RETURNING MATERIALS: )VIESI_J Piace in booR drop to remove this checkout from w your record. FINES will be charged if book is returned after the date stamped be10w. , J ,1} do“ A 3139‘" r~ M JAN“: " 031 99" .SEP ’2 3 “’9" JAN I‘a 2000 AN INDONESIAN FORESTRY OPTIMIZATION MODEL FOR TIMBER SUPPLY ALTERNATIVE ANALYSIS By Benjamin Dami Nasendi A DISSERTATION Submitted to Michigan State University in partial fquiIIment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Forestry 1982 ABSTRACT AN INDONESIAN FORESTRY OPTIMIZATION MODEL FOR TIMBER SUPPLY ALTERNATIVE ANALYSIS by Benjamin D. Nasendi Indonesian Forestry Optimization Model (INDO FOM) is an economic model for evaluating development alternatives for forest-based econo- mies in Indonesia. It is a linear programming model designed to identify the most efficient pattern of supplying forest products to domestic and export markets, including optimal locations for logging, harvesting and industry; efficient transport links; and industry/port capacity requirements. The model has goal and separable programming features. Its formulation is based on a Forestry Sector Planning Model for Indonesia developed by Dr. Joseph Buongiorno. The purposes of this study are: (l) to review and consider optimization models as methods for forest based economic planning, and (2) to exercise and to test the model to know its potential role and applications as well as its limitations. The INDO FOM was‘appiied,1r\ the East Kalimantan forest-based economic region to a selected ten HPH- (Hak Pengusahaan Hutan, Forestry Licensed Concessionaire) industrial companies that meet the Indonesian Forestry Agreement criteria. There were nine sawmill, seven plywood and veneer mill, one Chip‘bfiard and one pulp-paper operations; three ports; two local demand regions six 9 interinsular demand points and sixteen foreign demand points; and eight transportation links. Five development alternatives have been analyzed. The results of the case study have assisted in giving general sug- gestions or guidelines to the East Kalimantan regional economic develop- ment planners, including (i) resource development, (ii) industry development, (iii) port development, (iv) market development and (v) transportation linked development. INDO FOM has application to forestry in Indonesia: (l) as a training tool, (2) to provide quantitative information in identifying areas of research and development, and (3) as a computer-based economic planning tool. Aspects of INDO FOM that need to be revised in the future, in order to incorporate important related concerns are: (a) forest plantations, (b) port agglomeration economies, and (c) environ- mental goals. KEY WORDS: Optimization model, timber supply, forest-based economy, regional development VITA AUTHOR'S COMPLETE NAME: Benjamin Dami Nasendi DATE AND PLACE OF BIRTH: March 26, 1946 Maribu (Jayapura, Irian Jaya), Indonesia PARENTAGE -- Father: Johan Yandun Nasendi Mother: Tabita Sru Bonyadone PRIMARY & SECONDARY EDUCATION (Name, Location and Dates): Primary School (5.0. Naimbronbano and Yoka, Sentani, Jayapura, Indonesia, l954 - 1960). Junior High School (SMP/YPK Kotaraja, Jayapura, IndonesTa, l961 - I963). Agricultural Senior High School (SPMA/Ne eri-Kehutanan, Amban, Manokwari, Indonesia, l964 - 1967?. DEGREES: Ir. (B.S. equivalent, ForestrY), the Bogor Agricultural University, IPB, Bogor, Indonesia. M.S. (Natural Resource Management and Environmental Sciences), The Bogor Agricultural University, IPB, Bogor, Indonesia. PROFESSIONAL EMPLOYMENT (Position, Employer and Dates): Staff in the Forestry Planning and Training Division (Irian Jaya Provincial Forest Service, Manokwari, T968 - 1975). Staff in the Economic Development Planning Division (Directorate of Forestry Planning, Directorate General of Forestry, Indonesian De artment of Agriculture, Bogor, l976 - present . ORGANIZATIONS: Xi Si ma Pi (International Forestry Honorary Society) ISTF International Society of Tro ical Foresters) SAF (Society of American Foresters) ORSA (Operations Research Society of America) SPM (Society for Policy Modeling) PERSAKI (Persatuan Sarjana Kehutanan Indonesia, The Indonesian Forestry Professional Society) PII (Persatuan Insinyur Indonesia, The Society of Indonesian Engineers) 111' DEDICATION To my wife, Isye, and our children, Bogi, Rista and Rini iv ACKNOWLEDGEMENT I wish to express my gratitude to the Directorate General of Forestry of the Government of Indonesia for providing the opportunity to study in the United States of America. I am indebted to the United Nations Development Programme of the Food and Agriculture Organization of the United Nations for the fellowship which gave me the opportunity to pursue a Ph.D. program. My deepest gratitude and appreciation are due to Dr. Lee M. James, my major professor and Chairman of the Graduate Committee, who gave guidance and support throughout my doctoral program at Michigan State. Profound appreciation is extended to Dr. Daniel E. Chappelle, my Dissertation Director, and Dr. Victor J. Rudolph and Dr. Carl w. Ramm for their invaluable advice and assistance as my graduate and disser- tation supervisory committee. I would like to thank Dr. Nils H. Svanqvist, Mr. Bob Dixon, Mr. L. Waring, Mr. Victor Buenaflor, Mr. Brian Kingston, Dr. M. El Rashid and other friends on the FAO Projects INS/73/OlZ and INS/78/054 in Bogor, who have provided me with data on Forestry and Forest Products Development of Indonesia and other support material. I am also grate- ful to other colleagues in the Directorate General of Forestry and the Department of Agriculture of Indonesia who may have assisted me in any way. I thank Dr. Weldon A. Lodwick and Priscilla Prophet for their expert technical advice and for helping me with numerical analysis and computation. The study on which this dissertation is based was supported by FAO of the United Nations, the Directorate of Forestry Planning of the Government of Indonesia, the Ford Foundation Indonesia and the Indonesian Cultural Foundation. The East Kalimantan Provincial Forest Service, the South Kalimantan Provincial Forest Service, the PT. Inhutani I and II, the Perum Perhutani, and the HPH-company head- quarters in East Kalimantan, South Kalimantan, Jakarta and Surabaya/ East Java, respectively, provided me with data and their assistance during my field check trip in July-September, 198l. I am grateful to them all. Appreciation is due to Dr. Joseph Buongiorno for the model. Special thanks are also due to Marilyn Mitchell for her ex- cellent typing of the dissertation. Finally, I would like to express my heartful thanks and apprec- iation to my wife, Isye Tri Anita, our son, Bogi, and our daughters, Begirista and Begirini, for their cheerful encouragement, love, understanding, patience and suffering during my long years at East Lansing. vi TABLE OF CONTENTS PAGE ABSTRACT VITA ............................... iii ACKNOWLEDGEMENT .......................... v LIST OF TABLES .......................... x LIST OF FIGURES .......................... xv CHAPTER I NATIONAL GOALS AND STUDY PURPOSES AND INTRODUCTION TO FORESTRY IN INDONESIA .............. 1 Statement of Goals and Purposes .......... 1 Introduction to Indonesia ............. 3 Forestry in Indonesia ............... 6 Logging in Indonesia ................ 9 Wood-Based Industries in Indonesia ......... lO Forest-Products Trade in Indonesia ......... l5 Forest-Products Transportation in Indonesia . . . . l6 Marketing Problems in Indonesia .......... l8 II OPTIMIZATION MODELS IN FORESTRY: A LITERATURE REVIEW AND CRITIQUE ................ 2l Linear Programming Models ............. 2l Literature Review ................ 23 Critique .................... 27 Goal Programming Models .............. 3l Literature Review ................ 34 Critique .................... 38 III THE INDONESIAN FORESTRY OPTIMIZATION MODEL (INDO FOM) ..................... 42 Model Purpose ................... 42 Conceptualization of the Model ........... 43 Model Structure .................. 46 Model Formulation ................. 47 Numerical Analysis and Computation ......... 56 Required Data and Assumptions ........... 57 vii CHAPTER IV TABLE OF CONTENTS (cont'd.) POTENTIAL ROLE AND APPLICATION OF INDO FOM TO FORESTRY IN INDONESIA ............. INDO FOM as a Training Tool ........... Identifying Areas of Research and Development . . A Computer-Based Economic Planning Model ..... Critique and Limitations of INDO FOM ....... Forest Plantations .............. Port Agglomeration Effect ........... Environmental Goal ..... ‘ ......... AN EXAMPLE OF AN APPLICATION OF INDO FOM TO EAST KALIMANTAN ................. Introduction to East Kalimantan ......... Forestry in East Kalimantan ........... Forests and Their Potentials ......... Lowland Tropical Rain Forest ........ Hill Tropical Rain Forest ......... Logging Operations and Timber Production . . . Wood-based Industries ............. Forest Products Marketing ........... Forest Products Transportation ........ The Data for the Case Study ........... Alternative Analysis ............... Main Results of the Case Study .......... Resource Surplus ............... Industrial Capacity Development ........ Port Capacity Development ........... Market Analysis ................ Local Supply Shortage ........... Interinsular Supply Shortage ........ Foreign Supply Shortage .......... Transportation Patterns ............ Forest to Local Industry .......... Local Industry to Local Port ........ Forest to Local and Interinsular Demand . . Local Industry to Port ........... Local Industry to Local Demand ....... Port to Local Demand ............ Port to Interinsular Demand ........ Port to Foreign Demand ........... Guidelines to East Kalimantan Regional Economic Development .................. Resource Development ............ Industrial Development ........... Port DevelOpment .............. Market Development ............. Transportation Link Development ...... viii PAGE 67 67 68 69 69 70 7T 75 76 98 99 99 T00 TOT T02 T02 T03 T05 T06 TO6 T06 T07 T08 CHAPTER VI TABLE OF CONTENTS (cont'd). SUMMARY AND CONCLUSIONS .............. Structure of INDO FOM ............... Potential Role of INDO FOM ............ Limitations of INDO FOM .............. Alternatives Analyzed ............... Regional Economic Development ........... Resource Development .............. Industrial Development ............. Port Development ................ Market Development ............... Transportation Link Development ........ Conclusions .................... BIBLIOGRAPHY ........................... APPENDICES ............................ A INPUT DATA FOR AN EXAMPLE OF AN APPLICATION OF THE INDONESIAN FORESTRY OPTIMIZATION MODEL (INDO FOM) TO EAST KALIMANTAN ........... Resource Data ................... Industry Data ................... Port Data ..................... Market Data .................... Transportation Cost Data ............. OUTPUT DATA (OPTIMAL RESULTS) FOR AN APPLICATION OF THE INDONESIAN FORESTRY OPTIMIZATION MODEL (INDO FOM) TO EAST KALIMANTAN ........... Resource Surplus ................. Industry Capacity Development ........... Port Capacity Development ............. Market Analysis .................. Transportation Pattern .............. ix PAGE 105 TTO TTO TTT TTT TT3 TT4 TT5 TTS TT6 TT8 122 T25 T33 T34 T35 T39 T47 T50 T55 T70 T7T T74 T77 T79 T86 TABLE A3 A4 A5 A6 A7 A8 A9 ATO ATT AT2 AT3 AT4 LIST OF TABLES Definition of Variables Used in INDO FOM (In Order of Appearance) ................... Description of Alternatives Analyzed in the Case Study of an Example of an Application of INDO FOM to East Kalimantan ............... Log Out-turn (Annual Allowable Cut) and Logging Residue (Fuelwood Output) Potentials for Each Forest Point ....................... Industry Capacity for Each Industry Point as of the Year 1978/81, East Kalimantan ............ Average Recovery Rates in Processing at Industry Points as of the Year l978/8l, East Kalimantan ...... Fuelwood Potential to be Manufactured from Residues, East Kalimantan ................ Industry Capacity Expansion Cost, East Kalimantan . . . . Existing Capacity of Port, East Kalimantan ........ Port Capacity Expansion Cost, East Kalimantan ...... Estimated Demand Potential per Year as of the Year 1978/8l, Distributed by Demand Point, East Kalimantan ........................ Average Market Prices for Logs, Processed Products and Fuelwood at Local, Interinsular and International Market, Distributed by Demand Point as of the Year l978/BT, East Kalimantan ................. Average Transport Costs of Forest Products from Forest to Local Industry, East Kalimantan ............ Average Transport Costs of Forest Products from Forest to Local Port, East Kalimantan .............. Average Transportation Costs of Forest Products from Forest Point to Demand Point ............... PAGE 50 4 89 T36 T40 T42 T44 T45 T48 T49 T5T T53 T56 157 158 TABLE AT5 AT6 AT7 BT8 BT9 B20 BZT 822 B23 824 825 826 LIST OF TABLES (cont'd.) PAGE Average Transportation Costs of Forest Products from Industry Point to Port Point ............. l60 Average Transportation Costs of Forest Products from Industry Point to Demand Point ............ l6l Average Transportation (shipping) Costs of Forest Products from Port Point to Demand Point ...... l62 HPH-Industry Log (AAC) and Logging Residue (fuelwood) Out-turn Surplus (unused amount) under Five Different Alternative Strategies Distributed by Forest Point, East Kalimantan .............. T72 Optimal Industry Capacity Development: Expansion (+) or Stagnation (-) Required Under Five Differ- ent Alternative Strategies, Relative to Initial Capacity for Each Industry, East Kalimantan ........ l75 Optimal Port Capacity DeveTOpment: Expansion (+) or Stagnation (-) Required Under Five Different Alternative Strategies, Relative to Initial Capacity for Each Port, East Kalimantan ...... I78 Domestic: Local Supply Shortages for Forest Products Under Five Different Alternative Strategies Distributed by Demand Point, East Kalimantan ......................... TBO Domestic: Interinsular Supply Shortages for Forest Products from East Kalimantan Distributed by Demand Point ...................... l8l Foreign: Export Supply Shortages for Forest Products from East Kalimantan Under Five Different Alternative Strategies, Distributed by Demand Point ........................... l83 Optimal Transportation Patterns of Forest Products from Forest to Local Industry Under Five Different Alternative Strategies, East Kalimantan .......... 187 Optimal Transportation Patterns of Forest Products from Forest to Local Port Under Five Different Alternative Strategies, East Kalimantan .......... 190 Optimal Transportation Patterns of Forest Products Directly from Forest Point in East Kalimantan to Local and Interinsular Demand Points Under Alternative Strategy No. l for Domestic Supply ....... 192 xi TABLE 827 828 829 830 83T B32 833 834 835 836 LIST OF TABLES (cont'd.) Optimal Transportation Patterns of Forest Products Directly from Forest Point In East Kalimantan to Local and Interinsular Demand Points Under Alternative Strategy No. 2 For Domestic Supply ..................... Optimal Transportation Patterns of Forest Products Directly from Forest Point In East Kalimantan to Local and Interinsular Demand Points Under Alternative Strategy No. 3 For Domestic Supply ..................... Optimal Transportation Patterns of Forest Products Directly from Forest Point In East Kalimantan to Local and Interinsular Demand Points Under Alternative Strategy No. 4 For Domestic Supply ..................... Optimal Transportation Patterns of Forest Products Directly from Forest Point In East Kalimantan to Local and Interinsular Demand Points Under Alternative Strategy No. 5 For Domestic Supply ..................... Optimal Transportation Patterns of Forest Products from Local Industry to Local Port Under Five Different Alternative Strategies ....... Optimal Transportation Patterns of Forest Products from Local Industry to Local Demand Under Alternative Strategy No. l ............ Optimal Transportation Patterns of Forest Products from Local Industry to Local Demand Under Alternative Strategy No. 2 ............ Optimal Transportation Patterns of Forest Products from Local Industry to Local Demand Under Alternative Strategy No. 3 ............ Optimal Transportation Patterns of Forest Products from Local Industry to Local Demand Under Alternative Strategy No. 4 ............ Optimal Transportation Patterns of Forest Products from Local Industry to Local Demand Under Alternative Strategy No. 5 ............ xii PAGE T93 T94 T95 T96 T97 T99 200 20T 202 203 TABLE 837 838 839 B40 B41 842 B43 B44 B45 846 LIST OF TABLES (cont'd.) Optimal Transportation (shipping) Patterns of Chip-board and Pulp-paper Products: From Tarakan/ Nunukan Port to Samarinda and Balikpapan Demand Points Under Different Alternative Strategies ..... Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Interinsular Demand Point Under Alterna- tive Strategy No. l .................. Optimal Transportation (shipping) patterns of Forest Products from East Kalimantan: From Local Port to Interinsular Demand Point Under Alterna- tive Strategy No. 2 ............... Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Interinsular Demand Point Under Alterna- tive Strategy No. 3 .................. Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Interinsular Demand Point Under Alterna- tive Strategy No. 4 .................. Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Interinsular Demand Point Under Alterna- tive Strategy No. 5 .................. Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Foreign (export) Demand Point Under Alternative Strategy No. l .............. Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Foreign (export) Demand Point Under Alternative Strategy No. 2 .............. Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Foreign (export) Demand Point Under Alternative Strategy No. 3 .............. Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Foreign (export) Demand Point Under Alternative Strategy No. 4 .............. xiii PAGE 204 205 207 209 211 213 215 218 221 224 TABLE 847 LIST OF TABLES (cont'd.) PAGE Optimal Transportation (shipping) Patterns of Forest Products from East Kalimantan: From Local Port to Foreign (export) Demand Point Under Alternative Strategy No. 5 ............... 227 xiv LIST OF FIGURES FIGURE PAGE 1 Map Showing the Country of Indonesia Consists of a Broadly-Spread Archipelago ................. 4 2 Organization Structure of the Indonesian Forestry Administration ....................... 8 3 Forest Stand Potential in Indonesia, Distributed by Commercial Species Group and by Province ........ 12 4 The Basic Concept of INDO FOM in This Study ........ 44 5 An Example of a Transport Pattern of INDO FOM ....... 48 6 Map Showing Navigable Rivers, Road and Port and City, Town and Village in East Kalimantan ......... 80 7 A Map Showing Forest Point (HPH-Company Concession Areas) and Location of Industry Points and Ports of the Case Study in East Kalimantan ............. 85 8 Transportation Network (From Port to Importing Demand Point) Analyzed in This Case Study and Geographical Distribution of Demand Points (Global Orientation for East Kalimantan) .............. 88 XV CHAPTER I NATIONAL GOALS AND STUDY PURPOSES AND INTRODUCTION TO FORESTRY IN INDONESIA Statement of Goals and Purposes The Indonesian Forest Sector National Policy laid down in the Third Five-Year National Development Plan (Repelita III, 1978/79-1983/84) expresses the following long-term timber-based economic goals: Effort will be made to change the distribution of earnings so that the discrepancies which exists between social strata and regions will be reduced. The timber-based economic development will be geared towards doubling per capita income between 1980 and 2000. Management of timber production and other natural resources will give high priority to the protection and improvement of the environment. In Order to maintain these national goals, Government, i.e., the Directorate General of Forestry, Indonesian Department of Agriculture has been preparing steps to develop strategic plans for national forests development. These plans may be varied from region to region within the country depending on variation in social and economic conditions. One of the steps to develOp strategic plans for national forests development is by improving planning methods and techniques in order to facilitate improved efficiency in the management of our national forests. This is particularly true of our timber-based economic development alternative strategies, as required by the Basic Provisions on Indonesian Forestry, Law No. 5, 1967 and Government Regulation, P.P. No. 33, 1970 which call for responsibility that includes planning the development and utilization of forest resources. 1 It seems unlikely that Indonesia could achieve its timber-based economic goals with its current methods of timber supply management and planning system. Furthermore, present conventional planning proce- dures cannot guarantee the basic policy of the Second and Third Five- Year National Development Plans, which call for increasing the market value of produce from Indonesia. Consequently, emphasis was placed on local processing and industry expansion as reflected by the joint decision of the Departments of Agriculture, Industry and the Trade and Co-operatives on timber supply for domestic needs dated May 1, 1979.1 At the present time, 60 percent of the log out-put must be supplied to local log converting industries. It is in this setting of compliance with the above mentioned national goals and the achievement of optimum timber supply alternatives that the use of Optimization model as a planning technique has been evaluated and developed. The case study area is a forest-based economic region in the East Kalimantan Province of Indonesia. The solutions are quantitatively evaluated for their success in meeting objectives, con- straints and other specified levels of performance. The potential role 1The joint decision by the Minister of Agriculture, the Minister of Industry and the Minister of Trade and Cooperation (see footnote on p. 9), and which is based on a Presidential Instruction on Timber Supply of April 4, 1979, provides the basic rules for obligatory supply of round- wood to domestic industries and of processed timber to the domestic and export markets. The decision also divides the power of implementation and control between the three ministers. of the model in Indonesia's forestry economic planning is based on the results of this experience. Stated formally, the general purpose of this study is to indicate and to analyze timber supply alternatives in order to assist in the evaluation of development strategies of Indonesian Forestry planning, using economic distribution and efficiency criteria. However, other criteria can be introduced via constraints and/or weighted objectives. The general purpose may be defined in terms of specific purposes which are to: (1) review and consider optimization models as methods for forest based economic planning, e.g., timber supply alternatives; (2) exercise and to test the model on an East Kalimantan forest-based industries case study to know the potential role of the model. Introduction to Indonesia Indonesia consists of a broadly-spread archipelago with the major consumption centers separated from areas of industrial raw material supply by large expanses of water. The country is made up of 13,667 islands, of which 992 are inhabited. It stretches from 95°E to 141°E (about 5000 km along the equator) and between 6°N and ll°S (more than 2000 km). The main islands of Indonesia are Sumatra (lying almost parallel to West on Paninsular Malaysia across the straits of Malacca), Java, Kalimantan (part of the island Borneo and sharing common borders with East Malaysia), Sulawesi, and Irian Jaya (the western half of New Guinea). Figure 1 speaks for itself. The highest mountain is the 4,900 meter Puncak Jaya in Irian Jaya. The climate is humid tropical with an average daily temperature .mmmp .zgummcod 4o Fmgmcmm mangouumgwa "mumaom .ommpmnwgug< nmmcamizpumogm m 4o mumwmcou mwmmconcH we xgucaou on» mcwzosm an: .p “magma oON :- co 4' mwpmgumz< mvcumH .6 «$35 3: .66? u z " .......... ... .... 