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Improved forest harvest planning : integration of transportation analysis with a management unit cut.. Yamada, Michael M. 1980-03-20

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IMPROVED FOREST HARVEST PLANNING - INTEGRATION OF TRANSPORTATION ANALYSIS WITH A MANAGEMENT- UNIT CUT SCHEDULING MODEL by Michael M. Yamada B.S.F., University of British Columbia, 1974, A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES Department of Forestry We accept this thesis as conforming to the reguired standard THE UNIVERSITY OF BRITISH COLUMBIA September 1980 ©Michael M. Yamada, 1980 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the Head of my Department or by his representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of Forestry The University of British Columbia 2075 Wesbrook Place Vancouver, Canada V6T 1W5 Date October 15, 19 80 ii ABSTRACT Forest harvest planning involves determining, in time and place, the flow of timber to be generated from ths forest resource.. Existing planning models have addressed the.temporal aspects of timber supply.. However, the: spatial aspects of timber supply planning, particularly at the management unit level, have principally been ignored. This study presents.an analytical framework for examining the transportation system of a management unit, its interrelationship with the timber base, and the impacts on strategic harvest planning. The: transportation system is evaluated through network analysis technigues. Routing strategies from the stand to the mill are examined. The.costs of primary access development and log transport are integrated with the forest inventory, providing a more complete assessment of timber value. Homogeneous stand aggregations and associated yield projections, pertinent to management unit planning, are formed using factor and cluster analysis. Dynamic programming allows optimal allocations of the stand groupings across stratifications which recognize transport and accessibility costs.. The resulting timber classes are coupled with management prescriptions and evaluated through a cut scheduling model. Report generation capabilities then allow interpretation of the harvest scheduling results in terms of not only the timber iii classes, but in the spatial context of the individual stands. The methodology is applied to a British Columbia Public Sustained Yield Unit. The usefulness of the system is demonstrated through analyses which: 1) identify road development and transport costs, 2) evaluate alternative wood flow patterns, 3) identify the volume flow potential of the unit, 4) identify the dollar flow potential of the unit, and 5) illustrate the contribution of integrating the transportation system in the scheduling of harvests. . iv TABLE OF CONTENTS Abstract ................ ..ii Table Of Contents .........................................iv List Of Figures vList 3f Tables vii Acknowledgements vii1. . Introduction . .. 1 1.1 Forest Harvest Planning ...........................2 1.2 Levels Of Forest Planning ......................... 4 2., Forest Planning Models .......7 2.1 Aspects Of Models ..2. 2 A Review Of Mangement Unit Planning Models ......... 8 3.. Problem Analysis ...........o. ........... ............o. 14 3.1 Harvest Scheduling - Background 15 3.2 Harvest Scheduling - Need For An Alternative ...... 17 4., Model Components .21 4.1 Forest Subsystem 23 4.2 Transportation Subsystem .......................... 25 4.2.1 Derivation Of Transportation Costs ..........26 4.2.2 Network Analysis 28 4.2.3 Log Transportation Based On Minimum Routing .33 4.3 State Variable Subsystem 37 4.3.1 Initial State Variables 39 4.3.2 Data Analysis 42 4.4 Cut Scheduling Subsystem ...44 4.5 Report Subsystem 49 5.. An Application To Management Unit Harvest Planning ....51 6.. Analysis And Discussion ...............................63 6.1 Transportation Planning ........................... 63 V 6.2 Cut Scheduling 69 6.2.1 Case 1: Volume-Optimization - Long Term vs. Short Term 70 6.2.2 Case 2: Economic Optimization -Volume vs. Value ............................ 75 6.2.3 Case 3: Economic Optimization - With Transportation vs. ,Without ................. 80 7., Conclusions ...........................................38 BIBLIOGRAPHY . 90 APPENDIX I - Dijkstra's Shortest Route Algorithm 93 APPENDIX II - Land Classes Of The Westlake PSYO* ...?6 APPENDIX III - Prescribed Stand Treatments' For The Westlake PSYO .103 APPENDIX IV - Management Reports On Stands Of The Westlake PSYO 108 APPENDIX V - Stand Economics Report On Mature Stands ....112 APPENDIX VI - Factor Analysis Results ................... 120 APPENDIX VII - Volume And Value Yield Classes From Cluster Analysis ........................127 APPENDIX VIII - Transportation Economics By Compartment For The Westlake PSYO ..133 APPENDIX IX - Transportation Analysis Results For An Isle Pierre Appraisal Point - Stand 057 ......135 APPENDIX X - Summary Of Cut Scheduling Results - Case 1 .137 APPENDIX XI - Species Harvest By Timber Class - Case 1 ..138 APPENDIX XII - Summary Of Cut Scheduling Results - Case 2 14 1 APPENDIX XIII - Summary Of Cut Scheduling Results - case 3 4 2 vi LIST OF FIGURES 1. Levels of forest planning ...5 2. Components of the TRftCS system ....................22 3.. Examples of graph structures 29 4,, Components of the state variable subsystem ........38 5. BCFS volume over age curve ....40 6. „ Road network of the Westlake PSYU ................. 55 7.. Example of wood flow analysis 68 8., Comparison of volume flow - Case 1 .73 9., Comparison of species flow in decade 1 - Case 1 ...74 13.. Comparison of volume flow - Case 2 ............... 11 11. Comparison of species flow in decade 1 - Case 2 ..78 12. . Comparison of volume flow - Case 3 ..83 13. Comparison of species flow in decade 1 - Case 3 ..85 vii LIST OF TABLES 1. Age class distribution of the Westlake PSYO via ASAP ...52 2.. Basic access cost data ....54 3. Road segment report .....56 4.,. Stand distribution across accessibility classes ...60 5.. Timber class distribution across accessibility classes ..61 6.. Minimum routing distances and policies ............64 7.. Stand access report ............................... 66 8.. Differences in timber classes scheduled for harvest in decade 1 - Case 2 80 9. Comparison of stands harvestable in decade 1 within Compartment 20 - Case 3 ...86 \riii ACKNOWLEDGEMENTS I would like to thank Dr. D» H. Williams, supervisor, for his continued assistance and patience throughout my graduate program. I would also like to thank Mr. G. G. Young for his guidance and counsel during my university studies. Appreciation is also extended to Dr. D. Reimer for participating on my committee and reviewing the text. Finally, I would like to acknowledge the cooperation of the British Columbia Forest Service, Resource Planning Division in allowing me to address the topic of this thesis.. 1 1. INTRODUCTION The: scheduling of timber harvests within a forest management unit has far reaching conseguences. , Environmentally, the rate of harvest affects the long term ability of the land to produce both timber and non-timber benfits. Economically, harvesting rates affect industrial operating strategies. More than ever, mill and market expansions are :contingent on the continued availability of timber supplies. Decisions affecting the flow of timber, despite their critical nature, cannot always be deferred until complete infornation is at hand.. British Columbia has a valuable timber resource, and an industry developed for its utilization. Harvest scheduling can, however, be improved by planning.. Harvest planning involves not only the timber base, but also its interrelationship with the transportation network, accessibility and log,hauling reguirements are key factors which must be considered in the scheduling of wood flows. This thesis presents a quantitative, analytical methodology for harvest planning at the management unit level. The system developed integrates transportation considerations to improve cut scheduling decisions.. 2 1.1 Forest Harvest Planning Planning is any activity designed to provide efficient, controlled courses of action directed at achieving some identified end., It is a continuous activity, and as such must be flexible and dynamic. Planning allows assessment of change in economic, biological and social conditions.. It facilitates consideration of alternatives and resolution of conflicts, thereby guiding decision making. Hence, planning is the foundation for efficient management. Forest harvest planning encompasses those activities desigaed to provide efficient, controlled strategies for scheduling timber flows. The strategies are directed at achieving specified wood quantity and quality objectives, as well as allowing the integration of other resource uses. The main objective from a government standpoint is volume yield control.. In contrast, the:main objective of an industrial firm is the:generation of cash flow and profit, i.e. value yield control. However, the need for an assured raw material supply provides for a consolidation of the two objectives. Economics plays a fundamental role in the planning process. Scarcity, in turn, plays a fundamental role in the economic process. Economics is concerned with the efficient allocation of scarce resources so as to optimize a specified objective., If resources were not scarce, then there would be.little need for efficient allocation. Without need for efficient allocations there would be little need for planning. Hence, the concepts of scarcity and economics are;fundamental to planning. 