70-9644 THOMAS, III, Harley Hastings, 1941A MARKETING STUDY OF FINE WOOD RESIDUE IN SOUTHERN LOWER MICHIGAN. i Michigan State University, Ph.D., 1969 Agriculture, forestry and wildlife University Microfilms, Inc., Ann Arbor, Michigan A MARKETING STUDY OF FINE HOOD RESIDUE IN SOUTHERN LOWER MICHIGAN By Harley H^ Thomas, III A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Forestry 1969 ABSTRACT A MARKETING STUDY OF FINE WOOD RESIDUE IN SOUTHERN LOWER MICHIGAN By larley H. Thomas, III Historically, grade lumber has been the primary product of most hardwood sawmills in Michigan and the utilization of wood residue has amo anted to a disposal problem. Consequently, burning residues in bhe open or in teepee burners or dumping it at the back of th e already crowded mill site has been the quick, inexpensive m eans used to eliminate vast quantities of residue materials from the mill site. Today, new federal and state air pollution legislation is beginning to restrict waste burning. As a resul t, the production-oriented sawmill operator may be forced to select some alternative in an effort to dispose of wood residues. Last year (1968), 150 hardwood sawmills in forty-one southern lower Michigan counties alone produced approximately 500,000 tons of wood residue while processing an estimated 171 million board f^et of hardwood lumber. It is only reason­ able to assume that this great quantity of residue could pro­ vide support for ev^n more new industries and serve as the basis for expanding the profit margin of many existing industries Currently, a few markets are developing for sawdust and bark where the use of wood residue is considered a natural; such as an imal bedding, poultry litter, soil improvement, and mulch. Th ese basically agricultural and horticultural markets have a lot of potential in southern Michigan, but they will not expand to any degree without the aid of product information, advertising, a marketing program, and establishment of a depend­ able source of supply. In an effort to stimulate interest in greater wood resi­ due utilization and to compile the necessary facts upon which a residue processing plant could be established, the research study was initiated. The primary portion of the study centered around the development of an original heuristic simulation model which could be used as a management decision-making tool. The simulation is used in determining whether or not it is currently economically feasible for a processing plant to geographically concentrate and process sawdust and bark from hardwood sawmills on a large scale for sale to agricultural and horticultural markets. The secondary portion of the study investigated the present wood disposal situation at the sawmill site and the current use of sawdust and bark products in bulk quantities by dairy farmers, orchard growers and tree nurseries and the sale of packag ed bark products by lawn and garden centers. A field survey was conducted among sawmill operators, at which time general information was gathered and photographic documentation was made o£ the residue disposal problem. Follow­ ing the field survey, a mail survey was administered among the majority of sawmill operators to obtain specific wood residue handling and disposal data. To aid in determining the feasibil­ ity of a residue processing plant, a simulation model was developed. The model evaluates the potential success of a / processing plant which purchases bark and sawdust, provides inbound transportation, processes the raw material and sells * the finished product f.o.b. plant in relation to current agri­ cultural and horticultural market opportunities. Costs in the simulation are evaluated using the cost t center concept. The location and size of both raw material supply and market demand surrounding any given processing plant location constitutes a market configuration. Many configura­ tions were evaluated during the research and analysis, with three being included in the study as typical examples support­ ing the findings. The findings supported, even though in many cases on a marginal basis, the hypothesis that there presently exist agricultural and horticultural markets for fine sawmill resi4 dues, and that transformation of the sawmill residue disposal problem into a source of income through the establishment of a firm to collect, process and market bark and sawdust is economically feasible. ACKNOWLEDGMENTS During the course o£ this study, several men gave freely of their time and advice. Their efforts are deeply appreciated and deserve special recognition. i Professor Eldon A. Behr— academic advisor, consultant, and friend, who guided this project to a success­ ful completion at Michigan State University. » Professor Bernard J. LaLonde— a valued friend, whose valuable advice added genuine depth to the study and whose candid remarks and broad experience gave pragmatism to the study. Professor Otto Suchsland— whose timely remarks and reviews gave added strength to the final manuscript. Professor William B. Lloyd— for his views, opinions, and encouragement during the study. The author is also indebted to the Wood Science staff and graduate students for their encouragement and assistance. In conclusion, the author is grateful to his wife, Carol, for her constant moral support and tireless assistance during the entire degree program. ii TABLE OF CONTENTS Page ACKNOWLEDGEMENTS .......................................... LIST OF T A B L E S ........................................... LIST OF F I G U R E S .............. ii V viii « LIST OF A P P E N D I C E S ....................................... x INTRODUCTION....................................... 1 Part I. Statement of the Problem...................... 2 Background................................... 5 Scope of the Study.......................... 8 Hypotheses........................................ 11 Methodology ........................ . . . . . 13 L i m i t a t i o n s .................... .............. 18 Contributions ................................. 20 Organization......................................22 II. LITERATURE............................................ 24 III. RESEARCH D E S I G N ..................................... 44 Phase I: General Research Support...............45 Phase II: Sawmill Residue S u r v e y ............... 59 Phase III: Processing Plant Simulation . . . 69 IV. FINDINGS— RELATIVE T O PHASE I I ..................... 109 V. FINDINGS— RELATIVE TO PHASE I I I ...................140 VI. CONCLUSIONS A N D IMPLICATIONS....................... 152 Implications.....................................160 Suggested Areas for Further Research.......... 162 iv G L O S S A R Y .................................................... 165 LITERATURE C I T E D ........................................... 166 A P P E N D I C E S ..................................................174 « LIST OF TABLES Page Table 1. Approximate Composition of Wood ............... 33 Approximate Composition of Bark ............... 33 2. • * 34 3. Approximate Composition of Peat M o s s ........... 4. Sawmill Class Size and Production Data Used in 1968 Directory of Wood Using Plants in Michigan 48 5. Estimates .of Hardwood Sawdust by Diameter Class for Mills with Circular Headsaws............. 50 Estimates of Hardwood Bark by Diameter Class for all M i l l s ................................. 51 Estimates of Solid Residue Material by Diameter Class for Mills with Circular Headsaws. . . . 53 Mean Values for the Various Residue Components Based on Per M.B.F. Green Lumber Tally. . . . 54 Wood Residue Demand Estimated in Tons— A Summary of the Obtainable Market Share in Each County 57 6. 7. 8. 9. 10. Selected Sawmill Study Area Divided into Arbitrary Quadrants Showing the Stratification of Sawmills in Each by Mill C l a s s ...... 61 11. Selected Sawmills Included in 1968 Field Study. 12. Number of Sawmills Included in Mail Survey. 13. Deck Order for Control Data 6500........... 77 14. Processing Plant Fixed C o s t s ............... 93 v . 64 . 68 vi Table Page 15. Processing Plant Variable Costs ............... 16. Configuration No. 1— Listing of Supplying Mills for processing P l a n t s ............... 97 99 17. Configuration No. 1— Listing of Counties in Demand for Processing Plant.............. 100 18. Configuration No. 2— Listing of Supplying Mills for Processing Plant................. .. 102 Configuration No. 2— Listing of Counties in Demand for Processing Plant .................. 103 19. 20. Configuration No. 3— Listing of Supplying Mills for Processing Plant.................. 105 21. Configuration No. 3— 'Listing of Counties in Demand for Processing Plant .................. 106 Comparative Retail Prices of Mulches, Soil Conditioners, and Decorative Lawn Products, in Lansing, Michigan................. 124 22. 23. Equipment Owned by Hardwood Sawmills......... 125 24. Estimated Daily Hardwood Lumber Production. . . 126 25. Daily Lumber Production by Sawmill Class. .. . 127 26. Number of Sawmill Operating Days Per Year . . . 133 27. Time Required to Remove Residue from Sawmill Site.......................................... 134 28. Summary of Sawdust Sold Last Y e a r ........... 135 29. Summary of Bark Sold Last Y e a r ............... 136 30. Hardwood Bark Accumulated by Sawmills Operating Log Debarkers in Southern Lower Michigan. . . 31. Sawmill Residue Production Summary for Southern Half of Lower Michigan.......................139 137 vii Table 32. Page The Effect of Radius Change on Costs in a Processing Plant Configuration Simulation . . 142 33. Summary of Trial Configuration No...1 .......... 143 34. Summary of Trial Configuration No. 2............. 145 35. Summary of Trial Configuration No. 3 ............. 147 36. Summary ofCost Data from Three Test Configurations................................... 149 LIST OF FIGURES Figure Page 0 1. Materials Resulting From Sawlog Breakdown. ... 7 2. Location of Primary Wood Using Plants........... 15 i 3. 4. Location of Wood Residue Study Area Showing 41 Counties Divided into Four Quadrants . . . . 60 Distribution of 51 Hardwood Sawmills Included in Wood Residue Field Survey 1968 ............... 67 i 5. Grid Location System for Locating Geographical Points............................................ 71 Basic Flow Chart Showing Major Activities Performed by the Simulation P r o g r a m ........... 74 System of Material Movement Through Processing P l a n t ............................................ 86 8. Illustration of Proposed Processing Plant. 87 9. Outline of Counties Included in the Demand Phase of Configuration No. 1 .......................101 10. Outline of Counties Included in the Demand Phase of Configuration No. 2 .......................104 11. Outline of Counties Included in the Demand Phase of Configuration No. 3 .......................107 12. Vast Quantities of Wood Residue Found at Most Sawmill Sites..................................Ill 13. Close-up Photographs Showing Characteristics of Fine W ood Residues Compared to Engineered Wood C h i p s .......................................... 112 6. 7. viii ... Page Figure 14. Current Methods of Fine Wood Residue Disposal . 15. The Two Major Factors Determining the location of Current Wood Residue Markets— -Population and A g r i c u l t u r e ............................. 116 16. Current Uses for Wood Residues............... 118 115 i 17. Colorful, Informative, Consumer-Appealing Plastic Bags Used Successfully as Bark Mulch Packages 122 18. Percentage of Sawmills Advertising Residue Products for Sale............................128 19. Sawmill Operators' Estimate of Wood Residue Removal C o s t .................................. . 129 20. Summary of. Reported Methods of Bark Disposal. . 131 21. Summary of Reported Methods of Sawdust Disposal 132 LIST OF APPENDICES Appendix Page A Mail Survey Cover Letter and Questionnaire. B Estimated Quantity of Hardwood Residue Produced by Sawmills in Southern Half •of Lower Michigan 1968 .................... C—1 . 174 Excerpt from 1968 Michigan Standard Specifications for Landscaping Materials. 177 . 186 C-2 Lumber and Residue Fractions Developed from S a w m i l l i n g ............................... 187 C-3 Selected Markets for Wood Residues Visited During Field Survey . 190 C-4 County Code and Grid Location of Geographic Center.......................................... 191 C-5 Functions of Mulches and Soil Conditioners. D Computer Simulation Program .................. E-l Summary of Supply Data for Configuration No. 1 212 E-2 Summary of Supply Data for Configuration No. 2 213 E-3 Summary of Supply Data for Configuration No. 3 214 x . 192 193 PART I UTILIZING WOOD RESIDUES— A PROBLEM AREA / Introduction i More complete utilization o£ forest resources must be the goal of our forest products industries if they are to maintain favorable economic growth. tions on air pollution, Because of new restric­ increasing competition from other materials and rising labor and transportation costs, the woodusing industries have begun to scrutinize their costs for possible reductions. Wood residues are one of the prime areas where this can be done. Sawmills produce wood residue consisting of slabs, edgings, trimmings, sawdust, shavings and bark which accumu­ late incidental to the manufacturing of lumber. Within the sawmill industry only an average of 57 percent of the log is currently utilized for the primary product, lumber. remaining 43 percent ends up as sawmill residue. The This residue is commonly called wood waste and, by the connotation,, unfor­ tunately portrays the erroneous idea of having little value. 1 There are presently many uses for wood residues# but numerous economic £actors impose limitations that frequently make residue utilization unfeasible. Transportation costs and concentration of residues are factors that often cause economic roadblocks to utilization. It is obvious that uses t for residues must pay- their way or much of the material will continue to remain unused. Once wood residue markets are established as a paying proposition the cost of residue dis­ posal will be transformed into additional income for the sawmill operator. To date very little progress has been made in the development of Michigan markets for wood residue based products. Statement of the Problem The successful utilization of any material requires the consideration of many factors. of the material itself is essential. A thorough understanding More specifically in the case of wood residue this includes knowledge about avail­ able quantities# species, sawmill location# properties and characteristics of the wood residues themselves. important, Equally as if a marketing program is to be developed# is a knowledge of uses# consumer requirements, markets, processing methods, capital investment# operating costs and returns. Obviously, individual circumstances regarding these factors * will largely dictate which uses will be most advantageous. 3 It is generally accepted that data are available on the quantity of wood residue available in the State of Michigan. To date, most wood residue research efforts have been directed toward improving lumber production and increas­ ing the utilization of coarse sawmill residue and trim). (slabs, edging Out of this research came the wood hog and chipper which have made coarse residue more compatible with fuel requirements and pulp chips, respectively. Converting coarse residue to pulp chips has been progressing very well in Michigan and the economic picture continues to improve. The fine residues, sawdust and bark, have not been as fortunate as coarse residue in finding adequate markets. Even more than the coarse residues, the fine residues have often been considered over the years as only another disposal prob­ lem. At first bark and sawdust were used as fuel or disposed of in a variety of ways. Recently other uses have been developing which continue to make both bark and sawdust more valuable. Some of the first uses were directed toward using sawdust as a floor sweeping compound or charcoal briquets, but limiting factors such as (1) the high bulk of the product, * (2) scattered sawmill locations, and (3) small quantities produced at each of the mills have severely limited the growth of such markets. 4 To most sawmill operators, disposing of hardwood saw­ dust and bark represents a cost not only in dollars but in valuable space occupied, increased fire risks, insurance problems, and investments tied up in equipment. The direct costs of disposal alone encourage many operators to look for another way out. These costs range from $0.25 to $0.50 per thousand board feet of lumber pro« duced. A t $0.50 per thousand board feet, this amounts to $2,500 per year for a sawmill cutting 20,000 board feet daily. Annual insurance rates may increase as much as eight percent when residues are' piled or burned near a mill. Disposal costs will increase for many operators— especially those nearest to urban areas. New air-pollution codes and strict enforcement of current laws will force some operators to install pollution control devices on their tee­ pee burners or change from burning to dumping. Currently, a few markets are developing for sawdust and bark where the use of wood residue i3 considered a natural, such as animal bedding, poultry litter, soil improvement, mulch, and some pressed-wood products. These markets have a lot of potential in southern Michigan, but they will not expand to any degree without the aid of product information, education, a marketing program, a n d establishment of a dependable source of supply. 5 The sawmill industry and the individual sawmill owners are typically production oriented rather than market oriented. If they choose not to develop the markets or to show interest in supplying raw material to a processor, one of two things will happen: (1) it is conceivable that an independent oper­ ator will initiate a marketing program for sawdust and bark products, concentrating and processing them as necessary, or (2) the sawmills in general will continue to maintain an indifferent attitude and keep the status quo with the residue disposal problem becoming even worse, and a substantial profit opportunity will be overlooked. Background Historically, grade lumber has been the primary product of most hardwood sawmills in Michigan and the utilization of wood residue has amounted to a disposal problem. Consequently, burning residues in the open or in teepee burners or dumping it at the back of the already crowded mill site have been the quick, inexpensive means used to eliminate vast quantities of residue materials from the mill site. Today, new federal and state air pollution legislation is beginning to restrict waste burning. As a result, the production-oriented sawmill oper­ ator may be forced to select some alternative in an effort to dispose of wood residues.* *See Glossary for definition 6 Coarse residues, consisting o£ slabs, edgings and trim­ ming, constitute 21 percent of the total log volume. Fine residues, bark, sawdust and shavings, constitute 22 percent of the total b y volume. Figure 1 shows the average percentages of materials that result in the process of converting a log / into lumber at the sawmill. Of these two residue classes, only coarse residue has received the necessary attention from the wood industry to develop adequate processing systems and markets. The reason for this trend is that each individual sawmill in Michigan is relatively small and interested primarily in the production of lumber. Because of this size limitation, sawmill operators have given very little thought to the wood residues that accumulate incidental to the production of lumber other than disposing of them through inexpensive methods. Last year (1968), 150 hardwood sawmills in forty-one southern lower Michigan counties alone produced approximately 500,000 tons of wood residue while processing an estimated 171 million board feet of hardwood lumber. It is only reason­ able to assume that this great quantity of residue could pro­ vide support for new industries and serve as the basis for expanding many existing industries. Current indications are that immediate markets for large quantities of fine sawmill residue do exist and could 7 Figure 1 Materials Resulting From Sawlog Breakdown Slabs, Edgings End Trim Bark 2196 8# 3% Shavings 5696 Sawdust Lumber / be developed at a reasonable cost (8). Background information from sawmill operations on the west coast reveal that some sawmills have been actively processing wood residues and developing markets for some time. This information is cited in support of the idea that wood residue processing can also be done in Michigan. t From a preliminary investigation, indications were that a reasonable amount of opportunity may exist in Michigan for a firm to become established solely on the processing and marketing of wood residue based products. The proposed firm would purchase and concentrate hardwood bark and sawdust from several sawmills at one or more selected processing locations. The material would then be processed as necessary, scheduled for packaging or sale in bulk, the finished goods stored, and promotion and advertising done according to a basic marketing plan. The end result would be the beginning of a formal utili­ zation program for sawdust and bark that would add to the economic growth of Michigan and effectively utilize our wood resources. Scope of the Study The material in the study covers several areas. To describe the scope of the study in the most logical order, the individual parts are discussed in order of presentation. Part 9 II concerns a review of the literature in two areas: function of mulches and soil conditioners, (1) the the common miscon­ ceptions that surround their use, and how crops respond to their use, and (2) simulation as an analytical technique used in management decision making. Part III concerns the research design portion of the / study and is broken down into three phases. the research support data, * tions, Phase I contains including study assumptions, defixii- and the adoption of wood residue conversion factors used to compute residue quantities in the study. Phase II details the hardwood sawmill residue' survey which is broken down into a field study portion and a mail survey portion. Important information is obtained through the use of both surveys which is in turn used in Phase III. In each survey the primary purpose was to obtain reason­ ably accurate information about wood residue quantities pro­ duced at each sawmill and associated cost data that would contribute to the development of a realistic residue process­ ing simulation. The processing plant included in the study is a hypothetical one with realistic characteristics. The processing plant functions as the center of the simulation in that all raw materials must be brought to, and processed through, the plant. The cost centers within the processing plant are described and the effect of individual variations are discussed. 10 Phase III describes the simulation program, the cost center concept, processing plant costs, inputs and outputs of the processing plant, and h o w the simulation functions as a management decision-making tool for analyzing processing configurations. Three processing configurations are included to show the variety of results obtained from different configurations. Each configuration will have different sawmills supplying the raw material and different counties included in * the demand. This is readily done by changing the geographic locations of the processing plant. Important in each configuration are the markets to be considered for products produced by the processing plant. In the study the markets will be limited to agricultural and horticultural markets since the literature cites these as being the most logical ones at a time when wood residue utili­ zation is just beginning. The dairy industry, the nursery and orchard industry, and the home lawn and garden markets are the only ones included in the study. The lawn and garden market is considered to be a packaged product market; all the others are bulk markets. Estimates of market size are included in the study. Part IV presents the findings of Phase II, the field a nd mail survey. Supporting the visual observations made during the- field survey is a photographic documentation of 11 wood residue characteristics and sawmill residue disposal methods. Details o£ the mail survey sent to 106 selected sawmills in lower Michigan describe the methods di££erent sawmills are using to dispose of wood residues and the prob­ lems therein. Data which generally point out a lack of interest, on the part of sawmill owners, in wood residue / utilization are presented in the form of numerous tables accompanied by brief narrative comments. Part V discusses the findings of the simulation, des­ cribed in Phase XII, in relation to the hypotheses set forth in Part X. The resulting effect will be to prove or disprove the hypotheses. Part VX contains the author's summary, conclusions and implications regarding the potential for a wood residue processing plant in Michigan. Hypotheses Information about the characteristics of wood residue, the supply and location of raw materials, and the location and potential of markets, is all essential to the intelligent planning of a processing plant which will accumulate and process sawdust and bark into products for selected agricul­ tural and horticultural markets. The method chosen to aid in the evaluation of the basic factors is computer simulation. Ultimately the simulation can process great quantities of 12 data and act as a tool in evaluating the profitability of given market configurations. The simulation will also be able to illustrate the effect on profitability by changing the raw material supply area, raw material costs, competition, inbound transportation, market demand, plant production capa­ city, processing costs, or price of finished goods. t The thesis of the research study is that there presently exist agricultural and horticultural markets for fine sawmill residues, and that transformation of the sawmill residue dis­ posal problem into a source of income through the establish­ ment of a firm to collect, process, and market the material « is economically feasible. The testable hypotheses are as follows: Hq i Agricultural and horticultural use of sawdust and bark in bulk units dictates a raw material positioned processing unit. HQ 2 As scale of operations increase, unit costs will decrease up to an optimum size. HQ 3 The type.of ra w material used as product input (i.e., sawdust or bark) will influence the location of the processing unit. The study is formulated to accomplish the following secondary objectives: 1. Evaluate the present sawmill residue utilization situation by both field survey and mail question­ naire. 