 1 runs from February 1 to April 15. During this period,

approximately one-half of the soybeans in the warmer regions are

harvested, along with one-third of the soybeans in the cooler regions.'*'1

The transportation problem during this period is largely a local one

(from farm to collection point). Flows on the main arteries to

processing plants and the port are at their lowest levels.

The second period extends from April 16 to June 30. The remaining

soybeans in all regions are harvested, along with the commercialized 17 com. Congestion at processing and export terminals becomes great, 18 rail service cannot meet the demand and highways become congested.

The dividing line is considered the 24th parallel, located just north of a straight line from Guaira in western Parana to Arapoti in the east (Figure 2).

17 C o m matures about the same time as soybeans, but is often harvested later due to lower losses from delayed harvests. There is some tendency toward earlier plantings for soybeans in the north and west in order to plant wheat earlier and possibly avoid some frost damage. Harvest dates for soybeans vary with differences in microclimate, while non-commercial c o m is often left in the field for several months after maturity.

18 The Federal Rail Corporation (RFFSA) does not use peak demand pricing, so that there is a large unmet demand for rail cars during periods of peak shipments. s o

The third period begins July 1 and ends October There is excess demand for rail cars as well as tremendous congestion for both truck line hauls and terminal operations. The wheat harvest begins toward the end of this period.

Period 4 begins on November 1 and ends on January Jl, During

this period demands on the transportation system are low, as in period 1. During period 4, the main exports are soybean meal.

The wheat harvest is also completed, so that available storage space for c o m and soybeans in the producing regions is considerably re­ duced. The government commission CTRIN removes the wheat from

storage in the producing regions and transports the grain to storage units located at the flour mills in Parana and Sao Paulo. Storage

units are thus empty when the soybean/com harvest begins in period

1 of the following year, since there are no carry-over stocks for

these two products.

These considerations are summarized in Table 4. The 12

month period considered begins February 1 and ends on January 31 of

the following year. Thus the 1976 base year considered herein

actually extends through January, 1977, and there will be some

differences in export totals from the GEEMOS figures for 1976

due to the extrapolation of the GREMOS data to January, 1977.

4.2 The network model

4.2.0 Introduction

The capacitated network model used as an analytical tool in

this study is composed of a series of interconnected subsystems. 81

Table 4. Time periods for Parana grain transfers

Item Periods 1 2 3 4

Dates 2/1 - 4/15 4/16'- 6/30 7/1 - 10/31 11/1 - 1/31 Harvest soybeans soybean/corn wheat Storage empty 1/2 peak demand peak demand wheat enters low demand and leaves Transport low demand near peak peak demand low demand demand

The arcs and nodes of the railroad, highway, processing, terminal and port subsystems are connected to each other by axes representing terminal operations, with appropriate costs and capacities. Local storage units are connected to processing units where relevant and to transportation networks in each period. Nodes representing storage facilities at the same location but in different time periods are also connected.

4.2.1 Storage, processing, and terminal operations

The current production enters the storage units in the pro­ ducing areas during periods 1 and 2. Wheat has been removed to flour mills during period 4 of the previous year and carry-overs of c o m and soybeans are minimal. It is thus assumed that there is no carry-over grain and all storage in producing areas is avail­ able for corn and soybeans (subject to qualifications stated below).

Storage units are considered to be located in the principal city of each microregion following the convention adopted in Chapter 2

(Figure 2). Farm-to-collection point transfers within each micro­ region are thus excluded from the analysis. ■32

The storage units themselves are considered "nodal collection points" in the model. The grain produced in each region enters these facil­ ities for cleaning and drying, and no distinction is made for the small percentage stored on farms. Grain may he shipped out by truck or rail (-where available) during the same period, or stored at the collection facility for shipment in a future time period.

These concepts are expressed in network form in Figure 16.

In period 1, grain "enters" the system over the dummy arc(DO ,CS-^), where DO is the dummy origin (source) and CS-^ is the collection point in period 1 (Campo Mourao in the example). The exogeneously estimated exportable surplus for the region in this time period is represented by the upper limit u on the arc (DO .CS-,). In the second period, O -L production is given by u^1 on arc(D0,CS2). Grain entering CS^ is cleaned and dried and may be loaded onto trucks in the same period, as represented by arc(CS-^,CH^). The upper limit "G" on this arc

is an arbitrarily large value used to indicate that no capacity restriction is assigned to the arc.

Once the terminal operation is complete, the grain proceeds along the highway system to another terminal. The alternative to

the terminal-line haul at CS-^ is to store the grain for shipment

in a later period. Storage from period 1 to period 2 is repre­

sented by flow over one of the arcs^S-jCSg). The arc with zero

cost represents available bulk storage, taken as 80% of the capacity

listed in the 1976 census undertaken by the Brazilian Storage

Company (GIBRAZ^M). This discounts for: (1) units without con­

jugated drying space, requiring extra handling; and (2) reductions in 83

(o,u,o)

(1|G,0) DO

(l,GfO)

o

o

(1»G,0) GS CH

o O f 1,G,0) CS GH

Figure 16. Network representation of production and storage m effective space due to the large indivisibilities involved in most of Parana's large storage units. Since soybeans, com, wheat and occasionally rice are stored in these units, product separation often implies considerable unused space.

The other available storage facilities range from conventional storage (warehouse storage in bags), to sheds and rubberized infla­ table devices with capacities ranging from a few tons to several hundred tons. At a minimum, these alternatives add a handling cost of about Cr$it'5/'t°n beyond that of bulk storage. This occurs since the grain must still pass through drying and cleaning facilities within a short period after harvest to avoid spoilage. If storage is in bags, the cost of sacking must be added, doubling this amount.

The grain may then be stored for longer periods without spoilage, however.

In the network model, a zero cost is assigned to the bulk storage arc. This neglects the costs of storage over time once placed within the bulk facility. This is done for three reasons:

(1) storage over time as opposed to choice of location and type of facility is constrained by the seasonal nature of the final demands, so that it is not possible to ship grain in the first period to avoid these costs; (2) monthly product price increases are expected to exactly compensate for the expenses involved in accordance with the theory presented in Chapter 3; and. (3) "the costs are small in comparison with the other costs in the model. The handling costs from the storage unit onward are included in the model. Thus the alternative storage arc is assigned the handling cost of Cr$^5/ton. If the same grain is stored for two periods in the network model, it incurs a cost of Cr$90/ton. This corresponds to the estimated cost of storage in bags (Cr$89/ton), which is the only means of avoiding spoilage in the non-bulk facilities over the longer time periods. The alternative storage capacity is estimated as l/lp the capacity of the microregions' respective conventional storage capacity as declared in the GIBEAZ^M census. This figure takes into account that many of the units listed in the census cannot actually be used for grain storage, and many of the others are used for other products. The estimated amount of available

conventional storage, however, is still quite large in most regions.

The upper and lower limits are expressed in (metric) tons. The

lower limits are zero, since no amounts are required in storage

in specific facilities in a given time period.

Grain stored from period 1 to period 2 and grain produced in

period 2 compete for shipment by truck during period 2, as well

as for storage from Period 2 to period 3* These activities are

represented by arcs^S^fCH^) and (CSgjCS^j respectively. From

period 3 to period k, wheat competes for storage, and this is

represented by the reduction of the upper limit of available

capacity u on arc(GS^,GS^) to k0 % of bulk storage capacity of

the microregion. There is no reduction for the storage arcs

representing the alternative facilities, since the CTRIN rules 86 would not result in their use under most circumstances.

In Parana, some grain is produced in microregions without ade­ quate storage and is then transported to other microregions for cleaning, drying and storage. An example of a region without hulk storage and drying facilities is Iretama (node I in Figure

16). . Production is given by u^ on arc(DO,l). Drying and cleaning facilities are severely limited, as given by u. on arc(l,IS-,), IS -L forcing some of the available production to be taken to another collection area (in this case, node GS1# that is, Campo Mourao).

Transportation costs are higher for this operation than simple line haul costs from collection point to processing plants and export.

This occurs since the longer hauls create a bottleneck for the harvest itself, and tractors and wagons which are normally used to supplement local truck transportation cannot be used on the longer hauls. This is reflected in the model by assigning an additional l/k of normal line haul costs to these arcs. The arc for the Iretama region is duplicated for period 2 (not shown).

Since there is no production in periods 3 or *+» however, the 1^ and 1^ nodes are dropped, while the storage nodes IS^ and. IS^ remain in the model.

Processing and terminal operations are given in Figure 17.

As in the previous figure, production is represented by the upper limits on arcs from, the dummy origin DO to the nodes CS-^, CS2 ,

MS^ and MSg (storage facilities in Gampo Mourao and Maringa in periods 1 and 2, respectively). Grain at Gampo Mourao in period (0,G,0)

(O | d^, d^)

(ll,u,0) (0,G,0) (C,G,0)c,G,0 >— [MH, MAH H,G,0)

(i , 3 A p ,o )

3 A p ,o )

(1,G,0)

A5tu,o)

CO •S3 Figure 17. Processing and terminal operations 88

1 (CS^) is loaded on trucks, represented as a flow over arc(CS^,CH^).

The loading cost is minimal (Cr$l/ton) since queues are small and the actual loading operation takes only about 5 minutes. The grain goes to Maringa over arc(GH1 ,MH1), and may continue by highway over ) and subsequent arcs until reaching a terminal such as PAH^ The line haul costs incurred are additive for each arc connecting a nodal pair. There is a terminal cost at PAH1 (Paranagua) for the transfer of grain from trucks to the port storage facilities, represented by movement over one of the arcs(PAH^,PS-^). Each arc has a different cost parameter, starting at Cr$ll/ton for the first arc and increasing at Cr$10/ton on each additional arc. This represents a terminal cost which increases with volume, or a congestion cost, as described in the previous chapter (Figure 1^). The discontinuous cost function used is a linear approximation, by segments, of the congestion costs incurred by all trucks as the number of trucks at the Paranagua terminal increases.^

19 Each arc connecting the two nodes has a lower bound of zero and an upper bound corresponding to 100 trucks per day of average net weight of 19 tons (1,900 tons/day), multiplied by the number of days in each time period. The cost per ton is derived from a minimum terminal cost of Cr$ll/ton for 100 trucks or less, and increasing by Cr$l0/ton for each additional 100 trucks per day. The total cost function thus increases with volume, and the mar­ ginal cost function increases much faster than total costs. The function is a reasonable approximation of actual congestion costs in the port, given an estimated waiting cost of Cr$2o/ton for each day's wait in the port. 89

The terminal operation just described, is a transfer of grain from trucks to the port storage units. Once in these facilities, it may he loaded by conveyors onto ships or stored for shipment in

the next period. The ship-loading operation is represented by flow

over the arc(PS1 ,DD). The costs of loading are not considered in the model, since all export grain must pass through these facili- 20 ties. No constraint is placed on ship-loading capacity other

than the demand for export in each time period placed on arc(PS^,DD).

The loading operation only becomes a problem when ships are not available, and this type of variation is of limited interest over

the time periods considered in the model.

The storage of grain from period 1 to period 2 in the port

facilities is represented by movement over arcs^S-^PS^). Port bulk storage is assigned a zero cost and the so-called "reserve" storage outside the port area is assigned a cost of Cr$53/ton.

This figure is the financial charge for the use of the reserve storage over a short time period and corresponds to the additional handling costs involved in this congested area.

Figure 17 also shows the two additional options for grain

arriving by truck in Maringa: (1) transfer to the railroad over

arc(MH^,MR-^'), and (2) unloading soybeans at the processing plants

for crushing over arc(MH^,MP1). In the first case, a Cr$12/ton

The zero cost may not be too far from the financial cost to Parana's exporters. Premiums for loading ships in less than the alloted time can pay for the equipment and labor involved in a very short time period, given the scale of Paranagua's operation. 9 0 cost is incurred, based on the inexpensive loading operation (Cr$l/ton) and a Cr$ll/ton cost for the rail terminal operation. The second

(processing) option involves queues and a cost of Cr$ll/ton, based on estimated waiting time. For Ponta Grossa (not shown in the diagram), congestion costs are much more pronounced and are repre­ sented by additional arcs as in the case of Paranagua, with the cost/volume relationship adjusted to reflect the relationship in that processing region.

One-fourth of the weight of processed soybeans is represented as flow over the dummy arc(MP^,DD). This represents the 2.8% yield of oil and a 7% weight loss. The transport of oil is a small part of total volume and has diverse destinations (it amounts only to 5*6/6 of the grain flow into Paranagua, for example). Since it has no peaking problems and uses special trucks and terminals, its only effect on grain transportation is minimal competition for highway capacity. Therefore transportation and storage of oil is not included in the model.

The remainder of the raw soybeans' weight (75%) is transformed into pellets, represented by movement over the dummy arc(MP^,MP-^'), with processing capacity given as the the upper bound on the arc.

The dummy arc is assigned a negative cost to permit generation of node prices consistent with the in-kilter conditions of Table 3 of the previous chapter. These negative costs are then netted out of the final solution values. The artificial processing arc represents both capacity and demand at the processing plants, since two modes of transportation are available in Maringa and the choice of modes is left to the model. Similar dummy arcs exist for Londrina and Ponta Grossa (not shown). For processing regions with no rail transportation, however, capacity and demand are represented by bounds on the processing-to-truck arc, such as arc(MP-^,MH-^').

This avoids inclusion of unnecessary nodes and arcs in the network.

Processing capacity is defined as 80$ of the daily nominal capacity and 300 days of operation per year. These are generally accepted parameters in the state, although some plants operate above this figure and others somewhat below for a given time period.

The rail terminal operation for pellets is given by arc(MP^',MR^') with a cost of Cr$ll/ton. This cost is lower than the truck-rail transfer cost of Cr$12/ton since the plants usually have their own I rail sidings.

The Maringa highway node is split into two nodes, MH-^ and MH^', joined by a costless dummy arc with an arbitrarily large upper limit "L

This is to avoid routing the same flow through MH^ twice. In the real system, grain can arrive in Maringa for processing and leave it in the form of pellets by truck over the same roads. The creation of the dummy arc and node permits simulation of this shipment pattern without creating an internal loop within the network. Such nodes and arcs are added for every region with processing plants, as well as the railway subnetwork at Ponta Grossa, the only plant location which receives soybeans by rail. This latter restriction corres­ ponds to options permitted in the network. It would be physically possible for such plants as those in Londrina and Maringa to receive soybeans by rail. Economically, however, this alter­ native is not realistic due to high terminal costs and is not included as an option in the network.

4.2.2 The highway network

Many of the characteristics of the highway network were des­ cribed in previous chapters. There are a total of 46 nodes repre­ senting the microregions and subregions and the major intersections in the road network, plus 10 dummy nodes for those microregions with

processing facilities. Each node is duplicated for the four time

periods considered, along with the nodes and arcs representing

terminal operations and connected to the highway subsystem.

The additive line haul costs for trucks are assigned to each arc

connecting nodes representing road sections. These costs are based

on the weighted average of the number of kilometers of asphalt,

macadam and dirt roads. The Cr$/ton-km figures used in calculating

these costs are based on the all-asphalt Toledo-Paranagua haul in

the off-peak seasons (periods 1 and 4). These costs per ton-km are

virtually identical for other major shipping centers in the state,

such as Gampo Mourao, Maringa, Londrina, Pato Branco and Ponta

Grossa, once they have been corrected for terminal costs and random

fluctuations (e.g., rain, exceptional demands in brief periods from

a single microregion). This is expected in a region where trucking 93 is competitive and largely unregulated, As discussed in earlier chapters and Appendix G , these financial costs are underestimates of the real social costs involved, since they do not account for damage to infrastructure and congestion costs to non-grain traffic caused by truck transport of grain. The financial costs do weight implicitly, however, the cost reductions due to available backhauls.

The asphalt line haul considered is composed of level, rolling and mountainous stretches, and is of variable quality. There are frequent cases of inadequate bridges, signaling and shoulders

[Estado do Parana, 1976a: 10^]. The Cr$/ton-km cost for the Toledo to Paranagua stretch thus implicitly weights grade and road quality.

The only additional weighting necessary, therefore, is that needed for road type. The weights used are 1.00 for asphalt, 1.^2 for macadam and 1.8^ for dirt roads. These weights are those used in

GEIPOT vehicle operating cost studies for Glass 1 roads without grades, since implicit corrections are already included for grades and quality.

The grain trucking sector incurs congestion and mobilization

costs on line hauls in periods 2 and 3. These costs are estimated

at 20/S and 30/S of basic costs in the two periods, respectively, and

are added to the basic line haul costs for Parana highway arcs. For

arcs leading out of state, only the mobilisation costs are added: 10%

for period 2 and 15% for period 3- This cost is a premium paid by

grain shippers to acquire additional trucks from other uses and from

other states. A Cr$5/ton barge cost is included for Paraguay and

Mato Grosso for crossing the Parana river. The highway network is shown schematically for period 1 in

Figure 18. Aside from the Parana microregions considered in the network, the nodes include: Assis (660), Ourinhos (4-20) and Sao Paulo Capital (680), all in the state of Sao Paulo;

Dourados (317) in Mato Grosso; Paraguay (9^7); "the intersection of BR-373 with the road from Mangueirinha (373); BR-277 with

BR-373 at Tres Pinheiros (382); the Porto Vitoria-Uniao da Vitoria area (880); the intersection of BR-272 with BR-376 (376); BR-376 with

PR-^441 (^1); BR-277 with BR-376 (658); the western entrance to

Curitiha (Oil) and the eastern side (012); and Relogio (^50).

All these additional nodes represent either: (1) essential intersections in the 1976 basic highway network, or (2) locations at which significant improvements are expected by 1980. The inclusion of these nodes In the 1976 network thus permits both representation of the actual network for grain shipments for

1976 and facilitates the simulation of highway improvements for the future.

^.2.3 The railroad network

The basic railroad network is given schematically in Figure 19.

It is also duplicated for four time periods. The dummy nodes in

Londrina and Maringa were explained in Figure 17 and are not included here. The dummy node is included for Ponta Grossa to call attention to the fact that receiving is possible in this region, as well as outshipments. The Eng2 Bley and Eng2 Gutierrez

stations are included to facilitate simulations of future improve­ ments in the rail system. Other simulations such as the projected Assis Sao Paulo 660 Ourlnhos

Paranava£ Bandelrante

Londrln; liar In g£

Dourados Assal Clanorte Umuarama Apucarana Ipora llbaiti 3?6 Arapotl Borrazdpolls Gualra Marlluz

Iretama

Camplna da Paraguay Lagoa Pitanga Reserva Castro Cascavel

Ponta Grossa Rel6gio

■*-(^50f Laranjeiras 382 Oil 012 do Sul Guarapuava Capanema Irati PalmeIra Paranagu;

Mangueirinha Francisco Beltrao 880 Fato Branco Uniao da Vltdria

vO

Figure 18. A diagram of highway linkages and options omitting dummy nodes MaringA Londrina existing line Cianorte simulated line

Apucarana

SKO PAULO PARANA Campo Mourao

Guaira

Ponta Grossa

Ponta Grossa (d ) Gascavel Guarapuava

Eng2 Gutierrez Eng? Bley ParanaguA Atlantic Ocean

Figure 19. A schematic representation or the railroad network 97

Guarapuava-Cascavel line are later incorporated by adding the relevant nodes and arcs with their respective parameters.

Line haul capacities for railroads are based on the RFFSA calculations for different stretches. The Eng2 Bley-Paranagua line is approaching saturation on its limiting subsection - the serra - with an estimated capacity of 3*5 million tons. Traffic other than grain bound for the port reduces that figure to about

3 million tons. This quantity is divided among the four periods based on each period's percentage time in the year (Table ^).

The Guarapuava-Eng2 Gutierrez and Eng2 Gutierrez-Ponta Grossa lines are each limited to about 2 million annual tons, from which estimates of other traffic must be subtracted based on flow data

[ RFFSA, 197^a] .

The remaining stretches of rail lines included in the network model are of much higher standards than those considered above.

As a rough estimate, these lines should be able to sustain at least twice the present traffic on the serra stretch, that is, a minimum of 7 million tons per year. This upper limit allows for

considerable non-grain traffic on the lines. It could be reached

at lower annual volumes if severe peaking occurs. Capacity could

be expanded by addition and lengthening of sidings.

No cost data is available for line haul costs by specific stretch. Nonetheless, it is clear that costs are much higher on the Eng2 Bley-Paranagua stretch than any other. A reasonable estimate is preferable to assuming that costs per ton-km are equal on all stretches. The estimate is obtained by considering line haul costs to be proportional to the time spent on the line haul. The base for comparison is the average length line haul for grains.

This adjustment is reasonable since: (1) the serra section requires numerous switching operations to: (a) divide trains into 22 car units to furnish greater traction on the steep descent to the port and to permit passing operations on the short sidings of the serra;

(b) recombine them in a larger train at the bottom; and (c) reversal of these operations on the return trip; (2) twice as many loco­ motives and engineers must be employed on the downgrade; (3) main­ tenance costs on the line, plus derailment and accidents, are higher; and (^) the sharp curves and grades up to 3% imply increased resistance and high fuel consumption on the return trip, due to the dead weight of cars and locomotives.

For _the other stretches of line, there are no adjustments for differences in line haul costs. The Central do Parana line from

Ponta Grossa to Apucarana probably has the best operating character­ istics, but operating speeds do not greatly exceed those of other railways (with the exception of the serra stretch).

In contrast to the highway network, no adjustments are made for congestion or mobilization costs in the rail network. This is due to the presence of two conflicting tendencies in rail costs: (l) congestion on both line hauls and terminals, causing delay and increasing costs; and (2) more intensive use of fixed 99 investments in rolling stock and permanent way, and division of over­ head items such as administration by a greater volume of traffic, decreasing average costs. There is not enough data at present to estimate the point at which increased traffic actually increases average costs as the first tendency begins to predominate.

^.3 Transport cost parameters

The least cost model used in the present research allocates flows along the least expensive paths, subject to capacity con­ straints. For many commodities, ton-kilometer rates charged to shippers do not correspond to the costs borne by shippers

[Walters, 1968: 63-80]. This occurs since the mode with higher rates may also offer faster, more reliable service, reducing risk, the need to keep larger inventories and decreasing production delays.

Therefore, shippers avoid what superficially appears to be the more inexpensive mode, and actual flow differs considerably from that predicted from a least cost algorithm.

This is not the case with grain shipments in Parana, however.

Given lower charges and availability of service, rail will be used, since the quality of service is comparable in terms of speed and security, and both rail and highway shipments are usually insured 21 at the same nominal fee. In fact, shippers may even have a

21 Speed in this case refers to delivery time after loading, rather than turn-around time which affects rail capacity. Since trucks also travel at low speeds, and have long unloading delays, time differentials from the shippers' viewpoint for grains are not crucial. preference for rail service since it reduces the need for much docu­ mentation and the social problems created by extended queues of boisterous truck drivers. Some modal shift to rail would be induced by lower rail charges in the off-peak seasons. The shift would come from areas without convenient rail terminals where extra distance by truck to the terminals presently cancels the savings of rail line haul costs. The shift could be induced by limited use of peak demand pricing by the railroad: charging slightly less when rail cars would otherwise be idle, and increasing charges at periods of peak demand.

The question remains of the appropriate cost figures to be used in the model. The model will be more representative of actual flow patterns if the cost figures used are charges to shippers, rather than accounting costs used to represent underlying social or economic costs. On the other hand, the allocation pattern resulting from financial charges to shippers may be suboptimal if there are substantial divergences between financial and economic costs.

The present research uses the financial charges to shippers for the cost figures for highway shipments, and the long run average variable costs (LRAVC) for grain in the region as the cost figures for rail transportation services. The latter figures are derived from the Sander rail costing methodology. These costs approximate present charges in the aggregate, as grain services are 101 run at or slightly above costs (most other operations are deficitary for the 11th division, as indicated by the 1976 RFFSA Anuario).

In the short run simulations of the following chapter, these cost figures underestimate the social costs of highway grain transport, since:(l) congestion costs to other users are not in­ cluded in the financial charges to those who ship by truck, and

(2) these charges do not cover the diesel trucks' share of con­ servation, maintenance and overhead expenses for highways, while the LRAVG figure covers all conservation and overhead for the rail transport of grains.

For modifications in the rail system, however, the LRAVC figure becomes simply the long run average cost (LRAC), as all costs including the initial capital investment must be considered

[ Abouchar, 1967:2], At the present time, the capital costs of the projects are not known, and cost estimates frequently are unrealistic. Moreover, new lines and higher levels of traffic would reduce operating and maintenance costs. Thus it cannot be assumed that the LRAG figures of projected lines would be greater than for present lines.

Similarly, highway construction costs represent a large initial capital investment. When more complete cost-traffic relationships are known, perhaps a LRAG concept can be developed for the truck sector, and social costs of the two modes for grains may he examined and compared. At present, the financial charges for trucks and the 22, LRAG figures for railways are the data available for comparison,

Externalities and capital costs of new projects are discussed, however, as a supplement to the results of the simulations in the following chapter. Unlike some studies of U.S. grain transportation however, there are no simulations of marginal increases in rail charges [Fedeler, Heady and Koo, 1975a; 1975b]- While such uni­ form increases in rates are quite relevant to the American rail network and institutional environment, they are only marginally interesting in the case of Parana. As amply demonstrated in this research, the relevant issues for Parana concern the exis­ tence of rail service, the availability of service in areas with lines, and the proper discrimination of total transportation costs into terminal and line haul components.

22 As mentioned previously, the LRAG figures are approximately the charges to shippers. Published tariffs cannot be used as charge figures, since charges on all large shipments are subject to negociation between RFFSA and the user. Chapter 5

Results and Analysis

5.0 Introduction

The preceding Chapters discussed the Parana transfer problem and developed a framework of analysis for short and long range improvements in infrastructure. This chapter presents the em­ pirical results and their interpretation. Section 5-1 examines the optimal flows for the production-transfer conditions of

1976. The bottlenecks in the transfer system are located, with quantitative indications of the costs they impose on the system.

The relevant short run improvements are simulated. Section 5*2 uses the intermeditate and high production levels developed in

Chapter 2 for the next decade as a basis for analyzing future transport-storage problems and possible solutions. The most likely and/or promising improvements in infrastructure are simu­ lated for the time horizon of the mid-19801s when the economic and physical limits to grain production increases are expected to result in a more stable and permanent level of demand on trans­ portation and storage facilities.