3 Scarcity has also played a fundamental role in forest harvest planning.. The early stages of the.forest industry in British Columbia, and North America in general, were marked by an overabundance of forested lands. The most accessible, best guality stands were.harvested. Timber of inferior species, size and quality was not cut. . Sush harvesting activities have been considered wasteful exploitations, resulting from a lack of planning. However in economic terms, such "explotations" were rational actions. In the:face of excess timber supplies, there was little need for efficient allocation of timber resources, and thus no need for planning. As excess inventories are depleted, the issue of scarcity develops. Reduced timber supplies force harvesting activities to the margins. . Smaller diameter, lower guality stands are: harvested at the intensive: margins. More: distant, less accessible stands are harvested at the extensive margins.. At these margins the need for efficient allocation of resources becomes critical to viable operation. The majority of the industry is currently facing operations at the margins.. At the same time, there is ever increasing pressure for the production of a variety of goods and services from the forest land base. The forest industry is now aware of the necessity for proper planning to integrate timber supply needs with other forest land uses. 4 1.2 Levels Of Forest Planning The British Columbia Forest Service (BCFS), the public agency responsible for management of some 95% of British Columbia's forest lands, recognizes five levels of forest land planning (Figure 1) : 1) the provincial level, 2) the regional level, 3) the management unit level - Public Sustained Yield Units (PSYU) and Tree Farm Licences (TFL) , 4) the watershed level, and 5) the operational unit/cut block level. . These planning levels provide a framework for linking philosophical policies to actual, on-site operations.. At the higher levels, the planning horizon is longer and the objectives ace more broadly stated.. Conversely, at the lower planning levels the horizon is more:immediate and the: objectives more precisely defined. Williams (1976) noted the interrelationships that exist within the planning framework. Decisions at any particular level constrain the activities of neighboring levels. For example, if the objective at the: regional level is to develop the timber resource, then a corresponding industrial infrastructure must be established.r A suitable harvesting schedule would have to be developed at the management unit level, subject to the industrial needs.. Activities would involve: evaluation of land use potentials, including assessment of the :inventory and transportation system. The harvest scheduling strategy determined would then direct the Operational Unit Level 6 development of watersheds.. This example is a simplification of the actual process.. Factors such as public values and political issues also enter into the planning picture.,. Nevertheless, the multi-level planning framework facilitates coordination of the planning effort within a temporal and spatial continuum.. The: purpose of this thesis is to provide a methodology for explicitly integrating transportation considerations into management unit level harvest planning. . A computer modelling systen is presented which incorporates the effects of log transport and primary access development with the forest inventory, thereby providing a more complete assessment of timber value. This improved valuation, coupled with other management information is then assembled and evaluate! through a cut scheduling model. The result is improved long term and short term forest harvest planning. The following chapter reviews the development of pertinent harvest planning models. . A problem analysis of harvest scheduling is then presented.. Next, the methodology used in the study is described, followed by its application to an actual forest management unit in British Columbia.. 7 2. . FOREST PLANNING MODELS -2. 1 Aspects Of Models Models have been developed to facilitate effective, comprehensive forest land planning. They are an outgrowth of iacreasing management demands. More data, faster response time and the evaluation of alternatives, all under limited manpower, typify today's planning environment. Models simplify the complex nature of problems to a manageable degree.. The real problem is reduced to an abstraction. Only those factors identified as significant are represented, with less critical details ignored. The abstraction is then analyzed, typically with the aid of a computer. However, since the actual problem is not fully represented absolute answers do not result. A major limitation of planning models is that the actual planning process is not a well defined activity.. Conseguently, validating a model against present practices is very difficult. Models and the modelling process, nevertheless, do offer numerous advantages. The model formulation stage can reveal significant factors which may otherwise be obscured by normal analysis.. A model when properly formulated provides the capability for objective assessments on a reproducible basis. Further, a model provides a framework around which management knowledge and experience can be quantitatively expressed and 8 explicitly incorporated in the planning process. The.computerization of models has provided the ability to evaluate many factors on a dynamic basis, with greater speed and efficiency than possible under manual procedures. Such improvements have enabled the evaluation of new alternatives. The end result is improved utilization of data and a reduction in the.uncertainty around which decisions are:made., 2.2 k Review Of Hangement Unit Planning Models One.of the first published efforts which introduced the use of computerized models for forest harvest scheduling was by Curtis (1962). He applied Linear Programming (LP) in the cut scheduling of southern pine stands to maximize both volume and revenue production.. The application, however, considered a planning horizon of only one rotation and was specific to one forest company. Loucks (1964) extended the application of LP to cut scheduling by developing a more general model directed at sustained yield management. His formulation included a capability to consider a variety of management and silvicultural alternatives.. Leak (1964) used LP to examine the- maximum allowable yields generated from a series of final cuts and thinnings over a single rotation. He considered harvest alternatives which yielded equal areas, as well as equal volumes. Kidd et al. . (1966) incorporated biological factors of 9 site and age with silvicultural alternatives over a five-period horizon in using LP for forest regulation. Littschwager and Tcneng (1967) introduced LP decomposition techniques for solving large scale versions of the earlier formulated harvest scheduling problems. The technique of solving a series of smaller subproblems was found to be useful for scheduling cuts over a large number of forest compartments. Bare and Norman (1969) introduced the application of Integer Programming (IP) to harvest scheduling. . Their formulation included the scheduling of both stands and entire compartments. Previous LP applications had resulted in non-integral solutions. Through IP, harvest schedules can be found which preserve the integrity of existing forest stands. However, the lack of an efficient algorithm greatly limits the application of IP to scheduling problems of realistic proportions., Walker (1974) combined the economic concepts of supply and demand with the forest inventory to determine:rates of harvest. His model, named the Economic Harvest Optimizer (ECHO), maximizes present net value.. The solution strategy employed equates the marginal net revenue derived from harvesting for each time period. ECHO incorporates downward sloping demand curve relationships where timber price varies inversely with the volume harvested. This relationship was not characteristic of previous models. Also unlike past approaches, sustained yield or volume flow constraints were not imposed.. Instead, Walker criticized the criterion of sustained yield for determining optimal economic harvest levels. 