2. Evaluate current agricultural and horticultural market demand for sawdust and bark. 13 3. Adopt a set of wood residue conversion factors which reasonably represent the residue produced by hardwood sawmills in southern Michigan. 4. Summarize the functions of bark and sawdust mulches and soil amendments, and the accept­ ability of their use in relation to the soil. 5. Present photographic documentation of wood residue types and the various methods of handling and dis­ posal used b y sawmills. 6. Assemble current wood residue type, volume and location data for use by both producers and poten­ tial consumers of sawmill residue in southern lower Michigan. ■ Methodology To determine what had been written about the utiliza« tion of wood residues, a review of the technical and promo­ tional literature and reports of various individuals, associa­ tions, and government agencies was made. This initially involved a thorough search of the Michigan State University and University of Missouri library resources. Two of the most comprehensive wood industry trade journals, The Forest Products Journal and Wood and Wood Products, were extensively researched. Then, to find out what wood residue utilization programs were in progress or had been completed recently, letters were Bent to all universities with forestry programs, all U.S.D.A. Agriculture Experiment Stations, all U.S.F.S. Research Stations, the Southern Pine Association, Western Wood 14 Products Association, and the Forest Products Laboratory in Madison, Wisconsin. The major objective was to accumulate available research data as background material for this study and to avoid unneces­ sary duplication of previous research. Wood residue conversion factor data used to compute i quantities of residues produced during sawmilling was secured through library research of wood industry publications. Infor- i mation about heuristic simulation techniques was likewise obtained from researching the business management and market­ ing literature and textbooks. t A field survey of hardwood sawmills was designed and completed in the southern forty-one counties of lower Michigan. Only sawmills shown in Figure 2 and listed in the 1968 Directory of Wood Using Plants in Michigan (7) were considered for the sample. Data collection was done by personal interview and photographic documentation. The survey was designed to better acquaint the author with the actual wood residue problem and to obtain first-hand information about wood residue handling, < disposal, and the attitude of sawmill operators toward the local market potential for wood residues. Incorporated into the above survey were planned visits to the most logical markets for wood residues, see Appendix C-3. 15 Figure 2 LOCATION O f PRIMARY WOOD IISINO PLANTS • 19SI" . muiLi • O w t M M t • m* & M » l i t M N A u m to M M tot 0 W M t o M n i • m h n i m i l ■ Ito*r«*«m. qtMW K A M f to 6 * t o (C « M » U « t t o t o A titofMiftot to • Pm< i n i'll » ■ « M h 6 Clmito > M — totoPtoto O • T l M t o f N M 16 The mail survey was completed using the mail question­ naire in Appendix A. The mail questionnaire was designed to obtain detailed information about the quantities of wood residues produced by sawmills and how individual mills handle and dispose of sawdust and bark. A sample of 106 sawmills was chosen to receive mail questionnaires. In the development of an original and highly special­ ized computer simulation, which has the capability to handle t raw material supply data, market demand data, cost center data, and the many control parameters of a bark and sawduBt processing plant, several distinct steps were involved. The * major steps are outlined on the following two pages. The actual computer program, prepared with the assistance of a professional programmer in the office of Applications Program­ ming of the M.S.U. Computer Center, is included in Appendix D. Step One: The simulation program designed for this Btudy utilizes a uniform grid system for measuring distances and points. Before the processing plants could be located and the supply data or demand data used as data inputs the exact location of all 150 operational sawmills and the center point of each county included in the southern Michigan study area were plotted on a grid overlay of a Michigan map. Step Two: The geographic location of all mills in the study were obtained from a detailed locator card file prepared by the men in M.S.U. Forestry Extension. The four digit coordinates of all 150 sawmills were recorded. These data were stored on punched cards for later use in the actual simu­ lation. They were used in measuring distances and computing transportation costs for various configurations surrounding selected processing plant coordinate points. 0 Step Three: To determine the quantities of each type wood residue produced by hardwood sawmills, incidental to the production of hardwood lumber, a set of wood residue conversion factors were adopted from previous research. The conversion factors are detailed in Phase I of Part III. These calcula- ♦ tions were made for each sawmill using the annual production figures reported on the mail questionnaire. The resultant quantities estimated for each of the 150 sawmills and each of the forty-one counties are shown in Appendix B. Step Four: Demand data was next to be determined. Total market size and market share estimates f o r ,dairy cattle bedding, orchard mulch, nursery mulch, mulches and soil con­ ditioners for the lawn and garden markets in southern Michigan were made and the quantities demanded by each market shown in tons per county, see Table 9. Step Five: The processing plant is considered to be the center point (hub) of the simulation. The methodology used in this section is focused on material flow through the 18 processing plant and should bring about a better understanding o£ the operation to the reader. The cost center concept util­ ized to keep a logical accounting of the various types of costs involved in the total processing plant concept and details of the processing plant activities are shown in Phase XII of Part III. t The actual computer simulation program which evaluates the supply and demand relations relative to the cost centers and determines the profitability of selected configurations is shown in Appendix D. Limitations The field survey was not restricted by any major limita­ tions/ although the ideal situation would have included time during the field survey for on-site measurement of residues produced by each sawmill in the study rather than adopting conversion factors developed elsewhere. However, the author feels that this shortcoming was somewhat offset by knowledge gained during the field survey and cross-checking of mail questionnaire responses. The basic limitation, with respect to the mail survey, was the difficulty of having all questions in the question­ naire answered completely and determining the accuracy of the answers. 19 A second limitation resulted from insufficient funds to support a 100 percent survey; therefore, data from over 100 small, but important,, E, F and LTF-class sawmills had tq be estimated from a random sample of 12. The mail questionnaire is limited in the amount of information that can be asked at any one time; therefore, * much information about the sawmill industry and wood residues remains unknown. » The computer simulation used as an aid in the market analysis is limited, as far as the wood industry is concerned, to rather specific applications. It is designed to include only sawmills as suppliers of wood residue products to one specific type of processing plant. Because of the established logic this part of the program cannot be changed. Confining the market study to the agricultural and horticultural applications of wood residues also constitutes a limitation. Other industries such as those making floor sweeping compound, charcoal, paper, wood fiber products, pressed wood products and others present additional profitable market opportunities. As the technology is developed and the economic situation becomes increasingly favorable, bark and sawdust will be used as a raw material for more and more products. The study area included in the simulation was chosen arbitrarily and is limited geographically to the forty-one counties in southern lower Michigan. This area includes all counties within a 100-mile radius of Lansing. Within this area of southern lower Michigan is located over eighty percent of the hardwood growing stock and hardwood sawmills, most t of the dairy, nursery, orchard, lawn and garden markets, and over ninety-five percent of the population. Contributions Two major contributions of the field survey came out of the opportunity -to personally visit fifty sawmills in lower Michigan, exchanging information with the sawmill owners and operators about wood residue utilization technology and potential, and photographing the various wood residue handling systems and methods of disposal. This documentation is avail­ able, on a limited basis, in the body of the study. The conversation was, in many cases, the initiation of wood resi­ due utilization awareness, and the photographs are valuable in that they point out the tremendous waste of sawmill residues and help to make the case for increased wood utilization. The bulk of the color slides and black and white photographs were collected for the M.S.U. Forestry Department Extension Staff who will use them to plan future extension programs and research studies of the various sawmill operations. 21 During the £ield survey samples o£ various types of wood residues were collected, photographed and included in the study. In presenting photographs of various residue types is important to present utilization potential because it is now possible for persons not familiar with the sawmill process to compare the,physical characteristics of the different residue materials. The mail survey represents a valuable contribution of « new knowledge to the Michigan sawmill industry. Quantities of original data were successfully collected from two-thirds of the hardwood sawmills in southern lower Michigan, includ- t ing lumber production data, operating days per year, equipment owned b y the sawmills, the methods they currently use to dis­ pose of wood residues, amount of residue marketing and adver­ tising done, and the amounts and prices of residues sold. The data is made available in this report and the new profit opportunity should serve as a stimulus to the sawmill industry to improve wood residue marketing efforts and will also serve as a price and information guide to markets inter­ ested in purchasing quantities of residue. With the initiation of a marketing program for bark and sawdust products the problem of air pollution from the previous burning of these materials by sawmills will be significantly reduced. During this time when much attention is focused on 22 air pollution any method of reducing the problem would be considered a positive contribution. An original heuristic simulation was developed as a marketing management tool to determine the profitability of wood residue processing on a large scale. The methodology used is not new, but the application of this specific analysis i technique to the sawmill industry is considered a significant contribution. The secondary value of this computer simulation comes from the fact that it is not limited to use only in Michigan, but has the potential for broad applications through­ out the sawmill industry. t The real value of the simulation comes from its poten­ tial value as an aid to management decision making. For W> example, using this simulation it is possible to determine the quantities of raw materials within a radius of a chosen point, transportation costs to these points can easily be determined, the effect on unit cost can be seen by increases or decreases in raw material cost, transportation rates, or processing costs. Speed and accuracy as well as simultaneous considera­ tion of multiple factors is made possible by using simulation. Organization The remaining sections of the study consist of five chapters, each concentrating on a specific aspect of the research. Part 11 is a review of the literature. 23 The third Part is a discussion o£ the research design used in the study. The Part is divided into three phases. Phase I deals with general research support data that are used in the study as a whole. Phase XI concentrates on the design of the two hardwood sawmill residue surveys; one a field survey, the other a mail survey. Phase III discusses the design of the simulation and the important factors that are included in the basic simulation system. i Part Four of the dissertation discusses the findings relative to the surveys outlined in Phase XI. The discussion centers on the information obtained by the two surveys. The fifth Part discusses the findings resulting from the computer simulation configuration designed in PhaBe XXX. Part Six presents the conclusions drawn from the results presented in Parts Four and Five. Xn addition, Part Six dis­ cusses the implications of the conclusions and makes sugges­ tions for further research based on the findings of the present study. PART II LITERATURE 9 t Introduction Due to the nature of this study, the literature in a number of areas which relate to the research problem was consulted, but a review of literature for this research study covers only the first two of the following three areas: * 1. The heuristic approach to problem solving. 2. The use of fine wood residues in agricultural and horticultural applications. 3. Development and application of wood residue conversion factors. Research on each of these topics has been conducted independently from each of the others. Of the three basic areas mentioned above, the author does not consider it neces­ sary to duplicate a review of wood residue conversion factors literature for the following reasons. Only a very few studies have been designed to study wood residue conversion factors. Such studies have often been limited in application to other parts of the United 25 States and do not apply in this study because o£ differences in climate, species, soil conditions, and other influences. In most cases the studies were extremely brief in their des­ cription of methodology and results; therefore, for the con­ venience of persons interested in various methods of deter­ mining or applying wood residue conversion factors the best articles are footnoted below in order of significance (32) (41) (8) (2B) (51). For purposes of this research study, wood residue con­ version factors were adopted from King (32) because of his systematic approach to the problem and study of hardwood species with characteristics similar to those growing in Michigan. Heuristic Simulation Very little information is available on the applica­ tion of heuristic simulation as used in the study because it is an original program; therefore, because there are many who are unfamiliar with heuristics, some of the literature , included in this review will discuss heuristics as an approach to problem solving. W e b ster1s New International Dictionary of the English Language defines the adjective "heuristic” as "serving to discover or reveal." Heuristics, after Newell, Shaw and 26 Simon (39), are defined as principles or devices that con­ tribute, on the average, to reduction of search in problem­ solving activity. Simon (52) has referred to heuristics as rules of thumb selected on the basis that they will aid in problem solving. In an earlier paper, Simon, in collaboration with / Newell and Shaw, used the term "heuristic" to denote "any principle or device that contributes to the reduction in the * average search to a solution" (39). Making use of the lather definition, a heuristic program can be defined, after Tonge (56), as a problem-solving program organized around such « principles or devices. Simon (52) has distinguished between such programs and algorithms on the basis that only the latter guarantee solution of the problem to a desired degree of accuracy. Kuehn and Hamburger (33) do not believe that this is the.most appropriate way to characterize heuristic programs. They report the existance of many solution procedures, referred to as algorithms, which do not guarantee solutions to a desired degree of accuracy; but rather, as is possible with the heuristic warehouse location program, provide only upper and lower bounds to the solution. and Thompson matrix games. An example from Kemeny (31) is the fictitious play method for solving Furthermore, Courant and Robbins (18) report 27 the definition of algorithm generally used by mathematicians is "a systematic method for computation.11 Such a definition would include all computer programs. For purposes of the study, heuristic simulation is considered to be an approach to problem solving where the emphasis is on working toward optimum solutions rather than * optimum solutions. Tonge (56) supports this approach by saying heuristic techniques are most often used when the goal is to solve a problem whose solution can be described in terms of acceptability characteristics rather than by optimizing rules, « Bowersox, Smykoy and LaLonde (16) and Reynolds (45) discuss the development of a computer program for a heuristic simulation as a systematic order that closely parallels the thought process of the human mind. This step-like procedure of adding facilities allows managerial review of system development with related explanation of logic at each step. Thus, the solution, once derived, requires little managerial interpretation. Two limitations are also pointed out: First, heuristics does not necessarily result in selection of the best network among those facilities that appear plausible; and, secondly, although managerial intervention eases the process of understanding study results, the possibility of bias remains a constant danger. 28 Basic application5of the technique are varied. Tonge (55) has prepared a heuristic program to balance production t assembly lines in an appliance factory. Clarkson and Meltzer (19) have prepared a heuristic program to simulate investment activity under a trust fund. yet been published, Gere While no formal results have (24) has made several attempts to t construct heuristic programs for the job shop scheduling prob­ lem. Shycon and Maffei (50) use heuristic simulation tech- niques in the modeling of warehouse networks because of builtin flexibility which allows for rapid changes as required by new management decisions. t Recent interest in the heuristic approach to problem solving has led to the development of computer programs designed to: chess (13) geometry compose music (29), play checkers (49), play (38), discover proofs for theorems in logic and (40) (23), design electric motors and transformers (27), balance assembly lines (55), and locate warehouses (33). Of the many applications of heuristics, the one used by management that most closely approximates the processing plant feasibility problem in the study is distribution ware­ house location as discussed by Kuehn and Hamburger (33). The processing plant is the “hub" of the simulation and requires an efficient concentration system of raw material supply from many scattered sawmill locations. Similarly, the distribution 29 warehouse is the "hub" of a system, but works in reverse to efficiently distribute goods to many locations. According to Kuehn and Hamburger (33) the use of heuristics in problem solving has two prime advantages rela­ tive to the currently available linear programming formula­ tions and solutions procedures; First, computational simpli- city, which results in substantial reductions in solution times and permits the treatment of large-scale problems; * Second, flexibility with respect to the underlying cost func­ tions, eliminating the need for restrictive assumptions. Zt also represents an important extension to the simulation * approach to locating warehouses in that it incorporates a systematic procedure designed to generate at least one nearoptimal distribution system without reducing flexibility in the modeling of the problem. Wood Residues as Mulches and Soil Conditioners It is generally accepted that both bark and sawdust make excellent cattle bedding. Cattle bedding is also the largest current market for these materials. Because extensive use of either material for this purpose currently hinges on the economics of transportation and availability of substitute bedding materials, a review of the literature covering this point is considered unnecessary. Primary emphasis of this 30 portion of the literature review will focus upon bark and sawdust and their relation to the soil as mulches and soil conditioners. The principal uses of wood residues in agriculture and horticulture are for mulches and soil conditioners (30). Mater reports that both sawdust and bark are widely used for t both purposes in some sections of the country (36). Whether employed as mulches or soil conditioners, wood residue, as it i decomposes, results in complex transformations of carbon and nitrogen and ultimately supplements the soil humus. humus, in turn, The improves the tilling properties of soil and serves as a reservoir for nitrogen, phosphorous, potassium, sulfur, and other plant nutrients as well as water. The nutrients bound to the humus are slowly released and made available for plant growth by the action of soil organisms. Benefits of Wood Residues as Mulches and Soil Conditioners Wood residues are used as soil covers or mulches, or may be mixed with the soil to improve the physical and chemi­ cal properties. Dudley and Kelly (20) found that when used as a mulch, water intake is increased, runoff and erosion are decreased, soil temperature is lower, water loss through evaporation is decreased, weeds are controlled to some extent, and it offers a pleasing appearance, in comparison with 31 leaves and straw, Bollen and Glennie (14) found that wood residues are more easily applied, longer lasting, less susceptible to blowing and fire, and more pleasing in appearance. Dunn and Emery (21) and Wilde (61) reported that when incorporated with the soil, wood residues improve friability and prevent crust formation as effectively as peat moss, improve tilth in fine textured soils as effectively as peat, i increase initial infiltration rate, improve aeration, produce more rapid flowering of some plants and lower bulk density of soils. Although increased moisture retention is often % given as an advantage of soil amendments, Lunt and Clark (35) state that the incorporation of coarse organic matter in soil decreases rather than increases moisture-holding capacity and that evaporation rates are increased unless a mulch is also used. In certain cases, Lunt and Clark (35) report that potassium and phosphorus derived from bark appear to make a contribution to plantings for short periods, but, generally, undecomposed sawdust (and bark) would seldom be worth the cost of hauling if its only value was to supply mineral nutrients. Allison and Anderson (1) support this by saying "The principal effect of bark and wood particles on the macro­ element nutrition of plantings in soil mixes is that to be 32 expected by diluting the soil with relatively inert materials; in other words, more frequent fertilization is generally required." Lignin makes up approximately half the weight of bark and a quarter of the weight of wood. decomposition. It is quite resistant to For this reason, it is the most desirable / fraction of plant material from the standpoint of its benefi­ cial effects on the soil. Because of its slow rate of decom- 4 position, the total nitrogen demand which it creates is low. In addition, it supplements the native humus of the soil, thereby improving tilling properties, serves as a reservoir for plant nutrients, and holds nutrients against the leaching effects of water. The composition of wood varies somewhat among species, particularly as regards softwoods and hardwoods, and the importance of lignin content in both wood and bark should be noted. The approximate composition of a typical softwood and hardwood is shown in Table 1. Baxter (11) points