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AFV mN.w_. an FN.NF ANV ooo.ooo.m~ F ”mo O xJ _ (3) where cj's are optimization criterion weights or parameters (e.g., cost), of decision variables xj's; aij's are technological constraint coefficients of activities in question; bi's are resource constraint levels for particular activity, and Z is the scalar value of the objec— tive function or criterion variable. To put the LP problem in a form suitable for mathematical analysis, there exist three common elements: (1) an objective function; (2) functional constraints; and (3) non- negativity constraints. It is important to note the linearity, propor- tionality, additivity, divisibility and deterministic assumptions that this model implies as the following (Wu and C0ppins, 1981; Hillier and Lieberman, 1980; and Dorfman, 1951): o Linearity -- This assumption demands that the ratios between any two inputs and between any input and the output are fixed and hence, independent of level of production. If the objective function, cjxj, is not linear. we cannot use the LP technique as such. a Proportionality -- This assumption means that if decision variable, xj, were (say) doubled, so would its contribution to the objective function, cjxj, and to each constraint, aijxj’ be doubled. The implications are that there can be no increasing or decreasing returns to scale and also no setup costs in a LP model. 23 o Additivity means that optimization criterion weights (e.g., costs) are the sum of the individual costs and that total impact on the ith constraint is the sum of the individual impacts of the decision variables, xj's. o Divisibility -- This means that the decision variables, xj's, can be divided into any fractional level desired, i.e., noninteger values of the decision variables are permitted . o Deterministic -- The deterministic or certainty assump— tion is that all the parameters of the LP model (the c3, aij and bi values) are known constants. In real problems, this assumption is seldom satisfied precisely. LP models usually are formulated in order to select some future course of action. Therefore, the parameters used would be based on a prediction of future conditions which inevitably introduces some degree of uncertainty. One specific formulation of a LP model is known as a transportation model, it focuses on minimization of total transportation costs of any commodity from supply points to demand points. According to Black and Hlubik (1980), linear programming can be subdivided into seven parts, respectively, (i) mathematical background, primarily the theory of linear equalities; (ii) solution methods (e.g., simplex method); (iii) development of computer systems to handle LP; (iv) system manage- ment procedures including matrix generators, report writers and data base management; (v) data gathering, conversion, and transcription; (vi) modeling real world problems; and (vii) interpretation and pre- sentation of results in management decision making. My primary focus is formulation and modeling of real-world problems. The concept of LP is developed with a focus upon forest-based economic modeling. Literature Review According to Chappelle (1977), linear programming methods have been developed relatively recently. George Dantzig, the American mathematician 24 of the U.S. Air Force Operations Research Group, is generally credited with development and first application of linear programming techniques. Numerous problems of a linear programming type were formulated prior to the release of Dantzig's work, like Von Neumann and Leontief's pioneering solution techniques for large sets of simultaneous linear equations in the 1930's (Hadley, 1962), but a systematic method of solutions had not been presented up to that time. Dantzig presented the simplex method for solving linear programming problems along with the initial mathematical statement of the general linear programming model in 1947 (Gass, 1958). The first applications, and the basic reason for the development of LP, was for solving problems in military planning (e.g., World War II) and other aspects of operations research. Such applications as optimization of the transportation of war materials were of first consideration. Since that time, the models and solution method have been refined, modified and widely applied to problems in many areas. One of the first applications of linear programming oriented to the timber-based industry was concerned with the optimization of ply- wood production and distribution at the level of the individual plant (Bethel and Harrell, 1957), and a hypothetical analysis of site prepara- tion for pine plantations (Yoho and Row, 1958). A mill seeking a least- cost supply of pulpwood from its woodlands was the first instance of stand-cut scheduling application (Theiler, 1959). Coutu and Ellertsen (1960) used linear programming as an aid in farm forestry planning. Applications which were oriented to larger aggregates of decision-making units came later. An early application with a regional orientation was concerned with optimizing inter-regional flows of hardwood and softwood 25 lumber (Holland and Judge, 1963). Nautiyal and Pearse (1967) combined much of the early LP work in forest regulation with the work in economic analyses of timber-stand financial maturity (e.g., Gaffney, 1960; Kidd, Thompson, and Hoepner, 1966). They formally stated the forest stand's regulation problem as an LP optimization model and used it to show the economic implications of rotation length and the length of the conver- sion period. LP applications and studies continue to be reported in the liter- ature. In the United States, Martin and Sendak (1973) reported 105 applications of LP to forestry and the forest products industry in gen- eral during years 1955 through 1970. Field (1976) lists 174 references for the years 1955-1975. One of the well known LP models in private forestry sector has been the MAX-MILLION which was developed at the University of Georgia (Ware and Clutter, 1971). The model was designed to accommodate problems of industrial ownership -- continuous wood supply to the mill, and open market for wood, federal income tax accounting, and economic efficiency. MAX-MILLION's counterpart in the public sector is Timber RAM (Resources Allocation Method) developed by Navon and others at the Pacific Southwest Forest and Range Experiment Station of the U.S. Forest Service (Navon, 1971). In wood-based industries specifically, (i.e., processing and trans- portation of manufactured products to market or final demand) applica- tions in the United States have been increasing according to Martin and Sendak's and Field's reports, very rapidly since 1955. For example, Bruce (1969) applied the linear programming model to analysis of inter- regional competition of lumber in eleven western states of the United States. Holley (1971) used it to study the flow of softwood lumber and 26 plywood. Holley, Haynes and Kaiser (1975) applied LP model for inter- regional timber supply alternatives. Pearse and Sydneysmith (1966) developed a LP model to analyze the pattern of alternative allocation of logs and intermediate products among a utilization complex producing sawntimber and lumber, veneer and plywood, chip-board, pulpwood, and hog fuel to obtain the highest economic value from the timber output. Linear programming model applications were also being used in other parts of the world. For example, the Development Research Center of the International Bank for Reconstruction and Development (IBRD) of the World Bank has developed a successful LP model for forest sector analysis (Bergendorf, 1974a). This model was applied to a forest in- dustry planning problem in Turkey (Bergendorf, 1974b) and later tested by FAO in a special study of Malaysia (FAO, 1976). Because of the model's successful performances in Malaysia and Turkey, it was selected for use in an analysis of pulp and paper industry development strategies in the ASEAN region (Svanqvist, 1980; Staab, 1981). In Indonesian forestry and forest industry, a planning exercise was begun in 1976. The Food and Agriculture Organization (FAO), in cooperation with the Indonesian government, developed a linear program- ming model to identify the most efficient pattern of supplying wood products to domestic markets and of exporting these products to Singa- pore and Japan (FAO, 1978; Buongiorno, 1978). The model can be modified to solve location-allocation problems or to assist, in general, timber sector planning (Buongiorno, 1979 and 1980). 27 Critique Linear programming models for timber management alternative analy- sis such as MAX-MILLION and Timber RAM may be more powerful planning tools than either formulas or area-volume checks that are not optimizing techniques, since they permit the exploration of a wide spectrum of alternative silvicultural practices and harvest control policies, given certain assumptions and criteria (Hannes, Irving and Navon, 1971). However, from a realistic viewpoint, critics such as Chappelle, Mang and Miley (1976) contend that the Optimal solution generated is not likely to be a forest management optimum, because values of non-timber uses of the forest do not enter the various objective functions permitted. In terms of economic and social consideration, Timber RAM accepts only biological and production economic objective functions. It ignores completely regional economics, political and social goals which cer- tainly should apply in the case of public timber management agencies, and perhaps as well to many of the corporations that manage timber as part of an integrated wood products complex. Timber RAM fails to recog- nize the spatial and transportation dimension in an integral way. It seems appropriate to counter the reality argument by keeping the model relatively small and confined to those variables which can be most accurately modeled. Post Optimal procedures may then be used to test the sensitivity of solutions to parametric changes. The relatively small allocation model may be linked with sub-system models to provide input or to treat specific problems of the actual allocation. By link- ing models to solve specific parts of the forest-based economic problem, some of the difficulties encountered by very large LP models may be avoided. For example, an econometric model is linked to an industry or 28 forest production function specified as a linear program by assuming the minimization of the cost of meeting annual demand. Colletti (1978) developed such a model for the paper and paperboard industry in order to overcome some of these shortcomings. Another criticism of LP models which schedule future events is that they fail to consider the chance that those events may not occur. Omis- sion of the uncertainty problem is embodied in linear programming's deterministic assumption. Sensitivity analysis may provide some in- sights into the consequences of varying assumptions about the future. In this area the use of N-stage linear programming model which takes account of the different stages of nature involving stochastic events, can be more appropriate. The model in which chance-constrained program- ming can be incorporated to take account of risk and uncertainty inherent in certain parameters has been suggested by Wagner (1975) and Thompson and Haynes (1971). Because this iterative technique requires many solutions to the same problem, it is likely to be cost prohibitive for other than small models. Most of the other criticisms of linear programming are due to its linearity assumption. A special type of LP model which can accommodate certain non-linear constraints is separable programming. This technique separates non-linear constraints or objective function into linear seg- ments which may be represented by piece-wise linear approximation. It then considers them sequentially through modifications in the simplex algorithms, if the constraints are entered as sequential linear segments in the matrix and appropriate changes are made in the objective function. Separable