10 Johnson (1976) pursued the significance of a downward sloping demand curve on harvest scheduling and value maximization using a quadratic programming formulation. He demonstrated, contrary to popular belief, that value optimization could be achieved by restricting volume harvested. Under a price responsive situation, the market mechanism will act to constrain volume:flow. Hence, proper consideration of the price elasticity of demand allows achievement of optimal economic allocations, not possible under imposed sustained yield constraints. Hrubes and Savon (1976) demonstrated that a downward sloping demand curve could be incorporated into LP formulations by using separable programming in situations where volume harvested can affect stumpage prices. Clutter (1968) presented a more complete computerized forest management planning system. The system included an appraisal simulator in conjunction with an LP harvest scheduler and report writer.. The simulator calculated volume and value yields generated by alternative clearcutting policies for each cutting area. The cut scheduler then selected that set of alternatives which maximized present net worth subject to certain specified volume flow and area constraints.. His system, Max-Million, has been adopted as an operational tool for management planning of southern pine forests by a number of companies (Ware and Clutter 1971). The Timber Resource Allocation Method (RAM) is an LP timber planning system developed by Navon (1971) for the United States Forest Service (USFS).. This system is similar in concept to 11 Max-Million. It takes a forast inventory and a set of alternate management prescriptions for each forest type and determines the optimal harvest schedule according to either a volume, revenue or cost objective. The long range planning horizon (up to 350 years) and current operational use on a number of United States National Forests distinguish Timber RAM from previous scheduling models. .. In fact, a USFS review (Mass, 1974) of computerized planning systems recommended that Timber RAM be used for allowable cut calculations and long range volume.predictions1. . The Resource Capability System (RCS, Mass 1974) is a multiple resource planning tool. Forage, sedimentation, recreational visitor days, as well as timber, are products considered from the total land base. LP is used to schedule the mix of resource activities for a given management unit. . The primary benefit from RCS is a guantitative means of evaluating alternative land use combinations.. However, RCS has limited applicability for timber planning. The model is not well structured for evaluating detailed timber management alternatives and is only capable of handling eight time periods. Fowler (1978) discussed the need for estimating the impact of forest management decisions on broader socio-economic factors of a region. He presented a system of submodels to address this need. In addition to an LP cut scheduling model, he added: 1) a forest measurement simulator, *Further details of Timber RAM will be covered in subseguent chapters. r 12 2) an economic input/output model, 3) an employment estimation model, and 4) a tax projection calculator. This system enables one. to trace a given schedule:of timber flows through to its impact on levels of gross regional product, employment and taxes. Extensions to harvest scheduling models appeared in the planning of forest roads. Odendahl (1975) reviewed several of the planning models used by the USFS for forest transportation analysis.. The most pertinent of the models discussed, the: Timber Transport model, was designed for analyzing modifications to an existing road network which links harvest areas to mills. , This model is actually a system consisting of a route generator, matrix generator, an LP/IP optimizer and a report writer. Traffic allocations and assignments are produced based on the optimization of either a cost or revenue objective.. The Timber Transport model is particularly useful for examining traffic volumes, for costing alternative .route flows, and for assessing additions, improvements or deletions to the road network.. Navon (1975) presented two models for forest transportation planning. The first model addressed short run analyses of up to five years. It was assumed in the short run that both volume and location of harvest were known, with the planning problem reduced to determining the minimum cost strategy of hauling and road construction activities. The second model addressed long term planning. A detailed discussion is presented by Weintraub and Navon (1976).. The long term model included timber management activities as well as hauling and road construction 13 activities. The problem was to find that combination of activities which would optimize an economic objective, either minimum costs or maximum revenues. Both the short and Long run models use a mixed integer linear programming formulation.. Road construction activities were modelled as integer variables with silvicultural and hauling activities as continuous variables. As with most IP problems, the number of integer variables must be limited to keep the problem within solvable dimensions.. A progression in the.development of forest harvest planning models has become apparent upon reviewing the literature. Initially, applications emphasized the biological aspects of growth and volume yield, particularly in the context of a sustained yield philosophy. Models then began to place more focus on the bio-economic aspects of harvest planning.. From there the modelling effort has been characterized by the development of systems of models rather than a single model to address forest harvest planning. We are now at the stage where extensions to previous models are being developed (Williams, 1976).. The focus is on improving and combining existing tools, rather than on developing entirely new technigues or models.. 14 3. PROBLEM ANALYSIS The: problem analysis process basically involves the identification of; the decision maker, the objective, and the alternatives available to satisfy the objective.. Among the alternatives there has to be doubt as to which is best. Constraints affecting the alternative-objective relationship must also be identified within the problem environment.. The existence of a problem would not be clearly recognized if any of the above components were not identified. The decision makers to which this thesis is directed are the forest managers who plan harvests at the management unit level.. Included in this group are both government managers responsible for PSYU's, and industrial managers responsible for TFL•s.. The basic objective of the forest manager, in regards to harvest planning at the management unit level, is to schedule the harvest of the supply of timber over time in an optimal manner. . The desirability of possible cutting schedules is measured in terms of volume and value flow, giving due consideration to non-timber resources. Harvest scheduling encompasses the determination of: 1) the cut level, 2) the time period for harvest, 3) the species composition of the cut, and 4) the possible areas of harvest. Several alternatives exist in addressing the four aspects of harvest planning. Planning time reguirements and the ability 15 to evaluate a variety of scenarios are measures by which alternative harvest regulation methods can be judged. . Two particular alternatives are considered below; the present and historical method of determining harvest schedules, and a new approach which has gained considerable attention. 3.1 Harvest Scheduling - Background The historical approach to harvest scheduling has been based on biological yield criteria. Cut regulation policies were non-existent in British Columbia prior to 1947.. In the early 1950's sustained yield regulations ware instituted to determine annual allowable cut levels as a result of the second Boyal Commission of Inguiry related to British Columbia forest resources. The early attempts at determining harvest levels were based on the Hanzlik formula (Hanzlik, 1922).. This formula was designed for the regulation of mature and over-mature forests.. The basic form of Hanzlik's formula is shown below: Volume of Mature Mean Annual Annual Allowable Cut = +• Increment Rotation Age . of Immature Subsequently, harvest levels were determined by a more detailed computational procedure designed to maximize mean annual increments of growth. In this process, localized estimates of growth and yield are obtained from the timber inventory of the management unit in guestion.. Such estimates 16 provide the basis for determining total yields and annual yields of mature and immature, forest types. The: annual yields represent a cut level which maximizes the rate of physical production from the forest base.. A biologically optimal rotation age for each forest type is then determined by dividing the total yield by the annual yield. An iterative procedure called an "area/volume allotment check" is then carried out to verify the compatibility of the specified rotation age with the tabulated annual cut. If necessary the annual harvest level is adjusted so that the time period in which to harvest all age classes corresponds