out that the composition of bark iB quite different from that of wood in a number of important respects. As shown in Table 2, its lignin, extractive, and ash contents are considerably higher than that of wood, while its carbohydrate content is lower. As discussed subsequently, 33 Table 1 Approximate Composition of Wood (11) Acidity - pH Total Organic Matter Mineral Content (96) Water Soluble (96) Carbohydrate (96). Lignin (96)* Nitrogen (96) (96) Softwood Hardwood 5.1 98.8 0.2 4.1 68.5 26.0 0.1 5.3 98.6 0.4 2.5 71.9 26.5 0.1 *Based on extract-free weight Table 2 Approximate Composition of Bark (11) Acidity - pH Total Organic Matter Mineral Content (96) Water Soluble (96) Carbohydrates (96) Lignin (96)* Nitrogen (96) (96) Softwood Hardwood 3.5 99.0 1.0 23.0 46.4 52.6 0.2 3.7 91.7 8.3 4.0 55.1 41.8 0.2 ♦Based on extract-free weight these differences have a bearing on the relative efficacy of these two materials as soil additives. For purposes of comparison, analytical data for peat moss are given in Table 3. Peat moss is the most widely used material for mulches, soil conditioners, and other horti­ cultural and agricultural purposes in the United States 34 according to Anderson and Blake (6). Dunn and Wolfe (22) support the belief that since peat moss has many desirable properties and has become somewhat of a standard with which other soil additives are compared, its properties are of particular interest in a discussion of the properties of wood and bark as soil.additives. Table 3 Approximate Composition of Peat Moss Acidity - pH Total Organic Matter Mineral Content (%) Water Soluble (%) Carbohydrate (%) Lignin {%) Nitrogen (%) (11) 3.8 95.7 — 5.2 41.2 18.0 0.8 Overcoming the Disadvantages and Misconceptions About the Use of Bark and Sawdust as Mulches and Soil Conditioners There are several disadvantages and misconceptions concerning the use of wood residues, especially fresh material, arising from past experience in their use. These objections can be eliminated through processing and informed usage, and for this reason are discussed in detail. tions to the use of wood residues are: The primary objec­ one, they compete with growing plants for available nitrogen; two, they increase soil acidity; three, finer particles tend to pack, and dust and 35 slivers are objectionable; and four, they contain materials toxic to plants. Nitrogen Competition Decomposition of organic substances in soil is accom­ plished through the action of soil micro-organisms. These essential fungi and bacteria require a source of energy plus t nitrogen in order to survive and develop. Bollen and Lu (15) found that some residues cause more inhibition than others. Wood and bark particles provide the carbonaceous material needed for energy, but supply little of the necessary nitrogen. Micro-organisms must draw on the soil as a source of« nitrogen, competing with plants for the available supply. Unless sufficient nitrogen is present in the soil, either naturally or from supplemental applications, to supply the needs of both the growing plants and soil organisms, symptoms of nitrogen deficiency will appear. These symptoms are often mistakenly attributed to toxic materials present in the soil amendment. Although all carbonaceous materials react similarly when mixed with soil, most commonly used amendments naturally contain higher proportions of nitrogen than wood and bark, causing less nitrogen draft from the soil. If the C/N ratio is much wider than 25/1, Bollen and Glennie (14) report that 36 micro-organisms carrying on decomposition compete with plant roots for available nitrogen. The rate of decomposition of sawdust and bark is much slower than materials such as straw, which decomposes rapidly, causing a larger initial nitrogen deficiency but of shorter duration. Most proteins of plant and animal origin are / rapidly decomposed and, if nitrogen is adequate, so also are sugars. Celluloses are decomposed less rapidly, and lignins « very slowly. Wood residues are, therefore, most persistent. This is advantageous in providing longer-lasting mulches and more prolonged effects when incorporated in the soil. It may t be noted also that decomposibility of resistant carbonaceous materials such as sawdust is not greatly enhanced by additions of available nitrogen. The primary rate of decomposition of plant residues of mixed composition and wide carbon-to-nitrogen ratio responds to added nitrogen to a degree largely dependent upon their water soluble carbonaceous constituents which are responsible for initial nitrogen demand. Lunt (34) observed that the magnitude of nitrogen defi­ ciency is proportional to the rate of decomposition. The • duration of induced nitrogen deficiency varies with the rate of application of the amendment. Applications of 3 to 4 tons of dry material per acre will seldom extend nitrate depletion beyond the first season, provided conditions are suitable for decomposition. 37 The nitrogen assimilated by decay organisms again becomes available upon their death for use by other organisms or plants. Organisms appear to consume the major portion of this nitrogen until decomposition of the organic matter is completed. Nitrogen then is gradually released to plants as 4 the organisms decompose. Allison and Anderson (1) found that for sawdust this time may vary from four months to several years, depending on temperature, moisture, percentage of nitrogen added, quantity of sawdust applied, and the intimacy with which it is mixed with the soil. Allison and Murphy (4) (5) found that the wood and bark of softwood and of hardwood species each differ in their rates of decomposition. The hardwood species were more easily decomposed than the softwood species studied. Klein Allison and (3) found similar results on other softwoods. Lunt (34) found that birch chips decomposed more rapidly than either oak or pine and would require the most nitrogen to prevent deficiencies. The overall results of the above findings are summarized by saying that the most effective soil amendments are those with high lignin content, thereby holding nitrogen competition to a minimum and making them more stable in soil mixtures. 38 Soil Acidity Most barks and woods are acidic. McCool (37) reports ranges from pH 3.5 (approximate value of many peat mosses) to 7.0. Lunt (34) found that addition of sawdust to soil had no appreciable effect on soil acidity and that its initial effect was to decrease it slightly. Allison and Anderson (1) / reported that when sawdust is applied to a lime-requiring crop, any acid in it may be slightly harmful if the soil is already i near the lower limit of acidity tolerated by the crop. Lunt and Clark (35) suggest where desirable to maintain pH at levels near neutrality, 10 pounds of agricultural limestone per cubic yard of bark or sawdust is satisfactory. For acid- requiring plants such as azaleas, any resulting acidity is beneficial. According to Salamon (48) it is natural to assume that since ash of plants contains more basic than acid con­ stituents, the ultimate effect should be toward a less acid pH. Particle Size The nature and extent of the physical effects of mulches and soil conditioners vary somewhat with the size of the particles added. Coarse particles tend to decrease water- holding capacity if incorporated in the soil, and very fine particles tend to pack and exclude water and air from the soil. 39 Salomon (48) found that the depressive effects on plants caused by nitrogen deficiencies persisted longer from chips larger than one-fourth inch in diameter than from smaller chips. Likewise, Lunt (34) reported that particles larger than one-half inch reduce plant yields more than smaller sizes due to the presence of undecomposed wood. In general, it has / been found by Lunt and Clark (35) that particle sizes from * three to ten millimeters are satisfactory for most horticul4 tural and agricultural uses. Bollen and Glennie (14) found that wood chips, shavings, millrun sawdust, and gang sawdust make satisfactory mulches. However, they reported that resaw sawdust, because of its small particle size, tends to pack tightly and thus retard aeration and moisture penetration. All sawdust performed satisfactorily when incorporated if it was well mixed with the soil. Lunt and Clark (35) summarize by saying that it appears that nearly all sizes of bark fragments can serve satisfac­ torily in horticultural applications in short-term growing operations up to about three years, provided nitrogen rela­ tionships and acidity are properly controlled. Toxic Effects Toxic effects attributed to wood or bark soil amend­ ments and mulches are generally the result of nitrogen 40 depletion and can be prevented or corrected by applying supplemental nitrogen. Lunt and Clark (35) report that some materials do contain sufficient toxic material to retard growth, however. Walnut and cedar shavings adversely effect tomatoes. Allison and Murphy (4) found no indication that any of the hardwood products 'used in their study were toxic to organisms that carried out the decay processes. Using garden peas to determine toxicity effects of certain softwood species, Allison, et. al. (2), found that certain west coast woods and barks at low rates of application were very detrimental to growth. Other residues were found to be less detrimental at higher rates. The adverse symptoms observed on the first crop of peas were markedly decreased or entirely absent on a second crop of peas grown on the same medium. These observa­ tions are in agreement with those of Gibbs and co-workers (25) (26). Reuszer, et. al. (44) reported that cedar and walnut residues were detrimental to plants. Bollen and Lu (15) found that small amounts of walnut sawdust had no detrimental effect upon plant growth. Foundation On the other hand, Armour Research (9) has found that wood bark treated to neutralize the tannic acids can increase yield more than does peat moss. Crop Response to Mulches and Soil Conditioners The beneficial effects of using wood residues as mulches and soil conditioners for ornamental plants, have been demon­ strated by years of successful use of these materials by horticulturalists and nurserymen in the West, Mid-west and Northeast. Turk (57) reports that considerably less is known by the public * about the use of these materials on agricultural crops. Lunt (34) reports that natural well-rotted pure wood i chips or sawdust is a safe material to use under almost any condition. Sawdust and bark used as mulches have been reported to i be superior to other types of mulches for blueberries. and Mallenthin Roberts (46) found that sawdust and bark mulches four to six inches thick were particularly beneficial to blueberries because of their high moisture retention. were obtained with strawberries. nitrogen requirement, Similar results With crops having a higher they recommend the use of one hundred pounds of nitrogen per acre inch of sawdust (twenty tons per acre). According to Bollen and Glennie (14) farmers in the Bitteroot Valley and Flathead Region of Montana use sawdust almost to the limit of its availability. Some of it is applied directly to fields without having been composted. Dunn and Emery (21) concluded from field trials that properly composted sawdust is very beneficial to plant growth. Their work dealt with corn, rutabagas, peas, onions, beets, and other crops. Composted sawdust or shavings were superior in promoting plant growth over soil alone with fertilizer. Lunt and Clark (35) report that normal landscaping t and horticultural application of 1/10 to 1/3 by volume (fresh material) may cause nitrogen draft for periods of six months or more. They continue by saying that decomposition limits usefulness of single applications of bark and chips to about five to seven years. One pound actual nitrogen per 100 pounds dry wood or bark, preferably added in three or more applica­ tions, is required to offset nitrogen demand. Because inoculated legumes suffered no reduction in growth following sawdust application and due to erratic results with application of sawdust prior to planting, Lunt (34) recom­ mends that it precede a green manure crop, preferably a legume. Other possibilities are to use sawdust or bark as poultry or cattle bedding before field application, as a mulch preceeding incorporation with the soil or to compost the material before use. Sawdust mulches one-inch deep in various vegetable plots more than doubled yields and were better than black polyethylene film in experiments conducted b y Pratt and Comstock (43) of the N e w York Agricultural Experiment Station. 43 In summary, woods and barks, with few exceptions, can be used satisfactorily in agriculture as mulches and for soil humus maintenance, if adequate amounts of nutrients, especially nitrogen and sometimes lime, are supplied. Most woods behave similarly to common carbonaceous crop residues except that they decompose more slowly because they contain less available $ carbohydrate and more lignin. PART III RESEARCH DESIGN 0 Introduction Part III consists o£ detailed descriptions about each of the three separate components which comprise the research portion of the study. They are designated Phase I, II, and III. The first Phase discusses the general research support data that had to be located and examined prior to the initia­ tion of the second and third phases. Phase II presents the work plan for conducting a hard­ wood sawmill field survey and the preparation and administra­ tion of a mail survey. Both surveys are concerned with: (1) the gathering of data about the methods used to handle and dispose of sawmill residues, (2) developing insight into the local market potential for wood residues, and (3) determining the stage of development of local agricultural and horticul­ tural markets by the sawmill industry. The simulation model in Phase III represents a large scale bark and sawdust processing plant developed during the 44 45 present research to be used as an aid in management decision making. The primary purpose for the development of the model was to examine the profitability of processing wood residue. Secondary reasons were to show that a simulation model could be built and used to test hypotheses concerning the operation of wood residue processing plants and to illustrate the techt niques that might be used to build such a simulation. The model was limited to not more than four processing plants operating concurrently, no more than five separate cost centers, no more than three products, no more than 150 suppliers, no more than four m a r kets, and no more than forty-one counties serving as market demand centers. The model could be expanded, however, to model additional types of wood residues and as many markets as desired. The only limitation would be the physical limitations of the computer facilities used. Phase I: General Research Support General Study Assumptions Before significant work could begin on the research program it was necessary to strengthen the study by outlining a number of basic assumptions. 1. The most promising markets for bark and sawdust (wood residues) which are immediately available are agricultural and horticultural markets. Specifically these include: (a) Dairy— cattle bedding; (b) Nursery— stock mulch, soil condi­ tioning, and decorative applications; (c) Orchard— 46 fruit tree mulch; (d) Consumer packages— mulches, soil conditioners, and decorative material for home gardeners. 2. Most potential buyers of either sawdust or bark products (in bulk) own trucks and are willing to come to the processing plant for their needs and may even pay a premium for the products if they can depend on the following services: (a) a load being available when they get to the plant; (b) the material being loaded for them; (c) short-term credit being available. These same customers have tractors with front loaders to distribute the material once it is dumped near the site of even­ tual use. 3. Two cubic foot packages of bark, available in one ton units on pallets, will be sold through brokers to food chains, garden center chains, and other chain store organizations that handle lawn and garden products. These chains own large fleets of trucks and can schedule regular pick-up or back-haul the bark products. 4. Priced competitively, new bark and sawdust products will become accepted into the market in direct pro­ portion to the amount of advertising expenditure, product promotion, and industry education programs. 5. The 3 x 3 mile grid system effectively and accurately identifies the location of supplying sawmills, county market demand and the processing plant location in the computer program memory and in reality. 6. Estimated demand for finished products is accept­ ably accurate on a per-county basis for the study. » Sawmill Size Classes and Production Data The 1968 Directory of Wood Using Plants in Michigan ( ) was adopted as the basic source of information about the num­ ber of hardwood sawmills within the study area and their gen­ eral size relative to annual lumber production. Mills not listed in the directory were not considered in the study. 47 All sawmills listed in the directory that are in the 41 south­ ern counties o£ lower Michigan were included. Information in the directory was helpful in the prepara­ tion of the initial background for the study by describing the general characteristics of each sawmill, listing sawmill addresses, and indicating the general size of each mill's lumber production. Table 4 was prepared as a summary of the size and production of the 150 sawmills included in the study. In anticipation of questions about the accuracy of the production figures recorded in the directory, a high, low and average production figure is recorded in Table 4 for each * sawmill size class. Depending on the condition of the sawmill industry, this range can be used to compute a liberal or con­ servative estimate of lumber production which will reflect the amount of wood residues available. These data are impor­ tant to the planning of a processing plant which will require a large supply of wood residues from sawmills as raw material. For detailed information about individual sawmill lumber pro­ duction, see Appendix B. Adoption of Conversion Factors Information concerning wood residues and the quantities of the several fractions of which it is composed was determined through the use of conversion factors. Since it was not an 48 Table 4 Sawmill Class Size and Production Data* Used in 1968 Directory of Wood Using Plants in Michigan Lumber Production by M i l l 3 Mill Size Class No. Of Mills2 A 2 B 1 C 7 D 29 E 31 F 56 LTF 24 TOTAL 150 . Daily 50,000 44,000 37.501 37,500 31,000 25,001 25,000 20,000 15.001 15,000 10,000 5,001 5,000 3,750 2,501 2,500 1,500 501 500 250 000 Annually4 (000) Annual Lumber Production By Class5 (000) 10,000 8,800 7,501 7,500 6,200 5,001 5,000 4,000 3,001 3,000 2,000 1.001 1,000 750 501 500 300 101 100 50 00 20,000 17,500 15,002 7,500 5,200 5,001 35,000 28,000 21,007 37,000 58,000 29.029 31,000 23,250 15,531 28,000 16,800 5,656 2,400 1,200 000 Average Estimated Total Annual Production (000) 17,500 6,200 28,000 58,000 23,250 16,800 1,200 150,950 * All lumber production is in board feet green lumber tally. 1 Size class set forth in Directory of Primary Wood Using Plants in Michigan. 1968, Published by Michigan Department of Conservation - Forestry Division. 2 150 mills selected from Directory are in lower 41 counties of Michigan. 3 Estimated levels of production are high, average, and low, respectively. Assuming 250 working days per year. 3 The combined production of all sawmills within each individual class. 49 objective o£ the study to develop new wood residue conversion, a search was made o£ the literature for results o£ previous research investigations o£ the subject. Conversion factors, when multiplied times a thousand board feet of hardwood lumber produced by a sawmill, determine the tons of wood residue of each type that were also produced. The conversion factors developed by King (3 2 ) were adopted as an integral part of the present study because of 1 the very professional approach used by King and the fact that he considered hardwood species reasonably similar to those growing in lower Michigan. The average amount of hardwood sawdust produced by a circular headsaw is estimated in Table 5 to be 1.04 green tons* per thousand board feet (MBF) of green lumber tally produced. To determine the total quantity of sawdust produced over a period of time it is necessary to multiply the conversion factor (1.04) times the MBF of green lumber produced during the period and the results are in ton units. 1.04 (sawdust conversion factor) = 6.24 tons. Example: 6 MBF x The effect of log diameter variations on the amount of sawdust produced is also shown in the detail o f the table. Hardwood bark production is determined in a similar manner. Table 6 estimates the average quantity of bark *Green ton, see Glossary. 50 Table 5 Estimates of Hardwood Sawdust by Diameter Class for Mills with Circular Headsaws* Log Diameter Class (inches) 7.6-10.5 10.6-13.5 13.6-16.5 16.6-19.5 Weight in Tons with Confidence Limits at 9596 Probability Level Per 1000 Per 1000 Per 1000 bd. ft. bd. ft. bd. ft. International Doylegreen 1/4 in. Scribner lumber Log Scale Loq-Scale tally Green 1.11 ± 0.09 1.85 + 0.15 1.15 + 0.09 Oven-Dry 0.63 + 0.05 1.05 ± 0.08 0.65 + 0.05 Green 1.18 +. 0.09 1.57 + 0.12 1.27 ± 0.10 Oven-Dry 0.67 + 0.05 0.89 + 0.07 0.72 + 0.06 Green 0.95 + 0.07 1.24 + 0.10 0.91 + 0.07 Oven-Dry 0.54 + 0.04 0.70 + 0.06 0.52 + 0.04 Green 0.93 + 0.07 1.04 + 0.08 1,00 + 0.08 Oven-Dry 0.53 + 0.04 0.59 + 0.05 0.57 + 0.05 Average for all four diameter classes --Green Weight (Tons) Per MBF Green Lumber Tally 1.04 + 0.08 ♦Adapted from King, W. W. 1952. Survey of Sawmill Residue in East Texas, Texas Forest Service. Technical Report No. 3, p. 51. 51 Table 6 Estimates of Hardwood Bark by Diameter Class for all Mills* Log Diameter Class (inches) 7.6-10.5 10.6-13.5 13.6-16.5 16.6-19.5 Weight in Tons with Confidence Limits at 9596 Probailitv Level Per 1000 Per 1000 Per 1000 bd. ft. bd. ft. bd. ft. International Doylegreen Scribner 1/4 in. lumber Loq-Scale Log Scale ' tally Green 0.75 £ 0.14 1.30 £ 0.24 0.78 £ 0.15 Ovien-Dry 0.53 £ 0.10 0.91 £ 0.17 0.55 £ 0.10 Green 0.64 £ 0.09 0.91 £ 0.12 0.64 £ 0.09 Oven-Dry 0.45 £ 0.06 0.64 £ 0.09 0.46 £ 0.06 Green 0.50 £ 0.08 0.67 £ 0.11 0.47 £ 0.08 Oven-Dry 0.35 + 0.06 0.47 + 0 . 0 8 0.33 £ 0.06 Green 0.44 £ 0.10 0.53 £ 0.12 0.45 £ 0.10 Oven-Dry 0.31 £ 0.07 0.37 £ 0.08 0.32 £ 0.07 Average for all four diameter classes --Green Weight (Tons) Per MBF Green Lumber Tally 0.58 + 0.10 ♦Adapted from King, W. W. 1952. Survey of Sawmill Residue in East Texas. Texas Forest Service. Technical Report No. 3, p. 49. 52 accumulated per MBF sawn to be 0.58 tons, or roughly 1200 pounds. For purposes of this study, only mills with log debarkers are considered as sources for bark. Mills not owning a debarker accumulate an equal amount of bark but it remains attached to the slabs and edgings. t To make the study complete, the conversion factor for solid residue material is detailed in Table 7. The factor of 1.24 represents the quantity of solid wood residue produced incidental to the manufacture of one thousand board feet of lumber. No bark content is included in the factor. Table 8 is a summary of the conversion factors just discussed and a comparison is made of the related factors for Southern Pine. Xn addition, the amount of chippable material (solid wood) is estimated by respective conversion factors. The conversion factors will be used later in the study to determine the amounts of each type of wood residue pro­ duced by each sawmill in the study area. These quantitites in turn will serve as raw material supply inputs for the utilization simulation. For details about the quantities of each type of residue produced by individual sawmills, see Appendix B. * Estimation of Wood Residue Market Demand The demand for wood residue (bark and sawdust) products was estimated for four separate markets. The markets included 53 Table 7 Estimates of Solid Residue Material by Diameter Class for Mills with Circular Headsaws* Log Diameter Class (inches) 7.6-10.5 10.6-13.5 13.6-16.5 16.6-19.5 Weight in Tons with Confidence Limits at 9596 Probability Level Per 1000 Per 1000 Per 1000 bd. ft. bd. ft. bd. ft. International Doylegreen 1/4 in. Scribner lumber Loq-Scale Loq Scale tally Green 1.64 + 0.24 2.83 + 0.41 1.66 + 0.24 Oven-Dry 0.93 + 0.13 1.60 + 0.23 0.94 + 0.14 Green 1.36 + 0.13 1.95 + 0.18 1.40 + 0.13 « Oven-Dry 0.77 + 0.07 1.10 + 0.10 0.79 + 0.07 Green 1.04 + 0.13 1.41 + 0.17 0.99 + 0.12 Oven-Dry 0.59 + 0.07 0.80 + 0.10 0.56 ± 0.07 Green 0.92 + 0.16 1.09 + 0.19 0.93 + 0.16 Oven-Dry 0.52 +. 0.09 0.62 ± 0.11 0.53 + 0.09 Average for all four diameter classes --Sreen Weight (Tons) Per MBF Green Lumber Tally 1.24 + 0.17 ♦Adapted from King, W. W. 1952. Survey of Sawmill Residue in East Texas. Texas Forest Service. Technical Report No. 3 ( p. 50. 54 Table 8 Mean Values for the Various Residue Components Based on Per M . B . F . Green Lumber Tally* Estimates of Mean Values in Tons HARDWOOD PINE Residue Component Green Oven-Drv Green Oven-Dry Bark 0.38 0.26 0.58 0.41 Sawdust 0.85 0.42 1.04 0.60 Solid Material 1.18 0.58 1.24 0.70 Total 2.41 1.26 2.86 1.71 1.02 0.49 1.08 0.61 Chippable Material *Adapted from King, W. W. 1952. Survey of Sawmill Residue in East Texas. Texas Forest Service. Technical Report No. 3. 