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University Microfilms International 300 N. Zeeb Road Ann Arbor. Ml 48106 8415235 Levenson, Burton E. ECONOMIC ANALYSIS OF TREE IMPROVEMENT R E S EARCH IN MICHIGAN Michigan State University University Microfilms International 300 N. Zeeb Road, Ann Arbor, Ml 48106 Ph.D. 1984 PLEASE NOTE: In all cases this material has been filmed in the best possible way from the available copy. Problems encountered with this document have been identified here with a check mark V 1. Glossy photographs or pages_____ 2. Colored illustrations, paper or print_____ 3. Photographs with dark background_____ 4. Illustrations are poor copy______ 5. Pages with black marks, not original 6. Print shows through as there is text on both sides of page______ 7. Indistinct, broken or small print on several pages 8. Print exceeds margin requirements_____ 9. Tightly bound copy with print lost in spine______ 10. Computer printout pages with indistinct print______ 11. Page(s)___________lacking when material received, and not available from school or author. 12. Page(s)___________seem to be missing in numbering only as text follows. 13. T w o pages numbered____________.Text follows. 14. Curling and wrinkled pages______ 15. copy__ i / ' Other_____________________________________________________ __________ University Microfilms International ECONOMIC ANALYSIS OF TREE IMPROVEMENT RESEARCH IN MICHIGAN By Burton E. Levenson A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Forestry 1983 ABSTRACT ECONOMIC ANALYSIS OF TREE IMPROVEMENT RESEARCH IN MICHIGAN By Burton E. Levenson This dissertation describes and estimates the economic value of tree improvement research in Michigan. A classification of research products is constructed and applied to the forest products industry. Research activities are divided into three classes, primary, intermediate, and final research products. Only final research products are considered for economic valuation in this dissertation. The approach most appropriate to determine the economic worth of tree improvement research is calculation of net present value. tors which influence the value of tree improvement research are: Key fac­ the interest rate used in discounting; the degree to which the industry adopts use of genetically improved trees or how many trees are planted; the price of the forest product when sold at harvest; and, the genetic gain or percent improvement over the "wild population" resulting from tree improve­ ment research. The use of two valuation methods are employed to calculate net present value, discounting time, and discounting quantity or price. The results of the analysis indicate that primary benefits of research realized by the forest products industry may range from $52 million to $25 billion. A realistic estimate for the value of tree improvement research in Michigan is $262 million. Economic value of research using case studies representative of three forest product industry sectors were estimated. A typical pulp and paper company may derive a benefit of $1 million per mill from tree improvement research in Michigan. The State of Michigan public forestry program through the Department of Natural Resources may receive direct benefits from tree improvement research in excess of $5.5 million. A large Christmas tree farm in Michigan may derive a benefit of $895 thousand from tree improvement research. Surrounding states may receive direct benefits at least equal to those in Michigan due to the large number of seedlings exported by the Michigan nursery industry. Indirect and secondary benefits are not empirically calculated in the analysis, but appear to be large. Total costs of the research program when compared to the benefits are relatively ins ignifleant. ACKNOWLEDGMENTS Man/ people helped me in the preparation of this dissertation. My principal advisors, James Hanover and Larry Tombaugh were instrumental thesis. in helping me with the conceptual development of this They forced me to think clearly and carefully about its presentation. Their criticisms and encouragement helped me develop a high set of standards and convinced me I could meet those standards. I would especially like to thank James Hanover for the energy and unflagging support given to me while at the University. Lee James provided valuable insights about the broader implications of my work. Paul Rubin challenged me to be exact and helped in the mathematical aspects. He also painstakingly read the drafts of this dissertation and provided detailed comments and criticisms which resulted in significant improvement, both in the style and substance of the final version. John Gunter provided valuable advise and insights on the forest produces industry. Mel Koelling and Jonathan Wright were particularly helpful and a source of valuable information. My research could not have been completed without the cooperation and assistance of many people in the forest products industry. thank all those who helped. I I am primarily indebted to the United States Forest Service which provided partial funding to sponser the ii research. My fellow graduate researchers, Mike Morris, Art Gold, and the MICHCOTIP Lab were generous with their time and offered continual encouragement. I am especially thankful to Mike and Julie Gold for all their assistance and support. i am deeply grateful to Amanda, my wife, for her enduring patience, support, and understanding during one of the most challenging periods of my life. Finally, I wish to thank all the other individuals and institutions that helped me with this work. remain my own. iii Any errors or omissions TABLE OF CONTENTS CHAPTER INTRODUCTI ON The G en era l Economic Problem The Problem: Michigan Tree Improvement Research Timber Supply Outline of the Dissertation CHAPTER I - A RESEARCH CLASSIFICATION AS APPLIED TO TREE IMPROVEMENT RESEARCH The Research Process Tree Improvement Research in Michigan Tree Improvement Research Products CHAPTER II - THE ECONOMIC ENVIRONMENT AS IT AFFECTS TREE IMPROVEMENT RESEARCH Introduction Artificial Regeneration Levels Michigan Tree Seedling Industry Survey Genetic Gain Price of Forest Products Rotation Period Interest Rates Costs of the Research Program Time Table for Commercial Production CHAPTER III - MODELS FOR ECONOMIC ANALYSIS OF BIOLOGICAL RESEARCH Economic Analysis of Tree Improvement Programs Economic Analysis of Agricultural Research Ex-Post Studies Ex-Ante Studies Benefit-Cost Analysis Applied to Tree Improvement Research Summary CHAPTER IV - THE MQDEL USED IN THE ANALYSIS The Model Assumptions Created Valuation of Benefits Computer Programs Used to Run the Model Sensitivity Analysis CHAPTER V - RESULTS OF THE MODEL Benef its Costs Benefit-Cost Ratios Sensitivity Analysis Sector Analysis PAGE CHAPTER VI - ANALYSIS OF RESEARCH GAINS TO INDIVIDUAL PRODUCERS Introduction Reforestation - Private Sector Reforestation - Public Sector High Value Commodity Industrial Sector 119 CHAPTER VII - ANALYSIS OF RESEARCH GAINS AND POLICY IMPLICATIONS Introduction Interpretation of Results Direct Benefits Not Included in the Model Secondary Benefits Secondary Costs Analysis of Benefits Research Policy Implications Institutional Analysis Conclusions 133 APPENDIX Program ECON Program ANALYS Program ECNCOST 157 REFERENCES 168 v LIST OF TABLES TABLE PAGE Table 1.1 Commodity/Use at Primary Valuation Level 18 Table 2.1 Wholesale Price Range of Christmas Trees 1982 Season 37 Table 2.2 Parameter Values for Genetic Gain Estimates 38 Table 2.3 Parameter Values for 1983 Prices of Commodities 40 Table 2 . k Parameter Values for Initial Seed Orchard Production and Rotation Period 43 Table 2.5 Costs for One Progeny Test 46 Table 2.6 Site Preparation and Maintenance for Two Years: (Costs for One Trip) 47 Table 2.7 Total Cost for One Progeny Test 48 Table 2.8 Research Development Progress for Various Species 51 Table 5.1 Present Value Benefits - All Commodities, Infinite Rotations 97 Table 5 - 2 Present Value Costs of Tree Improvement Research 98 Table 5*3 Percentage Contribution to Present Value Benefits by Industry Sector - Constant Price Table 5 Percentage Contribution to Present Value Benefits by Industry Sector - Increasing Price 105 iq6 Table 5*5 Contribution to Benefits by Species 109 Table 5*6 Contribution to Benefits - Ranked by Species h q Table 5 .7 1983 Estimate of the Value for Raw Timber Produc ts in M ich igan Table 5*8 Tree Improvement Benefits as a Percentage of Timber Economy ^5 Table 6.1 Present Value Benefits of Tree Improvement Research - Pulp and Paper Company *22 vi Table 6 . Present Value Benefits of Tree Improvement Research - State of Michigan ONR 125 Table 6 . Present Value Benefits of Tree Improvement Research - Christmas Tree Company 129 Table 7. Present Value Benefits - All Commodities, Rotations From FI Seed Orchards Only 149 vii LIST OF FIGURES FIGURES PAGE 3 F igure 1 .0 Research Pathways of Tree Breeding for the Michigan Tree Improvement Program F igure 1.1 Years Remaining Until Production of Proven Genetically Improved Commercial Seed Orchards 17 F igure 1 .2 Market Chains of Commodities in the Analysis 20 F igure 2 .1 Population Distribution of Economic Traits 32 F igure 2 .2 Costs of Tree Improvement 50 F igure 3.1 Benefits of Tree Breeding Research 69 F igure 3-2 General Economic Model 71 F igure 3-3 Relationship Between Research Products, Markets, and Analysis Methodology 73 F igure 1*. 1 Equations to Calculate Economic Gain From Genetic Breeding 79 F igure h . 2 Projected Growth of Tree Planting in Michigan Reforestation Commodities 87 F igure *♦•3 Projected Growth of Tree Planting in Michigan Reforestation Commodities 88 F igure k . k Projected Growth of Tree Planting in Michigan Christmas tree Commodities 89 F igure *.5 Projected Growth of Tree Planting in Michigan Christmas tree Commodities 90 Figure If. 6 Projected Growth of Tree Planting in Christinas tree Commodities Michigan - 91 Figure k . J Projected Growth of Tree Planting in Ornamental Commodities Michigan - 92 Figure k . S Projected Growth of Tree Planting in Ornamental Commodities Michigan - 93 Figure 5.1 Discounted Costs for Tree Improvement Research ix 99 INTRODUCTION The General Economic Problem Calculating the economic return attributable to research is a problem in many scientific fields. problem is particularly acute. In the field of forestry, this Tree improvement research and development activities provide a potentially interesting case study, these activities are conducted under the assumption that the forest products industry and, ultimately, society will benefit. Research on tree improvement contributes to productivity in several ways: it increases the amount of raw material available, reduces the cost of obtaining the raw material, and improves the quality of the raw material. All three lead to increases in the total productivity of the forest products industry. If unlimited resources existed to conduct tree improvement research, all possible areas of potential productivity increase could be investigated at once. This is not the case. Resources available to conduct research programs are limited and decisions must be made as to which programs to fund. In the world of applied research, funding sources demand documentation of the expected returns from a research program. To justify the research, we must calculate the worth of the research investment and show the contributions the research makes to science. 1 2 The Problem: Michigan Tree Improvement Research Michigan State University has been a focal point for tree improvement research conducted in Michigan since i9 6 0 . Almost 1»00 forest genetic plantations have been established on 550 acres throughout Michigan, 96 % having been planted since i9 6 0 . purpose of these plantations are: The primary (1) to determine the type and degree of genetic variation for the commercially important species planted in Michigan; and (2) to serve as breeding arboretums for successive research and commercial establishment of seed orchards or vegetative propagation production centers. The tree improvement program implemented in Michigan is designed first to identify and quantify variation in a species through rangewide seed collection, and subsequent provenance planting or progeny testing. The next step is to identify the commercially valuable traits, and determine the extent of inheritance through progeny tests. The third step is to convert the progeny test into a genetically improved seed orchard by thinning the genetically inferior genotypes. A fourth step would be to do controlled crossing and breed a species to capture further improvement. Figure 1.0 shows the research pathways for production of genetically superior trees. The outline of the tree improvement program is basically the same as for highly successful agricultural crop plant breeding programs. Although similar in design to crop breeding programs, the time span for a breeding cycle in a tree species is measured in decades rather than the few years for most crop breeding. The long time span Figure 1.0 RESEARCH PATHWAYS OF TREE BREEDING FOR THE MICHIGAN TREE IMPROVEMENT PROGRAM SPECIES A Range—wide Seed Collection SPECIESB Provenance Tests Controlled Pollination Thin for Seed Orchard Large Breeding / \ Establish Papulation from Best Provenance Tests Sources Controlled Pollination / Progeny Tests Measure and Thin to Best Genotypes Inter-Species Hybridization Progeny Tests Measure and Thin to Best Genotypes F-) Hybrid Seed O rchard- Progeny Test F-j Seed O rchardProgeny Test Controlled Pollination I Controlled Pollination F«j Generation F2 Generation I 9 F3 Generation Fo Generation (Repeat breeding cycle again for subsequent generations) co A involved in tree breeding programs presents special problems to the research scientist and the economist documenting benefits to the tree improvement program. pathway options exist. It should be pointed out that other breeding Other tree improvement programs have used the breeding option of selecting in native stands using a "plus tree" phenotypic selection criterion. The validity of this breeding pathway for producing proven genetically superior trees has not yet been established. There are strong indications that the option used in the Lake States breeding program is the desired research pathway to obtain the maximum genetic gain per breeding cycle. (The program in Michigan has evolved to where this is the primary research pathway.) Special circumstances and individual needs of commercialization for some species do not rule out alternative research options. Relative to agriculture, tree breeding programs in the United States are a recent phenomenon. Only in the past three decades has a serious attempt been made at systematically establishing a commercial source of genetically superior trees for artifical regeneration. Timber Supply. One can see that eventually such a program to increase the supply of raw forest products might be attempted. Given an increasing population, growing economy and greater per capita consumption of wood-based products, the pressures on the forest as a raw materials supplier will increase. At the same time, pressures which demand a greater supply of wood fiber and forest products, also compound the demand made on the total forest resources through additional recreation, wildlife and wilderness demands. These 5 combined factors result in a decreasing forest land base from which to produce the raw forest products which are increasingly in demand. It is estimated that in Michigan alone, k k , 0 0 0 acres per year of commercial forest will be converted to agriculture, or developed for recreation each year until at least the year 2000.^ The United States Forest Service projects the demand for round wood from United States forest land will more than double by the year 2030 over the 13*7 billion cubic feet consumed in 1977* demand is for pulp products. Much of the increase in Timber product exports are also projected to substantially increase over the next 60 years. The Lake States region appears to be a major factor in future expansion of the nation's timber supply. From a national perspective the United States Forest Service anticipates a net wood supply deficit of approximately three billion cubic feet for the period 1990 to 2 0 3 0 . From a state perspective, Michigan's supply of timber products is seriously under utilized. Michigan is endowed with a land resource of 17*5 million acres of commercial forest land. A recently completed study shows positive ratios of net annual growth to removals for virtually all commercial forest species, ranging from 2.9 tor It.8. In terms of 1980 timber volume, Michigan had a net increase of over 600 million cubic feet. Abundance of the timber resource, close proximity to eastern markets and inexpensive international water transportation routes, present an opportunity for expansion of production. 6 In Michigan, the economic value of the 17*5 million acres is not entirely based on tree fiber products. A large but unquantifiable portion of the value is attributed to recreation, wildlife, and reserve values. Of the S.k million acres in public ownership (2.5 million acres in three national forests, 3*6 million acres in the nations largest state owned forest system, and the remainder in special public catagories) almost 1 million acres are either in reserve (no commercial access to raw wood products) or are managed for a primary product other than raw wood products (wilderness, grouse management, wild and scenic river land etc.). The time frame for planning in the forest products industry is unique in agricultural and natural resource production activities. Production cycles in excess of fifty years are common, with some exceeding 100 years. Even the relatively fas.t production of Christmas trees approaches 15 years to complete one crop rotation. This is one reason why so much attention is given to long-range projections of timber supply and demand. The number of years required for one cycle of a comprehensive breeding program is in excess of 50 years. A projection for the completion date of the first breeding cycle for all species in the Michigan tree improvement program is into the 21st century (approximately year 2008). To demonstrate how far into the future the effects of this program extend, the first harvest of genetically superior jack pine trees planted for a pulpwood harvest will begin 1*0 years from 1985 or in the year 2 0 2 5 , 7 The imposition of such long time spans, and future projections of dubious accuracy, create special problems in determining the economic value that can be assigned to the Michigan tree improvement program. Out!ine of the Pissertation This dissertation approaches the problem of measuring returns from research by looking at the actual situation of the Michigan tree improvement research program. (This situation has unique features in the returns from research class of problems). Although on the surface it is similar to agriculture and extension research programs, it possesses special characteristics which set returns from forest tree improvement research apart in a class of problems by itself. The long time periods involved, and very large potential supply needs mandate a model where the discount rate and projections of future use play an unusually important role. CHAPTER I describes the Michigan Tree Improvement Program as a comprehensive breeding research program. The research program is presented as a discrete program with both a beginning and ending date. The scope of total costs of the program are defined. The economic level of costs and benefits are confined to the same level of research product activity. The primary economic level of benefits and costs are associated with final product research activities. CHAPTER II presents the economic environment in which the research program is conducted. Gross benefits to the research program 8 are a direct function of at least four important variables. are: the quantity of research product used These (improved trees planted and harvested); the price of the trees when sold; the amount of genetic gain produced through the breeding program; and the discount rate used. The determination of values used in the model are given. CHAPTER III presents the various models which have been used in the past to estimate returns from research in biological situations. There are two common basic approaches. ex-post studies. The first basic approach uses These studies look at completed research programs and subsequent changes in output and prices. The ex-post studies can be further classified into consumer and producer surplus analyses (estimating average rates of return), or production function analyses (calculating the marginal rates of return). Neither is particularly suited for the tree improvement research situation. approach uses ex-ante studies. The second basic These look at future potential returns from completed or on-going research programs. Ex-ante studies are grouped into four classes: those using scoring models to rank research activities; analyses using benefit - cost methodology to establish ratios or rates of return; simulation models; and analysis using mathematical programming to select an optimal combination of research activities. A mathematical ex-ante benefit - cost is one suitable methodology for the tree improvement research situation. CHAPTER IV presents the model used. to the model are listed and explained. Major assumptions inherent The three computer programs used and necessary to analyze a case study are outlined. 9 CHAPTER V presents the results of a case study based on the model described in Chapter IV. The model shows large benefits from tree improvement research in Michigan. The range of estimates varies greatly according to future demand projections, relative scarcity of timber, and discount rate. Costs appear to be relatively insignificant when compared to even the most conservative estimates of benef its. CHAPTER VI shows how three different users of the forest resource individually benefit under the tree improvement program. The benefits to a large integrated pulp and paper company with a substantial artificial regeneration program are shown. The benefits to the Forestry division of the Department of Natural Resources for Michigan are presented. And, benefits -to a large integrated nursery and Christmas tree operation are shown. CHAPTER VII evaluates the research program in light of the model results. Discussion of secondary benefits and costs is presented. Policy alternatives are presented for completion of the research program. 10 NOTES - INTRODUCTION 1) Statistics are derived from many sources. Key studies used in this dissertation include; "Michigan Forest Resources 1979 * An Assessment", Michigan Department of Natural Resources; "An Assessment of the Forest and Range Land Situation in the United States", United States Forest Service; "Trends in Natural Resources Commodities", Potter and Christy; and the Forest Resources Inventory Survey currently being completed by the North Central Region of the United States Forest Service. 2) From, Lee James, Suzanne Heinen, David Olson, and Daniel Chappelle. 1982. Timber Products Economy of Michigan. Agricultural Experiment Station Research Report No. 446, Michigan State University; 23 P* <0 CHAPTER I A RESEARCH CLASSIFICATION AS APPLIED TO TREE IMPROVEMENT The Research Process When applying economic theory to the field of science, useful to view science as "the information industry." then the production process of new information. it is Research is As with more traditional industries, there is an ordered sequence of markets forming a market chain in which there is movement from "raw" to "partially fabricated" to "finished goods." At each level in the market chain, the number of potential uses or access to higher links in the market chain is reduced. Research production processes have problems or questions as inputs; information or answers as outputs. "innovative discovery."^ The process itself is Research production processes can be classified by two similar schemes of classification; process, or research output. innovative Industrial organi 2 ations find it .convenient to identify three stages of technological innovation. An example of such a classification would be the steps involved in bringing a new product to the commercial market. be the exploratory and discovery stage. The first, step would Second is the applied research stage, developing techniques and methods of refinement. third, is the development stage, And including such activities as market research and pilot scale production. 2 12 To apply economic theory in the analysis of research, it is more useful to classify the research production process in terms of output. Three classes of output or research products are defined: primary, intermediate, and final. The decision rule for classification is based on the diversity of the products' application to scientific disciplines and industrial processes. The logic behind this rule is to classify research products as to their relative effect on the economy. Research products which are the broadest in application, are primary research products. The biological research process can be viewed as a production activity resulting in three classes of research outputs; primary research products; products. intermediate reseach products; and final research In a two stage classification scheme, primary research is termed "basic research" or "pure research" and final research is called "applied research". Intermediate research products are blended into either primary or final research product classes. Primary biological research in forestry investigates the basic biological processes. Primary research topics tend to be discipline oriented or at least at the sub-discipl i.ne level. theoretical in nature. This research may be completely The research products further the frontier of knowledge at the most fundamental level of understanding. The impetus for conducting primary research may be anticipatory to a final research product. Public institutions such as the National Science Foundation are a primary funding source for this research class. 