to the optimal rotation.. complete details of the BCFS annual allowable cut calculation can be found in a policy paper (Haley, 1975) from the most recent Royal Commission into forest resources2. A number of administrative adjustments are subsequently made to the indicated allowable cut level to reflect volume losses due to land alienations for non-timber uses, fires, roads, regeneration delays and other silvicultural and harvesting induced losses. The net result is an administratively approved cut level for a one year period for the management unit of concern.. The above procedure has been used for determining annual harvest levels for both PSYU's and TFL•s.. The determination of the remaining scheduling aspects of 2Pearse, P.H., 1976.. Timber Rights And Forest Policy, Report Of The Royal Commission On Forest Resources 17 what and where to harvest have also been based on volume criteria. From the government viewpoint, assignment of cutting priorities to stand types based on an increment gain philosophy has identified what species to harvest. High priority is given to those stands which, when replaced, result in greater growth. These prioritized stand types are then identified on the inventory type map showing potential harvest areas. From the industrial viewpoint, product marketing requirements are becoming increasingly prevalent in dictating species flow from the harvest.. Potential harvest areas which will contribute to the desired species mix are again identified on the inventory type map. 3.2 Harvest Scheduling - Need For An Alternative There are several disadvantages to the harvest planning approach described above.. Foremost is the lack of economic considerations in the establishment of harvest regulation at the management unit level.. The rate of timber harvesting has been determined so as to maximize biological productivity, and not necessarily economic or social returns. Economic as well as biological assessment of the management unit will provide an improved basis for cut scheduling. In particular, the need exists for the economic factors regarding species flow and harvest area accessibility to be explicitly integrated in the determination of the rate of harvest. . Recently, the formal 18 incorporation of economic analysis with biological analysis was evidenced on one of the National Forests in California (Craig, 1979)., This study demonstrated that departures from a strict even flow sustained yield approach resulted in opportunities for increased levels of harvest, revenues and jobs without adversely affecting the long term biological capacity of the management unit. . & second disadvantage:of present harvest scheduling methods is the limited opportunity for assessing alternatives.. The need exists for assessing the impacts of possible:current decisions over a long range horizon. Previous planning methods did not facilitate analysis of a spectrum of harvest schedules in a timely manner, a deficiency largely a result of the manual process of allowable cut determination. A third major disadvantage is the absence of transportation considerations in determining timber flows for a management unit. The selection and timing of harvests are: principally a function of the timber value, its location and its accessibility.. Not only does the transportation network represent a major physical constraint in terms of access, but it represents a major expense. . Roads represent approximately 20% of the capital investment of a harvesting operation in British Columbia, with the costs of construction not uncommonly exceeding $70,000 per mile: ($43 ,497/kilometre). Substantial savings can be realized through thoughtful integration of cutting schedules with hauling and construction activities. . The integration of timber planning and transportation planning systems was endorsed in a review of OSFS planning 19 systems by Weisz and Carder (1975). In this regard, Weintraub and Navon (1976) state: "The sequential non-integrated approach leads to suboptimization on two counts: 1) the wrong set of harvest areas may be made accessible, and 2) the choice: of period of access to each node (harvest area) may not be optimal" In other words, transportation-related activities are major decision variables in harvest planning and should be given due consideration along with silvicultural activities. Sreater demand for other resources has forced alienations from the timber base. These:supply-reducing pressures, coupled with the increasing demand for wood products have substantially increased raw material costs to the forest industry., Such changes in the physical and economic environment only accentuate the deficiencies of past harvest planning practices.. The latest Royal Commission into British Columbia forest resources, the new Forest Act and the accompanying regulations are evidence that forest policy must be dynamic. Changes must be:made to keep in step with economic and social needs. Change is now appropriate in the regulation of harvests.. The traditional concepts and basis for harvest scheduling are no longer adequate or applicable to current planning needs. The alternative method presented in this thesis to address the deficiencies identified above is the Transportation Analysis-Cut Scheduling (TR ACS) system. TRACS makes use of resource inventory compilations and management prescriptions to provide growth, yield, cost and revenue data for harvest 20 schedule determination. Long range impacts of a variety of management strategies on both a volume and value'.basis can be evaluated.. Furthermore, TRAZS allows in its evaluations the explicit consideration of stand accessibility in terms of road construction and log transport. The result is a harvest planning system which integrates transportation in the biological and economic evaluation of cutting schedules for a management unit. , 21 4. MODEL COMPONENTS This thesis expands the effectiveness of existing management unit level harvest planning tools. In doing so, the TRACS system methodology draws largely from the components of the Computer Assisted Resource Planning (CARP) system (Williams £i §.i»r 1975)., CARP was developed for the BCFS as a prototype harvest planning system. The original methodology has been extended by developing a transportation modelling subsystem. The results from this transportation subsystem are subsequently incorporated in cut schedule determination. The flowchart in Figure 2 outlines the basic analytical structure of the TRACS system. The following five major subsystems are presented: 1) the Forest subsystem, 2) the Transportation subsystem, 3) the State Variable subsystem, 4) the Cut Scheduling subsystem, and 5) the Report subsystem. 22 Figure 2. Components of the TPACS System State Variable Subsystem Cut Scheduling Subsystem Report Subsystem On-line data storage j J Processing C7 Hardcopy reporting 23 4. 1 Forest Subsystem Ml methods of determining harvest levels first require an inventory of the physical resources of the management unit. Typically a map overlay system provides the foundation for compiling information on the supply of resources available. The overlays would include vegetation type and land classification as a minimum.. The type map provides information on species, age, site: and timber yield. The land classification map provides information on soils, landform, parent material and drainage characteristics. The overlay process delineates distinct land units for which area and productive, capability can be identified. All corresponding information is compiled as attributes of these physical geographic units.. Land use plans and management prescriptions related to the.identified land units accompany the physical resource information. Ose suitability and prescribed treatments are derived as a function of local knowledge and soil-landform characteristics, and provide the basis for cost estimation. Those areas having productive forest cover form the basic "stand" unit. Stands represent the finest level of resolution for unit planning. For each stand, the following attributes comprise the data base: 1) stand number 2) compartment number 3) geographic location 4) species type 24 5) land class 6) age class 7) site class 8) area 9) net volume per unit area 10) designated use(s) 11) harvesting method 12) season of harvest 13) earliest and latest harvest entries 14) expected site preparation 15) expected regeneration Ml of the above information is assembled and maintained on a computerized data management and retrieval system. A computerized data base serves three basic functions. Firstly, it provides rapid answers to on-demand user queries., Secondly, it provides for generation of standardized management reports. Thirdly, it provides for the generation of basic data for further analysis. As mentioned, the stand inventory provides an indication of productive capability of the land.. The BCFS derives localized estimates of timber growth and yield through sampling. These estimates reflect average volume production per area to a given utilization standard, less deductions for decay.. The yields are presented in graphical form in which volumes are plotted against age by species type, geographic location and site.. These BCFS Volume/Age Curves (VAC) are a rudimentary form of a whole stand-distance independent growth model. The.VAC's provide the 25 basis for the standard annual allowable cut calculation procedure., The same basis for growth and yield projection is used in this thesis because of availability and also to demonstrate how the same data can be utilized to generate more information to aid management. 