55 were: (1) dairy bedding, (2) orchard mulch, (3) nursery mulch and soil conditioner, and (4) packaged bark for the home landscaping and gardening market. The total market size for dairy cattle bedding was based on information from the M.S.U. Dairy Department that t a dairy cow requires approximately four cubic yards of bedding material each year, whether it be straw, sawdust, corncobs, i ground tree bark or something else. The number of animals in the study area was obtained from M.S.U. Extension Bulletin 582 (62). estimated number Figures include the (in thousands) of dairy cows in each county. At this point the author made four assumptions: 1. That every dairy cow uses approximately four cubic yards of some type bedding material. 2. That sawdust and/or bark bedding offered for sale b y a processing plant could obtain a 10 percent share of the total bedding market in all counties within a reasonable distance. 3. That bark and sawdust would split the 10 per­ cent market share in a 50-50 basis, each get­ ting 5 percent. 4. That demand is best calculated in units of one county to conform to available data. By multiplying the number of cows times 4 cubic yards (approximately one ton) the approximate tonnage of bedding material required by the market is determined. Ten percent of this total is then taken and divided 50-50 between the 56 requirement for sawdust and ground bark under the heading of dairy. The four markets within each county are shown in Table 9 and the quantities representing the estimated share obtainable shown in tons. t The demand for orchard mulch was estimated in much the same way as dairy cattle bedding. trees The number of fruit (apple, sour cherry, peach, sweet cherry and pear) in the study area was obtained from M.S.U. Extension Bulletin 582 (p. 58). county. The trees of each type are listed for each After talking with a M.S.U. horticulturist it was then assumed that each tree could be adequately mulched with ground bark or other material using an average of one cubic yard per tree. The second assumption was that, of the total mulch required for trees within each county, bark mulch supplied by a local processing plant could capture a market share of 10 percent. approximately one ton. Five cubic yards of ground bark weigh The tonnages, in Table 9 represent 10 percent of the total orchard mulch market. The demand for nursery mulch and soil conditioners was estimated using the 1969 Directory and B uyer1s Guide of the Michigan Association of Nurserymen as a data base. The Directory lists the nurseries in each Michigan county and the number of planted acres operated. The number of acres in 57 Table 9 Wood Residue Demand Estimated in Tons--A Summary of the Obtainable Market Share in Each County Sawdust____________ Bark County Allegan Barry Bay Berrien Branch Calhoun Cass Clinton Eaton Genesee Gratiot Hillsdale Huron Ingham Ionia Isabella Jackson Kalamazoo Kent Lapeer Lenawee Livingston Macomb Mecosta Midland Monroe Montcalm Muskegon Newaygo Oakland Oceana Ottawa Saginaw St. Clair St. Joseph Sanilac Shiawassee Tuscola Van Buren Washtenaw Wayne TOTAL Dairy Nursery Dairy Nursery Orchard 750 500 250 250 500 600 250 800 600 400 450 700 1, 200 750 800 700 600 250 750 1,000 600 650 450 400 100 200 650 250 450 250 200 700 600 800 350 1,950 650 700 300 700 50 23,100 2,150 160 ,1,120 5,500 60 1,190 530 510 140 1,420 190 150 170 1,420 140 110 320 2,650 1,830 830 220 450 2,620 40 590 3,120 770 1,660 60 3,140 160 9,770 1,890 2,910 960 50 140 170 6,880 580 3,100 59,870 750 500 250 250 500 600 250 800 600 400 450 700 1,200 750 800 700 600 250 750 1,000 600 650 450 400 100 200 650 250 450 250 200 700 600 800 350 1,950 650 700 300 700 50 23,100 2,150 160 1,120 5,500 60 1,190 530 510 140 1,420 190 150 170 1,420 140 110 320 2,650 1,830 830 220 450 2,620 40 590 3,120 770 1,660 60 3,140 160 9,770 1,890 2,910 960 50 140 170 6,880 580 3,100 59,870 14,080 45,680 1,840 1,100 2,160 1,040 1,340 13,560 820 1,820 740 2,840 2,860 1,760 26,820 4,440 21,260 1,100 145,260 Packac Bark 17 8 32 50 10 44 11 12 16 124 11 10 10 70 12 9 40 54 119 13 23 13 155 6 16 31 12 46 8 228 5 32 60 34 14 10 17 13 17 57 800 2,269 50 each county were tallied. After discussions with nursery operators in the Lansing area, the total mulch and soil conditioner market was estimated to be approximately 100 tons per acre of nursery operated. An assumption was then made based on the fact that bark and sawdust do make good mulching materials, that a processing plant for these materials could realistically capture a 10 percent share of the nursery markets located in nearby counties. . i The 10 percent market share amounts to an average of 10 tons per acre of nursery. The demand was estimated to be equally divided between bark and sawdust. The demand would, therefore, amount to a 5-ton per acre average for bark and the same for sawdust. The tonnage (5 percent bark and 5 percent sawdust) representing 10 percent of the total nursery demand per county is shown in Table 9. Due to the nature and use of packaged bark products sold to the rapidly growing and increasingly affluent lawn and garden market, it was arbitrarily decided by the author that the market was basically limited to households with incomes of $10,000 and over. The 1968 Michigan Statistical Abstract (17) was used as the source of information about the number of households in each county and their income category. 59 After talking with the managers of several Lansing area lawn and garden centers, it was apparent that the mulch and soil conditioner market is very large and absorbs great quantities of products. It was suggested during the course of conversation with the managers that a product of the type mentioned above could expect a minimum of sales in the first / year amounting to one ton for every 1000 households in the county having an income of $10,000 or more. Considering the estimated size of the market, the low price of the product, and the limited production of only one processing plant, this method of estimating the market share appears adequate until such a time when the question can be researched in detail. Phase II: Hardwood Sawmill Residue Survey Introduction Hardwood sawmills located within the 41 southern most counties of lower Michigan were selected as the population to be included in this marketing study. In Figure 3 the 41 counties in the study area were divided into four arbitrary quadrants to facilitate analysis. Each of the 150 sawmills within the area is listed in the 1968 Directory of Wood Using Plants in Michigan (7 ). Table 10 shows the number of sawmills within each quadrant; first by county and then by mill size M U M TALI Figure 3. Location of Wood Residue Study Area Showing 41 Counties Divided into Four Quandrants. ifeNMK.e»m IMIM VouL. ^ 1 _ _ IN TN IM _ 1 ' momt moa ! 1 , ! ' • •ouMMJeIoTc«rJ"*um Mum'a m .t n a k ; I ii i <• .!____ I....1_____ K O I C O I I .* H K M « ■ , M W .L . NAHM ““T|.**I MICHIGAN .1 . 1 . 1 .1.. r«eT.ur(I|1I flUMHlTMIMIC MUH|* a a n ila c U til.I I MONTCALM I CLINTON) .L fO A N INONAM | tA R R T I /mt CALMOMN I Of MIAN I _____ P c A l l lIT .M A IN N l i i a L ' JA C HIIM IM I ic h ir ' mAMTCMAM | W , M I I L L M A u |^ ( H « « ( ! “ 1M O N M t ' 61 Table 10 Selected Sawmill Study Area Divided Into Arbitrary Quadrants Showing The Stratification of Sawmills in Each by Mill Class (January 1969) » Class Size County Quadrant Total Sawmills* A B C D E F & LTF 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 2 1 0 2 2 0 2 0 1 1 0 2 3 5 2 0 3 3 0 1 0 1 3 2 0 0 3 9 14 13 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 0 0 0 1 1 0 3 2 0 2 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0 2 5 1 2 2 0 0 0 6 2 0 2 10 5 No . 1 6 6 0 5 5 8 7 2 Ionia Kent Mecosta* jMontcalm Muskegon Newaygo* Oceana* <0ttawa i i 'Subtotal 39 I 1 Quadrant No. 2 !l3ay* jciinton [Genesee 'Gratiot [Huron jlsabella* Lapeer Midland* 'Saginaw Sanilac Shiawassee Tuscola 1 1 0 2 6 3 4 3 5 3 0 9 — Subtotal 37 J 62 Table 10, coat. Class Size County . Total Sawmills ! F & | LTF I A B c » E 4 4 2 3 2 3 2 4 6 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 1 0 0 0 1 0 1 1 0 0 1 0 1 1 3 0 4 3 1 1 0 2 1 3 5 0 0 34 0 1 0 5 8 20 Quadrant No. 3, Hillsdale Ingham Jackson Lenawee Livingston Macomb Monroe Oakland St. Clair Washtenaw Wayne Subtotal » Quadrant No. 4 Allegan Barry Berrien Branch Calhoun Cass Baton Kalamazoo St. Joseph Van Buren Subtotal 5 9 2 2 4 5 5 2 1 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0. 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 2 0 1 1 0 0 0 0 2 1 0 0 0 1 0 0 0 4 7 1 0 4 3 2 2 1 5 40 0 0 2 5 4 29 29 31 # V GRAND TOTAL 150 2 1 7 80 *Means Region III, as described in the Michigan Conservation Commission Directory of Primary Wood Using Plants in Michigan (1966), has been expanded for purposes of this report to include the next tier of counties adjacent to the northern edge of Region III; thereby including six more counties in the study area and simultaneously reducing the size of Region II by this same amount 63 class. Stratification of mills is helpful in determining the general size and location of sawmills in southern Michigan. The Directory mentioned above indicates the general size of each sawmill b y dividing them into seven classes according to estimated annual lumber production. Detailed / information about this classification is shown in Table 4. To confirm the accuracy of lumber production figures i presented in the above mentioned directory and to better acquaint the author with the residue problem, a field survey was initiated in the summer of 196B. Later the same year a mail survey was developed that would provide cross-check data for the field survey findings and accumulate new data about the industry which was needed for the eventual industry simulation discussed in Phase III. Field Survey— Selection of Sample; In selecting a sample of sawmills to include in the field survey, it was arbitrarily decided to include all of the medium and large A, B, C, and D-class sawmills in the study area. small in size, Bven though four E-class sawmills, one from eafch quadrant, and eight F-class mills, two from each quadrant, were also included. No mills with production less than F-class were included. The name of each sawmill visited, (LTF) its size, the city and county in which it is located, are shown in Table 11. 64 Table 11 Selected Sawmills Included in 1968 Field Survey* Mill No. County Class Name City Quadrant No. 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Ionia Ionia Ionia Kent Kent Montcalm Montcalm Muskegon Muskegon Muskegon Newaygo Newaygo Oceana Oceana Ottawa D D , F C D D D E D D C C D D F Bauer Lumber Company Devereaux Sawmill, Inc. Hills' Crate Mill Schneider Lumber Co.,Inc. Teesdale Sawmill Custom Woodworking, Inc. Waldron's Sawmill Meyer's Sawmill Roger's Sawmill Wenting Bldg. & Mfg. Co. 0. J. Brigg Lumber Co. Dix Lumber Company Hesperia Crate Works Shelby Sawmill Anthony Elenbaas & Sons E D A D F F D C C D C D A D D D D DuRussell Lumber Co. St. Johns Hardwood Lbr. Fairhaven Ind. Wood Prod. Mobark Lumber Company Weber Brothers A. Inbody Sawmill D. T. Fowler Mfg. Co.Inc. Devereaux Brothers M.C. Richmond Lbr. Co. S & V Products Szepanski Sawmill, Inc. Grant Willsie Lumber Buskirk Lumber Company Gordon Ferguson McCarty Brothers, Inc. Cass River Lumber Co. H. Whittaker Hardwood Lbr Portland Pewamo Belding Sparta Cedar Springs Howard City Stanton Montague Muskegon Muskegon White Cloud Newaygo Hesperia Shelby Hudsonville Quadrant No. 2 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 Bay Clinton Huron Isabella Isabella Gratiot Lapeer Saginaw Saginaw Saginaw Saginaw Saginaw Sanilac Sanilac Sanilac Tuscola Tuscola Munger St. Johns Bay Port Winn Weidman Ithaca Lapeer Oakley St. Charles St. Charles St. Charles Freeland Sandusky Snover Ubly Tuscola .Cass City 65 Table 11, Cont. Mill No. County Class Name Citv Quadrant No. 3 33 34 35 36 37 38 39 40 41 Hillsdale Ingham Lenawee Lenawee Livingston Livingston Monroe Jackson Wayne F F D D D B D E D Cleveland Lumber Company Monroe Brothers Lumber R. Bradish Veneer & Hdwd. , Hawkins Lumber Company Dimension Hdwd. Lbr. Co. Thureson Lumber Company Lyle E. Farver & Son L o v e 's Sawmill Fair Lumber Company Hillsdale Webberville Adrian Rollin Milford Howell Ida Springport Livonia Quadrant No. 4 42 43 44 45 46 47 48 49 50 51 Allegan Barry St. Joseph Branch Branch Cass Cass Eaton Eaton Eaton D F F D D D C C D E Wayland Door Brothers Lbr. Co. Hastings Gordon Johncock Mill Shear's Sawmill Centreville Union City Superior Pallet, Inc. Union City Hardwood Co. Union City Cassopolis Marquette Lbr. Co, Inc. Richmond Lumber Mill, Inc.Dowagiac L. L. Johnson Lbr. Mfg.Co .Charlotte Sunfield Ind.Wood Prod.Inc.Sunfield Verhoeven Lumber Company Lansing *Mills are listed by quadrant to include: all A, B, C, and D class mills, (1) E class mill and (2) F class mills per quadrant. Quadrants were arbitrarily chosen. 66 Figure 4 shows the general location of the 51 sawmills included in the field survey. Each dot represents one sawmill. For the general location of all sawmills in the State of Michigan, see Figure 2. Mail Survey— Selection of the Sample: Reliable informa­ tion about the Michigan sawmill industry and their wood resir dues is almost non-existent. For purposes of the study it was, therefore, essential that current data of an original * nature be obtained. Hardwood sawmills to be included in the mail survey were selected in the following manner. First, the study area, as defined in Figure 3, was expanded to include the row of counties located along the northern edge of the basic study area in an effort to check for unusual sawmill operations adjacent to the arbitrarily selected study area. wood sawmills. Located within this area are 169 hard­ Each sawmill is listed in the 1968 Directory of Wood Using Plants in Michigan (7). It is possible that other mills may be located within this area, but since they are not listed in the Directory there was no opportunity for them to be included in the survey. « Second, it was arbitrarily decided to include 100 percent of the A, B, C, and D-class mills in the study along with 50 percent of the relatively small and numerous E, F, and iau mtali Figure 4. Distribution o£ 51 Hardwood Sawmills Included in Wood Residue Field Survey 1968 JteMMieawr 1 -^ l u e i i M O i ! ) ,I l._____ , j^ P % , L _ _ _£r»m 1 ANTRIM I ilnaT 1 • i \ I ---------1------- 1 ------1- - - - T- - r v ; « u l * * “ le * A * f O « N '0 , c 0 * * I A U O N A .mnzkmo .tnav.; i L . i . . J . . ' i I i !____ NOMDN. 'MIMA* I IOHO v£a»n| tAii z yeKttureto, 68 LTF-class mills. The 106 mills included in the mail survey are shown in Table 12. Table 12 Number of Sawmills Included in Mail Survey Mill Class A B C D E F LTF Total No. of Mills % Included in Sample 2 2 7 30 38 65 25 169 No. of Mills in Sample 2 2 7 30 20 33 12 106 100 100 100 100 50 50 50 The Mail Questionnaire; Once the sawmill sample was selected, a short mail questionnaire was developed A). (Appendix The original questionnaire was subjected to six revi­ sions through the combined efforts of both Forest Products and Marketing experts at Michigan State University in an effort to insure the best possible return. The overall effect of the revisions was to reduce the length of the questionnaire and make the questions as clear and concise as possible. Upon * completion of the initial revisions a pre-test was conducted among four sawmill operators. of a minor nature. Once again revisions were made The two-page questionnaire, along with a cover letter telling about the study and asking for the 69 individuals' cooperation was then printed. Nailing envelopes were addressed and self-addressed, stamped envelopes were enclosed along with the cover letter and the questionnaires. On February 1, 1969, all questionnaires were mailed. Fourteen days later the first follow-up was mailed; twenty-one days after the initial mailing the second and last follow-up was t mailed. The return was 86 percent. A computer data coding form was drawn up and as the returns came in the responses were recorded. These were later keypunched and analyzed by a simple computer program to determine the frequency of response for each question by mill class. The findings are discussed in Part IV. Phase III; Wood Residue Processing Simulation Introduction The method used to evaluate the potential success of a large scale bark and sawdust processing plant is heuristic simulation which is often used today by managers in business to aid in making improved management decisions. For purposes of the study the importance of the simulation should not be misunderstood. The development of the simulation is not the • primary objective of the study; rather the simulation is used only as a means to an end. The primary objective of the study is to prove or disprove the research hypotheses concerning the 70 feasibility of processing wood residues on a large scale for the agricultural and horticultural markets in southern Michigan. Within Phase III the factors directly related to the development of the grid location system, the inputs and operation of a proposed processing plant and the activities and cost centers influencing the actual simulation are dis/ cussed in detail. The Grid Itocation System Before prospective processing plant locations could be selected or supply and demand data used as simulation inputs, it was necessary to develop a means of locating given points on a scale map of Michigan with reasonable accuracy. A map location system was selected which used a transparent grid overlay. The scale of the base map and the grid overlay selected were compatible. A Michigan highway map having a scale of one-inch equals twelve miles was used as the base; making every one-quarter inch equivalent to three miles. It was then possible to use a kt x H inch grid overlay making each grid square equivalent to 3 x 3 miles. The construction of a uniform grid system was necessary, to facilitate the storage of location data in electronic com­ puter memory for later use in computing distance, quantity, time and cost data using the Control Data 6500 electronic computer. 71 Figure 5. Grid Location System for Locating Geographical Points. n I jMM4 i 1 i__ i — j «M vr i fiw J frfur r w U w -r7 j M M iir | U m m j i_4_._L.-l,— 1 I To prepare the input data for inclusion in the simula­ tion, coordinates of the following points were located: (1) All 150 operational hardwood sawmills (SUPPLY); (2) The geographic center point of 41 southern Michigan counties, Appendix C-4 (DEMAND); (3) Twelve prospective locations for processing plants. X-Y axes were .drawn on the grid overlay in such a way that most of the grid surface was in the upper right hand quadrant, -Figure 5. The center of the axes was located on the southwestern tip of southern lower Michigan and the X-axis lined up parallel with the Michigan-Indiana boundary line. To number.the grid squares, now enclosed on two sides by the X-Y axes, the zero point was located at the intersec­ tion of the X-Y axes. Using two position digits, the numbers were placed along the X-axis: 01, 02, 03, 04, 05...etc., placing the numbers under the respective columns of grids. The Y-axis was then numbered in a similar manner placing each number opposite a row of grid squares. All points (grid squares) can now be located by a four digit number; the first two digits being read along the X-axis, the second two along the Y-axis. (, In the study, distance from one point to another is always determined in relation to the X-Y axes, never across the shortest distance between the two points. To determine 73 distance in miles from one point to another, grid squares along the X-axis, first count the then along the Y-axis to the target point; add them together and multiply the total by three. For purposes of uniformity, each mill was assumed to be located in the center of the grid square that overlayed i the actual geographic location. In every case only one saw­ mill was located in any one grid square. i The Computer Simulation Program The computer simulation program (Appendix D) uses previous simulation technology to conduct one phase of the current research. The basic ideas involved and the originally written program are unique in their application of simulation to the sawmill industry. Preparation of the program was com­ pleted using the professional assistance of the Applications Programming Group of the Michigan State University Computer Laboratory. Operational Flow Chart The operational flow chart, Figure 6, shows the basic activities performed b y the program in a simplified manner. For specific details about the operations performed by the program, see Appendix D, General information describ­ ing the format of various inputs and controls are included on the next few pages of the research design. Figure 6. START o © • © * Basic Flow Chart Showing Major Activities Performed by the Simulation Program Set Costs, etc. in data — statements For each P. P. read Read in coordinate, minimum Read in No. ---a demand ----* annual production. * of P.P.# by county and the counties to to analyze include in supply Calculate distance from each mill to each P.P., check to see if the mill fits in the specified radius of the P.P. Accumulate bark along with variable costs and times required for load­ ing and transportation - Do X N = 1, No. P.P upply xhausted? Accumulate the Print out totals remaining mills to Print out totals obtain how much more — of bark ---9 bark is available accumulated in the P.P. area ♦P.P. = Processing Plant Sort the selected mills by distance to P.P. and list Read in supply by mill Print the demand to be included for this P.P. Demand atisfied? » © Print out totals accumulated for - * © bark Figure 6, cont. © * © . Accumulate sawdust along with variable costs and times required for load­ ing and transportation Accumulate the remain­ ing mills to obtain Print out totals of how much more sawdust sawdust accumulated -- » is available in the P.P. area ©-* Satisfy demand in the following order: Bark: 1. Packaged Sawdust: 1. 2. Dairy 2. 3. Nursery 4. Orchard Calculate gross sales and allocate all costs to the products sold on the basis of tons 0 0 - Print out totals accumulated for sawdust YES Dairy Nursery Calculate profits Print demand "H satisfied and all costs involved. Continue inue^ 76 Program Deck Order The order of the deck of cards used in the simulation Table 13* is included in the research design to better des­ cribe the data inputs and system control cards used by the program. It is not considered necessary to describe the individual control cards in detail; but, by having the deck order available, it will be easier to adapt the program to other computer installations provided the computer is compat i ible with the Control Data 6500. Format of Supply Inputs The actual sawmill supply data used in the simulation was determined earlier using the mail survey and the adopted wood residue conversion factors. For use in the simulation the information was put on data cards in the following format: Card Column 1-3 5-8 21-30 31-40 41-50 Description Sawmill code number Sawmill grid coordinate Estimated annual lumber pro­ duction in MBF Estimated tons of sawdust Estimated tons of bark 77 Table 13 Deck Order for Control Data 6500 (April 30, 1969) 1. PNC card 2. JOB card 3. PTN(L) 4. LGO 5.. 7 8 9 6. Program Deck 7. 7 8 9 8. Parameter Card No. 1 card (including all subroutines) card a. Parameter Card No. 2 b. Corresponding Parameter Card No. 3 10. County Cards with the Demand Data 11. Card with 99 in Column 1-2 12. Data Cards from Supplying Sawmills 13. Card with 999 in Columns 1-3 6 14. ^ 8 card 9 Note: A. B. Cards 8-13 are the data input cards Cards 1-7 and 14 are the system control cards *Item No. 9 (a. and b.): To get multiple runs these cards canbe repeated for up to six processing plants (P.P.). Both cards for P.P. No. 1 should be first, then both cards for P.P. No. 2, etc. 78 Format of Demand Data Demand data were estimated earlier and the information concerning each individual county demand center entered on data cards. The geographic center of each county is used as the demand center for each county. All units demanded are in tons. Card Column Description 1-2 County code number 3-6 County grid coordinate 7-16 Name of county 21-30 Tons of sawdust demand Dairy Industry 31-40 Tons of sawdust demand Nursery Industry 41-50 Tons of bark demand Dairy Industry 51-60 Tons of bark demand Nursery Industry 61-70 Tons of bark demand Orchard Industry 71-80 Tons of bark demand Package Market 79 Format of General Simulation Parameters The general parameters are included to not only detail the computer simulation inputs, but to illustrate that the multiple processing plant simulation is relatively flexible as shown by the notes pointing out positions of variable data entry. Three basic parameter cards are included as follow: Card>No. 1 : Card Column (one card for each computer run) Description 5 Number of processing plants— as many as six different ones in the same run 10 Insert (1) if it is desired that the supplying sawmills be included in only one processing plant's supply radius during a computer run; thus evaluating multiple processing plant locations. Leave the column blank if the mills can be included in more than one processing plant configuration. Note: If card column 10 is (1), the sawmills will be included in the first processing plant configuration where it can be used. 80 Card No. 2 : Card Column 1-5 6 10-13 14-15 16-25 26-27 *28-32 *33-37 *38-42 *43-47 *48-52 *53-57 (one card for each processing plant) Description Insert the letters "PARAM" to indicate this is a parameter card Processing plant number - must be in a sequence from 1-6. (These will be 1-N, where N is the number in column 5 of para­ meter card No. 1.) Coordinate of the processing plant Radius of the proposed supply circle around the processing plant— limits supply to only those sawmills inside the circle. Minimum annual lumber produc­ tion, in thousand board feet (MBF) for the smallest sawmill that the processing plant will include in its supply estimate potential. The number of counties (two digits) to be included in the demand portion of the configu­ ration. If all 41 counties in southern Michigan are to be included, insert the number 99. Conversion factor for dairy sawdust demand Conversion factor for nursery sawdust demand Conversion factor for dairy bark demand Conversion factor for nursery bark demand Conversion factor for orchard bark demand Conversion factor for packaged bark demand *The demand data are recorded assuming ten percent of the market, in which case these columns would have a (1) punched in the rightmost position. If the user wishes to assume a market share of twenty percent, he would insert a (2) or if he wished to assume only five percent, he would insert (.5). Within one computer run the conversion factors for all processing plants must be the same. 81 Card No. 3*: (one card for each processing plant) Card Column Description 1-3 6 9-10 11-12 13-14 ... 