103 1 0 4 -

5.1 Analysis of 197& grain transfers

5.1,0 Statement of problem and procedures

The analytical model used in this chapter was discussed in considerable detail in Chapters 3 and 4-. It is useful at this point, however, to organize the presentation of the empirical findings by briefly summarizing the economic problem and the specific goals of the quantitative analysis in this chapter.

Grains are produced and stored in Parana, Assis (Sao Paulo), 23 Dourados (Mato Grosso) and Paraguay. Soybeans are processed in the interior, and the meal produced goes to satisfy domestic demand in Sac Paulo and export demand in Paranagua, along with c o m and unprocessed soybeans. The year is divided into four time periods to represent seasonal production,! demand and pressures on the transportation and storage system. Soybeans and c o m are harvested In periods 1 and 2 (mid-February-June). Transpor­ tation demands intensify in period 2 (mid-April-June), and reach their peak in-period 3 (July-October). Period 4- has no grain pro­ duction and low transport demands, since little besides meal is exported. Effective storage capacity is reduced for c o m and soybeans, however, due to the wheat harvest toward the end of period 3 and the first month of period 4- (November through January).

23 -Tlows from Santa Catarina and Rio Grande do Sul are neglected. They are currently only a small percentage of total exports through Paranagua and may cease entirely with planned improvements in the respective ports of Sao Francisco do Sul and Rio Grande. Assis and Dourados represent regions. Only Parana, however, is subdivided into microregions. 105

The first goal is to minimize the major cost items in grain transfers from each region to the demand, points. These include:

(1) handling costs associated with terminal operations and use of alternative storage facilities; (2) line haul costs of highway and rail transportation; (3) costs emerging from the interaction of storage and transportation in meeting time-specific demands at processing plants, Sao Paulo Capital and Paranagua.

The second goal is to locate the most serious bottlenecks in the system and quantify the costs imposed by the binding capacity constraints. "Bottlenecks" are saturated facilities and tend to have low costs. Saturation forces use of more costly transfer alterna­ tives. Total costs to grain shippers can be reduced by expanding the capacity of the bottleneck facilities. The size of the marginal operational cost savings with expanded capacities are indicated by the absolute size of the negative CBAR values.

The third goal is to analyze the effects of improvements in the system. This may be done in some cases using the CBAR values alone, or when major modifications in the system are contemplated, by simulating the improvements. The simulation procedure permits sequential location of bottlenecks as the binding constraints in each iteration are relaxed.

The final goal is to analyze the effects of sequential improve­ ments on the modal distribution of grain transport. This provides important information on the external effects of truck transportation even though the externalities are not included in the model itself. 106

5.1.1 Storage in 19?6

The total cost of the OKA optimal allocation of grain for 1976 is Cr$789 million (iteration 1, column 3» Table 5). This includes the handling costs for alternative storage facilities and all line haul and terminal costs for grain and meal delivered to the two "final demand" points of Sao Paulo and Paranagua (the latter receives•over

90% of the total). This is about Cr$l6l/ton, in comparison with a pro­ duct price of Cr$l,500-2,200 for soybean meal and soybeans. Since c o m prices average only about k0% of soybean prices, transfer costs repre­ sent the highest percentage of product price for com.

As expected, bulk storage is a bottleneck for most regions.

Important exceptions are Ponta Grossa and Castro, which have excess capacity in all time periods. Some representative storage arcs and 24- their parameters and solution values are presented in Table 6.

The Paranagua storage facilities are used to capacity from period 1 to period 2, and from period 2 to period 3* The negative 25 net arc-costs indicate port storage is a bottleneck for the system.

The solution to each subproblem includes the parameters and solution values for all nodes and arcs in the subproblem. Each sub­ problem has about 500 nodes and 1,100 arcs. Complete results of simulations discussed herein for the 1976 year alone occupy over ^MDO pages of computer listing, making reproduction unrealistic. Basic information for the initial 1976 solution is given as Appendix F .

^^If CBAR is negative, x. . = u. if positive, x. . = 1. . (Table 3). Only integer values are used In the OKA problem formulation and solution. The x. u. . and 1. . values are in tons, and the c. . and ij ij ij ’ ij CBAR values are in Cr$/ton. The tabular presentation used here omits the lower limits (zero except for processing and final demands which are not shown in the tables). Also omitted are the kilter numbers and node prices. Kilter numbers are always zero in optimal solutions, and node prices are somewhat tangential to the main thrust of this study. Table 5- Optimal solutions for 1976 basic transfer problem and sequential simulations of improvements in rail transportation and storage

Transport First Storage First Difference in Savings Simulation3, Itera­ Total Additional Itera­- Total Additional tion Cost Savings tion Cost Savings

(1) (2 ) (3) ...... (4) . ... (5) . (6) (7) (8)

Cr$ millions Cr$ millions & Cr$ millions

Basic 1976 solution 1 7 89 -- 1 789 — - -

Paranagua terminal 2 763 26 3.3 6 697 24 3.0 2.3

S erra; inter. term. 3 695 68 8 .6 7 630 67 8.5 0.9

Ponta Grossa receiving 4 688 6 0.8 8 629 1 0.1 5.8

10% additional storage 5 676 13 1.6 2 776 12 1.6 0.2

23% additional storage 6 664 12 1.5 3 761 15 2.0 -3.7

50 $ additional storage 7 655 9 1.2 4 745 16 2.0 -6.2

No storage constraints 8 629 26 3.2 5 721 _25 _ ^ 2 0.7

Total: 160 20.2 160 20.2

^ h e iterations add each modification to all existing ones (e.g., the iteration 3 simulation of im- improved serra line capacity and interior terminals also contains the iteration 2 improvement in the Parana­ gua terminal. The storage modification in columns 5-8, however, refer to 1976 basic storage levels. ^Figures are percentages of original Cr$789 million. Rounding may result in slight errors in sums and differences. Table 6. Partial listing of storage arcs in optimal solution to basic 1976 transfer problem

Location Storage Type , Arc G. .(arc cost) CBAR (net U . (upper Xi j ( f low) ’(periods) arc cost) J limit) ( 1) (2 ) (3) (4) (5) ( 6 ) (?)

Cr$/ton 1.000 tons

Paranagua Port (1 ,2) 0 -54 200 200 1! fl 2,3 0 -87 200 200 II It ( 3 /0 0 99 200 0 Paranagua Reserve3, ( 1 ,2) 53 -1 21 21 II It (2,3) 53 -34 21 21 II II (3,4) 53 152 21 0 Campo Mourao Bulk (1 ,2) 0 1 152 0 ii II 2,3 0 -68 152 152 II it (3,4) 0 -5 ?6 76 Campo Mourao Alternative (1,2) 46 32 0 ii ii 2,3) 45 -23 32 32 it ii ( 3 /0 45 40 32 0 Londrina Bulk (1,2) 0 0 247 126 II II 2,3) 0 -45 247 247 II II ( 3 /0 0 0 124 0 Londrina Alternative (1,2) 45 45 305 0 II It 2,3 45 0 II 11 305 65 ( 3 /0 45 45 305 0

^Reserve storage is not directly connected to ship loading equipment. Its use requires extra truck loading and unloading operations with inferior equipment under congested conditions. 108

Alternative facilities include inflatable warehouses, conventional warehouses, sheds and storage on the ground. The arc cost includes only the additional handling cost, although spoilage and other losses may also result. 109

The savings from an additional unit of storage would be Cr$(5^ + 87) =

Cr$l^l. They are additive since a one unit increase in a storage facili­ ty is an increase of one unit for each time period. These savings would accrue from the avoidance of the high terminal and line haul congestion costs in period 3» as well as a reduction in use of alternative storage facilities in the interior. Conversely, there would he a high penalty for transfering grain to Paranagua at the peak congestion costs of period 3 for storage into period k (CBAR = 99).

The "flow" or amount of storage from period 3 f° period 4 is there­ fore zero in the optimal solution (column 5 of Tahle 6).

The port's reserve storage capacity is also a bottleneck for periods 1-3. The CBAR values for the reserve storage differ by the differential handling costs (column of Cr$53/‘ton. The analysis of reserve storage is otherwise identical to that of bulk storage.

The interior storage situation differs from that of the port.

The interior facilities, such as those at Campo Mourao and Londrina, are not used to capacity from period 1 to period 2 (the x. ,'s are less a 3 than the u. 3,'s). Only part of the harvest is completed in period 1, and congestion costs are lower for shipments to processors, Sao

Paulo Capital and Paranagua. From period 2 to period 3> however, capacities are reached in the bulk storage facilities in most regions (the X. .'s = the U. .’s). A unit increase in capacity in

Campo Mourao at that time would result in a Cr$68/ton savings, and in Londrina, Cr$Vj/t°n » These savings would accrue from decreased

use of alternative storage facilities and the avoidance of long 110

26 line hauls during periods of highway congestion.

For bulk storage from period 3 to period 4-, the Campo Mourao CBAR

value is -5 and for Londrina, 0. These values contrast with the high positive costs for storage in Paranagua in the same time

period, since they represent the converse of the underlying

economic situation. In the interior, storage from period 3 to period

4- permits avoidance of line haul and terminal congestion during the

peak transport period.

The combined negative CBAR values for bulk storage in other

regions are also quite high. Some examples are (not shown in

Table 6 ): -73 for region 5*2; -57 for 18,1; -65 for 21.2, 22.1 and

22.2; and -72 for 21.1. The CBAR values for regions with no bulk 27 storage do not differ systematically from the other regions:

-81 for region 11; -57 for 18.1; -74- for 18,2; -60 for 19.2; and

-4-5 for 19.4- and 20. Region 10 (Reserva) actually has a positive

CBAR value (2) for arc(1,2) and zeros for the remaining two arcs.

This occurs since production may flow out into the surplus storage

area of Ponta Grossa in periods 1 and 2 before peak congestion

occurs in period 3- However, producers under this situation

either have to sell their produce earlier or join Ponta Grossa area

26 The area with the largest relative investments in storage (aside from Ponta Grossa) is in fact the more distant western area, which also tends to transport much of its grain at a later date than more conveniently located regions.

27 The CBAR values are obtained by the use of an arc representing bulk storage as in other regions, but with a nominal upper limit of one ton. Regions are identified in Figure 2 and Appendix A. Ill cooperatives.

In order to meet the seasonal demands, 782 thousand tons of alternative facilities are used in the different time periods (not shown in the tables). This constitutes about 1/6 of the total volume handled. In absolute terms, it is a large deficit, especially in view of Parana's annual production increases.

The magnitude of the annual savings on handling and transport costs possible through expanded use of bulk storage is indicated by the CBAR values cited in Table 6. They are comparable to the

"annual" outlay of Cr$^7/'t°n f°r construction of the least expensive bulk units ("V" floor types). This figure assumes that 10% of the total construction costs of Cr$^70/ton are charged to each year over an economic life of 10 years (Appendix D). In the Maringa and Lon­ drina areas, conversion of IBG warehouses costs one-sixth of this amount.

Additional construction, however, is not justified on the grounds of savings on handling and transport alone, since operating costs are much higher than the indicated annual outlay against construction costs (Appendix D). Selected projects may of course be justified on an individual basis if the other benefits from increased capacity are included: (1) reduced spoilage; (2) ability to market at times of favorable prices; and (3) lower within-region transfer costs (from farm to collection point). The CBAR values, however, do indicate substantial reductions in handling and transfer costs, a factor generally ignored or not quantified in studies of storage and transport.

In sum, the limited capacity of bulk storage imposes consider­ able costs on grain transfers and requires use of alternative units 112

and long line hauls at periods of peak congestion. The grea st

savings to the system would accrue for increases in capacity in

the port of Paranagua and in the more distant western areas of

the state. Intermediate locations, such as Ponta Grossa and

Castro, have excess capacity at present. Storage at these

locations during peak transportation demands is less efficient than

in the areas more distant from Paranagua and Sao Paulo Capital,

since it avoids less line haul congestion.

5.1.2 Transportation in 1976

The most costly transportation bottleneck in 1976 was the rail

terminal in Paranagua. Due to the 2-3 day delay in the port terminal,

the rail corporation was unable to meet the demand that existed for

rolling stock during periods 2 and 3- The terminal handling

capacity is estimated from the GREMOS data for period 3 when the

excess demand for rail cars was greatest. The simulated flows

in the OKA solution for rail and truck into Paranagua are given in

columns_7- and 8 of Table 7, part (a). They correspond closely with ^ 28 the GREMOS data for actual flows in 1976.

The Paranagua rail terminal cost, based on delay time, is

Cr$29/ton (column 4). In the optimal solution, the facility is used

28 The somewhat greater total flows in the optimal solution are due to the fact that the GREMOS data is for the 1976 calendar year, and the present study defines the year as February, 1976 to January 31, 1977. Expected production increases from the two years account for the difference. Table 7. Partial listing of rail arcs for 197b transfer problems, with modal split indicated

’ EL Sir 3, Location Facility Period CL ^ CBAR IL^ X.^ Truck Transportation ------tons modal shift (Cr$/ton) (1,000 tons) (l,000 tons) (%) (1) (2) (3) (4) (5) (6 ) (7) (8) (9) (10)

(a) 1978 optimal solution to basic transfer problem Paranagua Terminal 1 29 -23 451 451 182 -- It II 2 29 -68 462 462 587 -- II 11 3 29 -84 750 750 1,283 -- II II 4 29 0 560 337 1?5 - Total: 2,200 2,227 --

Simulation of improved Paranagua terminal Paranagua Terminal I 29 0 720 475 141 41 23 II II 2 29 0 737 625 442 145 25 II II 3 29 0 1,196 1,011 1,022 261 20 II It 4 29 0 895 537 175 0 0 Total: 2,648 1,780 447 20

Serra Rail line 1 21 0 608 475 141 41 23 II II 2 21 -30 625 625 442 145 25 It II 3 21 -64 1,011 1,011 1,022 261 20 It II 4 21 0 756 537 175 0 0 Total: 2,648 1,780 447 20

Improved terminals in Paranagua and interior, serra line capacity for grain and meal = •H Serra Rail line 1 21 0 912 472 141 0 0 II II 2 21 0 937 860 210 232 40 II 11 3 21 0 1,516 1,395 638 384 30 IV It 4 21 0 1,13^ 53? 175 0 0 Total: 3|2d 5 1,164 ZIB 28 Table ? (Continued)

Location Facility Period C. .a CBARa U. a X. .a Truck Transportation i.l ______i.l i.l (Cr$/ton) (1,000 tons) tons modal shift (1,000 tons) (1) (2) (3) W (5) (6) (7) (8) (9) (10)

(d) Addition of unloading terminals in Ponta Grossa Paranagua Terminal 1 29 0 840 472 141 0 0 II 11 2 29 -10 86 o 86 o 210 0 0 II 11 3 29 _34 1,395 1,395 638 0 0 II It 4 29 0 i, o ^ 537 173 ___ 0 ___ 0 Total: 3»265 1,164 0 0

Ponta Grossa Terminal 1 29 0 451 263 not applicable it ti 2 29 0 462 95 11 II ti ti 3 29 17 75 0 0 11 II ii ii 4 29 8 560 0 11 11

aC. . and CBAR are the arc cost and net arc cost, respectively, of a unit of flow, U. . is ij iJ the upper limit on flow. X. . is the flow in the optimal solution. Totals are subject to dis- 10 crepancies from rounding. ^This is the tonnage arriving in Paranagua by truck. Modal shift comparisons refer to the same time period of iteration 1 in part (a). The values in columns (8) - (10) are derived from solution values for highway arcs not shown in the table for the same time period as the corresponding rail arcs in columns (1) - (?). 115 to capacity in the first three periods, with excess capacity in the fourth period. This also corresponds closely with the actual 1976 situation. The CBAR values of -23, -68 and -8^ indicate the savings which would be available from a one unit increase in terminal capacity 29 in each of the first three time periods.

The network solution shows one significant difference from the actual pattern of rail shipments: the more distant interior terminals such as Maringa and Apucarana, along with the unloading terminal in

Ponta Grossa, are used to capacity, whereas shipments from Ponta

Grossa are confined to period shipments from processing plants

(Table 8).^° In 1976, about 80% of the pellets and much of the soy­ beans from Ponta Grossa area cooperatives were shipped by rail

[GREMOS; Fink], The network solution selects the longer hauls since they lower the terminal/line haul cost ratio and the proportion of the expensive serra line haul to total line haul distance. Conversely, use of longer rail line hauls lowers the use of long hauls for trucks, which are about three times more expensive between any two geographical

29 Any increase in annual capacity would not be uniform for all four periods, since the lengths of the periods vary. A unit increase in annual capacity would imply a 0.20 unit increase in period 1, a 0.21 unit increase in Period 2, 0.3^ in period 3 and 0.25 in period There would be no savings in period k since excess capacity is currently available in that period. 30 ^ The longer hauls might require slightly more rolling stock than used in 1976 on these lines. Rail officials could have met this need by transfering some cars from other divisions to Parana. Since their cars were already being used as surrogate storage due to ter­ minal congestion in the port, they were understandably reluctant to do so. Their contracts with processors thus bound them to the short hauls due to their inability to handle more cars in Paranagua. Table 8. Selected interior rail terminal arcs for optimal solution to 1976 basic transfer problem

Terminal Period G. . (arc cost) CBAR (net U (upper Xij (flow) ij V ' arc cost) ^ limit) (1) (2 ) (3) (4) (5) (6).

Cr$/ton 1,000 tons

Maringa 1 oa -20 118 118 11 2 oa -26 122 122 11 3 oa -28 197 197 11 4 oa -13 147 147 Apucarana 1 12 -14 89 89 11 2 12 -18 91 91 If 3 12 -19 148 148 11 4 12 -7 110 110 Guarapuava 1 12 0 222 213 11 2 12 0 228 218 11 3 12 0 369 109 II 4 12 0 276 235 Londrina 1 oa 0 266 31 11 2 oa 0 273 32 11 3 oa 0 442 296 II 4 oa 0 330 b 38 Ponta Grossa 1 29 0 .1 -33 0llb 2 29 -24 0.l£ 0.1 (receiving) 3 29 -3 °.i£ 0.1 4 29 -13 o.ib 0.1 cL This arc represents capacity only. Terminal costs are included on other arcs (Figure 17). A nominal capacity. Cianorte also was assigned a nominal capacity (CBARs = -30,-39,-42,-23). 11? points.The exception is the serra line, where costs are nearly as high as for truck transportation (Chapters 2 and 4 and Appendix C ).

Least cost allocation results in the use of the more' distant rail terminals to capacity. The absolute value of the CBAR figures indicate V that savings would he obtained from increased capacity and use of the furthest terminals. These terminals are in order: Cianorte, Maringa and i Apucarana (Table 8). The Cianorte loading and Ponta Grossa receiving terminals are assigned nominal capacities only to permit obtention of S the CBAR values. Cianorte lacks an adequate grain terminal, while Ponta

Grossa terminals are not well equipped for receiving grain and preference is given for allocation of available rail cars to the Paranagua route.

The model result conform closely with observed behavior of the transfer system in 1976, with the exception of decreased use of rail transportation from Ponta Grossa to Paranagua. The network solution con­ sistently chooses the longest rail hauls available in the system, using distant terminals up to the limits imposed by the Paranagua terminal handling .capacity. The actual railroad pattern with more short hauls results from the railroad’s use of an average cost figure which does not accolont for the high cost of the serra line haul and the terminal operations (see section 6.3.2).

5.1.3 Simulations of short term rail and storage improvements

Short term improvements in the transfer system include rail sidings and terminals, increased storage capacity and some marginal highway improvements. The construction of new rail lines and major highways is a long term option due to their extended gestation periods. Since on For example, the rail distance between Londrina and Paranagua is 680 km by rail and 506 by highway, or y\% more. Rail costs per ton-km traveled are only about 22% of those of trucking for line hauls. 118 marginal highway changes are of little interest, this section simulates the short term rail improvements and increases in storage capacity.

The CBAR values of Tables 7 and 8 reveal the Paranagua rail terminal to be the most costly binding transportation constraint. A trainload volume movement terminal could reduce the delay time from the current average of 6o hours to a maximum of 10 hours for each 80 car train. This would decrease the round trip time from Maringa to Paranagua from 134 to

84 hours and imply an effective increase in the availability of rolling 32 stock by 6o%. Since it is also a precondition for further rail im­ provements, the new terminal is the first simulated change in the network

(Tables 5-8). It results in a savings to the system of Cr$26 million or 3.3/6 of total 1976 transfer costs (iteration 2 , columns 3 and. 4 t of Table 5). This assumes that the same terminal charge is made

to users and all the savings from the reduced turn-around time

accruing to the rail corporation are invested to pay for the capital 33 costs of the new facility. The reduction in rail operating costs

associated with the 50 hour reduction in terminal time are

Ci$23.75 'ton, yielding an approximate total of Cr$63 million.

32 J This is based on the longest haul in the system. It is thus an underestimate of the actual capacity increase, since the Maringa and Apucarana terminals are already saturated and capacity increases will be allocated to shorter hauls. This compensates for any possible overestimate of available rolling stock in the solution to the basic 1976 problem (footnote 30). The 60% increase results from a 37% reduction in turn-around time. As an illustration, a 2 ton unit carries 1 ton/hour if the round trip takes 2 hours, and 2 tons/hour if it takes 1 hour (i.e., a 50% time reduction yields a 100% capacity increase). 33 Detailed engineering studies are needed to provide firm cost es­ timates. However, full costs should be charged to the rail account only if specific to the rail operation: scales, track and other equipment not used jointly by truck. Storage facilities and other jointly used equip­ ment needed in the absence of rail improvements are not rail specific. 1 1 9

This means that by charging the same rates, the railroad would have Cr$63 million savings to apply against the terminal construc­ tion costs, while there are additional direct benefits to grain transportation of Cr$26 million resulting from the modal shift from more expensive highway transportation to railroads.

There are also substantial external benefits to other users of the highway system, as shown in Table 7» part (b). The improved terminal shifts W ? thousand tons of grain from the highway into

Paranagua onto the railroad over the solution in part (a) with the 1976 terminal (columns 8 - 10). This represents a reduction of truck traffic of 20%, and would help alleviate congestion and raise the level of service on highways from Paranagua to Ponta

Grossa, with additional benefits on the highways leading to

Guarapuava (BR-277/373) the north (BR-376).

With the simulated terminal improvement, the serra line becomes saturated in periods 2 and 3» with high negative CBAR values of

-30 and -6k (part b of Table 7). Thus continued increases in rail traffic now depend on the ability of RFFSA to increase the serra line capacity.

Although construction of a second line on the serra has been initiated, completion is not expected for several years. Work on the line has been stopped, as the government is giving priority to completing the "Steel Railway" linking Minas Gerais and Sao

Paulo. The alternative until a new line is installed is to augment the capacity of the existing line. It is thus necessary to increase 120

the number and frequency of sidings. The terrain is so difficult,

however, that it may not he possible to build additional sidings on

the serra itself. There remains the possibility of increasing the

length of some of the existing sidings to accomodate 25 cars instead

of the present 22 [Falavinha]. Longer trains are probably impossible

due to the extremely short 90 meter radius on some curves. Additional

sections could be built above and below the serra itself to permit

massing of cars for peaking and directionally-imbalanced flows.

These might increase the capacity in the absence of legal res­

trictions on individual shipment delays [Peat, Marwick, Mitchell

and Company, 1975’- &5]- Finally, passenger traffic could be eliminated.

Such service incurs high deficits for RFFSA, and bus service is

reliable and readily available throughout Parana. An optimistic

estimate of the effects of these measures is an increase in capacity.'

available for grain shipments to ^-.5 million annual tons.

The improvement of the Paranagua terminal and serra rail line

makes it possible to substantially increase the availability of

rolling stock at the interior terminals. They were found to have

high negative net arc costs in the basic 197& solution. Thus,

the next simulation is comprised of an improved Paranagua

terminal, increased serra line capacity of k.5 million tons and a

minimum loading efficiency standard for all interior terminals

(iteration 3 of Table 5 and part c of Table 7). The standard

of efficiency is the ability to receive one 80 car train per day

and load it with a single commodity within 12 hours. This is not

- a high terminal standard, but a tremendous improvement over the 121 34 present terminals (see Appendix G for details on terminals).

This 12 hour reduction in turn-around time implies a 16.7/S 35 additional increase in the availibility of rolling stock.

The simulation of these improvements in the network yields a Cr$68 million savings from the modal shift of 6l6 thousand tons

(iteration 4 of Table 5 and part c of Table 7). This is a reduction in grain transfer costs of 8.6%, and of highway tonnage of 28% beyond that of the previous solution with only the improved Paranagua terminal.

Truck traffic is reduced by 52% from the original 1976 level, alleviating congestion, improving the level of service and reducing the external costs borne by other highway users.

The above solution is based on 1976 costs. The increased traffic on the serra line (about 0.6 million tons) would have to compensate for any discounted annual costs of increasing the line capacity. Similarly, the expense of improving terminal operations

in the interior should be compared with the reduction in delay costs

of 50% in those terminals. For the total rail transported volume,

these savings amount to about Cr$18 million, considering an operating

cost reduction of Cr$5.50 per ton (i.e., half the present costs).

34 The minimum standard is about three times the capacity of the C0AM0 terminal in Maringa, probably the best grain loading facility in the interior aside from those of one or two processing firms in Ponta Grossa. By way of comparison, there are about 30 TVM terminals in the state of Ohio alone. Some are capable of loading 100 cars of 85 net tons in 10 hours I Kane ]. ; Gars in Parana are only 5^ net tons, due to the limitations of the narrow guage.

o cr The rolling stock constraint is represented on the Paranagua terminal rail arcs, rather than by creation of additional nodes and arcs. 122

Again, a detailed engineering study would be necessary to obtain firm estimates on the cost side. Most of the complementary facilities

(i.e., storage construction, conveyor belts etc.) will be needed in any case to accomodate future production, even if truck transporta­ tion is used. Thus much of the investment need not be charged to the rail sector specifica,lly. The sidings and scales for a terminal of three times the present GOAMO handling capacity would amount to something on the order of Cr$i)-.5 million. The annual Cr$l8 million reduction in operating costs is sufficient to make construction of several of these terminals attractive if improvements in the serra line capacity and the port terminal in Paranagua are realized, and if some of the operating cost savings are passed on to the owners of the terminals.