13 Primary research is valuable in expanding the frontier of knowledge and certainly is the foundation of most applied or final research. Determining a specific value of this class of research is virtually impossible without conducting a comprehensive global consumer and producer surplus analysis. In practice this type of analysis cannot be done. Intermediate research activities are those which enhance further investigation of primary and final research activities. These research products are more narrow in the scope of application than primary research products. This class of research includes discovery and testing of new scientific methods and analysis techniques. Examples of intermediate research are the invention and validation of new research methodology such as testing plant breeding experimental design, and discovery and refinement of analytical procedures such as high performance liquid chromatography. Intermediate research is also disciplinary in nature but tends to be limited to the discipline of the research process itself. Final product research or applied research is problem oriented and is highly specific in application. In the industrial world, this is the development class in a two-stage R&D classification scheme. Final research is the activity which combines knowledge and inventiveness to produce a commercial product. examples of this class of research. There are many Virtually the entire field of silviculture can be defined as producing final research products. The 14 applied research which produces genetically superior tree populations is another example. The final research product in this case is the identifed, evaluated, and genetically isolated population of trees which by some genetically controlled trait, on the average are "better" than the wild population. Tree Improvement Research in Michigan The scope of this dissertation is confined to investigating the potential returns to the final stage of research in the tree improvement programs at Michgan State University. of this research is information; superior seed orchards, The final product information to produce genetically (commercial seed orchards). The tree improvement research program designed to produce a commercial genetically superior tree began in i9 6 0 , 1961, and 1962 with Or Jonathon Wright's work on Scotch pine, eastern white pine, Austrian pine, red pine, Japanese larch and Hybrid larch (European/Japanese larch mixture). Of the 302 genetic plantations listed in "A Directory of Forest Genetic Planting in Michigan: June I98 O", only 15 were planted prior to i9 6 0 . Most of these very early plantings were jack pine and Scotch pine and are not considered as efforts included in the final product research stage. These 15 early plantations were established more for the.purpose of evaluating experimental design methods, and reforestation than as part of a comprehensive genetic breeding program.^ 15 This dissertation will consider 1961 as the effective beginning date for the final product stage of the tree improvement research activities in Michigan. By 19&1, a comprehensive plan for evaluating genetic variation and breeding selected populations of individual species for commercial use was established. receive major attention was Scotch pine. The first species to The primary need and potential beneficiary of the Scotch pine program was the Christmas tree industry. Many thousands of acres were being planted for Christmas trees in the early I9601s. Wright estimates that in i960 there were 100 million Scotch pine seedlings growing in Michigan nurseries, most of these intended for Christmas tree plantations. The research program expanded in the late 1960's with the addition of a second full-time researcher, Or. James W. Hanover. Currently, final research is in some stage of completion for 21 It different tree species representing 29 commodities/species. Many additional species are being examined under intermediate and basic research programs. investigation. Twenty-one species are in some stage of Because of the long time periods required between various research activities (such as the time between planting a provenance test and measurement) the investigation of a specific species may go "dormant" for several years at a time. The research program at its current size has the capacity to intensively investigate six species at a time. To intensively investigate a species in the context of the Michigan tree improvement research program means to allocate resources and active research effort to the 16 following activities: collect seed, grow seedlings, plant seedlings in genetic plantations, make controlled crosses (for progeny tests) and make grafted trees for vegetative propagation and seed orchard establishment. The research program is capacitated or constrained by greenhouse size, labor supply, and operating budget. Assuming a future commitment to funding at the present level, the research program is expected to be completed for all 21 species by the year 2008. Of the 21 candidate species for genetic improvement, five are close to commercial utilization in the next two years. Figure 1.1 graphically depicts the expected time of commercial seed orchard ava i1ab i1ity. Tree Improvement Research Products The 21 species investigated for final research products can be classified into three commercial uses: twelve species are in use or have potential for use in reforestation (pulp, fuelwood, and timber which includes both lumber and veneer); ten species are either used as Christmas trees or interest has been expressed for potential use as Christmas trees; and seven species are currently used as ornamental planting in the landscape industry. The species and commodities analyzed are listed in Table 1.1. Oifferent lengths of market chains are observed in the three commercial uses of species the research program is investigating. The sale of raw forest products is only one stage in a progressive chain of markets between trees and final consumer products. Products at Figure i.t YEARS REMAINING UNTIL PRODUCTION OF PROVEN GENETICALLY IMPROVED COMMERCIAL SEED ORCHARDS ♦* YEARS TO PRESENT (t983) 1983 1 2 3 4 5 6 7 8 9 ......14 * HYBRID PINE (proven better than either p i i i 1 i I i | * JACK PINE * SCOTCH PINE * BLUE SPRUCE a 15 r 16 e 17 n t 18 19 ) | l l I * EASTERN WHITE PINE l I I j * HYBRID POPLAR i 20..... 25 i I * WHITE SPRUCE * HYBRID SPRUCE (blue x white) i I * RED PINE ! * ASPEN * BLACK SPRUCE * WESTERN WHITE PINE * NORWAY SPRUCE * HONEYLOCUST * AUSTRIAN PINE * ENGLEMANN SPRUCE * OOUGLAS-FIR * EUROPEAN LARCH * HYBRID LARCH * BLACK WALNUT i ♦ WHITE FIR ** These estimates are based on the stage of research each species is at In the program, and biological parameters such as years-to-flowering. Initial commercial seed orchards are assumed to be constructed either from thinning progeny tests, or graphed from progeny test stock. 18 Table 1.1 COMMODITY/USE AT PRIMARY VALUATION LEVEL SPECIES COMMODITY USE CLASS - REFORESTATION HYBRID PINE JACK PINE HYBRID POPLAR E. WHITE PINE WHITE SPRUCE RED PINE HYBRID ASPEN BLACK SPRUCE HONEYLOCUST EUROPEAN LARCH HYBRID LARCH BLACK WALNUT Pulp Pulp Pulp Pulp Pulp Pulp Pulp Pulp Pulp Pulp Pulp Veneer (stumpage) (stumpage) (stumpage) (stumpage) (stumpage) (stumpage) (stumpage) (stumpage) (stumpage) (stumpage) (stumpage) (stumpage) USE CLASS - CHRISTMAS TREES SCOTCH PINE BLUE SPRUCE E. WHITE PINE WHITE SPRUCE HYBRID SPRUCE W. WHITE PINE AUSTRIAN PINE ENGLEMANN SPRUCE DOUGLAS-F1R WHITE FIR Cut Cut Cut Cut Cut Cut Cut Cut Cut Cut wholesale wholesale wholesale wholesale wholesale wholesale wholesale wholesale wholesale wholesale trees trees trees trees trees trees trees trees trees trees (F.0.8. (F.O.B. (F.O.B. (F.O.B. (F.O.B. (F.O.B. (F.O.B. (F.O.B. (F.O.B. (F.O.B. farm) farm) farm) farm) farm) farm) farm) farm) farm) farm) USE CLASS - ORNAMENTAL BLUE SPRUCE E. WHITE PINE WHITE SPRUCE NORWAY SPRUCE HONEYLOCUST AUSTRIAN- PINE DOUGLAS-FIR Wholesale Wholesale Wholesale Wholesale Wholesale Wholesale Wholesale landscape landscape landscape landscape 1andscape landscape landscape stock stock stock stock stock stock stock (F.O.B. (F.O.B. (F.O.B. (F.O.B. (F.O.B. (F.O.B. (F.O.B. nursery) nursery) nursery) nursery) nursery) nursery) nursery) 19 lower markets are inputs to higher markets. The effects of lower markets are transmitted upward through the supply functions of higher markets. The dynamics of an individual link in a market chain are directly effected by supply and demand factors of the other links in the chain. The factor demand of the higher markets are determined in part by the marginal revenue product and marginal factor cost of the factor input of the lower markets.^ This statement implies the profit of raw forest products effects the supply of many other higher market products. The reforestation species are at the bottom of a very long market chain. Trees are sold for pulp or timber in the forest to a woodcutter or wholesaler. This is the most basic or lowest level in the market chain at which a market exchange situation exists. In the example of trees sold to a pulp mill, six to ten subsequent market chain links or production processes are added until the consumer gets a final product and the ultimate direct value of the tree (now paper) is realized. The Christmas tree is linked to the consumer through a much shorter chain, composed of only two to four links. The first link is when the Christmas tree is cut and sold to a wholesaler. The Christmas tree is then sold to a distributor or directly to a consumer. The ornamental tree shows the shortest market chain, with only one or two links. The nursery sells a tree to a landscaper who uses the tree as an input into a consumer oriented production process (landscaping). Figure 1.2 is an example of the various possible market chains for the three commercial uses described. Figure 1.2 ; REFORESTATION ■ ■ ■ i ■ ■ Pulp Sticks Trees .W o o d . Cutter's Stumpage Bulk Roll Paper Pulp Mill' Stock - Reforesters or Fabrication Manufacturers or Distributors ► PrintersWholesalers CHRISTMAS TREES LEGEND Consumer 'Distributor ORNAMENTALS Landscape Input Trees Nursery' Wholesale PRODUCT Individual Traes Bulked Trees Trees Consumer MARKET Consumer 21 The economic analysis recognizes that various .levels of value are attained in each market. On a ton equivalent, a ton of stumpage is worth $3.20; a ton of wood chips at the pulp mill is $ 2 3 .0 0 ; a ton of pulp ready for paper manufacture or sale is worth $3 5 0 .0 0 ; and a ton of finished glossy magazine paper is sold for $2,000.00. At the consumer level, one ton of TIME magazines retails for $12,l»00.00. each market exchange level, additional At inputs are added to the basic raw resource (wood) to increase the value. The market exchange levels may be thought of as analogous to the three reseacch levels (primary, intermediate, and final) in their effect. research to final product development, As this analysis restricts it also restricts the valuation of the resource to the first link in the market chain. At this level in the market chain, there is a minimum of outside, value-laden inputs. The first level of valuation (assuming competitive markets) is where the most clearly defined (with respect to market imperfections) effects of tree improvement research are felt. To conduct the analysis at a higher level in the market chain, say at the paper mill level where the price of pulp is used as the index of value, would not only be counting gains to genetic improvement research but also gains to technological innovations in transportation, chemical refining processes and machinery control fields. It would be difficult to analytically separate the individual technical and research effects on production and the economy. The costs of the research are calculated over the life of the program, from year 1961 to 2008. During this time period, all 21 22 species are expected to have genetically superior populations segregated providing the opportunity for commercial seed orchard establishment. The benefits or value attributed to the research is carried to the same level for which costs of research are calculated. 23 NOTES - CHAPTER I 1) Schumpeter in "The Theory of Economic Development" discusses the classical theory of innovation, making the point that innovation itself is instantaneous but requires development, the process of creating a set of technical instructions to utilize the innovative discovery. The combination of the two processes will be called research in this dissertation. 2) This classification is given by Americo Albala in "Stage Approach for the Evaluation and Selection of R&D Projects". Joel Goldbar, Louis Dragaw and Jules Schwartz in "Information Flows, Management Style, and Technological Innovation" also present a similar classification system: Stage 1, idea generation and design concepts: Stage 2, problem solving and engineering; Stage 3* commercialization and marketing. These are two examples of many similar classifications dealing with industrial product development. 3) Personal communication concerning the history of tree breeding in Michigan with Dr. J. Wright. 4) Some species are used for more than one purpose. As an example, white pine is used for reforestation, Christmas trees, and as an ornamental. The term "commodities" will be designated to mean a species being used for a distinct commercial purpose. 5) Mills discusses this interaction in detail using the softwood lumber market as an example in "An Econometric Analysis of Market Factors Determining Supply and Demand for Softwood Lumber", PhD thesis MSU, 1972. p. 14-24. CHAPTER I I THE ECONOMIC ENVIRONMENT AS IT AFFECTS TREE IMPROVEMENT RESEARCH Introduction The economic environment in which the research process takes place is an important factor to consider in the tree improvement program. The economic environment often dictates the direction and scope of research. In conducting an economic analysis, one of the * first steps is to identify and measure the economic parameters which have an affect on the research process. Setting up and defining the economic enviromnment is critical to the analysis of returns to research. Environmental and industrial constraints put limits on the potential use and application of the final research product. In Michigan, potential gross benefits to tree improvement programs are a function of numerous variables. Four broadly aggregated variables interact to determine the gross level of benefits. These are: the number of genetically superior seedlings produced and planted; the price of the tree when sold (at the first level of valuation); the percent genetic gain of a trait (over the mean of the wild population) which can be expected to occur as a result of the breeding program; and the discount or interest rate used. These variables are broadly aggregated and incorporate several assumptions and "hidden" functions. As an example, price, as defined here, assumes a certain grade or quality of product and is represented 24 25 as an average price incorporating differences in regional demand, location differences, and seasonal factors. Artificial Regeneration Levels Quantity is an important variable in the analysis. Three different estimates reflecting three separate possible economic levels of activity were used. The first is the number of seedlings commercially planted in Michigan in 19 8 1. The second estimate is the number of seedlings which are currently planned for commercial planting in 19 8 6 . The third estimate is the projected number of seedlings planted, assuming logistic growth functions in the commercial planting industry. These three estimates of commercial planting represent conservative, middle-of-the-road, and optimistic outlooks respectively, on the future of the commercial tree planting industry in Michigan. To develop a minimum baseline economic level of activity in the tree planting industry, a comprehensive state-wide survey of the tree seedling industry was conducted.^ The purpose of the survey was to determine an accurate estimate for the number of tree seedlings planted in Michigan. 26 Michigan Tree Seed 1ing Industrv Survey In early 1982, over 500 potential members of the tree seedling industry were sent a four page questionnaire. The survey was designed to obtain information on the commercial tree seedling nursery, and commercial tree seedling planting activities in Michigan. Information was requested about current production levels (1981), and future production levels (1986). The response from the industry to the survey was excellent with over 70% returning completed questionnaires. members of the potential A random sample of nineteen industry not responding (the 30 % of the industry which did not return a questionnaire) was contacted by phone. By selecting a random sample of the non-respondents, an accurate estimate could be made for the entire industry. 2 The format of the survey allowed information to be tabulated by * firm and by species of tree seedlings both grown and planted. This was accomplished by aggregating the survey across species and across firms respectively. Two tree seedling sub-industries were identified by the survey: the nursery sub-industry which produces seedlings for sale, and the planting sub-industry, planting seedlings as part of a production process. In Michigan there are four important economic production activities which rely on trees as raw inputs in the production process. The raw inputs are classified according to the production processes as follows: reforestation (planting thirteen different species in 1981 primarily for pulpwood production); Christmas trees (twelve species planted in 19 8 1 ); ornamentals (five species produced from seedlings in 1981 and hundreds produced from 27 whips or cuttings); and fruit trees (primarily produced from whips or cuttings). This analysis will not be concerned with vegetatively propagated ornamentals or fruit trees. The scope is limited to reforestation, Christmas tree, and ornamental production processes that use planted seedlino derived trees. The survey polled only those companies, institutions and individuals considered commercial members of the tree seedling industry. Not included are the numerous "hobby" planters and small landscape nurseries each producing less than one thousand seedlings per year. The rationale behind excluding this non-commerical group from the survey is that commercial members need less convincing to implement use of genetically superior tree seedling stock (i.e., adoption costs of new products are close to zero) . The results of the survey provided accurate information on the number of commercial tree seedlings produced by nurseries and planted in Michigan both in I9 8 I and projected in 1986. in the commercial tree planting industry, a planning lead time of four to five years is necessary. The planting site must be cleared and prepared and seedlings contracted for (if quantities are large) several years in advance to assure adequate supply. Therefore, figures projected for 1986 also represent a realistic assessment of planting levels for 1986. There are approximately 300 commercial or industrial organizations which are extensively involved with growing and planting 28 tree seedlings in the state. These include the state and federal agencies which engage in reforestation for all purposes. The majority of these firms are in planting as a sub-production activity, usually for a future end-product such as Christmas trees, woody fiber or timber. (pulp), There are very few strictly professional planting companies in Michigan. Seventy percent of the tree seedling industry engages strictly in planting, buying all their seedling planting stock from nurseries. Ten percent of the tree seedling industry specializes in growing tree seedlings and does not engage in significant planting activities. The remaining twenty percent are combinations of nurseries and planters who grow their own seedlings for their own planting operations. In 1981 over 37 million tree seedlings were planted in Michigan. Virtually all the seedlings planted were produced by Michigan nurseries. At the present time, either by convenience or quality needs, the Michigan planting sub-industry is quite dependent upon Michigan nurseries. There is very little seedling stock brought in from nurseries in surrounding states. Plans for 1986 show even less willingness by commercial planters to rely on out-of-state nurseries for supplies of planting stock. Nine conifer species accounted for over 90% of the commercial planting in 19 8 1. These species are, European larch, white spruce, blue spruce, jack pine, Austrian pine, red pine, white pine, Scotch pine, and Oouglas-fir. This includes the "pine and spruce species-undifferentiated." Almost 40% of all the seedlings planted 29 were for Christmas trees and 50% were for reforestation purposes. The Soil Conservation District (SCO) supplied only a small number of seedlings to commercial planters (less than 600 thousand) with most of the SCO stock going to the "non-commercial11 planter or "hobby-farmer Over 86 million tree seedlings were grown in 1981 - The nursery industry is a strong export industry supplying many planters outside of Michigan. In 1981, 25 million Michigan-grown tree seedlings were planted in other states. The same nine conifer species account for over 80% of the total seedling production in number of seedlings grown. The species which are exported out- of the state are primarily for Christmas trees and high value ornamentals. In both the nursery sub-industry and' planting sub-industry, a few firms or organizations account for the bulk of the commercial production. The planting sub-industry has 7*5% of the firms controlling 77*5% of the total production. The nursery sub-industry has 8.9% of the firms, controlling 5^*5% of the total production. In this sense, the planting sub-industry is subject to less competitive pressures than is the nursery industry. Additionally, more of the nurseries rely on selling seedlings as their primary economic activity, than do the majority of the commercial planters. Most of the commercial planters are planting tree seedlings as one input into a larger production activity. The practical implication of this observation is that the nursery industry will most likely bend to the increased demand from the planting sub-industry and expand production. 30 The Christmas tree and reforestation industrial sectors account for most of the volume in tree seedling production and planting. ornamental The industry sector may account for a higher dollar percentage than the volume in ornamental seedling production indicates, (ornamental seedlings typically are much higher priced than Christmas tree or reforestation stock.) Fruit tree production is controlled by two or three companies with one nursery exerting virtual monopolistic control on fruit tree seedling production in Michigan. Within the Christmas tree industrial sector, the importance of various species to the commercial grower may change in the coming years. The basic fbur: Scotch pine, blue spruce, white spruce, and Douglas-fir will still account for the bulk of the industry in volume, but Fraser fir, Balsam fir, and white pine will become increasingly important. There has also been a large increase in the amount of Christmas tree planting over the past decade. Data from this survey indicate an increase of more than 20% per year in the total number of seedlings planted. This trend is calculated by combining results from a survey conducted by the Cooperative Extension Service several years, ago with the results from this survey. This survey anticipates that industry growth will be sustained if there is not a serious shortage of seedlings for the grower. The source of seedlings for the planting sub-industry is expected to be Michigan nurseries. There are no indications from commercial planters of a willingness to go out of state to purchase seedlings. Shortages of seedlings for Christmas trees may occur in blue spruce, white spruce, and White fir if 31 nurseries are unresponsive and planters implement projected plans. The reforestation industrial sector has also experienced a rapid growth over the past decade. Results from our survey predict this industry sector will continue its expansion, planting almost 27 million seedlings in 1986 for reforestation purposes. expansion is projected to occur in red pine. The primary European larch, and jack pine are also expected to increase in number of seedlings planted for reforestation purposes. The major future source of the seedlings is anticipated to be provided by "own production" facilities and, by private Michigan nurseries. There appears to be a strong market for high quality, genetically improved seedlings of red pine, white spruce, and European larch. The availability of commercial quantities of genetically improved seed for these species will further fuel seedling demand in the next decade. The implication of the tree seedling industry analysis is that the demand and potential use of tree improvement research products will continue to grow. Genetic Gain The amount of genetic gain is also an important variable. % Genetic gain is defined as the percent improvement of the mean of the selected population (Fjgeneration) over the mean of the-wild population for the trait being considered. Figure 2.1 shows the results of genetic breeding on the variation of economic traits found in tree populations. Gain can be applied to any genetically determined trait and population. Within the context of this analysis, Figure 2.1 POPULATION DISTRIBUTION OF ECONOMIC Without Selection TRAITS With Genetic Selection Original Population LMSt desirabk Most desirable OJ N> Population X X F breeding cycle genetic gain moves 1 the mean toward the desirable and of the distribution F2 Population F3 Population x= mean of population x 33 genetic gain is only relevant insofar as it effects the price of the product sold. As an example, it may be possible to select for and capture a 50% gain in fall leaf color in aspen (used almost exclusively for pulp structural timber). This would not effect the price of aspen as wood fiber material. and a Thus, in this model, that trait is not considered. For species used as timber (pulp), the primary characteristic or trait selected is volume or growth rate. specific gravity are also considered. Other traits such as In the current market structure for pulp stumpage, volume and species are the only factors considered in determining price (regional, economic, and geographic factors ceterus paribus). For Christmas tree species a multitude of genetically controlled characteristics contribute to the price formulation. Species, tree color, needle sharpness, needle stiffness, needle length, needle retention, natural form (which itself includes many traits), growth rate, and uniformity, among others, are all part of the price function. As in the case of Christmas trees, for species commercially grown from seedlings and used as ornamentals, price is calculated based on a "bouquet" of characteristics.