77-78 ' Insert the letters "CTY" to indicate this is a county demand card Processing plant number (1-6) (same as for card No. 2.) Demand county code #1 Demand county code #2 Demand county code #3 etc. Demand county code #35 Note: The counties in columns 9-78 do not have to be listed in numerical order, but the simulation does process and satisfy the demand for the first county listed, then proceeds to the next one. *If columns 26-27 of parameter card No. 2 is equal to 99, indicating all counties are to be included in the demand area, this card must be eliminated. Description of Simulation Cost Centers The cost center concept was used in this study. It was used to combine many general costs in the overall simula­ tion into fewer logical units containing all costs common to a specific activity. For purposes of this research study, the five following cost centers are utilized in the analysis: 1. 2. 3. 4. 5. Raw Material Cost Inbound Transportation Cost Fixed Variable Inventory Holding Cost Processing Plant Cost Fixed Variable Outbound Loading Cost 82 Considering that these costs are ail related to the purchasing, concentrating, processing and selling of bark and sawdust, their meaning is self-explanatory. The costs related to these cost centers will be included under the following section, the processing plant, because the simulation uses the processing plant as the hub of all activity. The cost centers used in this simulation are detailed below with the costs included under each activity. In all cases the figures used may not agree with estimates made by other people. This does not, however, cause great concern because the simulation is flexible enough to accept new cost £igures in place of old ones and the results observed in the following computer run. 1. Raw Material Cost: 2. Inbound Transportation Cost: Including: Sawdust Bark $2.00/ton $1.25/ton (two trucks) A. loading cost B. over-the-road C. unloading Fixed Cost Truck Depreciation Insurance License $3600.00/year 600.00/year 250.00/year Total $4450.00/year Variable Cost Truck Loaded mile charge Driver @ $3.00/hour $0.60 each truck $0.05 per minute for each driver 83 3. Inventory Holding Cost: Annual Raw Material Cost x 2% 4. Processing Plant A. Fixed Cost: Sawdust only All Bark to (Y) Bulk Bark after (Y) Package Bark after (Y) To be allocated B. $ 461.00/year 1,471.00/year 0.00/year 7,631.00/year 22,227.00/year Variable Cost: i Sawdust only All Bark to (Y) Bulk Bark after (Y) Packaged Bark after 5. (Y) $ 0.06/ton 0.18/ton 0.00/ton 13.14/ton Outbound Loading Cost: $ 0.10/ton 0.30/ton 0.25/ton Sawdust Bark (bulk) Packaged Bark Special Information Selling Price $ 4.00/ton 4.00/ton 63.65/ton Sawdust Bark (bulk) Bark (packaged) Capacity of packaging plant: Output/Day Output/Year (250 days) (one man inside plant) 600 bags or 8.8 tons 150,000 bags or 2200 tons 84 The Processing Plant The processing plant is designed to buy bark and saw­ dust from local hardwood sawmills, concentrate and process the material, and sell the finished goods f.o.b. plant. Supply for the processing plant is to be provided by hardwood sawmills located within a short radius of the processi ing plant and having a predefined minimum annual lumber produc­ tion. Raw material is to be purchased on a loaded basis; * therefore, it is essential that the sawmill have adequate loading equipment to be considered as a supplier. Raw material will not be purchased from the very small mills because they do not have the necessary material handling equipment or the capital to invest in equipment to load sawdust and bark into large trucks owned and operated by the processing plant. Sawdust will be purchased in truck load units of forty cubic yards weighing approximately ten tons; bark is somewhat lighter and will be purchased in units of forty cubic yards or approximately eight tons. Two trucks are used to haul raw material to the processing plant. The trucks loaded with raw material will be driven to 4 the processing plant where the loads are dumped into a forty cubic yard surge hopper. * Time to unload is minimal. The driver and truck are then ready to return for another load. 85 The raw material moves through the processing plant in one o£ three ways, shown in Figure 7. will be stored outside. All finished goods Sawdust is concentrated at the site and sold only in bulk units. Dark, on the other hand, is processed through a wood hog to reduce the particles to a uniform size, then conveyed to an outdoor storage pile. From t here it is either sold in bulk units like sawdust or sent through the packaging plant where it is put into colorful, i nicely printed, plastic bags for sale to consumers through grocery chains, nurseries, and garden supply stores. The processing plant, Figure 8, will employ one manager, one bookkeeper, two truck drivers, one front-loader driver to move raw material into the plant and load customer trucks, one machine operator to operate the bag machine, the sealing machine, load bags onto pallets, and drive the fork-lift to handle finished packaged goods. All finished products are to be picked up at the process­ ing plant, where plant personnel will load the outbound products on the customer's truck. Basic assumptions under which the processing plant i operation was designed are: 1. A bark and sawdust processing plant can operate all year (250 days) on the raw material supply accumulated by two company owned trucks working a basic forty hour week. 86 Figure 7. System o f Material Movement Through Processing Plant Inbound Raw Material in Trucks TTT Raw Material Input Hopper Wood Hog for Bark Processing I. Sawdust Storage Bin and Pile II. I 4. Bark Screen j' | Elevated Bin or X. Bark Storage Pile Input for Bark Packaging Plant | I Gravity Bag Machine | [ Baa Sealer IPallet X. J L_ Loading I 1 Y. Finished Good Storage Outdoor ------- 1 Outbound talk Sawdust (Loaded for Customer) ________ 31________ Outbound Packaged Bark (Loaded for Customer) Outbound Bulk Bark (Loaded for Customer) Keys Conveyors: 1,2,3,4,5 Front Loader: X Fork Lift: Y 87 Figure 8. Illustration of Proposed Processing Plant KEY 1. 2. 3. 4. 5. 6. Truck Dump Hopper # 1 Sawdust Bin Sawdust Pile Wood Hog Bark Pile 7. 8. 9. 10. 11. • 12. Front Loader Hopper # 2 Screen Bag Machine Bag Sealer Pallet 13. 14. 15. 16. 17. 18. Outside Storage Bag loading Dock Store Room Office Bulk Loading Bin Loading 88 2. Market demand will dictate the ratio of bark and sawdust that will be accumulated by the process­ ing plant during the year. 3. Packaged bark products return the most profit to the processing plant and, therefore, can justify the most processing expense. 9 4. The processing plant is able to adjust the product mix and markets served over the course of a year to reduce seasonal variation in demand. 5. All raw materials for the processing plant will be purchased in units of forty cubic yards— no partial loads. 6.„ Sawmills supplying raw materials to the processing plant will load the company trucks at the mill site. Most mills presently have the necessary handling equipment. 7. The demand for packaged units of bark will be given first priority in the product line; second priority will be given to bulk bark and third priority to bulk sawdust sales. 8. A purchase agreement is signed with each sawmill supplying new material to the processing plant. This will guarantee a source of supply at a given cost. 89 Specific assumptions that relate to the supply and demand phase of the processing plant simulation are outlined below: 1. All costs are allocated by tons of material processed. 2. Limits built into the computer program are: » 3. a. maximum of six processing plants b. maximum of 83 counties in demand configuration c. maximum of 170 sawmills in supply configuration Two dump bed trucks are used to concentrate a supply of raw material. If truck No. 2 is not utilized to seventy percent (70%) capacity, the wages of truck No. 1 driver are figured at overtime, and the cost of truck and driver No. 2 omitted. 4. Classes of costs cannot be added, but some can be left out. 5. The closest sawmill meeting the minimum production limitation is processed through the simulation first. 6. All bark in the supply area is brought into the processing plant first, then sawdust. 7. Pay rate of truck driver is 8. Driver works between: $3.00 per hour. 1870.5 and 1891.7 hours/year. i 90 9. Demand within the simulation is satisfied in the following order: A. 10. B. Bark: 1. Packaged 2. Dairy 3. Orchard 4. Nursery Sawdust: 1. Dairy 2. Nursery Raw Material Data Summary: Hardwood Bark Weight: (green) Per cubic foot Per cubic yard Cost: (loaded) Per cubic yard Per ton Per load Truck Capacity: Rated speed of truck : 15# 405# (5 cubic yards/ ton) $ 0.25 1.25 10.00 8 tons Sawdust 19# 513# 4 cubic ; ton) $ 0.50 2.00 20.00 10 tons 40 MPH The cost figures included under the general cost center categories mentioned earlier require additional background if they are to be evaluated for further use and up-dated. The next few pages briefly outline the costs and calculations used for this particular processing plant simulation that have not already been discussed. 1. Raw Material Cost - Completed 91 2. Inbound Transportation Costs: (two trucks) Fixed Costs: a. Two new trucks @ $10,000 each = $20,000 less $2,000 trade-in value ♦ 5 year depreciation life » $3,600 cost per year * 250. days = $14.40 daily cost. / b. Insurance ® $300 per year per truck = $600 * 250 days = $2.40 daily cost. i c. Truck license @ $125 per year per truck = $250 ♦ 250 days = $1.00 daily cost. Variable Costs: a. Truck (each) $0.60 to cover gas, per loaded mile charge oil, tires, and once a year overhaul of $250.00. b. Daily truck maintenance (each) $1.50 per working day to cover driver's maintenance time of thirty minutes. c. Driver wages (each) @ $0.05 per minute or $3.00 per hour; overtime calculated at a rate of $4.50. d. Truck loading time@ $0.05 per minute for driver who is idle; 20 minutes for sawdust and 30 minutes for bark. 92 e. Truck driving time @ $0.05 per minute for driver. f. Truck unloading time @ $0.05 per minute for driver who is idle; 10 minutes total for both bark and sawdust. 3. Inventory Holding Cost - Completed 0 4. Processing Costs: The plant and equipment needs are detailed in Table 14 and 15; first the fixed costs which are allocated according to the product receiving the greatest use each year, and then the variable costs which are allocated in dollars per ton of product produced. 5. Outbound Loading Costs a. Bulk Sawdust - using a sawdust bin, it requires two man-minutes per ton at $0.05 per minute; total cost is $0.10 per ton. b. Bulk Bark - using a front loader, it requires four man-minutes per ton at $0.05 per minute plus $0.05 per ton for loader gas, oil, tires, and maintenance; total cost is $0.25 per ton. i c. Packaged Bark - using a fork-lift truck, it requires five man-minutes per ton at $0.05 per minute plus $0.05 per ton for fork-lift gas, oil, tires and maintenance; total cost is $0.30 per ton. Table 14 Processing Plant Fixed Costs ' FIXED COSTS 1. Land: 10 acres @ $200/acre Total Cost New .Cost Allocation Breakdown SawAll Package To be Trade-in -dust Bark Bark AlloLife Value______ Only to Y After Y cated Years Dollars $/year $/year $/year $/year 40 $ 2,000 25 1,000 Plant Office (inside plant) 1,200 10 200 25 30 x 60 Package Bark Building 2,400 10 240 216 1,800 10 Interest on Investment @ 7% 2,436 0 0 6. Office Furniture 1,100 10 100 7. Interest of Office and Furniture @ 7% 2. 3. 4. 5. 8. 60 x 100 concrete' storage area @ 30$ per sq. ft. Manager’s Salary 75 180 917 707 25 75 160 40 120 12,000 3,000 9,000 186 626 Table 14, cont. Total FIXED COSTS Cost _______________________________ New 9. Bookkeeper's Salary Life Years 5,580 0 Cost Allocation Breakdown SawAll Package To be Trade-in dust Bark Bark AlloValue_Only to Y After Y cated Dollars $/year $/year $/year $/year 1,500 4,080 25 75 10. Property Tax 100 0 11. Insurance 300 0 0 75 225 12. Telephone 600 0 0 300 300 13. Utilities 1,052 0 0 452 600 14. Surge Hopper # 1, 40 cu. yd. capacity 1,200 10 200 100 400 10 0 40 Conveyor # 2 to sawdust bin and pile, 100 ft @ $20/ft. 2,000 10 0 200 80 cu. yd. capacity sawdust bin @ $5/cubic yard 400 10 0 40 6,000 10 600 540 200 10 0 20 15. 16. 17. 18. 19. Conveyor # 1 , $20/foot 0 20 ft @ Model 60 Mitts & Merrill Hood Hog Wood Hog Installation Table 14, cont.________________________________________________________________________________ _____ Coat Allocation Breakdown SawAll Package To be Total Trade-in dust Bark Bark AlloFIXED COSTS Cost Life Value Only_____ to Y After Y cated _____________________________________ New Years Dollars $/year $/year $/year $/year 20. Bldg. for Hog 10 x 10 f t . - 21. Conveyor #3 to bark pile 100 feet @ $20/foot 500 10 0 50 2,000 10 0 200 * 22. Front Loader with 2 cu. yd. bucket 8,000 10 800 720 23. Loader Driver @ $3/hr. 6,000 0 0 6,000 24. Surge Hopper #2, 3 cu. yd. capacity 200 10 20 18 1,000 10 0 100 25. Conveyor #4, 50 feet @ $20/ft. (yard into plant) 26. 6 x 6 ft. vibrating screen 300 10 0 30 27. Bag Machine 200 10 0 20 28. Electric Heat Type Sealing Machine 1,200 10 120 108 Conveyor #5 Portable (for finished product) 1,000 10 0 100 29. Table 14. c o n t . ______________________________________________________________________________ Cost Allocation Breakdown SawAll Package To be Total Trade-in dust Bark Bark AlloFIXED COSTS Cost Life Value Only to Y After Y cated ___________________ New Years Dollars $/year $/year $/vear $/year 30. 31. 32. 8 Electric Motors I Total Electrical Wiring Gas Engine Fork Lift TOTAL FIXED COSTS 800 5 0 20 20 80 40 1,200 10 0 15 J.5 60 30 4,000 10 400 360 $461 $1,491 $7,631 $22,227 Table 15 Processing Plant Variable Costs VARIABLE COSTS 1. Cost Allocation Breakdown All Saw­ Package To be Bark Bark dust Allo­ After Y to Y cated Only $/ton $/ton $/ton $/ton Processing Plant Operator one roan producing: 1.5 bags/roinute 50 minutes per hour 75 bags output/hour ' 2.50 0.10 2. Wood Hog Utilities @ $0.10/ton 3. Conveyor Motor Utilities @ $0.002/ton/motor 4. Electric Sealing Machine Utilities @ $0.05/ton 5. Wood Hog Maintenance; sharpening and replacements 6. Front Loader Gas and Maintenance to move bark into processing plant 0.20 7. 48 x 48 one-way block pallets 2.25 8. Multi-color Poly bags, printed @ $0.12 each S.04 9. Maintenance on conveyors and surge hoppers TOTAL VARIABLE COSTS 0.04 0.04 0.08 0.05 0.02 0.02 0.02 0.02 $0.06 $0.18 $13.14 98 To complete this section of cost data it is necessary to include the price of the finished good sold. The raw material purchase price is also included for sake of compari­ son with the selling price. Purchase Price (Per Yard) Selling Price Product (Per Yard) (Per Ton-} (Per Ton) $1.00 $4.00 Bulk $1.00 $4.00 Packaged $0.95 $63.65 $0.50 $2.00 Sawdust: $0.25' $1.25 Bark: $0.25 $1.25 Bark: Bulk Trial Configurations Three trial configurations selected from a total of four dozen computer runs are outlined in the research design. Each configuration is different from the other.two and serves to illustrate the supply and demand inputs of the computer simulation which were used to compute the findings in Part V. The supply sawmills for each configuration are shown first in Tables 16, 18, 20. Tables 17, 19, 21, summarize the counties included in the demand portion of the configuration. Lastly, a map showing the approximate location of the process­ ing plant and the surrounding counties constituting the demand area are shown in Figures 8, 9, 10. Information at the top of the supply table tells: 1. The plant number (1-6) of a multiple computer run. TABLE 16 Configuration No. 1— Listing of Supplying Mills for Processing Plant Coordinate 4635...Radius 21 Miles...Minimum Annual’Production 501 MBF Mill No. Coordinate Distance 139 138 140 137 141 75 4636 4735 4736 4732 4840 4238 3 3 6 12 21 21 Total Production (in thous.) Tons Sawdust 1800', 000 4000.000 11340,000 2400.000 1050.000 300,000 1872.00 4160.00 11793.60 2496.00 1092.00 312.00 6577.20 1392.00 21725.60 7969.20 Tons Bark 0.00 0.00 0.00 0.00 TABLE 17 Configuration No. 1— Listing of Counties in Demand for Processing Plant Coordinate 4635...No. of Counties County Isabella Clinton Shiawassee Genesee Oakland Midland Gratiot Saginaw Ingham Tuscola Macomb Bay Livingston Lapeer Coord. Distance 3544 3928 4728 5429 5021 4344 3836 4836 4320 5940 6722 4944 5120 6231 60 42 24 42 84 36 27 9 54 54 102 36 60 60 Totals Total Sawdust Total Bark 14 Tons of S a w d u s t _______ Tons'of Bark________ Orchard Nursery Dairy Nursery Dairy 700 800 650 400 250 100 450 600 750 700 450 250 650 1000 7750 22350.0 28623.0 110 510 140 1420 3140 590 190 1890 . 1420 170 2620 1120 450 830 14600 700 800 650 400 250 100 450 600 750 700 450 250 650 1000 7750 Percent of Supply Percent of Supply Pkg. Bark 110 510 140 1420 3140 590 190 1890 1420 170 2620 1120 450 830 0 0 0 * 1100 1760 0 0 0 0 0 1820 0 0 820 9 12 17 124 228 16 11 60 70 13 155 32 13 13 14600 5500 773 1.03 3.59 o o Figure 9. Outline of Counties Included in the Demand Phase of Configuration No. 1 ' LUCE [•eNOOLCMrr L _ _ _ _ _l _ I H tllM C ■ I f imiiH c n a h l k Jo i k L - .j j ! : j • Pll I ■I---- 1 | ALCOU . U N I I e Jm B.TIAv ' I I I L _ _ ; ___•___ !____I____L. ancon.'mum* ■10 -L.i- L L ' iCO«j LAKE • T M e * » w J ’e L » w j«U»»w| M M A I ! • ! ftL/' HURON TABLE 18 Configuration No. 2— Listing of Supplying Mills for Processing Plant Coordinate 3528.. .Radius 21 Miles.. .Minimum Annu'al Production Mill No. Distance 3529 3326 4028 3323 3223 3024 2931 3 12 15 21 24 27 27 Total Production (in thous.) 2640,000 1920,000 3000,000 3000,000 50,000 300,000 312,000 Tons Sawdust 2745.60 1996.80 3120.00 3120.00 52.00 312.00 324.50 11670.90 Tons Bark , 1531.20 1113.60 1740.00 1740.00 0.00 0.00 0.00 6124.80 102 93 91 69 73 71 95 94 Coordinate 299 MBF TABLE 19 Configuration No. 2— Listing of Counties in Demand for Processing Plant Coordinate 3528...No. of Counties County Montcalm Ionia Barry Kent Shiawassee Gratiot Clinton Eaton Ingham Oakland Wayne Coord. Distance 3036 3128 3720 2330 4728 3836 3928 3520 4320 6021 6212 39 12 48 42 36 33 12 24 48 96 129 Totals Total Sawdust Total Bark Tons of Sawdust Nursery Dairy Tons of Bark Orchard ' Dairy Nursery 650 800 500 750 650 450 800 600 750 250 • 50 770 140 160 1830 140 190 510 140 1420 3140 3100 650 800 500 750 650 450 800 600 750 250 50 6250 11540 6250 22350.0 28623.0 11 . 770 140 160 1830 140 190 510 140 1420 3140 3100 11540 Percent of Supply Percent of Supply Pkg. Bark 740 2160 0 13560 0 0 0 0 0 1760 0 12 12 8 119 17 11 12 16 70 228 800 18220 1305 . 1.03 3.59 104 Figure 10. Outline of Counties Included in the Demand Phase of Configuration No. 2. Ikmoolcrmt L if 'IpltTi ( t* J s ! , •LiVouL _ s ‘ v . j 1 __ 1 ANTRIM r r- - U / pH R IIIu « .T M « J ' r . I OICOtA , WXOM ■ I I llin^W O 'M * W mim UI koicm . I m ih a * I ItMO _ 1 » 1 i ' ! l_ . _ { }U L tIdI r o ieT ou r ( i n , , - J i a m V AM iue ~J i ! ! i I___>____I____I____ I__ <•«/ / m u m I m 'A tio •Hi c o . « , r . A m u ^ U W | ! ! ! / -"I UDILftC TABLE 20 Configuration No. 3— Listing of Supplying Mills for Processing Plant Coordinate 3520...Radius 27 Miles...Minimum Annual Production Mi: No Production Tons Tons Coordinate______ Distance______ (in thous.)________ Sawdust_______ Bark 3 15 18 18 18 21 21 24 24 24 24 24 27 27 26 27 27 27 30 36 36 3519 3323 3922 3817 3223 4119 2921 3815 3326 4219 2821 3116 3024 3529 4016 2818 2721 3016 2916 2825 3013 Total 3875,000 3000,000 750,000 -300,000 . 50,000 90,000 600,000 800,000 1920,000 750,000 750,000 300,000 300,000 2640,000 300,000 300,000 300,000 100,000 50,000 750,000 300,000 4030.00 3120.00 780.00 312.00 52.00 93.60 624.00 832.00 1996.80 780.00 780.00 312.00 312.00 2745.60 312.00 312.00 312.00 104.00 52.00 780.00 312.00 2247.50 1740.00 0.00 0.00 0.00 0.0 0.00 0.00 1113.60 0.00 0.00 0.00 0.00 1531.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 18954.00 6632.30 105 70 73 74 72 71 88 54 98 91 87 48 47 95 93 89 55 50 49 52 92 61 299 MBF TABLE 21 Configuration No. 3— Listing of Counties in Demand for Processing Plant Coordinate Countv Eaton Clinton Ionia Barry Kalamazoo Calhoun Jackson Ingham Shiawassee Kent Oakland Coord. Distance 3520 3928 3128 2720 2312 3312 4212 4320 4728 2330 6021 0 36 36 24 60 30 45 24 60 66 78 Totals Total Sawdust Total Bark 3520...No. of Counties Tons of Sawdust Nursery Dairv 11 Tonsi of Bark Dairv Nursery 'Orchard Pkg. Bark 600 800 800 500 250 600 600 750 650 750 250 140 510 140 160 2650 1190 320 1420 140 1830 3140 600 800 800 500 250 600 600 750 650 750 250 140 510 140 160 2650 1190 320 1420 140 1830 3140 0 0 2160 0 :.340 0 1040 0 0 13560 1760 16 12 12 8 54 44 40 70 17 11? 228 6550 11640 6550 11640 19860 620 18190.0 38670 Percent of Supply Percent of Supply .96 5.83 107 Figure 11. Outline of Counties Included in the Demand Phase of Configuration No. 3. iMNOMCROT ” L- - - AMUHM j 9 J \n / ' ' (fli____ •___ j.— , I \ !---- L - , - - 1 5 / M U “ “ l e t « W » ‘ 0 , e W ‘ I «W 0M . • h i ik S m . tbm , I » I L . r . - i - — l-___ y I i • / [_ I J_ ' S a *®*j l t < l " y 0 K W t i r e7 » Br • i i • j /kiUI gWMTM l ' l j ! I _ . _L _ J ilA w V A M M C < ri y w i*»i , ( SANILAC vm.> r s is U t. c l a ia ' ii i t « A M r n M tjt M AMTIM* I |CAM (•T.MMPMma KN "ItUMu JliNANK I 10B 2. The processing plant coordinate 3. The radius o£ the supply circle 4. The minimum annual lumber production of mills to be included as suppliers inside the circle. Now looking at the demand tables, two numbers are shown near the bottom after percent of supply. These two / ratios relate the amount of supply to the amount of demand within the configuration. The closer the number is to 1.00 i the better supply and demand are in balance. All other information is reasonably self explanatory. PART IV FINDINGS— RELATIVE TO PHASE II t Introduction The results of the field survey and mail survey are t discussed in the following paragraphs. Discussion of both field survey results and mail results are centered on the secondary objectives mentioned earlier and the results obtained from the mail survey presented in summary tables. Tables are presented in the text and in the appendix to facilitate the discussion. Field Survey Findings During the field survey it was observed that almost every sawmill site was the scene of vast accumulations of wood residues. Piles of slabs, edgings, end trim, sawdust, and bark were commonplace. In some cases slabs and edgings had been cut into firewood lengths and piled in equally large piles. 109 1X0 Wood Residue Accumulations and Characteriatics The quantities of wood residue accumulated around the typical hardwood sawmill in southern Michigan were photo­ graphed by the author. Figure 12 inadequately illustrates the quantities accumulated adjacent to the sawmill, but the general idea is clearly shown. t To be more specific about what the accumulations of wood residues are like, the characteristics can be seen in * the Figure 13 close-up photographs in comparison with engin­ eered wood chips. shown. In photograph (A) hardwood sawdust is The sawdust is relatively free of bark particles i because the sawmill uses a log debarker to remove the bark prior to breaking down the log on the head saw. Photograph (B) shows hardwood bark (American Beech) as it looks when removed from the log by a rosser-head type debarker. The bark is usually green and very wet and, depend­ ing on the species, the bark particles vary widely in both size and shape. It is not clearly evident in the photograph, but often as much as 25-50 percent wood fiber is attached to the bark. The percent of wood fiber attached to the bark % depends on several factors: 1. 2. 3. 4. 