programming may give a local optimum but mixed integer pro- gramming may give the global optimum. This approach has been suggested 29 by Hrubes and Navon (1976) to handle downward-sloping demand curves in timber production, and Buongiorno (1980) to handle economies of scale of the port capacity expansion costs when the level of production or demand rises. More drastic departures from the linearity assumptions require different solution techniques. Among those available are quadratic programming, which can use the simplex algorithm with minor modifica- tions to solve problems with quadratic objective functions and linear constraints, or vice-versa. Other non-linear methods can handle pro- blems with particular specifications. While there are well developed algorithms to solve such problems, they have not achieved the standard- ization and widespread availability that linear programming enjoys, simply because of the complexity of the solution techniques and their limited applicability to certain problem forms. The expanse of the solution generally makes non-linear programming suitable only to small problems and unique situations. For large scale and repetitive problems it is usually advisable to sacrifice non—linearity for easier problem formulation and computational efficiency (Moskowitz and Wright, 1979; Wu and Coppins, 1981). Another optimization technique, dynamic programming, can provide an excellent model of the problem conditions. However, it has disad- vantages similar to the non-linear programming approaches mentioned above. Furthermore, there are no standard solution algorithms. Each problem requires that a unique solution program be written for it by an experienced system analyst. Solution times are comparatively long and problems with many time periods become uneconomical to formulate, let alone solve. Small dynamic programs may, however, be combined with 30 linear programming to reduce the LP matrix and shorten the solution time. Non-optimization techniques such as simulation and gaming models have also been proposed for forestry planning models, for example, for calculating allowable cut using area and volume check method (Chappelle, 1966), for area regulation only (Chappelle and Sassaman, 1968) and for processing inventory records into a management guide (Myers, 1970 and 1974). Gaming models such as SNAFOR can simulate nearly all the multiple use aspects of a national forest system, including regional and inter- regional systems (Countryman, 1973). Gaming models allow input from the human decision—maker, feeding decisions as inputs and watching effects or consequences of the decisions on the outputs. Simulation and gaming models force the planner to look at a forest-based economy as a complete system, because it allows feedback. In general, such techni- ques seems to offer all the disadvantages of dynamic programming with none of the advantages. They require the same difficult and problem formulation, but offer no procedure for optimizationrun~do they guaran- tee feasibility, although hybrid models exist which utilize mathematical programming techniques for optimization. On the other hand, by using sensitivity or post-optimal analysis, most optimization models can function as simulation and gaming models. In general, special simulation models are not recommended where such an analytical approach is avail- able and cost-effective (Hillier and Lieberman, 1980). A final criticism of linear programming and the other models dis- cussed above is that they provide for the optimization of only one objective ataitime. Different optimal solutions based on different criteria are generally available from the same formulation. If the 31 conflicting objectives can be expressed in a common unit of measure, then a combined objective or criterion might be used for simultaneous optimization, however, it is beyond the capabilities of the LP techni- ques discussed in this section. Goal Programming Models Goal programming (GP), in its simplest form, is a special case or a modification of conventional Linear Programming (LP) aimed at minimiz- ing the departures from specified goals or targets, subject to the usual constraints on resources, operations, etc. It differs from that more familiar technique primarily in perspective. The technique was developed by Charnes and Cooper (1961) and was first applied by Field (1973) to a woodlot for timber production including hunting and camping. A primal (as opposed to dual) linear programming model focuses on the problem of determining an optimal allocation of scarce resources given a set of objectives. Goal programming, in a similar format, seeks a plan that comes as close as possible to attaining specified goals. Both procedures deal with constrained optimization. Both are limited by the assumptions that model variables are infinitely divisible and connected only by linear relations. Goal programming requires, further, the explicit Specifica- tion of quantitative goals and any preference structure that may be associated with these objectives. It is this orientation that provides the technique with the flexibility necessary to circumvent two major weaknesses of LP. A first shortcoming deals with the existence of an optimal solution. A GP model may incorporate two classes of objectives in the same problem: (i) an overall, single-dimensional, optimization criterion such as profit 32 maximization or cost minimization; (ii) a set of secondary requirements imposed by the decision-maker (distinct from absolute physical or eco- nomic constraints) such as the attainment of certain minimal production levels. Ordinary LP procedures yield an optimal solution to a quanti- tative allocation problem only if a feasible solution exists. Feasibil- ity is assured if the requirement specified by the analyst and the con- straints imposed by the problem environment are all mutually consistent. In contrast, the objectives specified in a GP format are approached as closely as possible but need not all be met completely. This flexibil- ity allows the specification of a problem in terms of multiple conflict- ing goals and the allocation of resources according to subjective priorities. Given the existence of a feasible solution to an ordinary LP problem, a second shortcoming is the requirement of a single-dimensional optimization criterion. Whatever the measure associated with the objec- tive specified by this criterion, the outcomes of the several activities included in the solution plan must be expressed in common units. The requirement has two particularly serious effects. First, analysts eager to apply LP to problems involving incommensurable values are tempted to search for indirect measures of relatively intangible re- sults in terms of those more easily valued. Thus, for example, vacation expenditures are used as a surrogate gauge of outdoor recreation bene- fits, and a wilderness preserve is valued in terms of timber harvests forgone. Secondly even with a clearly valid relationship between the optimization criterion standard and a particular activity does exist, that relation may be very difficult to specify. GP allows not only the simultaneous consideration of resource allocations to activities whose 33 outcomes cannot be valued in like terms but it also permits the analyst to specify directly activities whose levels can be associated with a common measure. For example, the consequences of a shortage of pulpwood at a mill can be expressed in cubic meters rather than requiring the difficult estimate of the overall dollar impact of such a shortage on the firm's operating costs and sales revenues. The mathematical expression of the goal programming model gener- ally has the structured form: n Minimize i = Z (wTdT + wTdT) (4) j=l J J J J objective function subject to IiT;I.-i..x.+a’. -dT=b.;j=1,2,...,m (5) i=1 ‘3 1 J J 3 goals '2‘ 1:]gkixi jgor Z bk; functizngl EOnstraTnts (6) and xi,dj; d330 dJ. . dj=0, (7) non-negativity constraints where d3’ +'s are the number of deviation units short (-) or in excess (+) of the goal (hi); "3’ +'s are the weights or priority factors given to a unit deviation short (-) or in excess (+) of the ij's are technical coefficients relating goals to the decision variables; xi's are decision variables or activity variables goal (bj); a now called subgoals; bj's are minimum desirable goals; gki's are con- straint coefficients; bk's are constraint levels or limiting amount of resource k available. 34 This model states the optimization problem as one of minimizing the aggregate sum of individual positive and negative deviations from specified goals. The d; and d3 variables are, in fact, the same as the slack and surplus variables of the conventional LP model. The distinguishing feature of the GP formulation is that it incorporates one or more of goal requirements directly into the objective function via the deviational variables and focuses the optimization procedure on these deviations by placing no value on structural variables xi. Thus we value not activity levels but the deviations from goals that are caused by those solution values. In general, the model is subject to the same linearity that applies to the usual LP model. Furthermore, the simplex algorithm guarantees condition (7). The nature of the functional, (4), and the specifica— tion of goals given rise to the majority of the variations in goal pro- gramming. Note that the coefficients of aij's and gki's must be explicitly stated. This means that while trade-offs between objectives need not be_quantified, interactions among resources must be given. For example, a hectare (ha) of land allocated to intensive pine pro— duction may be able to support less wild monkey than a hectare allo- cated to dipterocarps production. Literature Review Goal programming is not a new technique. The basic elements of the method were introduced by Charnes, Cooper, and Ferguson (1955) as simply an extension of linear programming. Charnes and Cooper (1961) apparently coined the term "goal programming". In the subsequent decade, the authors of the term have contributed much of the Sparse, but 35 rapidly growing, literature on the tOpic. Ijiri (1965) provided a more rigorous conceptualization, detailed variations in the functional weights, and introduced the idea of cardi- nal and ordinal weights on the several goals. Cardinal weights are simply coefficients on the variables in the objective function and the standard LP algorithms that can be used to solve these problems. If all weights are equal, then the achievement of one goal is generally assumed to be no more important than another. Unequal weights imply a differential preference in simultaneous goal achievement. If the preference among goals is so strong that maximum effort at achieving one particular goal is desired before attempting to achieve another, than one of the several pre-emptive or ordinally-weighted methods may be used. Ijiri suggested that the weights on higher order goals be made large enough to minimize their nonachievement. Field (1973) presented an algorithm for establishing "priority factor" weights which insures that they be no larger than is necessary to force the desired results. However, if there are many priority levels, the size of the priority coefficients may become impossibly large. Such an approach to goal ordering permits the achievement of lower order goals even if higher order goals cannot be met, and it can be solved using standard LP algorithms. Stepwise optimization of individual goals could produce the same result. Lee (1972) has developed a modified simplex algorithm which treats all goals sequentially without the use of cardi- nal weights to imply priority. While this approach is pre-emptive, it does permit differential weights to be used within priority levels. However, Lee's published program contains some computational errors, and it is restricted to small problems (Field, 1978). Bartlett, Bottoms and 36 Pope (1976) have corrected and improved Lee's algorithm but their ex- panded version is still too small for problems with more than 1000 activities. In spite of its recent