- Many of these characteristics are the same as those found in the Christmas tree price function and are genetically controlled. Growth rate is certainly highly weighed but tree form, color and hardiness (tolerance to cold, pollution etc.) are also important. 34 Specific determination of genetic gain is difficult to calculate before progeny tests are evaluated. F-l seedlings must be produced, allowed to mature, and harvested, to compare yields with similar environments of "wild" populations. be determined. The actual genetic gain can then None of the species being investigated in the research program are currently at this stage. Fairly accurate estimates of genetic gain can be made at the progeny test evaluation stage of development. Based on statistical estimation of heritability and roguing rates, numerous estimates of genetic gain have been made for a number of species commercially propagated in the Lake States. The most complete and accurate estimates are available for the genetic gain of jack pine when used as a timber (pulp) species and blue spruce when used as a Christmas tree or ornamental. A recent unpublished evaluation of jack pine 1/2-sib progeny tests designed for conversion to seed orchards show a genetic gain in volume ranging from 8% at a 62% roguing rate, to 13% at a 87 % roguing rate. A genetic gain of 1, 3.1% in specific gravity is estimated at a 75% roguing rate. A slightly positive correlation is observed between volume and specific gravity. The actual roguing plan may be determined by a linear programming model or other appropriate optimization technique to maximize overall genetic gain. Detailed information concerning the genetic variation in important Christmas tree traits has recently become available for blue spruce. Schaffer determined the variation in needle length to be 18% genetically determined, needle sharpness 28 %, and variation in needle stiffness to be 19% genetically 35 determined. Color or "blueness" is also observed to be under strong c genetic control. Wright has found substantial genetic differences in Scotch pine for several economically important Christmas tree traits. A 58 % difference in resistance to Pine Root Collar Weevil was observed in a Scotch pine provenance plantation consisting of trees from 108 different natural stands, collected from twenty-one different geographic regions. for height Similar differences in Scotch pine are observed g (growth), fall foliage color, needle length, and form. Work in Black pine (Pinus nigra) indicates a range of 11% to 19% of the growth rate may be attributable to genetic factors.^ Guries has reported that red pine progeny plantations after roguing and conversion to seed orchards will yield a 9-12% genetic gain in g volume. Work with several species of larch point to genetic gains of 10 - 15% in volume during the first breeding cycle (F^) using the q best geographic seed source. The range of values used in this analysis represent low, medium, and high estimates of genetic gain for respective species. The genetic gain estimates, as defined here, are the differences in total net revenue which can be expected from using genetically improved stock. For instance, a 10% genetic gain for a reforestation species means that the genetically improved stock, when harvested, will yield 10% more net revenue. This is because there is a direct price-volume relationship for stumpage. For the reforestation species this is brought about primarily through an average increased growth rate raising total gross revenue, (with costs held constant). Other 36 genetically controlled traits may contribute to lowering the cost thereby increasing net revenue. Genetic gain is represented in Christmas trees as an increase in net revenue brought about through improvement in a multitude of traits. Christmas trees and ornamentals propagated from seedlings exhibit a tremendous price differential at the wholesale level. An unpublished 1982 survey of the Michigan Christmas Tree Growers Association (accounting for 60% of Michigan Christmas tree production) showed price differences as wide as 1*50% from low price to high p r i c e . T h e differences in the price of Christmas trees are shown in Table 2.1. After extensive and detailed discussions with key figures in the reforestation, Christmas tree, and ornamental industries, (and with the primary researchers conducting the tree improvement program) the following estimates of genetic gain were determined for all twenty-nine species/commodities which are in the economic model. These estimates are presented in Table 2.2. Price of Forest Products Prices of the commodity at the designated market link are taken from the competitive market place. Reforestation commodity prices are shown as dollars per tree for stumpage. the following method. This price is derived using The base price used is the average price per cord stumpage in the appropriate region as reported by Timbermart North Price Reporting Service 1st Quarter 19 8 3 • A cord is assumed to have 100 cubic feet (since harvest methods assume whole tree 37 Table 2.1 WHOLESALE PRICE RANGE OF CHRISTMAS TREES - 1982 SEASON SPECIES SIZE CLASS (FEET) LOW PRICE ($/TREE) HIGH PRICE ($/TREE) SCOTCH PINE 5 - 6 1/2 2.50 1 0 .5 0 SCOTCH PINE 6 1/2 - 8 3.00 13.50 BLUE SPRUCE 5 - 6 1/2 6.00 1 7 .0 0 BLUE SPRUCE 6 1/2 - 8 7.35 20.00 DOUGLAS-F1R 5 - 6 1/2 6.00 20.00 OOUGLAS-F1R 6 1/2 - 8 7.35 2 5 .0 0 WHITE SPRUCE 5 - 6 1/2 6 .3 2 12.50 WHITE SPRUCE 6 1/2 - 8 6.00 17-50 AUSTRIAN PINE 5 - 6 1/2 2.00 7.50 AUSTRIAN PINE 6 1/2 - 8 12.50 17.50 WHITE PINE 5 - 6 1/2 5-50 9.00 WHITE PINE 6 1/2 - 8 8.00 11.33 From the annual Michigan Christmas Tree Association Marketing Survey Department of Forestry, Michigan State University, 1983* 38 Table 2.2 PARAMETER VALUES FOR GENETIC GAIN ESTIMATES COMMODITY/SPECIES GENETIC GAIN ESTIMATE -----------------------LOW MEDIUM HIGH (PERCENT) REFORESTATION/ HYBRID PINE JACK PINE HYBRID POPLAR E. WHITE PINE WHITE SPRUCE RED PINE HYBRID ASPEN BLACK SPRUCE HONEYLOCUST EUROPEAN LARCH HYBRID LARCH BLACK WALNUT 5 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 10 10 10 10 10 10 10 30 15 30 30 30 15 30 15 15 30 30 15 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 10 10 10 10 10 30 30 30 30 30 30 30 30 30 30 5 5 5 5 5 5 5 10 -10 10 10 10 10 10 30 30 30 30 30 30 30 CHRISTMAS TREES/ SCOTCH PINE BLUE SPRUCE E. WHITE PINE WHITE SPRUCE HYBRID SPRUCE W. WHITE PINE AUSTRIAN PINE ENGLEMANN SPRUCE DOUGLAS-FIR WHITE FIR ORNAMENTAL/ BLUE SPRUCE E. WHITE PINE WHITE SPRUCE NORWAY SPRUCE HONEYLOCUST AUSTRIAN PINE DOUGLAS-FIR 39 chipping). One individual tree at harvest is assumed to yield fifteen cubic feet of chips. Lake states industry trends show a progression of harvest methods from the chainsaw skidder method to whole tree chipping. By the time most of the new timber will be harvested (a minimum of 35 years from 1983) it is expected that virtually all the commercial harvest of pulp plantations will be by whole tree chipping. Using Miller's Hybrid pine biomass equations, a tree with a volume of 15 cubic feet would be of O . k , and a 7*5 inch DBH. 32 feet high, have a specific gravity Using Smalians formula it would be a log 37 feet long with a butt diameter of 10.575 inches and a top diameter of 6 inches.11 In a plantation setting, a tree yielding 15 cubic feet of usable chips would have dimensions between the above two extremes. The price per cord is converted to price per tree using 100 cubic feet per cord and a 15 cubic foot tree as parameter values. Prices of Christmas trees are derived using weighted averages of wholesale prices received by Christmas tree producers in the 1982— 1983 season. These are reported in .the Michigan Christmas Tree Growers Association market survey.1^ The price of ornamental species is derived from a telephone survey of four large area wholesale ornamental nurseries. According to these nurseries the most common size of ornamental tree sold is the six foot size stock with price averaging $10 per foot. A wide variation exists depending upon the quality, form, and general condition of the tree.1^ The prices used for all commodities in each use class is shown in Table 2.3* AO Table 2.3 PARAMETER VALUES FOR 1983 PRICES OF COMMODITIES COMMODITIY/SPECIES UNIT/TREE REFORESTATION cu ft/tree HYBRID PINE JACK PINE HYBRID POPLAR E. WHITE PINE WHITE SPRUCE RED PINE HYBRID ASPEN BLACK SPRUCE HONEYLOCUST EUROPEAN LARCH HYBRID LARCH 15 15 15 15 15 15 15 15 15 15 15 BLACK WALNUT 40 CHRISTMAS TREES PRICE/UNIT PRICE/TREE stumpage $/cord stumpage $/tree 7 .0 0 1 0 .0 0 8 .0 0 8 .0 0 6.50 7 .0 0 5 .0 0 6 .5 0 8 .0 0 5 .0 0 5 -0 0 $ M/bd ft 1 ,0 0 0 .0 0 individual tree cut at the farm BLUE SPRUCE E. WHITE PINE WHITE SPRUCE NORWAY SPRUCE HONEYLOCUST AUSTRIAN PINE DOUGLAS-FIR 1 .5 0 1 .2 0 1 .2 0 0.975 1.05 0.75 0.975 1 .2 0 0.75 0.75 480.00 $/tree wholesale F.O.B.. farm SCOTCH PINE BLUE SPRUCE E. WHITE PINE WHITE SPRUCE HYBRID SPRUCE W. WHITE PINE AUSTRIAN PINE ENGLEMANN SPRUCE DOUGLAS-FIR WHITE FIR ORNAMENTAL 1.05 8.75 12.00 11 .00 10.00 12.00 1 1.00 12.00 12.00 12.50 7-00 hei ght (feet) 6 6 6 6 6 6 6 $/foot 10.00 10.00 10.00 10.00 10.00 10.00 10.00 $/6 foot tree 60.00 60.00 6 0 .0 0 6 0 .0 0 6 0 .0 0 60.00 60.00 41 Rotation Period Inherent in the total net revenue function is the number of years each rotation requires for a given forest product. All revenues are counted at time of harvest (end of a rotation) and discounted back to 1983 . The longer the rotation, the greater the effect the discount rate will have on total revenues. In present value dollars, longer rotations are worth less than shorter rotations, ceteris parabus. In this way the length of the rotation is important in determining the magnitude of the total revenue. For a given environmental site, price and demand structure, an optimal rotation period can be determined. For timber species, this range where the optimal rotation period occurs begins when the average revenue starts to decrease (diminishing average returns).^ The values for rotation length were taken as average production rotation periods expressed in years. The rotation period for reforestation species is standardized for 15 cubic foot tree yields. (The number of years it takes to produce a tree with 15 cubic feet of chips.) The number of years required to grow a tree with 15 cubic feet of chips is dependent upon many factors such as site index, stem density, and numerous silvicultural inputs. The rotation values used are within the range of feasible optimal rotation periods for pulp 15 production. J The value for rotation period of the Christmas tree species is an approximate value derived from two marketing surveys and discussions 42 with both Christinas tree producers and the Cooperative Extension Service. As with reforestation species actual rotation lengths may vary according to local climatic conditions, soil fertility, and a host of management practices. Rotation periods for ornamental species are set according to the number of years required to produce a saleable six foot tree in the wholesale ornamental nursery environment. The parameter values for the rotation period of each commodity is presented in Table 2.4. Interest Rates Economic analysis of production processes which necessitate long time requirements are sensitive to interest rates when present value determinations of benefits and costs are made. A wide range of interest rates was used in the analysis to discount benefits and costs. The range spans the "typical" interest rates used by the range of producers in the forest products industry. There are two institutional groups of forest products producers (potential direct beneficiaries of genetic tree breeding programs): private companies and public institutions. Each uses a different discount factor. Public institutions use a relatively low discount factor, an interest rate of 4-8%, while private companies use 10-14% and higher as typical interest rates. Interest rates throughout the analysis are presented as real interest rates, net of inflation. This implies prices of goods and labor are constant; further, the assumption is made that prices are constant with respect to each o t h e r . ^ 43 T ab le 2 .4 PARAMETER VALUES FOR INITIAL SEED ORCHARD PRODUCTION AND ROTATION PERIOD COMMODITY/SPECIES INITIAL PRODUCTION SEED ORCHARD (YEAR) ROTATION (YEARS) REFORESTATION HYBRID PINE JACK PINE HYBRID POPLAR E. WHITE PINE WHITE SPRUCE RED PINE HYBRID ASPEN BLACK SPRUCE HONEYLOCUST EUROPEAN LARCH HYBRID LARCH BLACK WALNUT 1984 1985 1985 1986 1987 1988 1988 1988 1998 2003 2003 2003 . 20 45 15 35 40 40 15 45 20 30 25 50 CHRISTMAS TREES SCOTCH PINE BLUE SPRUCE E. WHITE PINE WHITE SPRUCE HYBRID SPRUCE W. WHITE PINE AUSTRIAN PINE ENGLEMANN SPRUCE DOUGLAS-FIR WHITE FIR 1985 1985 1986 1987 1987 1998 2003 2003 2003 2008 10 12 10 12 10 10 10 12 12 12 1985 1986 1987 1998 1998 2003 2003 8 8 8 8 5 8 8 ORNAMENTAL BLUE SPRUCE E. WHITE PINE WHITE SPRUCE NORWAY SPRUCE HONEYLOCUST AUSTRIAN PINE DOUGLAS-FIR 44 Costs of the Research Program Costs of the research are not allocated to specific activities such as seed collection, 'and seedling production. This kind of detailed accounting is not possible with the wide spectrum of concurrent primary, intermediate and final product research investigations taking place at Michigan State University. A fairly accurate "lump sum" cost of the final product research program may be estimated. Two methods of cost estimation were used: past budgetary allocations to tree improvement research, and a detailed cost budget constructed from expenditures made during the 1982 planting season. Costs were calculated for the duration of the research program, to 2008. 1961 The two major research cost inputs are primary investigator's labor and operating expenses. research is highly labor intensive. Tnee improvement Much of the research activity is performed by advanced degree research personnel. The salary of the professional researchers (portion allocated to research if teaching duties are also part of the scientist's responsibilities) comprises approximately 50% of the total program cost. calculated over a long time period Operating expenses were (22 years) and includes the capital cost of major equipment, along with seasonal operating funds. The operating expense costs are determined by averaging the grant funds and other budgetary items not including salary of researchers from 1961 to 1 9 8 2 . These costs are determined before the University extracts "indirect" costs. In this way, overhead (capital costs and other maintenance costs) were included in the cost of the research 45 program. Two trends of grant funding were observed. From 1961 to 1971 approximately $ 2 5 ,0 0 0 per year was allocated to the operating expense category for tree improvement. For the period of 1972 to 1982, $50,000 was allocated to tree improvement operations.^ All salaries and other operating expenses were deflated using the Gross Domestic Product Consumer Price Index to reflect 1983 constant dotlars. Actual salary expenditures for tree improvement research were obtained for the period 1971 to I9 8 2 . Estimated costs based on the 1971 to 1982 period are extrapolated for the period 1961 to 1 9 7 0 * Salary costs from 1983 to 2008 are assumed to be held constant (in real dollars) at the 1983 level. This assumption reflects the conservative flavor of the analysis, going against the observed downward trend of real expenditures for salary. A second method of cost estimation is possible by constructing an operating budget for the most expensive research activity. Based on the 1983 spring planting season, the cost of a progeny test for one species is determined to be $13,000. The detailed budget for this research activity is given in Tables 2.5. 2.6, and 2.7« represents the expenditure for the operating expense. This cost For the 21 species considered in the analysis, there would be 42 such activities, one provenance test and one progeny test for each species. The total expenditure would then be $546,000 for this research activity. Added to this amount is $25,000 per species for rangewide collection, and the sum total operating expense is $ 1 ,0 7 1 ,0 0 0 in constant dollars. 46 T ab le 2 .5 COSTS FOR ONE PROGENY TEST (CONIFER SPECIES) (Based on a 6000 Tree Progeny Test) PLANTING COST FIGURES TIME (hrs) MATERIALS Seedlings bundled in the cooler ready for planting: -Mix soil, band and fill cases -Sow seeds -Thin and transplant -Maintain in greenhouse -Move to shade -Bring in and replicate -Greenhouse fuel and light expense (hrs) - ferti1izer $ 100.00 16 2 - banding and moss $ 200.00 42 -------- ------- SUB-TOTAL 6,000 bands, 122 cases $ 250.00 , $ 74.00 - 11 15 3* 120 $ 8 0 0 .0 0 ----------$ 1424.00 PLANTING AT THREE MICHIGAN SITES: (4 PLANTING DAYS @ 1500 TREES/DAY) -Load up -Pre-week maintenance -Extra vehicle -Travel mileage -Tractor run-time for 30 hours -Planting labor (five worker crew) -Hotel (4 nights) -Unload -Mapping and record keepi ng 8 8 1 200 repair material $ 400.00 $ 6 0 .0 0 $ 160.00 (.40/mile) $ 60.00 ($2/hr) per diem for 5 days $ 500.00 ($20/day) $ 320.00 8 20 -------- SUB-TOTAL (hrs) 245 • ---------$ 1500.00 47 Table 2.6 SITE PREPARATION AND MAINTENANCE FOR TWO YEARS: (COSTS FOR ONE TRIP) ‘Load up -Pre-week mai ntenance -Travel mileage •Tractor run-time for 18 hours -Labor (2 worker crew) -Hotel (3 nights) -Chemicals -Record keeping -Unloadi ng 8 8 64 repair material $ 400.00 $ 160.00 (.40/mile) $ 3 2 .0 0 ($2/hr) per diem for 4 days $ 1 6 0 .0 0 ($20/day) $ 9 0 .0 0 herbicides $ 6 0 0 .0 0 8 8 SUB-TOTAL (hrs) 96 FOR THREE TRIPS (X 3) $ 1442.00 (hrs) 256 $ 4326.00 48 Table 2.7 TOTAL COST FOR ONE PROGENY TEST * $7/hr SEEDLING COST: 120 labor hours material $ 1424.00 PLANTING COST: labor hours 245 material $ 1 5 0 0 .0 0 MAINTENANCE: labor hours 256 material $ 4326.00 TOTAL $ LABOR COST $9/hr $11/hr 840 $ 1080 $ 1320 $ 1715 $ 2205 $ 2695 $ 1792 $ 2304 $ 2816 11 ,5 9 2 .0 0 12,839.00 14,081.01 $ PER SEEDLING COST FOR ONE PROGENY TEST (6000 SEEDLINGS) $7/hr LABOR COST $9/hr $ 11/hr SEEDLING IN COOLER: .38 .42 .46 .54 .62 .70 1.02 1 .10 l.!9 1 .9 4 2.14 2.35 PLANTING: MAINTENANCE: TOTAL $/SEEDLING * Calculated by MICHCOTIP personnel, summer of 1983- All costs are based on the actual level of planting and material used for the 1982—83 MICHCOTIP planting season. 49 The total operating expense calculated under the lump sum method is $2,050,000. The more economically conservative method and larger figure of $2,050,000 is used in the analysis. Salary and total nominal costs for 19 6 1 to 2008 are shown in Figure 2.2. Time Table for Commercial Production The tree improvement research program moves through 5 steps from research initiation to final research product. seed collection is made. in a provenance test. crosses are made. a progeny test. number of years. Initially, a rangewide The seeds are grown to seedlings and planted The provenance test is evaluated and controlled Seeds from the crosses are grown and planted out in The progeny test is measured and evaluated for a Finally, the progeny test is rogued and genetically improved seed and vegetative cuttings are available for commercial seed orchard production. The nearest commercial production date for the species considered in the analysis is 1984, when F^ hybrid pine stands will produce commercial quantities of seed to be used in reforestation. The anticipated initial production of commercial seed % orchards for each species is the projected year F^ be converted to seed orchards. progeny tests can Table 2.8 lists the current research stage for each species considered in the analysis. The actual value for initial production year is based in part on the observed research interest and priority, and in part on the biological constraints in the breeding stages (year to flowering, possibility of early evaluation etc.). Those species being considered 50 Figure 2.2 SALARY ♦ OPERATING EXPENSES MSU Accounting Record! 59 66 73 80 87 94 101 108 115 122 129 136 143 150 COSTS of TREE IMPROVEMENT 45 52 SALARY ALONE YEAR 51 Table 2.8 RESEARCH DEVELOPMENT PROGRESS FOR VARIOUS SPECIES SPECIES CURRENT RESEARCH STAGE AUSTRIAN PINE JACK PINE RED PINE SCOTCH PINE E. WHITE PINE W. WHITE PINE HYBRID PINE (jap x‘ nigra) BLACK SPRUCE BLUE SPRUCE ENGLEMANN SPRUCE NORWAY SPRUCE WHITE SPRUCE HYBRID SPRUCE (b 1ue x wh ite) DOUGLAS-FIR WHITE FIR LARCH (all species) ASPEN HYBRID POPLAR BLACK WALNUT HONEY LOCUST F-ij: YEARS UNTIL COMMERCIAL SEED ORCHARD PRODUCTION F-02 F-05 F-03 F-05 F-OL F-02 F-05 F-Oit F-05 F-03 F-03 F-Oit F-Oit 20 2 5 2 3 15 1 5 2 20 15 it it F-02 F-02 F-02 F-Oit F-OA F-02 F-02 20 25 20 5 2 20 15 i - breeding generation; 0 * breeding from wild populations 1 ■ is a genetically improved population j « research development stage; 1 - Rangewide seed collection 2 - Provenance test 3 “ Progeny test from controlled crosses it - Measuring, evaluation and testing 5 - F(i) seed orchard construction 52 for only Christmas tree and ornamental uses will be able to take advantage of early evaluation methods and procedures. Since the production age of the Christmas tree is only 10 to 12 years, evaluation of progeny tests could be carried out in 8 to 10 years, rather than in 20 to 25 as is required for reforestation species. 53 NOTES - CHAPTER I I 1 ) Levenson, Burton E.t and J. W. Hanover. 19 8 3 • Michigan Tree Seedling Industry Survey. Michigan State Agricultural Experiment Station Research Report, In Press. 2 ) Cochran, William G. 1977* Sampling Techniques. Wiley Press; 428 p. 3 ) James, Lee M., Victor J. Rudolph and Melvin R. Koelling. Production and Marketing of Christmas Trees in Michigan. Michigan State University Agr. Exp. Station Research Report 412; 8p. 4 ) MICHCOTIP research results compiled byGlenn summer, 1983t Michigan State University. Howe and Steve Ernst, 5 ) Unpublished statistical results by Schaffer from research conducted at Michigan State University, 19 8 1— 19 8 3 • 6 ) Jonathan W. Wright, and Louis Wilson. 1972. Genetic Differences Scotch Pine Resistance to Pine Root Collar Weevil. Michigan State University Agricultural Experiment Station Research Report 159* in 7 ) N. C. Wheeler, H. B. Kriebel, C. H. tffee, R. A. Read, and J. W. Wright. 1978. 15 -Year Performance of European Black Pine in Provenance Tests in North Central United States. Silvae genetica, 25: 1; P- 1~5 8 ) R. Guries, and A. Ager. 1980. Red Pine Seedling Seed Orchard: 10 Year Results. University of Wisconsin-Madison Department of Forestry Research.Notes No. 242, Dec. 1980. 4 p. 9 ) R. F. Calvert and R. M. Rauter. 1979* Status of Larch Improvement. LSTIC Proc. 1979. P. 145-152. 10 ) Annual Michigan Christmas Tree GrowersMarketing Survey, Department of Forestry, Michigan State University, January, 19 8 2 . 11 ) Miller's formula is found in; James W. Hanover. 1983* Short Rotation Woody Crops Program, Annual Technical Report, 1 9 8 3 * Report Submitted to Union Carbide, March 15, 1983. Smalian's formula is from; Reginald D. Forbes, 1955* Forestry Handbook. Ronald Press Co., p. 1-51. 12 ) This survey classified Christmas trees by species; 2 size classes; wholesale, retail, and cut-your-own. The price used here is the average wholesale price of both size classes weighed by the number of trees sold. 13 ) Telephone inquiries were made to either the owners or managers of the following Michigan nurseries: Cottage Gardens, Bosmon's Evergreen Garden Nursery and Landscape, Summit Nursery, and Lincoln Nursery. 54 14 ) The theory of forest production economics on here. The reader is directed to three texts understanding: "Forest Resource Economics", by 1972; "Price Theory and Applications", by Jack "Managerial Economics", by Pappas and Brigham, will not be expounded for a more complete G. Robinson Gregory, Hirshleifer, 1980; and 1979* 15 ) There are few well managed plantations for pulp production. A review of L. Zsuffa's two 1979 working papers titled, "A Breeding Program for Short Rotation Poplar Biomass Production in Ontario" point to the recent efforts in this field. A review of the literature and consultations with the faculty of the Forestry Department at Michigan State University, along with discussions with R. Woessner of Mead Corporation and Richard Sirken from Champion Timber lands, have resulted in fairly accurate estimates of rotation lengths. 16 ) The one exception is for the sensitivity analysis on price, when the real price is allowed to rise at 1.2$ per year. 17 ) The University accounting system is not set up for cost accounting purposes. Exact expenditures are not available on a single research activity basis. These estimates represent the high cost figures for tree improvement expenditures. 