4.2 Transportation Subsystem As previously discussed, the transportation system represents major decision variables in forest harvest planning. The importance of transportation considerations has been evidenced in a study by Herrick (1976). He found that hauling distance is one of the most critical determinants of successful logging operations. Reliable estimates of the costs of moving logs from the landing to the manufacturing plant are reguired for proper stand valuation. . However, models for evaluating such costs have not been long established in forestry. TRACS allows for the generation and evaluation of transportation-related costs. The transportation subsystem presents a procedure for deriving cost estimates for truck transport based on a minimum routing network analysis technique. 26 4.2.1 Derivation Of Transportation Costs Two main factors affect the estimate of transportation costs for a given stand: 1) the transportation network, both existing and proposed, and 2) the location of the forest stand in relation to both the network and the manufacturing plant.. The typical forest road network can be characterized by two attributes; road class and, type of haul (i.e. on vs. off highway haul). These two characteristics determine the guality of the road network and the type of transport medium which utilizes the network.. Road classes relate to the design and capability standards in terms of maximum allowable vehicle speeds and traffic concentrations for the: road.. They are significant as they directly affect "cycle" times for travel from landing to mill and back to landing. . The type of hauling medium permissable, either on-highway or off-highway trucks, is also a significant factor.. This characteristic directly affects allowable load limits and truck speeds. More specifically, cost estimates for truck transport, in dollars per unit volume, are a function of four components: 1) distance 2) speed 3) machine rates 4) load size Distance divided by allowable truck speed, loaded and unloaded, provide cycle times. . Cycle times applied against machine rates 27 for logging trucks yield costs for log transport in strict dollar terms. This cost whan divided by load size generates log hauling cost in dollars per unit volume of wood., The derivation can be summarized as follows: 1) Distance/average Speed = Cycle Time (miles) (miles/hour) (hours) 2) Cycle Time X Machine Rates = Cost (hours) ($/hour) ($) 3) Cost/Load Size = Transportation Cost ($) (cunits) ($/cunit) Thus, hauling distance is the initial factor which contributes towards transportation cost derivation. Distances from the stand to the road network and through the network to the mill are required.. Network analysis provides a means for determining the necessary hauling distances and facilitates the assessment of hauling strategies. The following section defines some basic terminology which will be introduced in the discussion of minimum routing and its application to stand valuation. 28 4.2.2 Network analysis a basic characteristic of graphs and networks is their combinatorial nature. . a graph is a collection of nodes joined by a collection of arcs. Graphs define purely structural relationships. a network is a graph containing in addition, flow, distance or some other measurable attribute associated with the member arcs and/or nodes. Thus, networks provide guantitative descriptions as well as defining structure.. In mathematical notation, the set of nodes can be represented by N = {i I i = 1,...,n}, and the. set of arcs represented by a = { (i, j) or (j,i) | iSN, j&N} . Given the above two sets, a graph can be defined as the set G = {N,a'} where A'sa. Extending the notation, node attributes can be represented by B = [bt| i&N}.r Similarily, arc attributes can be represented by C = [c(i,j) and/or c(j,i) | (i,j)&A}._ Given these additional sets, a network can be defined as the set W = {N,a',B, C'} where C'cC (A») . a number of other "graph-network" terms also reguire definition. a "branch" is an arc together with its corresponding end nodes. If all branches are unordered, where arc (i,j) = arc (j,i), then the graph, G, is "undirected". Conversely, if the branches are ordered yielding some sense of direction between the nodes (where arc (i,j) # arc (j,i) ), then the graph, G, is said to be "directed".. a "source" node is oriented such that arcs lead away from it, whereas a "sink" node is one:in which arcs are directed towards it.. Figure 3 presents examples of both an undirected and a directed graph. Figure 3. Examples of Graph Structures 29 30 Corresponding set definitions accompany the diagrammatic representations. Note also, intersections occur only at nodes, not where arcs are shown to cross each other.. The "degree" or "order" of a node is the number of arcs incident upon it. A node.of degree 1 is an extreme point3, and its corresponding arc is a terminal arc. Further, arcs are defined to be adjacent if they are incident on a common node. Completing the terminology, a "path" is a series of ordered, adjacent branches leading from a given node i to another node j such that each intervening node is encountered just once. A path initiating and terminating at the same node is called a "cycle" or "loop". Conversely, an acyclic path is referred to as a "simple path".. A graph, G, is "connected" if there exists at least one path connecting any two nodes i and j, where i&N and j&N and i # j. As a final term, a "subgraph" of G is that subset of nodes, N'sN together with the appropriate subset of incident arcs, A'aA. The Shortest Route problem involves finding the feasible path of minimum distance from a particular source node: to a particular sink node. The problem is characterized by a directed graph, G = (N, A). , The node set, N = (i| i=1,... ,n) , can be partitioned into three subsets: 1) N, = source nodes, 2) N2 = intermediate nodes, and 3However the converse is not true.. It is not necessary for an extreme point to be a node.of degree 1. 31 3) N3 = sink nodes The arc set, A = C(i/j)|i&N,j&N} where A > n-1, connects every pair of nodes. . There, exists a set of attributes, C = [C (i, j ) | (i, j) &A} , associated with each arc between nodes i and j.. The feasible path between nodes i and j can ba represented by xLj . . The following additional conditions also hold: 1) the arc attributes cLj need not be symmetric, i.e. c "Lj / c j i , 2) the attributes cLj are non-negative, i.e.. ,Cy > 0 , 3) the value of an attribute from a nodeito itself is zero, i.e. c^ = 0 t and 4) where no arc exists between any particular pair of nodes, the attribute c y- is assumed to be infinite. Siven the above specifications, the Shortest Route problem can formulated as follows: MIN Z = E E cu xi} i j J J subject to : 1-1 for i&N, , where N, = {1} 0 for i&N2 , where N2 = {2,...,n-1} 1 for i&N3 , where N3 = [n} ii) Xy- > 0 for all i The objective is to find the route which minimizes the total 32 distance travelled from a specified source to a specified sink. The first set of constraints specify that only a single unit flows out of the source (N, ) and into the sink (N3) , while: flow is conserved at the intermediate nodes (Nz). . The second constraint states all flows are to be positive. In the usual case, the arc attributes, cLj , represent distances between respective nodes.. However, the arc attributes need not be restricted to distance. They may be times, for determination of the minimum duration route, or probabilities of delays, for determination of the most reliable route, or the attributes may be costs, for determination of the minimum cost route.. Note that as with most problems the optimal value (i.e. the minimum distance) is not of key concern, but it is rather the decision strategy yielding optimality (i.e..the minimum route) which is of primary importance. Closely associated with the basic Shortest Route problem is the determination of the shortest path between a selected sink and all other nodes. As Elmaghraby (1970) points out, almost all algorithms that solve the- basic one source to one sink problem, also solve the all sources to one sink problem.. The all sources-one sink Shortest Route problem is the one of particular interest.. The algorithm considered to be most efficient in determining the shortest path between a specified pair of nodes is a tree method developed by Dijkstra (1959).. The method is a permanent labelling, iterative process in which the distance from a