arrival, goal programming has already seen use in many areas. Since Field's (1973) introductory example, there have been numerous applications in natural resources, particularly in the area of land use planning. Straightforward uses of goal programming in this area have been reported by Bell (1975), Dress (1975), Bottoms and Bartlett (1975), Schuler, Webster and Meadows (1977), Dane, Meador and White (1977), and Steuer and Schuler (1978). In other countries, like West Germany, the GP is named a "psychometric method", and called "Nutzwertanalyse" as reported by Henne (1978). Its popularity has been enhanced for Forest Service planning by the availability of Bartlett et al's program for general use, and by a general technical report on goal programming for land use planning (Bell, 1976). Turner (1974) reports that a study project based on the RC5 allocation package may also be modified to incorporate a GP structure. The natural resources literature also contains some interesting variations of goal programming. Porterfield (l976) evaluated tree improvement programs by using a separable programming procedure to solve a goal formulation. Neely, North and Fortson (1976) report the use of an integer GP model to allocate entire and indivisible projects which may statisfy the multiple objective of water resources planning. Meadows and Schuler (1976) have suggested a GP framework with quadratic loss functions to account for decreasing marginal utility. There are also reports of GP variations similar to those for linear programming. Ruefli (1971) described a generalized goal decomposition 37 model while Contini (1968) used a goal interval programming model to allocate Coast Guard activities to prevent the pollution of the marine environment. Here they described each goal as a range of values so that the decision maker could examine trade-offs within as well as among effectiveness measures. They indicate that the results would be enhanced through an interactive approach. Indeed, almost all GP appli- cations must involve interaction between the model (analyst) and the decision maker, due to the subjective specification of the trade-off relationships. It is highly unlikely that an "acceptable" preferred solution will surface during the first running of the model. Dyer (1972) has developed an algorithm which precisely monitors the interaction between the decision maker and the model and selects the preferred solution in a finite number of steps. However, this procedure comes close to the class of vector optimization techniques which simply generate non-inferior solutions for consideration by the decision maker. Such an approach which drops the formulation altogether is being con- sidered by Steuer and Schuler (1978) for national forest planning. Goal programming is a useful tool for the manager-analyst; however, Dyer, Hof, Kelly, Crim and Alward (1979) recommend that it only be used with a thorough understanding of the algorithm and its economic implications, and with extensive sensitivity analysis on each problem. Recent studies made in timber management planning of National Forest indicate that goal programming can be used complementary with linear programming to produce operationally feasible solutions as reported by Field (1978) and Field, Dress, and Fortson (1980) because it provides improved sensitivity testing and strategy selection in timber harvest scheduling. 38 Transportation and marketing literature is growing rapidly with examples using a GP approach to special cases of transportation method, since it uses only one objective criterion -- minimization of transport- ation cost. The basic assumption of the transportation method which is based on LP model is that management is concerned primarily with cost minimization. However, this assumption is not always valid. As demon- strated by Lee and Moore (1974), Lee and Nicely (1974), and discussed in more detail by Lee (1976), in an examplecyftransportation schedule contracts, concurrence with union contracts, the provision of stable employment levels in various plants and transportation fleets, balanc- ing of the work among plants, and minimization of transportation hazards may also be important to management. Thus, the transportation problem includes not only the determination of optimal transportation patterns, but also the analysis of production-scheduling problems, transshipment problems, and assignment problems. If one accepts the premise that the transportation problem may involve multiple, possibly conflicting objec- tives, then the GP approach is an appropriate technique to use. For Indonesian application of the transportation method in the multiple objective problem, goal programming has been used in a timber-based economic problem to design the optimal pattern of timber supply alterna- tives, port capacity and locations, shipping patterns, and other alterna- tives, as indicated by Buongiorno (1978, 1979, and 1980), and FAQ (1978) for broad, long-term National Forest Sector analysis. Critigue The most obvious advantage of the GP model is that it permits the simultaneous consideration of multiple but incommensurable objectives. 39 It is one of the most computationally efficient techniques of vector optimization compared to conventional linear programming, the transport- ation method, and to those vector optimization methods which use a single utility criterion. Bottoms and Bartlett (1975) and Lee (1976) found goal programming more akin to the actual thinking process of a manager than linear programming. The GP formulation also posses a less obvious computational advantage, even for problems with a single objec- tive. The possibility of an infeasible solution is effectively elimi- nated because the sizes of the variables in the function (the goal deviations) are virtually unconstrained. From another viewpoint, this may not be an advantage after all. Generally, infeasibilities are the result of incorrect problem formulation or inattention to binding constraints. These problems may be ignored by the GP model but they are not eliminated. An analyst viewing the feasible GP solution may be unaware of such hidden deficiencies and accept the invalid results. The most frequently cited disadvantage of goal programming is the difficulty in setting the goals, priorities and weights. This drawback cannot be considered unique to goal programming for it will occur in any attempt to objectively consider multiple goals. The same may be said for resource interactions where they enter the problem. Bell (1975 and 1976) has reviewed the goal-setting aspect of this problem in detail and has suggested some ways to obtain the necessary coeffi- cients. Regardless of the method, the goals, priorities, weights and interaction used must generally be considered assumptions. Specifi- cally, they would be subject to sensitivity testing. The inherent 4O disadvantage that goal programming possesses in this area is related to the property of "universal feasibility". Almost any specification of goals and weights will apparently yield a candidate for the preferred solution. Cohon and Marks (1975) plainly illustrate that goal program- ming could produce an inferior solution which would generally be unaccept- able as a preferred solution. Careful setting of the goals can eliminate this possibility but may then lead to implicit and undesired assumptions about weights on each goal. Each problem situation calls for complete recognition of the assumptions and their implications and requires explicit steps for testing their sensitivity. This approach is illus- trated in Field, Dress and Fortson (1980) on their test case study in 0conee National Forest in Georgia, where the possibility of an inferior solution was ignored. Rustagi (1976) and McMinn (1977) also used the same approach, in their applications of goal programming to forest management planning and regulation problems. Dyer et al (1979) fully recognized the implications of goal programming in forest resource allo- cation problems and pointed out that goal programming, generally because of its demand for ranking objectives, does not generate theoretically desirable solutions, i.e., welfare economic (Pareto-efficient) solutions to the public resource allocation problem. Goal programming is then analyzed as a "satisficing" alogorithm and as a production feasibility test, indicating that GP will not generate income distribution solutions since variously weighted (preemptive) GP solutions are inferior to cardinally weighted GP solutions, inaccurately reflecting the real world situation. Therefore, Field et a1 (1980) dismissed the use of preemptive GP and recommend the exclusive application of cardinally weighted GP for considering the trade-offs among multiple criteria or objectives. 41 Generally, these refinements to the standard GP model as presented in equations (4) to (7) are for special cases and small, well defined problem situations. Their formulation and solution typically requires an experienced analyst and special algorithms. For the purpose of this study and any evisioned application of its results, goal programming in its cardinally weighted linear form, which may be solved using standard LP algorithms, is preferable. This permits easy and efficient solution, transformation from linear to goal models and the use of the full range of post optimal procedures available with standard computing packages. Indonesian Forestry Optimization Model (INDO FOM) was developed and has, indeed, a goal programming feature. However, because mathemati- cally, it does not differ from linear programming, I prefer to keep the linear programming appellation as found in Buongiorno's papers (Buongiorno, 1978 and 1980). Some peOple prefer to keep the term goal programming for the cases in which there are multiple objectives, that are ordinally weighted (in fact, INDO FOM has cardinally weighted formulation). CHAPTER III THE INDONESIAN FORESTRY OPTIMIZATION MODEL (INDO FOM) Model Purpose The Indonesian Forestry Optimization Model (INDO FOM) is an eco- nomic model for evaluating development alternatives for the forest-based economies in Indonesia. It is a linear programming model to identify the most efficient pattern of supplying logs and processed wood pro- ducts to domestic (local and interinsular) and export markets. However, the model may be used also for non-timber forest products resource allocation. This model is designed to identify the optimal locations for logging or harvesting and industry, the most efficient supply routes, and the port capacity requirements for domestic supply and export. 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Azpm>wpocmpwv om53mmm cmmo no; ocm .zom oozH mcu mo cowuocom m>wpomnoo mcm mo mcouoocam mumoo ~ouou mzm mo cowmcoo comma o m? umoo cowumucoomcochm .Ammcoco mocp o mo poc Ame umv mwmz_mcm mcp mo mocmwcm>coo mfi. com bmimocmfi moms cmmo mo; ocoemo cocooco cw mmmmcomo co mmomcocc mmmcoco m>$occmfi unwow mvo>mm-wco»:::H op oNF mm 1- -1 1- - - 1- 1- - moumna_;u a 1- -1 - 11 -1 - eo— NP com me m3: acoo mccH w 1- - - 1- - - com me com vpp aHumv mmwgumzucH ummgou :muquwpmm m -- -- ow, em ome mm omp om. owe Fem AHUHHV mwmmcoucH .agou guns?» Paco-pmcgmucH o - - me mm NPF mo op PF we KN mwmmcoucm gm>wm cmxmx m - - om mm em Nu op m «N mP gaucH wuxmm wucocmz a - 1- mm e um m m_ «- wm cm H PamngcH m - - mm mm «m an op ¢_ ca mm H mm: Longsm N - - cop cw oov FPN ow Fm cop mm mwcmEWme \owmmconcm uwmwuma m_mgomo _ u<< “=0 usawzo pampzo u<<, 8:8 “amuse “gauze u<< 3:8 asaz pewoa .xmz. _m:pu< .xmz .cwz .xmz wngoK1 .xoz .cw: .xmz .wmzuu<1 mgmmcowmmmucou wmmgou mac; mmauwmwm mcmwmob moo; cmmcm> moauwmmm moo; 38m vsmom-awzu mecm>-noczz_a 1noozx~a mcwmmog 3mm .cmucmeppmx “mom .Apm\m~op me> mzp mo mm ccm> gm; E.