18 ) Consumer Price Index series is from the 1982 "Economic Report to the President". CHAPTER I I I MODELS FOR ECONOMIC ANALYSIS OF BIOLOGICAL RESEARCH Economic Analysis of Tree Improvement Programs There are two fields of plant breeding research where major analysis of productivity and research efficiency have been attempted: forestry and agricultural field crops. in forestry a great deal of research has been devoted to improving silvicultural management technology, yet little research has been directed toward improving the biological resource base through, genetic breeding. It is not surprising, then, to find only a few economic and financial analyses of tree breeding (tree improvement) research programs. Perry and Wang authored a short note in the November 1958 issue of Journal of Forestry describing the value of genetically superior seed.^ Their intention was to show the potential value due to selection from the "best" geographic source. The extent of detail in this analysis is sparse, and the assumptions inherent in their calculations are broad and largely unspecified. The application is limited to loblolly pine in the southern forestry region. The article does not mention breeding programs, but implies "genetic gain" derived through proper geographic seed source selection. Perry and Wang did initiate the investigation of the value of tree improvement programs. In 1965 Allen Lundgren was one of the first economists to recognize the potential returns to be realized even from slight genetic improvement. 55 2 Lundgren used an entirely 56 hypothetical situation of tree improvement through limited provenance progeny and plus-tree selection as applied to jack pine and red pine in the Lake States. The criterion for improvement was expressed as an increase in the site index (a proxy for volume or growth rate increase). The evaluation criterion was net present value. Lundgrens model included detailed costs of grafted seed orchard establishment and maintenance. Seed orchard establishment and maintenance are not part of the tree improvement research program. The Lundgren model also was analyzed under the static assumption of only 16,500 acres of planted forest. There was no distinction between returns to research activities and returns to seed orchard production activities. Davis (1967 ) performed a similar analysis on cost-return relationships of tree improvement programs in southern pines.^ Davis also included seed orchard establishment and management costs in his analysis, stressing these activities. It is unclear what, research activities were included in the Davis analysis. if any, Davis uses a net present value methodology but neglects both quantity effects and interest rate sensitivity. The study implies a break-even approach was used to calculate the genetic improvement needed to cover investment costs in seed orchards, but this is never formally presented. Several studies have noted the potential for tree improvement or opportunities for tree improvement in species commercially utilized for timber. Dawson and Pitcher (1970), Silen and Doig (1976)* Zobel 0974), Callahan and Smith (1974), and Marquis (1973) » are a few of 57 the early proponents documenting opportunities and potential rewards from tree improvement. if Carlisle and Teich (1970) recognized the difference between the research component and the seed production component, but found no way to separate these costs in the hypothetical case they analyzed. 5 In 1971, Carlisle and Teich presented the results of the first computerized model (implemented on a DEC POP-8 computer) costs and benefits of tree improvement programs.^ to analyze This model did not segregate the research and production components nor did it perform a sensitivity analysis on a key variable, the number of trees planted. The model assumed 100,000 acres of commercial forest land would be planted yearly in white spruce. The model provided sensitivity analysis on other important variables, namely interest rate, genetic gain and price. After a series of introductory papers on opportunities in tree improvement from 1958 to 1971• Schreuder presented a marginal analysis of the economics of tree improvement in a short course at the Center for Quantitative Sciences.^ This analysis was one of the first to look at the efficiency of tree improvement programs, attempting to optimize the program. The situation is hypothetical and the analysis is simplified, but it represents the beginning of the next phase in the investigation of economic returns to tree improvement. Van Buijtenen and Saitta (1972) subsequently used a linear programming Q model applied to the economic analysis of tree improvement. Their definition of tree improvement consisted primarily of seed orchard 58 development and propagation. Using plus-tree selection as a "research method" for tree improvement, Van Buijtenen and Saitta used the linear programming model to optimize southern pine seed orchard size and roguing intensity. In 1975* Porterfield performed the most detailed economic q analysis of tree improvement programs to date. Porterfield used the southern pine (loblolly pine) tree improvement program as a case study to perform a goal programming analysis of tree improvement efficiency. Although Porterfield's model separated the research costs from seed orchard management, the goal programming model optimized seed orchard management. Porterfield also ignored limits on quantity of seedlings assuming instead that an unlimited number of seedlings would be planted. In subsequent studies Porterfield and Ledig (1981) used a % break-even, benefit-cost analysis to determine the minimum gain needed for tree improvement research.'® Both these studies, one dealing with white and black spruce in an eastern United States setting, and the other looking at ponderosa pine and Douglas-fir on the west coast, segregate costs associated with selection (provenance and progeny test programs) and with seed orchard development and management. These studies also recognized the quantity question, but performed no sensitivity analysis. They simply assumed a fixed number of acres planted annually for the life of-one seed orchard. The progression in economic analysis of tree improvement programs has gone from broad speculative analysis to rather detailed optimization studies of seed orchard management. Host of the analyses 59 have centered around the financial returns to seed orchard development and management. Few studies delineate the research component as a separate input in tree improvement programs. analyses address the question of quantity. Only a handful of No study goes beyond tree improvement research for products other than timber and pulp. In addition, no study has modeled tree improvement programs with more than two species. The works completed in this field so far cannot be classified as economic analysis of research programs. Studies have investigated tree improvement programs, making little distinction between seed orchard management and the research necessary to lay the foundation for commercial production. mentioned. Rarely are quantity and market effects The definitions of market and benefit levels are vague. There is virtually no mention of secondary effects from tree improvement programs (non-market cost and benefits and multiplier effects). Most of the studies in the literature fall into the category of financial analysis of seed orchards. These studies calculate the return to seed orchards, not to research. None of the papers address the returns to research question as a separate component in tree improvement programs. To investigate the models and methods used for this type of economic analysis the work in agricultural economics is reviewed. 60 Economic Analysis of Aqricultural Research Ex-Post Studies. There are two broad classes of methodologies relating to analysis of returns to research in agriculture. is ex-post studies. One class These studies analyze completed research programs and review the resulting changes in output or price. The ex-post studies can be further classified into consumer and producer surplus analyses, estimating average rates of return, and production function analyses which determine marginal rates of return. Shultz (1953) was among the first to attempt a major quantitative evaluation of agricultural research investments by calculating the value of inputs saved through more efficient production technologies compared to the costs of research programs.1^ He estimated what the output of the agricultural community would have been in 1950 using 1910 technology and material inputs, in effect calculating the increase in consumer surplus resulting from the savings in inputs. To do this, it was necessary to calculate a marginal per unit cost of production using 1910 and 1950 technologies. Shultz further made some rather sweeping assumptions concerning demand. No attempt was made to segregate * individual research components (new machinery development versus crop breeding research etc.). Since Shultz's initial work, two landmark studies have calculated net consumer surplus for discrete research programs. Griliches (1958) calculated the loss in net consumer surplus if hybrid corn were to disappear. 12 The basic assumptions inherent to Griliches' study are that use of hybrid corn has shifted the supply curve for corn downward 61 (greater output for a given price) and that the net consumer surplus is the value of the hybrid corn research program. This study is extremely thorough in its sensitivity analysis, estimating net consumer surplus for all intermediate and all polar cases of supply and demand (perfectly elastic versus perfectly inelastic) and for a variety of interest rates. Schmit2 and Seckler (1970), in their study on social welfare (consumer surplus) as affected by the mechanized tomato harvester, used Griliches1 approach but went into more detail on non-marginal effects of the increased mechanization and production. Specifically, they attempted to "appraise both the heightened production efficiency and its effect on the welfare of the workers."1^ Not only did Schmitz and Seckler compute the gross social benefits and research costs, but also calculated a net social rate of return by including the cost of wage loss of the displaced labor force. This was one of the first major studies to recognize and quantify the full economic implication of research programs resulting in wide spread use of more efficient production technologies. Griliches, and subsequently Schmitz and Seckler, of necessity made broad and simplistic assumptions concerning the dynamics of the supply and demand curves for corn and tomatoes respectively. Even today, with more accurate data series and powerful computers, estimation of these curves is complex. At a national commodity level, the construction of a dynamic supply and demand curve is difficult. When Griliches, and Schmitz and Seckler did their work, construction of accurate and meaningful national level supply and demand curves was clearly infeasible. In the last decade, many papers have been 62 presented dealing with the effects more complex and dynamic supply and demand curves have on the conclusions of Griliches and Schmitz and Seckler. Nonetheless, the basic conclusions of Griliches and Schmitz and Seckler have stood up to the past decade of scrutiny. Lindner and Jarrett (1978) recognized that total benefits are inflated by the nature of the research generating the supply shift. 14 They hypothesized that some innovations are more likely to generate divergent and others convergent supply shifts. Their reasoning focused on the effects different types of innovations (biological, chemical, mechanical, and organizational), on the average costs of marginal and lower cost firms (less than marginal) and the location of those firms on the industry supply curve. The production function approach is the other ex-post methodology used to estimate returns to agricultural research. Research is included as an input in the production function for a commodity. The basic model which is log-linear in its inputs, is: Equation 3-1 Q=AlT X*TlRf*_,U i-l ' j- 0 *“ J Where Q is value of agricultural output, A is a shift factor, X. is the ith conventional production input, R t_j research in the t-jth year, B. ith conventional input, at_j 's the expenditure on is the production coefficient of the is the partial production coefficient of research in the t-jth year and u is the random error term. 63 This methodology has been applied using primarily cross-sectional data. A major difference in the various studies using this approach has been the length of the time lag reflecting the impact of research expenditures on output. The production function approach is attractive to economists because it yields the value of the marginal return to research. The production function approach has been primarily used at the national level of output. Griliches (1964) and Davis (1979) used it to calculate aggregate output for the United 15 States. J This model has been particularly popular in Third-World countries, where production functions are simpler to construct. Evenson (I967 ) first applied this approach in the United States to calculate the marginal product of research in the United States.^ The production function methodology is useful for separating the production effects of research from those of education and conventional inputs (materials and labor) among geographic areas. A major difficulty is obtaining detailed data on production inputs such as labor, machinery, and management. The production function methodology is best applied at the individual firm or farm level. In theory, large aggregate national production functions for a commodity % can be constructed by summing individuals' functions. In practice, these large aggregate production functions are at best, fiction. Aggregation of individual functions does not yield manageable national functions. Assumptions dealing with non-marginal effects by the individual do not translate to the national level. Since research as an input affects the total production, there is some doubt as to the 64 v a 1id ity of th is approach. Neither methodology is particularly well suited for analyzing tree improvement programs. There are no completed tree improvement programs for which a sufficient number of years of economic data exists to perform the analysis. Consumer surplus methodology centers around the ability to construct a demand curve for the consumer commodity research effects. If sufficient data on the demand of seedlings existed in the Lake States (which it does not), 29 separate demand curves would be required to investigate returns of the Michigan tree improvement program as each species/commodity has its own unique market structure and characteristics. The production function approach relies on accurate cross-sectional data on individual firms' production functions. This data does not exist for the forest products industry in the Lake States.1^ Ex-Ante Studies. Ex-ante methodologies can be classified into four groups: those using scoring models to rank research activities: those using mathematical programming to select an optimal mix of research activities; those studies using stochastic simulation models; and those employing benefit cost analysis to establish returns to research. Scoring models have been used to evaluate research alternatives primarily by public institutions (USDA, land grant colleges etc.) which have a sizable and diverse research component. Shumway and 65 McCracken (1975) use the results of a scoring model analysis in ranking research activities. 18 Mahlstede (1971). in another study, states "the validity of the study rests heavily on the premise that scientists, through a systematic group effort, can predict, to some degree, the outcome of scientific inquiry and thus improve the basis of selecting research activities that will offer the highest return."19 This type of analysis does not return a cardinal value for research benefits. An ordinal value is returned, useful for comparison to other research alternatives under the same management umbrella. Evaluation of just one isolated research activity or program is not possible with scoring models. •Mathematical programming models (linear programming, dynamic programming, and goal programming) have been used to optimize a given research program. 20 These models, although theoretically useful, require assumptions which limit their practical use.^1 Detailed data on the research process and returns to separate research activities are also necessary to fully utilize this methodology. Several studies have used stochastic simulation models to investigate the returns to research. large, complex and costly to run. 22 These models are generally Simulation models are more widely used for research evaluation in the private sector than for public research evaluation. This is because private sector research is often more narrow and select than public sector research. Private research 66 generally involves only processes and reward schedules which are well known. A simulation model for tree improvement programs would theoretically be possible and the methodology conclusive to the research environment. These models require extensive data series to estimate the parameters; not enough data exists to create probability functions necessary in simulation models for various tree breeding act ivi t ies. Benefit cost studies, the last category of ex-ante methodologies, are similar to consumer and producer surplus methodology. One major difference is that ex-ante benefit-cost studies must project what the future yield or gain will be, whereas consumer surplus studies ♦ calculate the yield based on past production. Fishel (1971) describes and reviews a comprehensive computerized model for collecting and processing information needed to evaluate research activites and to select an efficient allocation of resources 23 among research activities. J The model, called the Minnesota Agri cultural Research Resources Allocation Information System involved three major steps: specification, estimation, and analysis. Benefit-cost ratios, net present value, and internal rate of return are returned by the model for each research project. The Minnesota model relied on surveys sent to scientists in the field to estimate annual expenditures, time requirements, and technical feasibi1ity of research. This model is extremely complex, but considering the accuracy of the estimation step, the complexity may be spurious. 67 Ramalhode, Castro, and Schuh (1977) created a model with a slightly different approach. 2k Their study focused on growth and distributional effects of technological change along with direct and indirect effects of research. This approach is quite similar to consumer surplus methodology in that it also assumes the supply curve shifts in different directions for various crops. model The data for this is derived through secondary sources, to project yield increases, adoption rates, and probability of success. Two key studies, Easter and Norton (1977) and Araji, Sim and Gardner (1978), applied benefit-cost methodology to specific commodity 35 research programs. ** Easter and Norton used scientists' estimates for yield and cost effects of various research conducted at land grant universities. These estimates were then compared with the 1978 United States Department of Agriculture budget requests for soybean and corn production research. An important aspect of this study was the sensitivity analysis performed. The benefit-cost ratio sensitivity to variations in probabilities of success, yield increases, commodity prices, and research program completion time, were analyzed. The Araji, Sim, and Gardner study evaluated returns to agricultural research and extension programs for sheep, fruit and vegetables, potatoes, cotton, and rice in the western United States. An important aspect of this study is the distinction between research programs and extension activities. Both research and the agent to facilitate widespread use (extension) are needed to garner benefits. 68 Their methodology was similar to that of Easter and Norton. Personal interviews of agricultural researchers and extension specialists were conducted to gather data for the study. The main parameters of the analysis were all determined through interviews. They include: the initiation and termination dates of research projects for each commodity; the probability of success; probability and rate of adoption of research results (with and without extension); and extension resources required to implement and maintain the new technology. The Araji, Sim, and Gardner study did not perform sensitivity analysis on the several key variables in the model. Benef it Cost Analysis Applied to Tree Improvement Research Considering the unique economic environment of forestry and limited data available, an ex-ante benefit cost methodology is best suited to analyze the returns to tree improvement research. A benefit-cost analysis applied to tree improvement research will compare the increase in net revenue resulting from using genetically superior trees to the costs of the final product research. The cost of the research is a one-time expenditure yielding a. product (information and genetically improved stock) which produces m infinite stream of benefits. The realization of benefits relies on the ability of forest managers to maintain segregation of the genetically superior population from the wild population, and to maintain the regenerative capability of the superior population. A graphic depiction of tree improvement research benefits is shown in Figure 3.1. Both benefits and costs are converted to present value 69 Figure 3.1 BENEFITS OF TREE BREEDING RESEARCH Total Net Revenue/Tree Incrementa^Net Revenue From Fg Generation Stock NET REVENUEATREE Incremental Net Revenue Of :.:j Fj Genetically Improved Trees ik Wild Population (YEARS) Net Revenue/Tree Increases With Each Generation Of Tree Breeding 70 terms using 1983 as the base year. A generalized economic model is shown in Figure 1 . 2 . The benefit cost methodology is chosen for two primary reasons: to determine the magnitude of potential economic benefits or losses; and to conduct a sensitivity analysis to see how changes in key variables affect the outcome. Four key variables which affect the economic analysis show a high degree of uncertainty. These are the interest rate, the genetic gain expected (in terms of the effect on price), the future price of the commodity itself, and the .future level of production. Because of the extremely long time periods involved on both the cost and benefit side, the interest rate used to calculate present values is a highly sensitive variable. The interest rate is one variable on which sensitivity analysis is performed. The price of the commodity and level of production (quantity) are both variables with a degree of uncertainty attached to them. Price and quantity are critical variables to the benefit cost methodology since revenue (from which present value benefits are calculated) product of price and quantity. a sensitivity analysis. is determined by the These two variables are the subject of The genetic gain variable is used to calculate the difference in revenue between commodities not affected by the research and those which are. to this variable also. Sensitivity analysis is applied Figure 3.2 GENERAL ECONOMIC oo n y Total Value of Genetic Research * MODEL ^ ^ (PVBjj — PVBjj) — i-h+F, j-1 PVB*» Present Value Benefits of Genetically Superior Population P V B * Present Value Benefits of Wild Population P V C a Present Value Research Costs h = Rotation Length F-j * Year F-j Seed Orchard Begins Production p » Year Research Program Ends r ■ Year Research Program Begins J * 1r»M*»n Species i « Years 72 The major drawback with this methodology is the static nature of the model. For each estimate of net benefits, the values for the variables in the model are fixed. static. The sensitivity analysis is also Only one variable is changed at a time while all others are held constant, implying independence of all the variables. Obviously, variables such as price and quantity are not independent, but other models which may take this factor into account are equally unsuited for reasons already stated, or are infeasible with the available data. Summary There is a relationship between market levels, analysis methodology, and the scope of effect from research products. shown in Figure 3»3« application. This is The final research products are specific in Primary market levels are directly influenced by final research products. The methodologies most suited for this level of economic analysis are benefit-cost, and net present value. Both are methodologies which have few built-in assumptions, and both methodologies are highly flexible in the amount of detail allowed in analytical model construction. Intermediate research products have a wider effect on market levels. This type of research product not only affects primary market levels but also directly affects higher market levels. To account for these effects, a more encompassing economic analysis methodology is needed. Studies at this level are usually industry-wide studies. Frequently used methodologies are those using multipliers (such as Figure 3.3 RELATIONSHIP BETWEEN RESEARCH PRODUCTS, MARKETS, AND ANALYSIS METHODOLOGY (INTERMEDIATE PRIMARY IFINAL •PRWARY PROOUCT RANGE* ■INTERMEDIATE PRODUCT RANGE- RESEARCHPRODUCT -FINAL PRODUCT RANGE- CONSUMER (DISTRIBUTOR (WHOLESALER |IF" FINISHER/ (MANUFACTURER PULP MILL STUMPAGE SALE REFORESTATION f _ FINAL PRODUCT _ Cut Paper, Newsprint SECONDARY PRODUCT, Roll Paper | .PRIMARY PRODUCT. Logs.Pulp oj CONSUMER I d ISTRIBUTOR I WHOLESALER ICHRISTMAS TREES SOLD ON FARM CHRISTMAS TREES CHRISTMAS TREES- LANDSCAPER (c o n s u m e r NURSERY ORNAMENTALS — — 1 ANALYSIS METHODOLOGY THEORETICAL I INSTITUTIONAL I APPLIED • Total Consumer-Producer • Analysis Using Multipliers • Benefit/Cost Surplus Analysis • Index No. Approach # Production Function • Net Present Value MARKETS I I 74 input/output analyses), and production function approaches. Analytical studies at this level have more assumptions than studies of final research products. These assumptions are needed to account for the greater economic complexity at this level.' analysis at this level The detail of the is constrained by the number and scope of economic assumptions. Primary research products are most encompassing in their affect on the economy. The effects of primary research products envelop all levels of consumer markets. theoretical at best. quantitative accuracy. The analysis methodology at this level is Studies at this level have a questionable Those few analytical studies which are attempted at this level are highly constrained in detail. These studies incorporate the greatest number and broadest assumptions. 