The only other significant short run improvement possible for the rail system is that of increased receiving capacity at the processing plants in Ponta Grossa. The firms which have loading terminals (the INGOPA firm does not) are not well equipped to receive soybeans by rail. Present incentives to modify their terminals are small indeed, given the inability of the railroad to furnish cars to shippers in the interior. In previous iterations, receiving capacity was constrained to a nominal 100 tons per period, since the efforts of the railroad to serve both Ponta Grossa and Parana­ gua would likely have created a rolling stock shortage.

Iteration of Table 5 shows a modest savings of Cr$6 million for removal of the constrain on rail receiving capacity in Ponta Grossa, assuming other improvements in the system are in operation and the same 123

Cr$29/ton unloading charge is maintained. If the terminal's physical efficiency were raised to the level of the new Paranagua terminal,

the associated reductions in operating costs would be about Cr$8.5 million. If truck transportation were used exclusively from a

joint terminal (e.g., through conversion of the new CIBRAZlSM storage unit), these reductions would be about Cr$4.9 million. There are also beneficial externalities with the increase in use of the Ponta

Grossa terminal. They result from a reduction of 358 thousand tons

of grain arriving in Ponta Grossa by truck, significantly reducing

congestion on the interior highways.

The rolling stock constraint (represented as terminal capacity

in Paranagua) is binding in periods 2 and 3> with GBAR values of

-10 and -34 (Table 7» part d). Although not shown in the table, the

same constraint was binding in the previous iteration when the increase

in serra line capacity was simulated (CBARs = -16, -34). Continued

increases in rail traffic would require new acquisitions of rail

cars and-locomotives by RFFSA. The serra rail line, however, is

not saturated in either simulation (c) or (d) of Table 7.

The only significant short term improvement remaining to be

simulated is an increase in bulk storage capacity. Table 5 presents

the results of simulated increases. In columns 2-4, the storage

increases are simulated after the rail improvements (i.e., itera­

tion 5 assumes all rail improvements have been made, and merely

This assumes the 1978 level of Cr$lo/ton for transport by dumptruck from the Ponta Grossa cooperative to the nearby industries with scheduling to avoid terminal congestion [Fink ]. 12k adds a 10% bulk storage increase in all microregions. The simu­ lated 10% increase over 1976 hulk capacity yields a savings of Cr$13 million. For a simulated increase of 2.$% over 1976, there is an additional savings of Cr$12 million, or a total of Cr$25 million.

For an increase of 50%, the figures are Cr$9 and Cr$3^ million. The removal of all storage constraints results in a savings on handling and transportation costs of Cr$6o million, or Cr$26 million over a

50% storage increase.

Three points are of note regarding these results: (1) Although most of the reduction in costs is due to savings on handling by using bulk storage, there are important interactions between the transportation and storage sectors. For example, the Cr$13 million savings on a 10% storage increase are composed of a Cr$9-5 million reduction in handling costs, with the remainder resulting from the ability of distant regions to ship by rail during periods of highway congestion (not shown in the tables). Furthermore, the use of alternative storage becomes progressively concentrated in

Maringa, Apucarana and Campo Mourao as the percentage increase in storage is changed from 10 to 25 to 50%. The bulk storage arcs for these cities have large negative CBAR values (not shown).

The first two centers have important rail terminals, while Campo

Mourao ships much of its production to Maringa for transfer to the railroad. The production in these areas is thus stored to take advantage of cost reductions due to long hauls by rail as opposed to truck; (2) Considerable reductions in transfer costs are obtained even with very large increases in storage capacity; and (3) Alternative storage facilities are used in some regions 12 5 even with a 50% increase in bulk storage (148 thousand tons).

Clearly, indiscriminate storage increases of the magnitude simulated are not economically justified, due to the difference in regional storage adequacy and the high operating costs of storage units (Appendix D). The capital costs of construction, however, are probably compensated by reductions in handling and transfer costs for the 10 and 25% options. The values correspond roughly with the previous calculations based only on the negative CBAR values for the storage arcs in the basic 1976 optimal solution. For this particular case, the CBAR values were indicative of savings over a fairly wide range of increase in capacity, although they are precise measures only for a unit change in the upper limit on each arc taken separately.

Since storage and transportation are interdependent sectors, there may be a difference in the benefits from rail and storage pro­ jects if the order of implementation is altered. This is examined in columns 5~8 of Table 5» where the order of implementation is re­ versed. - As in the previous simulations (columns 2-4 of the same table), the first iteration represents the 1976 basic transfer problem.

As shown in column (5)» however, iteration 2 now simulates the 10% storage increase with no transportation improvements, iteration 3 the 25% storage increase etc. Only after iteration 5 when all storage constraints are removed will the first rail improvement be simulated, so that the improved Paranagua terminal is given as iteration 6 in the "storage first" sequence of columns (5) - (8).

The savings from the simulated 10% bulk storage increase are

Cr$12 million, approximately the same as before when the same increase 126

37 was simulated after all rail simulations (column 2, iteration 5)•

For the 25 and increases, the benefits from the storage pro­ jects are substantially greater than if rail improvements are realized first: Cr$3-7 and- Cr$6.2 million, respectively (column 8). These inter­ mediate levels minimize long truck hauls during peak congestion and their relative effect would be greater for the 1976 system. This does not hold for the removal of all constraints, however (iteration

5, columns 5~8).

The major difference in absolute savings for the two sequences is for the Ponta Grossa receiving terminal. If the terminal is in­ stalled first (iteration k, columns 2-4), the savings are Cr$6 million for the transfer system. If realized after all storage restrictions are removed (iteration 8, columns 5~8), the savings are Cr$0.5 million.

This occurs since Ponta Grossa has excess storage. When storage in the rest of the interior is a binding constraint, considerable savings accrue by rail shipments in periods 2 and 3» since this results in less highway congestion and truck terminal congestion at the processing plants. When storage in the interior is unconstrained, however, the processing demand may be met by allocations from nearby areas during the congested periods, so that the rail option results in lower savings to the system.

Despite the interaction between storage and rail transportation,

Table 5 reveals that the differences in absolute values are not large between the two sequences when compared with the total savings from

37 Column (8) shows a difference in savings of Cr$0.2 million, rather than Cr$13 - Cr$12 million = Cr$l million, since the latter numbers were subject to large rounding errors when expressed in Cr$ millions for the table. Computer figures are precise to one cruzeiro. 127 each improvement. Thus investments in one sector do not tend to eliminate the benefits from investments in the other sector.

The exception is the Ponta Grossa receiving terminal in the extreme case of prior removal of all storage restrictions. For most realistic ranges of storage improvements, investments in either sector are likely to reduce total costs of transfer without substantially reducing the benefits from investments in the other sector.

5.1.4 Summary of 19?6 results and simulated short term improvements

The 1976 basic solution shows the rail and storage sectors to be costly bottlenecks to the grain transfer system. The most costly rail bottleneck is the Paranagua rail terminal. The pattern of flows in the optimal network solution differs from the actual pattern in one significant respect: rail cars are allocated to more distant terminals until their capacities are reached.

The actual 1976 pattern allocated cars largely to the Ponta Grossa no area, with the more distant regions served as residual claimants.

The network solution minimizes system costs by choosing long line hauls, where the railroad has a distinct advantage. Like the actual system, however, the OKA solution results in excess capacity for the rail system in period 4, and the serra rail line is not saturated in any period. Total costs are approximately Cr$l6l/ton in the original solution.

OQ The network solution does not take into account the railroad.’s desire to maximize its cargo nor the resulting contracts which produced the actual pattern. These factors are discussed more fully in the implications section of Chapter 6. 128

The rail improvements which are relevant in the short run are:

(1) improvements in terminals; (2) improvements in the serra line capacity; and (3) increases in rolling stock. The inefficient ter­ minal operation in Paranagua acts as a constraint on rolling stock, in addition to being a costly operation in terms of labor and equip­ ment. An improved terminal capable of reducing rail car delay in the port to 10 hours constituted the first simulation. Total costs to the grain sector were reduced by 3-3% and 20% of the grain tonnage was removed from the highways. The rail corporation had a reduction in operating costs of Cr$63 million to apply against the capital costs of construction for the project. The effective increase in rolling stock from reduced terminal delay was approximately 60%.

The effective increase in rail car availability led to the satura­ tion of the serra rail line in periods 2 and 3 and. brought an obvious need to upgrade interior terminals, since an increase in the supply of cars to the interior would be forthcoming. The joint simulation of improved _interior terminals and expanded capacity of 50% for grain and meal on the serra rail line provided the largest savings to the grain transfer sector of any set of simulations (8.6%) and the largest decrease in truck traffic (28%). The hypothesized reduction in interior terminal time from 2k to 12 hours resulted in a further in­ crease in rolling stock capacity of 16.7%, and an operating cost re­ duction of Cr$l8 million to be applied against the construction costs annually. The increased traffic on the serra line was about 0.6 million tons, leaving the line with excess capacity as rolling stock again became the constraint. 129

The final rail simulation was an improved terminal for unloading soybeans in Ponta Grossa, along with some increase in rolling stock for that purpose. The savings were a modest 0.8% of total costs, but there was a significant external benefit in the form of a reduction of 358 thousand tons of grain arrivals in Ponta Grossa.

The other simulated improvements for the short run were storage increases. The basic 1976 solution showes that returns would be highest for storage investments in Paranagua and in the more distant northern areas of the state. Ponta Grossa and Castro had excess capacity.

Storage in these intermediate regions does not have the favorable effect of diminishing long line hauls at times of peak congestion provided by storage in the port and more distant interior regions. The storage simulations were therefore used to provide information on the degree of competition between sectors regarding benefits. The Ponta Grossa receiving terminal was found to provide substantially greater benefits when storage supply was tight than when all storage restrictions were removed* - For the other improvements, the difference in savings due to the ordering of projects tended to be small in relation to the total savings induced by each improvement. A uniform increase in bulk storage capacity of 10% would reduce total transfer costs by 1.6%.

This reduction in handling and transport costs would approximately cover the capital costs of the least expensive new units over a 10 year period. Other benefits would have to be included to justify construction on an individual project basis, however, since the operating costs of storage units are quite high in relation to construction costs. 130

The simulated rail improvements reduced costs to grain and

meal shippers by Cr$l00 million, or 13% of the total cost of handling

and transporting all grain and meal delivered to Paranagua and Sao

Paulo. These figures are based on present rate charges, so that

reductions in operating costs could be applied against the capital

costs of the improvements. Engineering and cost studies are needed to

ascertain if operating cost savings are greater than the capital

costs of the projects.

The favorable external effects from a reduction in truck traffic

are not given a monetary value in the model, but are likely to be

the largest savings of all. The improvement of the Paranagua

terminal shifts 20% of incoming truck tonnage to rail, and the

improvement of the serra line and interior terminals elevates the

cumulative modal shift to kd%. This reduction of truck traffic

on the highway leading to Paranagua would substantially reduce

congestion on Parana's major arteries and reduce delay costs to

other users. Increased use of rail for shipments of soybeans to

processing plants in Ponta Grossa would have a similar, if less

dramatic, effect,

5.2 Long run improvements in transportation and storage facilities

5-2.0 Introduction

Parana is expected to increase its exportable surpluses by two

or three times during the next decade (Chapter 2 and Appendix B).

Based on intermediate and high production estimates, this section

explores: (1) the effects of the most relevant transportation al­

ternatives on the costs and flows of the increased volumes; and 131

(2) the increase in bulk storage required to meet seasonal demands.

5.2.1 Assumptions

A number of assumptions are required regarding the probable physical and economic environment in the mid-1980's. First, for the railroad sector it is assumed that: (1) physically efficient trainload volume terminals will be added as needed at all rail nodes included in the analysis, and rolling stock will be acquired as needed, so that neither terminals nor rolling stock will become bottlenecks; (2) costs of terminal operations remain at the 1976 level, a very conservative assumption in view of the savings in operating costs; and (3) the serra line capacity is initially at the 1976 level of 3 million annual tons available for grains and meal.

Secondly, two very liberal assumptions are made with regard to the highway system: (l) the Paranagua truck terminal will be able to handle any volume at the same average cost as in 1976; and

(2) highway capacities for truck traffic are unrestricted and line haul costs remain at the 1976 levels. These assumptions are dis­

cussed where relevant in the sections on simulations and inter­

pretation below.

Thirdly, it is assumed for the storage sector that: (1) any

new storage facilities for grain will be bulk units, so that al­

ternative storage facilities remain at the 1976 level; and (2)

bulk storage will be perfectly distributed among regions according

to the present statewide ratio of storage capacity to production.

Fourthly, the only changes in the processing sector are

assumed to be the proposed plants in Gascavel and Ponta Grossa 132

(the Anderson Clayton addition). They are assumed to operate at 80% of their nominal capacity of 1,500 and 2,400 tons/day, respectively.

Finally, the demand structure is taken to be proportional to that existing in 1976. That is, the same proportion of total production will go to Sao Paulo and Paranagua in each of the four time periods as in 1976 (Chapters 3 a-nd 4).

5.2.2 Simulations of long range improvements under intermediate production levels

The simulations of long range improvements are given in Tables

9, 10 and 11, along with the most significant empirical results. The intermediate production level leads to shipments to the port roughly double the 1976 levels (Chapter 2 and Appendix B).

The first simulation includes the intermediate production levels and the assumptions of section 5-2.1. The assumptions limiting storage increases to bulk storage expansion proportional to the 1976 ratio of bulk storage to production result in an infeasible solution. The total storage facilities are insufficient for the flows to meet the seasonal demands (Chapter 3)- The bulk storage facilities will have to expand at a greater rate than production unless: (1) producers market a larger proportion of grain at harvest time when prices are lowest, an option not simulated in the model* or (2) considerable costs are incurred from spoilage and extra handling, since more alternative facilities would have to be used than the estimated available units in 1976.

A feasible solution is obtained in iteration 2 by increasing bulk storage capacity an additional 10%. The total transfer cost Table 9. Total transfer costs for simulated, transportation and storage improvements under intermediate and high levels of future production

Bley-Gutierrez Sequence Guarapuava-Cascavel Sequence Difference in Description of Simulation Itera- Total Savings'" Itera- Total Savings Savings tion Cost Cri|> % tion Cost JL (1) (2) (3) (4) (5) (6) (?) (8) (9) (10)

Intermediate Production: Cr$ millions Jk. Cr$ millions % Cr$ millions Mid-1980's production 1 infeasible 10% additional storage 2 1,428 - - 25% additional storage 3 1,392 36 2.5 Highway improvements 4 1,347 45 3.2 Serra capacity 14 mil. 5 1,241 107 7.5 Eng5 Bley-EngS Gutierrez 6 1,198 42 2.9 7 1,107 41 2.9 1 Eng2 Gutierrez-Guarapuava 7 1,146 52 3.6 8 1,009 98 6.9 -46 Guarapuava-Cascavel 8 1,009 137 9.6 6 1,148 92 6.4 4 5 Maringa-Campo Mourao 9 995 14 1.0 C ianorte-Guaira 10 989 6 0.4 Total: 439 30.7 Table 9 (Continued)

Bley-Gutierrez Sequence Guarapuava-Cascavel Sequence Difference in Description of Simulation Itera- Total! Savings Itera- Total Sayings Savings tion Cost Gj|> % tion Cost frij> % (1) (2) (3) W (5) (6) (7) (8) (9) (10)

High Production: Cr$ millions Cr$ millions % Ci$ millions

Mid-1980's production 1 infeasible 10% additional storage 2 infeasible 25/£ additional storage 3 2,112 - - Highuay improvements 4 2,038 .7^ 3.5 Serra capacity 14 mil. 5 1,900 • 138 6.5 Eng2 Bley-Eng2 Gutierrez 6 1,850 50 2.4 7 1,752 55 2.6 -5.1 Eng2 Gutierrez-Guarapuava 7 1,698 152 7.2 8 1,472 279 “ 13.2 -127.3 Guarapuava-Cascavel 8 1,472 225 10.7 6 1,807 93 4.4 132.4 Maringa-Campo Mourao 9 1,451 21 1.0 C ianorte-Guaxra 10 1,442 9 0.4 Total: 669 31.7

Monetary values for savings are differences from the previous iteration. Percentages refer to percent of the cost of the first feasible solution. Table 10. Modal split for grain arriving in Paranagua in mid-1980's under intermediate and high levels of future production

Bley-Gutierrez First Sequence Guarapuava-Cascavel First Sequence Description of Simulation Iter- Truck Rail Shift Iter- Truck Rail Shift ation tons ^ ation tons ^ (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Intermediate Production: 1,000 tons % 1,000 tons %

Mid-1980's production 1 infeasible 10% additional storage 2 5,661 3,000 -- 25% additional storage 3 5,661 3,000 -- Highway improvements 4 5,661 3,000 -- Serra capacity 14 mil. 5 3,397 5,263 2,263 40.2 Eng2 Bley-Eng2 Gutierrez 6 2,987 5,674 410 7.2 7 2,878 5,783 296 5.2 Eng2 Gutierrez-Guarapuava 7 601 8,060 2,386 42.1 8 601 8,050 2,277 40.2 Guarapuava-C ascavel 8 611 8,050 -10 -0.2 6 3,174 5,487 224 4.0 Maringa-Campo Mourao 9 611 8,050 - -

C ianorte-Guaira 10 611 8,050 - - Total: 5,049 89.2 Table 10 (Continued)

Bley-Gutierrez First Sequence Guarapuava-Cascavel First Sequence Description of Simulation Iter- Truck Rail Shift Iter- Truck Rail Shift ation tons ~~^ ation tons % (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

High Production: 1,000 tons % 1,000 tons JL

Mid-1980's production 1 infeasible 10% additional storage 2 infeasible 25% additional storage 3 9,693 3,000 - - Highway improvements 4 9,693 3,000 - - Serra capacity 14 mil, 5 6,358 3,335 3,335 34.4 Eng2 Bley-Eng2 Gutierrez 6 5,691 7,002 667 6.9 7 5,617 7,076 504 5.2 Eng2 Gutierrez-Guarapuava 7 1,4-94 11,199 4,198 43.3 8 1,494 11,199 4,123 42.5 Guarapuava-C ascavel 8 1,494 11,199 - - 6 6,121 6,572 237 2.4 Maringa-Campo Mourao 9 1,524 11,169 -30 -o,3 C ianorte-Guaira 10 1,524 11,169 - - Total: 8,169 84.3

Expressed as a percentage of truck shipments in first feasible solution Table 11. Rail lines which operate at capacity under simulated 1980's conditions, time periods and net arc costs of capacity constraints

i Description of Simulation Iteration Saturated Lines Periods Net Arc Costs (CBAR Values)

Intermediate Production:

10% additional storage 2 Eng2 Bley-Paranagua (serra) 1,2 ,3,4 -16,-47,-80,-19

25% additional storage 3 Eng2 Bley-Paranagua (serra) 1,2,3,4 -22,-56,-76,-15

Highway improvements 4 Eng2 Bley-Paranagua (serra) 1,2,3,4 -13,-47,-67,-6

Serra capacity = 14 million 5 Ponta Grossa-Eng2 Bley 2,3 -30,-49 Eng2 Gutierrez-Ponta Grossa 1,2,4 -7,-5,-5

Eng2 Bley-Eng2 Gutierrez 6 Apucarana-Ponta Grossa 2.3 -4,-4 Ponta Grossa-Eng2 Bley 2.3 -12,-22 Eng2 Gutierrez-Guarapuava l,2,3,4 -13,-33,-55,-11

Eng2 Gutierrez-Guarapuava 7 Ponta Grossa-Eng2 Bley 2,3 -3,-10

Guarapuava-Cascavel 8 none none

Maringa-Campo Mourao 9 none none

C ianorte-Guaira 10 none none

H -O Table 11 (C ontinued)

Description of Simulation Iteration. Saturated Lines Periods Net Arc Costs 1 (CBAR Values)

High Production:

25% additional storage 3 Eng° Bley-Paranagua (serra) 1,2 ,3,^ -28,-61,-80,-21

Highway improvements 4 Eng2 Bley-Paranagua (serra) 1,2,3,^ .-25,-58,-76,-17

Serra capacity = 14- million 5 Ponta Grossa-Eng2 Bley 2,3 -34,-49 Eng2 Gutierrez-Ponta Grossa 1,2,4 -7,-1,-5 Apucarana-Ponta Grossa 3 -8

Eng2 Bley-Eng2 Gutierrez 6 Ponta Grossa-Eng2 Bley 2,3 -34,-49 Apucarana-Ponta Grossa 3 -8 Eng2 Gutierrez-Guarapuava 1,2,3,^ -13,-41,-55,-11 Eng2 Gutierrez-Guarapuava 7 Ponta Grossa-Eng2 Bley 2,3 -12,-29 Apucarana-Ponta Grossa 3 -19 Eng2 Bley-Eng2Gutierrez 2,3 -7,-35 1 1 •p" Ponta Grossa-Eng2 Bley 0 G uarapuava-C ascave1 8 s*

2,3 ■K Apucarana-Ponta Grossa 3 -6 Eng2 Bley-Eng2 Gutierrez 2,3 -16,-46 Eng2 Gutierrez-Guarapuava 3 -4

Maringa-Campo Mourao 9 Ponta Grossa-Eng2 Bley 2,3 -12,-40 Apucarana-Ponta Grossa 3 -10 Eng2 Bley-Eng2 Gutierrez 2,3 -12,-46 Eng2 Gutierrez-Guarapuava 3 -4

C ianorte-Guaira 10 same as iteration 9 same as iteration 9 139 is Cr$l,^28 million (Table 9)-"^ The cost per ton is Cr$lh-9, slightly below the Cr$l6l/ton cost of the 1976 basic solution. This is due to:

(1) the absence of rail terminal constraints; (2) shifts in crop production towards microregions closer to the final demand points; and (3) the assumptions of constant highway costs and unrestricted highway capacities. In reality, this solution is probably infeasible as well. The increased volume of truck tonnage into Paranagua of

2$\% over 1976 levels would probably be possible only with the con­ version of all major arteries connecting Guarapuava and Londrina with Paranagua into four lane highways. The existing two lane roads were already at or near saturation in 1976 (Appendix G ). Specific capacities were not assigned to highway arcs, since more grain can be accomodated on a saturated roadway by forcing other traffic off the facility. This process, however, cannot continue indefinitely on a two lane highway. Similarly, it is hard to imagine any improve­ ment in the truck terminal within the port area capable of handling this volume of trucks at 1976 average terminal cost levels.

Since the 10% increase still requires almost all of the avail­ able alternative storage facilities to remain in use, the third simu­ lation substitutes a 2$% storage increase to allow greater flexi­ bility in allocating transfers to meet the seasonal demands.

This yields a 2 . ^ savings for the transfer system (Table 9). The

simulated 2$% increase is retained for all the intermediate production

level simulations to follow.

■^Costs in text and tables are given in 1976 cruzeiros. The per ton costs cited include shipments to Sao Paulo not shown in the tables. OAO

Since the highway improvements of Chapter 2 are expected to he largely complete before any major transformation of the rail network occurs, they form the next simulation (iteration ^ of

Tables 9 and 10). These include all programmed extensions and improvements in the highway system through 1980. To be a feasible solution for the required volume of traffic, however, the changes would probably have to include the conversions of highways into freeways mentioned earlier. Since the cost figures used in the model are the 19?6 financial charges to shippers per ton-kilometer, they underestimate the social cost of trucking. The social cost includes provision for the infrastructure and the external costs borne by other users.

The effect of all the highway improvements on grain traffic is a savings in operational costs of Cr$^.5 million, or 3*2% of the costs of the first feasible solution (intermediate production section of Table 9, columns k and 5). This figure does not include, however, the benefits from improved local (farm-to-collection point) grain transportation nor benefits to other traffic. There is no modal shift among arrivals in Paranagua from the highway improvements.

The rail lines continue to have lower costs and are saturated by traffic (Table 10).

The remaining simulations in Tables 9 - H are alternative rail improvements, since the preceding iterations answered the principal questions regarding the storage and highway sectors. i4i

Given the results of the short term analysis and the assump­ tions that neither terminals nor rolling stock will constitute a bottleneck in the mid-1980's, the first priority for improved rail service is the completion of the new serra line. This is simulated in iteration 5 of Tables 9-H* for an assumed capacity of grain 40 traffic of 14 million annual tons between the new and old lines.

The LRAC figure is taken as two-thirds of the 1976 level for the serra line (i.e., 1,76 times the 197& LRAC on the remainder of

Parana's rail lines). This cost figure should make a substantial contribution to the capital costs of construction. The elimination of passing operations in both directions and the high technical standards of the new line should reduce operating costs to the level of those on the rest of the lines in the state.

The reduction in the total transfer bill is substantial:

Cr$l07 million, 2.4 times all the planned highway improvements and

7.5% of all handling and transportation costs (Table 9). There is a modal shift of 2.3 million tons from truck to rail, reducing truck tonnage into Paranagua by 40% (Table 10).

The effect of each of the above simulations on rail line bottlenecks is shown in Table 11. Until the simulation of in­ creased serra line capacity, that line was the only rail bottle­ neck, with very large negative CBAR values. These values were

40 The new line would probably duplicate the old line rather than replace it. The old line would probably still be used to go down the serra into Paranagua, and the new one used for the return. The elimination of passing operations should increase capacity about 4 times, as occurs when a 2 lane highway is converted to a 4 lane freeway [Highway Research Board: 284; 203-4]. 142 only slightly reduced from their values in the first feasible solution by the simulated 25% bulk storage increase and by the highway improve­ ments. The 14 million ton annual capacity, however, eliminates a bottleneck on the serra. This leads to the saturation of the Ponta

Grossa-Eng2 Bley line in periods 2 and 3 (CBARs = -30 and -49) and the Eng2 Gutierrez-Ponta Grossa line in periods 1, 2 and 4 (CBARs =

-7, -5 and -5). As expected, these bottlenecks are less costly than the serra line in the previous iteration (CBARs = -13, -47, -67 and

- 6 ).

The Ponta Grossa-Eng2 Bley line reaches saturation in peak transport periods 2 and 3 since it must carry all traffic from the northern and western parts of the state (Figure 4). The rail line capacity could be marginally increased by increasing the number and frequency of sidings. Still, traffic from the west cannot be in­ creased since the Eng2 Gutierrez-Ponta Grossa is also saturated and its capacity probably cannot be increased without major recon­ struction-. This is not an attractive alternative for grain traffic since the construction of a direct link from Eng2 Bley to Eng2 £> / Gutierrez would reduce the line haul from western Parana to Paranagua by 121 km, avoiding Ponta Grossa and alleviating congestion on the

Ponta Grossa-Eng2 Bley line.