5. the the the the the species being experience of season of the uniformity of condition of debarked the machine operator year the log surface the cutter head 111 Figure 12. Vast Quantities of Wood Residue Found at Most Sawmill Sites End Trim C Hardwood Bark Edgings F Slabs cut into Firewood • ii ]'T"pT7]"Tri^rj;iTi'i,i'iTiJ'iT''r|'1'iJiri'r A Sawdust from Mill with Debarker B Hardwood Bark Removed by Debarker D Hogged Bark and Sawdust Mixed ■'U'i jrryrrj C Sawdust from Mill without Debarker Contains Bark Particles •In v te&KfifiC: ■rrnT?iTP|TPi'|lTl E Engineered Wood Chips (if•11!.:I'J'W'J!y- v;:nV i>jji\- Dry Planer Shavings Figure 13. Close-up Photographs Showing Characteristics of Fine Wood Residues Compared to Engineered Wood Chips 113 Photograph (C) shows the characteristics of sawdust coming from a sawmill without a log debarker. Rather than being a light uniform color, like the sawdust shown in (A), the sawdust contains a considerable amount of dark bark particles. The hogged bark and sawdust mix seen in photograph * (D) represents a combination of bark and sawdust. It can be seen that the bark has more uniform characteristics than in t (B). The uniformity is obtained by processing the bark through a mechanical wood hog. Mixing processed bark and sawdust together is often done by sawmills having customers who prefer a product with the basic characteristics of bark, but also want the additional bulk furnished by the sawdust. Photograph (E) shows the characteristics of uniform engineered wood chips prepared for the pulp and paper industry. The chips are carefully manufactured from coarse wood residue at many sawmills in Michigan. They are shown here for compari­ son with the fine wood residues. Dry planer shavings are shown in Photograph (F). Wood shavings have the characteristics of being dry, fluffy, and relatively dust free. Because of these good qualities, there are many profitable markets for shavings. 114 Current Methods of Residue Disposal The methods used to dispose of wood residues by differ­ ent sawmills were observed b y the author to be many in number# but essentially the same in that most methods involved an expense to the sawmill rather than a source of income. The most frequently used methods of fine wood residue disposal are shown in Figure 14. one basic fact: The photographs point out that currently there are many more unprofit­ able ways being used to dispose of fine wood residues than profitable ones. The greatest percent of wood residues at small sawmills is either burned in the open# given away, or dumped at the back of the mill site. operate teepee burners. Some of the larger mills Of the total mills in southern Michigan# only a very few mills make any effort to sell bark or sawdust. Current Markets for Wood Residues Finding out what the current available markets are for hardwood bark and sawdust in southern Michigan was an important part of the field survey. The data in Figure 15 served as the initial indicators for residue markets. Upon interviewing the sawmill operators that are currently engaged in marketing sawdust and bark products# it was found that population density and amount of agricultural activity within a county are in fact reasonably good indicators of the market 115 Figure 14. Current Methods of Fine Wood Residue Disposal A Conveying Residues to Teepee Burner B Open Burning C Dumping at Back of Mill Site D Conveying to Pile — ■ - — .. Selling Sawdust and Bark for Dairy Bedding, etc. Giving Residues Away 116 M OST HIGHLY POPULATED COUNTIES, 1965 est. TOTALS Id C o u n lk i Slate 4,601,440 1,200,000 •H aied on o tim atei prepared by the Center for H ealth Stalltllct, Michigan Department o f Public Health. May. 1967. COUNTIES WITH HIGHEST FARM PRODUCT SALES, 1964 (Each exceeding $15,000,000) as tot u 22 Countlei State $496,600,000 $767,198,000 'S ource; — l9 6 4 C e n tu io f Agriculture. Figure 15. The Two Major Factors Determining the Location of Current Wood Residue Markets— Population and Agriculture 117 size. The best general indicator for packaged bark markets » is population since the product is sold primarily to the home gardener. The amount of agricultural activity within a county was found to be a good general indicator for potential sales of bark and sawdust. Sawmill operators reported that, of the bark and sawdust they sold, the greatest quantity was t purchased by dairy farms for cattle bedding. Some bark was reported sold to the orchard industry for fruit tree mulch, i and some bark and sawdust had been sold to tree nurseries for mulch. To check out the reported markets mentioned by the sawmill operators, several visits were made to the purchasers of sawmill bark and sawdust. common uses for wood residues. Figure 16 shows five of the most In all cases the users were well pleased with the actual material even though they were still unsure about the validity of the "old wives' concerning the uses of sawdust and bark. tales" Most were in agree­ ment about the cost being too high and the general difficulty in obtaining the material. It was observed during the field survey that between 25 and 50 percent of all sawdust produced is being sold-, but very little bark. Because of the apparent difficulty in establishing markets for bark, special attention was directed to the problems encountered by sawmills that consider marketing 118 Figure 16. A Current Uses for Wood Residues Orchard Mulch C D Decorative Ground Cover B Nursery Mulch E Dairy Bedding Fruit Mulch 119 bark products. The findings are summarized as follow: 1. Great quantities of bark are available, usually in scattered locations. 2. The disposal problems surrounding bark are severe and becoming more severe. Burning is not an efficient or profitable disposal tech­ nique at this point; air pollution legislation is imminent. 3. Barks are not uniform. Each specie differs and there is a wide range of quality within species. 4. Each bark specieshas certain advantages and dis'advantages. Consideration must be given to color, structure, density, sorptive capacity, resistance to decomposition, and fiber characteristic. 5. Barks in general are considered a waste product or, at best, a low-value product and have little consumer appeal in their natural form. 6. When processed as a decorative mulch or soil conditioner, care must be given to uniformity of color, texture, and size. 7. Foreign matter such as wood fiber, slivers, and splinters have varying degrees of importance upon the finished product. 8. Low cost and effective substitutes for any known bark products are available in local markets at competitive price and volume levels. 9. Demand is limited because bark products are relatively unknown to the consumer. Wood Residue Market Competition During the field survey it was found that sawdust and bark used as dairy cattle bedding receives the greatest compe­ tition from straw, the traditional bedding material. But it 120 was pointed out that as more hybrid grains are grown the stalks are becoming shorter and shorter making less straw bedding available on the farm. Another current practice is for the dairy farmer to grow less grain crops and spend more time specializing in dairy management. Dairy managers were quick to point out that the competition for suitable bedding matert ial will continue to increase. It was found out during talks with nursery operators i that the use of sawdust as a nursery mulch has always received stiff competition from peat moss and straw. This has primar­ ily been because of the nitrogen depletion problems that arose if the user was not familiar with the use of sawdust (or bark) as a mulching material. Misinformation and old wives' tales about the toxic content of sawdust were also found to limit the use of wood residues as mulches in nurseries. Many requests were made for up-to-date information on how to use wood residue mulches. The use of bark as an orchard mulch was found to be limited, not by a competitive material, but by the fact that adequate information is not available on how to use the mulch * or resulting benefits. The orchard operators are reluctant to try wood residues as mulches without knowing more about the possible effects. 121 The utilization of packaged bark from various parts of the United States used for soil improvement, soil amend­ ment, growing mediums, and decorative covers was found to be a rapidly growing business. Packaged bark sold through lawn and garden centers was found to be in direct competition with traditional soil amendments such as peat moss, sludge, manures, * humus, sand, leaf mold, composted waste products, etc. A summary of the limiting factors for domestic hardwood bark * utilization, cited by operators of lawn and garden centers, was that today the customers are demanding a quality bark product free from wood particles, of uniform size and color, and sold at the same price they paid several years ago. It was reported that the most recent competition in the decorative ground cover market was coming from substitutes such as volcanic rock and ash, colored stones, and wood chips. Upon visiting Lansing and Grand Rapids area lawn and garden centers it was found that bark is usually sold in bags. Home owners and other small quantity users consume most of the hardwood bark mulch produced. Thus most producers, including one small Michigan producer, market their mulch in i colorful plastic bags holding two or three cubic feet, Figure 17. It was reported that retailers and consumers seem satis­ fied with these bags because they are easily handled, weather­ proof, and resistant to damage— especially from internal 122 Uak Ur o*ke» OwMMr’f prwiutH to m m k yeor gerden Tnr peWlwi m MUn MMk 2CU.FT. PftO C C tK D BARK MULCH Bag A - Back tMCVWW. WfrM M I Bag A - Front Bag B - Back Bag B - Front krfei— ^Sqj^S&S&SSSSffi aSESE&3SE3SS?«w MULCH /Or •Mika Figure 17. Colorful, Informative, Consumer Appealing, Plastic Bags Used Successfully as Bark Mulch Packages 1 123 moisture. Retailers reported greatly increased sales of bark products now that attractive, colorful, informative bags are used to merchandise the product as well as func­ tion as a container. Comparative retail prices for mulches, soil condi­ tioners and decorative lawn products observed during the t 1968 field survey are presented in Table 22. Competitive bark products from the west coast are presently selling on a t tight margin because of high freight rates. The current prices cannot be lowered more than 14 percent and still remain profitable. Zn bulk sales, sawmill operators having log debarkers reported selling hardwood bark mulch for $2 to $5 a ton f.o.b. plant. Sales are made to nurseries, orchards, dairy farmers, and other large users. landscapers, To date only a few bark sales in bulk quantities have been reported. Mail Survey Findings A total of 106 mail questionnaires were mailed to selected sawmill owners and operators. pleted questionnaires were returned. A total of 92 com­ This represents a return of 87 percent which is exceptional. In some cases the respondents did not answer all of the questions that were asked; therefore, the total number of responses on the following tables will seldom equal 92. 124 Table 22 Comparative Retail Prices of Mulchea, Soil Conditioners, and Decorative Lawn Products. in Lansing, Michigan* Price at Garden Item (Summer 1968)______________________Center or Nursery 1. 2 cu. ft. Hardwood Bark Mulch 2. 3. 4. 5. 6 7. 8. 9. 10. 11. 12. 13. 14. . 15. 16. 17. 18. 1.77 3 cu. ft. Pine Bark Mulch 2.29 4 cu. ft. Pine Bark Mulch, fortified 3.98 3 cu. ft. Vita-B^rk Ground Cover 3.98 5 cu. ft. Shredded Hardwood Bark (45#) 3.98 Baled Wheat Straw (35-45#) 1.25 50# Ground Corn Cobs 2.40 1 cu.,ft. Sphagnum Peat Moss 0.89 3.97 4 cu. ft. Canadian Peat Moss 2.95 50# Buckwheat Hull Mulch 25# Cocoa Shell Mulch 1.99 50# Dairy Compost 1.59 25# Dairy Compost 0.97 1 cu. yd. engineered wood chips (local del)10.00 (local delivery) 4 cu. ft. Vermiculite (18#) 2.99 50# White Decorative Stone (Marble) 1.99 50# Black Decorative Stone (Obsidion) 2.99 50# Crushed Vitrified Tile 1.65 ♦Prices listed are extremely variable, depending upon freight charges, sales outlets, local prices of competing goods, and other factors. None of the questionnaires returned from the six counties outside the basic study area represented any circum­ stances not common to the basic study area other than the fact that mills further north process softwood species in part or in total. The findings of the mail survey are included in tables and figures on the following pages along with brief narrative 125 comments. For sake of order, the tables and summary figures are presented in the same order sequence as the questions listed on the questionnaire General question (Appendix A). (A) asked the sawmill owner or operator to check the appropriate box in front of each piece of equip­ ment used around the sawmill. Table 23 presents the responses. / The important point to note is the number of log debarkers and the size mill operating them. Table 23 Equipment Owned by Hardwood Sawmills Wood Hoq Tractor Fork lift Class No. of Responses Debarker Chipper A B C 0 E F LTF 2 2 6 24 17 28 9 2 2 4 12 4 2 0 2 1 4 11 1 2 0 1 1 2 1 0 0 0 2 0 5 5 10 13 5 2 2 6 23 14 24 6 Total 88 26 21 5 40 77 Source: Teepee burner . 1 2 3 3 0 0 0 9 Mail Questionnaire - January 1969 General question (B) was self-explanatory in asking for the approximate daily lumber production. The responses to the, question were recorded in Table 24 showing the sawmill produc­ tion by sawmill class and daily production of lumber which is helpful in comparing the actual capacity of mills within the general classes. Table 24 Estimated Dally Hardwood Lumber Production by Individual Sawmills Class No. of Responses 1 1 1 2 3 4 5 6 7 8 9 1 | A 2 B 2 C 6 D 24 E 17 P 28 4 4 9 2 2 88 7 6 T lousand Boar d Peet 10 11 12 15 18 25 30 45 1 55 1 i LTP Total 1 1 1 1 3 1 1 3 6 8 5 1 2 9 2 2 8 4 13 1 2 3 5 1 2 1 6 4 1 2 1 1 1 2 1 1 1 3 3 9 1 8 1 ■ 6 i 5 1 . 1 ; 1 : 1 1 • Source: Mail Questionnaire - January 1969 127 Average daily lumber production by sawmill class is presented in Table 25. The figures serve as indicators of sawmill size relative to the other mills in the same class and point out where the greatest volume is produced. Table 25 Daily Lumber Production by Sawmill Class Average Daily Production (MBF) Class % of Total Production Contributed b y Each Sawmill Class A 32.5 37 B 16.5 18 C . 19.7 21 D 8.8 10 E 7.5 8 F 2.9 3 LTF 2.9 3 90.8 100 Total General qut tion (C) asked the sawmill owner to check the methods of advertising used to promote the sale of wood residues. A summary of responses indicated less than 5 per­ cent of all sawmills in the study area advertise any barb, sawdust, slabs, firewood, bedding or mulch. Figure 18 shows the percentage of sawmills advertising in some manner at this time. Figure 18. Percentage of Sawmills Advertising Residue Products for Sale. 95% No Advertising 5% Advertise General question (D) asked sawmill owners to place a check (v') in front of the approximate quantity of wood resi­ due they produced each year. The responses were determined to be invalid and are not presented in the study. General question (E) asked sawmill operators to indi­ cate how costly they consider wood residue removal from the sawmill site. The responses are presented in Figure 19. 129 Figure 19. Sawmill Operators' Estimate of Wood Residue Removal Cost. 50 to <1) to c o a m 40 U-l o I ltll 30 • • • • • • • * * » • • • « * « * • * § iv.^v.v.vv.v V .V > • « + « » • • * * 20 ! • # * « • • • • • » « « • * • • • • • » « * • * • • • • • w ®SS Jv X v . vav. 10 » * + « • * » • • • • * « * • • • • • * • « « • • • • • » • • » • • • • • « ) • • • • • • • • « k&S¥SSW:i Very Costly Costly * • » » • • • * * * Not Costly Residue Removal Cost 130 In answer to general question (F) sawmill owners each reported several methods of residue disposal. Figures 20 and 21 present a summary of current wood residue disposal methods for sawdust and bark. Because each mill made multiple responses to the question, only general trends can be concluded. Figure 20. Summary of Reported Methods of Bark Disposal Give Away Sell 19% 14% Fuel 7% Dump at Back of Mill S.ite 8% 36% 16% Teepee burner Burn in open’ Figure 21. Summary of Reported Methods of Sawdust Disposal Burn in open Teepee burner 133 In general question (G) sawmill owners were asked to insert the number of operating days they worked last year. Table 26 presents the responses of 83 mills. By multiplying the number of operating days given in (G) times the daily lumber production figure given in (B) an approximate annual lumber production figure is found for each individual sawmill. t The results of these calculations are marked with (*) in Appendix B. Table 26 Number of Sawmill Operating Days Per Year Less No. of wt. Class Responses than 101- 151- 201- 226- 251* 276- 301- Over Avg. 100 150 200 225 250 275 300 325 325 A 2 0 0 0 0 1 1 0 0 0 250 B 2 0 0 1 1 0 0 0 0 0 200 C 6 0 0 2 0 1 2 1 0 0 234 D 22 0 1 2 1 10 1 6 0 1 246 E 15 1 2 3 2 3 1 3 0 0 209 F 28 : 5 5 8 2 5 0 2 1 0 201 LTF 8 4 2 0 0 2 0 0 0 0 126 83 10 10 16 6 22 5 12 1 1 Total Source: Mail Questionnaire - January 1969 X34 General question (H) asked the approximate number of man-hours spent each week in removing wood residues from the mill site. The responses shown in Table 27 were used to estimate the expense involved in residue removal. Of the total responses, 70 percent estimated less than 10 hours were required each week to remove residue. 9 Table 27 Time Required to Remove Res idue from Sawmill Site ------ No. of Class Responses 1-5 6-10 A 1 B 1 C 4 1 1 D 16 8 3 B 15 1 6 F 25 16 6 7 6 1 69 33 17 LTF Total Man-Hours Per Week 16-20 21-25 26-30 11-15 1 1 Source: 1 1 1 2 2 2 3 3 1 2 3 7 1 8 Mail Questionnaire - January 1969 Only two of the remaining questions on the question­ naire are important to the Btudy. The others were designed to lead the respondent into the "target questions." Table 28 presents a summary of estimated sawdust sales during 1968 as a percent of the amount produced by individual 135 sawmills. Only positive responses from mills that did sell some sawdust last year were recorded in the Table. Table 28 Summary of Sawdust Sold Last Year No. of Class Responses 110 A 1 B 1 C 3 D 13 E 7 P 7 LTF 3 20 2130 (1966) Percent So!Ld Annually 31- 41- 51- 61- 7170 90 40 50 60 91100 1 * Total 1 1 . 35 1 1 1 3 1 1 1 1 2 3 2 6 1 3 1 5 Source: 2 3 2 5 2 3 3 14 Mail Questionnaire - January 1969 t’able 29 summarizes estimated bark sales for calendar year 1968 in terms of percent produced. The quantity of bark accumulated by sawmills in southern lower Michigan was determined as a direct result of information included in the mail questionnaire. Table 30 summarizes the quantities of bark available by mill class. The data are presented here as a major finding which can be used b y the industry in resource evaluation and market planning. 136 Table 29 Summary of Bark Sold Last Year* (196B) Percent Sold Annually 31-40 41-50 j 91-100 11-20 21-30 Class Mo. of Responses 1-10 A 2 2 B 2 2 C 2 1 1 D 8 5 1 E 2 F 0 LTF 0 1 1 \ t 1 1 1 1 i Total 16 2 12 2 ' * By sawmills with log debarkers. Source: Mail Questionnaire - Jaunary 1969 137 Table 30 Hardwood Bark Accumulated by Sawmills Operating Log Debarkers in Southern Lower Michigan* (1968) Mill Class A B C D E F Total Estimated Board Feet Lumber Production in 1968 2,500.0 12,500.0 6,250.0 2,880.0 3,875.0 4,420.0 11,340.0 2,500.0 2,000.0 3,525.0 3,000.0 3,000.0 * 1,920.0 2,640.0 2,760.0 2,000.0 2,500.0 2,000.0 2,400.0 2,000.0 3,600.0 2,400.0 3.360.0 300.0 300.0 1,380.0 Quantity of Bark at Each Mill (Green Tons) 1,450.0 -7.250.0 3,625.0 1,670.4 2,247.5 2,563.6 6,577.2 1,450.0 1,160.0 2,044.5 1,740.0 1,740.0 1,113.6 1,531.2 1,600.8 1,160.0 1,450.0 1,160.0 1,392.0 1,160.0 2,088.0 1,392.0 1,948.8 174.0 174.0 800.4 50,663.0 Total Quantity of Bark by Mill Class (Green Tons) 8,700.0 3,625.0 13,058.7 22,181.1 1,948.8 1,148.4 50,663.0 *Annual green lumber production data for 1968 determined by mail questionnaire, January 1969. MBF then multiplied by conversion factor (0.58) to determine bark quantity produced in green tons. 138 Table 31 concludes the findings in Part IV. summarizes the residue production for the study. The Table These tables are then multiplied times estimated values for each type of residue to give the cumulative annual gross value of fine and coarse residues. These figures could also be considered the amount of value added to the forest products industry if they were sold. In summary, Part IV presented the findings of the field 4 and mail survey. Almost in all cases the data presented indi­ cated gross waste of wood residue materials. Most of the current methods used to dispose of wood residues do not yield a return to the sawmill. For the most part little advertis­ ing is done to promote the sale of wood residues. Question­ naire responses indicated that the sawmill operator does not consider wood residues to be an unmanageable problem or the cost excessive. Personal interviews with most sawmill oper­ ators revealed that few alternatives to current wood residue methods have been considered. The sawmill operators' concern is sawing lumber, whether or not the residues are valuable or what happens to them does not seem to interest them to any measureable degree. The findings relative to Phase III are discussed in Part V. Table 31 Sawmill Residue Production Summary for Southern Half of Lower Michigan^ (1968) Fine Res: Ldue Accumulated Annually Estimated Bark Total Sawdust Saw­ No. (green (green mill of Lumber Sawn Class*3 Mills Annually0 tons) tons) (000) ■j Coarse Residue Cumulative Annual Accumulated Annua]-ly Gross Value W/0 Bark W/Bark of Fine (green (green Residue^ (dollars) tons) tons) Cumulative Annual Gross Value of Coarse Residuee (dollars) A 2 15,000.00 15,600.00 8,700.00 42,075.00 18,600.00 0.00 93,000.00 B 1 6,250.00 6,500.00 3,625.00 17,531.25 7,750.00 0.00 38,750.00 C 7 32,265.00 33,555.60 13,058.70 83,434.57 27,918.60 17,745.00 175,083.00 D 29 63,995.00 66,554.80 22,182.80 160,837.22 47,423.80 46,865.00 330,849.00 E 31 32,095.00 33,378.80 1,948.80 69,193.60 4,166.40 52,297.70 125,427.40 F 56 22,037.00 22,918.50 1,148.40 47,272.50 2,455.20 36,503.70 85,283.40 LTF 24 2,125.50 2,210.50 0.00 .4,421.00 0.00 3,868.50 7,737.00 Total 150 173,767.50 180,718.20 50,663.00 424,765.15 108,314.00 157,279.90 856,129.80 a41 counties in southern half of lower peninsula of Michigan ^Defined in Directory of Primary Wood Using Plants in Michigan, 1968, Michigan Department of Conservation-Forestry Division cBoard feet of green lumber sawn; reported on January, 1969, mail questionnaire ^Estimated at a value of $2 per green ton for sawdust and $1.25 per ton for bark loaded on e customer's truck at the sawmill Estimated at a .value of $5 per green ton for debarked slabs and edgings and $2 per ton for coarse residue with bark attached. , PART V FINDINGS— RELATIVE TO PHASE III / Introduction The results of the experiments using heuristic simula­ tion are discussed in the following paragraphs. The discus­ sion is centered on the hypotheses presented earlier and presents the results from three of the most typical process­ ing configurations. Tables are presented in the text and in the appendix to facilitate the discussion. Findings Relative to Hypothesis I The first hypothesis, agricultural and horticultural use of sawdust and bark in bulk units dictates a raw material positioned processing unit, was investigated using the specially designed heuristic simulation program to calculate all of the costs. The same configuration was processed through the computer six times using a different supply radius. Various supply radii used were 9, 15, 21, 27, 33, and 39 miles. The distance between the processing plant and the sawmills included 140 141 within the supply radius was found to be critical for highbulk and low-value products like sawdust and bark. Table 32 illustrates that it is not profitable to transport these materials to a processing plant except over very short distances. The inbound transportation cost differ­ ence between 15 miles and 39 miles is more than double and t the increase in tons of raw material increased from 27,389 to only 30,567. The per unit cost of bulk sawdust and bark increases very rapidly as distance between raw material loca­ tion and processing plant are increased. cost is inbound transportation. factor to a minimum, The most critical To hold this most important it was found necessary to locate as near as possible to the raw material supply. Findings Relative to Hypothesis II The second hypothesis, as scale of operations increase, unit costs will decrease up to an optimum size, utilizes the data from three separate configurations. configurations, Tables Even though the 33, 34, and 35, are not located in the same geographic part of the state, the data included from all three will be similar in the general trends because the least cost per unit radius in each configuration was selected. Detailed supply data developed by the computer for each con­ figuration is shown in Appendix E and the individual costs are summarized in Table 36. Table 32. The Effect of Radius Change on Costs in a. Processing Plant Configuration Simulation Supply Radius (Miles) 9 15 21 27 33 39 50,950 51,680 27,543 42,195 Raw Material Cost 43,860 48,780 48,780 48,780 Total Inbound Transportation 17,178 20,761 20,761 20,761 % 45,844 45,844 45,844 45,844 45,844 45,844 Total Variable Cost 12,625 13,286 13,286 13,286 13,773 14,379 877 975 975 975 1,019 1,033 123,849 133,619 133,619 133,619 Profit 35,762 37,938 37,938 37,938 34,981 24,505 Total Tons Processed 24,403 27,389 27,389 27,389 29,129 30,567 5.08 4.88 4.88 4.88 • 4.93 5.24 Inventory Holding Cost Total Cost Average Unit Cost 143,536 160,162 142 Total Fixed Cost Table 33 Summary of Trial Configuration N o . _1 (Coordinate 4635— Radius 21 miles) * Packaged Bark Lawn and Garden Sales Tons County Isabella Clinton Shiawassee Genesee Oakland Midland Gratiot Saginaw Ingham Tuscola Macomb Bay Livingston Lapeer 9 12 17 124 228 16 11 60 70 13 155 32 13 13 572.85 763.80 1082.05 7892.60 14512.20 1018.40 700.15 3819.00 4455.50 827.45 9865.75 2036.80 827.45 827.45 Bulk Bark* Dairy Tons Sales 700 800 650 400 250 100 450 600 750 700 450 250 650 446 2800.00 3200.00 2600.00 1600.00 1000.00 400.00 1800.00 2400.00 3000.00 2800.00 1800.00 1000.00 2600.00 1784.80 Sawdust ! Dairy Nursery i Tons Sales Tons Sales 1 : 1 i [ j I j j i : = i ; i 700 800 650 400 250 100 450 600 750 700 450 250 650 1000 : Total pemand 'Satisfied 773 — - 49201-45 7196 28784.80 ; 7750 2800.00 3200.00 2600.00 1600.00 1000.00 400.00 1800.00 2400.00 3000.00 2800.00 1800.00 1000.00 2600.00 4000.00 110 510 140 1420, 3140 590 190 1890 1420 170 2620 1120 344 0 440.00 2040.00 560.00 5680.00 12560.00 2360.00 760.00 7650.00 5680.00 680.00 10480.00 4480.00 1374.40 0.00 • 31000.00 13664 54654.40 Gross Sales 6612.85 9203.80 6842.05 16772.60 29072.20 4178.40 5060.15 16179.00 16135.50 7107.45 23945.75 8516.80 7401.85 6612.25 ! j 163640.65 1 I T I ♦No bulk bark was available to satisfy demand of nurseries or orchards, dairy consumed total. Table 33, cont.