75 NOTES - CHAPTER I I I 1 ) Thomas Perry and Chi-Wu Wang. 1978. The Value of Genetically Superior Seed. J. For. 58: 11; p. 843-845 2 ) Allen Lundgren and James P. King. 1965* Estimating Financial Returns from Forest Tree Improvement Programs. Working paper from the Lake States Forest Experiment Station, St. Paul, Minnesota; 17 p. 3 ) Lawrence S. Davis. I967• Cost-Return Relationships of Tree Improvement Programs. Working paper from Virginia PolyTechnic Institute, Blacksburg Virginia; 6 p. Problem Analysis. USDA Forest Service North Central Forest Experiment Station k ) David H. Dawson and John A. Pitcher. 1970. Tree improvement Opportunities in the North Central States as Related to Economic Trends, A Problem Analysis. USDA Forest Service North Central Forest Experiment Station NC-40; 30 p., and David Marquis. 1973* Factors Affecting Financial Return From Hardwood Tree Improvement. J. For. 71: 2; p. 79-83, and John C. Callahan and Robert P. Smith. 1974. An Economic Analysis of Black Walnut Plantation Enterprises. Purdue University Research Bulletin No. 912, August, 1974; 20 p., and Bruce Zobel. 1974*. Increasing Productivity of Forest Lands Through Better Trees. S. J. Hall Lectureship in Industrial Forestry, April 18, 1974; 19 P>, and Roy Silen and Ivan Doig. 1978. The Care and Handling of the Forest Gene Pool. Pacific Search, 10: 8 , June, 1978; p. 7“9 5 ) A. Carlisle and A. H. Teich. 1970. The Costs and Benefits of Tree Improvement Programs. Petawawa Forest Experiment Station Information Report PS-X-20, June, 1970; 28 p. 6 ) A. Carlisle and A. H. Teich. 1971. The Costs and Benefits of Tree Improvement Programs. Canadian Forestry Service Publication, No. 1302; 3* P. 7 ) Gerald Schreuder. 1971* The Economics of Tree Improvement. WFGA Short Course, Mimeo Handout; 11 p. 8 ) J. P. Van Buijtenen and W. W. Saitta. 1972. Linear Programming Applied to the Economic Analysis of Forest Tree Improvement. J. For. 7 0 : 3 ; p. 164-167 9 ) Richard Porterfield. 1974. Predicted and Potential Gains from Tree Improvement Programs— A Goal Programming Analysis of Program Efficiency. North Carolina State University School of Forest Resources Technical Report No. 52; 112 p. 76 10 ) F. T. Ledig and Richard Porterfield. 1 9 8 1 . West Coast Tree Improvement Programs: A Break-Even, Cost-Benefit Analysis. Pacific Southwest'Forest and Range Experiment Station Research Paper, PSW- 156 ; 8 p. 11 ) Theodore Schultz. 1953* The Economic Organization of Agriculture. New York, McGraw-Hill Book Co.; 37** P* 12 ) Z. Griliches. 1958. Research Costs and Social Returns: Hybrid Corn and Related Innovations. J. Pol it. Econ. 6 6 : p. 419 —1*31 13 ) Andrew Schmitz and David Seckler. 1970. Mechanized Agriculture and Social Welfare: The Case of the Tomatoe Harvester. Amer. J. Agr. Econ. 52: 10; p. 569~576 14 ) R. K. Linder and F. G. Jarrett. 1978* Supply Shifts and the Size of Research Benefits. Amer. J. Agr. Econ. 60: 1; p. 48-56 15 ) Z. Griliches. 1964. Research Expenditures, Education, and the Aggregate Agricultural Production Function. Amer. Econ. Rev. 54: p. 961-974, and J. Davis. 1979- Stability of the Research Production Coefficient for U.S. Agriculture. Ph.D. thesis, University of Minnesota. 16 ) Robert Evenson. 1987* The Contribution of Agricultural Research to Production. J. Farm Econ. 49: P- 1415-1425 17 ) A recent input-output study on the timber products industry is in the final stages of completion and should contribute to the cross-section data void which exists. One publication, "Timber Products Economy of Michigan" by James, Heinen, Olson, and Chappelle has resulted from this study. 18 ) C. R. Shumway and R. J. McCracken. 1975* Use of Scoring Models in Evaluating Research Programs. Amer. J. Agr. Econ. 57: 10; p. 714-718 19 ) J- P- Mahlstede. 1971* From, "Long-Range Planning at the Iowa , Agricultural and Home Economics Experiment Station" in, "Resource Allocation in Agricultural Research" edited by W. L. Fishel, Minneapolis: University of Minnesota Press; p. 327 20 ) For examples of detailed studies, see: R. W. Cartwright. 1971* Research Management in a Department of Agricultural Economics. Ph.D. thesis, Purdue University; and Richard Porterfield. 1974. Predicted and Potential Gains from Tree Improvement Programs— A Goal Programming Analysis of Program Efficiency. North Carolina State University School of Forest Resources Technical Report No. 52; 112 p.; and D. G. Russell. 1977* Resource Allocation Agricultural Research Using Socio-Economic Evaluation and Mathematical Models. Can. J. Agr. Econ. 23: 2; p. 29-52, and Donald Keefer. 1978. Allocation Planning for R&D with Uncertainty and Multiple Objectives. IEEE Transactions on 77 Engineering Management, Vol EM-25, No. 1; p. 8-14; and Bernard Tailor, Laurence Moore and Edward Clayton. 1982. R&D Project Selection and Manpower Allocation with Integer Non-linear Programming. Mgt. Sci. 28: 10; p. 1149-1158; and Tom Lee. 1982. A Nonsequential RSD Search Model. Mgt. Sci. 28: 8 ; p. 900-909. 21 ) Ronald Heiner. 19 8 3 * The Origin of Predictable Behavior. Amer. Econ. Rev. 73* 4; p. 5 6 0 -5 9 5 22 ) Robert Evenson and Yoav Kislev. 1976. A Stochastic Model of Applied Research. J. Pol. Econ. 82: 2; p. 265*281; and P. E. Winkofsky, N. R. Baker, and D. J. Sweeney. 19 8 1. A Decision Process Model of R&D Resource Allocation in Hierarchical Organizations. Mgt. Sci. 27* 35 P. 268-283 23 ) Walter Fishel. 1971. The Minnesota Agricultural Research Resource Allocation Information System and Experiment, in "Resource Allocation in Agricultural Research", W. Fishel, ed. University of Minnesota Press, 1971• 24 ) J. P. Ramalhode de Castro and G. E. Schuh. 1977* An Empirical Test of an Economic Model for Establishing Research Priorities: A Brazil Case Study, in, "Resource Allocation and Productivity in National and International Agricultural Research", ed. T. M. Arndt, D. G. Dalrymple, and V. W. Ruttan, University of Minnesota Press, 1977* 25 ) K. W. Easter, and G. W. Norton. 1977* Potential Returns from Increased Research.Budgets for the Land Grant Universities. Agr. Econ Res. 29: 1; 127*133; and A. A. Araji, R. J. Sim, and R. L. Gardner. 1978. Returns to Agricultural Research and Extension Programs: An Ex-Ante Approach. Amer. J. Agr. Econ. 60: 11; p. 964-968 CHAPTER IV THE MODEL USED IN THE ANALYSIS The Model The model in this analysis uses a benefit cost methodology to return an estimate of net present value for the tree improvement research in Michigan. Equations 4.1 and 4.2 in Figure 4.1 show the basic structure of the model. Pij represents the price per unit (tree) of the ith year, for the jth commodity. Qij is the number of the jth commodity in units entering the market or sold in the ith year. The rotation length (h), is the number of years necessary to produce one crop of the jth commodity. The genetic gain (g) , is the FI breeding cycle gain for the jth commodity. is real interest net of inflation. The interest rate (r), The year (i), is when the genetically improved crop is harvested for commodity j. Years are normalized so that the year 1983 corresponds to i~0. These equations contain the parameters most sensitive and important in determining benefits. In developing a model for a benefit cost analysis, there is a trade-off between model detail (formulation), execution cost, and accuracy. The execution cost is somewhat dependent on technical model programming skills. The accuracy of the results, however, is a function of the raw data inputs and the ability of the model to utilize the degree of detail represented in the raw data. The complexity and detail of the model presented in this dissertation reflects the same level of detail and 78 79 Figure 4.1 EQUATIONS TO CALCULATE ECONOMIC (hjfl.) GAIN FROM GENETIC iQ i f — ~ Lei-*-*') 1 00 BREEDING Formula 4.1 n zz i=h+F-jj i=1 LpiQi(1+"i)fc^r]._h Q'Lo' 1 P» Price/Unit Q» Quantity (Units) g * Genetic Gain (decimal) (%/100) r * Interest Rate (decimal) (%/100) F^ * Year F-j Seed Orchard Begins Production j ■ 1,...., n Species h * Rotation Length of Unimproved Stock 1 ■ h+Fi,...., «» Years Formula 4.2 * 80 accuracy found in the raw data. Certain economic assumptions are inherent to the model. For the most part, the level of complexity determines the assumptions. This model was constructed realizing the limitations of the data availability, and knowledge of the production functions and operations of the three commodity qroups included in the analysis. Further detail incorporating dynamic interaction of the following assumptions would cloud the observations of the more important sensitivity analysis for the key parameters: genetic gain, price, quantity and interest rates. Assumptions. The level of complexity of the model requires the following major assumptions to be built into the analysis: 1. Real price of the commodity is constant with respect to time and with respect to all other commoditites. This assumption is dropped in the sensitivity analysis in which the real price of all products rise according to historical trends. 2. Prices of all resource commodities and costs of research represent the opportunity cost or shadow price of the resource. the timber resource (pulp, timber etc.) For and Christmas trees this implies the price of the resource includes economic rent of the land. Research costs, are also assumed to be the opportunity cost of the research activity. Prices and costs are therefore always assumed to 81 be what the price or cost would be under perfect competition with perfect knowledge by all producers and consumers. Firms are also assumed to be pure price takers. 3. The interest rate (net of inflation)) constant over time. time. is assumed to be Obviously, real interest rates fluctuate over Since interest rate is one of the more sensitive variables in the analysis and future interest rate changes difficult to predict, a constant rate is used in the model. k. Rotation periods are assumed to be infinitely divisible. Forest management practices treat rotation period as an integral number of years. In this model, rotations can involve a fraction of a year. 5. The model assumes an infinite number of rotations for each commodity included in the analysis. The each rotation remains the same, with the number of trees harvested in exception of the analysis sensitivity to industry growth assumptions. Trees of in this model, are being treated as an infinitely renewable resource. 6. The model does not allow for extraordinary capitalization on onetime profits resulting from genetic improvement. It is assumed that all normal profits resulting from sales of the commodities in the analysis are reinvested in the production operation to sustain further rotations. Any windfall profits from using genetically superior stock are also assumed to be reinvested in the production operation or are assumed to be invested at the same interest rate specified for the 82 same time period as one rotation ad infinitum. 7. Availability of land in Michigan for planting trees is assumed not to be a constraint or limiting factor. This qualification is necessary to avoid negative joint impact effects a land constraint would have on the economic valuation of the commodities in the analysis. The maximum land use for artificial regeneration under the expanding industry assumption is set to one half the commercial forest land area in the state. 8. Several management assumptions are needed primarily for the timber industry sector commodities in the model. One assumption is that all plantations are on "good" growing sites. For all industrial sectors, it is an assumed that all labor and material inputs and the timing of these inputs into the production of "wild trees" are identical to inputs into genetically improved trees. This says that no extra inputs are required.to produce trees with superior genes.^ Valuation of Benef its Benefits are evaluated in the model Equation 4.1 "discounts time". in two different ways. The genetic gain is seen as a reduction in the rotation period of each crop. The value to the producer of trees is the difference in the timing of revenue realization (interest charge) between longer "normal" rotations and shorter rotations resulting from faster growing trees. "discounts quantity." Equation 4.2 The genetic gain is reflected by either a higher price for the commodity or a greater volume of the commodity. 83 The value is the difference between the quantity or price of the normal tree and the higher quantity or price of the genetically superior tree. A basic assumption is that no extra inputs are required to grow a genetically improved seedling to maturity. Net revenue per tree (value) can, therefore, be expressed three independent ways. The first is increased net revenue due to a decrease in interest charges resulting from shorter rotations grow faster). (trees The second is an increase in net revenue due to higher p r i c e s received in the market (trees have better qualities which command higher prices). The third expression of an increase in net revenue is due to a reduction in the cost of the production process (trees are more uniform and easier to manage and harvest). The model assumes only one method of calculating net revenue for each commodity. Of course, if two or three valuation assumptions interact in a positive manner, the total benefits would be greater than under a single valuation assumption alone. As an example, there is little price variation within a species used for pulp stumpage based on quality (form of the tree, specific gravity, etc.). The price function for reforestation species within a commodity group is based on volume of fiber. Genetic improvement of these commodities results in a faster growing tree. The increase in net revenue is from decreased interest charges; the trees are harvested earlier. Some cost reduction may also occur if genetically controlled traits, which may decrease silvicultural management costs, are favorably correlated with growth rate. The total net revenue with the cost reduction is greater than when calculated based on decreased interest charges 84 alone. For each commodity the model calculates the net revenue using both valuation methods (discount time and discount quantity). The method which returns the highest value is used to calculate the benef its. Computer Programs Used to Run the Model The model is available in the form of a series of Fortran V (ANSI 1977 standard) programs which reside on magnetic tape under the control of the Michigan State University Forestry Department. The face validity of the code was checked and confirmed by qualified programmers. Appendix I is a listing of the three programs used to calculate benefits and costs. The main program (ECON) calculates the present value benefits for each commodity and performs the sensitivity analysis. Program ANALYS sums the benefits and provides other information. Program ECOSTS determines the present value of the research costs for the same interest rates used in ECON and ANALYS. In this dissertation, program ECON calculates the present value benefits gain for each of the 29 commodities in the analysis. program internally determines which equation ( k . l o r U . 2) each commodity. The to use for The decision rule is to use the equation which yields the greater net revenue. Program ECON approximates an infinite number of rotations at 300 years plus the commercial life of the FI seed orchard. A test for the accuracy of this approximation was conducted. The test consisted of comparing a present value sum calculated at each interest rate using the model (300 plus years) and calculating a 85 present value using an infinite period formula. Program ECON returned the same answer as the infinite period formula, accurate to 32 decimal places, for the range of interest rates used in the analysis. The formula to calculate the present value for an infinite series could have been used, except in the sensitivity analysis in which both real price and quantity are expanding. To be consistent in the presentation of results between sensitivity runs, the approximation was used. Inclusive of all commodities and sensitivity analysis, program ECON in this dissertation calculated the net revenue of 1,01*9,220 rotations. Program ANALYS uses the output file from ECON to calculate the benefits from tree improvement research. A benefit estimate is returned for each sensitivity analyses, 108 in all. Program ANALYS also calculates the contribution of each use category (reforestation, Christmas tree, and ornamental) estimate. to the benefit The contribution of each commodity to the benefit estimate is also calculated for two representative sensitivity analyses. Finally, program ANALYS recalculates the benefits under the assumption that $0% of the reforestation commodities/species will not reach the market to be harvested due to thinning, fire, insect damage, etc. In this dissertation each benefit estimate is the sum qf net revenues from 9 ,7 1 5 rotations of the 29 commodities included in the analysis. Program ECOST calculates the costs in present value terms for the six different interest rates used in the analysis. 86 Sensi tivity Analysis Sensititvity analysis is performed on four critical parameters: price, quantity, genetic gain, and interest rate. Two price assumptions are used, the first fixes price as a constant, as explained earlier in Chapter III, and the second assumes a 1.2$ real increase per year in the price of forest products. Three quantity assumptions are used: first that the quantity planted is a constant value equal to the 1981 planting level for each commodity: second that the quantity planted is a constant value based on the projected 1986 planting level; third quantity involves an increase in the number of seedlings planted. The last assumption is based on the observed growth pattern in seedlings planting from 1971 through 1986 projected levels. Available production data for each commodity was used to determine the commodity growth rate. from 1971, 1981, and 1986 In most cases, production data (projected) were used. If 1971 data was not available for a commodity, 1981 and 1986 data alone were used, if 1988 data was not available, a minimum commercial threshold level of production was assumed. A logistic curve representing a theoretical economic growth pattern was then fitted to the data. For comparison these industry expansion curves for all commodities are shown in Figures 4.2 to 4.8. Under the expanding industry assumption, the I9 8 I value for production level is used until the year commercial seed orchard production begins. determine the quantity. The industry expansion curves then Figures 4.2 to 4.8 show all the curves beginning at the same year for comparison only. MILLION! _______ ftEP PINE EUROPEAN LARCH HYBRID PINE E.WHITE PINE O M°NEV LOCUST ro BLACK SPRUCE ■n o (O o Q O 88 Figura4.3 REFORESTATION COMMODITIES O. U7 MAX O MILLIONS PLANTED/YEAR O (0 20 30 40 50 YEAR 60 70 80 90 100 89 Figure 4.4 cb MILLIONS PLANTED/YEAR CHRISTMAS TREE COMMODITIES MAX CO 10* AUSTRIAN PINE 40 60 80 100 YEAR 120 140 160 180 200 90 Figure 4 .5 CHRISTMAS TREES COMMODITIES CVJ E < MAX Ul Qi u w 2 & Z CO z o S ' h y b r id s p r u c e W. WHITE PINE ENGELMANN SPRUCE Q- 20 30 40 50 YEAR 60 70 80 90 100 91 Figure 4.6 CHRISTMAS TREE COMMODITIES cn o. OD CM CO CM MAX SCOTCH PINE MILLIONS PLANTED/YEAR a CM oCM (O S Christmas trees 21.1%, and ornamentals 1*7.8% of the gross present value. Reforestation # sector production processes show a high degree of sensitivity to the discount rate. This sensitivity to discount rate is the result of long rotation periods. Using a higher discount rate reduces the economic value of reforestation sector products and accordingly the contribution to gross present value. When a discount rate of H % is used, reforestation accounts for only 1.2% at a high genetic gain and constant price, and 1.6% at a high genetic gain estimate and increasing price. The reforestation sector contributes more to present value benefits under the expanding industry assumption relative to the static industry assumptions. This is due to a much larger potential for expansion in the reforestation sector relative to the Christmas tree and ornamental sectors. At the maximum expansion allowed in the model a total of 180 million trees are planted each year. This is an increase of nine times over the 1986 projected 112 commercial planting level of 19*7 million seedlings (for those species included in the research program). The Christmas tree sector has an assumed expansion capacity of only four times the 1986 projected planting of 28.6 million seedlings. The ornamental sector has an allowed capacity for expansion of nearly 66 times its current projected level, but accounts for the same range of gross present value as under the static 19 8 1 planting level This is due, industry assumptions. in part, to the relatively slow growth anticipated in this industry sector (relative to reforestation and Christmas trees). • To put the dollar value of the present value benefits in perspective, it is useful to express benefits as a percentage of the value of the forest products industry. An accurate figure for the value of the forest products raw resource (at the first level in the market chain) is difficult to determine. Most studies which do attempt an estimate are broad in scope (usually at the national level), and generally restrict the investigation to timber or reforestation products. The Christmas tree industry is rarely included in these studies, and the ornamental ignored altogether. industry is often National surveys often rely on state natural « resource agencies to supply the raw data for the econometric models, which ultimately produce a resource value estimate. Many of the state agencies in turn, gather their raw data from previous national surveys. Interviews with officials in the United States Forest Service, Michigan Department of Natural Resources, and the Michigan Department of Agriculture indicate this practice is wide-spread. 11 3 Original data series created from economic surveys are rare in this field. The State of Michigan is fortunate in being one of the few states where an original data series is available. An on-going study on the timber products economy of Michigan has published preliminary estimates of the 1980 value of raw timber products. Several other previous assessments of the Michigan resource base have also presented estimates of the value of the forest products in the state. 12 James (1982 ) estimated the I98 O value of raw timber products harvested in Michigan to be $265 million.1^ This value is inclusive of raw products from the reforest ion and Christmas tree sectors, specifically including; pulpwood, timber sawlogs, Christmas trees, fuelwood, raw forest non-fiber products (primarily maple syrup) and wood residues. This is the most accurate and recent estimate of forest products production in Michigan. From this value of $265 million, an estimate of the 1983 value is derived for the three industrial sectors (reforestation, Christmas tree, and ornamental) this analysis. in The 1980 production level of pulpwood, sawlogs, fuelwood, raw forest non-fiber products and wood residues are assumed to be the same for 19 8 3 . There is no data to indicate these sectors have increased or decreased. The 1982-1983 Christmas tree harvest is estimated at 6 million, 2 million more than in 1 9 8 0 .' The 1983 estimate of the value of raw timber products is $482 million, using 1983 prices and adding $100 million as the estimate of the states's ornamental nursery wholesale business. 14 The value is then expressed as the present value terms of an infinite series for the same interest 114 Table 5*7 1983 ESTIMATE OF THE VALUE FOR RAW TIMBER PRODUCTS JJN MICHIGAN 1980 $ (millions) Reforestation Fuelwood Wood Residue 137 82 14 (add 1983 $ (millions) 10% inflation) 233 > 310 100 Ornamental Christmas tree 72 Total 482 rates used in the analysis, similar to the gross present value of research. At a discount rate of 4% the total value is $12,050 billion, at 14 % the value is $3*44 billion. Assuming a real price increase of 1 .2% the tota*l value at a 4% discount rate is $17.42 1 billion.1^ Table 5*8 shows the estimated total value and percentage of value attributed to genetic research. The value of the genetic research as a percentage of the total forest products industry value is small. At a 4 percent discount rate, low genetic gain estimate, 1981 planting levels and constant price, the value of the research is 3 *3% of the total value of the industry. The highest estimate of genetic research value in block 1 is still only 9 .8 % of the total industry value. With a 1.2% real price increase in raw forest resources, the research value is 11% of the total value. The value of research as a percentage of the total value is estimated for only the 1981 planting level of estimates. is not calculated for the assumptions of 1986 planting levels and It 115 Table 5-8 VALUE OF GENETIC RESEARCH AS PERCENTAGE OF TOTAL FOREST PRODUCTS INDUSTRY ** Constant Price 1nterest Rate 1» 6 8 10 12 ]k Present Value of Industry, Infinite Rotations (million $) 1 2 ,0 5 0 8,033 6,025 1»,820 1*,017 3,W*3 Research Value as Percent of Total Forest Products Industry * (Genetic Gain Estimates) [ low med high ] (3) (3) (3) 3.3 2.6 2.2 1.8 1.6 1.5 6.6 5.3 *».3 3-7 3.3 3.2 9.9 8.0 6.5 5-7 5.2 5-1 Increasing Price 1nterest Rate 11 6 8 10 12 1A Present Value of Industry, Infinite Rotations (million $) 1 7 ,^ 2 0 10,162 7.173 5,5^3 *♦,516 3.811 Research Value as Percent of Total Forest Products Industry (Genetic Gain Estimates) [low med high ] (3) (3) (3) 3.8 3.0 2.5 2.1 1.8 1.7 7.5 6.1 i*.9 k.2 3.8 3.6 11.5 9.1 7-5 6.5 5-9 5.8 * Value of Research Estimated for 198 I Planting Levels ** Forest products industry is valued at the primary raw material level. Products include: stumpage for pulp, timber, veneer, fuelwood, wood residues, maple syrup, Christmas trees, and woody ornamentals. 