The direct Eng2 Bley-Eng2 Gutierrez line is thus simulated

as iteration 6 in Tables 9-11. The cost savings are relatively

modest: Cr$42 million or 2.9% of the total costs in the first feasible

solution (Table 9)• The savings are limited largely to cost reductions from, reduced distance, since the EngS Gutierrez-Guarapuava line becomes a bottleneck to increases in grain shipments from the west. The CBAR values for the Eng2 Gutierrez-Guarapuava line indicate that it is a very expensive bottleneck: -13, -33» -55 and - H (Table

11). The Apucarana-Ponta Grossa line also appears as a bottleneck for the first time. It is saturated in periods 2 and 3 (CBARs =

-4 and -^). This occurs since the northern area of the state now supplies more tonnage by rail to Ponta Grossa crushing plants, but the costs imposed by the constraint are minor. There is a beneficial externality from the Eng2 Bley-Eng2 Gutierrez link since it results in a truck-rail shift of ^10 thousand tons among arrivals in Parana­ gua. (Table 10).

Since the EngQ Gutierrez-Guarapuava line is the ir. in rail bottleneck at this point, its improvement to 1976 standards for

the Apucarana-Ponta Grossa line is simulated in iteration 7 of Tables

9-11 (much of the line might have to be completely replaced to achieve this improvement). The addition of this improved link reduces transfer costs by Cr$52 million and shifts 2 ,k million tons of grain arrivals in Paranagua from road to rail (Tables 9 and 10).

Total truck arrivals thus drop from about lyvfo of 1976 truck tonnage

to the port to a modest 27% or 610 thousand tons. For the first

time, the grain traffic is no longer a major contributor to con­

gestion on the main arteries east of Guarapuava. For 1976 levels

of other traffic, there would be no need of converting all these

arteries into freeways (drastic gasoline taxes have stopped growth

of automobile traffic). For the railroad itself, the only existing 144 bottleneck is now the Ponta Gr.ossa-Eng2 Bley section in periods 2 and 3 (CBARs = -3 and -10, seen in Table 11).

At this point, however, the railroad still does not reach the

Gascavel area - Parana's most important producing region. The ex­ tension of rail service from Guarapuava to Cascavel is simulated in iteration 8 of Tables 9-H. The cost figure assumed is again the

1976 LRAC for the line haul, and the large volumes and distances involved result in an additional savings to the grain sector of

Cr$137 million (Table 9). There is a negligible modal shift from rail to truck, and there are no longer any rail bottlenecks (Tables

10 and 11, respectively).

There are two other long range improvements in rail transporta­ tion to be simulated: (1) the Maringa-Campo Mourao branch line and

(2) the Gianorte-Guaira extension. Both of these additions involve construction of new lines (Chapters 2 and 4). This implies a substan­ tial capital outlay, although the terrain presents no major problems for construction. Since Campo Mourao already has access to a rail terminal in Maringa and the area to be served by the second line is not a major producing area, these projects are not considered priority items by RFFSA. These additions are simulated in iterations

9 and 10 of Tables 9-H. The Maringa-Cianorte line yields a reduction

in transfer costs of Cr$l4 million, while the longer Cianorte-Guaara

line saves only Cr$6 million (both figures are based on 1976 LRAC

for line hauls). The limited savings for the rail extension to

Guaira are found despite the simulation of terminals in the inter­ mediate points of Umuarama and Ipora. This project is clearly inferior Ik 5 to the Maringa-Campo Mourao line with respect to the impact on grain shipments, The cost reductions in both cases are modest, however, and neither extension results in further modal shifts of arrivals in Paranagua.

As discussed in the section on short term improvements, the benefits from individual projects may vary with the sequence in which they are adopted. There is one important alternative to the sequence of long range rail improvements just described: the addition of the Guarapuava-Cascavel line without prior improvements on the

Eng2 Gutierrez-Guarapuava line or the construction of the direct link from Eng2 Bley to Eng2 Gutierrez. This latter sequence is given in columns 6-9 of Table 9 and- columns 7-11 of Table 10.

As in the previous sequence, all the modifications in iterations

1-5 are introduced, so that the starting point is "Serra capacity 1^4- mil." with a total cost of Cr$l,24l million and 3,397 thousand tons sent by truck to Paranagua (columns 1-3 of Tables 9 and 10, respectively).

For the'Guarapuava-Cascavel first sequence, however, iteration 6 is located in column 6 of Table 9 and column 7 of Table 10. The description of the simulation is read in column 1 in both tables

(i.e., for iteration 6 in the tables, "Guarapuava-Cascavel" is found in column 1 for the Guarapuava-Cascavel sequence on the right hand side of the tables). Thus, when the Guarapuava-Cascavel addi­ tion is simulated after the increased serra line capacity, the total cost is Cr$l,l48 million, a Cr$92 million savings (6.k%) of the grain transfer costs (columns 7”9 of Table 9). The savings are Cr$^5 million 146 less than in the previous sequence when the line was added after the

Eng2 Bley-Eng2 Gutierrez direct link and the new or upgraded Eng2

Gutierrez-Guarapuava line (column 10, obtained by subtracting column

8 from column 4). The modal shift among arrivals in Paranagua is a modest 224 thousand tons (column 10 of Table 10). The savings and the modal shift are both limited in this sequence by the fact that the lines between Guarapuava and Eng2 Bley are unable to handle in­ creases in traffic.

The subsequent addition of the direct link from E n g 2 Bley to

Eng2 Gutierrez permits an additional savings of Cr$4l million, Cr$l million less than in the Bley-Gutierrez first sequence (columns 8 and 10 of Table 9). The modal shift among arrivals in Paranagua is

296 thousand tons (column 10 of Table 10). The savings are due to the reduction in the length of the line haul to Paranagua and the minor diversion of traffic from truck to rail. Both savings and total rail

traffic are limited by the inability of the E n g 2 Gutierrez-Guarapuava

line to 'carry more traffic.

The last link to be added in the Guarapuava-Cascavel first

sequence is the new or upgraded Eng2 Gutierrez-Guarapuava line

(iteration 8 in column 6 of Table 9 and column 7 of Table 10).

The savings are Cr$98 million, or 6,9% of the total transfer costs in

the first feasible solution (columns 8 and 9 of Table 9). This is

Cr$46 million more than in the Bley-Gutierrez first sequence since

its low capacity had prevented greater use of the low cost long rail

hauls from Gascavel as well as increases in overall rail traffic

(column 10 of Table 9). The addition of the line permits a dramatic 1^7 truck to rail shift of 2,277 thousand tons of grain arriving in

Paranagua. The same final network structure emerges in iteration 8 for both the Eng2 B l e y - E n g 2 Gutierrez and Guarapuava-Cascavel sequences.

Thus the total cost (Cr$l,009 million) and the modal distribution of freight (611 and 8,050 thousand tons by truck and rail, respectively) are identical for this iteration in the two sequences.

The modal shifts induced by the different sequences are of par­ ticular importance. If the Eng2 Bley-Eng2 Gutierrez line and the

Eng2 Gutierrez-Guarapuava lines are constructed first, the total shift induced is 2.7 million tons, and total tonnage into Paranagua by truck is reduced to only O.o million tons, less than l/3 of the

1976 volume (Table 10, columns 1-5)* If the Cascavel line comes first, the shift is only 0.2 million tons and a volume nearly 1.5 times that of 1976 still flows into the port by truck. Highway congestion east of Guarapuava is not alleviated until both the Eng2 Bley-Eng° Gutierrez and Eng° Gutierrez-Guarapuava lines are installed (Table 10, columns

7-10). Xn the absence of ^ lane highways, such a volume would impose very high costs on other highway users. Similarly, the implied con­ gestion costs in the truck terminal in Paranagua would be much greater than those of 1976.

The two sequences result in differing benefits within the grain sector and between the grain sector and other highway users.

The completion of the Guarapuava-Cascavel line first benefits the western part of the state and the railroad corporation by permitting the substitution of long truck hauls for rail transport. Grain shippers in other regions benefit from the Bley-Gutierrez sequence, since it 1^8 alleviates both highway and truck terminal congestion. Other highway

users would benefit from the Eng2 Bley-Eng2 Gutierrez first sequence

through reduced highway congestion. These externalities are probably much greater than the cost reductions to the grain sector itself from either sequence of improvements (Appendix C ).

The Eng2 Bley-Eng2 Gutierrez first sequence can be made more attractive to the western portion of the state if the subsequent ex­

tension of the line from Guarapuava to Cascavel is realized in

stages, although this option is not simulated in the tables. A TVM

terminal at Tres Pinheiros, 60 km to the west of Guarapuava, can

pick up the truck traffic from the southwest and shorten the truck

line hauls from the western regions. Subsequent terminals can be

installed in Laranjeiras do Sul and possibly other municipios before

the line actually reaches Cascavel.

5.2.3 Simulations of long range improvements under high production levels

The high production level for the mid-1980's represents nearly

13 million tons of grain and meal arriving in Paranagua - a threefold

increase over 1976 levels. As with the simulated intermediate pro­

duction, the initial solution is infeasible (Tables 9-H) • Again,

the proportional increase of bulk storage and production does not

provide sufficient storage space to permit satisfaction of the seasonal

demand structure. The storage deficit is even more serious than under

the intermediate production level, however, since a 10% increase in

bulk storage does not result in a feasible solution (iteration 2 of

the "High Production" section of Tables 9-H) • Thus a greater per­

centage of output would have to be sold during harvest than in 1976 149 unless much greater use is made of alternative facilities than appears possible at present.

A simulated 25% increase in bulk storage space provides a feasible and optimal solution at a total cost of Cr$2.1 billion (Table 9). This value represents Cr$150/ton for all shipments, including those to Sao

Paulo not shown in the tables. The per ton costs are approximately equal to those of the initial feasible solution under intermediate pro­ duction levels. The qualifications regarding this solution are even stronger than for the intermediate production case. The modal split for arrivals in Paranagua is 9.7 million tons by truck and 3 million tons by rail (Table 10). The volume of truck arrivals is over 4 times that of 1976 and would clearly require 4 lane highways for successful delivery. The problem of port congestion under such a large amount of trucks is difficult to conceive. It is doubtful if the limited physical space of the port area would permit construction of sufficient receiving and unloading facilities to handle so many trucks. Much of the space on the required 4 lane highway would be used for parking trucks waiting in line to unload (currently the trucks park on the shoulder of the highway leading into the port).

The simulated highway improvements result in a savings on grain transportation of Cr$74 million and no modal shift among arrivals in

Paranagua (iteration 4 of Tables 9 and 10). As under the inter­ mediate production level, the total savings from highway projects currently planned are quite modest in relation to the overall transfer bill. 150

The simulated increase in the serra line capacity yields a savings of Cr$138 million to the grain transfer sector, based on the 1976 LRAC figures used in the previous simulations. This represents a savings of 6.5$ of the transfer costs in the first feasible solution, and is accompanied by a modal shift of 3*3 million tons from truck to rail among arrivals in Paranagua (iteration 5 °f Tables 9 and 10). The volume arriving in the port by truck, however, is nearly 3 times the 1976 level, so that the caveats stated above continue to apply.

Iterations 6 and 7 of Tables 9 and 10 now simulate the addition of the Eng2 Bley-Eng2 Gutierrez and Eng2 Gutierrez-Guarapuava rail lines.

Together, they reduce transfer costs by 9.6% and truck arrivals in

Paranagua to 1.5 million tons, a level about 1/3 less than actual

1976 arrivals. This solution is feasible for the present system without k lane highways or drastic alterations of the truck receiving and unloading facilities in the port area. Transfer costs on a per ton basis with these improvements would be about l/k less than in 1976, guaranteeing the competitive position of the region's soybeans, c o m and meal on the international market.

The simulated extension of the rail line from Guarapuava to

Cascavel at this point results in an additional 10.7% savings on transfer costs as a percentage of costs in the first feasible solution (Table 9)• There is no modal shift among arrivals in

Paranagua (Table 10).

The analysis of the Guarapuava-Cascavel first sequence parallels that given for the intermediate solution: the line does not result 151

in a significant modal shift among arrivals in Paranagua nor alleviate

congestion of highways and terminals east of Guarapuava until the

Eng2 Bley-Eng2 Gutierrez and Eng2 Gutierrez-Guarapuava links are

completed (columns 7-11 of Table 10).

The simulated additions of the Maringa-Campo Mourao and Cianorte-

Guaxra lines again result in very modest decreases in transfer costs

of 1.0 and 0M%, respectively (Table 9). The extension to Gampo Mourao

results in savings of 2 1/2 times those of the extension to Guaira,

but the overall effect on grain transportation is minor in comparison

to other rail projects simulated.

Table 11 reveals that more lines become saturated under the high

production levels than under intermediate levels after the serra line

improvement is simulated in iteration 5- Under high production, the

Apucarana-Ponta Grossa line is saturated in iteration 5 and. all

subsequent simulations in period 3» The new Eng2 Bley-Eng° Gutierrez

line becomes saturated as soon as the Eng2 Gutierrez-Guarapuava line

is added,' and this latter line becomes saturated with the addition

of the Guarapuava-Cascavel line (iteration 8). The bottlenecks

remain the same when the Maringa-Campo Mourao and Cianorte-Guaira

extensions are simulated.

The rail bottlenecks, however, are much less costly to the

system after the new lines reach Guarapuava, as evidenced by the decline

in the absolute values of the negative net arc costs. From that addi­

tion through successive simulations, no rail line is saturated in

more than two time periods. Furthermore, there would exist the possi­

bility of increasing rail line capacity marginally on all congested 152 lines "by increasing the number and frequency of sidings, thus avoiding the bottlenecks which appear in Table 11.

5 . 2 A Summary of results for intermediate and high production levels

Bulk storage space will have to increase more than proportionately to production to meet time specific-demands if alternative facilities remain constant at the 1976 levels. The planned improvements in the highway network through 1980 have a very modest effect on grain trans­ fer costs. The maximum cost reduction from principal cities to processing plants, Sao Paulo and Paranagua would be only 3«5%*

Unless rail participation is greatly increased, the projected intermediate or high volumes of grain can only be delivered if the truck terminals are greatly improved and 2 lane highways converted into k lane freeways. The grain and meal traffic moving by truck is respon- sable for the need to increase highway capacity, since current volumes of other traffic do not warrant conversior of these roads into ^ lane arteries. Such a solution would involve a large implicit transfer of general funds to road construction, given the present structure of user charges for highway services. If implanted, most stretches would rank among the most lightly travelled ^ lane highways in Brazil.

The cost and capacity analysis reveals the following strategy for increasing rail participation to be the most economic sequence:

(1) implantation of trainload volume movements in all principal regions with rail service through TVM loading and unloading terminals;

(2) conclusion of the new serra line; (3) the Eng2 Bley-Eng2 Gutierrez direct link; (k) the Eng2 Gutierrez-Guarapuava line (new or improved), and (4) the Guarapuava-Gascavel line, preferably in stages. 153

The use of TVM terminals and the addition of new lines as far west as Guarapuava could lower the truck shipments of grain to below

1976 levels for highways from Apucarana and Guarapuava to Ponta

Grossa and Paranagua. This would significantly lower the congestion costs borne by other highway users. Assuming 197& LRAC figures for rail and 1976 financial charges for trucking, transfer costs would be about Cr$l00/ton, compared with Cr$l6l/ton in 197& and Cr$128/ton for 1976 volumes with all simulated short term improvements. Thus, with the indicated improvements in infrastructure, Parana could expand its exports to double or triple the present levels with lower per ton transfer costs than at present. Chapter Six

Summary, Conclusions and Implications

6.1 Summary

6.1.1 The economic problem

Grain from the state of Parana contributes significantly to Brazil’s foreign exchange earnings and to Brazilian and world food supplies. The state may double its soybean production within the next decade, resulting in a total harvest of some 8 million metric tons. This is equivalent to 67% of the 1976 production for the entire nation — the world's second leading soybean producer. The rapid expansion of grain production in recent years has overloaded the state's storage and trans­ port facilities, including nearly saturated two lane highways, an anti­ quated rail system and limited bulk storage space. Parana's transfer infrastructure is subject to additional strain from beyond its borders, since it serves as the "export corridor" for the Republic of Paraguay and parts of the neighboring states of Sao Paulo and Mato Grosso.

The high level of transfer costs threatens the competitive position of the area's grain in the international market. C o m in particular is penalized due to its low price/weight ratio. Lower transfer costs would encourage the expansion of c o m exports and rotation of c o m with soybeans on Parana's better land. This would reduce pesticide and herbi­ cide use and ease downward pressure on soybean prices resulting from continued growth of the area's soybean exports. Regional c o m exports are not large enough, however, to effect world c o m prices. 155

The capacity of the export corridor's transportation and storage facilities must be increased in order to physically handle the increased volumes of grain production. Transfer costs must be lowered simul­ taneously, however, if potential volumes are to be produced and marketed without incurring the heavy social costs of special agricultural subsidy programs.

This research provides a framework of economic analysis for under­ standing Parana's short and long term transport-storage problem and the probable gains associated with alternative strategies for improving the infrastructure. This framework can indicate where future cost-benefit studies of individual projects should be made and constitutes a valuable supplement to such analyses. The specific objectives are to:

(1) locate bottlenecks and associated costs in:

(a) the 1976 (present) transfer system;

(b) the present system with simulated short term improvements;

and

---(c) possible future systems under projected increases in grain

production over the next decade; and

(2) evaluate alternative strategies for improving the infrastructure

on the patterns and costs of grain transfers.

6.1.2 Methodology

The export corridor's grain transportation and storage system is formulated as a capacitated network. The model developed minimizes the costs of transporting and storing available grain to meet time-specific demands for processing and export through the port of Paranagua, as well as the lower level of domestic demand in Sao Paulo. Costs are minimized subject to the existing capacity restrictions for transportation and storage facilities. The year is divided into four time periods to per­ mit representation of the time-specific characteristics of production, demand, storage, processing and congestion. Truck and rail shipments are divided into their line haul and terminal components. Individual microregions, highway and rail facilities, terminals, processing plants and storage facilities are represented through the use of nodes (points) and arcs joining the nodes. Costs and capacities are represented by parameters assigned to the arcs, and changes in the physical system are simulated through alterations in the parameters of selected arcs in

the network. This process permits investigation of short term improve­ ments in the transfer system under the 1976 grain production level.

For the mid-1980's, "intermediate" and "high" production estimates are

used, and new facilities are simulated through the addition of arcs.

The model provides a significant extension of transportation and storage

analysis and of network modeling applications. The network problems are

solved using the Fulkerson algorithm.

6,1.3 Simulation of short term improvements

Short term improvements include the installation of trainload

volume movement (TVM) terminals, increasing the capacity of the

existing serra rail line leading into the port and augmenting bulk

storage space. Major highway and rail projects are long term options

due to their extended gestation periods, and are therefore considered

only in regard to the models for the mid-1980’s.

The basic solution to the 19?6 problem reveals which rail and

storage facilities are bottlenecks to the low cost transfer of present 157 volumes of grain and meal. The total transfer cost is Cr$789 million, an average of Cr$l6l/ton. This compares with a product price of

Cr$l,500 to Cr$2,200/ton for soybean meal and soybeans. Corn prices are only about k0% of soybean prices, so that transfer costs represent a higher proportion of its product price than for either beans or meal.

The most costly rail bottleneck is the Paranagua rail terminal.

The simulation of a trainload volume movement terminal in the port results in a 60% increase in the effective availability of rolling stock. The TVM terminal yields a Cr$63 million annual savings on rail

costs which could be applied against the construction costs of the pro­

ject, plus a savings of 3-3% on costs to the grain sector from reduced

use of trucking if the 1976 rail charges are maintained.

The capacity of the serra line is saturated from April to October

by the effective increase in available rolling stock resulting from the

TVM terminal simulation. The capacity of the existing serra line

can be expanded through new and/or longer sidings and the elimination

of passenger traffic. A simulated 50% capacity increase to ^.5 million

annual tons, along with added TVM capacity in the interior of the

state, reduces transfer costs by an additional 8.6%. More importantly,

the rail improvements halve truck shipments of grain by truck on

Parana's main highways. This significantly reduces the external costs

imposed on other highway users. These costs are not included in the

•> model, but are separately estimated to be about as large as the

financial costs of truck transport paid by the grain shippers them­

selves (Appendix G ). 158

The analysis also reveals that: (1) the limited availability of bulk storage space forces the use of sheds, conventional warehouses and rubberized inflatable warehouses for storage of about one-sixth of the grain passing through the system, incurring additional handling costs;

(2) Ponta Grossa and Castro are the only two microregions with excess bulk storage capacity; and (3) the use of alternative storage facilities becomes progressively confined to areas at or near the more distant rail terminals as bulk storage increases are simulated. These locations allow substitution of rail transport for long truck shipments during periods of peak highway and truck terminal congestion.

6.1.^ Simulation of long term improvements

"Intermediate" and "high" production estimates for the mid-1980's form the basis for simulations of long term improvements in the transfer system (Appendix B) . These levels represent approximate two and three­ fold increases in the volumes to be handled on the export corridor.

The bulk of the area's soybeans and c o m is expected to continue to be exported between the March-June harvest and the U.S. harvest some six months later (Chapter 4). The model incorporates future time-specific demands as proportional to those of 1978.

The long term solutions are infeasible unless bulk space is in­ creased more than proportionately to production. The infeasibility results from the requirement that seasonal product demands be met and the assumption that the absolute supply of non-bulk facilities will remain at present levels. The subsequent simulations incorporate a

25% increase in bulk storage space beyond the proportional increase to production. 159

All highway improvements planned through 1980 are simulated, with a combined effect of only a 3*2 to 3*5% reduction in transfer costs. In the absence of improved rail transport capacity, the pro­ jected intermediate and high volumes of grain can probably be delivered only if highway improvements are expanded to include conversion of the state's principal two lane highways into four lane ones. Grain truck transportation causes the saturated conditions on these highways since other vehicles are not large in absolute numbers and a higher level of service is maintained during the off-peak grain shipment periods. The necessity for highway expansion therefore derives largely from grain truck induced congestion.

For the "intermediate" production level, implantation of TVM terminals in the state's principal microregions and the completion of the new serra line might be sufficient to avoid the costly conversion to four lane highways. For high production levels, however, the Eng2

Bley-Eng2 Gutierrez and Eng2 Gutierrez-Guarapuava rail projects are required. These projects would: (1) reduce truck grain traffic on Parana's main highways below the 1976 level; and (2) reduce per ton transfer costs to less than 75% °f the 1976 level. These results would guarantee the physical ability to market increased production, improve the international competitive position and ease highway congestion.

If the Guarapuava-Cascavel link is added without 'the Eng2

Bley-Eng2 Gutierrez direct link or improvements in the other inter­ mediate lines, tonnage is limited by the low line capacity from Guara- puava to Ponta Grossa. If the intermediate Eng2 Bley- Eng° Gutierrez and Eng2 Gutierrez sections are added before the extension of the l6o

rail line from Guarapuava to Gascavel, the shift of grain from road to rail is sufficient to reduce highway congestion below the 197^

level. There are also substantial line haul savings on shipments

from Guarapuava to Paranagua, since the Eng2 Bley-Eng2 Gutierrez

direct link shortens the distance by 121 km. The extension to Gascavel

can then be added in stages, progressively reducing traffic on the

BR-277 highway west of Guarapuava as intermediate terminals absorb

traffic from the southwest and municxpios between Guarapuava and

Gascavel.

The Maringa-Campo Mourao and Cianorte-Guaxra lines reduce transfer

costs by relatively small amounts and do not affect the modal distri­

bution of traffic on main arteries. The grain transfer savings from

the Gampo Mourao addition are about 2 l/2 times those on the line to

Guaxra. Other than the absolute size of the traffic and the savings

on grain transfer costs, there is little difference between the inter­

mediate and high production estimates regarding these two additions.

As'expected, benefits from the rail projects are greater in

absolute terms for the high production level than under the intermediate

production level. An important result of the long run simulations,

however, is that the rail line additions are physically able to handle

the high level of production at per ton costs of transfer approximately

equal to that under the intermediate production level. Neither the

economic choice of projects nor the sequence of adoption is highly

sensitive to the volumes of production within the ranges simulated. l6l

6.2 Conclusions

The most significant conclusions emerging from this study regarding future project analyses are:

(1) Rail operating costs can be substantially reduced through the installation of trainload volume movement terminals. Improved terminals increase the effective capacity of the rolling stock and should be regarded as a precondition for further improvements in the rail system. The benefits to the rail corporation and to the grain sector are quantified in the text. The construction costs for TVM terminals cannot be calculated without detailed engineering studies. The rail-specific costs for track and other items not jointly used by trucks, however, appear to be small in relation to the potential savings from the projects;

(2) The serra line becomes a bottleneck as soon as the effective capacity of rolling stock is increased through the use of TVM terminals.

Expanding the capacity of the existing line is a difficult engineering feat, but essential to increased use of the rail system. Completion of the new serra line cannot be expected before 1980, due to the rugged terrain and construction delays which have already occurred. It must be completed, however, if the railroad is to maintain or increase its relative participation in grain and meal transportation in the face of the production increases expected by the 1980's;

(3) The congestion costs which highway transportation of grain imposes on other highway users approximates the financial costs of truck transportation paid by the grain shippers themselves; and

(^) Production levels $-10 years from now cannot be transferred with the 1976 system, but can be transfered at per ton costs less than 162

75% of the 1976 level through selective improvements in the rail infra­ structure. These savings are obtained at a level of financial charges to the grain sector which would permit the rail corporation to cover all its long run average variable costs after construction and still apply some fraction of the charges against the capital costs of con­ struction. Construction costs and quantities of other traffic must be known, however, before the benefit-cost ratio of these projects may be determined. Due to the long gestation of such projects, they must begin soon unless all major highway routes are to be converted into four lane highways. This conversion would involve high social costs and would not reduce costs to the grain sector significantly. The conversion would have the beneficial effect, however, of eliminating congestion affecting other highway users

(5) If the rail project option is undertaken, the sequence which maximizes the combined benefits to the grain sector and other highway users is: (a) conclusion of the new serra line; (b) addition of the

Eng2 Bley-Eng2 Gutierrez direct link; (c) addition of a new or upgraded

Eng2 Gutierrez-Guarapuava line; and (d) extension of the line from

Guarapuava to Gascavel with implantation of TVM terminals at inter­ mediate locations.