______________________________________________________________________________ Packaged Bulk Sawdust Gross Costs_________________ Bark__________ Bark________ Dairy_________ Nursery_________ Sales 8993.90 2749.64 3803.44 1089.86 7642.94 179.88 3974.19 0.00 1328.32 0.00 0.00 5302.50 0.00 1295.32 0.00 0.00 1295.32 1799.05 15490.16 1930.48 3074.87 1173.73 6179.08 309.80 4280.03 166.84 0.00 0.00 0.00 4446.87 465.00 0.00 0.00 0.00 465.00 775.00 27309.84 3403.52 5421.13 2069.34 10893.99 546.20 7545.89 294.16 0.00 ' 0.00 0.00 7840.04 819.82 0.00 0.00 0.00 819.82 1366.36 52760.00 8379.00 12708.00 • 4450.00 25537.00 1055.20 16227.00 461.00 1471.00 7631.00 0.00 25790.00 1284.82 1434.46 10157.22 0.00 12876.49 4172.31 Total Costs 20535.26 25213.58 27665.91 48776.25 122191.00 Profit (Loss) 28666.19 3571.22 3334.09 5878.15 41449.65 144 866.10 . Raw Material 295.36 Driver 408.56 Variable Truck Fixed Truck 117.07 Total Inbound Trans. 820.99 19.32 Inventory Holding 426.90 Allocated Fixed Cost Sawdust Fixed Cost 0.00 All Bark Fixed Cost 142.68 Pack. Bark Fixed Cost 7631.00 Bulk Bark Fixed Cost 0.00 Total Fixed Costs 8200.58 Sawdust Variable Cost 0.00 All Bark Variable Cost 139.14 Pack. Bark Var. Cost 10157.22 Bulk Bark Var. Cost 0.00 10296.36 Total Variable Cost Loading Costs 231.90 Table 34 Summary of Trial Configuration No. _2 (Coordinate 3528— Radius 21 miles) ----------------------------------------------------------- . Bulk Bark* Dairy Tons Sales Packaged Bark Lawn and Tons Sales County tontcalm ionia Jarry Cent Jhiawassee Gratiot Clinton Baton Jngham Oakland Wayne 12 12 8 119 17 11 12 16 70 228 800 763.80 763.80 509.20 7574.35 1082.05 700.15 763.80 1018.40 4455.50 14512.20 50920.00 * 650 800 500 750 650 450 800 220 0 0 o 2600.00 3200.00 2000.00 3000.00 2600.00 1800.00 3200.00 879.20 0.00 0.00 0.00 650 800 500 750 650 450 800 600 750 250 ! 50 2600.00 3200.00 2000.00 3000.00 2600.00 1800.00 3200.00 2400.00 3000.00 1000.00 200.00 4820 19279.20 ! 6250 25000.00 Nursery ' Tons Sales 770 140 160 1830 140 ' 190 510 140 1420 69 0 ! 3080.00 560.00 640.00 7320.00 560.00 760.00 2040.00 560.00 5680.00 275.60 0.00 > Gross Sales 9043.80 7723.80 5149.20 20894.35 6842.05 5060.15 9203.80 4857.60 13135.50 15787.80 51120.00 i i • Total Demand Satisfied Dairy Tons Sales r ■■;■ ■■■ ■ 1 Sawdust 1305 83063.25 » i » I 5369 21475.60 148818.05 ■ i ------------------------------- *No bulk bark was available to satisfy demand of nurseries or orchards, dairy consumed total. Table 34, cont. Costs Packaged Bark Raw Material 1627.84 646.86 Driver 1285.18 Variable Truck 327.29 Fixed Truck Total Outbound Trans. 2259.33 Inventory Holding 32.56 1193.45 Allocated Fixed Cost Sawdust Fixed Cost 0.00 All Bark Fixed Cost 313.42 Packaged Bark Fixed Cost 7631.00 Bulk Bark Fixed Cost 0.00 Total Fixed Costs 9137.87 0.00 Sawdust Variable Cost All Bark Variable Cost 234.90 Pack. Bark Var. Cost 17147.70 Bulk Bark Variable Cost 0.00 Total Variable Costs 17382.60 Loading Costs 391.50 Bulk Bark Sawdust Nursery Dairv 6012.16 2389.09 4746.62 1208.77 8344.47 120.24 4407.81 0.00 1157.58 0.00 0.00 5565.39 0.00 867.56 0.00 0.00 867.56 1204.95 12479.67 2238.51 5210.15 1567.46 9016.12 249.59 5715.76 247.98 0.00 0.00 0.00 5963.74 375.00 0.00 0.00 0.00 375.00 625.00 * Gross Sales 10720.33 1922.94 4475.65 1346.48 7745.07 214.41 4909.98 213.02 0.00 '0.00 0.00 5123.00 322.13 0.00 0.00 0.00 322.13 536.89 30840.00 7197.40 15717.60 4450.00 27365.00 616.80 16227.00 461.00 1471.00 7631.00 0.00 25790.00 697.13 1102.46 17147.70 0.00 18947.30 2758.34 Total Costs 30831.71 22114.78 28709.13 24661.83 106317.44 Profit (Loss) 52231.54 -2835.58 -3709.13 -3186.23 42500.61 Table 35 Summary of Trial Configuration N o , 3 (Coordinate 3520— Radius 27 miles) ^ ' Packaged Bark Lawn and Garden Tons Sales • County Eaton Clinton Ionia Barry Kalamazoo Calhoun Jackson Ingham Shiawassee Kent [Oakland [Total {Demand [Satisfied a Bulk Bark * Dairy Tons Sales Sawdust Dairy Tons Sales Nursery Tons Sales Gross Sales 16 12 12 8 54 44 40 70 17 119 228 1018.40 763.80 763.80 509.20 3437.10 2800.60 2546.00 4455.50 1082.05 7574.35 14512.20 600 800 800 500 250 600 600 750 650 462 0 2400.00 3200.00 3200.00 2000.00 1000.00 2400.00 2400.00 3000.00 2600.00 1849.20 0.00 600 800 800 500 250 600 600 750 650 750 250 2400.00 3200.00 3200.00 2000.00 1000.00 2400.00 2400.00 3000.00 2600.00 3000.00 1000.00 140 510 140 160 2650 1190 320 1420 140 1830 3140 560.00 2040.00 560.00 640.00 10600.00 4760.00 1280.00 5680.00 560.00 7320.00 12560.00 6376.40 9203.80 7723.80 5149.20 16037.10 12360.60 8626.00 16135.50 6842.05 19743.55 28072.20 620 39463.00 6012 24049.20 6550 26200.00 11640 46560.00 136272.20 1 i i '— ■ — - ■«' 1 ------------ i *No bulk bark was available to satisfy demand of nurseries or orchards, dairy consumed total. Table 35, cont. Costs Packaged Bark Bulk Bark Sawdust Dairy Nursery Gross Sales 773.10 331.13 706.05 111.15 1148.33 15.46 405.31 0.00 137.51 7631.00 0.00 8173.82 0.00 111.60 8146.80 0.00 8258.40 186.00 7496.90 3211.07 6846.75 1077.85 11135.67 149.94 3930.40 0.00 1333.49 0.00 0.00 5263.89 0.00 1082.21 0.00 0.00 1082.21 1503.07 13071.19 2783.68 7213.35 1174.25 ' 11171.28 261.42 4281.91 166.00 0.00 0.00 0.00 4447.91 393.00 0.00 0.00 0.00 393.00 655.00 23228.81 4946.87 12818.85 2086.75 19852.47 464.58 7609.38 295.00 0.00 ' 0.00 0.00 7904.38 698.40 0.00 0.00 0.00 698.40 1164.00 44570.00 11272.75 27585.00 4450.00 43307.75 891.40 16227.00 461.00 1471.00 7631.00 0.00 25790.00 1091.40 1193.81 8146.80 0.00 10432.01 3508.07 Total Costs 18555.11 26631.69 29999.81 53312.63 128499.24 Profit (Loss) 20907.89 -2582.49 -3799.81 -6752.63 7772.96 Raw Material Driver Variable Truck Fixed Truck Total Inbound Trans. Inventory Holding Allocated Fixed Cost Sawdust Fixed Cost All Bark Fixed Cost Packaged Bark Fixed Cost Bulk Bark Fixed Cost Total Fixed Costs S Sawdust Variable Cost All Bark Var. Cost Packaged Bark Var. Cost Bulk Bark Variable Cost Total Variable Costs Loading Costs 149 Table 36 Summary of Cost Data from Three Test Configurations* Product and Con­ figura­ tion no. (A) No. Tons Packaged Bark lefield, Milton 1954. Economic Considerations for a Successful Utilization of Sawmill Wood Residues. ForeBt Products Journal 4 ( 4 ) :11A-17A. Arimour Research Foundation 1957. Soil Builder from Bark. Chemical and Engineering News 3 5(5):30. 168 169 10. Basham, B. M. and W. S. Thompson 1967. An Economic Study o£ the Production and Use of Sawdust and Bark as Mulches and Soil Amendments for Horticultural and Agricultural Purposes. Mississippi Forest Products Utilization Laboratory, Information Series No. 6. 11. Baxter, H. O. 1964. Summary of Research on Sawdust and Bark for Agricultural Uses. Agricultural Experi­ ment Station, College of Agriculture, Athens, Georgia. 12. Bear, Firman E., and Arthur L. Prince 1951. Organic Matter in New Jersey Soils. N. J. Agricultural Experi­ ment Station, Bulletin 757. 13. Bernstein, A., M. V. Roberts, T. Arbuckle and H. H. Belsky • 1958. A Chess-Playing Program forthe IBM 704. Procedures of the 1958 Western Joint Computer Conference, pp. 157-159. 14. Bollen, W. B. and D. W. Glennie 1961. Sawdust, Bark, and Other Wood Wastes for Soil Conditioning and Mulching. Forest Products Journal 9 (4): 39A-42A. 15. Bollen, W. B. and K. C. Lu 1957. Effect of Douglas Fir Sawdust Mulches and Incorporation on Soil Microbial Activity and Plant Growth. Soil Sci. Soc. Amer. Proc. 21:35-41. 16. Bowersox, D. J., E. W. Smykay and B. J. LaLonde 1968. Physical Distribution Management. New York: MacMillan, p. 323-347. 17. Bureau of Business and Economic Research 1968. Michigan Statistical Abstract. Graduate School of Business Administration, Michigan State University, East Lansing. 18. Caurant, R . , and H. Robbins. Oxford University Press. 19. Clarkson, G. P. and A. H. Meltzer Selection: A Heuristic Approach. December. 20. Dudley, F. L. and L. L. Kelly No. 608. 1941. What is Mathematics. 1960. Portfolio Journal of Finance, 1941. U.S.D.A. Circular 170 21. Dunn, S. and J. D. Emery 1959. Wood Wastes in Composts. Forest Products Journal 9(8):277-281. 22. Dunn, W . , L. P. Wolfe, Jr., W.. A. MacDonald, and J. R. Baker 1950. Field Plot Studies with Sawdust for Soil Improvement. Plant and Soil 4:164-170. 23. Geleinter, M. and N. Rochester 1958. Intelligent Behavior in Problem-Solving Machines. IBM Journal of Research and Development, Volume 2, pp. 336-345. 24. Gere, W. S., Jr. I960. Heuristics in Job Shop Schedul­ ing. O.N.R. Memorandum No. 70, Carnegie Institute of Technology, June. 25. Gibbs, W. M. and H. W. Batchelor 1927. Effect of Tree Products on Bacteriological Activities in Soil. II. Study of Forest Soils. Soil Science 24:351-363. 26. Gibbs, W. M. and C. H. Werkman 1922. Effect of Tree Products on Bacteriological Activities in Soil. I. Ammonificatioh and Nitrification. Soil Science 13:303322. 27. Gomary, R. E. 1961. An Algorithm for Integer Solutions to Linear Programs. Princeton— IBM Mathematics Research Project, Technical Report No. 1. 28. Haataja, B. A. and L. W. Hooker 1965. Mill Residue Survey. Institute of Wood Research, Michigan Technological University, Houghton, Michigan. 29. Hiller, L. A. and L. M. Isaacson Music. McGraw Hill, New York. 30. Ivory, E. P. and P. Field 1959. Utilizing Bark at Medium-Sized Mills. Forest Products Journal 9(4): 22A-30A. 31. Kemeny, J. G. and G. L. Thompson 195B. The Modified Fictitious Play Methods. Dartmouth Mathematics Project Report No. 3. 32. King, W. W. 1956. Survey of Sawmill Residues in East Texas. Texas Forest Service Technical Report No. 3. 1959. Experimental 171 33. Kuehn, A. A. and M. J. Hamburger Program for Locating Warehouses. 9(4):643-666. 1963. A Heuristic Management Science 34. Lunt, H. A. 1955. The Use of Wood Chips and Other Wood Fragments as Soil Amendments. Connecticut Agricultural Experiment Station Bulletin 593. 35. Lunt, 0. R. and B. Clark 1959. Horticultural Applica­ tions for Bark and Wood Fragments. Forest Products Journal 9(4):39A-42A. 36. Mater, Jean Projection. 37. McCool,, M. M. 1948. Studies on pH Values of Sawdusts and Soil-Sawdust Mixtures. Boyce Thompson Inst. Contrib. 15:279-282. 38. Newell. A. J. C. Shaw and H. A. Simon 195B. Chess Playing Programs and the Problem of Complexity. IBM Journal of Research and Development, Volume 2, pp. 320335. 39. Newell, A., J. C. Shaw and H. A. Simon 1958. T he Process of Creative Thinking. The RAND Corporation Paper, P-1320. 40. Newell, A. and H. A. Simon 1956. The Logic Machine Theory. IRE Transcript on Information Theory, IT-2. 41. Page, R. H. and J. R. Saucier 1958. Survey of Wood Residues in Georgia. Georgia Forest Research Council, Resource-Industry Series No. 1. 42. Pinck, L. A., F. E. Allison and V. L. Gaddy 1946. The Nitrogen Requirements in the Utilization of Carbonaceous Wastes in Soil. American Soc. Agron. Journal 38:410-420. 43. Pratt, A. J. and S. Comstock 1958. Mulches or Cultiva­ tion for Vegetable Crops? Farm Research, 4-5 January, New York Agricultural Experiment Station. 44. Reuszer, W. H., R. L. Cook and E. R. Graham 1957. Wastes and Soil. Crops and Soil 9(4):12-13. 45. Reynolds, W. H. 1968, Heuristics for the Businessman. Michigan State University Business Topics, Winter, pp. 14-22. t 1967. Bark Utilization, A review and Forest Products Journal 17(12)i15-20. Wood 172 46. Roberts, A. N. and W. M. Mellenthin 1959. Effects of Sawdust Mulches. II. Horticultural Crops. Oregon Agricultural Experiment Station, Technical Bulletin 50. 47. Salomon, M. 1951. Decomposition of Wood Chips in Soil. N. E. Wood Utilization Council Bulletin 33:81-82. 48. Salomon, M. 1953. The Accumulation of Soil Organic Matter from Wood Chips. Proc. Soil Sci. Soc. Am. 17:114-118. 49. Samuel, A. L. 1959. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, Volume 3, pp. 210-229. 50. Shycon,* H. N. and R. B. Maffei 1960. Simulation— Tool for Better Distribution. Howard Business Review 38(6):65-75. 51. Simmons, F. E. and A. R. Bond 1955. Sawmill "Waste" in Maryland. U.S.D.A. Northeastern Forest Experiment Station, Station Paper No. 74. 52. Simon, H. A. 1961. Modeling Human Mental Processes. Procedures of the 1961 Western Joint Computer Conference. 53. Simon, H. A. and A. Newell 1958. Heuristic Problem Solving: The Next Advance in Operations Research. Operations Research 6(1):1-10. 54. Tenny, F. G. and S. A. Waksman 1929. Composition of Natural Organic Materials and Their Decomposition in Soil. IV. The Nature and Rapidity of Decomposition of Various Organic Complexes in Different Plant Materials Under Aerobic Condition. Soil Science 28:55-83. 55. Tonge, F. M. 1960. Summary of a Heuristic Line Balancing Procedure. Management Science 7(l):21-42. 56. Tonge, F. M. 1961. The Use of Heuristic Programming in Management Science. Management Science 7 (2):231-237. 57. Turk, L. M. 1943. The Effect of Sawdust on Plant Growth. Michigan Agricultural Experment Station Quarterly Bulletin 25:10-22. 173 58. Waksman, S. A. 1926. The Origin and Nature of the Soil Organic Matter on Soil Humus. III. The Nature of the Substances Contributing to the Formation of Humus. Soil Science 22:323-333. 59. Waksman, S. A. and F. G. Tenny 1927. The Composition of Natural Organic Materials and Their Decomposition in the Soil. II. Influence of Age of Plant upon the Rapidity and Nature of its Decomposition— Rye Plants. Soil Science 24:317-333. 60. White, A. W . , Jr., J. E. Giddens, and H. D. Morris 1959. Effect of Sawdust 'on Crop Growth and Physical and Bio­ logical Properties of Cecil Soil. Soil Sci. Soc. Amer. Proc. 23:365-368. 61. Wilde, S. A. 1958. Marketable Sawdust Composts: Their Preparation and Fertilizing Value. Forest Products Journal 8(11):323-326. 62. Wright, K. T. and D. A Caul 1967. Michigan's Agriculture. Michigan State University Cooperative Extension Bulletin 582. APPENDIX A Mail Survey Cover Letter and Questionnaire December 16, 1968 Dear Sawmill Operator:' I am a graduate student at Michigan State University, and I am presently doing a study on the nature and uses of wood residue products in Michigan. If I am successful in my research, I hope to help you and other sawmill operators convert your wood residue from a nuisance to a by-product in the,lumber industry. In order to complete m y research, I need some important information that only you as a sawmill operator can supply. I wonder if you would take a few minutes and fill out the attached questionnaire. I know all these figures will not be at your fingertips, so I would appreciate your best esti­ mates if you can't find the exact figures. Thank you in advance for your help, and you may be sure that all of your answers to the attached questionnaire will be held strictly confidential. Sincerely, ] Research Associate Eldon A. Behr Professor 174 175 MICHIGAN SAWMILL DATA FORM January 1969 GENERAL QUESTIONS; A. Check (✓) the following equipment if used by your sawmill: □ Debarker □ Chipper (pulpchips) □ Wood Hog □ Tractor with front loader bucket □ Fork-lift truck or tractor □ Teepee burner B. Approximately h o w many thousand board feet of lumber each work day? ______________ MBF do you saw C. Place checks i’n the columns under each wood product indicating what methods you use to advertise. (NOTE: Use as many checks (✓) as necessary) SAWFIRE"HOGGED" BEDDING BARK DUST SLABS WOOD & MULCH_______ Sign at the mill □ Cl □ □ □ Newspapers D □ □ 3 3 Trade journal □ □ □ □ □ DO NOT ADVERTISE □ 3 □ □ □ D. How much wood residue did you produce last year? (Check one) □ Large quantity (over 1000 green tons) □ Medium quantity (500 - 1000 green tons) □ Small quantity (under 500 green tons) E. Generally speaking, how costly do you consider wood residue to remove from your sawmill? (check one) 0 Very costly F. □ Costly □ Not Costly Place checks in the column under each of the four wood products indicating all methods of residue disposal you used last year. (NOTE: Use as many checks as necessary) TRIM & BARK SAWDUST SLABS EDGINGS Selling (pulp chips, □ □ □ □ firewood, etc.) Give Away 3 □ □ □ Puel □ □ □ □ Burn in Open Burn in teepee burner Dump at back of mill site 3 □ □ 3 □ □ □ □ □ 3 □ □ 176 6. How m#nV days did your mill operate last year? ______________ days H. Estimate how many man-hours it takes each week to remove the wood residue from your mill. _ _ _ _ _ _ _ _ _ _ _ _ _ man-hours SAWDUST: A, What percent (%) of your sawdust did you sell last year? % B, How do you sell sawdust? (Check one) q Truck load, ___________ size truck □ Cubic yard p pounds or tons C, How much do you charge per unit? 1. Loaded by customer _ per unit 2. Loaded by m i l l __________ per unit 3. Delivered Per unit BARK: (Answer only if you have a debarker) .... _ — % A. What percent (%) of your bark did you sell last year? B. How many cubic yards of bark did you "peel" last week? _ _ _ _ _ _ cu, yd. C. Approximately how many cubic yards of bark do you usually "peel" in one week7 _ _ _ _ _ _ _ _ _ _ cubic yards D. Do you process bark through a "wood hocf1? □ Yes □ No E. How much would you charge me for 10 cubic yards of bark if I parked my truck under the bark conveyor at your mill? _ _ _ _ _ _ _ _ _ dollars SLABS: A. (Answer only if you DO NOT produce chips) Do you sell slabs? □ Yes □ No B. In your mill do you direct your slabs past a "cut-off1* saw where they are cut into FIREWOOD? DYes □ No C. How much per cord do you charge for FIREWOOD picked up at the sawmill by the customer? _ _ _ _ _ _ _ _ _ price per cord - short cord 2x^x8' If you deliver? ______________ price per cord - average WOOD CHIPS: (Answer only if you are a chip producer) A. Do you buy debarked slabs7 . □ Yes □ B. Where doyou sell chips?D Detroit□ Otsego No □ Muskegon C, How many road miles is it (one way) from your mill to: Muskegon _ _ _ _ _ _ _ _ Otsego _ _ _ _ _ _ _ _ _ Detroit ■ D. How many chip vans do you OWN? _ _ _ _ _ _ _ RENT OR LEASE? CONTRACT? _____________ E, How many TONS of chips didyou haullastweek?_ _ _ _ _ _ _ _ TONS of chips do you haulin an green tons F, How many C. If you had the opportunity to go into pulp chip production, and were not already in the business, would you make the investment? □ Yes □ No Thank you. average - week? _ _ _ _ _ _ green tons 176 6, How many days did your mill operate last year? _ _ _ _ _ _ _ _ _ days H, Estimate how many man-hours it takes each week to remove the wood residue from your mill. man-hours SAWDUST: A. What percent (%) of your sawdust did you sell last year? % B. How do you sell sawdust? (Check one) □ Truck load, ___________ size truck □ Cubic yard □ pounds or tons C. How much do you charge per unit? 1. Loaded by customer _ _ _ _ _ _ _ Per unit 2. Loaded by mill _____________ per unit 3. Delivered __________________ P®r unit BARK: (Answer only if you have a debarker) A. What percent (%) of your bark did you sell last year? % B. How many cubic yards of bark did you “peel" last week? _________ cu. yd. C. Approximately how many cubic yards of bark do you usually “peel" in one week? cubic yards D. Do you process bark through a "wood hog"? □ Yes □ No E. How much would you charge me for 10 cubic yards of bark if I parked my truck under the bark conveyor at your mill? _ _ _ _ _ ^ _ _ _ _ dollars SLABS; (Answer only if you DO NOTproduce chips) A. Do you sell slabs? B. In your mill do you direct your slabs past a "cut-off" saw where they are cut Into FIREWOOD? DYes □ No How much per cord do you charge forFIREWOOD picked up at the sawmill by the customer? price per cord - short cord 2x4x8' If you deliver? price per cord - average C. WOOD CHIPS: □ Yes □ No (Answer only if you are a chip producer) A, Do you buy debarked slabs? B, Where do you sell chips?D □ Yes Detroit □ No □ Otsego □ Muskegon C, How many road miles is it (one way) from your mill to: Huskegon _ _ _ _ _ _ _ _ _ Otsego _ _ _ _ _ _ _ _ _ _ Detroit _ _ _ _ _ _ _ _ _ D, How many chip vans do you OWN? RENT OR LEASE? CONTRACT? _____________ E, How many TONS of chips did you haul last week? haul in an average week? . green tons F, How many TONS of chips doyou G, If you had the opportunity to go into pulp chip production, andwere not already in the business, would you make the investment? □ Yes □ No Thank you. _ _ _ _ _ _ _ green tons APPENDIX B ESTIMATED QUANTITY OF HARDWOOD RESIDUE PRODUCED BY SAWMILLS IN SOUTHERN HALF OF LOWER MICHIGAN 1968 6 7 8 9 10 11 12 13 14 Total 67.5* . 2500.0* 300.0 50.0* 480.0* 3397.5 70.2 2600.0 312.0 52.0 499.2 3533.4 0.0 1450.0 0.0 0.0 0.0 1450.0 Total 300.0 750.0 100.0* 300.0 750.0 50.0 300.0 600.0* 300.0 3450.0 312.0 780.0 104.0 312.0 780.0 52.0 312.0 624.0 312.0 3588.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 LTF D F F F F E F F E LTF F F F 3100.0 0.0 0.0 0.0 3100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Sawmill Grid Coordinate^ 122.9 0.0 546.0 91.0 873.6 1633.5 42 43 44 45 46 1818 2121 2023 1521 1442 546.0 1365.0 182.0 546.0 1365.0 91.0 546.0 1092.0 546.0 6279.0 47 48 49 50 51 52 53 54 55 3116 2821 3016 2721 2621 2915 2517 2921 2818 177 Barry 1 2 3 4 5 o • o Allegan Sawdust ____________Mill Quantity in Tons Solid Wood Solid Wood Code Bark W/O Bark With Bark N o .— o • o County Ac tua 1 lumber Directory production fin. thousTT Class No, County Actual lumber Directory production Class (in . thous.) No. Bay 15 Branch Calhoun Cass 16 17 780.0 312.0 1092.0 0.0 0.0 0.0 18 19 Total 2000.0 2000.0 4000.0 2080.0 2080.0 4160.0 0.0 1160.0 1160.0 20 21 22 23 Total 300.0 300.0 50.0 300.0* .950.0 312.0 312.0 52.0 312.0 988.0 24 25 26 27 28 Total 700.0* 300.0 150.0* 3525.0* 2880.0* 7555.0 728.0 312.0 156.0 3666.0 2995.2 7857.2 E F D D F F LTF LTF F F F D C 2 5339 0.0 0.0 1365.0 . 546.0 1911.0 56 57 1102 0705 0.0 2480.0 2480.0 3640.0 0 0.0 3640.0 58 59 3107 3007 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 546.0 546.0 91.0 546.0 1729.0 60 61 62 63 3509 3013 2813 2909 0.0 0.0 0.0 2044.5 1670.4 3714.9 0.0 0.0 0.0 4371.0 3571.2 7942.2 1274.0 546.0 273.0 0.0 0.0 2093.0 64 55 66 67 68 1806 1906 1705 1504 1306 * 1 o o • • o o Berrien 455.0 455.0 o • o Total 750.0 300.0 1050.0 Total 250.0* 250.0 Sawmill Grid Coordinate 178 260.0 260.0 o o • o o E Sawdust Quantity in Tons______________ Mill Solid Wood Solid Wood Code Bark W/0 Bark With Bark No. Clinton 29 30 31 32 33 34 Total 3000.0* 3000.0 3120.0 3120.0 1740.0 1740.0 3720.0 3720.0 Total 3875.0* 50.0 300.0 3000.0 750.0 7975.0 4030.0 52.0 312.0 3120.0 780.0 8294.0 2247.5 . 0.0 0.0 1740.0 0.0 3987.5 4805.0 0.0 0.0 3720.0 0.0 8525.0 312.0 187.2 499.2 0.0 0.0 0.0 0.0 Total 300.0 180.0* 480.0 C LTF F D E 69 4028. 0.0 91.0 546.0 0.0 1365.0 2002.0 70 71 72 73 74 3519 3223 3817 3323 3922 75 76 4238 3835 • D Sawmill Grid Coordina 0.0 546.0 327.6 873.6 Hillsdale 37 38 39 40 F F F F 300.0 285.0* 300.0 90.0 975.0 312.0 296.4 312.0 93.6 1014.0 174.0 0.0 0.0 0.0 174.0 372.0 0.0 0.0 0.0 372.0 0.0 518.7 546.0 163.8 1228.5 77 78 79 80 3903 3702 3804 3602 Huron 41 42 43 44 45 43 F A LTF LTF F LTF 300.0 2500.0* 50.0 50.0 495.0* 50.0 3445.0 312.0 2600.0 52.0 52.0 514.8 52.0 3582.8 0.0 1450.0 0.0 0.0 0.0 0.0 1450.0 0.0 3100.0 0.0 0.0 0.0 0.0 3100.0 546.0 0.0 91.0 91.0 900.9 91.0 1719.9 81 S2 83 84 C5 85 6745 6047 7050 5950 7049 6650 Gratiot 35 36 F F Total • o • o o o Eaton Sawdust Quantity in Tons_____________ Mill Solid Wood Solid Wood Code Bark W/O Bark With Bark No. o o County Actual lumber Directory production Class (in. thous.) No. Ingham 47 48 49 50 Ionia Jackson Kalamazoo 57 58 59 60 61 62 63 Sawmill Grid Coordinate 780.0 93.6 312.0 312.0 1497.6 0.0 0.0 0.0 174.0 174.0 0.0 0.0 0.0 372.0 372.0 1365.0 163.8 546.0 0.0 2074.8 87 88 89 90 4219 4119 4016 4421 Total 750.0 90.0* 300.0 300.0 1440.0 1996.8 780.0 2745.6 324.5 312.0 52.0 6210.9 1113.6 0.0 1531.2 0.0 0.0 0.0 2644.8 2380.8 0.0 3273.6 0.0 0.0 0.0 5654.4 0.0 1365.0 0.0 567.8 546.0 91.0 2569.8 91 92 93 94 95 96 3326 2825 3529 2931 3024 2831 Total 1920.0* 750.0 2640.0* 312.0* 300.0 50.0 5972.0 3494.4 2870.4 1435.2 7800.0 1948.8 1600.8 800.4 4350.0 4166.4 3422.4 1711.2 9300.0 0.0 0.0 0.0 0.0 8 9 10 3444 3440 3544 Total 3360.0* 2760.0* 1380.0* 7500.0 104.0 832.0 936.0 0.0 0.0 0.0 0.0 0.0 182.0 1456.0 1638.0 97 98 4110 3815 Total 100.0* 800.0* 900.0 312.0 780.0 1092.0 0.0 0.0 0.0 546.0 1365.0 1911.0 99 100 2215 2309 Total 300.0 750.0* 1050.0 D E D F F LTF E D F F E F F 0.0 0.0 180 Isabella 51 52 53 54 55 56 E F F F Sawdust o o to. Quantity in Tons___________ Mill Solid Wood Solid Wood Code Bark W/0 Bark With Bark No. • County Actual lumber Directory production Class (in . thous.) o « o Lapeer Lenawee Livingston Macomb 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 Sawmill Grid Coordinate 31.2 358.8 312.0 1315.6 4596.8 2496.0 9110.4 0.0 0.0 0.0 0.0 2563.6 0.0 2563.6 0.0 0.0 0.0 0.0 5480.8 0.0 5480.8 54.6 627.9 546.0 2302.3 0.0 4368.0 '7898.8 101 102 103 104 105 106 2524 2231 2334 2226 2132 2234 Total 30.0* 345.0* 300.0 1265.0* 4420.0* 2400.0* 8760.0 2080.0 312.0 312.0 780.0 3484.