116 increasing growth in the forest products industry. Since estimation of the total value of the industry under these assumptions involve too many unknowns. 117 NOTES - CHAPTER V 1 ) Benefit-cost ratios are not calculated for blocks three and six (expanding industry assumptions) since substantial additional extension costs (sometimes referred to as "adoption costs") may be involved in applying the genetic gain to the expanded industry. 2 ) Willis L. Peterson. 1971* The returns to Investment in Agricultural Research in the United States, in, Research Allocation in Agricultural Research, 1971; Walter L. Fishel ed. p. 139~162. 3 ) Industrial growth in forest products industry is supported by Burt Levenson and J. Hanover 1983* Michigan Tree Seedling Industry Survey, (in press), 1982 Mead Corp. Annual Report, and personal communications with the woodland managers of Mead Corp. and Champion International Corp. k ) A minimum initial commercial level of planting was established to be 100,000 trees planted. This estimate was used for hybrid pine, hybrid spruce, hybrid aspen, hybrid larch, western white pine and Englemann spruce. 5 ) Value increases are presented here as a percentage increase over the base value. As an example, a doubling of the value from 100 to 200 is a 50% increase. A quadrupling of value from 100 to U00 is a 75% increase. 6 ) Burton Levenson and James W. Hanover. 1983* Michigan Tree Seedling Industry Survey. Michigan State Experiment Station Research Report, In press. 7 ) Neal Potter and Francis T. Christy. 1962. Trends in Natural Resource Commodities. Johns Hopkins Press, Forest statistics section; P. 3* 8 ) ibid; p. 30-31. % 9 ) Har.old Barnett and Chandler Morse. 1963. Scarcity and Growth. Johns Hopkins Press; p. 210-216. 10 ) Robert Manthy. 1978. Trends in Natural Resource Commodities. Johns Hopkins Press; Kerry Smith. 1979. Scaricity and Growth Reconsidered. Johns Hopkins Press. 388 p. 11 ) Robert Manthy. 1981. Notes to Natural Resource Economics. Michigan State University, Department of Forestry, Winter 1981. 12 ) Forest Statistics of the U.S., 1977. Washington D.C.; Research Report Lee James, Suzanne Heinen, David Olson, and Daniel Chappelle. 1982. Timber Products Economy of Michigan. Agricultural Experiment Station Research Report No. M 6 , Michigan State Unversity.; An 118 Assessment of the Forest and Rangeland Situation in the United States. USFS, FS-345. 1980.; Darius Adams et a l . 1 9 8 2 . Private Investment in Forest Management and the Long-Term Supply of Timber. Amer. J. Agr. Econ. 64: 11, p. 232-241; USFS. 19 8 2 . Lake States Forest Inventory Survey - Preliminary Results. North Central Forest Experiment Station. 13 ) Lee James, S. Heinen, D. Olson, and D. Chappelle. 19 8 2 . Timber Products Economy of Michigan. Agricultural Experiment Station Research Report No. 446, Michigan State University; 23 p. 14 ) Unpublished figures obtained through the Michigan Department of Agriculture from a yearly survey by Walter Gammel Inc. (Miami), show the seedling portion of the nursery business to be worth $100 million at the wholesale level. Although this figure must be taken lightly since Gammel Inc. receives their data from the Michigan Department of Agriculture, (which in turn uses national estimates derived from state estimates), it appears to be a reasonable figure for the state-wide value of this industry. 15 ) The formula to calculate the present value of a perpetual annual series is from; Warren A. Flick. 1978. A Note On Forest Investments. For. Sci. 22: 1; p. 30-32. # CHAPTER VI ANALYSIS OF RESEARCH GAINS TO INDIVIDUAL PRODUCERS Introduction This chapter investigates the benefits of tree improvement research from the economic perspective of different institutional users. A large pulp and paper company with a paper mill and extensive land holdings in Michigan will represent the private reforestation sector. A public agency modeling the Michigan Department of Natural Resources and the United States Forest Service is chosen to represent the public reforestation sector. A large commercial Christmas tree farm shows how tree improvement research will benefit the individual operator in the Christmas tree and ornamental sector. Figures for the sample institutional users are based on real but annonomous companies and public agencies. Reforestation - Private Sector Artificial forest regeneration for the production of wood fiber is an activity showing large economies of scale. Owners of small land parcels do plant a substantial number qf trees in Michigan; however, the economic motivation is a mixture of fiber production, enhanced recreation value, and esthetic motives. Two institutional groups engage in artificial regeneration strictly for fiber production: large pulp and paper companies (or other large land owners leasing holdings to these companies), and public natural resource agencies. In Michigan, these two public agencies are the Michigan Department of 119 120 Natural Resources (DNR) and the United States Forest Service (USFS) . A recent survey estimated the 1980 population of wood-using mills to be 1.637* The majority of these are secondary manufacturing companies. The primary manufacturing companies include 276 sawmills and 8 integrated pulp and paper mills.1 Only three pulp and paper companies actively engage in artificial regeneration of the forest, with a fourth company about to begin. The sample pulp and paper company represented in this study is located in the Upper Peninsula of Michigan with a planting schedule of four million trees per year. The land holdings of the company exceed 200,000 acres, all of which are suitable for artificial regeneration of one or more species. The paper mill is operating at capacity on the yield from four million trees per year, and no mill expansion is planned or taken into account in this analysis. take 35 years to mature. is $1.20 per tree. The planted trees all The current price of stumpage in this area Six diverse species are planted and grown, with genetically improved seedlings becoming available in 1987* The purpose of this exercise is to see what a genetic tree breeding research program is worth to this hypothetical company. benefits are expressed in present value dollars per mill basis. (base year« 1983 ) on a The sensitivity analysis is performed as in the main analysis for all parameters. is analyzed. All Only one level of industrial production The genetic gain estimates used are 5%* 10%, and 15% for all species planted. The genetic gain in this case is reflected by increased growth rate, resulting in shorter rotation times. For 121 example, harvesting trees with a 10% genetic gain means the company will be able to meet its raw fiber requirements in 31*5 years as opposed to the "normal" 35 year rotation time. The method of valuation is discounting time (decreased interest charge). The results of this analysis are presented in Table 6.1. potential benefits are very sensitive to the interest rate. The When an interest rate of 4% is used, the present value benefits range from $1.3 million at 5% genetic gain to $6.2 million for a 13% genetic gain. An interest rate of 14% reduces the benefits to $61,000 and $233,000 for 5% and 13% genetic gain respectively. Both these estimates are made under the assumption that real price is constant. Increasing real price at 1.2% per year has a large effect on the benefits the company will receive. $61 thousand to $103 thousand. The lowest estimate increases from The high estimate of benefits is increased by $7«8 million to $14 million. A third block of estimates is presented with a constant price set at $6.00 per tree. This price is used to illustrate potential benefits using a stumpage price actually predicted by a large pulp and paper company for the Upper Peninsula of Michigan. company are substantial. At this stumpage price, the benefits to the The benefits are in excess of $1 million, even using the very high long-term interest rate of 14% and high genetic gain estimate. The high potential benefits which can be realized by individual companies in the pulp and paper industry would appear to provide a strong incentive to fund tree improvement research. Although pulp and 122 Table 6.1 PRESENT VALUE BENEFITS OF TREE IMPROVEMENT RESEARCH Infinite Sequence of Rotations - PULP AND PAPER COMPANY (begin production 1987) (thousands 1983 $) Constant price " $1.20/tree Rotation ■ 35 years L million trees planted/year Interest Rate (%) ! ; ! ! ! ! * 6 8 10 12 H* low Genetic Gain Estimate high med iurn 1,291 938 L68 233 118 6l 6,181 3.127 1 .60 L 833 1*38 233 3.978 1.977 956 508 262 137 j | • J ! Constant price * $6.00/tree Rotation ■ 35 years If million trees planted/year Interest Rate (%) ! < ! ! ! ! i» 6 8 1° 12 ]k low Genetic Gain Estimate med iurn high 6,1*55 4,691 2,322 1,164 591 30if 19,889 9,885 4,979 2,51*1 1,313 686 3 0 ,9 0 5 1 5 ,6 3 7 8,019 If, 166 2,192 1.167 ! ! | j | { Increasing price 6 1.2%/year Rotation ■ 35 years If million trees planted/year Interest Rate (%) ! ! i ! ! ! 1* 8 8 10 12 ]h low Genetic Gain Estimate high med iurn 4,368 1,876 870 421 209 105 9,Oif6 3.935 1,866 919 464 239 11*,057 6 ,2 2 5 3.005 1 .5 0 8 776 606 ! ! j ! J ; 123 paper companies are members of a competitive industry, products and prices are relatively standardized. From the long-term corporate perspective, a positive motive is apparent for. corporate contributions to university research. The research product (genetic material to construct seed orchards) directly benefits the private companies. Whichever individual company first implements genetically improved populations will have a comparative advantage over other regional firms. The company with improved trees planted will have raw material available to them at a lower real cost. Conversely, the company which does not plant improved trees will surely be at a comparative disadvantage. This competitive edge will exist for as long as this company has the only genetically improved trees planted. It is suprising that more leverage is not placed on the university research system by these companies requesting genetic materials. Reforestation - Public Sector The second institutional group planting trees for fiber production is that of the two public natural resource agencies, the DNR and the USFS. The ONR controls over 3*635 million acres of commercial forest and the USFS 2.U23 million acres in Michigan. agencies have extensive artificial regeneration programs. Both To investigate the benefits these agencies would receive, a hypothetical public agency, representative of both, is constructed. The public agency plants on sites of poorer quality (or lower site index) than the private pulp and paper companies. The private companies are able to choose parcels of land for regeneration based on 124 productive capacity. The public agencies are granted land and take what is given to them, often because no one else wants it. Due to the poorer quality of land for timber regeneration, the average rotation period is assumed to be US years to produce a tree yielding 15 cubic feet of chips. The price is assumed to be the same as in the private industy, $1.20 per tree. The genetic stock, mostly jack and red pine, will be available for planting in 1987- The public agency is also assumed to plant 20 million trees for fiber production, of which 15 million survive to harvest. The public natural resource agency is charged with the care, management, and "wise use" of the natural resources. Applied to forest lands in the lake states, this means multiple use. The agency not only manages forest land for fiber production, but also for recreational uses, wilderness, wildlife, and many other uses. Genetic tree improvement research directly effects only the fiber production part of the agencies' mandate. The research contributes indirectly to the other multiple use goals of the agencies. The direct benefits to the public agency are shown in Table 6.2. 1 Even with the longer rotations and poorer sites, the most conservative is $1 million using an 8% interest rate.** The highest estimate is $20.7 million, assuming constant prices, a 15% genetic gain and a U% interest rate. Assuming that real prices increase 1.2% per year in the forest products sector, the estimates of benefits range from $2.2 million to over $53 million. 125 Table 6.2 PRESENT VALUE BENEFITS OF TREE IMRPOVEMENT RESEARCH Infinite Sequence of Rotations - STATE OF MICHIGAN ONR (begin production 1987) (thousands 1983 $) Constant price ■ $ 1 .20 /tree Rotation - 1»5 years (assumes poorer sites) 15 million trees planted/year Interest Rate (%) ! : ! ! ! 6 s 10 12 i* low Genetic Gain Estimate medium high 6,318 2,563 1,058 13.220 5.W6 2 ,3 1 6 2 0 ,7 5 8 8,818 3,811 1,675 kkk SSh 189 433 7M 82 192 339 | ; | ! j | Increasing price 6 1 .2%/year Rotation - k5 years (assumes poorer sites) 15 million trees planted/year Interest Rate (%) ! ! ! J ! ! * 6 8 10 12 14 low Genetic Gain Estimate high med iurn 1 6 ,1 8 9 5.7*9 2,233 905 377 160 33,873 12,303 A, 888 2 ,0 2 6 863 376 53.188 19.775 8,Oi45 3.*15 1,1491 666 | ! | | j j 126 Obviously, the people of Michigan, through the public natural resource agency, receive more than the direct benefits of raw resource production. The public feels the effects of the secondary benefits. There are increased jobs from higher productivity; a greater property tax base to support schools and other public programs, and more efficient production on the fiber producing commercial forest releases other public lands for alternative uses. The direct and indirect benefits the public receives from the tree improvement research are large by any estimation. From the institutional perspective, public agencies should have a strong motive to promote and contribute to tree improvement research. Public natural resource agencies' primary function is one of management. Although research activities are conducted throughout public agencies, particularly in the USFS, the public agency research is centered around research of management practices. Genetic tree improvement research is partly management oriented but primarily biological nature. in Additionally, a tree improvement program requires a very long-term funding commitment (with respect to the yearly budget process). Few public agencies have sustained a steady direction of management goals and objectives for as long as a genetic tree improvement research program requires. Most public agencies see a change in priorities with each new administration. The relative autonomy and personnel stability at university research institutions is an attractive environment for parenting new research products. While public priorities are conducive for allocation of public funds 127 to genetic research, direct support of the university research programs will enhance the continuity and potential the university research program offers. High Value Commodity Industrial Sector The forest products industry has two sectors which produce a high value commodity directly from planted trees: the Christmas tree grower; and the wholesale ornamental nursery. The economics of the two are sufficiently similar to be included in the same case study. Both production processes are highly labor and management intensive, and both produce a high value product directly from a planted seedling in 8 - 12 years. A Christmas tree operation is modeled representing the magnitude of benefits possible to a similar ornamental nursery. The Christmas tree farm is 1,000 acres of plantation on a 12 year rotation (83*3 acres planted per year). The operation plants 100,900 seedlings yearly at 1,210 trees per acre. Although some trees mature in fewer than 12 years, this analysis, for convenience and to be conservative, assumes that all tree are harvested and sold at 12 years from planting. The real price F.O.B. .farm is $12.00 per tree. Two methods are used to determine the value of genetic gain: discounting time, and discounting quantity. The first refers to the decrease in interest charges by shortening the rotations. The second refers to a genetic gain translated into a higher quality and, therefore, a higher priced tree. If all the genetic gain were to result in a faster growing tree, a 10% gain would mean that rotations 128 could be produced in roughly 11 years. The decrease in interest charge from shorter rotations would value benefits according to discounting time. If all the genetic gain were to produce a Christmas tree which could sell for a 10% higher price ($1 3 *20) the valuation method would be discounting quantity. at work. In reality, both actions may be For this analysis, the most lucrative valuation method alone is used, realizing that some degree of under valuation occurs. The potential benefits to the grower are not as sensitive to interest rates as in the reforestation sector due to the shorter rotation period. This results in a higher value of benefits, $108,000, for the low genetic gain estimate and 14% interest rate. The greatest benefit estimate is $4.8 million. Increasing real price at 1.2% per year has less effect on benefits to Christmas tree growers than to the reforestation sector, again due to the shorter rotations. The range of estimates, assuming increasing real price, is from $242,000 to $8.5 million. A complete listing of the benefits is in Table 6.3* The state-wide economic analysis shows the Christmas tree and ornamental sectors to be the greatest beneficiaries of genetic tree improvement research. The individual producer in these sectors is generally quite small compared with public agencies and pulp and paper companies. The yearly gross revenue of a pulp and paper mill is measured in the hundreds of millions of dollars. The gross revenue of the model Christmas tree farm (relatively large at 1,000 acres) only $1.2 million. is The problem of capturing the enormous potential 129 Table 6.3 PRESENT VALUE BENEFITS OF TREE IMPROVEMENT RESEARCH Infinite Sequence of Rotations - CHRISTMAS TREE COMPANY (begin production 1988 ) (thousands 1983 $) Constant price * $12.00/tree Rotation - 12 years 100,833 trees planted/year Interest Rate (%) : ! ! ! ! ! 6 8 10 !2 14 low Genetic Gain Estimate high med iurn 1,615* q 794 q 441 q 319 t 239 t 108 t 3.230 1.587 895 677 51* 392 q q t t t t 4,845 2,381 1,409 1.077 829 640 qj qj tj tJ tj t! Increasing price § 1.2%/year Rotation - 12 years 100,833 trees planted/year Interest Rate (%) ! ! ! ! ! : 6 8 10 12 I** low Genetic Gain Estimate hi gh med iurn 2 ,8 2 5 1 ,2 1 5 636 444 326 242 q q q t t t 5.650 2,431 1.290 942 699 526 q q t t t t 8,475 3.647 2 ,0 3 0 1 ,5 0 0 1,128 857 * q, and t, represent the highest value: q » valuation by discount quantity t - valuation by discount time q' q| tj tj tj t! 130 benefit to the industry is that no one individual can afford any extra costs. The short rotations, and large number of small producers (many of them part-time), create a highly competitive market where no single grower can afford the lag-time between the cost outlay for research (or contribution to university research) and the revenue from genetically improved trees. situation is a tax. structure. The usual economic solution in this Several options are available for a tax A direct tax on the producers benefit the most) is one feasible option. (those groups who stand to At the public regulation level, the state trade agencies may impose a tax, proceeds of which would go to genetic tree improvement research. A second alternative is self regulation through the trade association. This method of generating funds for mutually beneficial activities is common in many industrial areas. The plywood companies pay so many cents per thousand square feet of plywood produced. The paper companies pay a fee to several trade and lobby organizations based on paper output. At the retail level, automobile dealerships pay fees or dues to the local "greater area dealership association". This method of self-taxation works best when there are large numbers of individual units or products produced. If a small percentage of the value of each unit is taxed, a large amount of revenue is generated without noticeable effect on the cost of production. The Christmas tree and ornamental .operations are ideally suited for this method of self-taxation. 131 The Christmas tree market is developing into several regional competitive areas. Species suited for Christmas tree production are specific to the lake States. Genetic research products in this area could not be transported to other regions. The potential gains in Michigan from costs saved, increased price, and larger nationwide market share are substantial. These gains translate into a competitive advantage, an advantage which may help sustain the projected growth and prosperity of the industry. 132 NOTES - CHAPTER VI 1 ) Lee James, S. Heinen, D. Olson, and 0. Chappelle. 1982. Timber Products Economy of Michigan. Agricultural Experiment Station Research Report No. 446, Michigan State University; 23 p. 2 ) The analysis is conducted on an individual tree basis so prices are expressed as dollars per tree. This price is based on a tree 35 years old yielding 15 cubic feet of wood chips and a conversion factor of 100 cubic feet per cord. 3 ) This price is derived from the 1982 Mead Paper Company Annual Report. Mead reported purchasing future timber options valued at five times the current stumpage price. Mead Annual Report to the Stockholders, 1982. p. 34-35* 4 ) Public agencies rarely use an interest rate greater than 8%. The full range of estimates using higher interest rates are presented for comparison only. CHAPTER VII ANALYSIS OF RESEARCH GAINS AND POLICY IMPLICATIONS Introduction The goal of applied research in forestry is to increase the productive efficiency of the industry. Pursuant to this goal, the primary objective of the tree breeding program is to increase the genetic quality of stock material for the establishment of commercial seed orchards. These seed orchards, in turn, are used to increase the efficiency of producing the raw forest products. The purpose of this analysis is to estimate potential benefits to the industry which may result from the research program. Begun in 1961 , the research program has not yet been completed. The first commercial products are expected to be available in 1984. A continuing stream of products will follow through the year 2008, as research on more species is completed. The effective agent today of the genetic based tree improvement program in Michigan is the Michigan State Cooperative Tree Improvement Program (MICHCOTIP). MICHCOTIP, since it's organization in 1974, has at one time or another, conducted genetic research on over 45 species. These species are classified into three market categories: reforestation commodities, which includes trees grown for pulp or fiber, timber (lumber and veneer), and fuelwood or energy; species used for Christmas trees; and species used for ornamentals. 133 134 A review of the research status of each species coupled with the Michigan Tree Industry Survey has identified 21 species for which commercialization of the genetic research is feasible in the near future (specifically within the next 25 years). All benefits, as calculated by this analysis, will occur in the future. There is always some degree of uncertainty about the future economic environment, so several different economic assumptions are used to calculate a range of potential benefit estimates. The range of estimates represents a broad spectrum of outlooks on future economic trends. Interpretation of Results The results derived by the analysis clearly show that under all assumptions tested, even the most economically and biologically conservative, economic costs incurred by the research program are more than covered by the economic benefits. The lowest benefit-cost ratio is 3-37, reflecting economic benefits of $52 million.