6.3 Implications

6.3.O Introduction

Sections 6.1 and 6.2 summarized the findings and conclusions derived from the analytical results of previous chapters. This final section is more qualitative in nature. It discusses some of the limita­ tions of the study, presents some general considerations regarding transportation costs and pricing policies and outlines some areas where 163 additional research is needed.

6.3.1 Limitations of the study

There are three major limitations regarding the network analysis

used in this study. First, highway capacity is not restricted within

the network model itself. The highway capacity available for grain

traffic cannot be precisely specified, since more grain trucks can

use a saturated roadway by forcing other users to cease traveling or

to take more expensive routes. Nonetheless, as observed in the text

and developed more fully in Appendix G , substantial increases in

grain traffic by truck probably can be accomodated only by converting

the existing two lane roads into four lane highways.

Secondly, the data on rail costs and capacities are somewhat

arbitrary as emphasized in the text and Appendix C . The rail capacity

figures used in the text allow for substantial amounts of non-grain

traffic, providing conservative estimates of capacity available for grain

and meal. Since the majority of rail cars on the key lines either carry

grain and meal presently or will under likely mid-1980's conditions,

rail capacities can be more satisfactorily estimated than highway

capacities. They should not be regarded, however, as precise and

immutable figures. Capacity of rail lines can be increased by the

addition of sidings, the lengthening of sidings and better scheduling.

Fluctuations of non-grain traffic will influence the capacity and

delay times for grain transport. Conversion to 1.6 meter guage

(not analyzed in the text) would increase capacity well beyond the

probable demands of the grain sector. The operating costs of the new

lines, new TVM terminals and wider guage system would be much lower 164 than 1976 costs. Additional information is needed regarding the construction costs to evaluate the effect of these projects on long run average cost (LRAC) figures including initial capital outlays. To the best knowledge of the author, this is the first attempt to deal explicit­ ly with the different aspects of rail capacities and costs for specific commodities within a network model. It should be a productive area for future research.

Thirdly, as indicated repeatedly in the text, project evaluation requires much more information on social and private costs of providing storage, rail and highway services than it has been possible to include in this study. For example, a unit storage capacity increase in Parana- gua would result in the highest benefits from storage expansion found in the system. The construction costs, however, are likely to be higher in the port area than elsewhere and the high temperatures and humidity do not favor long term storage in the port area. Furthermore, the encroachment of the remaining port area by grain storage units would pre­ clude tha possibility of exporting other products from Parana's agricul­ tural and extractive sectors or from its emerging industrial park.

Increases in port storage, therefore, should be limited to those neces­ sary to permit product separation and rapid unloading of trucks and trains rather than supplanting storage increases in producing regions.

With regard to the railroad projects, no attempt is made in this study to evaluate the total benefits of new lines or increased capacities on existing lines. The construction of the Cianorte-Guaira line, for example, might result in considerable amounts of non-grain traffic.

Likewise, the new lines from the serra to Cascavel would provide 165 low cost transportation for construction materials to be used for the

Itaipu dam. The model may be updated as better data becomes available.

6.3.2 Implications for transportation and storage policy

Given the above caveats, it is nonetheless apparent that some of the results and conclusions in this study are applicable to a wider range of conditions than those explicitly considered in the text. The economies of trainload volume movements are potentially available at several locations besides the nodes included in the present model.

Other microregions with rail service and substantial grain production may be candidates for TVM terminals when the Paranagua terminal and the serra line are upgraded even though they have been omitted from the network representation of this study. TVM movements to Sao Paulo

(not considered in the text) may also be viable if buyers in that area can adopt TVM receiving terminals. The lack of TVM receiving terminals for domestic shipments in the United States has limited the economies of TVMs in the United States, although they are used extensively for export movements IWright, Meyer and Walker; Kane], Such shipments may be viable in Brazil, however, since the narrow guage and lower tech­ nical rail line standards limit the tonnage in a TVM to about half that carried on a U.S. TVM. Wheat could also be moved by TVMs to Paranagua for subsequent cabotage to the North and Northeast if future production increases beyond levels now expected (Appendix B). Excess rail capacity from November through March could be used for wheat shipments to either

Sao Paulo or Paranagua.

The network analysis points out some of the difficulties with the current rail traffic patterns and cost/pricing calculations. Since 166

RFFSA does not use peak demand pricing, there is a large unmet demand for rail cars in the north of the state during periods of highway and truck terminal congestion. Rate increases and pricing flexibility are limited by "inflation controls" imposed by the Interministerial

Commission on Prices (CIP). The rail corporation is attempting to reduce its deficit by using the Sander long run average cost methodology and by 'charging prices in line with the cost of providing rail services.

Due to CIP controls and the requirement of providing a variety of uneconomic services, deficits are inevitable. Thus the use of the

Sander methodology attenuates losses but does not eliminate them.

The railroad officials in each of the geographical divisions of

RFFSA along with other officials of government agencies are inclined

to adopt cargo maximization as an unstated goal. Annual tonnage becomes in effect a surrogate criterion of accomplishment. RFFSA's

performance is decidedly more favorable when viewed against the

tonnage yardstick than against Its loss statements [Rede Ferroviaria

Federal, 197&].

The limits on price flexibility encourage the railroad to enter

into long term contracts with processors to guarantee cargo throughout

the year rather than allocate cars to longer hauls during peak soybean

and c o m shipment months. This tends to maximize cargo, but does not

minimise costs for the grain transfer system and may not maximize the

railroad's net income from grain shipments. This is due to the

expensive Ponta Grossa-Paranagua haul for most meal shipments. The

line haul costs per ton-km are over twice those of other lines in the

system, and the terminal costs are the same as for longer hauls. Costs

in the off-peak season are probably as great or greater than transport 167 by truck. In the network model such short hauls continued to flow into Paranagua by truck even though simulations removed rail capacity constraints in one or more periods.

The LRAC methodology used by the railroad gives an overall cost figure per ton-km. The intent is to encourage a more realistic pricing strategy than has existed previously [Baer, Kerstenetzky and Simonsen;

Abouchar, 1967, 1969a, 1969b]. The costing techniques are not yet sufficiently refined to produce a ton-km cost for specific lines and to net out the effect of terminal costs on shipments from different locations. This limitation plus^ cargo maximization can easily lead to unduly low charges to shippers in nearby locations. Since most of the meal from Ponta Grossa moves to the port by rail, the concentration of processing plants in that city may originate from an unintended trans­ portation cost subsidy to that region.

If terminal handling and/or rail line capacities are the limiting constraints, cost minimization to the grain transfer system and rail­ road profit maximization both require shipment from the network's most distant terminals. This derives from the railroad's comparative advantage over truck transport on the line hauls. If the railroad can handle additional traffic, short hauls may become economically sound from the railroad's point of view if TVM terminals lower costs below those of trucks (Appendix C ). Such savings will have to be passed on to shippers to encourage use of the railroads. Some subsidization of required construction projects may be warranted given the external benefits to highway users as the railroad absorbs more grain traffic and alleviates highway congestion. The LRAC approach promotes a concern with realistic pricing, but its scope does not normally include examination and promotion of new

systems which may lower rail costs. The LRAC analysis may therefore

be gainfully supplemented by: (1) the use of long range incentives

for improvement of terminal facilities and formation of trainload

volume movements; (2) the disaggregation of average costs into terminal

and line haul components; (3) the use of more flexible pricing schemes,

including peak demand pricing; and (k) the modeling of specific systems'

physical and economic characteristics, such as that contained in the

present study. The implementation of such a strategy will naturally

involve certain institutional changes. These include CIP regulations

and the formation of joint receiving terminals in Paranagua to replace

the current proliferation of small sidings under the control of individ­

ual cooperatives and companies.

As pointed out in the text, the benefits from individual pro­

jects may be dependent on the order in which they are implemented.

One possibility not simulated in the text is the completion of four

lane highways from Paranagua to Gascavel in the west and to Maringa

in the north before major rail projects are initiated. Grain traffic

by truck would no longer impose significant congestion costs on other

users. Line haul costs of trucking, however, would be reduced only

by the present 10 and ±$% congestion costs of the peak periods. The

mobilization costs would still be incurred in order to provide an in­

creased supply of trucks during peak shipment periods. The congestion

costs of truck terminal operations would continue undiminished, if

it would be possible to handle the projected volume of trucks at all. 169

Thereafter, operating costs could only be further reduced through the implantation of rail projects such as those considered herein. The relevant economic question would then become simply the determination of the cost/benefit ratio for the rail projects. The cost of the completed highways would be considered a sunk cost, while the construc­ tion costs of the rail projects would enter the cost calculations.

The financial charges for trucking used herein favor the trucking sector, since financial costs do not cover the grain trucks' share of highway maintenance costs and make no contribution toward the con­ struction of new highways. The LRAC railway cost figures, in contrast, cover all operating, maintenance and overhead costs incurred by grain shipments over railways in 1976. The simulated improvements assume 1976 LRAC figures, although operating costs would obviously be lowered with the improved rail facilities. The rail cost figures are sufficient to cover part of the capital cost of the construction of new facilities when they are simulated in the network models of the text. Nonetheless, the rail projects are the only simulated improve­ ments which substantially reduce transfer costs to the grain sector.

This result would be accentuated by future increases in the price of diesel fuel or use of the proposed cargo tax on truck shipments which the federal government may use to bring the financial costs of trucking closer to the total social costs it involves. Both the fuel price increase and the cargo tax would increase the financial attractiveness of rail transportation to grain and meal shippers.

In 1976, grain and meal exports were financially attractive to the export corridor's producers and processors despite the high costs 170 of transfer. The financial attractiveness is partially due to subsidies for production and storage (Appendix D). Distortions are created in those sectors, and the government attempts to recoup its losses on sub­ sidies by taxing output and exports [Meyer and Wright].

A side effect of the subsidies is to mask the present need for cost reductions in the transfer sector. Since output and exports can be taxed, exports are generally regarded as profitable and the transfer problem appears to be one of capacity rather than cost in the short run. The need to minimize costs is clear when the social costs of production and storage are considered, as opposed to the subsidized financial costs paid by farmers. The government is currently considering reducing the subsidies due to the high social costs and widespread distortions involved in the programs. Such a reduction would high­ light the need to reduce transfer costs. Furthermore, continued pro­ duction increases and uncertain future grain prices make the Parana export corridor's competitive position in the world market dependent on simultaneous increases in physical capacity and per ton cost reduc­ tions in the transfer sector.

6.3.3 Future research

This study outlines a number of areas where additional research

is needed. One is the local (farm-to-collection point) transfer problem

(Appendix D). The ideal size, location and drying capacity of the

collection-storage units needs to be determined for the state's microregions. The methodology developed in this study and that

indicated by Fuller, Randolph and Klingman could be adapted for such a purpose, but a great deal of additional data is required. Care 171 should, he taken to avoid the common error of designing a minimum cost storage facility.without accounting for the costs involved in farm-to-storage transportation and the queues which develop at large units. There is a related need to evaluate the benefits and costs

of higher quality storage units which permit long term storage of a variety of grains with reduced losses and indivisibilities, and

the energy requirements and costs of different types of units.

An improved transportation policy requires improved estimates of

the financial and social costs of truck and rail transportation in

Brazil. Realistic cost estimates of TVM terminals and proposed railway and highway projects would permit the development of a LRAC approach

for both modes when modeling or evaluating new facilities. Continued research is required on highway and rail capacity in order to deter­

mine an overall strategy for transportation development, as well as to

establish the relation of congestion to per unit costs of shipment.

Much of the previous literature on agricultural commodity transfer has been concerned with the determination of commodity flow patterns.

Capacities of physical facilities are generally assumed to be unre­

stricted. Financial charges are assumed to reflect social costs,

with some notable exceptions such as the Iowa rail line study by

Baumel, Miller and Drinka. The present study deals explicitly with the

capacity restrictions of a grain transport-storage system and the

problem of social and financial costs of transportation. The network

flows are evaluated in terms of the costs of congestion to the grain

sector and to other highway users. To the best knowledge of the author, this is the first time these topics have been jointly treated using the framework of economic analysis and network models. The model proved useful in locating bottlenecks in the 197& transfer system and in simulating improvements in the system as it may exist several years from now. Flow data.yielded important information on external costs from congestion. The analysis provides considerable insight into the characteristics of the underlying economic situation and the likely effects of alternative strategies for dealing with changes in the transportation and storage infrastructure. Future research along the lines suggested in this study presents a challenge to economists to provide a refined theoretical and methodological basis for transfer policy. Appendix A

i Municxpios„ Included in the Study by Microregxon

Microregion 5.2 (272) Microregion 11 (278)

1. Palmeira 1. Carlopolis 2. Conselheiro Mairink Microregion 6.1 (273) 3. Curiuva 4. Guapirama Ponta Grossa 5. Ibaiti 6. Jaboti Microregion 6.2 (273) 7. Japira 8. Joaquim Tavora 1. Castro 9. Pinhalao 2. Pirai do Sul 10. Quatingua 3. Telemaco Borba 11. Salto do Itarare 4. Tibagi 12. Santana do Itaraxe 13. Sao Jose da Boa Vista Microregion 7 (274) 14. Sapopema 15. Siqueira Campos 1. Arapoti 16. Tomazina 2. Jaguaraxva 17. Venceslau Braz 3. Senges Microregion 12 (279) Microregion 9 (276) 1. Abatia 1. Imbituva 2. Andira 2. Irati 3. Bandeirantes 3. Mallet 4. Barra do Jacare 4. Prudentopolis 5. Cambara 5. Rebougas 6. Congoinhas 6. Rio Azul 7. Comelio Procopio 7. Teixeira Soares 8. Itambaraca 9. Jacarezinho Microregion 10 (277) 10. Jundiai do Sul 11. Leopolis 1. Candido de Abreu 12. Nova America da Colina 2. Ipiranga 13. Nova Fatima 3. Ivai 14. Ribeirao Claro 4. Ortigueira 15. Ribeirao do Pinhal 5. Reserva (continued) 41 IBGE numeration in parentheses. Decimal indicates subd'

173 174

Microregion 12 (279) (continued) Microregion 15 (282)

16. Santa Amelia 1. Atalaia 17. Santa Mariana 2. Doutor Camargo 18. Santo Antonio da Platina 3. Floral 19. Santo Antonio do Paraiso 4. Floresta 20. Sertaneja 5. Itambe 6. Ivatuva Microregion 13 (280) 7. Mandaguagu 8. Mandaguari 1. Assai 9. Marialva 2. Jataizinho 10. Maringa 3. Rancho Alegre 11. Ourizona 4. Santa Cecilia do Pavao 12. Paigandu 5. Sao Jeronimo da Serra 13. Sao Carlos do Ivai 6. Sao Sebastiao da Amoreira 14. Sao Jorge 7. Urai 15. Uniflor

Microregion 14 (281) Microregion 16 (283) 1. Alto Parana 1. Alvorada do Sul 2. Amapora 2. Arapongas 3. Cruzeiro do Sul 3. Astorga 4. Diamante do Norte 4. Bela Vista do Paraiso 5. Guairaga 5. Cafeara 6. Inaja 6. Cambe 7. Itauna do Sul 7. Centenario do Sul 8. Jardim Olinda 8. Colorado 9. Loanda 9. Florestopolis 10. Marilena 10. Florida 11. Mirador 11. Guaraci 12. Nova Alianga do Ivai 12. Ibipora 13. Nova Esperanga 13. Iguaragu’ 14. Nova Londrina 14. Itaguaje 15. Paraiso do Norte 15. Jaguapita 16. Paranaciti 16. Lobato 17. Paranapoema 17. Londrina 18. Paranavai 18. Lupionopolis 19. Planaltina do Parana 19. Miraselva 20. Porto Rico 20. Munhoz de Melo 21. Presidente Castelo Branco 21. Nossa Senhora das Gragas 22. Querencia do Norte 22. Porecatu 23. Santa Cruz do Monte Castelo . Primeiro de Maio 23 24. Santa Isabel do Ivai 24. Rolandia 25. Santo Antonio do Caiua . Sabaudia 25 26. Sao Joao do Caiua 26. Santa Fe 27. Sao Pedro do Parana 27. Santa Ines 28. Tamboara 28. Santo Inacio 29. Terra Rica 29. Sertanopolis 175

Microregion 17.1 (284) Microregion 19.1 (286)

1. Borrazopolis 1. Goioere 2. Faxinal 2. Mariluz 3. Grand.es Rios 3. Moreira Sales 4. Ivaipora 5. Jardim Alegre Microregion 19.2 (286) 6. Sao Joao do Ivax 1. Campina da Lagoa Microregion 17.2 (284) 2. Nova Cantu 3. Ubirata 1. Apucarana 2. Bom Sucesso Microregion 19.3 (286) 3. California 4. Cambira 1. Araruna 5. Jandaia do Sul 2. Barbosa Ferraz 6. Kalore 3- Boa Esperanga 7. Marilandia do Sul 4. Campo Mourao 8. Marumbi 5. Engenheiro Beltrao 9. Rio Bom 6. Fenix 10. Sao Pedro do Ivax 7. Janiopolis 8. Mambore Microregion 18.1 (285) 9. Peabiru 10. Quinta do Sol 1. Altonia 2. Alto Piriqui Microregion 19.4 (286) 3. Ijoora 4. Perola 1. Iretama 2. Roncador Microregion 18.2 (285) Microregion 20 (287) 1. Cruzeiro do Oeste 2. Icaraxma 1. Manoel Ribas 3. Maria Helena 2. Palmital 4. Nova Olxmpia 3. Pitanga 5. Tapira 6. Umuarama Microregion 21.1 (288) 7. Xambre 1. Guaxra Microregion 18.3 (285) 2. Marechal Candido Rondon 3. Santa Helena 1. Cianorte 4. Terra Roxa 2. Gidade Gaucha 3. Guaporema Microregion 21.2 (288) 4. Indianopolis 5. Japura 1. Assis Chateaubriand 6. Jussara 2. Capitao Leonidas Marques 7. Rondon 3. Cascavel 8. Sao Tome 4. Catanduvas 9. Tapejara 5. Ceu Azul 10. Terra Boa 6. Corbelia 11. Tuneiras do Oeste (continued) Microregion 21,2 (288) (continued) Microregion 23.2 (290)

7. Formosa 1. Guarapuava 8. Foz do Iguagu 2. Inacio Martins 9. Guaraniagu 3. Pinhao 10. Matelandia 11. Medianeira Microregion 24.1 (291) 12. Nova Aurora 13. Nova Santa Rosa 1. Glevelandia 14. Palotina 2. Mangueirinha 15. Sao Miguel do Iguagu 16. Toledo Microregion 24.2) (291)

Microregion 22.1 (289) 1. Biturana 2. Cruz Machado 1. Capanema 3. General Cameiro 2. Perola do Oeste 4. Palmas 3. Planalto 5. Paula Freitas 4. Realeza 6. Paulo Frontin 7. Porto Vitoria Microregion 22,2 (289) 8. Uniao da Vitoria

1. Ampere 2. Barracao 3. Dois Vizinhos 4. Eneas Ma.rq.ues 5. Francisco Beltrao 6. Marmeleiro 7. Salgado Filho 8. Salto do Lontra 9. Santa Izabel do Oeste 10. Santo Antonio do Sudoeste 11. Vere

Microregion 22.3 (289)

1. Chopinzinho 2. Goronel Vivida 3. Itapejara d'Oeste 4. Mariopolis 5. Pato Branco 6. Sao Joao 7. Sao Jorge do Oeste 8. Vitorino

Microregion 23.1 (290)

1. Laranjeiras do Sul 2. Quedas do Iguagu Appendix B

Estimates of Intermediate and High Production Levels for Soybeans and C o m

This appendix presents some background on the Parana crop situation which provides a rationale for the assumptions underlying the intermediate and high level production estimates in Table 1 of the text. It also explains how the levels are calculated, given the assumptions.

The assumptions regarding the two estimates of mid-1980's production levels are: (l) soybeans will continue to be the most lucrative option for many farmers and thus will continue to expand into areas now occupied by other crops, pasture and timber; (2) c o m will continue to occupy the marginal lands it now occupies at current levels of productivity (intermediate level estimate); it will also expand in a rotation with soybeans on better land (high level estimate); (3) the expansion of soybean acreage will be limited to areas suitable to mechanized cultivation; (4) competition from other crops, possible deficiencies in the new land and lags of tenure and

land clearing will allow incorportation of only %% of the additional mechanizable land within the decade; and (5) productivity for soybeans

177 will remain at the 1976 level.

The first two assumptions are derived from relative costs and

productivities. Cattle raising is discouraged by price controls on

milk and meat prices and by the current export market. Crops such

as cotton, rice and the different types of beans commonly grown on

small plots are expected to maintain much of their current acreage due to improved prices, the existing tenure situation and other factors.

Coffee areas are expected to maintain their current levels, down

1/3 from those of the IBGE 1970 agricultural census, baring another

killer frost [156-7; Stresser], The number of trees will be nearly

the same as in 1970 due to new planting patterns, and should soon

reduce the output price through supply increases. Since coffee is

very profitable at present, it seems unlikely to be further dis­

placed by annual crops unless severe frost damage occurs. On the

other hand, both the government and producers are cautious about

further coffee plantings due to the likelihood of future price declines.

Soybeans are currently much more profitable than c o m on good

lands, since Parana soybean yields are even higher than in the U.S.

(2,077 kg/hectare compared with a 1976 U.S. estimate of 1,725 kg/ha),

while c o m yields are only about b0% of the U.S. levels -(2,2^5 kg/ha

and an estimated 5»^97 kg/ha in 1976).[ ACARPA, 1976a: 1; U.S.D.A, 1976 ].

Farm production costs are Cr$l,426 per hectare for soybeans and Cr$772 and

Cr$2,720 per hectare for c o m using traditional and m od e m cultivation

practices, respectively [ Secretaria da Agricultura, 1976: 36-^5 ] •

Since prices for soybeans are normally about 2.U - 2.8 times those 179 for com, c o m yields will have to be greatly increased for the crop to compete with soybeans for quality land.

The third assumption restricting soybean expansion to areas suitable for mechanized cultivation is related to the labor requirements for harvesting. Some soybeans are in fact produced on small farms with­ out mechanical harvesting in regions such as Francisco Beltrao and

Pato Branco. Losses from d layed harvests are larger for com, however, and the manual harvest is quite labor intensive, so that manual harvesting on a large scale would be limited by a seasonal labor constraint. Currently, even most small holders use mechanical harvesting through rental agreements. The labor constraint, coupled with severe erosion problems with soybeans in hilly areas, is expected to confine soybean expansion to land with topography suitable for mechanized agriculture. C o m , in contrast, is currently planted on areas where topography, tree stumps or size of plots make mechaniza­ tion difficult or impossible. Statewide, only 21% of the land in c o m has motorized mechanical operations [ACARPA, 1976b].

The data for the estimates are drawn from ACARPA figures for crop production, GREMOS export figures and a GEIPOT study con­ taining figures on mechanizable land by microregion [1976b; 1976;

1975: 66 ]. Since kjfo of current c o m production remains on farms and much of the remainder is consumed regionally, the ACARPA and

GREMOS figures are used to estimate the "exportable surpluses" of c o m from each microregion. The ACARPA corn and soybean data for 150

the 1976 year are unofficial and are published only in aggregate f o m

[1976a] . This study used the agency's unpublished municipal estimates

for the 221 of 289 municxpios covered in 1976. The IBGE official

figures by municxpio had been released only through 1973 when data

collection ended in December, 1976. The IBGE state total had been released by that time, but this statistic is useful only in comparing the overall totals for the state. It is largely irrelevant to a study of transfer problems by microregion.

The data for municxpios missing from the ACABPA survey were

imputed based on the percentage of land in c o m or soybeans in

each microregion in 1973* Inaccuracies due to the transformation

of the agricultural situation from 1973 to 1976 are thus confined

to within-region variation, rather than variations among micro­

regions. This latter bias would be serious since the 1976 state

total would be disaggregated into individual microregions based

on the 1973 shares, which have changed considerably in the interim.

Within-re'gion variation is of minor importance, since local (within microregion) transportation is not included in this research.

The figures on lands suitable for mechanized cultivation are

given in column 3 of Table 12 . From this total area, estimates of

the area currently in mechanized com, soybeans and permanent

crops (columns 5> 6) are subtracted out (permanent crops

are largely coffee). Column .(7) gives the potential increase in

soybean hectares as one-half the remainder. For microregions not

well suited to soybean cultivation (9, 10 and 20), the figure is Table 12. Potential increase in hectares of soybeans by microregion

1976 Potential Increase as 1976 Mechanizable 1976 1976 Po;:ential Increase0 Percent of 1976 Soybeans Region Restricted Mechanizable Area0, Area in C o m Soybeans Permanent Crops in Soybeans In Region In State (l) .. (2) (3) (M ■ f5T (6) (7 ) (81 19)

0 5 .2 17,523 691 10,687 207 2 ,9 6 9 28 0.1 0 6 .1 181,090 371* 38,000 903 7 0 ,9 0 6 187 2.9 06 .2 165,769 5,269 31*, 785 826 6 2 ,1*1*1* 180 2.5 07.0 1*3,510 6,820 11,650 958 12 ,0 5 6 103 0.5 09.0 I 76,550 13,306 12,770 1*63 12,503 98 0.5 10.0 I 117,680 8,382 1* ,169 331 33,700 608 1.1* 11.0 372,600 17 ,291* 3,81*1* 21,390 1 65 ,036 1,293 6.7 12.0 518,1*20 69,892 111*,606 75,92** 1 28 ,999 113 5-2 13.0 130,100 10,852 1*1* ,01*6 11,512 31,995 73 1.3 1U.0 811,000 61,301 99,25l< 182,152 235,61*6 237 9.6 15.0 297,760 1* ,679 198,656 81* ,1*98 1* ,961 * 2 0.2 16.0 I 1*96,500 309 8,305 110,591* -- - - 17.1 276,1*55 29.267 72,915 55,1*26 59,1*11* 81 2.1* 17.2 1 6 0 ,61*5 11,987 1*2,370 32,208 37,01*0 87 1-5 1B.1 312,586 71*0 23,512 75,979 106,178 1*52 1*.3 1 8 .2 200,511 2,081* 15,082 UB,730 67,301* 1*1*6 2.7 16.3 171,502 1* ,029 1 2 ,9 0 0 1*1,686 56,1*1*1* 1*33 2.3 19.1 102,022 2,750 1*6,130 7,201* 22,969 50 0.9 19.2 90,327 3,810 1*0,81*2 6,378 19,61*8 1*8 0.8 19.3 626,591 31,1*27 283,317 l*l*,2l»6 133,800 1*7 5.1* 19. U 36,319 11,309 16,1*22 2,565 3,012 18 0.1 20.0 I 270,81*0 7,055 3,5 9 0 561* 61*, 908 1 ,8 0 8 2.6 21.1 21*7,910 12,1*68 155,695 5,981* 36,882 2>* 1.5 21.2 1,371,050 39,600 861,060 33,018 218,686 25 8.9 22.1 208,623 2,756 50,1*13 1,156 77,11*9 153 3.1 22.2 376,025 25,1*1*3 90,865 2 ,0 8 3 128,017 11*2 5.2 22.3 228,892 32,721 55,311 1,268 69,796 126 2.8 23.1 35,1*79 11,093 1*,050 21* 10,156 251 0.1* 23.2 613,761 9,1*30 70,062 1*10 266,930 381 10.8 2fc.l 111*,282 1* ,722 22 ,0 8 6 687 1*3,391* 196 1.8 21.2 97,838 10,251 18,908 587 3I1,01*6 180 1.1* TOTAL 8,803,‘*90 1*52,131 2,1*66,302 81*9 ,969 2,217,791 - W!