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3640.0 546.0 546.0 1365.0 6097.0 107 108 109 110 6030 6634 6534 6434 Total 2000.0 300.0 300.0 750.0 3350.0 1040.0 1560.0 2802.8 5402.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1820.0 2730.0 4904.9 9454.9 111 112 113 4903 4404 5206 Total 1000.0* 1500.0* 2695.0* 5195.0 o o Kent Sawdust Quantity in Tons____________ Mill Solid Wood Solid Wood Code Bark W/0 Bark With Bark No. 2080.0 6500.0 8580.0 1160.0 3625.0 4785.0 2480.0 7750.0 10230.0 0.0 0.0 0.0 114 115 5420 5120 Total 2000.0 6250.0* 8250.0 1664.0 52.0 1716.0 0.0 0.0 0.0 0.0 0.0 0.0 2912.0 91.0 3003.0 116. 117 6819 7023 Total 1600.0* 50.0 1650.0 LTF LTF F E C D D F F E D D F D B E LTF * County Actual lumber Directory production Class (in . thous.) No. Monroe Montcalm Muskegon 81 82 83 84 85 86 87 88 89 90 91 92 . 93 94 95 Sawdust Quantity in Tons_____________ Mill Solid Wood Solid Wood Code Bark W/O Bark With Bark No. Sawmill Grid Coordinate 3120.0 52.0 312.0 3484.0 0.0 0.0 . 0.0 0.0 0.0 0.0 0.0 0.0 5460.0 91.0 546.0 6097.0 20 21 22 4343 3946 4047 Total 3000.0* 50.0 300.0* 3350.0 2704.0 156.0 2860.0 0.0 0.0 0.0 0.0 0.0 4732.0 273.0 5005.0 118 119 5704 6007 Total 2600.0 150.0* 2750.0 o o Midland _____ __ 2600.0 780.0 624.0 780.0 2080.0 68S4.0 1450.0 0.0 0.0 0.0 1160.0 2610.0 3100.0 0.0 0.0 0.0 2480.0 5580.0 0.0 1365.0 1092.0 1365.0 0.0 3822.0 120 121 122 123 124 2537 2436 3135 3237 3235 Total 2500.0* 750.0 600.0* 750.0 2000.0* 6600.0 748.8 780.0 2496.0 2080.0 780.0 6884.8 0.0 0.0 1392.0 1160.0 0.0 2552.0 0.0 0.0 2976.0 2480.0 0.0 5456.0 1310.4 1365.0 0.0 0.0 1365.0 4040.4 125 126 127 128 129 1333 1039 1132 1232 1733 Total 720.0* 750.0 2400.0* 2000.0* 750.0 6620.0 E LTF F D LTF D E F E D E E D D E • County Actual lumber Directory production No. Class (in. thous.) Newaygo Oakland 104 105 106 107 108 109 110 111 112 113 114 115 Total 1000.0* 4350.0* 1400.0* 1000.0* 750.0 50.0 1000.0* 2000.0 11550.0 1040.0 4524.0 1456.0 1040.0 780.0 52.0 1040.0 2080.0 12012.0 0.0 0.0 0.0 0.0 0.0 ' 0.0 0.0 0.0 0.0 Total 300.0 2000.0 135.0* 50.0 300.0 2785.0 312.0 2080.0 140.4 52.0 312.0 2896.0 0.0 0.0 0.0 0.0 0.0 0.0 Total 400.0* 2000.0 300.0 3600.0* 50.0 50.0 750.0 7150.0 416.0 2080.0 312.0 3744.0 52.0 52.0 780.0 7436.0 0.0 0.0 0.0 2088.0 0.0 0.0 0.0 2088.0 E C C E E LTF E E ■ F E LTF LTF F E D F D LTF LTF E 0.0 0-.0 0.0 0.0 0.0 0.0 0.0 0.0 Sawmill Grid Coordinate 1820.0 7917.0 2548.0 1820.0 1365.0 91.0 1820.0 3640.0 21021.0 23 24 25 26 27 28 29 30 1842 1942 2039 1542 1846 1640 2042 1740 0.0 0.0 0.0 0.0 0.0 0.0 546.0 3640.0 245.7 91.0 546.0 5068.7 130 131 132 133 134 5619 6026 5816 5716 5826 0.0 0.0 0.0 4464.0 0.0 0.0 0.0 4464.0 728.0 3640.0 546.0 0.0 91.0 91.0 1365.0 6461.0 31 32 33 34 35 36 37 0945 1442 1242 0943 1045 1043 1541 183 Oceana 96 97 98 99 100 101 102 103 ______________ Mill Quantity in Tons Solid Wood Solid Wood Code Sawdus t W/0 Bark With Bark No. Bark o • o County Actual lumber Directory production No. Class (in. thous.) County. St. Joseph 129 Sanilac 130 131 132 135 136 1826 1829 Total 2496.0 4160.0 1872.0 11793.6 1092.0 21413.6 1392.0 0.0 0.0 6577.2 0.0 7969.2 2976.0 0.0 0.0 14061.6 0.0 17037.6 0.0 7280.0 '3276.0 0.0 1911.0 12467.0 137 138 139 140 141 4732 4735 4636 4736 4840 Total 2400.0* 4000.0 1800.0* 11340.0* 1050.0* 20590.0 312.0 52.0 257.9 780.0 52.0 312.0 1765.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 546.0 91.0 451.4 1365.0 91.0 546.0 3090.4 142 143 144 145 146 147 7430 7036 7228 7029 7525 7130 Total 300.0 50.0 248.0* 750.0 50.0 300.0 1698.0 312.0 312.0 0.0 0.0 0.0 546.0 546.0 148 2304 Total 300.0 300.0 13000.0 3120.0 1196.0 17316.0 7250.0 0.0 0.0 7250.0 15500.0 0.0 0.0 15500.0 0.0 5460.0 2093.0 7553.0 149 150 151 6938 6639 6742 F F D C D C D F LTF LTF E LTF F F A D D Total 12500.0* 3000.0* 1150.0* 16650.0 312.0 312.0 624.0 184 123 124 125 126 127 128 546.0 546.0 1092.0 0.0 0.0 0.0 o • o St. Clair 118 119 120 121 122 0.0 0.0 300.0 300.0 600.0 o • o Saginaw 116 117 Sawdust Grid Coordinate o • o Ottawa Directory No. Class Actual lumber __________ Quantity in Tons_____________ Mill production Solid Wood Solid Wood Code (in. thous.) Sawdust Bark W/0 Bark With Bark No. County Tuscola Washtenaw Wayne 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 Mill Quantity in Tons Solid Wood Solid Wood Code W/O Bark With Bark No. Bark i Sawdust Sawmi] Grid Coordinc 312.0 312.0 2080.0 312.0 780.0 312.0 1497.6 52.0 1300.0 6957.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 546.0 546.0 3640.0 546.0 1365.0 546.0 2620.8 ' '91.0 2275.0 12175.8 152 153 154 155 156 157 158 159 160 5735 6040 5536 5637 6342 5939 6236 5738 6443 Total 300.0 300.0 2000.0 300.0 750.0 300.0* 1440.0* 50.0 1250.0* 6690.0 312.0 312.0 52.0 457.6 312.0 1445.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 546.0 546.0 91.0 800.8 546.0 2529.8 161 162 163 164 165 1316 1508 1711 1714 1611 Total 300.0* 300.0 50.0 440.0* 300.0 1390.0 468.0 780.0 1300.0 2548.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 819.0 1365.0 2275.0 4459.0 166 167 168 5313 4909 5508 Total 450.0* 750.0 1250.0* 2450.0 2080.0 2080.0 0.0 0.0 0.0 0.0 3640.0 3640.0 169 5915 Total 2000.0 2000.0 173767.5 180718.2 50663.0 108314.0 157279.8 F F D F B F F LTF D F F LTF F F E E E D GRAND TOTAL 185 Van Buren Actual luniber Directory production Class (in. thous.) No. APPENDIX C-l Excerpt From 1968 Michigan Standard Specifications for Landscaping Materials State of Michigan Department of State Highways 7.21.02 Mulching Materials: a. Manure.— Manure shall consist of well rotted cow manure or well rotted horse manure aged for at least 3 months in a building or large pile. It shall be free from shavings, saw­ dust and cornstalks. Straw or similar bedding may be present to the extent of not more than 15 percent by volume, provided that it is well rotted. In lieu of the above a uniform mixture of 50 percent well rotted, pulvarized sheep manure and 50 percent salvaged soil may be used. Only well rotted cow manure shall be used in planting areas intended for roses or evergreens. b. Well Rotted Deciduous Leaves. c. Wood Chips.— Wood chips shall be the product of a mechani­ cal brush chipper. Not more than 5 percent of the chips shall be over 4 inches in size. At least 50 percent of the chips shall be one inch or less in size. Suitability of chips material and size will be determined by visual inspection. d. Shredded Bark.— This material shall consist of tree bark which has been stripped and shredded from saw logs by means of a de-barking machine. The material shall be sufficiently fine and free from extraneous material so that it will readily pass through a conventional mulch blower. e. Coarsely Ground Corncobs. Special Note: Sawdust and peat moss were deleted from the specifications as a mulch on July 30, 1968. 186 187 APPENDIX C-2 LUMBER AND RESIDUE FRACTIONS DEVELOPED FROM SAWMILLING (1)______ Cubic Foot Cubic Foot Weight per Weight Fractions Volume Per Volume in MBF in in ______________________ MBF (2)_______ Percent Pounds (2) Percent Bark: Green bark (3) Green logs w/o bark (4) Green logs including bark 28.66 13.9 1, 260 10.3 178.10 86.1 10,970 89.7 206.76 100.0 12,230 100.0 33.12 18.6 2,040 18.6 20.12 20.88 6.84 11.3 11.7 3.8 1,240 1,290 420 11.3 11.7 3.8 80.96 45.4 4,990 45.4 Lumber: Rough Green Lumber (6) 97.14 Rough Dry Lumber 88.62 Water in Lumber 8.52 54.6 49.8 4.8 5,980 3,710 2,270 54.6 33.9 20.7 28.22 1.86 15.9 1.0 1,330 70 12.1 0.7 30.08 16.9 1,400 12.8 111.04 62.3 6,390 58.2, 58.54 32.9 2,310 21.1 Sawdust: Green sawdust Solid Residue: Green slabs Green edgings Green trim ends Total Green Wood Residue (5) Dry Wood Residue: Dry shavings Dry trim ends Total Dry Wood Residue TOTALS: Total Green & Wood Residue Total Dressed Dried Lumber SOURCE: Dry (8) & (9) Applefield, Milton, 1954. Economic Considerations for a Successful Utilization of Wood Residue. Forest Products Journal 4(4):11A-17A. 188 Appendix C-2, cont. In order to better evaluate the data in the previous example an explanation of the basis for the calculations of the author and the interpretations are listed in items (1) through (9) below, corresponding with the parenthesized num­ bers in the table. (1) The various fractions are the result of processing 1,000 board feet, mill tally, of average southern yellow pine logs into 1,000 nominal board feet of finished and dried 4/4 lumber. , (2) The volumes and weights represent solid wood values. (3) Single ,bark thickness averages .41 inches per log. The bark fraction has not been considered wood residuo. (4) The average pine saw-log on which these data are based is 9.4" in diameter at the small end, inside bark, and 14.6' including 3" trimming allowance. This log scales 50 board feet mill tally, and has an average, inside bark, taper of 2.4". This average green log, without bark, represents the entire wood volume (100%) from which all residue fractions and percents were calculated. (Log diameter is the principal variable affecting avail­ able volume of sawmill wood residue.) (5) The weight, per cubic foot, used for all green wood fractions, is 61.6 pounds, obtained as an average of numerous weighings. This coincides very closely with the weight given in U. S. Forest Products Laboratory Technical Note No. 218 which gives cubic foot and board foot weights for various species and moisture contents of round and sawn wood. (6) Green lumber is actually sawn 3/32" full in thickness and 1/2" full in width, but is nominally considered 1" lumber. Thus, true volume of 1,000 nominal board feet of rough green lumber is 1,181 board feet and it requires a con­ version factor of 10 board feet to make one cubic foot. 189 Appendix C-2, cont. (7) The water in lumber cannot be considered waste or residue, though it is not used in the final lumber end-product. It has been isolated in order to determine more accurately the remaining fractions, and it must be pointed out that the drying and consequent shrinkage of wood does not represent a straight line volume-weight ratio. This frac­ tion represents the water in green lumber, with a 110 percent moisture content which has been kiln dried to about 12 percent 'moisture content, based on oven-dry weight. (8) Excludes the fraction representing water loss from lumber drying. (9) Finished lumber, though scant 7/32" in thickness and 3/8" in width, is considered nominal 1" lumber. Thus, there are only 748 actual board feet per nominal MBF of this lumber which requires a conversion factor of 15 1/2 board feet per cubic foot. Average weights of southern yellow pine lumber per MBF (nominal dimensions) are available from the Southern Pine Association, New Orleans, Louisiana. Applefield's data has been presented on the basis of both weight and volume. In examining the residue fractions, however, most people will prefer to use weight as the standard of measurement because it is simpler to apply in practice, and is also more accurate because there are few variables involved. Based on these weights, the manufacture of one thousand board feet of finished, nominal 4/4 lumber produces 2,040 pounds of green sawdust and 2,950 pounds of solid green wood residue (slabs, edgings and trims). Dry residue, consisting primarily of shavings, weighs 1,400 pounds. In addition, there is also a bark fraction weighing 1,260 pounds. 190 APPENDIX C-3 Selected Markets for Wood Residues Visited During Field Survey Included in the field survey were dairy farms, nurseries, and lawn and garden centers. t DAIRY FARMS: (1) Smith's Dairy - Potterville t (2) Green's Dairy - Leslie (3) Meadow's Dairy - Swartz Creek NURSERIES: (1) Maple Hill - Charlotte (2) Smith - Lansing (3) Cottage Garden - Lansing LAWN A N D GARDEN CENTERS: (1) Fruit Basket - Grand Rapids (2) Frank's Nursery - Lansing (3) Meijer's Thrifty Acres - Lansing 191 APPENDIX C-4 County Code and Grid Location of Geographic Center Code No. Grid 03 08 09 11 12 13 *14 19 23 25 29 30 32 33 34 37 38 39 41 44 46 47 50 54 56 58 59 61 62 63 64 70 73 74 75 76 78 79 80 81 82 1820 2720 4944 0804 3204 3312 1604 3928 3520 5429 3836 3904 6648 4320 3128 3544 4212 2312 2330 6231 4804 5120 6722 2744 4344 5804 3036 1235 1942 6021 1244 1528 4836 7229 2304 7039 4728 5940 1512 5312 6212 i Name Allegan Barry Bay Berrien Branch Calhoun Cass Clinton Eaton Genesee Gratiot Hillsdale Huron Ingham Ionia Isabella Jackson Kalamazoo Kent Lapeer Lenawee Livingston Macomb Mecosta Midland Monroe Montcalm Muskegon Newago Oakland Oceana Ottawa Saginav; St. Clair St. Joseph Sanilac Shiawassee Tuscola Van Buren Washtenaw Wayne 192 APPENDIX C-5 Functions of Mulches and Soil Conditioners A mulch is used to: 1. Reduce evaporation of the soil moisture. 2'.., Lower soil temperatures in the summer and protect plants from extremely low temperatures in winter. 3. Improve the appearance of landscaped areas. 4. Control water run-off and, to a degree, prevent erosion. 5. Aid in controlling weeds. A good mulch may take the place of frequent cultivation in the control of many kinds of weeds. 6. Protect fruits and flowers from soil spattered by rain, as in the case of strawberries, tomatoes, etc. 7. Aid seed germination. Because mulching materials reduce evaporation, assist in maintaining uniform temperatures and aid in preventing erosion, they may be used frequently. A soil conditioner is used to: 1. 2. 3. 4. 5. Source: Improve the porosity of the soil (making it more friable), which in turn improves the admission of water and oxygen into the soil. Improve the water-holding capacity (unless the ■ particles are too large). Help prevent crusting. Assist and improve the biological processes that occur within the soil. Lower the bulk density of soil, which is important for nurserymen who grow plants in containers. Basham, B. M. and W. S. Thompson 1967. An Economic Study of the Production and Use of Sawdust and Bark as Mulches and Soil Amendments for Horticultural and Agricultural Purposes. 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S 7OT2|H)atOT2(N»/60 TOTM(H]aroTP»tHl/60, 198 SUBROUTINE PRSUP FORTRAN EXTENDED V 1,0 09/16/69 ,22.29,0V. SUBROUTINE PRSUF (I) c o h m q n /S u p p i y /S u p .n m i l l p p .m i l l p p COMMON/SORT/1SN0S(ISO) DIMENSION SUP(7(170)«ISUP(7«170).NMILLPP(6),NILLPP(2«190*6) C0MH0N/A/DEHC95*6)*NCTV 09 COMMON/n/TOTSUP(2) 10 4» 50 20 55 500 505 FORHAT(1 HO* 10 X**TOTAL* 39X*Fl2t2*5x*F9.2) RETURN 29 30 39 C C C HFADING ROUTINE 100 PRINT 105* I,(0EH(KK*I),KKal*4) 105 FORMAT (1H1*30X«*LISTING OF SUPPLYINS HILLS FOR PROCESSING PLANT • 1I4/-0*2§X,-COORDINATE *213* RADIUS *14* MIN a n n u a l PHOD *1X0) PRINT H O 110 FORMAT flMO/lH ,-MILL N0-5X-C0QRD*5X-DlSTANCfe*9X»-PRQDtIN Tm O u S)15X.-T0N3 SA«DUST*5X*T0NS BARK-//) IOVF a Q GO TO 45 END 199 19 E0UIVALENCE(SUP*1SUP) t y p e INTEGER DEM T0TSUP(l)»TOTSUP(2)«0, NMa NMILLPP(I) S IOVF-43 DO 500 JJ«1*NM K«]SNOS(JJ) J»MILLPPC1.M,I) IFCIOVF ,GE, 43) GO TO 100 PRINT 50* J,ISUP(1*J>.ISUPI2*J)»MILLPP(2»K»I).ISUPIM*j)*Ma3.9) FORMAT (IN lI7f5X.I2tX«I2.5X*I8*5X*Fl4f3*5XlFl2.2*9V*F9.2) IOYF*!OVF*l DO 55 N«l*2 M-N-3 TOTSUPIN)*TOTSUPCN)*SUP(M»J) CONTINUE PRINT 505* TOTSUP PROGRAM 600 SAtaSlM 1035 1040 1145 1050 619 ,2 2 . 2 9 , 0 V . J*1«1R PRINT 1035,.K«1,2),IC0ST*1«1#7> F0RHATC1H . 2 A 10» lX fFl 2l2 . 5 ( 6 X . F l 2 l2).Fl2.2) CONTINUE PRINT 1045.PROCCST FORMAT(tHO.•TOTAL C0STS*4X,6(6X,F12,2).F12.2J PRINT 1050,PROFIT FORMAT(1HO,#PROFIT (L0SS)*2X.6(6X,F12.2)*F12,2) PRINT 1060,1 FO RM AT( 1H 1,*END OF PROCESSING PLANT *15) 1060 C C END OF DO LOOP C 2000 CONTINUE PRINT 2005 2005 F0RMAT(1H1*EN0 OF RUN*) ENO 200 610 09/16/49 N«DEM{M,I> PRINT 1 0 2 0 , ICTYI3.N)« (SATtK.HK),K*i,l3) 1020 FORMATflH ,A 10, 5X * 6 ( F 7 I0 >F 1 1 , 2 ) IF12,2) 1025 CONTINUE PRINT 1 03 0. TOTS 1030 FO RNAT(lHO,3xa. *T OTAL*.7X.6(F7lO . F l l l2 ) . 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M O Q J k « J ft- u v 4 0 l « 4I P ft Ml ■ < 0 t W I ft ft III UI 9 M i a OH f t (A Jl IQ ft ft A ft j 1 11Ok I 8kMl m H IU 3 O k ■ ) « A J o MM O — ft. 203 ft 204 M N MIX X kX 4K • * ** O X U * * ( I I I A 4 11 f t *H* UK «l¥* ft*4 4 « »-* h II Ui j U o jr • U O 4 ** 19M *4 1 1 X • VaTOTS(3)«TOTS(5l*TOTS(7) DO *30 J«3,7.I IFCt.EO.O.t 00 TO t«* IaTOTI|J|/r 00 TO *«0 OM K *u xu u u uuu • • ♦< uuu f tftft H O O f e • «4U U I U « U *** 1 C CJ Cl k 4 a rt «4 w • MC * aU • K O B H • U K U O Q HUMl W M O O J O Q O f e U I Q U U b ♦ I I OB I d f* ■ H M 4 vt x c n aa K ft b V 4VI OUik O wMO • « =» oO O l UIM3 BVI X . * * 4 h R B * M ftX • M B *4 fW k>*~ *4 3 O • • ** k ^ * 4 \ ' M 4 V U 1 I I » I U U • M »ft ■t c k 9« *M **• V ►• o•f»t f I• » x **k J f k 4 ft M o I X * 4 i w n * M »-n « M*< « 9 oI Ql IA SOX 410 M4I • X • MU* M M non MOB O O O O ftO ft ooukuk • ■ K ■ * 0 001 x s B f t V M S 4 W H M I 4 X I I B k Ik k V MO IANBI k4 •« I I Ult x**o MX **BkM« 4ft X V O IftO O U O • ft W B f tftfto. • M • MM O X UUU X 4 X o A ft v CMCCft YEARLY production 205 206 61/ I * O ■ M a O N » d ■ Ik a w*m tfuia *#r t k • k l m.vs t s« u *•« ««)k n « fve o « «» o saw \W M «4 ---4 m m • m -*► f 1t tu C U I O ■ p ® t P o • O • U O O O ^ ^ iO O P Pa p o V u ui ••►►►•• ►► «*• « • I I " k* II a N 9 > V p a » M * N m * « M « > « > £ # m f a * P 0*00* 2’9 O O us w u 1 * 19 in 2 - * roait*TiiMa*rauc« full - smhill •■ill nut nr truc* tovtailNE ppID por •f»,i» mours*/im ,«*ll op hill supply upousht in 2 • ruatNER procpssins stoppso*) SO TO 292 210 PRINT 2B9.K 207 208 #* ift # _i Ift Uf • C; 41 K • X *» ft M Ui U M * * « m « o * ■» ' •> x % « 4 a i n * 4 O ft H O W N X U • M * i l N k m C ^ O « Q O O « ^ ^ m u k t l k H O (ft ft o ft X a t u *. o e n o ' 3 C •^ » O w •o y UI o M* in 4/1 O rv * «* ft# It Ift 0 Cl n 9 tt ft> ft o M m 0 ^ r t 4ft O f i m < « v f t O Ift ft* (ft i ~ l # * W • — ft W ft ft#• o u. 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MMO»_XW X X X S M 4 U K O h N m J W M - » U k D l UK# m O l » U talk S » S S X V K « x x < * > * «> S S 1 1 1 KCBUt » * ■ •<»MUiS^OO B » S KtK * M 4 U 3 B XX a O 0 9 Vf B M M f t M •« « % * B • « § « - tt J 0 « • 4 J 4 3 « 4 J 4 3 x s x x x s x x x a s »a • a a N N M N N N M N N N N M M M A I f t a 0 0 0 m * * * * « * B 0 4 4 •, • M M • M « * • * * • • * * * • • * M M M M M H M M H M M M M MvtHMMU **«klllliai«iaillll£ m m i m • ■ » m p • « w M m #4 0 ■ a • 1 m B m m < » M B « + • a a M c •o«u « s o •m o f > f *X1X __ ^ ^ * 4 •• o k O i w • • f4nO<»HN««4f Wf MH l J I t J U J J I J U J A A I S # — - a * a *X I t t M I H k l O X 4 M 4 B H N M N ^ U l 4 0 e 0 U 9 B • •* • • 44 E X • N M « X 3 w * N X A -J N « I4MNUU • * -I I » « • X X • M X 3 %X M « X O N a * *44 O 4# X > X A A A « Q « • X * X a * a US k « N « • • « A A X A • X Ax 4 0 4 * H • 4 0 A M X A A X A M H U 3 f t * « 4 • » N • • « < « H O * H O 4 B 4 I H m » O 9 * X- X X X 3 X X 44 X m A 44 o A m 4 • N XAQ X • A X * N O « X 44*4 X A M • X 44 X A 4 A XI) m X X 44 m X X m x X A * * _l H U * X fW 3 X X 4# X o u * A A 4 • If A 44 A % U9 M o X X X 4 X X > O A V X A o 4 A • A A B 0 O O -JA O • • A M X ■ A N X X O t M N I M o • N 4* M 4 A A X I X » « U I B U 4* u w MM 4 k E N W 1 > 4X X X * - 1I M A X X X X 4 A X X w X A o kM X M •O * • * OX N 3 E N 3 O • N 3 X • A * A 4 « M X O A 0 X 0 A X A » J • * M B A A X A X * A X • - «> M a w 4 X * • « « * « 44 X WUElM* H 3 a J N O B S U S " l-NN » «X 0 0 0 «I A X w X A X *L • U> X w X A X * A n « N • 44 4 «* S E > H ^ N 4 E M X o 3 N *o X 3 X JB m A a 3 M O x 4»* ^ N • o mi H f i n i r •»«#«#« X XX X 0WVIM xxxx XX XX * * * • k d J a i J j M n « k ^ A n M n * k « k * A » M A A M A O M a . • • k a a t l N n * i \ « t k * » a « M f l * l # * a a M n k W M A n a a A A H A H A A , A •. a ■ • A A f c n . a. a . a . a a ( a , a, a a, a, A a , a, * a, a M A A A a a A a a a « * * « * • k» ■ a|ai a | J w W M W * i W M W V k » « t « » W W W M M M M M M W AM*4fNl0UJUJIUUJU«UJUJUJUJUlU*UJUIU*UJ O U O U O U O U O O w I K K k K l l t l 4 4 x xxx. x x*********x I I K I I K I f l l f i l l k « a 4 f « 4 « 4 S I o o 44«44« o a u > X B U M l t f i k 50EMfMEM«XBK(XKKK03&t, W304HII» v i *■* j»*« • • m h tt( M m 1 v in <*"i « ■ 3 0 « • A « * 0 K » 0 ^ S m R It J I U M > Jl o (9 4 « s ^ o t - B U R * H 3 H i h it *- a ► * v < * 0 4 x « *** a M h n A • i I A » M n O I/I M i : mJ H v *• ^ • a M ■ • O M O ft M M U I «J ** H (V H 9 « M O «« t r ►- *- > > 31 * * 4 N M U H M V H M H t H O O t • O h | | 4 0 0 f e l M I I I M N B l H » 4 H V I * ^ a 4 U ■ H M 4 4 ■ H M 3 M i « * U M R < 9 ft U ft * 0«04< 4 1> J H M N M M M M J h M H A IM IU M H J U ^ M ft«ftft.ftj»ftlflft4 ^A l -3MVI i ■ DOM *J 4I K » / f • N I H 0 M A M h > h 1 0 ^ OM»- J ■4aM•I .| • O A k 4 W I I f M l H 4 H H K |M Q |« M •O H H M 4 S I O O I ^ h ^ M f < U M 4 > u a * » > 3 ^ H « a W H H M ^ H » 3 > > O O O O O O A C O W l * . I I)► < I• ■ ■ *toft*► >ONk IU O • ■ O h E « U « # 4 O «U x ** «* m «r < W ^ t f ^ K * « h K V O * • •HIM • M > V I I«HO >V • fr» V) • ar — — o U it *4 M 269 O O CMECK * o to see if GOES to TOUCH 1 uo ? i APPENDIX E-l Summary of Supply Data for Confiquration No. 1 Mill No. Coord 40 37 4736 4732 Supplyinq Mills of Bark Var. No. Cost/Raw Truck Loads Material Cost Total Cost Total Time (hours) 2-383.80 661.20 13563.00 3654.00 794.6 220.4 4212.00 3045.00 17217.00 1015.0 9960 4212.00 3045.00 17217.00 1015.0 0 0.00 0.00 0.0 Dis Tons 6 12 6577 1392 822 174 8220 1740 2959.20 1252.80 Total 7969 0 9960 Truck 1 7969 0 Truck 2 0 0 Cost of Driver 0.00 ' 212 Supplyinq Mills of Sawdust 1872 4160 11794 2496 1092 187 416 1179 249 109 3740 8320 23580 4980 2180 336.60 748.80 4244.40 1792.80 1373.40 364.65 811.20 2829.60 821.70 506.85 4441.25 9880.00 30654.00 7594.50 4060.25 121.5 270.4 943.2 273.9 168.9 Total 21414 0 42800 8496.00 5334.00 56630.00 1778.0 Truck 1 12094 0 24180 3267.00 2630.25 30077.25 867.7 Truck 2 9320 0 18620 5229.00 2703.75 26552.75 901.2 39 38 40 37 41 4636 4735 4736 4732 4840 3 3 6 12 21 APPENDIX E—1 Summary of Supply Data for Conficruration N o . _1 Mill No. Coord 40 37 4736 4732 Dis Tons 6 12 6577 1392 Supplyinq Mills of Bark Var. No. Cost/Raw Truck Loads Material Cost 822 174 Cost of Driver Total Cost Total Time (hours) 8220 1740 2959.20 1252.80 2383.80 661.20 13563.00 3654.00 794.6 220.4 9960 4212.00 3045.00 17217.00 1015.0 Total 7969 Truck 1 7969 0 9960 4212.00 Truck 2 0 0 0 0.00 3045.00 ' 17217.00 0.00 0.00 1015.0 0.0 212 Supplyinq Mills of Sawdust 1872 4160 11794 2496 1092 187 416 1179 249 109 3740 8320 23580 4980 2180 336.60 748.80 4244.40 1792.80 1373.40 364.65 811.20 2829.60 821.70 506.85 4441.25 9880.00 30654.00 7594.50 4060.25 121.5 270.4 943.2 273.9 168.9 Total 21414 0 42800 8496.00 5334.00 56630.00 1778.0 Truck 1 12094 0 24180 3267.00 2630.25 30077.25 867.7 Truck 2 9320 0 18620 5229.00 2703.75 26552.75 901.2 39 38 40 37 41 4636 4735 4736 4732 4840 3 3 6 12 21 APPE N D I X E — 2 Summary of Supply Data for Configuration N o . 2^ Mill No. Coord 93 3529 3326 91 4028 69 3323 73 Total Truck 1 Truck 2 Dis 3 12 15 21 Tons 1531 1114 1740 1740 6125 6125 0 Supplyinq Mills of Bark Var. Cost/Raw No. Truck Loads Material Cost 1910 191 343.80 139 1390 1000.80 217 2170 1953.00 217 2170 2734.20 0 7640 6031.80 0 7640 6031.80 0 0.00 0 Cost of Total Driver Cost 467.95 2721.75 528.20 2919.00 922.25 5045.25 1117.55 6021.75 3035.95 16707.75 3035.95 ' 16707.75 0.00 0.00 Total Time (hours) 156.0 176.1 307.4 372.5 1012.0 1012.0 0.0 Supplyinq Mills of Sawdust 3 12 15 21 27 27 2746 1997 3120 3120 312 324 11619 8462 3156 274 199 312 312 31 32 0 0 0 5480 3980 6240 6240 620 640 23200 16900 6300 493.20 1432.80 2808.00 3931.20 502.20 518.40 9685.80 5490.00 4195.80 534.30 656.70 1170.00 1450.80 172.05 177.60 4161.45 2640.00 1521.45 6507.50 6069.50 10218.00 11622.00 1294.25 1336.00 37047.25 25030.00 12017.25 178.1 218.9 390.0 483.6 57.3 59.2 1387.1 880.0 507.1 213 3529 93 3326 91 69 4028 73 3323 95 3024 94 2931 Total Truck 1 Truck 2 APPENDIX E-3 Summary of Supply Data for Configuration N o . 3^ Supplying Mills of Bark Mill No. Coord 70 3519 73 3323 91 3326 93 3529 Total Truck 1 Truck 2 Dis 3 15 24 27 70 73 74 72 54 98 91 87 48 47 95 93 89 55 50 92 Total Truck 1 Truck 2 3 15 18 18 21 24 24 24 24 24 27 27 27 27 27 36 - 4030 3120 780 312 624 832 1997 780 780 312 312 2746 312 312 312 630 18190 7580 10610 403 312 78 31 62 83 199 78 78 31 31 274 31 31 31 62 0 0 0 Cost/Raw Material 2800 2170 1390 1910 8270 8270 0 Var. Truck Cost 504.00 1953.00 2001.60 3094.20 7552.80 7552.80 0.00 Supplying Mills of Sawdust 8060 725.40 6240 2808.00 1560 842.40 620 334.80 1240 781.20 1660 1195.20 3980 2865.60 1560 1123.20 1123.20 1560 620 446.40 620 502.20 4438.80 5480 620 502.20 620 - 502.20 620 502.20 1339.20 1240 20032.20 36300 15160 3997.80 21140 16034.40 Cost of Total Driver Cost 686.00 3990.00 922.25 5045.25 778.40 4170.00 1155.55 6159.75 3542.20 ' 19365.00 3542.20 19365.00 0.00 0.00 Total Time (hours 228.7 307.4 259.5 385.2 1180.7 1180.7 0.0 785.85 1170.00 327.60 130.20 288.30 423.30 1014.90 397.80 397.80 158.10 172.05 1520.70 172.05 175.05 175.05 427.80 7730.55 2136.45 5594.10 261.9 390.0 109.2 43.4 96.1 141.1 338.3 132.6 132.6 52.7 57.3 506.9 57.3 57.3 57.3 142.6 2576.8 712.1 1864.7 9571.25 10218.00 2730.00 1085.00 2309.50 3278.50 7860.50 3081.00 3081.00 1224.50 1294.25 11439.50 1294.25 1294.25 1294.25 3007.00 64062.75 21294.25 42768.50 214 3519 3323 3922 3817 2921 3815 3326 4219 2821 3116 3024 3529 4016 2818 2721 2825 Tons 2247 1740 1114 1531 6632 6632 0 No. Loads 260 217 139 191 0 0 0