particular source: node, 1, to every other node, i, (i=2,...,k,...,n) is determined in ascending order until the 33 specified sink nods, kf has been processed, or until all other nodes have processed. The: algorithm is capable of handling non-symmetric arc lengths an! both positive or negative arc attributes.. A detailed description of Dijkstra's algorithm is presented in Appendix I. 4.2.3 Log Transportation Based On Minimum Routing The forest road system of a management unit can be represented in digital form.. Two-dimensional spatial relationships of the road network can be captured from a map through a process of digitization*. Road segments can be delineated on the basis of road class, road status and haul type. In other words, road segments represent sections of road of uniform characteristics. Lengths of the individual road segments can be computed directly from the digitized data. Empirically observed cycle times from centres of active operation can be supplied along with the road system., These cycle times, combined with current machine rental rates and average load volumes, provide transportation costs per volume of log. Distances from the active operations to milling sites allow generation of haul costs in dollars per cunit per mile digitization is the process of recording x and y coordinate values relative to a predefined base origin* The recording of a series of coordinate pairs enables the geographic location of such features as roads to be numerically represented. 34 ($/cubic metre/kilometre). These costings from observed operations can then be used as the basis for establishing hauling cost zones.. Within a particular zone, the transportation cost for a given stand can be derived by multiplying the distance from stand to the mill by the respective dollars per cunit per mile ($/cubic metre/kilometre) figure.. Alternatively, distance to a pre-determined location for -ost appraisal purposes could take the place of the mill site. The distance from a stand to the mill or point of appraisal involves two components. The first component is the distance from the stand to the access road. The coordinate location of each stand is captured through digitization of a visual centroid. The selection of an access road for a particular stand is based solely on linear distance. In other words, the closest road will be accessed., Pythagorus' Theorem is used to determine this linear distance. This approach is a simplification in at least two respects.. First, the distance will be underestimated, since in most cases the path of access from a stand to the road will not be linear.. Second, no regard is given to topography which may hinder access of the closest road.. Nevertheless, to facilitate an estimate.of the first distance component it is assumed that the nearest road will be accessed. . The second component is the distance from the point on the access road through the:road system to the point of appraisal.. The criterion employed in selection of the route is one of minimum distance. Dijkstra's algorithm, discussed in Section 35 4.2.2, is used in determining this second distance component.. The forest road system can be represented as an undirected, symmetric network5.. The node set, N = (i|i=1,2,...,n}, becomes the points of the road class transitions (and road segment end points). The corresponding arc set, A = [(irj) I i&N* j&N) is the road segments themselves with distance as the quantitative attributes cLj , of interest. The source nodes i, i&N, , are the geo-coordinate centroids of the forest stands.. The sink node j, j&N3 is the point of appraisal, usually a specified mill site.. Thus, the situation is formulated as a minimum routing problem involving multiple sources and one sink.. The objective is to minimize the distance travelled in proceeding from a source nodei, through the network to sink node j. The decision is to determine the routing strategy, x^j , which yields minimum total distance travelled. The approach of using Dijkstra's algorithm in conjunction with digitized data is unique relative to applications reviewed in the:literature. The distinguishing featureiof this approach is that as part of the process of determining minimum distances and routings, the precedence relationships of the network are constructed.. Node:and arc relationships of the road system are assembled and maintained from the initial digital representations, and are not expressly identified., Cost estimates for primary road development can also be SAlthough distances are symmetric, travel times may not be. However, the simplifying assumption is that cycle times are directly related to distance.. 36 generated by the transportation subsystem. Lengths of proposed main roads, by road class, within the.network are determined by the subsystem. . These distances, when combined with construction costs for given conditions of terrain, parent material and road standard, yield a cost estimate for the proposed road development. The construction costs are then proportioned among the timber volume of the stands which will use the proposed road for access.r The result is an additional par unit volume cost estimate reflecting access development. A further use:of the subsystem is for evaluating road class selections for proposed construction or upgrading. Tradeoffs between the extra costs for developing better class roads versus the estimated savings in transportation costs can be examined. The transportation subsystem can also be employed on a stand alone basis for evaluating alternative wood flow patterns from stand holdings to mill complexes. Routing strategies both within and between management units can be examined. Impacts of fluctuations in unit costs for transportation and road construction can be assessed. For example, forecasted fuel price increases, suggested practices of end hauling and other such considerations could be evaluated. To summarize, the transportation subsystem provides the capability for generating both transportation and primary road construction cost estimates for stand access. . Tha estimates provide a more comprehensive assessment of stand value.. This improved appraisal can be used as the.basis for independent analysis or can contribute to the overall scheduling of timber harvests at the management unit level.. 37 4.3 State Variable Subsystem Resource managers are-being forced to deal with a diverse and ever increasing data base. Under such conditions, the efficient utilization of data becomes a significant concern. The degree and extent to which data should contribute to planning must be identified for rational analysis to take place. The issue is one of data resolution. The reguired level of resolution is very much connected with the concepts of planning levels. . For regional planning only broad, incisive parameters need be considered. . Conversely, for cut block planning very detailed data are necessary. Between these two limits is a wide range in levels of data resolution.. The user should be able to select a level appropriate to the.planning needs. This section describes a methodology which allows base data, in the form of variables which describe the state of the resource, to be : synthesized to varying levels of resolution.. In this study, data are transformed into information pertinent to management unit level harvest planning.. The:components of the state variable subsystem of TRACS are outlined in Figure 4. Figure 4. Components of the State Variable Subsystem Factor Analysis Cluster Analysis Dynamic Programming Timber Classes & Yield Classes 39 4.3.1 Initial State Variables The finest level of data thus far has been the stand, characterized by species type, productive capacity and management prescriptions.. However, for policy decisions concerning harvest scheduling at the management unit level a broader level of resolution is appropriate. Groupings of stands or "timber classes" can be derived which are homogeneous with respect to their response to management treatments. Since cut scheduling decisions are based on yield attributes, the:condition or state of each stand can be reflected by volume and value yields. Any consolidation of stands should be based on similarities in these yield characteristics.. This necessitates determining the volume and value yields for individual stands, for both the present and the future. Such yields represent the set of initial state variables. .. Current volume per area estimates for mature and over-mature stands are derived from the type:map, with current stand stocking used to adjust future volume yields projected by the BCFS VAC's. . An example of a VAC is presented in Figure 5. , The curve identifies volume yields which can be: expected over time from logdepole pine (Pinus contorta Dougl.) stands of medium site quality. Yields from immature stands are derived directly from the VAC's, assuming stand management will result in necessary stocking conditions for the corresponding volume flow.. Estimates of stand value are obtained from a simulation of Figure 