^ The high estimate of economic benefits (using the most optimistic assumptions) is $24 bi11 ion. The validity of these results depends on the validity of the assumptions made in defining the economic environment. assumptions are made in a somewhat subjective manner. assumptions are chosen to clarify economic behavior. The Economic The fundamental assumptions in this model are concerned with maintaining a competitive exchange market for "raw" tree resources. The phrase "raw tree 135 resources" refers to tree products which are exchanged at the first level in the market chain. These assumptions neglect any market imperfections which may confound the analysis or cast doubt on the validity of the results. model. Said assumptions are not varied in the Other assumptions concern the uncertain future. These are varied and, due to the uncertainty attached to them, the results are open to interpretation. The greatest degree of subjectivity in any returns-to-research analysis lies in setting the level at which research costs and benefits are determined. The model used here sets the cost and benefit level at the point where a minimum of outside factors contribute to either costs or benefits, and where analytical estimation of costs and benefits .is feasible. The level of costs is defined as those research activities which uniquely contribute to increased production of the raw forest input. On the benefit side, the level is defined at the point where the raw forest resource first attains a market value and a competitive market exchange exists. To value either costs or benefits at higher levels would necessitate a far broader approach in the analysis, diluting the accuracy of estimates for any one research or benefit component. Research activities which are a precursor to the final product research stage (such as basic tree physiology research) potentially benefit not only the production of raw forest products, but also the production of other commodities. The question is one of allocation of costs to multiple different production systems. Theoretically, this question 136 can be solved by a global general equilibrium economic model, but of course such a model is not practical. Direct Benefits Not Included in the Model Within the valuation constraints of this model, other costs and benefits do exist, which are not reflected in the estimates of potential benefits or costs. The unique structure, operation, and production characteristics of the Michigan tree seedling nursery industry accounts for a potentially large benefit not included in the model. The 1982 Michigan Tree Seedling Industry Survey found that approximately 30% of all seedlings sold were exported out of Michigan to surrounding states. An implicit assumption in the model is that the nursery industry in Michigan will assume the role for production of genetically improved seedlings. If the demand for seedlings in Michigan stays at the 1981 or projected 1986 levels, an expected 25 million seedlings of genetically superior stock will be shipped yearly to other states. The survey did not delineate by species the intended production use of the exported seedling stock, so it is difficult to assess an accurate value for these exported seedlings in the context of the model. The survey did show that 56 % of the exported seedl ing,s may be used for Christmas trees, 24% for ornamentals and 20% for reforestation production. To put the seedling export component in perspective, the export segment of production is compared with the numbers used in this analysis to calculate the benefits. of the 1981 planting level used in the model The analysis is based on a total number of 24.1 million seedlings planted, split 47.4% for Christmas 137 trees, 43.2$ for reforestation, and 9.4$ for ornamentals. The export segment of nursery production has a higher percentage of seedlings to be used for the more valuable commodities. The total number of seedlings exported is approximately the same as the number used in the analysis for Michigan. The high percentage of more valuable production categories exported may indicate greater economic benefits going to other states than what was derived for Michigan alone. The estimate of economic benefits derived from tree breeding research might easily double if the benefits accrued by other states as a result of importing Michigan produced genetically superior seedlings were to be included. A second major benefit not shown in the model is observed under the assumption of a fixed constant supply requirement. This benefit will be realized by the companies which derive most of their supply through artificial regeneration on lands which they own or lease. This situation applies to large pulp and paper companies, and to most Christmas tree growers. If the supply of raw material needed is constrained by factory capacity (pulp companies) or market saturation (Christmas tree growers), a fixed number of acres is needed to produce the supply. As each rotation of genetically improved trees is planted, less land is needed resulting in savings in the value of the land, lease payments, taxes, and/or economic rents paid. As an example, suppose that a paper company has annual requirements of 250,000 cords of wood chips met by a harvest on 5«000 acres of plantation stocked with "wild" unimproved trees. The unimproved stock 138 is grown on a kO year rotation, each acre producing 50 cords at the end of i*0 years. needed. To supply the company's mill, 200,000 acres are The first plantation of 10% genetically improved stock in the series of rotations will mature in 36 years. The 10% improvement reflects an increased growth rate; the same amount of raw material as can be produced on 5*000 acres of unimproved stock in kO years now takes only 36 years. This assumes a linear relationship between volume production and time. A full series of plantations in genetically improved stock will require, a total land base of only 180,000 acres, a "savings" of 20,000 acres. The land savings will occur incrementally in 5*000 acre blocks four years before genetically improved stock is planted. Rotations numbered 37f 38, 39* and kO, each 5*000 acres large and normally needed under the l»0 year rotation plan, are now unnecessary. replanted, and can be sold. When harvested, this land need not be As calculated in the model, the primary method of valuation is the time value in the reduced rotation length. The "savings" of 20,000 acres is a one-time benefit not included in the model. The same analysis can be applied to the Christmas tree grower desiring to sel1 a fixed number of trees each year. The reduced variation in the genetic base means the trees will mature more evenly. As an example, instead of four years required to "clear" a plantation, genetically improved trees require only three years. With a total land base of 2,000 acres, and assuming a 12 year rotation including clearing time, the rotation period of genetically improved stock is reduced to 11 years, including clearing time. For 11 years as a rotation time, only 1,833 acres are needed, creating a one-time 139 savings of 166 acres (the land area of one rotation). A third major potential benefit (not included in the model) is the possibility of more than one genetically controlled trait simultaneously being improved through a breeding program. Christmas trees and ornamental commodities, qualitative traits is assumed. For improvement in multiple For reforestation commodities, reduction of the reduced time required to produce the resource is the lone valuation method, with no assumed increase in qualitative characteristics of the resource. Recent work on the genetics of jack pine indicates that specific gravity is positively correlated with growth rate. 2 This implies that qualitative traits may indeed add additional value. Only one primary valuation method (reduced time or increased quality) was used for each commodity in constructing the gross present value benefit estimates. Obviously, in some commodities, more than one valuation method is at work. The valuation method used in the model calculates the greatest direct benefit for each commodity. The benefits reported by the model are those with the least amount of uncertainty and are most directly applicable to the individual production process and Michigan's economy. While additional direct benefits.may indeed exist, they are not included in the reporting of results. It has been stressed that the nature of this analysis is conservative. Inclusion of additional benefits with a high degree of uncertainty, or benefits which are not quantifiable, will add little to the impact and nothing to the integrity of the analysis. 140 Secondary Benefits There are further additional benefits which are not so easily quantifiable, but nevertheless deserve mention. These fall into the category of secondary benefits: economic or employment income multipliers, and induced indirect benefits. Under the assumption of less than full employment in the economy, any rightward equilibrium shift of supply results in either greater local production or increased export of the product. This, in turn, creates a greater number of jobs with a result that more people are employed. pocket. These new jobs and income represent new money in someone's Given a positive marginal propensity to consume, an increase in the National effect. Income will result.^ This is called the multiplier Multiplier effects can also be felt through the market chain from raw material resources to manufactured wood~based consumer products. Raw timber products harvested in Michigan in 1980 were worth $265 million. When value is added by the manufacturing, transportation, marketing, and construction activities, the portion of « the economy dependent on raw timber products is worth $4.7 billion. A fundamental change in the economics of supply is felt at each level all the way up the market chain to the consumer. In addition to multiplier effects, there are induced benefits, which effect sectors of the economy only indirectly tied to the forest products industry. Induced benefits are realized when economic activities outside the the forest products industry are able to make 141 use of scarce resources freed up by the increased efficiency of growing wood resources. A prime example of this type of benefit is the value of production resulting from alternative uses of land saved by shortening the rotation period. This was discussed in the case of a company requiring a fixed supply of forest products. For instance, land used to produce wood fiber for pulp attracts only wood cutters. If this land is sold and used as a national lakeshore area, hotels, tourist stands, and other economic support activities are induced. The additional economic activities are the induced benefits. A second type of potential induced benefit may be the attraction of more wood based manufacturing and processing companies due to the availability of a cheaper source of raw wood resource supply. With respect to artificial regeneration of supply, the pulp and paper industry has traditionally located in the southern United States. The industry has also traditionally regarded the Lake States area as an unproductive and slow growing source of wood fiber. Decreasing the rotation period and increasing the quality of the wood resource will move toward reversing this plant location trend. As more plants locate in the state, more economic support activities are needed and more induced benefits realized. In addition to indirect benefits, which theoretically are quantifiable, certain intangible benefits exist which are much more difficult to quantify. Two intangibles are related to the value of information or knowledge about a previously unknown or untested hypothesis of biological production functions. The value to the 142 industrial user of the raw resource supply is enhanced by greater understanding of the production process. Greater understanding of how trees grow eliminates a degree of uncertainty in the supply of raw materia]. This model assumes an infinite sequence of rotations of timber, Christmas trees and ornamental species. Under the assumption of increasing industry size certain constraints are reached with respect to land and market saturation. Currently, The primary constraint is land. 17*5 million acres are classified as commercial forest area in Michigan. Much of this land is unsuitable for artificial regeneration due to a low site productivity. regeneration defined in the model The limit of artificial is 9*3 million acres of reforestation (timber plantations) forest land and 1.1 million acres of Christmas trees. Ornamental species are insignificant with respect to the land constraint since those species are grown in nurseries- that L compete with agricultural land. Of the commercial forest land, 2.4 million acres are in National Forest, 3*6 million acres are in State Forest, 4 million acres are owned or leased by the forest products industry (primarily pulp and paper companies), 3*5 million acres are owned by farmers in'small woodlots and 4 million acres are in miscellaneous private ownership. The current institutional ownership patterns for commercial forest indicates that the maximum limit (as defined in the model) of artificial regeneration is feasible. Studies addressing future management possibilities also confirm the feasibility of these limits.** The primary constraint on the maximum 143 limit of land for artificial regeneration supply is site productivity. A genetically derived increase in productivity, without additional silvicultural inputs or costs, will allow poorer sites to be artificially regenerated. This is an obvious benefit when the limit as constrained by the site productivity is reached. This benefit is not included in the formulation of estimates as calculated in the model. Another intangible benefit of the research is the possibility of an unforeseen technological breakthrough. When engaged in research and development, the possibility exists of revoluntionary discovery. Obviously this is not quantifiable; nonetheless, it is still a benefit to the research and must be taken into account when deciding on the total worth of the research. Secondary Costs Indirect costs also exist which are hot included in the model. For the most part, these are negligible compared with both direct and indirect benefits. One-cost not included may have a limited effect on the economics of tree improvement research. With the increased « efficiency of the supply function, particularly under the cost savings assumption, a certain amount of labor displacement may-occur, particularly in the Christmas tree sector. A narrowing of variation in the stock planted for Christmas trees results from genetic breeding programs. This in turn, may result in cost savings through fewer labor inputs in the production process of growing the Christmas trees. 144 An accurate estimate of this cost reduction is not possible with today's information, thus was not included in the model. A rough estimate of the magnitude of labor displacement is possible with the available data. A 1979 state-wide survey found 711 Christmas tree growers in Michigan. The majority of these (83*5%) are non-commercial growers or part-time operators.^ Labor requirements vary greatly from grower to grower, depending on size of operation, quality of growing stock, and degree of mechanization. A reasonable estimate of average labor requirements for the average grower would be 3 person-years per farm. Rounding to the high side, approximately 2,500 person-years of labor are engaged in the production of Christmas trees in Michigan. Further assuming that all the 30% genetic gain (using the high estimate), constitutes a direct labor savings, a labor displacement of 750 person-years would occur. If this displaced labor is compensated at 5 0 % on a $ 1 5 ,0 0 0 yearly wage basis for two years (time to seek alternative employment), an additional cost of $11 million is incurred. The bbove labor displacement analysis is valid only under two unlikely assumptions: that there would be no expansion of the industry with respect to labor requirements; and that labor is not mobile. Recent surveys based on planting trends show the Christmas tree industry to be expanding, throwing some doubt on the validity of the first assumption. The majority of the labor inputs in the Christmas tree production process are filled by highly mobile temporary or seasonal labor, negating the second assumption. 145 A second indirect cost not accounted for by the model is the difference in yield between research plantations, of provenance and progeny tests, and commercial plantations. Both research plantations and commercial plantations yield a wood product. Because many research plantations are composed of both superior (some) and inferior (many) trees, as compared to the "wild planted" forest, the yield of research plantations when harvested is often less than what a plantation of commercially planted and managed trees would be. data needed to estimate this cost is not available. assumed to be minor. The The cost is again An upper bound for this cost would be the total commercial value of the area in research plantations; this assumes research plantations have zero salvage value. There are approximately 550 acres of land in research plantations in the state. If all 550 acres were in Christmas trees, the highest value wood crop possible, the total gross value would be almost $8 million. Assuming a high 20% profit margin, the net value, and therefore the cost to the research program, would be $1.6 million. Experience is proving that the salvage value of research plantations is not zero, further reducing this cost.^ The cost of adopting the new genetically improved' species in the production process must also be addressed. A recent study evaluating the returns to agricultural research, segregating the extension program component as a separate input, found that the extension programs accounted for 25 % to 60 % of the return to the investment in p experiment station research. The logic behind the efforts of 146 extension programs to assist in new technology transfer is clear. Researchers themselves are generally not in a position to distribute or demonstrate the new technological production possibilities. If the industry does not use the new technology, there are no benefits. For the specific case of tree improvement research in Michigan, the initial adoption costs of the "new technology" improved stock) are negligible, if any. (meaning genetically The structure of the tree seedling industry in Michigan and institutional structure of MICHCOTIP make adoption costs an unnecessary consideration. The majority of tree seedlings are produced by only a handful of nurseries; of the total seedling production is accounted for by only 8 .93 % of the firms. The few large nursery operations are eager for genetically improved stock. There is no problem in getting the nursery industry to adopt genetically improved stock into their production processes. Once the nurseries are growing the genetic stock, the planters will buy the new genetic seedlings. tree improvement cooperative) In addition, MICHCOTIP .(the state-wide is active not only in the finai product research activities but also in disseminating information and genetically improved stock. At the 19 8 1 or 1986 industry production levels, there appears to be little extra cost involved with adopting genetically improved stock. Under the expanding industry assumption, it is reasonable to expect that a portion of gross benefits would be due to cooperative extension programs. would then be germane to the model. These extension program costs 147 Analysis of Benefits It is clear that there is some room for interpretation in the results and conclusions of the study. The model presents the user with a wide range of estimates, the estimates indicating the direct or primary benefits which are felt in the state. It is also clear that when the total social welfare of the state or region is considered (including direct, primary and indirect induced, and secondary benefits), the estimates derived from the model are conservative. The order of magnitude of secondary benefits is much greater than secondary costs. To state quantitatively how much the model underestimates the total value of research is not possible. There simply is inadequate information and data on the various economic components to conduct such an investigation. When all factors and economic trends are considered, a reliable but still conservative figure for the value of tree improvement research in Michigan is $539 million. This estimate uses the 1986 projected planting level, constant prices, medium genetic gain estimates, and a long term interest rate of 6%. The costs involved are only $6.4 million, a small amount compared to the benefits. benefit-cost ratio is 84.2. The This estimate is based on real projected planting levels and a widely used government institutional discount rate (6%). Due to discount factors and the long time spans experienced in forestry, most of the $539 million value is realized in the first 50 years. At very low discount rates, the present value of 148 a revenue realized 400 years in the future is close to zero. Even though the model calculates the value based on infinitely many rotations, the estimate approximates the short-term (less than 50 years) returns to the research program. do not differ greatly when the model to the productive life of the Fj Estimates of gross benefits is run within a time span equal seed orchards. The average Fj seed orchard life is 35 years for the species used in the analysis. this time span, the conservative estimates (constant price, 1981 planting levels) are only 2% to 30 % less than when the model for infinite rotations. (with infinite rotations) to $51 million for only those rotations resulting from the Fj million. is run The most conservative estimate under these assumptions is reduced from $52 million orchards. For seed The high estimate is reduced from $1,188 billion to $830 A greater percentage of the benefit occurs in the earlier years with lower discount rates. The present value benefits . calculated for only the rotations resulting from one cycle of F^ seed orchards are shown in Tabie 7-1- The greatest potential for benefits from tree improvement research, as estimated by the model, is realized by the higher priced and shorter market chain Christmas tree and ornamental sectors of the industry. When secondary benefits are included in the analysis, the longer market chain reforestation industry sector will contribute proportionally more. This is true particularly under the assumption of an expanding industry and when multiplier effects are included. The longer the market chain, the greater effect multiplier effects Table 7.1 PRESENT VALUE BENEFITS - ALL COMMODITIES. ROTATIONS FROM FI SEED ORCHARDS ONLY. APPROXIMATELY 50 YEARS INCREASING PRICE (THOUSANDS OF 1983 DOLLARS) BLOCK 4 ESTIMATES: 1981 PLANTING CONSTANT PRICE BLOCK 1 ESTIMATES: 1981 PLANTING INTEREST RATE % 4 6 8 10 12 14 MEDIUM GAIN LOW GAIN { ■ 552,701 j 354.557 { 236,697 168.848 128.815 108.953 j 276.190 177,161 118.127 82.759 62.785 51.259 HIGH GAIN 830.137 532,571 357,098 258.816 203.326 174,015 358.398 ! 223,538 145,186 j 101,047 81,054 66,323 I HIGH GAIN MEDIUM GAIN 717.357 447.481 291,359 210,782 170,657 141.986 1.079,010 673,135 442.086 330,652 271,903 228,566 BLOCK 3 ESTIMATES: INCREASING PLANTING (1981) INTEREST RATE % LOW GAIN MEDIUM GAIN 4 6 8 10 12 14 373,550 233,829 152,768 105,370 78,930 63.712 HIGH GAIN MEDIUM GAIN 747,779 468,133 306,239 215,131 162,055 135.455 I j ! j | 1,123,754 703.579 426,418 330.049 255,911 216.419 j j INTEREST RATE % LOW GAIN 4 6 8 10 12 14 494.735 300,617 191.052 130,792 103.388 83,583 BLOCK 6 ESTIMATES: INCREASING HIGH GAIN INTEREST j RATE % LOW GAIN MEDIUM GAIN i 990.677 j 602,064 383,609 272,933 217,756 178,971 j 149 ____ J z O < -I <9 4 6 8 10 12 14 LOW GAIN BLOCK 5 ESTIMATES: 1986 PLANTING BLOCK 2 ESTIMATES: 1986 PLANTING INTEREST RATE % INTEREST RATE % HIGH GAIN 1.491.857 906.737 582,875 428,495 347,129 288,221 j PLANTING (1981) MEDIUM GAIN HIGH GAIN | I 4 6 8 10 12 14 691.173 398,711 243,647 159,309 113,777 88,217 1.383,522 ! 798,112 488,431 325,247 233,572 187,553 j 2.078.B11 1,199,205 737,839 498,898 368,794 299,617 4 * 8 10 12 14 i 1.013.440 561.927 332.714 212,535 148,864 113,545 2.029.819 1.125,455 667,397 434,306 305.868 241,284 3,052.678 1.692.534 1.009.531 666,923 4B3.223 385,616 j 150 will have on total social benefits. Research Pol icy Imp!ications The results presented with the analysis have definite policy implications both on the direction of scientific research, and on the institutional structure of the tree improvement program. program in Michigan is at a pivotal stage. The research The main emphasis up to present has been on establishment of provenance and progeny tests for a wide variety of commercial tree species. The direction and thrust of research in establishment of these tests has been toward the reforestation sector of the forest products industry. The Michigan Tree Seedling Industry Survey identified three distinct industry sectors which could potentially benefit from tree improvement. Recent developments in Michigan point to a fourth sector of agro-forestry/biomass as being a beneficiary of tree improvement efforts in the future. In light of the immediate great potential the Christmas tree and ornamental in industry sectors, the future direction of tree improvement should be reviewed. Activities to be completed in .the research program (measurement of progeny tests, and thinning for seed orchards or distribution of genetic stock for other commercial seed orchard establishment), are focused more toward a specific industry sector and a specific product. A research program geared toward accomodating the high potential returns in the Christmas tree and ornamental sectors would experience a greater rate of return than one looking only at reforestation commodities/species. 