Sources: For hectares subject to mechanization, GEIPOT [1975, p .66]; for permanent crops, IEGE [1973; pp. 156-571 and Stresser[l976] ; all other figures derived from ACARPA data [1976b] aFor subdivisions, divided according to 1976 acreage in soybeans °0ne-half of the mechanizable hectares not in corn , soybeans or ]permanent crops. For regions 9. 10 and 20 , one-fourth Of this area. Ho increase allotted for region 1 6 . 181 182 taken as l/^ the remainder. No increase is allowed for region 16, due to severe erosion problems.

The increases in soybean hectares are expressed as a percentage of the 1976 acreage of the microregion and the state in columns (8) and (9), respectively. Gascavel (21,2), Campo Mourao (19.3) and

Londrina (l^) account for the largest volumes, so that production continues to be concentrated in the northern and western regions of the state. Importatant volumes, however, are also produced in

Borrazopolis (17.1), the southwest (22.1, 22.2 and 22.3) the north­ east (11 and 12) and Guarapuava (23.2), with the largest percentage increase in terms of the state total occuring in the latter micro­ region.

The total hectares in soybeans (columns 5 anti 7) times current yields in each microregion give the estimates of future production.

This figure and the exportable surpluses for com, adjusted for a 6,5^ reserve for seeds and losses, yields the intermediate production estimates for grains given in Table 1 of the text.

For the high production estimates, there is a change in an assumption regarding com. As previously, it is assumed that yields of c o m on present lands will remain unchanged. However, it is now assumed that c o m becomes competitive with soybeans on the superior lands soybeans occupy presently and will occupy in the future. This could occur if the relative price of soybeans falls, or if pests, disease or ecological considerations encourages a soybean/com rotation. C o m on such lands under mod e m cultivation practices 183 and with prompt mechanical harvesting will have a much higher pro­ ductivity than at present. The high production estimates assume c o m yields of ^-,500 kg/ha. This is very optimistic given the 2,2^5 kg/ha state average in 1976, and serves to give the desired upper estimate on mid-1980's production when combined with the liberal assumption of a 1/3 rotation with soybeans on quality land.

The high estimates are not beyond Parana’s capability, however.

The low mean c o m yield for the state hides the fact that some municxpios have means ranging from 3»000 to ^-,800 kg/ha [AGARPA,

1976b ] . These yields are obtained even though c o m occupies less favorable lands than soybeans within each municxpio, and modem cultivation practices for c o m continue to be limited to small areas. Farm level yields have been obtained in Parana of 9»000 kg/ha without fertilization [Trevizan]. Furthermore, the soybean-corn expansion could go beyond the hypothesized figure of 50% of the available mechanizable land.

As explained in the text, although the intermediate and high

estimates are derived from very specific assumptions, the production

levels they represent could be approximated by a variety of com­

binations of soybean expansion, soybean/corn rotation and levels

of productivity for the two crops. APPENDIX C

Transportation Costs and Capacities

Operational Costs

The standard analysis of operational costs holds that the most economic mode of transportation is determined, in large part, by the distance of shipment. Typically, the cost structure is defined as in Figure 20, with small trucks the least expensive means of shipping a given commodity for distance up to 0A; large trucks for distances from 0A to OB; rail for distances OB to 0C; and water for distances greater than 0C. The graph reflects the relative cost structures of these modes in many parts of the world in recent decades.

Superficially considered, Figure 20 provides graphic evidence for the widespread belief that railroads are economical only for long hauls.

This conclusion, however, is based on the assumption of fixed terminal costs. If these can be changed substantially, the relation­ ship of distance to economic mode also changes, and the inherent com­ plementarity of the different modes is enhanced. This is the basis of the containerization "revolution" in the maritime industry and has broad implications for rail service as well [Whittaker]. Sward shows that transport costs on rail lines can be much more closely related to terminal efficiency than to distance per se. Through a 185

trucks $/ton small large rail water w

r

1

s

A0 B G km

Figure 20. Distance determines economical modes when terminal costs I are fixed.

$/ ton

R

R'

T

0 F G km

Figure 21. Modal shift with decreased rail turn-around costs meticulous time-motion-cost study of a Minnesota rail operation,

Sward demonstrates that improved terminal operations reduced car ownership costs per gross ton from $3.25 to $0.15 on the line, and that continued improvements in terminal operations could lower this cost to $0.06 [1973: 31-3^].

Sward's pioneering study provides an empirical base for the development of an economic model with wide applicability in trans­ portation economics. The central concept is that the least expen­ sive mode in Figure 20 is determined by two distinct cost com­ ponents: (l) line haul costs, which increase linearly with distance and (2) terminal or turn-around costs, which increase with time rather than distance. Line haul costs are composed primarily of capital costs (interest on investment and physical wear), fuel, lubricants and oil, and labor. Turn-ai’ound costs, on the other hand, are basically a function of time. They include labor, the interest on the investment in rolling stock and loading/unloading equipment, and "overhead" items such as administration, license fees and insurance. Several of these expenses may also be considered line haul costs. They vary, however, with the time spent on the line haul rather than the distance traveled.

A very high percentage of railroad costs can be classified as turn-around costs. Administration, terminal employees, rolling stock and maintenance of the permanent way involve cost items which rise less than proportionately with increases in traffic. On the other hand, railroads have tremendous cost advantages over trucks 187 with respect to some line haul costs such as salaries and fuel: a

single train with an engineer and brakeman can haul a net payload

superior to that of over 200 trucks and drivers. Therefore, greater physical efficiency in terminal operations can significantly reduce turn-around times, lowering total shipping costs. Traffic will shift from road to rail if charges to shippers accompany declining costs.

This concept is illustrated graphically in Figure 21 where a reduc­ tion in terminal costs from OR to OR* enables the railroad to cap­ ture traffic of distances OF-OG formerly held by trucks. Cost savings are realized for all distances greater than OF up to a maxi­ mum of BC(=RR') for distances 0G or greater.

The above analysis implies that railroads can be economical on

short as well as long hauls, if large volumes of bulk-handled com­ modities permit efficient terminal operations. A more subtle point is that for a fixed supply of rolling stock, more efficient terminal operations increase the total capacity of the rail system to handle goods. The less time rail cars and locomotives spend in terminals, the more time they can actually transport commodities.

Congestion, costs and capacities: railroads

There is no clear relationship between overall operating costs

for railroads and congestion. As mentioned above, such items as

administration, terminal labor and maintenance of the permanent way

involve items which rise less than proportionately with traffic.

Operating costs, however, begin to rise due to delays in line haul 188 traffic caused by congestion as rail line capacity is approached.

Rail service capacity can be restricted by rail car availabil­

ity, terminal handling capacity or rail line capacity. The factor with the lowest capacity will be the effective constraint on the

system, and the three are to some extent interdependent. Ineffi­

cient terminal operations delay loading and unloading, thereby reducing the effective hauling capacity of the rolling stock. Sub­

standard tracks similarly reduce rail car availability by reducing travel speed. The weight of these three factors will vary from one rail operation to another. In Parana, there is a seasonal rail car

shortage, but this is caused by terminal delays, particularly in the port of Paranagua. A contributing factor to reduced car availa­ bility is the delay time on the serra stretch between Curitiba and

Paranagua. Shippers perceive the problem as a lack of cars and peti­ tion for more. To some extent, increases in rolling stock may help alleviate the shortage from the shippers' point of view, but the additional cars often function merely as storage on wheels as terminal operations continue to be clogged. A real increase in transport capacity would be obtained, however, if shippers reduced delays in loading and unloading. This is a case of technical exter­ nalities and non-optimal adjustments by the many economic agents

involved. Individual shippers own their loading and unloading sidings, while the railway owns the track and the rolling stock. There

are no price incentives for long term improvement of terminals since

the efforts of any individual company would affect only marginally 189 the availability of rolling stock, and there is no guarantee that decreased turn around times would be reflected in lower individual rates for shipment. In Paranagua, the whole rail terminal operation needs to be reoriented by installing separate TVM terminals for corn, pellets and unprocessed soybeans, and conveyors to join indi­ vidual terminals within the port. The problem of multiple sidings and individual ship-loading terminals results from the port authority's

^previous lack of capital. The authority leased the terminal rights to companies and cooperatives which constructed small, individual sidings.

Rail terminals

The Sward study documents the reductions in operating costs which may be obtained through increased physical efficiency of ter­ minal operations. It also indicates the factors required for physi­ cal efficiency:

1) uniformity of product;

2) a single origin and destination for each shipment;

3) adequate track design to eliminate or reduce switching oper­

ations; and

U) facilities for rapid loading and unloading, including appro­

priate rolling stock.

The Parana grain transfer system can meet most of these require­ ments if they are developed as part of a coherent transport strategy.

The uniformity of product requirement implies the need for terminals of size sufficient to load a train with a single product in a short period of time (e.g., a minimum standard of 12 hours for an 80 car \ 190 train, with each hopper car having a not load of 5^ tons). Suf­ ficient siding capacity in the form of parallel tracks or (ideally) a continuous "loop" is required for train formation. The terminals could be operated privately (e.g., any of the processing firms in

Ponta Grossa), by cooperatives as in the case of the C0AM0 terminal in Maringa or by the Railway Storage Company (AGEF). Terminal effi­ ciency cannot be achieved, however, with the current proliferation of small sidings, which accommodate as few as 2 or 3 cars and rarely more than 8 cars.

The single origin - single destination requirement implies the need for large shipments from producers and some increased ✓ storage and reception capacity in Paranagua. Receiving bins for quick unloading from hopper cars must be built in the port area.

Ideally, the cars could be unloaded by gravity flow while in motion

(3-5km/hr). Conveyors would then remove the grain from the receiv­ ing area to individual or common storage. In the interior of the state, new physical locations and equipment are required, as virtually all terminals are located within urban areas with little room for expansion.

The limitations of existing rail lines do not permit the econo­ mies associated with unitrain movements, if correctly defined as trainloads of a single commodity moved on a regular schedule between a single origin and a single destination without switching operations

[Sward: 1973: 2]. The state's single line tracks and the implied passing problems require frequent switching to sidings and limit 191

the maximum train size on the serra stretch going to Paranagua to

22 cars. Although true unitrain movements axe impossible, the

economies of trainload volume movements can be achieved. The TVMs

would differ from unitrains due to the required on-line switching

operations. They would, however, provide a substantial share of

unitrain savings through improved terminal efficiency.

The last requirement of rapid loading and unloading facilities

is partially discussed in the above paragraphs. Hopper cars are

required and are currently being used in Parana. Flood-unloading with continuous motion is possible with such equipment, but cars

must remain stationary while loading. This is due to the neces­

sity of being able to close the car to entrance of rain and foreign

matter after loading. This slows loading somewhat by eliminating the

use of gondola cars which are widely employed in mining operations.

With the exception of the siding itself, there is relatively

little additional investment required for the loading terminals.

Additions to storage capacity will be required within the state to

accommodate increased production in each coming year. From a social

point of view, the marginal cost involved in their construction does

not include the storage bins themselves, merely the additional equip­

ment and expense involved with the location and rail loading equipment.

Since storage occurs only for brief time periods in these terminals,

construction may be of the more inexpensive ”V" or "W" floor units.

Conveyors and overhead loaders are also required, but are not

rail-specific since they can also be used for loading trucks. 192

Highways

For a given highway or terminal facility, truck operating costs increase with congestion, becoming very high as capacity is approached.

Each additional truck using the facility creates a negative exter­ nality for all other vehicles using the facility if traffic is heav­ ier than that minimal amount classified as "free flow." Decisions by shippers regarding the choice of highway versus railway consider only privately incurred vehicle costs. The costs of providing and maintaining the infrastructure are considered only to the extent gasoline taxes and other variable user charges are incurred. When excessive traffic damages the roadbed or it becomes necessary to provide additional roadways, there are substantial divergences between private and social costs of trucking, as explained below.

Financial charges to shippers under Parana's competitive truck­ ing conditions include an implicit calculation for licensing fees, fuel taxes, and availability of backhauls. The inclusion of the fuel tax_ and other transfer payments in the charges makes financial charges for the trucking operation greater than the economic costs of vehicle operation alone by perhaps 20% [GEIPOT: 18].

There are two problems, however, in using an economic "accoun­ ting cost" of vehicle operation instead of the financial charges to shippers. The first problem is that an accounting cost is heavily dependent on the assumptions regarding the intensity of vehicle use and the availability of backhauls, as well as the type of truck

studied. GEIPOT's accounting cost, for example, is for a truck with 193

a net load of 10 tons and no empty backhauls. The estimated economic

cost, however, is superior to the actual charges by truck owners for

grain in Parana, even though these include transfer payments and

empty backhauls. This is presumably due to the intensity of use

of vehicles (little time lost in search of cargo, although terminal

delays are substantial) and larger vehicle size (average of 19 net

tons).

The second problem concerns the social costs incurred by vehicle

operation. The transfer payments by truckers via the fuel tax,

license fees and other items are much less than the costs incurred

by society in providing and maintaining the roadways on which they travel, so that the financial charges underestimate the total costs

of truck operation even if congestion costs to other users are

neglected.

In Brazil, the available figures (1970) indicate that diesel

trucks account for 2b.3% of the ton-kilometers of highway traffic,

while originating only 9 -9% of the imposto unico (fuel tax), the

principal user charge for highways [Paola and Azeredo, 197 : 137].

In order to originate an equal share of the tax in terms of ton-

kilometers of traffic, the heavy diesel truck share would have to

be increased by 250 percent. If this increase is added to GEIPOT’s

accounting data for operating costs, the economic costs of vehicle

operation exceed the financial costs which include the current levels

of transfer payments. This analysis assumes that the 1976 cost of

Cr$2.10 per liter for diesel fuel is representative of the social 19^ cost, including foreign exchange losses. Since diesel fuel is not penalized ty the heavy taxation applied to discourage gasoline con­ sumption by passenger cars, this assumption seems reasonable. There may even be an implicit subsidy for diesel fuel which would lead to an underestimate of fuel costs and consequently the financial charges by the competitive trucking industry would further underestimate the social costs of vehicle operation.

The imposto unico in 1976 was the only important specific tax on trucks (license fees were only about U.S.$^00 per year for a 30- i+0 ton truck). The imposto unico does not even cover expenses for conservation of existing roadways. In 1970, it provided only 60% of conservation expenses [Paola and Azeredo: 1-U5] * In Parana at present, it is a relatively small share of the funds destined for road construction and maintenance. The Fundo Rodoviario Nacional (national road fund), composed of the highway sector's contribution to the imposto unico, accounted for only 16% of Parana's transportation budget in 1975. The state spent over 2.6 times this amount ( of its budget) in that year just to pave 280 km of highways [Estado do

Parana, 1975: 92, 100].

The imposto unico thus does not cover conservation costs, it makes no contribution towards the construction of new highways, and * the trucking sector's contribution to the imposto unico is only about h0% of the diesel trucks' share of total ton-kilometers. The ton-kilometer criterion in turn is an underestimate of the percen­ tage of costs incurred by heavy trucks for highway construction and maintenance. Tremendous increases in costs are associated in 195 building roadways to accommodate heavy trucks as opposed to other traffic. Trucks are also responsible for most of the wear on high­ ways once they are in operation. Abouchar suggests that trucks should be charged with 90% of the cost of construction and upkeep of paved roads [1967: 52].

A few examples indicate that Abouchar's argument is quite reason­ able, even though precise data is not presently available in Brazil:

1) estimates of road wear factors used in determining the stan­ dards for highway construction are: 1.05 for heavy trucks; 0.33 for light trucks; and only 0.26 for buses, while passenger cars are not considered [Ministerio dos Transportes, 197^: 52];

2) during the peak grain transport season, Parana's major high­ way arteries are saturated with traffic, a condition created by con­ gestion induced by grain trucks (see below);

3) extensive damage to highways is caused by the grain traffic.

In one case, a 12.h km stretch designed for passenger cars to the

Santa Clara mineral water resort was subsequently used by grain traffic crossing an adjoining river by ferryboat. After only three months of such traffic, the pavement was extensively damaged; and

h) overloading of trucks can further reduce road life. Although

Brazilian law establishes ^0 tons as maximum permissible gross weight

(excluding special carriers), grain traffic occurs on vehicles with a gross weight of h8 or more tons (a 20% overload). Such overloading can reduce road life from 15 to as little as 6 years, yet the absence of scales and inspections results in non-compliance with load limits. 196

In Rio Grande do Sul, (a state with longer experience with intense

grain traffic), roadways have been virtually destroyed in less than

5 years.

Finally, there are congestion costs to other users resulting from

increased travel time, use of more expensive routes and inability

to travel (for marginal traffic). In Parana, the increase in travel

time places a considerable burden on other users (see below).

Thus, financial charges to shippers are used as the cost esti­ mates in the analytical model in the text. No percentage is sub­

tracted out for transfer payments to get at an estimate of "economic

costs," since the transfer payments are already insufficient to cover

the economic costs of trucking to society (the transfer payments do

not cover the trucking share of conservation costs, and neglect

entirely the provision of new facilities and congestion costs to

other users). The financial charges are thus an underestimate of

the social costs of vehicle operation. This will be pointed out

when comparisons are made with rail costs. The rail costs include maintenance expenditures for the permanent way and administrative

costs under the concept of long run average costs (LRAC) using the

methodology developed by Sander. The LRAC concepts constitute the

only means presently available for obtaining cost estimates for

individual commodities on the Parana rail lines.'

Railway line capacity

The terminal, rail car and rail line factors were cited above

as limits on rail service capacity. The investments needed to relieve 197 the rail car and terminal constraints are relatively minor in compar­ ison with those necessary for increasing rail line capacity. Actual line capacity is a function of several variables, including gauge, gradient, station size and spacing, weight of rails, tractive force, degree of maintenance, type of cargo, speed and adequacy of bridges and other special types of construction. Most of these variables are interdependent. For example, speed is diminished by the narrow (l meter) gauge, excessive gradients and curvature and by light rails on some sections. The Federal Railroad Corporation (RFFSA) has some estimates of rail line capacity using mathematical formulas and there is some recent work in the United States by Peat, Marwick,

Mitchell and Company. The general conclusion is that rail capacity can become a binding constraint much sooner than previously supposed.

There is also some empirical information resulting from peak traffic operations on certain lines [Falavinha]. Therefore a somewhat con­ servative figure of 7 million annual tons is used in this study, for the better lines such as that from Ponta Grossa to Apucarana.

This figure allows for substantial amounts of non-grain traffic.

Estimates for low capacity lines are given in Chapter The annual capacity does not accurately indicate the ability of the line to move traffic if there is sharp peaking, as it may be saturated during a few months and have substantial available capacity idle during the rest of the year. This is, however, accounted for in the analytical models in Chapters U and 5 by dividing the year into four separate time periods. 198

It should be noted that these capacity estimates are not abso­ lute limits. As the Peat, Marwick, Mitchell and Company study points out, rail line capacity is the ability to move trains over a line without undue delay, rather than to move them at all [1975: 67].

Increasing sidings, directional imbalances and peaking are ways of marginally increasing capacity.

Highway capacity

The U.S. Highway Research Board's Highway Capacity Manual, 1965

is the basis for most capacity calculations in Brazil as well as

the United States, although studies are underway which may someday

allow adjustments for Brazilian conditions. The formulas and tables

in the Manual are derived from decades of study and enormous invest­ ments in data collection. They are intended as guidelines rather

than rigid values applicable to all areas. Among the difficulties

in applying these values to Parana's roadways is the very high pro­

portion of trucks and the absence of hourly traffic counts on the

highway network (capacity is defined on an hourly basis in the

Manual, rather than a daily basis, while only daily figures are

available for Parana). With these reservations, however, it is

still possible to document the effect of truck traffic on highway

capacities and levels of service for Parana's main highways.

The service volume of a roadway can be defined in terms of a "level

of service," in turn associated with driving speed. If Parana's two

lane highways had no restrictions with regard to width, lateral

clearance or other factors, a level of service "B", associated with 199 a driving speed of about 80 km/h (50 mph) would be possible with a total of 900 cars per hour (total, both directions) [Highway Research

Board, 1966: 302]. An increase in volume to 1,1*00 cars would result in a level of service "C", with speeds of about 6U km/h. These are * typical speeds for trips on such routes as Curitiba-Paranagua; Curi- tiba-Ponta Grossa, or from Ponta Grossa to Guarapuava or Londrina.

However, during the peak grain transport season (April to September), heavy grain trucks further lower the speeds to around Uo km/h, indica­ tive of a level of service "F" and capacities of less than the maximum of 2,000 cars per hour (which occurs at about U8 km/h with the un­ stable level of service "E"). The implied increase in travel time for cars and buses is a major cost borne by other users. Although no attempt is made to include these externally in the models developed in the text, they can be separately estimated. Taking the Transpor­ tation Ministry study of value of travel figures and correcting for inflation, the value of travel time is Cr$13.19 per hour for passenger cars and Cr$39*^9 for buses [Ministerio dos Transportes, 197^-: 129].

At the Tevels of traffic on the BR-277 near Ponta Grossa from July

31-August 7 5 19755 the congestion costs borne by car and bus users amounts to Cr$l*3 million for the Curitiba-Ponta Grossa stretch alone during the six months of heavy grain traffic [Ministerio dos

Transportes, 1975s vol. 2, p. 5]« Although no traffic count is available for the Curitiba-Paranagua stretch, delay times and observed speeds indicate that the above figures would be reasonable approxi­ mations for that highway as well, for a total of Cr$86 million for 200

the two stretches. The figures for the value of travel time are

probably very conservative estimates, especially for buses, and

the Cr$86 million figure neglects delay costs to trucks carrying

other goods. Costs to users forced onto higher cost routes and from

reductions in marginal trips are not included in the totals.

The costs to other users from a grain traffic-induced reduction

in the level of service are substantial. At the above rates, a

"back of the envelope calculation" indicates that if expanded to

the entire highway system, they may have amounted to as much as 1/3 to 1/2 of the entire grain transfer bill, and could approach the total cost of truck transportation borne by grain shippers from the P principal production areas to processing plants and Paranagua.

The grain traffic may soon impose even greater costs to society

by requiring conversion of several two lane highways into four lane

freeways. This may result as the volumes of grain traffic produce

saturation on highways that are lightly traveled in terms of total

vehicles. This seeming paradox occurs since heavy trucks are the

"equivalent", in capacity calculations, of several passenger cars,

and trucks compose about 50% of total vehicles on Parana's main

highways [Ministerio dos Transportes, 1975a, Vol. 2]. This implies

that saturation on a two lane highway (2,000 passenger car equivalents,

total both directions) occurs with an hourly volume of vehicles con­

siderably less than 2,000.

For the off-peak season at the observed level of service "C",

the truck equivalency factor may be taken as 5 for rolling terrain 201 and 10 for mountainous terrain. Both are found on the Curitiba-

Ponta Grossa and Curitiba-Paranagua highways and upper capacity for the route is given by the most limiting stretch. At levels "D" and

"E" of service (corresponding to peak grain movements of April to

October), the equivalency factors are 5 and 12, respectively [High­ way Research Board: 101]. With a truck equivalency factor of 5 and

50% of total traffic composed of trucks, maximum hourly capacity under ideal road and driving conditions is reduced from 2,000 to

667 vehicles, obtained from the capacity formula [Highway Research

Board: 102]:

100______100 - Pt - EtPt where P^ is the percentage of trucks and E^. is the truck equivalency factor.

For the mountainous substretches, the factor is E^. = 10, and hourly capacity is only 36^ vehicles.

The above calculations can be used to compare saturation levels with Parana vehicle counts. Considering a conservative figure for the percentage of total daily traffic in the maximum volume hour

(12$), saturation would occur at 5,558 average daily traffic (ADT) on the rolling terrain and 3,033 on the mountainous terrain [American

Association of State Highway Officials: 56]. These figures provide a capacity equivalent of the observed low levels of service and average speeds. For the Curitiba-Ponta Grossa stretch, the observed

APT from July 31-August 7, 1975 was 5,696 (trucks composed 5 ^ of the traffic) [Ministerio dos Transportes, 1975 a: 5; 32-37]. For 202 the Ponta Grossa-Cascavel stretch, the ADT was 2,1+90 and for Ponta

Grossa-Londrina, 3,100.

The above figures place all of these highways near saturation levels. Furthermore, the theoretical capacities refer to levels of

service on ideal roads and under ideal conditions. Low lateral

clearances, narrow bridges and lanes, sharp curves and obstructions would lower the theoretical volumes below those discussed earlier.

Since such problems are present on Parana’s rural highways, "satura­ tion" is more than a peak hour phenomenon on Parana’s highways.

Rather, it is found from early morning until late at night.

In summary, the grain traffic is responsible for reducing the vehicle carrying capacity and the level of service on Parana’s highways. The reduction is of such magnitude that saturation occurs at ADT volumes as low as 3-5 thousand vehicles, rather than at levels of 12-16 thousand vehicles for volumes composed of passenger cars, buses and light trucks. Observed speeds are reduced from over 6 h km/h in the off-peak season to less than 10 km/h during the time

of peak grain shipments, causing delay costs to other users. The

delay costs may approach the financial costs of the non-local grain

truck movements, and the duplication of highways which may have to

occur would involve considerable additional social costs.