=u ucmmaosu :wv p:_om pmmgom :omm com mpmwucmpom Ap28pao oozpmamv mauwmmm mcwmmo; new Apsu mpnmzoPF< szccoga caucmsHme ummm ucm .HHmmH .cugmz .muH>cmm pmmcom HmHu=H>oca zmpcmEHme “mam "mucHngmm~,HmmH nommxxoz Hmmwmcou HmcoHpmz L0H umgmaoca .meH\ome gmm> mg» Ho “Locum Hmzcc< muH>me ummcom HmHucw>oLa cmpcmsHme “mom “Hmmmamcmd cmHmmcoucH :HV me>Huumqmmg .mpcmsauou mcwonHoH mg“ no comma mew: m on H mchoa ummgoH sow mwczmHm Hu< choa muo>am-mem»=;cH oH om NN oNH mm -- - -- -- om NN moumuawgu m eoH NH com me -1 -- -- -- -1 1- a2: mean mecH w HHmmv mmwgpmzucH ummgou :mamaxHHmm n HHQHHV mHmmcoucH .agou gmaawp com me oom «HF 11 11 11 11 11 1 com onp oom mmv 11 11 11 11 11 1 HmcowpocgmucH e we om ooH om - - 1- 11 11 1- mHmmcoucH cw>Hm caxux m me om on cm 11 11 1- - 11 11 smucH Huxmm Hpcmcwz q mm mH mm me - 11 -1 -1 - - H HcmpascH m mm we NmH eHH 11 - 1- 11 - 11 H mm: Lwnsam N com moH com com 1- 1- - 11 -1 11 m_:mEHme \mHmmcoccH onHuam mngomw H uaauzo azauao o<< use uzapso “sauna cgzuuso ccaypao uzmuzo unquao msmz Nchoa .xmz .cHz .xmz Hmzuu< .xwz .2»: .xmz .cHz .xmz .cHz mgHmcowmmmucou ummcom .mmaowmmm mac; Hope» mmauHmmm won voozamzm mmzuHmmx-wchmoH v rmHOH- acmmmoHlvoozarza ugmcmnnwcu .H.u.p:ouv m< mam<~ 138 .xeumaucw muuzuoeq uewon vcm awgm cuH: xHHmmHmHuwam mHmwv new .mcmwcwx mmmxewx be an cw::o mH omwcawzo .HHHmzuwHpmw mchmoH suH: NmmH :H :onuzuoea mpH acmpemum ma uHso: mweswcw> ucHoa muo>mm-Hcmp=;=Hm .waw: meHmconmwwcow w>kuwamwe we» we conmwucou wcu ewuca wwew umweoe mcwws ucHoa umweoum .AmeH .xwz .meouowewo mo venom ”muemxmwv .ocH .H Hcmuach wzp mo swap peonwm Hwacc< web co uwmmn mew: Hmwezpcm> pcmow wuo>wm1chngch oH ucHoa umweom eoe mwezmwu .HHmmH .xewzcwo .H.v.p:oov mwuocuoom m< mmmqh INDUSTRY DATA 139 140 H.u.ucouv .MmHmecwuoa mauHmwe mchmoH new seam-mac momv mxewHamh cw u<< saewas ucw mam Hmswuw eoH mm wsmm mew sz>Huuw mwe om op HH mucmoa xeumzucH eom mwueaomH omm - com -1 -1 1- NoH - «NH 1- vwezpcw> ucHow mvo>wm1Hcmpsch cm 2: or -- -- 2: 02 -- -- -- -1 $0835 2 omH om 1- 1- 1- -1 1- - omH mm «:1 ago: wccH wH oqm om - - -1 1- 11 1- owm om HHumv meepmzucH umweou :wnmaxHHmm HH owe omH 1- 11 1- 1- ooN om omw ooH AHUPHV mwmwconcH .qeou ewnEHH HmcowuwcewucH oH oeH om - 11 - 11 ooH ow ow om mwmwcoccH ew>Hm cmxwx mH omw omH 11 -1 1- 11 omH om ooH ow swccH Huxmm Hucmewz «H mom mm - 1- 1- -1 om om mmH we H chuach mH com ooH 11 1- -1 11 omH om om cm H mm: ewnszm NH com oHH 11 1- 1- 11 ooH om ooH om mwcmEHme \mHmwcoocH uHHHuma meeoww HH .amu .qmu .awu .awu .mmu .mmo .mmu «mwu .amu .awu wsmz :qusoo ucHoa .xmz stum< .xwz szum< .xwz Hwaum< .xwz Hmspw< .xwz mzpu< NxeumsucH :upmswcH kuop HHHz HHHz HHHz ewmam» HHHz :wm ewawa1QH=¢ venom-nwgu coo: coo:me .cmpcmEHme “mum .HmmH\w~mH eww> wcp Ho mm choa HeumsucH comm eow Heww> ewe HuHumawo wxwch 2.3m ucmmzosu :Hv x Humane HeumzucH .¢< m4m1uoo:xqu «mmH .HHHHE :mmv mme eww» ”m:oHHoH mm mHHHE w>Huowamwe mpH mcwuempm ma quo: weap=w> ucHoa mco>wm1chazchw .ucchew>ow wgp seem mmcmeH mchwewao zepmzncH ewuc: HHHE m we Hu_umawu :mHmmu m mH HaHuwamu Ezewxms can .HHm\mHmH emm» me» me mm amHgm ewa emw» emu 5.:wv mcwpmewao cwwa mm; HHHE m mews: HuHuwamo wamew>w cm mH Huwumnwu Hmsuukuwamwe wgu mo chmewc:o we» ewuca :oHpmuoH :eumaucH mcmws choa HeumzucHN .H.u.w:ouv mwwocuooe v< mmm

moH:wm we“ we Non eo on.o mH mnnHmwe HHHE:wm mew en mseHo> mew .wmww mHeH eH meme: .HwHHewHoe wenHmwe1noo:Hw:H mw nwuwmoHHw we ewu HH mo pmwe wen Hwep nwsemmw mH pH .nwew>ouwe mH wano> mmoH:wm mew Ho Nmn eo s.=u mn.o xHeo.eeHm=neH meHHHHe:wm eH nwmmwooee we on wwaeH mmoH:wm 2.30 H EoeH Hweu ewws m¢.o mH HwHewam eomv noo:e:wm eoH wpwe Hem>oume .HmNmH .N .Ho> .H Heoewm waHeeumH .mHO\mH\m .zwo weu eoe muummmoee .HHmmH ..awe .HH .oz ewewe meeroz .nmo\mm\mzH Noe .meweoneH mo Hemseew>oo we» we Hewmweoe Ho Hweweww wHweOHmweHo eHH: eoHuweweoom eH meoprz nwuHen we» we .o.<.e ”eomomv meeHmnneH nwmwm noo: zewEHee .meHew: .4 .HwNmH ..>oz .H ewewe meeroz .NHO\mH\mzHuom ..o.<.e mew Ho wanoee Hewseon>wo mmwnnoee Hmweoe new zeamweoe "eomomv .meweoneH eH mw>HuweemHH< xHemam emnEHH mo mmemwe< w>HkuHneH e<.mwaHnwh eoH mw wswm ”sz>Huuwemme mew: mmuwe aew>oume wowew>w mmHeamnneH HHHE ewawa-eHee new HHHE newon-eHew eoH mw HHm: mw .meepmnneH HHHE ewwew>1noo:aHe new HHHe:wm eoH mwuenom H wN. o.m an. on.m Nn.H m.n m.H o.m ueHem mN.N NN. mm.N mm. mo.N nN.H m.m n.H mm.m HHHem mH.H mH.o mm.H NN.o em.H NN.o o.N nom.o mNN.N uHHem H ”HemeHHHmou pagp:¢-u:aeH oH.o mN.o 11 11 -1 11 11 11 moon ewewe-eHze -- -- mH.o ww.o -- -- -1 -- meow wewow-eHew -- - - - on .o 8.0 11 11 30.. ewwew> non-SE -1 11 11 - 11 11 o¢.o mn.o mmoH:wm N N mmnnHmwm ewewe mwanmwm newom mwnnHmwm ewwew> mwnnHmmm non: ”wxwueH 1noo:Hw=e 1eHee 1noo:Hw:e 1eHeu 1noo:Hw:e 1noo:>He noo:Hw=e e:wm moon HHHe ewewe-eH=a HHHe wewow-mHen HHHe emwem>-woozeHe HHHeewm wsewao Hwnnoee .ewuewEHwa Hmwm H.Hw\mNmH ewm> wep we mw mHeHoe ermnneH pw meHmmwwoee eH mmpwm Hew>omwm wmwew>< .m< wHawH 143 .mo.o u mn.o\H x o¢.o x om.o u 3owme meHHHHezwm ww.o ww moH :wm ewe Heme eonuawoee eowwH wHHem H Ho eoHpHneoo weu ewne: Hm3ouwe w epH: Hn< anwH eweuene wwmv HwHuemuoa meHengwwmaews noo:Hw:H mw HmHmv nwew>owwe we ewu HwHewaw eoHV mwenHmwe :eumnneH HHHe:wm me Now eo “He: om.o uwep nw53mmw mw: “He .NN.N u mn.0\— u 3ouwe mmwem>w men an Hnmv meoHHweweo HHHem H emne: .wHewaw eom .Hmzouwe HHHE:wm eoH pewHuHHHmou uneven-uneeH wepm .H.w_weouv wwwoewooe we mewH 144 TABLE A6. Fuelwood Potential From Residues,1 East Kalimantan. Fuelwood Manufacturing Input-Output Logging Residues Potential Coefficient Saw Logging 0.852 1.183 Plywood-Veneer Logging .85 1.18 Chip-Board Logging .85 1.18 Pulp-Paper Logging .85 1.18 Industry Residues Sawmill 0.90 -- Plywood-Veneer Mill .90 -- Chip-Board Mill .95 -- Pulp-Paper Mill .95 -- 1Data based on the author's field check as of August 1981, and on assumptions made in J. Buongiorno, A Timber Supply Model for Indonesia, Model Description and Users Manual (Bogor: Forestry and Forest Products Development Project of the F.A.O. of the United Nations, FO: INS/73/012, Working Paper 2, Dec., 1978). 2For example, it was assumed that when the unit of fuelwood is cut from saw logging residues (FSC), 0.15 unit or 15% of raw material is lost or wasted, so residue potential left for fuelwood is only 0.85 unit or 85% FM . 3The input-output coefficient for fuelwood-residue manufacturing potential which can be transported (IOFM) from saw logging residues area, for example, is IOFM = FSC/FM = l/O.85 = 1.18. 145 H.n.weouv ewuewEHwa Hmwm eH mmHeHmzneH we» .eoprewHexw eH .mHHHem HemHe + eooeempew + meHeeos eo mN.N new .mpeHem eooe 1empew + meHeeos Ho mH.H .HeHem meHeeoe Ho o.H .an>Huumemwe mmEomwn emea awn ewe eoHpunnoee EnewaE weH .Huemmeme om mw nmmwe we ewm HHHHHnw meHmmwmoee wv HHHem HemHz "wee ammo-wee oomN .HHHem meHeeos om nmeweeom mw memo 1ewe mu mo HuemHmHHHm ew m:oem AHHwnm: .HuH>Hum:noee eH mmeHHumnvnrHteweooeewuee "wee oomN-mee oonH .Huewmewe ooH mw nmpweHeoe we ewm umHem ewe eonmsnoee memv HHHem meHeeoz "wee oonH1mee omno ”mH eempuwe peweHEonwee me» man .ewuewEHwa Hmwm eH mmHewesom HememHeHn eH Hew> muHHem Ho mEHu Hwnuu< N .m< new 2 $38. 5 mw mmmeaom w>.5wmemwe mmm .mwmeeomH ommN 11 oonN 11 omen wezpem> meHon wno>wm1Hewm=eeH ON 1- oonN 1- 11 1- ommneHeo mH -1 1- 11 1- omen w:: memo meeH NH 1- 1- 11 11 ommw %Hmmv meeHmnneH Hmmeoe ew weeHme NH -- -- wwwN comm -- AHUHHV wemmeoweH .eeou emaEHh HweoHuweewHeH nH 1- 1- ommN omeN 1- wHmmeoneH ew>Hm ewzwx mH 11 11 mNmN 1- come ewneH Huewm Hpewemz eH .- -- wNHN wewN .- H HewwzeeH wH 11 11 ommN wmnN 1- H mwz emnsnm NH -1 1- oomN 11 some mHewsHwa \memeoneH uHHHuwe meeomm HH weeem wN.N meeem wN.N weHem wN.N weHem-mN.N weeem wN.H we 2 Newggom weHoe HHHe HHHz nHeeewmem> HHHe zwm eewmaweH ewewe1eHne newom-eHeu 1noo:zHe .ewuewEHng Hmwm .HmeH\mNmH ewm> mew en mw eoHHwemeo an ewe ”53.6 PE: emu em :5 H38 HummsemHHawumm 55 e .Hmewexm. 3.838 HwHeumnneH .3 39¢ 146 .ueeuao 2.3m eme mmmN em eoHuwemeo awn ewe HHHem mN.N new .ueeueo 2.3m ewe ONme em mH eopremeo zwn ewe peHem mN.H "aHm>Hmumemme mpmom eonewexm mmwew>w .H.H.e.zv HpmHmom eweeHH ememeoneH we» Eoee eoHqueoHeH we» eo nwmwmm a; .8 ..wlm .mm .Hme: 9.th .e .ewepeee mmm .mpw HmemseoeH>ew meHeeo: nmeoHHHneou eHw1eoev Heommwe mew eH wwwe .memeeo: eo memHme we wmemmn w He nwmnwm me ewm eooempew we“ eH HHH>HHuenoee meHeHHmmo .uemHszn on nmmoeeo mw mmmeeewn Ho meeoe we» eH meweeo: xe e:oem new HHmH emueo mH memHHwH Ho wmemmn emuwmem w new w>HmemueH eoewH mew .H.n.meomv mwuoemooe N< wHewH PORT DATA 147 148 .eww» ewe wneHewEwm Heme uw HemseHemmeweu mmoH eoH 5.:u newmeoeu mNeH n m x com x omen .mHewam e3 .5253 HeHoe ueoe wmeHm: 3.2858 mm ewes? x man uemseHemmeweu m>.5 1meem com x HaHuwewm meHnone=1meHnon awn ewe mw nmmeanwm mw: ewm» ewe :uHuwewm pewEeHemmeweu moee .mmnnHmwe HepmnneH meHHHHE :wm new meHmmoH :wm aHeo nmE=mmw emwe mwe enemm mHeu eH HewEeHemmeweu noo:Hm:e1m:nHmme .Hemewe-eHee new .newoe 1eHem .emmew>1noo:xHe .noo:e:wmv mmmnnoee nwmmmmoee HHw meHneHueH HemEeHemmeweu muonnoee nmmmmmoee .HmmoH emewe1eH=e new .mmoH newoe-eHeo .mmoH emmew>1xHe .mmoH :wmv meoH HHw meHneHmeH mewEeHemmewem moem .mueHoe Heme ewxee32\ewewewH new eweweeHme e8 0me anHeew PH 5.52298 2.3 we: .xwm wneHewswm new ew>.& ewxwewz m5 meon wwew meHeewemomm wneHewswm eH nmuwmoH mpeHoe mememem\meHnon ememo m mo awn ewe muwe meHnon mmwew>w mew mw anwHemem mw: HwHewaw eoev HewseHemmeweH mmoH eom mmwe HemEeHemmewep an ewe wneHewswm ueHoe peoeN .m< new m< mmHewe eH mw mmmeeom m>Heomemme mew ome wwm .eweeH...ume .w ewewe eeHeeoz .NHO\we\mzH ”we .meoewwz wmeee we» we .o.<.e eo uwmnoee pewseon>me mumenoee pmmeoe new :eumweoe ”eomomv memeoneH eH meHeeHem .Eoeumemmmm .o ”mmeeom H om mwo. one own. Nwm mmo. cwene32\ewxwewh mN moH meH. ome omm. mom noo.H eweweeHme NN omH ooH.o mNHH ome.o mNeH omm.o wneHewswm HN ewm> ewe meHnwom ewm> ewe meHnwoe ewm> ewe .TmeHnwom mez Heme ueHoe HHHmwewu an eme memwewu Awn ewe ezuHmwewu Awn ewe N ueoe uemEeHemmeweH Hemeewemmeweh pewEeHemmeweh meow noo:Hm:e-mwenHmme memenoee nmmmmmoee m .ewuewEHwa umwm .Hm\memH ewm> we» we mw pews -eHemmewep mpmznoee pmmeoe eo E.:u newmeoep eH .HpeHoe uemEeHemmew my Heoe eo quuwewu meHumem .m< memHHmwemwe men eoe zmHmwewm ueoe en 2.3m mo mnewmeoeu eH mmeHw> mew mmmweuemewe eH mweanem .quHHHmwe Heoe ewe awn ewe meHem mN.N we eoHpHneom we» ewne: xaHuwewm meHnone=1meHnonv m mHenoz .HHHHHHme peoe eme Hwn eme ueHem me.H Ho eoHpHneom mew emne: quowewm meHnonee1meHnonv N mHznoz . qu 1HHmwe peoe ewe an ewe ueHem H mm eoHaneom we“ emne: euHmwewm meHnone=1meHnonv H mHanoz .aHm>HHmw mwe mew meme» om .=wH:noE w= anme mH Hmnoe mHep eH quHHmwe ueoe ewe HHHem ewe eopremeo an emeN .w< new me .m< mmHewH eH mw mmmezom w>Hummemme eweuo new ...mmm.amm .HmemHv Eoepmememm .m ome wmm .HmmH mmame< eo mw eoHHwEeoHeH emwem nHmHe m.eoep=w mew eo nmmwm ”mmeeomH HeH e ewH V eow v eomHe eom e eoowv eewwme AooHv emw e cweaezz eHe NHw omm emeN ,ooom comm ooe ooeH eoeH \ewxwewh mN eow e eon e .mH e eova eooee eoome eeowe eoowe Hone com eon oooH enHH emem memm new mom eonH eweweeHme NN ewe v eow V eww e eooee eoowe ewNHV eowee Aeowe meeva enN com ONm wmnH eeHN mmNN oON oom emm wneHewEwm HN HeHem HeHem meHem HHHem HHHem HHHem HeHem meHem NHeHem mswz meoe ueHoe mN.N me.H -e mN.N me.e11 H mN.N me.H H peoe pemEeHemmeweH Hemeewemmeweh HemeeHemmeweH meow noo:Hm=e1m:nHmme memenoee nwmmmmoee .ewpewEHwa umwm .Awo ewe we Heme 1eHemmeweH .mumznoee ummeoe Ho E.=u ewe em eHV .umou HpemEemHHemem :mzv eonewmxm quuwewu Heoe .m< memwn umm3\eoemeHu we -1 11 -1 - o.N 1- 1- - o.m wHHwepme< me 1- 11 11 o.w 11 11 -1 o.m 11 eewsewe ee 11 -1 1- o.e o.em - 1- 11 o.NH anuH me 11 11 1- 11 o.m - - 11 o.m Hewsemw umm: Ne 1- 11 11 o.mH o.e -1 11 -1 o.e mnewHemeuwz He 1- 11 11 o.em o.NH 1- 1- -1 o.m .¥.= oe 11 11 11 o.NH o.e -1 -1 11 -1 Hmwm .z\wHewe< Hnewm mm 11 o.mH - -1 o.om -1 11 - - meszwz mm 11 o.oH - 11 o.NH -1 - 11 1- newHHweH em 11 11 1- o.oe o.e - 11 -1 11 .