5. BCFS Volume Over Age Curve *>ool Zone 4 Volume/Age Curves 9.1"+ and 13.1"+ D.B.H. For Growth Type 12 - Pi Medium Site AGE IN YEARS the BCFS Interior End Product Appraisal System,, Inventory cruises to industrial standards provide compilations of stand volume by log grade. Recoveries in terms of lumber and chips for a representative mill are used to generate end product outturns. Corresponding market prices for the products provide gross revenue for the stand. Such revenue estimates are derived for each stand as a function of age, site:and species type. Harvesting costs, including felling, bucking, skidding and loading, for each stand are derived as a function of age, volume, species type, soil-landform class and management prescription. Area costs for landing construction, skid road construction and site preparation are also included in the stand appraisal. These costs together with the costs derived from the transportation subsystem are then subtracted from the gross revenue figures, resulting in net value estimates for timber delivered to the mill. Projections of stand value over time are generated on the basis of the volume yields projected from the VAC s. , 42 4.3.2 Data Analysis The initial state variables of each stand provide:the basis for generating timber classes. Data analysis techniques6 provide the capability for reducing data sets to manageable dimensions while minimizing the loss in information. Factor analysis is performed on the original stand yield characteristics which transforms the variables to an orthogonal, normalized state space. Basically, the procedure involves first an extraction of the principal components of the input variables. . These components are then rotated to delineate underlying dimensions of the input variables.. An orthogonal rotation reduces the.amount of inter-correlation that may exist. In simple terms, independent factors which contain the essence of the original state variables are extracted to yield a smaller set of stand attributes. This step eliminates redundant information which may bias the generation of timber classes. . Next, stands are aggregated into timber classes based on the factors extracted above. Cluster analysis is employed to perform the aggregations. It is a descriptive, statistical technique whose successful application relies oh the existence of inherent natural groupings. Groups are formed sequentially so as to minimize the total variation in the factor values among each stand member. The process begins with each stand as an 60thers have better covered the computational details of the techniques to be discussed (Ward, 1963; Gower, 1967; Veldman,1967). 43 individual group. Groupings are made, one at a time, until eventually all stands are members of one group. . At each step the decision to combine particular stands or groups of stands is based on the minimization of the increase in total intra-group variation.. Examination of the variances associated with each successive grouping level may indicate a particular number of groups worthy of consideration. Reduction to the next lower level may result in a substantially large .increase in error. Typically there are.a number of significant error increases. The determination of significance is mainly subjective and dependent on the user's objective. If minimizing loss in information is of prime.concern, then the grouping level that exists prior to the first substantial increase in error should be selected. If however, a particular range of grouping levels is of interest, then the error increases only within that range should be examined.. An extension to theclustering process has been developed where:special gualitative:attributes can be used to segregate stands in determining timber groupings. A dynamic programming formulation is used to allocate: grouping levels among the stratifications in an optimal manner. A paper by Williams and Yamada (1975) describes the: procedure in detail with an application which preserves species type within the timber class groupings. . The net result of the data analysis subsystem is the formation of timber classes which have similar silvicultura1 and economic yield characteristics.. The same process is applied to the yields over time to form concise classes for volume and 44 value projections. The. resulting timber classes and yield classes are the state variables which are used as input for harvest schedule determination. 4.4 Cut Scheduling Subsystem The TRACS system schedules timber harvests based on the LP model, Timber RAM.. Other papers have described the Timber RAM model in detail (Hennes et al. , 1971; Navon, 1971). The major aspects of the model will be reviewed here.. Timber RAM was developed by the OSFS for formulating long range timber management plans. The model has the capability for considering planning horizons of up to 35 decades.. Such long range: horizons allow assessment of the future implications of short term decisions. Given an inventory of timber classes and a set of management prescriptions and responses, RAM will determine a cutting schedule that optimizes a specified objective subject to specified constraints.. The objectives may be:to maximize volume production, maximize discounted value production or minimize discounted costs over any number of decades7. Various constraints on periodic levels of volume, revenue, costs and forest accessibility can be specified.. The resulting schedules indicate the area of each timber class cut, 7The:first planning period can be split into two 5 year periods H5 and the corresponding flow of volume, costs and revenues generated for each decade of the planning horizon., The:activities to be scheduled represent a sequence of management treatments for each timber class over the span of the planning period. The timber classes and the volume and value yield classes generated from the state variable, subsystem are used to formulate RAM activities.. An example.of a sequence of management treatments may be to clearcut employing an 80-year rotation with precommercial thinning at 20 years.. One corresponding timber class activity would be to clearcut and regenerate in decade two, precommercial thin in decade:four and clearcut and regenerate again in decade ten, repeating the sequence over the planning horizon. Hence, activities can differ not only in the type of treatment but also in the timing of treatments.. In this way a multitude of timber class activities can be generated and evaluated with Timber RAH.. There are three major types of constraints which can be imposed on timber class activities: 1) area and accessibility constraints, 2) period constraints, and 3) harvest control and regulation constraints. Area constraints restrict the maximum area available for management of any timber class. Alternatively the total area to be managed of each timber class can be controlled. Accessibility constraints restrict the area of each timber class accessible during the.first five planning periods. Constraints on minimum acceptable levels of volume or revenue, or maximum 46 acceptable levels of costs can also be specified for any period in the planning horizon. Harvest control constraints can be used to control volume flow8 during the conversion period. Harvest regulation constraints can be used to regulate volume flow during the post conversion period. The conversion period is that span in which old growth is liguidated, with the post conversion being that period in which second growth management is in effect.. During the conversion period three types of harvest control can be implemented: 1) arbitrary control, where harvest levels are restricted to absolute upper and lower limits9 2) seguential control, where upper and lower limits on harvests are restricted to a percentage of the harvest specified in the preceding period., This allows smooth transitions in decade harvests. 3) conventional control, where harvest levels are restricted to a percentage range around the average.harvest level of the conversion period. During the post conversion period conventional control is used to regulate harvest levels. The optimal scheduling of timber class harvests which 8The option also exists to regulate the area harvested rather than volume. 'Arbitrary control is the same as instituting periodic volume constraints. 