Additional detail on highway costs

In the off-peak seasons, the basic cost for the Toledo-Paranagua

shipment of soybeans was Cr$0.2022/ton~km in 1976. Waiting costs are at

a minimum at this time, as is line haul congestion. Nonetheless, 2J% 203 of total time spent on the off-peak round trip is spent in terminals.

The cost of waiting time is estimated as b0% of the expenses con­ sidered for the line haul. The difference in waiting and line haul costs is attributable to fuel and wear. About 3b-50% of total costs can be attributed to fuel, oil and lubricants alone. The amount of fuel used depends on the fuel efficiency of the vehicle and the amount of congestion. The fuel cost calculation is based on GEIPOT figures for the price of diesel fuel, Cr$2.10 per liter when adjusted for inflation from December, 1975 to mid-1976. Thus the cost figure for trucking is not exaggerated by the taxes placed on gasoline to discourage consumption.

Using these percentages, the minimum waiting cost is about 9% of the off-peak round trip cost, or Cr$12/ton. This may be further broken down into a Cr$l/ton loading cost in the interior and a Cr$ll/ ton delay cost in the port. The costs for terminal delays are derived from a total cost of Cr$20/ton for each day spent with driver and truck in a terminal, considering about 17 hours of this as useful time (i.e., allowing for rest for the driver).

During periods 2 and 3, there is congestion on both line-hauls and in terminals (both in Paranagua and in Ponta Grossa). Terminal congestion costs in these and other terminals are based on average waiting times with differing volumes of shipments into the terminals.

In Paranagua, waits of 2-3 days are normal during the peak season, and can become even longer as waiting lines stretch along the highway for 20 or more kilometers. Line haul congestion increases time and fuel costs on Parana highways, as traffic conditions during periods 2 and 3 are charac­ terized by average speeds of 1*8 km/h (30 mph) or less, indicating over­ loading. Assuming that the additional costs to trucks are proportional to the time spent on the line haul, one obtains an increased line haul cost of 10$ in period 2 and 15$ in period 3, based on seasonal variations in driving times. Empirically, costs throughout the state increase much more than this during periods 2 and 3, even after isolating the terminal costs. Therefore, the "mobilization" cost is assigned to bring the line haul costs for the two periods up to the empirical level. This cost is 10$ of the line haul cost for period 2 and 15$ for period 3. It represents the premium necessary to attract additional trucks from other states and other uses to the seasonal grain traffic in Parana. The congestion and mobilization costs together increase line haul costs 20% in period 2 and 30% in period 3. Appendix D

The Storage Sector

Both local transportation and storage costs in bulk units are peripheral to the focus of the present research and are thus not developed in the text. There are a number of economic issues involved, however, which serve as background for the treatment accorded in the text and which are the subject of this appendix.

Parana's semi-tropical climate combines high humidity and high temperatures for most of the production areas during about nine months of the year. Unless the harvest season is unusually dry, grains must be cleaned and dried within a few days of harvest or spoilage results. Relatively few farmers possess the scale of operations and mechanical expertise necessary for on-farm grain drying. Therefore, grain is generally transported directly from the harvestors to principal cities in the production regions for cleaning, drying and subsequent storage. These collection point storage units are owned by cooperatives, private firms or the state and national storage companies (COPASA and GIBRAZ^M, res­ pectively) .

205 The financial costs of building and operating a storage unit are difficult to specify satisfactorily. The per ton costs vary with the percentage of space occupied and with the turnover in a given time period. They also vary with the services offered,

such as cleaning and drying, and the quality of storage. The variation among types of storage units in the quality of storage provided make construction costs ambiguous. For example, a large

"V" floor unit with ^2,000 tons of capacity has a construction

cost of Cr$l('6‘7 per ton [ACARPA, 1976b], The cost of a set of 96

cells from the RICASILO company, in comparison, is Ci$865/ton,

based on an 18,000 volume [Kottel, 1976b] . However, the second

unit provides high quality storage over long time period and

is more flexible. It can be used for simultaneous storage of many

grains without space losses from indivibilities and product separ­

ation, permits temperature control and ventilation of individual

cells, and can be used for storing seeds and inputs. Thus the

per ton-construction costs cannot be directly compared, since the nature and quality of the storage provided are different.

The storage units without conjugated cleaning and drying

facilities, however, incur extra costs from additional handling

operations. These are included in the cost calculations and

models developed in the text. Such units vary from conventional

warehouses where grain may be dumped for short time periods, to

inflatable devices for small volumes on farms or larger volumes

at more central locations. Some of the simulations in Chapter 5 include increases in storage capacity. The benefits are expressed in monetary terms, and refer to savings on handling and transportation costs due to increases in bulk storage capacity. Other benefits from these units are not included, such as lessened spoilage and ability to sell the grain at times of more favorable prices. Additionally, the cost figures referred to in Chapter 5 for purposes of comparison are construction cost figures.

For the least expensive bulk units with "V" floors, construction costs are only 26% of total costs if 10% of total costs of construction are charged against a given year and the economic life of the unit estimated at 10 years [ACARPA, 1976b] . The remainder of the costs are for administration, cleaning, drying and other operating expenses.

As indicated earlier, facilities which offer better quality storage characteristics have higher construction costs. Certain superior facilities, however, have lower economic costs. These are the IBC -coffee warehouses. They have a near zero opportunity cost since the IBC recently removed virtually all its coffee from its vast chain of warehouses above the 2^-th parallel. These facilities can be converted into bulk storage units for grain at one-sixth of the construction costs of the "V" floor units [Catanho, 1976]. The conversion involves the installation of drying and cleaning units and metal bins or cells within the warehouses. The warehouse roof provides overhead shade and ventilation, while individual cells permit effective monitoring of grain temperature and reduced indivisibilities when handling different grains. 20 8

Despite the lower economic costs, however, these facilities may not he converted to bulk storage as rapidly as desirable.

The cooperatives using the warehouses must pay the IBC 50% of the

GIBRAZ^M level of charges for storage services as rent, in addition to the conversion cost. t Since the operating costs are large in proportion to the construction costs, the divergence between the economic costs of conversion and the added financial charges could discourage cooperatives from choosing this option.

An additional source of divergence between economic costs and financial costs originates in the negative interest rates for storage construction. In 197&, cooperatives and private firms received loans for construction and other facilities at a nominal

interest rate of 15% per annum, while producers received loans at

8%o per annum. Given the k8,2%o inflation rate for the year, the

implied subsidy for 197& was 33.2% f°r firms and cooperatives, and

^■0.2 for producers. This artificial cheapening of capital to

the grain sector can lead to construction in situations where

operating costs are higher than for existing firms. This can lead to

conflicts between cooperatives and their members due to the differences

in the rates of subsidy.

Finally, there is a wide variation in both financial costs and

financial charges among cooperatives and firms in Parana. Each firm

or cooperative offers a wide variety of services and charges the

users a variety of fees. The attempt of management is to recoup

costs in the aggregate and provide a surplus for profit or expansion. 209

There is often very little correspondence, however, between the cost of providing a specific service and the amount charged for it.

Aggregate costs per ton stored also vary greatly from firm to firm, along with their component construction, fixed and variable costs

(Hammerschmidt, 19731■ Charges to users for specific services also vary considerably among firms.

To further complicate the picture, the firms with lowest aggre­ gate charges may not represent the least expensive alternative to the user. Very large units have the lowest construction costs, and there may be additional economies of scale in drying and administra­ tion. A local congestion problem may develop, however, as trucks and wagons arriving from farms create a bottleneck at the unloading facilities. A number of cooperatives now recognize the inherent difficulties in trying to deal with large numbers of vehicles and are locating new collection units in towns and villages nearer the producers. Appendix E

Partial 1976 Basic Solution

Table 13. Identification of origin and destination nodes for com­ puter printout of 1976 basic solution

Columns Identifies Code Origin Destination

6-7 1 3 -1^ microregion follows IBGE, with consecutive 1-2^ numeration

8 15 subdivision of follows Figure 2 microregion

9 16 artificial nodes 0 = represents real facility 1 = artificial node used to impose capacity con­ straint

10 17 type of node 0 = storage 1 = truck 2 = not used 3 = processing k = rail 5 = region without drying capacity

11 18 time period 1 = period 1 (see Table 2 = period 2 3 = period 3 k = period ^ 211

Table 1^. Computer printout of optimal solution to 1976 basic transfer problem, omitting node prices and kilter numbers

ARCS COST UPPER LOWER C3AR

* *» n o 1 62011 1 709 0 00 ) 0 0 16 5?t)02 52012 1 70 3000 3 0 56 8 c *700? 5 201 3 7000 00J G 29400 o ? ^00^ 52014 1 707.9700 0 0 4 6 1001 m c u ’ 70)0000 0 0 Q? M 00? ‘ I 0’ ? " ...... ' i 7000 )00 0 0 76 * 1 0 0 61 01 3 i 700000 0 0 0 2 61 00^ 61014 i 7000 )po 0 0 27 °0001 a30’. 1 V 7000900 0 7 225 0 ^000? 90011 i 7000 300 0 A 1 A 1 A 0 ononT 9001 3 i 7000 000 0 1 01 42 0 90004 90014 i 700(3 300 0 0 0 6 ? 0 0 L 62011 ...... 1i .. 7 00000 '3.... 0 0 8R 6 ’ 00,n~' '67 01 7 ' '7030000 0 73 e?no3 62013 I 7090030 0 6001 5 0 62004 62014 i 70J0C00 0 0 7 0101 70 011 i 7000 000 0 0 0 7C002 7 3012 l 7 000 00.3 0 1 5065 0 7 C 0 0 7001 3 i 70)000.) 0 1 0?? fc 0 7U004 70014 7000000 0 0 0 1 0 0 0 J1 10 0011 ...... ^ 70)0000 0 0 0 \onno?" rOOffrj'" 7000 300 ...... 0 o' 0 1 OCCO 13 0013 i 7 000000 0 1 0 100004 100014 I 7000000 0 0 0 m o o i 11 )O U . 7000000 0 3469 0 11000 2 110012 I 7000000 0 4 4 0 5 ‘ 0 110003 111013 i 70)0000 0 2U4q 0 110004 1 1 .3 01 4 i 7000000 0 0 0 1 TOO 01 12 0'U 1 ... _. ._ i . 7090000 0 ft1-1'. 0 1 20 0 0 n 12 301? ' 7000 900 O’" ■ P653A 0 120 )C 3 1 7 0 01 3 i 7090000 0 103645 0 12 0004 120014 i 7000000 0 2610 r 0 l?onoi 130011 l 7000000 0 ■> 7 t ; 0 13000? 13 0012 l 7003C00 0 401 5 7 0 1 3000? 13 11)13 i 70 30000 0 2541 4 0 130 004 13001 4 i 70)0000 0 0 0 14 000 1 140011 7 7000000 0 J 5 14 0 00 ? i'4'oorr*' —* ■7000000 0 ■ 0 -> 1 ALOO*5 140013 ’ 700000.0 0 2 1 0 4 7 1 0 1 4 )004 14 3014 1 7 000000 0 0 3 1r inoi 16 0011 1 7030000 0 0 2 l r-000? 15001.2 1 703 3000 0 0 2 13 r oo? 1 5101 1 1 7000000 0 2? 7 050 0 10 o 0 0 4 ’ 40014 1 7000000 0 11374 0 160001 16 7011 . .. ,r1 7 09 0000 0 a 38 16000? 160012 ' 7onrrooo ' " 0 ■ n 1 8 1 6000 * 16 0013 1 7000900 0 o 2 160004 160014 1 7000000 0 0 IP 171Q01 17101.) 1 7 000000 0 A 0 0 0 0 171002 1 71012 1 7010003 0 ' 0 0 1710 0 3 171013 1 7000000 0 10200 0 171 )0£ 171014 i 7000000 0 0 0 177 001 17201! 1 7090000 0 6 4633 0 17??0? ' H 7 n n ■■ - 7000009 ------' -o - 217260 0 17200-* 172013 i 7000000 0 106652 0 177004 172014 1 7000000 0 0 3 1*1001 181011 1 70)0000 0 0 1 6 1pion? • 1 41 01 2 ------' 1 7000000 0 0 IP 191 00'S 181 013 I 70)0000 0 1 0 1H 1004 16 1014 ) 7000003 0 1 0 ’ .i ' iii** ’. «?011 i 7 9.9.9 30 9 0 007 0 io 2dUc 1620L2------1 708t)tjth> 0 o 1<» 1 R 7 003 182013 i 7000300 0 3691 3 0 1ft300^ 152014 i 7000000 0 0 2 m o o t 143011 l 7010000 0 211290 0 1P310?" ~ I’P 3 01 2 - ...... i 7 09 0 0 00...... 0 9761 2 0 1 q t rj.'j-j 1.6 301 3 l 7.90030 3 0 4664 0 IP 300 4 15 7014 i 7 990000 0 4 3? 0 *o » )? l 1 0) .11.!. ) 70OO00.) 0 2 9 ‘ 5 0 0 1<) 1 1 9 3 (11 I 7 090991) 0 9 4()?qi> o i 6>no? ! 9 1 0 1 ? 1 7000000 0 2 35014 o i o - n.rf 197013 i 7000000 0 107- 3 3 o ^ - j IIIIV, 1 n -> n i i 7090000 0 76’ 45 0 in/.ooi 194011 i 799 0 03.) 0 0 1 ^ 0 0 ? 194 012 i 7 00090 0 0 0 194()0? ’ 94 01 1 i 709 3 00.) 0 3 1«; 4('i?4 j 44014 v 7O9()009 0 0 .09001Jo o o h i 7) 1001)3 0 0 j-noo'C- _r* c* ■- iN U'uM(v) f\i*vj fvjf -J rv f)"J M fJ i - r > j. j jh, -»p-.coo 0 .->xv4 -4 -o:7'0 ‘>n«'>>ai'* ^*v> •. -. — .» x* *' •'’ Is •* •*■ 1 ►’ •■ I J JU ' 1 ••.< j -\j .... .

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. -.-OOOO “3 0 0 OOC OOOOO 2Qi 30330000000^-

! 1 ■ ! —J-4-4—J-4-4-4-4 4 -J 4—4-4-4 -tI-X-J-O-J-X-J-X-J-J-X-X-J—J-X-J -J—J—J —J—4-X-4-4-J-4-4-4—J-X-4—l-j-4-4*4-* K—r-* rvj.-o .\J0j D 003-J O-CCn^OOOOOOOOJOCOOOOCOOOOOOOOOQOjOOOOOd^ c’'- m 'm 'j /j 'T • / ’ j X X f O i l X X X J’l-'J 1>J f—• m 043 OO O 0 O O w ■ 3 O O O _«»O -3 C 3 O *“*■ O OC ._■ w O O O O O OO 0 0 3 3 -O w w O 3QC O t_* O O 33<-< J WC p> •£>':; -j -4 «J -4 -4^4 .n rO’XX ♦-■*-X',0 3fc-'»,0'\JT0-X-4-4r-X'OO'33OOOC_;3OOOOCC.0C3O'3 33COQOC3O30O. 3 C Z . O O O C O O 3 O O r } >3O —>30000 0 - 4 Xs X" 0 * 0 w— .ro-X -j'/^'Ji“JffOOOC.COUO^ O C - O O C O O o O O C O O w O O d O O O C C O O O C OOO C ood O C ^ u OOO j ^ ' J C - O OOC S f DJ'fOOCTMMmnOD^OOOCOC 'OOCCCOOOCC.3 ^COOOCQOOCi13COOC .‘iCOOOOcjOSOOOO 3 00 OOC iCOO J.^. ,4 0 -4- 4 > '33 x X' 23 »»- 'XJXOUOXJaJ'«xxOOQOOOOGCOOG300000003.'JOGOOOOqOOCOOCCQwC/J3COOqOCOJC;CC30CCC

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ARCS COST UPPER LOVTER X CBAR i / ico<; i f LOO? 45 I S4fc 4 U 9 ( ' \J 0 1.71.003 171004 4 5 18 4 6 4 0 0 45 17 7001 172 002 45 9P460 0 0 40 112002 I 7 ?00* 46 9 Q 46 9 0 03460 -2 5 172003 172004 45 98460 0 0 45 m o o i 18100? 45 6933 0 0 45 1n1002 1 q T 0 0 0 4 5 *• 33 ) 0 I 0 1^1(1.03 181C04 6 5 4 9 3 3... 0 0 33 1°7001~ ...... 45 - 4 7760 ...... 0 ...... 70" l 002 102000 6 5 4? 260 0 42260 -3 1P 2 00 3 1 H2C )4 4 6 1033 3 0 0 34 103001 lo?oo*> 45 4737 0 0 45 1 [>->007 ! n ?003 45 6?3 2 0 42 3 2 i a3003 183004 6 5 423*> 0 0 41 191001 191002 4 6 15868 0 0 49 l Ql f 02 191003 4 5 15869 0 150 6 8 -31 I 'll (TOT- I17! orrs- ■ - 4 5 ------Q ----- TJ 4 5 1 9 2 001 19200 2 4 5 705 i 0 0 45 1«2C02 1 ° 2 0 0 3 45 7053 0 1 0 l Q2 0O3 19 ? 004 45 7053 0 0 30 193001 19300? 45 31 549 0 0 46 1° 3 002 1O3003 45 31 548 0 3 1 54 9 -2 ? 1 9 3 0 J i 1.93004 45 31545 0 0 40 l^OOJ. 10400? 4 i 1 6? 4 0 0 45 '1 9/V 007 ~ "19 40 O'? 45 'T6'74'"“ — n - 0~ - 0 !■=* 001 1^4004 4* 1624 6 0 45 200001 200002 4 5 569 0 0 4 5 200002 200008 4 6 569 0 I 0 2 r 0 ° 0 3 ? o o n 4 45 56n 0 0 4 c 2 1 K 01 211002 .4 5 13103 0 0 46 2 11 10? ?1100? 45 13103 0 1 *>10 3 — Q ?! 1003 21 1004 45 1 3 10 ? 0 0 ? 7 ? i7 P o r "--?1?'QT2------45 — 7073 2------‘ - -0 ...... 9 4 *> 2 1 2 10 2 212 0) *? 4 5 70 H 7 0 7 07 7? -20 212003 212 004 4 5 70 73? 0 0 45 2?1001 221002 65 6 720 n 0 6? 221 OQi 2210O7 4 5 >6 32 0 0 o3 7 0 -1 7 221001 2 ? 10 J 4 45 6 12 0 0 0 35 222001 2 7 ? 0 0 ? 65 1 7947 0 0 6 6 2 2710 2 222003 6 > 17-367 0 ] 7347 -20 2 2?rOT 277004 ...... 4*r 1T“T6'7 0 0 ...... 44 223001 2? 0002 45 i j 0 0 45 273002 7 1 3 0 0 3 46 11 56? 0 11 562 -21 223001 2? 1004 45 11 86? 0 0 45 2 3100) 73 1 00? 46 453 n 0 4 6 2?100? 27 1 003 46 4 3 8 0 458 -21 2 3 IC 0 3 2?1004 46 4 6 9 0 0 6 6 3 7 2 101 2 ? ? m ? 46 1 k 7qo 0 0 4 6 ?3 7002 2^27107 ' ------4 5 1 S *0*5 0 1 6 79? -2 6 ? :■ 3061 2 32 004 6 5 i 6 76G 0 0 4 S 3 410 01 24100? 46 6 b 0 0 43 241002 24 100 3 45 66 0 6 6 -21 241003 241004 46 66 0 0 45 347001 742002 45 1 986 0 0 4? 242002 24200 3 6-j 198 6 0 19fl6 -23 24?103 2^2004 45 1 986 0 0 46 3 1 700*- - 3 ^ C 9 r ‘ ...... O' ■---7000990 ...... 0 ------• -*120?-?- ...... 0 117002 31 7 00 3 0 7 000900 0 2 4654 0 317001 317004 0 7 0° 990 0 0 7654 0 660001 600002 0 7000000 n 10 008 5 0 660002 66000 3 0 7000000 0 200170 0 660003 660004 0 7060000 0 166084 0 947001 94 7 00? 0 700JOOO 0 5 5 9 6 2 0 14700' 94 7 00? 7 90 900 0 0 1 ) T 0 >4 0 9-r 7t/0 3 9^700-* O 7000000 0 1 li 9 2* 0 6 1101 6110? 0 1 48.9 8 0 0 132071 0 6 I 1 02 61103 0 7 48 *180 0 140880 — 60 4 110 3 6110 4 0 1483P0 0 0 5 0 1 101 * 11 0 I 10 7000300 0 ' 0 60 6 100? M 1 02 10 70 XJOOO 0 0 50 6 1003 6)103 10 7 009000 0 130281 0 6 1 004 6 ) ' 0 4 ) 0 7030000 0 0 6 A 1 01 1 61 041 1 2 51 06 0 n 0 ?3 <- i 01 2 6 1 04 ? 12 6 2 960 0 0 30 61013 61 ()«* ? 1 y 3 4 8 71 0 9 33 t 1 0 1 4 61 044 1 5 S3 479 0 0 7 3 140011 140141 1 ? 2 4 5660 0 0 *>0 140012 14 014? 12 272341 0 0 24 140013 140143 1 > 441574 0 2 4 4 M 1 7 9 1 4 0(11 4 1M11 44 1? 3 30 ? 7 4 0 0 12 160011 1 501 41 1 3 11 840 0 0 8 , • f ’ 0 I 3 00 12 16014? t > 121600 8 .. T J 1 ~ ■) n * i 1 r* 0 1 4 \ . 1 96 30*' 0 ) 4'. ■ '■> 0 i s 0 o i '* 1 c. 0 1 4 i* \ / 147197 ■ ) 1 l: ' ’ ’ ' > 0 14 0141 1^0041 0 26 5 66 0 0 117-'/, 0 140)42 1 4 0 )4 I 0 ? 7? 341 0 T 9 i 4 0 1 4 > 140041 0 4616 74 0 2?' )7 ? 0 14 0)44 14 004 6 0 3 50 *>7 4 0 ? • 7 6 1 0 15 0:41 15 J 041 0 112 4C 0 0 11-400 -2 0 I c 0 I 4 ? 16 0 04? 0 1 21 ‘>00 0 121600 -2ft is o : 4 ? 1 K 0 06 “a 0 1 °6 8 0 0 I 9'»n0? -2 3 1 9 >; 4 4 I c 034 4 (.1 147197 0 14/107 - 1 ? CM rH

Table 1^ (continued) cn yl cr x f\jv0 p' o o Vi i i iii ii iii iiii iiii «V»iiiii C P oc r.;f^OrCO^fv«NNOC^OOCNfvOC -v niOCMf\j f“C 4 OOc OCOOCOOCCOOOfJOCOOOOCnoOOOOOODt C OOOQ' rr . p . V p ' Q O O O C t D O O O O O O o n C O O O O C O J f O O O C C O O C O O C O c O O CC 4 f*“ jC \ f M C O i in -Cv. r g r ( \ r . \ r J \ c , v r j r . ' r i N f O . \ f C i S « C C C O v f N C O O ^ C O N N « v f ^ O C r O ^ f ; . r c o P- TC C 4 - n r: mOr rj , ~ ,r;f4<*~uJ' v— »i\p>J£-4fi- ^-Nr^ | u ^ r N - ^ rv,—< »-if\ipi>}J>£^-u4nf\i«-' ,^r\;rf-4u<.*'~au>Jr', r~« r>jC n, mrO^ru I cca c HO"I cr *-4r- c OOOOOOCXOCODOCGDDCOCooc cococ ooooc . coooC-COOCO o o o cc o c o o o c o o r.o o o o co o o o co o c c o o o o co o c o cc o o co o o OOOOaOCOOOOOOCCXOOCCOOCDCOOCOGCDODOCCOCC ocD O OC O: rpjsC*^ oocc ^ «H 30N _«,-4 r x i rj'r o* , ^i cocorf,'• (*T.vjrp< ocococcoCoocccoooocr cccococcpcpc' c p c p c c o c o c c rc n o c o c c o c ro c o o o o c c _(<* c o o C o Tv.rvrj^r^p c c o c <* o c o f rif',M' • o c o c c r ^ i "ir o r jrvjp'-r” v c t c o c w o n T ror*r c r ir r,T j :^ v r — <4 )4444r'r( ^ rr r"P'r*\rtr<'roCT OCOOPOOCOCOOOOCOOOC^OOCOOOOCOCCCOC OOCO o c O o o O o c O o o C c o o o ^ c c * o r o j c o o o i o o c N o i o . c c r o c a O o o o c o r—4 c o T o c c f - fr'rr o n o f—* coo •;r o c a o c o o a c -. o c a o o c o x o c o o o c c c o c o o c o c ^ o o o c c o c c o o ^ o o o o o o o o n o o o D 4 »)-4 (< t

c-r~- c-r~- 0 - H 1 ^ 4 <>4 h p 0 4 ri\ r-^'Njr’ if\r r rr f. •J44i ^T o *c C C•<•** < r-i c f\4^\.rvjf . r\; «jc\*t r . j (v.oj»'rjrM(\*c'jf',r-« \;, r f r •—< C CC r* crx oo •—* i *• L^tT if 4 4 J • f^.r c a rr rr . f\i rvf\j ^ rccccccc h h h h r~- — * 0 « - » • / “ i T O C O O f M ^ 4 WHr rvCr 4 x rs*a f- rof-C4 fMr-—»rg O rva:cjx.o OC OC C fM'VsTh-C O * - ^ o p * * p o ^ - * '•''vOOOOOCOCOOC/OU'CT OwOCOOCOOCOCCOOCOOOOCOOOOOOOOOOOOCOOCC OOC h h >} J 1 icOCOCOCOOOOOOOOCO'f ^oooocooc ooooc ooooooooc c n o co o o o o o o o co o o o o o o o o co o o o co c o c o o c o ire o o ircOOOCOOOCOOoCCOOOOOOOOOOOOOOOOCOOr'Of^ o r^ o 1 —< ,- rg 0 4 *4 4 .-nfMrr r-:r\,p' .^(\onir'^oiT'C-:'Oa ooooooooocoooccr^oc• coo-cocoooocoo.ocroccoccocoooooo-jcccaoooccc ooooooooocoooccr^oc• >.^(\onir'^oiT'C-:'Oa r\u* 4 rccsti'cr cvrgr f\ir\ rvrvjr c rvjpjojrif T.K j%,\f.c . r i r*ccCNCrf, O if - r v f M ( j r ^ - h o o aaiotr r\jr-cc C- O‘CO-41,Cf-C r . * ? \ rprcrr c r-p~r-c-r-r- 4 4 4 4 <\, •* 4? crv. r‘. 4

p - r ‘r- r - p - . r - - p - ' p - h - K - r ' - r ‘ -Nr‘ - p - r - r - —p' c - p - o r - c - 1 f- O O O O OOO cOC oc C 0 0 OC 4 N p~ CC c o * - ■ r \ j ' r t c o c OOC r-s- OOC 0 0 0 0 •*-’ c ^ o ►*'^