<.m.e em ow ow ow OH 9: -- -1 -1 -- £89...wa mm w.e o.m o.N 0.0 o.eH 11 11 11 11 wneHewEwm em N.H -1 - o.mH o.mH 11 11 -1 11 Hmm:wH:m Hwepewu mm e.m 11 1- o.mH o.ON - 11 - 11 Hmm:wH=m epeom Nm w.e o.ON o.oH o.me o.oe 11 -1 -1 -1 wxwe wwewewn1er Hm e.m o.oH o.m o.me o.me 1- 1- - 11 w>wn Hwepewo\mewewsmm om mH.N - 11 o.mN o.oH 1- 11 o.mw o.om ew:HwH mN - 11 -1 o.m o.N 11 11 o.om o.mm wweox euzom wN N.H 11 o.oH o.ooH o.m 1- 11 o.oN o.oH meoxmeo: RN e.N o.oH o.oH o.Hm o.mN o.om o.om o.omH o.ooH ewewn eN e.N o.oH o.m o.o~ o.om o.m o.w o.eN o.mH meoewmeHm mN o.m o.mH o.oH o.on o.mw 11 11 o.ooH o.om w>wn,pmwm\wxwewe:m «N. noo:Hmee emmwe newoe emmew> non: mmoe Immoe mmoe mace wswz ueHoe menHmme N1eH:e 1eHeu 1noo:xHe e:wm emewe newom emmew> :wm newsme 1eHee eHeu noo:»He .ewueweHwa pmwm H.Hmumenoee emepo eoH 5.3m newmeoe» new .emewe1eHee eoH eoh .E newmeoeu eHv HeHoe newsme xe nmpeeHeumHe .Hw\memH eww> we“ en mw ewm> ewe Hmeempoe newsme nwquHumu .oH< memeH 152 .Home .mmH .ez mewmem wHprmmeoe aneum .zepmmeoe em memHHou .mmmemem FwenuHemHem< eo qumew>Hee emHnm:m meH “emnm:m .wmeeeev oo0N ewm> men on e: Hmeemmoe Hemeeon>me en mmeHwe< e< 1 eonme zeew>m .2 en nmmwe mew: mnewemn emewe1eH=e new newom-eHeu eoe wpwn wsomN . .w< new m< .m< mmHewH eH mwoenom eo nmmwe mew: wpwn mEom wmwm mHeswm w mw Hwnos we» meHummH mo mmewHem>eom mew eoe eonwe ewmewEHwa pmwu mo meewesom Hwew>wm eHH: aHeo nm>Ho>eH mw: xnnpm we» mmeHm eoHHwHeeoe ewee: new Hweee emm:uwe eoHpesemeom eH mmmememeHHn new eoHpeeHeH 1mHn eoprHeeoe eo nmmwe woe mw: pH .mmeeeHe mermxews HmeoH .eanmeHempeHv mHmmeon new .mmeemHe ueoexm eo nmmwe HHw-wemHv meww» e Hmwe eH meHumxews peHoe newEmn meHeoemHe we» om nmeememe mew wHwn newsmn HH< .newn 1ewpm eoHumeeHmmem HmHHwee new mnwem meHuweewz zepmmeoe eo muweommweHe we mw HmmoH ewewe1eHee new .mmoH newom 1eHeu .mmoH :wmv e new H .m .HmmoH emmem>1xHev e\e sz>Hpmmemme .mmem quHwee eo mnwem eo nmmwe anemeem mem: mnewsmn Heoexm meme .me>HHmwemme newsmn Heoexm new .newswn eanmeHewmeH .newsmn meoH ewe mmesmHe Hm \memH we» eo nwmwe eonwEHumm .meH umeme< mo mw eoHuweeoeeH emwem nHmHH m.eoeu:w we“ eo nmmwm ”mmeeomH Anhteoov mmuocuooe oz 39: 153 e.n.weoue - - - ewwwmw - - - -- -1 wwwewm Ne owee oomww omemw emNmm woeHe - - - - w>ww wmw3\eoemeHm we - -- - 1- owwmeH - - - ewewe wHHwewmee we - 1- - owNmeH - - - oweww 1- eewECme ee - - - owNoeH owewwH - - - wowee emeH we - - - -- coweeH 1- - - oeHNw eewEeww “mm: Ne 1- - - ewemeH owewwH 1- - -1 ooo~w wwewHewewmz He -- -- -- wwewmw owewHN - - - oowee .e.= ee - - - wweweH emewH - -- -- - Hmwm .e\wHewe< Hwawm em -1 weHNeH - - oooweH - -- -- 1- meewHwe ww - wwNoeH - - wewme - - - -- wewHHweH em - 1- - wwemHH oowwwH -- -- - - .e.m.= ww wow eewmw ooom- OOQNQ OOONN - - - - ewewexHwa mm owm oeoew ooowe ooeoe OOOON - - - - wweHewEwm em oowN - -1 owwew ooeww - - -- -- Hmmszsm Hweeeme mm comm -- 1- ewewe ewewe - - - - Hmweszm ewaom Nw comm owemw oweoe OOONe oowHe -1 1- - - wewe wwewemw-Hee Hm come eooww ewewe woowe ewewe - - -- - w>ww Hwepemo\mewewemm om some - 1- wawON eooewH - - oooow mNHwe ewszH mN .- - - ewewe ooowwH - -- ewewe ooowe wmeoe ewaom wN - - ooowwH HwHowH owewwH 1- - woon ewewe ecceeeoe NN ewew oooomH ooeoeH mNewNH owowwH nooee ewewe omNHw oooow ewewe wN wwew woowwH coco-H weeewH NwoewH wooee ewewe ewewe ewewe weoeweeHm mm come oooow woemm comma cocoa 1- - ooowm oooow w>ww pmwm\wewewe=m «N noo:Hm=e ewewe newom emmew> noo: mane mmoe 1wmee mmoe mez HeHoe -m=nHmwe 1eH=e -eHeu 1noo:>He e:wm ewewe newom emmew> :wm newswe -eHze -eHem eooeeHe 2.3m ewe em new .emewe1eHee eom eoH .E eme em eHv 1ewueH new .ewHemeHewmeH.meoe eo noo:Hw=e new we .ewHewEHng pmwm N.Hw\wemH ewm> meu Ho mw Hmpmenoee eweuo en» HeHoe newsme ee nwpeeHepmHe mw .meewz Hweopre wnnoee nmmmmmoee .mmoe eoe mmmHee meewz wowew>< .HH< mem<~ 154 .Hmnoe mew eH eoprHemeu en mmemHem>eom mew ewe mNe em 1 a we H mo mpwe mmewemxm mew eo nwmwe ewHeee ememeoneH oueH mmeHw> ewHHoe m: eoee nmEeonewep mew: mmmHee .e.H.u new .m.o.eN .meoe ewewe-eHee .meoe newom-eHeu .mmoe :wm .mmoe emmem>1xHe eoe .aHm>Huumemwe. .He .H .m .e\ee newnewpm mmem auHHwee new mnwem meHumxewe eo nmmwe mew: eoHmeHHHmwem mace .nome .ewesmome .mmHmmHawmm Ho ewmeem Hwepemo memeoneH "wwewewne meeH .meweoneH em eooeeww> meHpmHmem ”AHmmH emewz .xewmmeoe eo Hwememm meweommmeHe "wuewewn\eomome meeH .mmHumHmem emummeoe ememeoneH eo nmmwe mem: A.e.H.o eo .m.o.ev wpwn mwxewe meow .m< new .m< .m< mmHewH eH mmmeeom mwm "mueeomH .e.w.w=ouv mwpoepooe HHe mHewH TRANSPORTATION COST DATA 155 156 .mmeane nwuwsHumMN .mpeoemewep Hemeepv newH new empw: eoe mpmom mawew>w mew memoo HH< .HmemH .emeouuo .H\meHmmoe ueoewe eonmHz nHmHe .emO\we\mzH Hoe .meoHuwz nmpHee we» we .b.<.e wee eo ummmnee pemEeon>me mmmanoee emmeoe new zemmmeoe "eomomv ewueweHwa Hweuemu new eeeom .Hmwu eH mmHewesou-meHmmoe Hwem>mm op meeoe ennum eo nwmwe mew: wpwn meow .Hmnoe mew mermmu eH wmemHem>eou eoe mnwe mew: mmezmHe eH mmmememeeHe .HHmmH .emewz .oH .oz ewewe eeeroz .emo\we\mzH nee .memeoneH mo HemEeew>ow mmw we Hepmweoe mo Hwewemw muweoummeHe euH: eoHHwemeoou eH meoHuwz nmuHe: me» we .o.<.e "eomomv eoHuwpeoeweweH new mammaoe .eoHewemem .> "mmenomH o.omHH - o.omHH o.omHH meeuem> peHon wno>wm1HewueeeH oH -1 o.omNH -1 1- oumneHeu m 1- - - w.omeH as: eeoe meeH w - 1- - e.ome Meeme xeumeneH pmmeoe ew weeHme e -1 - o.oonH o.ooeH AHUHHV wHmmeoneH .eeou emeEHH HweoHHweemmeH e 1- - o.oomH o.oomH w.HmweoneH em>He ewewe m 1- - o.meeH o.meeH w.HmmeoneH Huxwm Hpewemz e 1- - o.ommH o.ommH H HewueeeH m - -1 o.omeH o.omoH H mwz ewesem N - 1- o.mmmH o.mmmH mHewEHwa \memeoneH uHeHuwe meeomw H mace mace meoe emmew> meme :wm mswz meHweonmmmeou ueHoe ummeoe uemewe-eHee newom-eHeu noo:xHe Eoee O ~0N.....NH.HHV newoelxeumnneH no» .ewuewsHwa umwm .HHmmH\memH ewm> me» me mw 5.3m ewe em eHV xemmzneH meoe om emmeoe seem wuuenoee ummeoe Ho mu ou aeoemeweh wmwem>< .NH< mem ueHon wno>wm1Hewu=eeH oH - -1 o.ommH - - ommneHeu m o.ommH - 1- 1- o.memH w:: meme meeH m o.oemH - - -- o.mewH eeewe xeumeneH ummeoe ewewexHme e o.oowH - - o.oweH o.oweH eHmHHv wemmeoweH .eeou emeEHh HweoHHweempeH e o.omeH - - o.ooeH o.ooeH w.HmmeoneH em>He ewewe m o.meeH -1 - o.memH o.memH w.HmmeoneH Hpewm Haewewz e o.ooeH - -1 o.omeH o.omeH H HewueeeH m o.ommH - 1- o.omeH o.omeH H mwz emesem N o.oowH - - o.mmeH o.mmeH mHeweHwa \memeoneH mHHHmwe meeomu H noo:Hm:e mmoe mace meme emmew> mmoe :wm mez ueHoe 1w:nHmme emmwe-eHee newom-eHeu noo:>He umweoe HmN.NN.HNV weHoe weoe "OH "eoee ewweweHHwe wmwm .HHweH\wemH ewme we» eo mw 5.3m ewe em eHv Hueoe meoe om emmeoe Eoee memenoee Hmmeoe mo mumou ueoemeweh mmwem>< .mH< memHe ewxwx m - 11 - o.omme o.OONe noo:Hm:e -w=nHmme o - 1- -1 - o.o0Ne meme emmew> . noo:HHe o memeoneH - - -- -- o.ooee mace :wm . Hwewm Heewewe e - - - o.oome 1- noo:Hw=e -w=nHmme o H HeweeeeH m 1- - - - o.omNe noo:Hm:e -m:nHmme o 11 11 11 1- o.oomm mmoe emmew> noo:HHe o - - -1 1- o.oome meme :wm o H mwz eweE=m N mHewEHwa -1 -1 - - o.oome noo:Hm:e \memeoneH wenHmme o mHHHmwe meeoww H Home Aewv eHmv Acme Heme meeH mamz eeHoe eweweHwewm mnewewEwm- wuewan-onl-w>wn Hwepemo w>mn,umwm\wxweweam Hmnnoee ummeoe HeHoe newsme HOH "soee .ewuewEHwa umwm m.HHm\mHmH ewm> wee mo mw 5.3m eme em eHv HeHoe neweme om meHoe Hmmeoe Eoee memenoee Hmmeoe e mumou eoHuwmeoemeweH mmwem>< .eH< memeH 159 .mumom meHeeHem HeweHHv omewm new HmemHeewm HwHeHmeneHv mmeewe new Amnewemev meHem Hme Ho mmeemHe mmwem>w mew mmeane HH< .NH< new .w< .m< .m< mmHewH eH mmme30m mmm ”mmenomH - - o.ooom - -1 noo:Hm:e meeuem> ueHon 1menHmme . wno>wm-Hewu:eeH oH - - 1- - - -1 ommneHeu m 33 :3 :2 82 :3 25 me; 358 eweweeHme wneHewEwm wwewewn-er w>wn Hwepemo w>wn Hmwm\wxwewe:m Huanoee ummeoe HeHoe newsme “0H ”Eoee 46.208 3 meme: 160 .mmeemHH anwEHummm .me new w< mmHewe. mmm .HmNV eweee32\ewxwewH .HNNV eweweeHme .HHNV wneHewEwm HHm>Hummemwe mew mHeHoe ueoeN .HmmH .umemz< Ho mmeamHH xmmeu nHmHH m.eoeu:w we» eo nwmwe mew wewn wee Ho meom .NH< new .w< .m< .m< mmHewH mmmeeom mmm "muezomH o.meN o.mom -1 o.meN o.mmN meeuem> peHon wno>wm1HewH=eeH ON - - 9me - 1- 8835 2 o.mem -1 - 11 o.mmN w:: meme meeH wH 9E 1- - 1- owwm Ea eepmeneH Hmmeoe ewewexHme NH o.meH 1- - o.ooN o.ooN AHQHHV wHmmeoneH .eeou emeEHH HweopreemHeH eH o.oom -1 -1 o.meN o.meN w.HmweoneH em>He ewewe mH o.omm 1- -1 o.oom o.oom wHmmeoneH Hpewm Hpewemz eH o.omm 1- - o.oom o.omN H HewueeeH mH c.omN - - o.omN o.omN H mwz emeE:m NH o.OON - 1- o.oom o.oom mHeweHwa \memeoneH mHeHuwe meeomw HH noo:Hm:e ewewe-aHne newom1aHeu emmew> noo:e:wm mez ueHoe 1w:nHmme m -noo:»He HemmeneH NSNJNHNV 2.8.. 20.. "E ”.8: .ewmewEHwa Hmwm .HHm\memH ewm> we» we mw muuanoee emepo eoe 5.3m eme em new .emewe-eHae eoe en» .E ewe em eHv pmHoe peoe om ueHoe HemmeneH seem memenoee mmmeoe mo mumou eoHuwueoemeweH mmwem>< .mH< NemHm,ewHw¥ .m o.e o.H - - m o.NH o.HH -1 1- e 1- -1 1- - m 1- - -1 1- N ewneH - - -1 - e HprMTHHewemz e o.mH o.mH - - m o.mH o.mH - 1- e He.oH He.oH - -1 m mm.eH mm.eH - 1- N Ne.m Ne.m -1 11 H HueeweneeH m o.mH o.mH 1- - m o.eH o.eH - - e eN.m eN.m - - m HH.e HH.e - 11 N mm.m mm.m - 1- H H mwz emesem N o.Nm o.Nm - - m o.oe o.oe - - e o.em o.em - - m mHeweHwa o.mN o.mN - 1- N \memeoneH C.HN o.HN - N- H uHeHuwe wHeeomm H menHmme mmoe Hnoo:HmnHV mane mmoe mmoe mane Hmme mswz ueHoe HwHoH menHmme ewewe ewom noo:HHe :wm 1wrum m>HH weHweonmmmeou emmeoe meHmmoH:wm 1eHee -eHeu 1weemeH< .ewmewEHwa umwm .Hmumenoee Hmmeow Ho eme eme 2.3m newmzoep eHv HeHoe Hmweoe He nmueeHepmHn mmHmmuweum m>HHweemun HemewHHHn m>HH emne: HueeoEw nmmeeev mnHeeem eenu-Heo Hnoo:Hm:HV wenHmme meHmmoH new Hu<Hpmwv wwwn oe mewms xewHe meHm .mHewHHw>w peaosw mnHeeem eo nmmne: oe mewms H-1V emwn meHN .m empeweu Ho N mHewH op nmeeweme mew Hm op Hv mmHmmpweHm m>HHweemuH HeHon o.ee -1 -o»ee - 1- - H wno>wm-HewmeeeH 0H 1- - 1- - m - - - 1- e -1 1- - - m - - - -1 N - -- -- -- H ouwweHem m o.mw - o.mw - w o.eoH -1 o.eoH - e om.He - om.He HM m W.WW HM w.mw -- H we: eeoe meeH w o.oeH 1- o.oeH .- m o.oo~ 11 o.oo~ -- e HHemv o.omH -1 o.omH 1- m mmHeHmen H o.mm - o.mm - N emmeoe o.mH -- o.me - H, eweweXHwa -H o.eeH - o.eeH - -w m o.owH wH.NH o.wwH - wH Ne H AHQHWV o.omH - o.omH - - m memeon a o.wNH - o.wNH - - N .eeom emeeHH o.an -1 o.emH - - H HweoHHweemHeH e menHmme mmoe Nnoo:Hm:HH mane mmoe mmoe mmoe -Hmme mez HeHoe . menHmme ewewe newom noo:HHe :wm -weum m>Hu meHweonmmmeou Hmmeoe meHmmoH:wm 1eHne 1eHeu -weemurm .H.w.Heoov wHe meweH INDUSTRY CAPACITY DEVELOPMENT 174 175 ee.em - mm.Hm 1 m ee.mm 1 ee.em 1 e ee.oN - Hm.em - m me.Nm - oH.Nm 1 N w.HmmeoneH HH.HN - NH.NN - H em>He ewewe wH m mH.neH- me.em 1 e Hm.HNH1 eH.wH - m ON.NoH- mN.mm - N ewneH ee.we 1 eH.mm 1 H prwm Hmewemz eH mN.Nw 1 o.meH- m Ho.Nw - o.weH1 e Hw.Ne - oe.wm - m ee.Ne - eH.ee 1 N N.HN 1 mm.wm - H H HewmeeeH mH em.HHH1 om.wH 1 m He.eHH- mm.NN - e mH.HmH- mm.em - m Nm.Hm 1 mN.em - N mm.em - em.oH 1 H H mwz eme53m NH eN.HeH- we.mm 1 m mm.emH- me.ee - e HH.eoH1 mm.Hm - m mHewEHwa Hm.Hm - wn.mm - N \memeoneH mw.wN - Hoe.m - H uHeHuwe wHeeomw HH emmew> noo: Hmmpwepm m5szNeweEou peHoe ewewe-eH:e mnewom1eHeu -noo:HHe -e:wm m>HHweemHH< HemmeneH .ewuewEHwa Hmwm .Heme ewe .muuenoee nmmmmmoee1HwHeHm=neH ewemo 5.3m newmeoep new mpmnnoee ewewe-eHne Ho en» .E newmeoep eHV HemmeneH emwm eoH HuHmwewm HwHHHeH om m>HHwHwe .mmHmmeeum m>HuweemHHw HememHHHn w>HH emne: nmeHeewe H-V eoHemweHeomeo H+V eonewexu ”Hemseon>me HHHuwewu HepmeneH stHueo .mHm memHHmw oe mewms mowem eewHe meH m .m>HHweemun em>Hm w emne: HHHmwewm HwHHHeH o» w>prHme HHHHEV HepmnneH ewHemeewe Hweu eoH nmeHneme eonewexm mmuwaneH emHm H+V mzHe meHN .Hmmmwepm m>HmweewHHw ew>Hm w emne: HuHmwewm HwHHHeH op m>Humee uewEeon>mn HHHmwewu HHHHEV HepmeneH we» Ho HeoHumnnme .eoHHwemem eov eoHuuweueom wwemeneH emHm H1V meeHE weHH o.oow- ww.wwH- wH.oNH- w o.QON1 ww.me- mH.eHH- H o.wHH- Hw.NwH- wN.HH - m o.owH- Nw.wHH1 wN.Hw - N mmeHHemH HeHow o.oeH- HH.wH - wo.HH - H wmo>wm-HewweeeH ON Hw.NwH1 w wH.HwH- H HH.mmH- w wH.OHH- N Hw.on- H ouwweHem HH Ho.eeH- w om.wH 1 H ww.ow - m mN.Hw - N .o 0H.HH - H we: eeoe meeH wH n” Ne.wHN1 w mw.ew - H wH.Nw - w NN.Hw - N HHemv mmHeHmeweH wN.wN - H HmmeOH eweweeHwa HH e.o0N- No.HwN1 w Hm.eHH- mo.owH- H HHmHHV Hm.mH - wH.HH 1 w memeoeeH Nw.HH - NN.NH 1 N .eeom emeeHH mH.w + NwH.HH + H HweoHHweemHeH wH emmew> noo: Hmmuwewm mez Hewesou HeHoe ewewe-eHee newom-eHeu 1noo:HHe 1e:wm m>HHweepr< HemmeneH .H.w.Heoue HHw meweH PORT CAPACITY DEVELOPMENT 177 .Hmmpwemm w>HHweemHHw em>Hm emne: HHHmwewm HwHHeH op m>prHme nmeHneme HemEeon>mn HHwaewm Ho eonewexm mmemHneH H+e emHm mnHe .uemEeHemmewem moH eoH HHHHHHme peoe eme HHHem o.Hv mHenoE HmeHH eoe ueoe Ho HHHmwewm mHewHHw>w uemeeem we» we eonHnnw eH Hm.H HHHee we on nmmz 178 N .HHHmwewm HwHHeH om m>HHmee nmeHeewe HeoHHmenmev eoHHmwepeom HHHmwewm peoe mewms H-v emHm meeHzH HH annozv o.oeH - HN.momH- NHHHee Hm.H + m o.oeH 1 mN.mwwH- H.eNH - H o.0NH - we.momH1 o.Nwm - m o.OH 1 mH.NHm 1 o.Nmm - N 0.0m - mN.NHm - o.Nwm - H ewe=e=Z\ewewewH mN o.mwm - Ho.Hmwm1 o.NeH 1 m o.HmN - HN.NemN- om.on - H Hm.mNH - oe.NHwH- o.eNe - m NH.HoH 1 mo.eemH- mm.mme - N Hw.HN - NN.wHH - o.owm - H eweHeHHwa NN 0.0mm 1 No.HoeN1 o.NHHH- m o.mHH - mm.HHwH- o.mNHH- e o.omm 1 Hm.eHmH1 o.mNHH- m o.OON - Hm.emoH1 o.mNeH- N o.omH - Ho.omm 1 Ho.mNHH1 H wneHewEwm HN HemeewemmeweH HemamwemmeweH Heme Hmmuweum mewz Heme HeHoe noo:Hm:e1mm:nHmme memenoee nmmmwmoee -eHemmeweu meme m>HuweemHH< Heme .ewpewEHwa Hmwm .Heme ewe .mumenoee nwmmmmoee1HwHeumeneH emeao 5.3m newmnoep new memenoee ewewe -eH=e Ho emu .e newmeoeu eHv Heme emwm eOH HuHmwewm HwHHHeH op m>HHmee .mmHmmeemm m>Huweemun mememHHHn m>HH emne: nmeHzcme H-veoHHmweHeomeo H+v eonewexm ”Hemseon>we HuHmwewu ueoe HwEHueo .ONm memHpmw oe mewme muwem eeme N .FwUOE 2.3 .12. UwHMLOQLOUCw mszUSLHm “moo co ummmn mmz cowumzpm>m .FwUOE we: on mzocmmoxm mLm mwom-Hn: oo.m 11 oo.e om.mH om.mH m OH.m - cu.m oe.HH cu.mN H - - mH.H ow.wH ew.ew w wwewHeoem .