151 A definite research direction toward blue spruce Christmas tree/ornamental, Scotch pine Christmas tree/ornamental, and Dougias-fir Christmas tree/ornamental commodities would prove to be highly beneficial to these industries. This is based on the potential economic returns, and research stage each species is currently at. Insti tutiona1 Analysis Why has not more funding been allocated to tree improvement research? is germane. With potential returns apparently very high, this question The answer to this question is complex and embedded in the nature of the research product, and in the institutional framework * of the research institution and forest products industry. The research product, (information about which individual trees in a progeny test will yield the best off-spring and how to construct seed orchards from these parents), has many characteristics of a joint-impact good. q By definition, the maintenance costs for the research product are zero. After the product is produced, (the information obtained), the marginal cost of an additional user of that research product is zero. products is extremely high. The exclusion costs of tree improvement The information is of a public nature, coming from the university system. Widely planted genetic stock from commercial seed orchards would be equally difficult to exclude unwanted users or "free-riders." Benefits to users are only attained with wide-spread use of genetically superior stock. Once dispersed on a large scale, to exlude others from gathering seeds or snipping 152 cuttings would be virtually impossible. The private producer of a product will attempt to maximize profits by selecting the level of production at which marginal cost equals marginal revenue. Despite a demand for the product,'the problem from the private producers' point of view, is that with the optimal price at zero, there is no way to pay for total costs (which are positive). From the income perspective, research is counted against income without contributing to immediate profits. Many executives and company managers with fund a 1locating responsibilities, receive income bonuses based on profitability. profit. Research expenses lowers corporate Consequently, there is a strong incentive for managers to allocate funds to capital expansion, an expenditure which can be capitalized and depreciated over a number of years. (Capital expenditures also usually generate income.) From the strategic planning perspective, there are two strategies ' for obtaining information which can increase productive efficiency: develop it through expensive research and development programs (RSD); or acquire it from some other company which conducts the RSO. Abundant examples of both strategies exist at the firm level as well as at the national level. itself. Information espionage is an industry in As has already been pointed out, exclusion costs for tree improvement research products are high enough to dissuade any company from choosing the RSD strategy. 153 Conclusions Tree improvement research appears to be a highly profitable economic activity for the economy of the State of Michigan. Immediate potential direct benefits which may be realized over the next half century are enormous. The costs of the program relative to the size of the benefits are insignificant. The analysis calculates only the direct benefits which are realized by the primary producers in the forest products industry. When secondary benefits are added to the direct benefits, the total worth of the research program may grow tremendously. Surrounding states in the Lake States area are potentially large beneficiaries of the research program in Michigan. Secondary costs appear to be small or non-existent. The unique joint-impact characteristics of the research product present severe barriers to the private sector for engaging in tree improvement research, at least at the development stage. The joint-impact nature of the research product would indicate that the university setting may be a proper place for this kind of research. The research program is at a mid-stage of completion. A research direction aimed at providing genetically improved material to the Christmas tree and ornamental sectors of the forest products industry would yield the highest economic return. potential Emphasis on developing the in Scotch pine, blue spruce, Oouglas-fir, and white spruce 154 for Christmas tree and ornamental stock will account for over half of the present value benefits. The policy implications of these conclusions are many. goal can be generated for each conclusion. A policy A policy dialog between researchers, private industry, and public administrators could be an initial step in utilizing these conclusions. The next step in the economic analysis of tree improvement research will be to investigate more accurately the specific micro-economic aspects of the problem. Specifically, at what level should private industry now enter into the research process to optimize the research gains created so far? On a somewhat broader level, what are the gains to other nearby states resulting from tree improvement research? Finally, on the macro-economic scale, the investigation into suitable methods for analysis of gains to society .(the question of consumer surplus) should be considered. 155 NOTES - CHAPTER VI I 1 ) All costs and benefits are expressed in present value terms dollars). (1983 2 ) Unpublished measurement data generated by MICHCOTIP personnel on the Pickford jack pine progeny test in the Upper Peninsula of Michigan has a statistically significant positive correlation between specific gravity and growth rate. When rogued on the basis of volume, one to two percent gain is shown in specific gravity. 3 ) National Income is equal to Net National Product minus indirect business taxes. Net National Product is considered to be a true measure of the output of the economy and reflects "how well off we are." When Net National Product increases, on the average, people are better off. A ) The individual species limit for reforestation is 15 million trees planted per year. At 600 trees/acre and an average 31 year rotation, the 12 species planted for reforestation requires 9«3 million acres for continuous rotations. Christmas trees at the production limit will need 1.1 million acres for the 115 million trees planted at 1,210 trees/acre on a 12 year rotation. 5 ) Michigan Forest Resources 1979: An Assessment, by Michigan Department of Natural Resources; Adams, Haynes, and Dutrow's Private Investment in Forest Management and the Long-Term Supply of Timber, Am. J. Ag. Econ. 1982; and the USFS North Central Forest Survey, Preliminary Results, 1982; all show the possibility that roughly half the current commercial forest area in Michigan could ultimately be artificially regenerated. 6 ) Production and Marketing of Christmas trees in Michigan, James, Rudolf, and Koelling; Research Report No. 1*12. 1979* 7 ) Sales of a White pine stand for lumber and a blue spruce provenance test at Kellogg Research Forest in Kalamazoo County, Michigan have shown that the salvage value of research plantations can be substantial. 8 ) Araji, Sim and Gardner in their 1978 study on research and extension programs in sheep, fruits and vegetables, potatoes, cotton and rice in the western region conclude: "Depending on the commodity and nature of the research program, 25 % to 60% of the expected returns to public investment in agricultural research will not be realized without extension involvement." 9 ) Allan Schmid explains the terminology of joint-impact goods with respect to other authors definitions in Political Economy of Public Investment, 19 8 2 , and in Property, Power, and Public Choice: An Inquiry into Law and Economics, 1978. Frequently, other literature 156 refers to joint-impact goods as "public goods" or as Samuelson's definition of "consumption externality". APPENDIX Program ECON Program ANALYS Program ECNCOST 1.J c C C C C C C C PROGRAM ECON THIS IS THE MAIN DRIVER PROGRAM FOR CALCULATING GROSS **** REVISED 8/15/83 **** **** CALCULATION OF GPV FOR INFINITE SERIES **** **** APPROXIMATED WITH 100+ YEARS **** INCLUDES LOGISTIC CURVES CALCULATED FOR EACH SPECIES **** PRESENT VALUE BENEFITS FOR ALL SPECIES CHARACTER*80 TITLE,YES,SPEC IE,DISTT.DISQQ DIMENSION SPEC IE (29) C * INTEGER ROTAT.GENER,YEAR,I,K ,J ,INITY,JK,J3.FLAG 1,RROTAT,TFLAG,QFLA G,I II,IIJ,I I,JJ,JJI,PFLAG,SFLAG * * REAL PRICE,QUANT,GAIN.I NT,PVTK,PVTJ,PVK,PVJ,PVY1K ,PVY2K,PVY1J ,PVY 2 J ,DI SC,GAN,IN T 1,PRICET.TEMP2.TEMP3.PLANT,GPV,PR ICC,ABTGPV,PERGP V C COMMON /MAIN/ PV K (500),PV J (500). PVTK,PVT J .INT.GAIN,QUANT.PR ICE(29) ,I,K ,ROTAT,PVY1R (500).PVY2KJ500),PVY1J (500),PVY2J (500),SF LAG,GA N (2 9 I3 ).PLANT (29.2 ,DISC,I NTT,PR ICET (29)t ,TEMP2,J3 2 9 ) ,TEMP3»GEN ER (2§) .ftROTAT (29) ,G^V(29.3.6,3,2) ,INITY(29 ,YEAr1505) OPEN- (6,FILE-'TABLE') »— — --— |\ OPEN (7,FILE-'ARRAY . OPEN (fi.FILE-'SUMTAB') C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C c C C C C C C C VARIABLE LISTING ********** INTEGERS********* RROTAT - ROTATION PERIOD OF THE CROP OR SPECIES ROTAT -ROTATION PERIOD ADJUSTED FOR DISCOUNTING BACK TO 1983 % GENER -GENERATION OF GENETIC VIABILITY (YEARS) (FI USEFULNESS) YEAR - PRESENT YEAR OF DISCOUNTING I. K, COUNTERS QFLAG - QUANTITY OPTION FLAG TFLAG -THINNING FLAG FOR REFORESTATION OR CHRISTMAS TREETHINNING SFLAG -FLAG FOR SHORT CUTTING PRINTING OPTION PFLAG « FLAG FOR PRINTING DETAILED TABLE (WARNING, THIS IS EXPENSIVE) FLAG 1 -FLAG FOR PRICE INCREASE OPTION J3 (I) -DIFFERENCE BETWEEN 1983 AND BEGINNING YEAR OF FISEED ORCHARD INITY - BEGINNING YEAR OF FI SEED ORCHARD ************REALS*********** PLANT(1,1) - AMOUNT PLANTED IN 198 ] FOR EACH SPECIES PLANT(I.2) - AMOUNT PLANTED IN 1986 FOR EACH SPECIES % PRICE » PRICE OF THE PRODUCT AT HARVEST (TO THE GROWER) QUANT -QUANTITY OF PRODUCT STATEWIDE GAIN - GENETIC GAIN EXPECTED FOR THE PRODUCT OR SPECIES INT - INTEREST RATE NET OF RISK AND INFLATION PVTK - PRESENT VALUE TOTAL FOR 'K* OPTION (DISCOUNT TIME) PVTJ ■ PRESENT VALUE TOTAL FOR 'J' OPTION (DISCOUNT QUANTITY) ABTGPV - PRESENT VALUE OF THE TOTAL CROP PERGPV - GENETIC GAIN VALUE AS PERCENTAGE OF TOTAL VALUE PVYIK - IS THE TEMPORARY STORAGE FOR EACH PV PVY2K m 11 11 11 11 11 11 PVY1J m 11 11 11 11 11 11 11 11 PVY2J ■ 11 11 11 11 11 11 11 11 DISCT - GAIN RELATED DISCOUNT FACTOR FOR TIME IN OPTION 1 (K) INTI - INPUT VARIABLE FOR INTEREST RATE PR ICET(I) - TEMPORARY PRICE STORAGE FOR RE-SETTING PRICE TO BASE YEAR TEMP2, TEMP3 - TEMPORARY STORAGE FOR YEARLY PV SUMS GPV(SPECIES,GAIN,INTEREST,QUANTITY,PR ICE)- STORAGE ARRAY FOR SUMS PR ICC - TEMPORARY PRICE VARIABLE FOR CALCULATING PV *********CHARACTER******** YES - PROMPT TO GO AGAIN (FOR INTERACTIVE VERSION) TITLE - IS THE TITLE OF THE PARTICULAR RUN SPECIE - THE NAME OF EACH SPECIES DISTT - TITLE FOR DISCOUNTING TIME DISQQ - TITLE FOR DISCOUNTING QUANTITY READ IN DATA FOR 20 SPECIES: DATA (SPECIE(I),1-1,29) / 'HYBRID PINE REFORESTATION','JACK PINE R *EFORESTATION' 'HYBRID POPLAR REFORESTATION','EASTERN WHITE PINE RE *FORESTATION','WHITE SPRUCE REFORESTATION1,'RED PINE REFORESTATION' *,'HYBRID ASPEN REFORESTATI O N 1,'BLACK SPRUCE REFORESTATI O N ','HONEYL *OCUST REFORESTATION','EUROPEAN LARCH R E F O R E S T A T I O N H Y B R I D LARCH *REFORESTATION','BLACK WALNUT REFORESTATI O N ','SCOTCH PINE CHRISTMAS * TREE','BLUE SPRUCE CHRISTMAS T R E E 1EASTERN WHITE PINE CHRISTMAS * T R E E ' . ‘WHITE SPRUCE CHRISTMAS TREE' 'HYBRID SPRUCE CHRISTMAS TREE' * , 'WESTERN WHITE PINE CHRISTMAS TREE 1,'AUSTRI AN PINE c c c C C c C C C * CHRISTMAS TREE','ENGLEMANN SPRUCE CHRISTMAS T R E E D O U G L A S FIR C *HRISTMAS TREE'.'WHITE FIR CHRISTMAS TREE'.'BLUE SPRUCE ORNAMENTAL' *,'EASTERN WHITE PINE ORNAMENTAL'WHITE SPRUCE ORNAMENTAL','NORWAY * SPRUCE ORNAMENTAL','HONEYLOCUST ORNAMENTAL','AUSTRIAN PINE ORNAME *NTAL','DOUGLAS FIR ORNAMENTAL'/ READ IN DATA FOR AMOUNT PLANTED IN 1981 AND 1986 DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA i 18s;/ . iooo / I \ 0,100 / f 0,k2 / I f. £ 5 0 ,17A3 / / 0,100 / / 1,3 / ?0,JI,J 11,J ,J 12,J ) , J PLANT(13,J PLANT 14,J PLANT 15, J PLANT 16, J PLANT PLANT 118,J PLANT 19.J PLANT 2 0 , J PLANT 2 1 , J PLANT 22,J ,J-l,2 ,J-l, 2 ,J-l,2 ,J-l,2 ,J-l,2 ,J-1 ,2 ,J-l,2 .J-l.2 ,J-l,2 ,J-l,2 { fauio f / / / / 0,100 / 0,100 / 12 . 2 , 8 / 0 ,1 0 6 / / 1 4 5 0 ,2 3 8 0 / / 1 3 ,3 0 / PLANT PLANT PLANT PLANT PLANT PLANT PLANT 2 3 ,J 24, J, 25,J, 26,J, 2Z ’J ,28, J, 29, J, ,J-l ,2] / ,J-l ,2, ,J-1,2, / 80,To4 / ,J-l,2 / 100,103 / .J-l,2 / 0 ,5 0 / . J-l,2 / 4 i,3 2 ,J-l ,2, / 10;T36 / J READ IN DATA FOR GAIN ESTIMATES, 20 SPECIES (GAN 1l.J .J-l, GAN 2, J .J-l. GAN! 3.J, .J-l. GAN 4,J ,J-1. GAN 5, J .J-l. GAN 6, J •J-l. GAN Z»J ,J-1. GAN o.J ,J-1, GAN 1.J-l. GAN :?6f:J) ,J-1 GAN .11.-J) ,J-1 GAN .12,,J) ,J-1 / .05,•1,.30 / I / .05, *1 S *30 4 / / .0 5 . . 1 . . 3 0 / / .05,.1,.30 / / .05,•1,♦15 / / .0 5 ,.1 ,.3 0 / / .0 5 ,-1.-15 / / .0 5 ..1,.15 / , 1 /.05,•1,.30 / 1 /-05,-1,.30 / / .0 5 ,.1 ..1 5 / 1 REM THIS IS THE END OF REFORESTATION DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA C C C 92 / REM THIS IS THE END OF CHRISTMAS TREE DATA 1-22 DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA C C C (1 »J) *J™ 2,J),J J .J— REM THIS IS THE END OF REFORESTATION DATA 1-12 DATA DATA DATA DATA DATA DATA OATA C C C PLANT PLANT PLANT PLANT PLANT PLANT PLANT PLANT PLANT PLANT PLANT PLANT GAN GAN GAN GAN GAN GAN GAN GAN GAN ,GAN ,J ,J-1.3! ,14,J ,J-l.3 ,J .J-l.1 16 .J .J-1.3 ,J .J-l.3 18 ,J .J-l.3 ,J .J-l.3 20 ,J .J-l.3 ,21 ,J ,J-l.3 22 ,j ! ,J-l ,3! / / / / / / / / / / • 1, •2, • 1,. 2, # |, •2, • 1, •2, •1 • 1f • * • 1,. 2 , • 1, •2 , • 1..2 , • 1,.2 , ..a. I REM THIS IS THE END OF CHRISTMAS TREE DATA DATA DATA DATA DATA GAN GAN GAN GAN (23,J (24,J (25,J (26,J .J-1,3 »J-l.3) .J-l ,3) ,J=1.3) / / / / -1,-2,.3 - 1,-2,.3 .1,.2,.3 -1 , -2 - .3 / / / / (GAN(27.J) .J-1,3) / - 1,-2, .^ / 28,J ,J> • • '.1..2..3 / (GAN(28tJ ,J-1,3) / DATA (GAN(29,J) ,J-1 .3) / .1..2,- 3 / oo o DATA DATA READ IN ooo * DATA FOR PRICE 20 DATA (PRICE(I). 1- 1 ,29 )/ 75,. 75, W O , SPECIES (29 VALUES) 1 .0 5 . 1 -5 .1 -2 ,1 .2 ..975.1.05.-75.-975. 1 -2 .. REM THIS IS THE END OF THE REFORESTATION PRICE DATA non +8.75,12,11.10,12,11,12,12,12.5.7. REM THIS IS THE END OF THE CHRISTMAS TREE PRICE DATA nnnn +60,60,60,60,60,60,60/ READ IN DATA FOR INITIAL YEAR OF SEED ORCHARD PRODUCTION 20 SPECIES (29 VALUES) DATA A (INITY(I) . 1- 1 .29 ) / 1981*. 1 9 8 5 .1985* 1 9 8 6 .1987,1 9 8 8 .1988 ,1988,1 * ooo A READ IN DATA FOR LIFE OF FI SEED ORCHARD 20 SPECIES nnn * DATA (GENER(I).1-1,23) / 30,30,15,35.30.60,20,35,30,35,35.60.30,30 .35.30,30,35.*5.W , 35,<0,30,35,30,45.30.U5.35 1 READ IN DATA FOR NORMAL ROTATION LENGTH OF SPECIES USE (29) DATA (RROTAT (I) ,1-1,29) / 20,L5xl5.35.1*Q,feO, 15.1*5.20,30,25,50,10,1 o nnnnnnnnnnnnnnnnnn * 2 ,10 ,12 ,10 ,10 ,10 ,12 ,12 ,12 ,8 ,8 ,8 ,8 ,5 .8 .8 / DATA TO BE OUTPUTED IN THE FOLLOWING FORMAT: SPECIES(I) ;A80 PLANT (1) ;F20.2 PLANT (2) ;F2 0 .2 GAN (1) ;F3.2 G A N (2) ;F3.2 GAN (3 ) ;F3.2 PRICE ;F8.2 INITY ; \ k GENER ;I3 RROTAT ;13 **** INITIALIZE VARIABLES **** LOAD J3 (I) DO 10 I - 1,29 J3 ( (I) INI 1) - inTty (i)-1983 10 CONTINUE non PVTK - 0.0 PVTJ - 0.0 ABTGPV - 0.0 PERGPV - 0.0 GAIN - 0.0 FLAG1 - 0 SET SFLAG TO A VALUE GREATER THAN 0 FOR SHORTEST OUTPUT SFLAG TFLAG PFLAG FLAG ISTT DISQQ nnn S - 1 0 0 0 'DISCOUNT TIME' 'DISCOUNT QUANTITY' DO LOOP FOR THE TWO PRICE OPTIONS DO 180 III - 1,2 IF (III.EQ.2) THEN FLAG1 - 1 TITLE - 'FI BREEDING CYCLE GROSS PRESENT VALUE W/ INCREASING PRICE *' C C C BRING PRICE UP TO LEVEL OF FI FIRST YEAR LOAD STORAGE ARRAY FOR PRICE 160 20 DO 20 I = 1.29 ,„ „ PR ICET (I) = PR ICE (l)*1.012** (J 3(1) +RR0TAT (I) — 1) CONTINUE oo o ELSE TITLE - 'FI BREEDING CYCLE GROSS PRESENT VALUE W0/PRICE OPTION' END IF DO LOOP FOR QUANTITY OPTIONS o QFLAG - 0.0 ooo DO 170 IIJ - 1,3 QFLAG - QFLAG+1 DO LOOP FOR SPECIES 1,...,29 ooo DO 160 I - 1,29 INTEREST RATE LOOP ooo INT - .02 DO 150 J - 1,6 INT - INT+.02 SET THE YEAR ADJUSTMENT TO ACCOUNT FOR DISCOUNTING BACK TO 1983 o o ooo IF (INITY(I) .LT.I983 ) THEN PRINT *,1THIS PROGRAM CANNOT HANDLE CALCULATIONS OF PAST PRODUCTIO *N ' PRINT *,'FOR FI SEED ORCHARDS. YEAR MUST BE GREATER THAN I9 8 3 1 STOP END IF ROTAT - RROTAT (l)+J3 (I) DO LOOP FOR GENETIC GAIN ESTIMATES i4o jj DO 140 JJ - 1 .3 IF (JJ.EQ.lf GAIN - GAN (1,1 IF (JJ.EQ.2) GAIN - GAN (I,2 IF (JJ.EQ.3) GAIN - GAN (I,3 DISCT - RROTAT(I)AGAIN ooo PVTJ PVTK ABTGPV PERGPV 0.0 0.0 - 0.0 - 0.0 SET PRICE TO BASE LEVEL FOR THE NEXT ROUND IF INCREASE PRICE OPTION OOO IF (FLAG1.EQ.1) THEN PR ICC - PRICET(I) ELSE PR ICC - PRICE (I) END IF CHECK FOR QUANTITY OPTION OOO IF (QFLAG.EQ.3) THEN CONTINUE ELSE IF (QFLAG.EQ.2) THEN QUANT - PLANT (1,2) ELSE , x QUANT - PLANT (I,1) END IF DO LOOP FOR FI LIFE OF SEED ORCHARD TO CALCULATE GAINS FROM HARVEST ooo DO 30 II - ROTAT, (GENER (I)+300) K - ll+l-ROTAT TEMP2 - 0 TEMP3 - 0 CHECK FOR QUANTITY OPTION IF (QFLAG.EQ.3 ) THEN , , „ „ IF 7l.EQ.lf QUANT - 15000/(1+ (2999*(2.718281 828*5? (-. 600417571*7*(II-RROTAT (if)))) J) IF (I.EQ.2) QUANT = (15000/ (1+ (3.0021345“ (2.7 In I 18281828 ** (-.0624524 33* (I I - R R O T A T (I) )) ) ) )) UANT 8 * * ( - .0 2 8 . F jl •E Q . 3) (15000/(1+(256.2 (-•I1*1376 F (I.EQ.6) QUANT - ( 5000/(1 28 **(-. 35 8 3 5 1 8 9 3 *( t-RROTA 18281 5000/(1+1 f1-RR0TA1 ..... ■ '18281 . 121 * (I .EQ. 10) QUANT 28** {-.409723899*( 182 iWWU.JV ft ft 2(i j i 8 2 8 ? y * I!'^8iiH281!5N(l.3uiA?822^ \T,(?R8§?A¥^f ??)( ft ft UANT ('•ECT 28 ** (-. 05 ^ 484 8 5 * (t ?-Irotat???? ) / 2) l 2 (I.EQ.16) QUANT 2 .j 1 8 2 8 1828 ** (-.00 ft ft ft ft ,( A i S 8 * ft ft m FF }Z> 6 8 V 8 p j 4 5 6 i \-\ I???)*/)2 ) 7 1f ® )S2) V 1 fiiiS* 681 O p 4 9 6 i ( 1-RROTAT}???f (I-EQ.191 QUANT - (10Q00/(l+(8l8.67213 ^MiW281^.oifl?8»«iT^HlHfr)( ft ft ft ft ft ft ft ft i:TfSiiUaSa^i.572^8§ii«u. (ziid?2?7(t???)( 'i17!8ii?fe285i5N(I.To5(ii282aU S?I????/ (I.EO.24) OUANT - (10000/ (1+ (379.2281369*( 2 .7 18281828 **(-.032977923*(I I“RROTAT(I)))) II iEQ.25) _QUANT_- .(10000/ 0 + (J?4*.(?..718281 If!gS . (26?51 8 S ^ 1^ (f 7 ,8 2 8 18 28** (-.005972375* (111-RROTAT (iff)) D J „ Q flsiwz 71 i i o s ft ft nnnnn I???*)(f)-r828' ELSE CONTINUE END F DISCOUNT TIME CHECK FOR PRICE INCREASE OPTION IF (FLAG1.EQ.1) PRICC - PRICC*1.012 PVYIK(K) - PR ICC*QUANT* (1/ ((1 + 1NT) ** (I I-Dl SCT) )) PVY2K (K) - (PRICC*QUANT*(l/(0+INT)**l I))) PVK(K) - PVY1K (K)-PVY2K (K) TEMP2 = PVK(K) PVTK = PVTK+TEMP2 nnn YEAR (K) - IN ITY (I)+K-1 NOW TO THE OTHER OPTION - DISCOUNTING VOLUME PVYIJ(K) - (PR ICC*QUANT* (1+GAIN) * (1/ ((l+INT) **l I non * PVY^j \ k ) - (PR ICC*QUANT*(1/((1+1NT)**||))) PVJ(K) - PVY1J(K)-PVY2J(K) TEMP3 - PVJ (K) PVTJ - PVTJ+TEMP3 WRITE OUT THE ABSOLUTE VALUE OF THE CROP INTO ABTGPV o ABTGPV » ABTGPV+PVY2J(K) o nnnnn 30 CONTINUE CALCULATE THE VALUE OF GAIN AS PERCENTAGE OF TOTAL VALUE WRITE TO FILES IF (PVTJ.GE.PVTK) THEN IF (ABTGPV.LE.O) THEN PERGPV - 0.0 ELSE PERGPV « (PVTJ/ABTGPV)*100 END IF GPV(I,JJ,J,IIJ,III) - PVTJ IF (SFLAG.EQ.O) THEN ,, WRITE ( 7 ,kQ) SPEC IE(I).GAIN,I NT,QUANT.PR ICE(I * ) ,PRICC,DISQQ.GPV(I,J J ,J, IIJ, flI),ABTGPV,P * ERGPV 40 FORMAT (/4X,A,2X/.4X,'GENETIC GAIN - ',F3.2,4X . 'INTEREST RATE -',F *3.2/,4X,'QUANTITY «',F15.2/,4X,'PRICE ,f 8.2,4X,'FINAL PRICE IS *\F8.2,2X/,10X,'0PTI&N IS ',A/,4X,'GROSS PRESENT VALUE-'.15X.F20.2 */,4X,'TOTAL GROSS PRESENT VALUE OF THE CROP IS',5X,F20.2./4X,'GENE *TIC VALUE AS PERCENTAGE OF TOTAL IS*,10X.F10.4) END IF „ WRITE (8,50) GPV(I,JJ,J,IIJ,I II).ABTGPV * * nnn 50 SKIP AROUND THE LONG DETAILED FILE IF (PFLAG.gT.Oj THEN WRITE (6.60) TITLE FORMAT (Sx.A) (8X,A) WRITE (6,60) (6 ,60 ) SPECIE (I) 60 nnn ELSE , IF (ABTGPV.LE.O) THEN PERGPV - 0.0 ELSE PERGPV - (PVTJ/ABTGPV)*100 END IF GPV(I,JJ,J,I IJ.Ill) - PVTK IF (SFLAG.EQ.O) THEN ,. WRITE (7,kQ) SPECIE(I).GAIN,INT,QUANT,PRICE(I ),PR ICC,DISTT,GPV(I,JJ,J ,IIJ,II I),ABTGPV,P ERGPV END IF WRITE (8,50) GPV(I,JJ,J,IIJ.II I).ABTGPV FORMAT (F20.2.4X.F20.2) END IF PRINT PARAMETERS WRITE (6,70) PRICE (I).QUANT,INT,GAIN,GENER(I),RR OTAT(I) 70 FORMAT (4X,'PRICE- ',F8 .2.4X,'QUANTITY- ',F15.2.2X/.4X,'INTEREST R *ATE- '.F3.2.4X,'GENETIC GAIN- T ,F3.2,2X/,4X,YGENERATI0N PERIOD - ' *,I3.6 X,'ROTATION PERIOD- ' I 3) WRITE (6.80) 80 FORMAT (7X,'*****ALL VALUES ARE IN 1983 DOLLARS*****') WRITE (6 ,90 ). 90 FORMAT (3X,'YEAR',6X,'DISCOUNT TIME',10X.'DISCOUNT QUANT.') WRITE (6,100) (YEAR (JJ I) ,PVK (JJ I) ,PVJ (JJ I) ,JJ 1-1 * .GENER (I) j 100 FORMAT (3X,I4.4X,F14.2.6X,F14.2) WRITE (6,110) WRITE (6,120) 110 FORMAT ('------------------------------------------') * it) 1 120 130 FORMAT {) WRITE (6,130) PVTK,PVTJ FORMAT (hX,F 2 0 . 2, U X ,F 20.2) ELSE CONTINUE C C END IF IJiO CONTINUE 150 CONTINUE 160 CONTINUE 170 CONTINUE l80 CONTINUE END 164 **** VERSION 8/18/83 **** THIS IS THE PROGRAM TO MANIPULATE THE OUTPUT FILES FOR ECONTHESIS MODEL. C H A R A C T E R S TITLE,SPECIE,GAIN,PRICE,OUAN, INTER DIMENSION SPECIE (29) .TITLE (20) .GAIN (3) .PRICE (2) ,QUAN(3) , INTER(6) n n oonno PROGRAM ANALYS INTEGER I,JJ,J,I IJ, III REAL GPV.GPVSUM,TEMPI,TEMP2,TEMP3,TGPV,GPVSUT,REFPER,CHTPER,0RNPER ,RE FGPV,CHTGPV,ORNGPV,SPPCT,RATE,ABT o * COMMON /MAIN/ G P V (29,3,6,3,2).GPVSUM,TEMPI.TEMP2.TEMP3.TGPV(3,6,3. n * 2) * o ooo nno * t o . ,FILE-'TAB5') r “ / DATA (QUAN(I),1-1,3) / '1981 PLANTING LEVELS','1986 PLANTING LEVELS','INCREASING PLANTING LEVELS USING LOGISTIC EQ.'/ DATA (INTER (!),1-1,6) / '4 PER ' , ' 6 PER ' , ' 8 PER','10 PER','12 PER', * '14 PER'/ READ IN DATA FOR 20 SPECIES: DATA (SPECIE(I),1-1,29) / 'HYBRID PINE REFORESTATION','JACK PINE R DEFORESTATION','HYBRID POPLAR REFORESTATION' 'EASTERN WHITE PINE RE *FORESTAT ION',1WHITE SPRUCE REFORESTATION','RED PINE REFORESTATION* *,'HYBRID ASPEN REFORESTATION','BLACK SPRUCE REFORESTATION','HONEYL *OCUST REFORESTATION','EUROPEAN LARCH REFORESTATION'. 'HYBRID LARCH *REFORESTAT ION','BLACK WALNUT REFORESTATION', 'SCOTCH PINE CHRISTMAS * TREE','BLUE SPRUCE CHRISTMAS TREE','EASTERN WHITE PINE CHRISTMAS DTREE','WHITE SPRUCE CHRISTMAS T R E E ' H Y B R I D SPRUCE CHRISTMAS TREE' * , 'WESTERN WHITE PINE CHRISTMAS TREE1,'AUSTRIAN PINE * CHRISTMAS TREE','ENGLEMANN SPRUCE CHRISTMAS TREE','DOUGLAS FIR C *HRISTMAS TREE','WHITE FIR CHRISTMAS TREE','BLUE SPRUCE ORNAMENTAL' *,'EASTERN WHITE PINE ORNAMENTAL','WHITE SPRUCE ORNAMENTAL','NORWAY * SPRUCE ORNAMENTAL','HONEYLOCUST ORNAMENTAL','AUSTRIAN PINE ORNAME DNTAL','DOUGLAS FIR ORNAMENTAL'/ LOAD IN THE ARRAY DO 60 III - 1,2 DO 50 I IJ - 1 " 0 40 I - ?,29 . 30 J - 1,6 DO 20 JJ - 1,3 o n 5,FILE-'SUMTAB') 6,FILE-'TAB1' 7,FILE-'TAB2' 6,FILE-'TAB3' 9,file-'tab4'; READ IN TITLES AND GAIN HEADERS DATA (TITLE(I),1-1,5) / 'TABLE-TOTAL GPV FOR ALL SPECIES WITH OUT * THINNING','SPECIES PERCENTAGE OF GPV FOR TWO CASES'.'TOTAL GPV *FOR 50 PERCENT THIN OF REFORESTATION SPECIES','TOTAL GPV FOR EACH *USE CATAGORY AND PERCENTAGES WITHOUT THINN ING','TOTAL GPV * - INTEREST RATE SENSITIVITY'/ DATA (GAIN(I),1-1,3) / 'LOW ESTIMATE GENETIC GAIN','MED IUM ESTIMAT *E OF GENETIC GAIN1,'HIGH ESTIMATE OF GENETIC GAIN1/ DATA (PRICE(I),1-1,2) / 'CONSTANT PRICE OPTION','INCREASING PRICE' o n n non OPEN OPEN OPEN OPEN OPEN OPEN READ (5,10) GPV (I,JJ,J .IIJ,II I),ABT FORMAT (F20.2,4X,F20.2) 10 non 20 CONTINUE 30 CONTINUE 40 CONTINUE 50 CONTINUE 60 CONTINUE INITIALIZE TEMPORARIES FOR THE FIRST MANIPULATION GPVSUM - 0 . 0 nnn non o nnn n nnn Lt-o WRITE TITLE TO THE FILES WRITE (6,70) TITLE (1) 70 FORMAT (h X,A) WRITE (7,70) TITLE WRITE (6,70) TITLE WRITE (9.70) TITLE DO 170 I I I - 1,2 DO 160 11J - 1,3, DO 150 J - 1,6 DO 140 JJ - 1,3 RESET GPVSUM FOR NEXT CASE GPVSUM - 0.0 REFGPV - 0.0 CHTGPV - 0.0 ORNGPV - 0.0 GPVSUT - 0.0 DO 80 I - 1,29 FIRST DO THE THINNING OPTION FOR REFORESTATION SPECIES IF (I.LE.12) THEN GPVSUT - GPV(I,JJ,J,IIJ,II I)/2+GPVSUT ELSE GPVSUT - GPV (I,JJ, J, I IJ, I I O+GPVSUT END IF NOW FOR THE SUM TOTAL, PERCENTAGES WON'T CHANGE n * GPVSUM - GPV(I.JJ.J,II J, II I)+GPVSUM IF (I.EQ.12) REFGPV - GPVSUM IF ((I .GT. 12) .AND. (I .LE.22))CHTGPV - GPV(I,JJ,J, I I J, II D+CHTGPV IF (I.GT.22) ORNGPV ■ GPV (I,JJ,J,IIJ,I Il)+ORNGPV 80 * CONTINUE WRITE (6,90) PR ICE (III) ,QUAN (IIJ) , INTER (J) ,GAIN (JJ) ,GP VSUM FORMAT (/4(4X,A/),4X,'GROSS PRESENT VALUE -'.F20.2) * WRIviirf8,90) PR,CE1 ,QUAN(I,J) » , N T E R (J> »G A I N (JJ) »G P nnn n 90 WRITE OUT THE USE CATAGORY FILES WRITE (7r,100) PRICE (I II) ,QUAN (I IJ) , INTER (J) ,GAIN(JJ) ,R EFGPV 100 FORMAT (, [AX,A/) ,4X, 'REFORESTATION SPECIES GPV - \ F 2 0 ft •2 ) (7 , ’,110) PRICE(I II) ,QUAN (I IJ) .INTER (J) ,GAIN(JJ) ,C WRITE ft HTGPV FORMAT (> ■X,A/) ,4X,'CHRISTMAS TREE SPECIES GPV «',F20 110 ft •2 ) (7 , WRITE ft RNGPV FORMAT I 120 REFPER - REFGPV/GPVSUM)*100 CHTPER - CHTGPV/GPVSUM)*100 ORNPER - ORNGPV/GPVSUM)*100 WRITE (7.130) REFPER.CHTPER.ORNPER 130 FORMAT (4X,'REFORESTATION SPECIES PERCENTAGE OF GPV ,F7-2/.4X,' *CHRISTMAS TREE SPECIES PERCENTAGE OF GPV -',F7.2/.4X,'ORNAMENTAL S *PECIES PERCENTAGE OF GPV -',F7.2) TGPV(JJ,J,I IJ,I I I) - GPVSUM 140 CONTINUE 150 CONTINUE 160 CONTINUE 170 CONTINUE WRITE OUT SPECIES BREAKDOWN FILE - PERCENTAGE OF GPV DO 210 II I - 1,2 GPVSUM - 0.0 DO 180 I - 1,29 nnn o n ft lbn GPVSUM * GPV (I,2,3,2,I I I)+GPVSUM 180 CONTINUE nnn DO 200 K - 1.29 SPPCT - (GPV(K,2,3,2,I I I)/GPVSUM)*100 WRITE (9,190) SPECIE(K) ,SPPCT 190 FORMAT 7AX,A/,AX,'PERCENTAGE OF TOTAL -\F7.2) 200 CONTINUE 210 CONTINUE WRITE OUT INTEREST RATE SENSITIVITY FILE WRITE (10,220) TITLE (5) 220 FORMAT (/Ax, A/,AX.'INTEREST RATE',AX,'TOTAL GPV') RATE - .02 DO 2AO J - 1,6 230 RATE - RATE+.02 WRITE (1,8 ,230 ) RATE,T GPV(2,J ,2,1) FORMAT (8X,F3»2,8X,F20.2) 2AO CONTINUE WRITE (10,250) PRI CE (1) .GAIN (2) ,QUAN (2) 250 FORMAT (3 (A/)) REWIND REWIND REWIND REWIND REWIND REWIND END ~ 7 6 9 10 1b7 THIS IS THE PROGRAM TO CALCULATE PRESENT VALUE COSTS CHARACTER*80 TITLE o non PROGRAM ECNCOST o INTEGER J,I,YEAR REAL INT,COST,PCOST,TCOST,SALAR,OPER,TEMP COMMON N /MAIN/ INT (6) ,COST (1*7) ,PCOST (1*7) ,SALAR (47) .OPER (47) OPEN (7,FILE-'DATA' .FILE«'I OPEN (8 ,FILE ,FILE" 1I COST1, OPEN (g.FILE-'OUT') ,FILE»'I REWINO 7 o nnn § FILE DATA IS THE INPUT FILE, FILE COST IS THE OUTPUT FILE. DO 20 I - 1,22 READ (7.10) YEAR,SALAR (I) 10 FORMAT (|1»,F8.3) 20 CONTINUE DO 30 I « 23,^7 , % READ IN THE DATA FOR OPERATING COSTS K - I960 DO 70 I - 1 ,1*7 IF (I.LE.ll) THEN OPER (I) - 25.0 ELSE OPER (I) - 50.0 END IF o non SALAR (I) - SALAR (22) 30 CONTINUE K - I960 00 50 I - 1,47 K - K+l WRITE (9,40) K,SALAR (I) 40 FORMAT 1l4,2X,fl8.2) 50 CONTINUE nnn K m K+l COST (I) « SALAR(I)+QPER(I) WRITE (9.60) K,COST(I) 60 FORMAT 1l4,2X,F18.2) 70 CONTINUE READ IN THE VALUES FOR INTEREST DATA (INT(I) ,1-1,6) / .01*,.06, .08,.10,.12,.11* / „ WRITE (8,80) 80 FORMAT (2X,'NET PRESENT VALUE COST INTEREST RATE') DO 120 J - 1,6 K - I960 DO 100 I - 1,1*7 IF (I.LE.22) THEN PCOST (I) - COST (I) * ((1+INT (J) )** (23“ I)) ELSE IF jl.GE.2l*) THEN r PCOST (I) - COST (I) * ((1+INT (J)) ** (23“ 0) ELSEIF (I.EQ.23) THEN PCOST (I) - COST (22) END IF K - K+l WRITE (9.90) K, PCOST (I) 90 FORMAT XlS,2X,Fl8.2) C c c 100 CONTINUE TCOST - 0 110 do 110 1 - 1 ,1*7 TCOST - PCObT(I)+TCOST CONTINUE WRITE (8,130) TCOST,I NT (J) 120 CONTINUE 130 FORMAT ]4X,F20.2,1*X,F1*.2) REWIND 8 END REFERENCES REFERENCES Abbott, Herschel G. and Stanley D. 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