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rrjh0— 444 0 n" 4 4 40 4 4r-r\jGh“0.—4 c «-• ^ ^ rsi *M rvj cm f\j«'J ^ ^ ^ - ^ «-•rsi *M^ ^ f\j«'J rvj cm r- c 0 c 0 4 4444444444 4 r, ‘rCP^r‘iG«?.

j -» h ( . r \ » f \ j r \ J■ c — \ . « { \ * , —(\ f v r \ . r ' j r * - — fJ'prCl- »—4 f'Jf'JpgrOCNl^-l —4 ,-4 c c c 4

4 4444444444

'; r 4 *ir>r.o T - r— i r* j c c o c c c c t .-•*"« ^ r- ^ —'t- ^ r- ^ .-4,-> .-•*"« r'r-.r-.rtjf.r'frr.r.r.r.rir’ 44 >r 4 4 pr (*'•04444*,*p>rr t i m^T'fo pp.f 4 4 rp.p'.rf. 4 11 coccoccccc - r-. ^ •-* - . -

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216

Table 14 (continued)

Y ARCS COST UPPER LOWER C3AR 20014 20 00 * 51 174 nr 0 0 0 4? pool 4 ?OOOA 71 174 -(po 0 0 *2 20014 20004 ? 1 1749QQ 0 0 ‘? 20 014 rooo4 01 174*00 0 0 T 2 ? 0 0 1 4 ■> 1 304 101 174*00 0 0 P? 20014 ? 0 0 0 i 111 1749Q0 0 0 42 20001 1000100 0 412173 41213 3 4 1 2 133 2 0? re ^02 !0 0 0 10 0 ... 0 g- 104CIP,0 1 04 51*0 10 4 y 1 « 0 2 b b ococn •1 0 0 0 1 0 0 " - 22*54097 “ * 2254057 ‘ '2254097 343 20004 1000100 0 71212* 71 212* 712129 24*. 1200 31 120111 -10 1 3 Q6 3 1396 9 1 3h* 9 -1 0 12 0 0 3 2 12 0112 -1 0 14 24 7 1424? 14242 -1 0 120033 1P011? -1 Q 23050 23050 2 30r 0 - 1 3 120034 12 011^ — 1 0 1 7740 17240 1 7? 4 0 - i o 14003^ 140 I? 1 -?p 30774 30776 307 76 ?0 ^ CD 0? ? t'- o i ip 31 603 316 0 9 : 4 1400 3 3- -i-rO l i l ------— 2 V •• - 5-i-i *>f>- • • -5 1 1 5 5 • - 511 ------— 1 <2 140034 l M j n * - ?Q 3B261 3R261 38241 ? 150031 150131 -?0 31 749 31740 3 1745 -16 15003? 1 501 0.2 -2 0 1? 497 32607 3 ? 6 ' 7 -1 c 1 500**? 1^0113 .....~ ...... ——- _?Q 52^70 57773 q 7 7 ^ 3 -12 l c 0 11 ? 4 1 ‘■•oi i t -?0 75471 3f34 7 1 7 v. / 1 - 1 7 1 401 3 1 1 5') 1 1 ! 1 9 ? 2 2 P 0 0 9 14 -:r 2 1 '• 0 1 1 7 1 94H? 3 0 0 I 40 1 *>3 14 0 1 1 ■= 1 15 3 465 0 2 140114 1 ^-0 ’ l 0 1 1 -> 1 * ? 40 7 0 0 ? 1*30133 • n o n : i 7 ■------"I 5 2 *7 } 0 0 7 150:34 15 0 1 1'. 1 39471 0 0 2 1600*1 ’ 501 l) -1 9 7 1 F 96 21 306 2 1 H J 6 79 0 16 0 0’? 100112 -19 ??4f- 3 22^9 P ? ? 4 ^ 8 q 1 4Q0** 150113 -1 5 *6 30 5 3639 5 3 4-> ‘ 5 <■? I 6 0 0 * 4 15 0114 — \ 0 2 7 ? 2 I 27221 ? 7: ? 1 1? 1 P 2 9 ? 1 1H 2 1 1! -19 4 744 4744 7 M 4 -1 9 1 apo *3 7 1C 2 1 1 ? - 1 9 4^7* 4977 4 r. 7 2 -6 7 c a r 1p ?03* 1 c " 1 7. 3 “ -...... - t c 71P 5 793* -10 1 P2034 I C 2 11 4 — 19 5 39 3 5 P. ) R - H l q t r 11 1 0111 1 -1 9 ?1 P 9 2 1 q O 21 "9 -10 1° 1 03 2 1 "l' 11 2 -1 9 ? ?4 q 2 24 3 2 2 4 R -10 1 o i o a a 101113 -10 2 64 0 36 4 0 3 6 0 0 1 010 "»4 l ' U U 4 -1 9 2 7? 3 2 7.2 7 2 7 - 3 -1 0 2 i \ > * 1 m m -1 O I 094 q 10a-V3 I 0 f-4M 0 2 1103? 21111 ? -19 11 ?6 4 1 1 ? ■' 4 1 1244 -a pn *• - 7T1113 - '-1 9 • K U O ? 1 P1 ’> 7 181^7 -10 2 1103 4 2 11114 -14 1361 1 1361 1 1 ? 6 1 1 -10 2 17 03 1 2 12111 -K ■>i 196 2 1 J J 6 21 4 96 - 1 9 - i n ?! 2 03 2 ?’ 2’ 1? -19 2 2 4P * 2 24 3 9 5 2 4- 9 2 12 0 3? 2 1 211 3 -1 5 *>6 Jos 3 6 3 7 6 3 6"96 - 16 2 1 ? 0 34 ? 2 1 ' 4 -1 0 *> 7 1 ?. 7 ? M 2 7?" 1 - 10 22 3 03 1 2 •’ 3 1 11 -19 2 o? j 292 0 2'*P0 _ q 2 ?'a )* ? 2 ' 11 2 -14 ? cc * 2 94 0 ? 9 (J q -10 *2 3 0 ” - J-J-M 1 ------1 0 -- 4 Q *> 3' 4 45? 4 C6 3 -10 2? 3 u? 4 2 2 3114 -19 3 6? 0 36? 5 “ 6?9 -P 1^ 10b 1 211001 7 5 7000000 0 0 4? iP l «*5? 21 1002 30 700001; 0 0 0 60 1*1051- -112 CO! 14 7000000 0 1 7fc44 0 1°1 >5? 11 ? 0 r 1 6 7000 ICO 0 4 1 1. 6 0 0 ^ F ? i) 61 1 030)1 ?<}. 7000000 0 1 2 7 ? p c 1 p ? 0 5 2 1 J 30 )? ? 4 7000000 0 6-7666 0 1 m no: ------j-n 7000090 • 0 - 0 19 1 P?06? 1 0 : CO.? *» 1 7000000 0 0 2 3 i'o or.i 132001 \ « 70)0000 0 O 1 5 1 O 1 M c, T 132002 23 7000000 0 1109 0 101051 l l ' l 001 45 7900000 0 0 -> 0 3 c I/'10 >2 1 '>30.1? r 4 7000000 0 0 1 P.tOp'l ’ 1 ? n i l i. 7000000 0 opl 7 ' 6 10 4 0 3? 103 002 ? 5 7000000 0 7 416 6 0 I^ - O M 1.310)1 4 0 7090000 0* r poo? H 1 c 3 ''H ? 1 n r .'ji 4 H 7000 100 0 73°? 2 'J 1 O 0 5 1 1 12 001 41 7 0 00000 0 0 7 Iop 0 52 2 i?i;o? c0 7000000 0 9 7 2 01 05 1 212001 M 7030000 0 l? P l 0 2 713 5 2 2 12 0:)? 5 0 ■*000 000 0 1 r’ 4 ? ? 0 2 Z I 1 51 2 ’ ? n 1 4 - 7 00 0 000 0 0 •) 7 7 \ j ** 7 ? 1 i 0 0? 6 8 7000000 0 0 20 74 10*- 1 22 3 001 7 000or 0 0 0 1 ?4l >4? 7 3 3 0 )2 3l' 7001)900 0 0 ? 741 .,51 2 '.2001 1 /. 70 mono 0 7 6 6 0 74 1 jO? 1 V 3 p ) ) 4(i 7 0)00(0 0 1 U 9 6 0 17' )M 1 n o ii ' i. 70)0009 0 9 74‘»H 0 171 )5 7 1 Of.:),’ 7 6 7000000 0 2 4 6 1 ■* ? 9 p OOO 0 ) ’ 12 0 ) ’ 40 7 09090 9 0 ) ‘)72 0 20005 ? 4 fl 709000 3 0 40**72 0 212002 n » 4 1 0 0 * 1 0 7 r, 9 r» c, ry n 9 :-4'M 1 >.> m 1 1 ' i ’> 7,900.VI ) 0 *♦ 0 1 "L ) '1 1 ’ OOil 1 0 7000003 0 1 n 1 7 v ' > > ? 1 •> n rM .> 10 7000.09 n 1 . 1 - M vi -» ’ > ).H ' , 0 70 1090 ) 5 3 • 7 3 “ ■ ■ 1... 1 .< i ..iii 1.. 1 f ‘ 70 >300 ) 0 , -iw ; .1 t :,,, 1 .. 1 . m 1 n 1 1 ’ M »:• v* , .' > ’..,> ’- m i > 1 lj ’ ' U, ) ■*> • •• cv H CN-

Table 14 (continued) s 8 o E- c o o c o b o c o o § -J o o m cn SHOooO'flOO'OO'iooooooooocc oc *; SHOooO'flOO'OO'iooooooooocc n'vj-* —

»-'^'-Jr- r' ■.'■oPCtDiT cr -< .H^HHOcooococ-<<-'*MfMf'!r^r''rcA c r ' ' r ^ r ! ' f M f M * ' - < < - c o c o o o c O H H ^ H . < .- r * r c T i D t C <■*.•'■■*o i - P r u p . p ^ - . ' * r '- r x.ooooQuxcrxc - r J - ' ^ ' - <» - M , ‘ r j \ ( ' ' f O P O P C c w 0 c o o c ^ c ’c o c 5 ' ~ o c r o ' ~ , c o c c c c o o c c o o c c o c : c c c c c c c c w . r c c o r ; c w « - M * - i r --• r-; _ _ c ~ c »- .-• - - w c — ^ ^ c - c ^ ^ — c w - .-• »- - c ~ c _ r-; _ --• r i - * M - « w c ; r o c c r . w c c c c c c c c : c o c c o o c c o o c c c c o c , ~ ' o r c o ~ ' 5 c o ’c c ^ c o o c 0 w c n' r~rt' p* nmPPmoOc^if^rOf^rf rprc r■ p o o o o o t o c o c o o o o o o o o o c c o c o o o c o c o ^ c o o o c o o o c c o o C'OC.'r~’ o o c o o o o o o o o o o c ^ o o o o o o c o c o c c o o o o o o o o o c o o o o o o c »—!• r H H O C C C O C C O O O O O O O C O C C O O C " C O C 0 ‘ / ^ • O t c ^ t r x a a c r c - - x .x v X C c O ‘ c o o x c c i:s :c i:< ra :n :c c a :* * i.-'.K * -* c c c c :rc r:r r x . x ix x ^ x **- x -*c ^ x ix x . x r r:r :rc c c c c -* * i.-'.K * :* a c :c :n ra i:< :c i:s c c x o o c ‘ O c C X v .x x c - - r c a a x r t ^ c t O • ^ / ‘ h O x. C\. ,_ i 0 0 ^ m 0 Pg 0 0 0 r ^ « ~ w ( v J P J r f . P — J i ' r - < < \ * , ——i c \ j P J < \ , p - r p . p * . ^ - . r - i r w ^ - x . P i P j r f m < V P r ' s ! r \ J f \ . P j ' \ , p j r . ( \ < C v j { > j f M . \ j ( V f — » ^ r — 1 1 • — COOCCOCOC < r ~ OOOCCCC » — OOcQC ■ mhCO 0 -O^.0'Or C «£C r O >! ' 0 r- . 0 ^ O Cr in*-«c c incv-tr O'coou'vcrJN.^^ cr *t ' f ^^trr'Cr'f',r'ccir«jNif.r,xCOtreo>r O.rO^MnOOOOOff O O 0 0 0 0 0 0 0 0 0 ^ ^ OOO^^CoOOO«MNOOOMOOi/'lOOOo&?'*'P'Off O 'f ^^trr'Cr'f',r'ccir«jNif.r,xCOtreo>r ^ OOO^^CoOOO«MNOOOMOOi/'lOOOo&?'*'P'Off ^ 0 0 0 0 0 0 0 O 0 O 0 O.rO^MnOOOOOff 1 r~*■ •*-'-* x n K v r ^ - j ' ^ ' ^ r ^ o f N i * s . o j o . ^ O o v t — . r*~ v • r **■— » x oooooooooccocoooocoooocooooccooooocoooocoooooocoooooccccooccooooooooocooococooo o o c o c o o o c o o o o o o o o o c c o o c c c c o o o o o c o o o o o o c o o o o c o o o o o c c o o o o c o o o o c o o o o c o c c o o o o o o o o o 0 0-0-0— r— < , r\;m >*ccCroOrpfnOxc-x-fp- x - ■Crr'*.*pf-CJ' c x O n f p r O f*xcccsC‘r X ; \ r ,- j «_ 00 i ,- aH-rr * ,H-*<—»«-^.~xxL'; u cooococ--^'rrf-'CCCc *-«»■•• •- *-<•—o ,^Hr- *p »-«r-<^-.L~xxuxxL''L; • ccoooooo-ccoocr-r-r^r'-r-r-f'-f'CC'C'Crcc u ^ a H'-'rir- m c i <-HO,rgfNjrru(\jP'jPw’MfvJf'irN*' -4i— I k 3 or.!rvrjp’ ' (CaCh C (XC a m O'J- i - v r M r f x t - x u r * ' J ‘ ^, f \ j r r N t ^ r s i r r ' v p ^ : ( X p ' : 4 - ^ r s » JO^ - « c v i r ^ - r *-* r\ ♦r- O O w M iO x . r^r-a. - r^r-a. . x iO M w O O »» — ««p - i ^ -r»~»« . r - g > —«— « — <1» ~ < r - ' i N - r o . f ' - >j x c c r~a - r - m - 1 00000 0000 0 0 0 00*0*000*00 1 o-o 0 0 r-t Crv-v j C r- r C 0 C xrvsjx j s v r x r r t O r f r i x S I v lO rsgIA rvJ H f— r-r-r-f— ^**

n / 1

•— ■ < — » •* •■*oc e ir »-4 sT s - Nt -> f i ( N j f r , » r Vjvjvivj'f <-*•- •■*« vrr Cf ’r'r r r —<»-<*-•.-« ' C r •*■»*-« C Cwf\ rv.r.r1 r’ jr'ifM irv jrv jrv rV r* #»• p-j*,■»

0 ' r r o C ' i n O o O O x r - c c r ' O c o O i p r ' - o r v i C o o O p ' i P - i r . - f O o O ' l c * * « »■*»—*•—< *~i»— —<•—«»-•-•#-•'v-*>—»-■«*-• r H ^ «. • •-« r_«j ^ «-< ^ p- r_«j •-« • r-X OC& r-X (NJ^- O O ‘~-,'*ir~lZlZ-r r4 x»r^ orv# X»

218

Table 14 (continued)

ARCS COST UPPER LOWER X C5A.H

ICO 113 1 A 3013 23 7000000 0 99°? 0 12 0 1 1 A 1A001A IP 7000000 0 ABA? 7 0 1 ?0)11 14)011 7 7000)00 0 37 ?1 9 3 1’ 3012 1A0012 P 7 0 )o ro o 0 A 01 5 7 •*> ------o '1?0'!)VV 1A 101 7 ' ------c Toonrroo ' ' ” ? 5A1 A' ‘ - ' \j 1 3 0 01 A 1 A 0 01A 7 7000000 0 0 1 130011 123C11 1 5 7000000 0 0 26 13001? 12031? 16 7000000 0 0 29 120313 12 0013 17 7000000 0 0 31 I 30 31A 12 101 A IS 7 000000 0 0 27 1 AO 111 3 76011 ' r 7000000 0 0 18 1 A 0 112 3 76 01? . _ 2Ci t . 7000000 0 0 ?3 1A01I3”” 376013' ------7000000 ' "" ■■■■...9------..... ” 0 ”■ ? 3 1 AOUA 37601 a 1 5 70)0000 J 0 12 1A011 1 123011 in 7000000 0 0 Q 1 AO 112 120012 22 7000000 0 0 2 ! l A o m 120012 23 7030000 0 0 46 1 A 0 11 A 12001 A IP 7000000 0 0 26 1 •= 0 111 172011 9 70)0000 0 3? 92 ? 7 o 13011 2 J 7201? 11 7009000 0 306B2A 6 lS O U -*" ”T7~2 013 ------' 12 700 0000 0 ”'2P0?51 " n ISO' 1 A 17201A o 7000000 0 1 1.0 )° 9 3 u o m 153011 1 A 7000000 0 2 1 9° 6 160! 12 1 5 301 2 17 7000000 0 2 2A9 9 5 3 6Q’13 IS 0013 IS 7000000 0 36395 0 16011A 1 5 301A 1A 7000000 0 272? ] 0 1710! 1 37601 1 7 7 7000000 0 6900 0 171 012 3760] 2 2 9 7000000 0 ■ 0 ?*. V'TOIB' 376 013 ' ' ...... - 30 ' TOOOaCTO " 0 1 020 3 a 17101 A 7 7601 A ? 3 7000000 0 ) 0 1 72 31 1 376011 10 7000000 0 2 5 ° r ! 1 0 172312 37601? 12 700000 3 0 A 3 2 q 7 4 i 172 313 376013 1 2 7 0 ) one: 0 0 2 1 0 1 72 01 A 37601A 1 0 7000000 0 0 1 172011 A? 0011 3 A 70)000 0 0 A A 6 5 5 0 172012 A23012 AO 7030000 0 0 10 17 7 013 A? 0013 A3 7000000 n 0 7"* 1 72)1.4 A 7 > o 1A 3A 7 000009 0 0 2 u 1 «131 1 311 .311 ?P 7000000 0 0 AQ 16 1012 21 1 012 2 A 7)00000 0 0 41 1 0 3 03 3- '211731 3 ------7 6‘ 7090000 ' - 0 0 ' '' 52 1 1 o 1 A 2 11 01 A 2 0 7030900 0 0 4 0 10 13 11 13 2 011 1 ' 7000090 0 6 A 0 ? 6 0 1 0131 2 H 2 012 1 7 790)000 0 6 4 0 h 0 u n n 132017 1A 7000000 0 0 !<" 31 A 1 7201 A 1 1 7000000 0 0 1 ;! 1, ) 1 1 2 ’ 2 0 1 ' i A 7090300 0 0 27 10 1312 ? 1201? 53 70)090 3 0 0 33 1"1313 21 ? H 7 - - - S7 7090000 0 0 35 I t v j u 212 ) 1 A AA 70 30030 0 0 14 1h 2 111 1)1011 1 s ’ 00)000 9 0 ■» n. 1 p ? ’ 1 7 101012 ’ p 7 000000 0 J 2 3 102110 1'H 017 ?U 7000900 0 0 15 16? 11 A 1 ’’ 1 0 ! A 1 5 7000 000 n ■1 19 10 2 11' 132011 1< 70)0900 9 c: 9 6 7 7 0 1 0 2 1-1 2 1 “ 3 012 1 7 7000000 0 4 ‘1 7 2 0 1B2113 ■ 1C3017 ■ ------21 ’TO 9 9 00 0 ...... 0 0 1 fl 7 1 1 A 1 ’) 3 01 A 16 70009)0 0 Q 7 7 u 4 0 1 0 3 )1 ’ 1500!1 1A 7 0 )0 )0 ) 0 11 7 71^ 0 10 33!2 !. 6001? 1 7 7000 )0 0 ) £t>7hQS 0 ’ 01017 1 S 3013 lb 70)0000 n 53102 0 10 70’2 1 6 0 0 !A 1 A 7000)00 0 10 3 ^ 3 0 1. a 1 13 1 ? ? o n 3(7 7000000 I) 0 20 1011 I 2 17701? A 7 7)cooon 0 J 24 l c ’ 11 3 177012 '...... A 7 7 6)0 00 0 0 0 26 '.ll 1 i A 17701 A if 703 007 9 0 o 20 1Ol 111 117011 20 70C9C0 0 0 31 7 /^ 0 1 01 U 2 1 3?0'2 74 7) 3.3 00.9 I) CCiT? £ o 1 9' ' ' 3 1 32.017 26 7000000 0 V>40 0 1 Q 1 I 1 A 1 13014 2 0 70 31)000 .) 1 o l o 3 0 1 A 1 1.11 1 >1 7 011 1,5 707)000 (1 0 11 l c l 112 IS 201? 1 6 7 0 1000 0 0 0 11 i " 11 n 1 ° 2 01 7 ------70 7000000 0 0 101 1 ' A 1 A ? 01 A 16 70 30000 0 0 11 19 2)11 ? l?011 77 7000000 0 0 19 13731? 212012 AO 700000.3 0 0 26 1 0 ?o 1 3 2 1 ? O’. 3 4 ? 70900.90 0 0 9 1r; ? 0 1A 2 1 2 01A 77 70 31)000 0 0 1 4 lo ? 3 l I 17 3 0 1 ) 7 7 7990 30) 0 0 12 10 2 ) 3 7 1)701? 7P 7JOOOOO 0 0 19 1 ” ?)7 ? 173017 ------A? 7000000 0 1 0 1 3 ? 0 ’ 4 1 ) 7 0 1 4 7 ? 7o.:o(’!io 0 I 'J 1 ” 7 3 1 1 1 ) A 011 17 7 0 3 0.913.) 0 107)12 1 7 A (' 1 ? 20 7 OC9000 0 v) 3 '007 1 7 1 ’’ A 0 1 7 ?? 7 9 (;(.('0 9 0 0 23 107 J1 A T'AOIA 17 7000000 0 0 23 ’ 0 AO 11 1 17011 17 7)09009 0 3 a 4 ’ 0 4 3 ] 2 1 7 3 01 2 2 0 7 )9 0 0 )0 0 J 4 0 1 0 4 3 1 7 1 7 7 0 1 7 ...... 2 ? 70)0099 o 0 21 1 o 4 ) ’ 4 1 ■) 3 o 1 A 17 70)0000 0 11 1 3 7 I S )(.” ' ’ P 7931)000 0 ? 90 ? a 3 3 l r- M'l ? l r’) 01? ? 2 7)90300 9 2 3 5 o j 6 0 CM H ON

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Table 14 (continued)

ARCS GOST UPPER LOWER X CBAR H i 1 00 200051 0 0 -f.o 1 QQJ 7 ‘ 100 200052 0 90*73 0 -6 1 1 on 21 10 01 B 4 3 5 1 0 e 4 i5 i -n 19470 * - 1? i.in 21100? ----Q0 194703 0 '00 212001 3 9 2 T7 n • 0 • "* 9237 1 -SO 0 784759 0 7 P47^1 -SO :u o 212002 -a 100 ?7i 05i 0 27201 0 2 7201 0 54402 0 5440? 0 : 00 2 ? 1 0 * ? - t , a > 00 222001 0 43945 0 4 3 946 1 jo 222002 0 3 7 990 0 P 7 8 M -48 100 223001 0 2 6 634 0 2 6 4 3 ♦» - 6 C 1 00 2 7 30 02 0 53269 0 532^ ' — 6 5 1 oc -T31T01 0 r * 0 9 - 0 IBC'5 -6 5 ! 0 0 23100? 0 3 c 6H 5 0 3 5 4 8 5 -65 ' O') 0 130*7 0 2 805 t -1 0° .00 0 10 410 4 0 1 0 4 '-4 -1 06 ’ 10 241 0 51 0 8 655 0 . -•>7 * )0 741052 0 2 3 ? 5 4 0 2 12 r r 1 00 247001 0 8263 0 M ? *■ 7 — 75 ’ 10 24?00? 0 37975 0 3 78 >, -7 7 100 * 660001 ..... 0 r o a r 8 5 0 1 0 0 0 " 5 -1 4 7 1 00 640002 0 100GO5 0 1 0 0 0 ° 5 -147 1 )0 317001 0 1 232 7 0 I?*1; 7 -107 l or. 3 1700? 0 17 3 p 7 0 1 ? 3 - 7 -1 0 7 1 in 947001 c 35967 0 *59f.r -40 1 0 0 947002 0 55962 0 5 5 0 * ? -4 0 61031 -4 0 194630 19 4630 1 9 4 6 m) 1.? M 101 ) CQOOj -L. 61 ■ 12 4inb2 -4 0 1 39301 1 9 0 A 9 ' ft X 1 O'? • 6T033' ‘ * = 4 0 •373 a : 0 32351 6 2 2 3 510 -PS- 3 419 8 M 1 O'* 4 1 03 4 -4 0 ? 41 768 241968 13 101010 0 0 7 P1 ?4 981 24 9p1 1 4 200 ' 8 0 ! ’ \ £ IOOOIOO 0 10 0 7 ■* 6 100776 1 or) 76, 217 . 0 0 n ! 4 1010100 n 163039 1 63r,9p 16 3 0' * ?T> : 4 1000100 0 12138 9 1219 3 4 1215 •} 24 > 6 11*1 4 5 3 011 ] 2 7 000 JCO 0 8 4 '-0 0 rlii' 65 501? 14 7000000 0 0 1 6111"* * 6 5 8 013 16 7 0 0 0 *7 0 0 0 n 3 H I ! A 653014 12 70C0CC0 0 0 0 )C 1 ' on 100 0 7 0 0 JOOOO 0 b4H4b’*? 0

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