- wH.w oH.H mw.mH ww.mw N HHeeam oo.n oo.e oo.H oo.mH oo.mm H meoH proH o oo.m -1 oo.m om.oH om.mN m OH.m -1 om.H om.e om.mH H -1 1- OH.N OH.m oH.ON m - wo.m mo.N mH.H mH.NH N oo.w oo.m oo.N oo.H we.HH H :HeweeHwa mm - -1 oo.m oo.m oo.HN m - - ow.H oH.m OH.HH H - -1 mo.N ON.H ON.mH m - wo.m mo.N mH.e OH.eH N -1 oo.m oo.N oo.e oo.eH H wneHewEwm Hm noo:Hm:e ewewe newom emmew> noo: mane made mace mmoe Hmmu mez HeHoe -m=nHmme -eH=e -eHeo non: -e:wm ewewe newom emmew> :wm -weum m>HH newsme 1HHe, 1eH:e -eHeu no::HHe -weemHH< emepo eoH 5.3m newmzoeu new memenoee ewewe-eHee Ho ecu .5 new .mmHmmpweHm m>HHweempr mememHHHn m>HH emne: mpmnnoee pmmeom eoe mmmwpeoem HHeeem meoe HonmmEoe .ewpeweHwa pmwu .Heme ewe .mmmenoee meoep eHV ueHoe newewn He nwpeeHeumHn .HNm memwn Hwememu 11 oo.OH. once oo.mH oo.me H \mewewsmm om om.mH om.NN oo.mH oo.om om.HNH - - m oH.w om.w oo.m oo.Hm oo.eN - -1 H 11 oo.wH oo.NH oo.NN oo.NoH oo.ONH - m - mm.mH mN.oH om.He mH.Hm om.NoH - N w>wn Hmwm - oo.mH oo.oH oo.ow oo.mw oe.on 1- H \wHweweem HN noo:Hm:e ewewe newom emmew> non: meme meme me2 HeHoe -m:nHmme -eH=e -eHeu non: -e:wm emmew> N :wm -wemm m>HH neweme 1HHe noo:HHe H.Heme ewe .mumznoee emepo Ho 5.3m newmeoep new memenoee ewewe-eHee Ho eou .E newmeoep eHv HeHoe newsmn He nmueeHeHmHn ewHeweHwa Hmwm EoeH mmmanoee Hmmeoe eoe mmewpeoem HHeeem ewHemeHempeH HmHHmwsoe .NNm memm .Hmnos mew op meoemmoxm mew mmmHee .meepmeeem “mom nmsemmw emne: nmenmmo mmmwmeoem oe mewma H-V emwn weH .mHmHHwew we» eH nwneHmeH HHH>HHmw oe mewms muwem xeme m N H om.mm oN.HH om.Nm oo.HNm oo.HHH - - m mm.mm om.w oo.Hm 0H.HmH OH.mmN -1 - H "mmmwueoem Nm.H ow.Hw oo.ew ON.ewN oN.Hmm oo.eNH -- m HHeeam me.m mH.wm ww.mm HH.HNN Hm.NmN om.NoH 1- N ewHemeH oe.m oo.Hm oo.mm oo.mew. -pcmmeN oo.ooH 1- H -emHmH-kumh,o OH.HH ON.H oo.mH oo.HN oo.HN m oe.m 11 oo.m ON.eH ON.eH H 11 oe.mH oo.NH oe.HN oe.HN m 1- om.NH mN.oH mH.mH mH.mH N w>wn pmmz 11 oo.NH oo.oH oo.mH oo.mH H \eoemeHu eH noo:Hm:e emewe newom emmew> noo: mmoe mmme mmoe mmoe Hmmp mez pewmm 1m:nHmme 1eH=e 1eHeu noo: -e:wm emewe newoe emmew> :wm 1wepm m>HH newewe -HHe 1eHne 1eHeu noo:HHe 1weemHH< .H.w.Heome NNw mewHH 183 .HmHmHHwewv Hneum mHep eH nmneHmeH HHH>Humw oe mewme wuwem eeme m .meepmeeem ammo nm52mmw emne: nmeemmo mmewpeoem HHeenm Heoexm oe mewme H-V emwn meHN .Hwnoe we“ eH nmuweoeeoueH weeemneum pmom eo nmmwe eoHHweHw>m .Hmnos we» on mnoewmoxm mew mmeee H HH.H - - - - m Ne.H - - - 1- H 1- - -1 oo.NoH -1 m - - - HH.Hw 1- N - - 1- wo.mw -- H ewszH eN wN.m oo.m oo.wwH -- w wH.w - - -- H ON.H eH.N oe.on - w HH.H wo.N wN.NH - N HH.H - oo.oe -- H Hmeoe eezom we ow.H Nm.NH oo.owH - - -- m wo.H 1- wm.mw - - - H HH.H - wH.Hw - oo.HN - m wN.H ow.m ON.Hw - ow.ON - N ON.H - No.wm -- ow.ON -- H1 HeoHeeo: NN ow.m - .- om.wH - ow.ONH oo.0NH - -- m wH.N 1- - we.wH - oo.NN oe.NH - . -- H ww.N -- - eN.Hw -- oo.we oo.wm oo.owH - m wH.N wH.H wN.Nw - wo.Nw oo.Nw HH.HmH .- N OH.N oo.oH -- we.Hw - 00.0w oo.ow oo.omH - H, ewewe wN ow.m - ow.H oo.moH Hw.wH ow.H oo.NH - - m wH.N - Hw.m oo.mw .- oo.H - - 1- H 1- - HH.H oo.Hw -- oo.w ow.e ON.Hw -- m - wN.oH HH.w HH.HH -1 mH.m ON.w HH.HN - N - oo.oH HH.H ow.OH - oo.w oo.w oo.wN - H weoeweeHm wN noo:Hmee memewe newom emmew> noo: mace mmme wmoe Nmmoe Home me2 HeHoe manmme 1eH:e 1eHeu noo: 1e:wm emewe newom emmew> :wm 1wepm m>HH newsme -eHe -eHze -eHem woeemHe -weemHHe memenoee emewe-eHne en en» .E newmeoeu eHv Hem .Heme eme .muuenoee emeuo eoH 2.3m newmnoem new oe newsmn He nmueeHeHmHn .mmHmmeeum m>HmweemHHw HewemHHHn m>.HoH emne: ewueweHwa pmwm soeH memenoee Hmweoe eoH mmowueoew HHeeem Heoexm HemHmeoe . mm m NEH; 184 oo.HN om.oH 1- m ON.eH - - H oe.HN OH.w - m mH.wH wH.H - N oo.mH oo»H - H mnewHemeumz HH He.m -1 11 m 1- -1 - H - 1- -1 m 1- - - N 1- - - H .x.: OH oo.mH oo.m m oo.oH -1 H 1- -1 m 1- -1 N Hmwm mHnnHz oo.NH 1- H \wHewe< anwm mm oo.HN - m ON.eH -1 H oe.HN - m mH.mH 11 N oo.mH -- H HHHHHHwe mm oo.mH -1 m oo.m oo.wH H oo.NH oH.HH m mN.oH om.NH N o.oH .Twm.H H newHHweH Hm oo.om oo.n m oo.Hm oe.m H oo.NH ow.H m om.He oH.H N oo.oe oo.H H .<.m.e em noo:Hm=e emewe newom emmew> noo: wmoe mmee wane mmme Ham» me2 HeHoe menHmme 1eH=e -eHeu non: 1e:wm ewewe newom emmew> :wm -wepm m>HH neweme -HHn -eH:e -eHeu noo:HHe -weemHH< .H.w.weoue wNw meweH mHNH oo.NH No.8 emNmm em.emHom.HNH oonH oo.mmH - m Nm.H oN.mN Hm.m HN.oHN on.m oo.eH oo.NH 11 11 H "mmmwpeoem Nm.H oe.mm OH.H mm.HNm HH.mm oo.NoH oe.moH ON.mHH - m HHee:m Hweoexmv me.m OH.OH mm.w Ne.emN Hm.me mH.Hw ON.om wN.omm 1- N emHmeoe proH o oe.m, oo.mH He.m. Nm.enN ee.HN oo.mw oo.mm oo.HHm 1- H . D.Hm m 1- H 1- m - N - H wnwewu NH oo.m 1- m -1 1- H -1 - m -1 - N - 1- H wHHweHmee mH oo.NH 1- m ON.H - H oe.m - m as ON.w 1- N no oo.m - H HewEemo -1HNI i. oo.e oo.Hm - m OH.m 1- - H 0N.H HH.mN - m mH.e me.Hm - N oo~m1 1- - H HHHHH MH om.H - m 11 -1 H oe.m -1 m wo.m - N oo.w - H Heweeww Hmme NH noo:Hm:e emewe newom emmew> noo: name name mmoe mace Hump mswz peHoe menHmme -eHne 1eHeu noo: -e:wm emewe newom emmew> :wm 1weum m>Hp newEwe -HHe -eHee -eHem HoozHHe -w:ewHH< .H.n.ueouv mmm memHe ewHwe w oH.Nw Nm.HH wH.oH m Hw.Hw Ne.wm mN.wN H Hw.Nw om.ow Nw.NH m ww.ew HH.HH we.HH N memeoneH Hw.mm HH.HH wN.eH H HHewm HHeweme H ow.wH HH.NN HH.NN w HN.ww HH.HH HH.NN H OH.HH HH.HN HH.HN m oH.Nm HH.HN HH.HN N wN.wN HH.H ww.ON H H HewHHeHH m Ho.HH ww.HN HH.HH w HH.HH Ho.ww HH.HN H Hw.HN HH.HH HH.HN w mN.HN Ho.Nm wH.NN N HN.ww HH.HH we.ON H H mm: eweEHm N Nw.eNN Nw.HHH HH.HH w HH.HHN Nw.HmN ww.ew H wH.wNN HH.HNN He.ww m chweHHHe HH.HHN wH.NHH HH.HH N \memeoeeH Ho.wwH HH.HNH HH.Hm H UHHHUHe HHHeomw H mace mace Nwwoe wane emmew> .mmoe Hmmuwepm mswz HeHoe HH< emewe-eHne newoe-eHeu noo:HHe :wm m>HHweemHH< meHweonmmmeou ummeoe EN. .N-H .HHEfioflmeewee NE. ”:2: H.ewpewsHHw¥ Hmwm .Heme eme 5.3m newmeoeu eHV mmHmmeeHm m>preemun Hem -emHHHn m>HH emne: HemmnneH meoH op Hmmeoe soee mpmnnoee Hmmeoe Ho meemepwe eoHpreoemeweH stHHeo .HNm memwm-HeszeeH oH wH.ow wH.ow m Nw.Hw Hw.Hw H ww.ww ww.ww m HH.HH HH.HH N wewueme NH.NN Ne.Nw H meHeHHNHUmHeHee m HH.HNH HH.HNH m HH.HHH HH.HHH H HH.HOH HH.HoH m ww.ew ww.ew N mN.wN wH.wN H we: eeoe meeH w Nw.HMN Nm.mmN m OH.HHN OH.HHN H Hw.HwH Hw.HeH m HHewv HH.wNH HH.wNH N HeHmeeeH Hmmeoe wN.ww wN.ww H eweweHHwa H Hw.mHN 1- Hw.mHN m we.NwN HH.w HN.wNN H HHeHHH NN.ewm HH.HHH HH.HeH m meweoeeH HH.HHm wN.wNH NN.me N .eeou eweeHH He.wwN om.om HH.HON H HweoHHweemHeH w mmoe mmoe mane .wHoe emmew> mmoe Hmmewepm mez peHoe HH< emewe-eHee newom-eHeu non:HHe :wm m>HHweemHH< meHweonmmmeou Hmmeoe HON .... .NH .HHH.HeHoe HeHHHHHH HH: ”some .H.w.Heomv HNw HHwHH 189 .Hmmpwepm w>HHweemHHw em>Hm emne: new .weeemneem “mom nwsemmw ewne: HHHmeeweHH\HHmeHEoeomm mHewH> Hoe mH eoHHmmee eH HHH>HHmw ueoemewee memHneH H1-v mmemwn meHm .mHmHHwew wee eH nmnszeH HHH>HHmw oe mewme mmwem xemeN .weepmeeem Hmom eonwueoemeweH nm53mmw emne: HeumeneH meoH op Hmmeoe Eoee nwpeoemewep Heme\e.:m newmeoeuv Hmenoee emmeoe Ho HwHewH> HHmeHEoeomw .HwEHHeov ueeoEw Hmme wee wmmeneH mHewH wee eH mmeemHH meHH .Hn.ueouv HNm memHe ewwa m memeoneH HHewm Heewewe H H HewHHeeH m I I I I I I I I v—NMQ'LDr—NMQ'LDr—NMQ’LDFNMQ'LDF-NMQ'LD 1- - -1 1- H mwz emeEnm -w - - 1- -1 mHewsHwa -- - - 1- \memeoneH - 1- - - mHHHmwe meeomw H mmoe noo:Hm:e meme mmmme meme emmew> Nmmoe Hmwuwepm mswz ueHoe HH< 1m:nHmme ewewe-eHze newom1eHeu noozHHe :wm m>HHweemHH< pmmeoe HMN .NN 1HWH eque Heoe uOH Hzoee H.ewuewEHHw¥ Hmwm .Heme ewe 5.3m newmeoep eHv mmHmmuweHm w>Huweemun HememHHHn m>HH ewne: Heme meoH op Hmmeoe soeH mpmenoee emmeoe Ho meemupwe eoHHwHeoemeweu HwEHHeo .mNm mem HeHon 1- 1- 1- - 1- H wno>wm-wHewH=eeH oH - -1 m 0N.H 0N.H m -1 1- N wewmemx 1- - H wmeew¥\ommneHeu m - - 1- m -1 -1 - H 1- - - m -1 - - N - - - H w:: meme meeH m - - -1 m - - 11 H - - - m 1- 1- 1- N HHemv HeHmeneH - 1- - H Hmmeoe eweweHHme H oo.HHH -1 oo.HHH - m om.mHH - om.mHH - H -1 - 11 - m HHuHHv w.HmmeoneH 1- - - 1- N .eeou emeEHH 1- - 11 - H HweopreewHeH e mmoe noo:Hm:e mmoe mmoe mace emmew> mmoe meuweum mewz ueHoe HH< -m:nHmme emewe-eHee newom-eHeu noo:HHe :wm1, m>HHweemHH< ummeoe HmN .NN .HNV HeHoe Heoe HOH ”some .H.n.Heouv mNm mem peHon menHmme o wno>wm-Heww=;eH oH wewmemx -11-1- wmeew¥\ommneHeu m 1- 1- noo:Hm=H mnnHmme o w::.meoe.meeH m - - noo:Hm:H HHemv HepmeneH menHmwe o emmeoe eweweHHme H HHUHHHmemeoneH - - -1 - noo:Hm=H .eeou emeeHH menHmme o HweoHuweemueH e - 1- noo:Hw:H memeoneH -m:nHmme o em>He ewwa m mm.m mm.m - noo:Hm=H -m:nHmme . - 11 mace emmew> noo:HHe o memeoneH -- - mmoe :wm o [wawm Huewemz H He.m He.m noo:Hw:H 1menHmwm o H HeweneeH m HH.H HH.H 82.ng -m=nHmme o 1- - mmoe emmew> noo:HHe o 1- - mace :wm m H mwz emeE=m w mHewEHng -- - noo:Hm:e \memeoneH -m:nHmme o mHHHmwe meeomw H eszmeH wae .HHwH HHwH HowH HHNV meeH mEHZ HeHoe meoe -emmeH ewewexHme wneHewEwm wpewewn w>wn w>wn Hmwm Hmenoee ummeoe HHHeasm HwHoH Hmeoe -H¥e Hweuemu \wHweweem Hzoee HememmHemHeH1 mpeHoe newsme "DH ewuewEHwa mmwm eH ueHoe Hmweoe EoeH HHHumeHn mumnnoee Hmmeoe Ho meewuewe eoHHwaeoemeweu HwEHueo .HHeeem mHHmmEon eoH Heme ewe 5.3m newmaoeu eHv H .02 Hmmuweum m>preempH< emne: meeHoe newsmn ewHemeHemHeH new meoH op .on Nem HeHon -m:MHmme o wno>wm1HewH3eeH oH wewmewx 1111-- wmeewX\oumneHeu m - - noo:Hw:H -m:nHmwe m w:: meme meeH w 1- - noo:Hm:H HHemv HepmeneH 1m=nHmme o ummeoe ewewexHme H HHUHHV w.HmmeoneH 1- - -1 1- noo:Hm:H .eeou emeEHH -m:nHmme o HweoHHweempeH e - - noo:Hm:H memeoneH 1mnnHmmm o emewe ewwa m wH-e NH.e - noo:Hw:H -m:nHmme o - -1 mmoe emmew> noo:HHe o memeoneH -1 - mace :wm o Huewm Hpewemz H eo.m eo.m noo:Hm=H -m:nHmme . H HeweeeeH m mN.m mN.m noo:Hm:H -m=nHmme o 1- 1- mane emmew> noo:HHe o - - meme :wm o H mwz emesem N mHeHEHwa 1- -1 noo:Hm:H \memeoneH -m:nHmmm o mHHHmwe meeomw H LHHHWHH emme HHHV -HHNV Home HHNH meeH memz HH.H. meoe -emHeH ewewexHme wneHewEwm wpewan w>wn w>wn Hmwu Hmenoee pmmeoe HHHeenm HwHOH Hmeoe er HweHemu \wHwewenm Hzoee eanmeHemHeH mHeHoe newEme HOH H.HHeeem mHummson eoH Heme ewe 5.3m newmeoem eHv N .02 Hmmpweum m>HuweemHH< emne: meeHoe newsmn ewHemeHewHeH new meoH op ewuewEHwa Hmwm eH HeHoe Hmmeoe EoeH HHHmmeHn memenoee emmeoH Ho meemuuwe eoHpreoemeweH stHpeo .HNm mem HeHon 1menHmme . wno>wm1HewH=eeH oH wewmemx ------ wmeew¥\oumneHeo m ON.H ON.N wooszae 1w=nHmwm10 w:: meme meeH w - 1- noo:Hw=H HHemv HeumeneH -m=nHmme o emmeoe ewewexHme H HHUHHV w.HmmeoneH - -1 11 - noo:Hm:H .eeou emesHH -m:nHmme . HweopreempeH n - -- noo:Hm:H 1menHmwe o em>Hm ewwa m NH.e wH.e -1 noo:Hm:H -m=nHmwe o -1 - mane emmew> noo:HHe o memeoneH - - mmoe :wm o -prwm Huewemz H HH.H HH.H noo:Hm=H -mnnHmme o H HewuaecH m ow.oH om.oH noo:Hw=H -m:nHmme o 1- - mane emmew> noo:HHe m -1 - meme :wm o H mwz emesem N mHeHEHng -1 - noo:Hm=H \memeoneH 1menHmme o mHHHmwe meeomw H ewHemeH mev HHmV Hva Home -HHNV meeH wEHZ HeHoe waoe 1emHeH eweweeHme wncHewswm wmewan w>wn w>wn Hmwm Hmenoee Hmmeoe HHHeqem HwHoH meoe er Hweuemu \wHweweem "some ewHemeHemHeH mHeHoe newsme HOH .HHeeem mHHmmson eoH Heme ewe 5.2m newmeoep eHv m .oz Hmmpweem w>preemHH< emne: mmeHoe newsmn ewHemeHemHeH new meoH op ewpewsHwa Hmwm eH HeHoe ummeoH EoeH HHHmmeHn mmmnnoee emmeoe Ho meemppwe eoprHeoemeweH HwEHueo .wNm mem ueHon 1menHmme o wno>wm1HewH=eeH oH wewmewx --- wmeewaomwneHeu a - -- noo:Hm:H 1mnnHmme o w:: meme meeH w 11 1- noo:Hw:H HHemv HeumeneH -w=nHmme o emmeoe ewewexHme H HHmHHHmeweoeeH - - - noo:Hm:H .eeou emeeHH 1m=nHmme . 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