47 satisfy the imposed constraints is found using LP.. Generally, allocation decisions are based on a series of evaluations under a variety of constraints and objectives, not solely on a specific optimal situation. . The :underlying benefit of Timber HAM rests in its ability to examine alternative policies.. Such alternatives are formulated by varying objectives, activities and/or constraint combinations. Different objectives can be specified by changing the planning horizon, discount rate or output criteria (i.e. volume versus revenue). Activities can be altered by manipulating rotation ages or silvicultural treatments., Similarily constraints can be changed, for example, by varying volume flow reguirements or land accessibility allowances. Evaluation of such changes provides not only an indication of desirable strategies, but also an indication of the stability of various management policies. To summarize, Timber RA3 provides: 1) a schedule of timber classes to be. cut with the corresponding volume and value flows per decade, 2) an estimate of the productive capability of a management unit in terms of both volume and value, 3) a means of evaluating impacts of alternative: management policies, 4) a framework in which to assemble and utilize a comprehensive forest data base, and 5) an assessment of the opportunity costs of non-timber land uses and alienations 48 There are.also several disadvantages of Timber RAM, and LP in general. First, the model is deterministic with no allowance for risk. All specified activities must be implemented for the indicated results to hold.. Second, all variables are continuous. Hence, any even age structure that exists within timber classes or stands may be violated.. Third, all relationships are linear. . Changes in responses that may occur at varying rates cannot be reflected10. This is a particular disadvantage where economies (or diseconomies) of scale, or downward sloping demand hold.. Further disadvantages inherent in the Timber RAH model itself have been presented by Chappelle et al..(1 976). Timber RAM nevertheless provides a means of addressing the harvest scheduling problem. It has proven to be a very useful tool for providing guidelines in the planning of management unit timber harvests. l°Separable programming techniques can be employed to reflect non-linearities.. 49 4,5 Report Subsystem Each subsystem of TRACS has report generation features.. The forest subsystem allows for the generation of standard management reports.. The transportation subsystem reports road network, and stand access descriptions. . Economic valuations of each stand are reported by the state variable subsystem. Examples of such reports will be cited in the discussion of results. However, the reporting facilities directly concerning the.harvest scheduling plans deserve brief discussion here.. The. Timber RAH model itself generates a variety of reports which describe the optimal cut schedule. A detailed harvest schedule can be generated, listing for each timber class the area to be managed by the selected activity and the resulting volume: yields (in total and per unit area) for each decade in the planning horizon. A corresponding report of the resulting economics can be generated on the same basis.. Summary reports of the periodic levels of volume and value flow across all timber classes can also be generated. A graph of harvest volumes over time is a particularly useful output feature. The value of the objective, the average long run sustainable yield and other plan statistics are also reported. All results reported by Timber RAM are in terms of timber classes.. The inability to relate the harvest plan to stands has been identified as a serious drawback (Chappelle et al., 1976).. Reports relating harvest schedules to identifiable stand units have been developed to augment the timber class reports.. These reports allow interpretation of the cut schedule. in a spatial 50 context for the management unit. Recognition of the spatial implications of scheduling results is necessary for realistic management assessments. Specifically, the reports identify the individual stand members of the timber classes which are to be harvested in a particular decade.. The species type, soil-landform class, age class, area and volumes of the candidate stands are reported.. ft species composition report for the decade harvest is also generated. An option exists which allows the plotting of candidate stand locations. This feature is facilitated only where geographic coordinates have been recorded as a part of the basic stand data. In this manner, potential stands which could comprise the specified decade cut are identified., This is the first step towards linking management unit harvest plans to watershed level planning. 51 5. . APPLICATION TO MANAGEMENT - UNIT HARVEST PLANNINS The TRACS system was applied to an actual forest management unit, the Westlake PSYU. The Westlake PSYU, a part of the Prince George Forest District, is situated in the central interior of British Columbia.. The unit is approximately 600,000 acres (242,803 hectares) in size.. It is in the Montane forest region with the principal commercial species being lodgepole pine and white spruce (Picea glauca (Moench) Voss). Individual stand units were delineated on the basis of three map overlays. A forest cover map containing 42 inventory types provided the:first overlay.. A soil-landform map provided the second overlay. Nineteen different land classes were identified for the Westlake PSYU. Descriptions of each land class can be found in Appendix II. The third overlay identified desigaated use in terms of timber production, grazing, wildlife, fisheries, recreation, agriculture and deferred use.. A total of 2,441 stand units resulted.. Prescribed stand treatments also accompanied the overlay information. The treatment seguences which are based on land class and growth type are detailed in Appendix III.. From the.above information the.fifteen attributes listed in Section 4.1 were compiled for each stand. . This stand information together with BCFS VAC's provided the initial data base. A computerized data management system called ASAP11 was iiASAP, an acronym for As Soon As Possible, is a product of Compuvisor Inc., Ithaca, New York. 52 used for storage and retrieval of the Westlake data base. . An example of the guery capability from a computerized data base is shown in Table 1. Table 1.. Age Class Distribution Of The Westlake. PSYO" Via ASAP Run 2 12/13/79 page 1 Output 1 Summary agedist Regt 1 Task 1 Line 19 244 1 records selected ********************************************* Age class distribution by volume and area *********************** ****************** Age Class 0-20 Yrs 21-40 Yrs 41-60 Yrs 61-80 Yrs 8 1-100 Yrs 101-120 Yrs 121-140 Yrs 141-250 Yrs 250+ Yrs Other Subtotal Total Volume (cf) 25 64510 263672 830337 1553442 929724 764107 692530 4000 14480 5116827 Total Acreage 26729 97004 51504 1 17307 152105 49893 22513 31200 466 51351 600072 The table shows the results from a request for the age 53 class distribution in terms of both area and present volume across all 2,441 stands of the unit. The Westlake PSYO does not have a balanced distribution of age classes., The greatest portion of the volume:and area are from stands between 60 and 130 years of age. Hence, harvest scheduling for continuous volume flow is not directly apparent. In addition to guery capability, management reports as those shown in Appendix IV can be generated, giving detailed descriptions of each stand. The forest road network of the Westlake PSYO was obtained in map form. Empirical costings from areas of active operation were also supplied. The data gave rise to three sets of hauling cost zones and six sets of road development costs., Table 2 shows the basic access-related costs for the unit. .. The primary access roads were digitized with the attribute information and precedence relationships established through the transportation subsystem.. The forest road network of the Westlake PSYO consists of 46 primary access roads. There.are in fact three separate networks within the management unit.. Two of the networks lead to Prince Seorge mills, while the third leads to an Isle Pierre mill.. The node network constructed and the precedence relationships are shown in Figure 6. The large, underscored numerals represent the individual road segments. The smaller numerals correspond to the nodes generated during network construction. . A summary of the road segments within the network is shown in Table: 3. For each road there is a description of its length, node precedence relationships, status, road class, haul cost zone assignment and cost for development, if any. Table 2. Basic Access Cost Data COST OATA SUNMAftV TRANSPORTATION COSTS ZONE $/CUNlT/NlLE 1 0.22 2 0.18 3 0.15 4 0.0 s o.o 6 0.0 7 0.0 8 0.0 9 0.0 10 0.0 R0A0 DEVELOPMENT COSTS ROAO CLASS S/NILE 1 6S000.00 2 50000.00 3 40000.00 • 33000.00 5 12000.00 6 8000.00 ) Table 3 . R°*° SEGMENT REPORT RD. • RO. LENCTH INILESt 1ST NODE 2ND NODE ROAD STATUS ROJ CL* ID HAUL COST DEVELOPMENT kSS ZONE COST It) 1 13.94 1 2 ON-HMV, EXISTING k I 2 3.22 3 2 ON-HMV, EXISTING I 1 3 1.36 4 3 ON-HMV, EXISTING i4 1.69 3 3 ON-HMV, EXISTING i 1 9 1.49 6 3 ON-HMV, EXISTING i6 1.10 5 7 ON-HMV, EXISTING I I T 3.22 7 8 ON-HMV, EXISTING k 1 8 T.61 T 9 ON-HMV, EXISTING i9 3.03 10 9 ON-HMV, EXISTING I 1 10 2.16 9 11 ON-HMY • EXISTING i11 6.08 12 11 ON-HMV i EXISTING i 1 12 2.87 11 13 ON-HMV. EXISTING >13 6.63 14 13 ON-HMV, EXISTING . 1 14 9.66 13 13 ON-HMV, EXISTING I19 12.73 16 19 ON-HMV, EXISTING i 1 1* 6. 09 13 17 ON-HMV, EXISTING ^ i IT 7.09 18 17 ON-HMV, EXISTING i18 1.61 17 19 ON-HMV, EXISTING i l 19 3.14 20 19 OFF-HMV, EXISTING >20 3.61 20 21 OFF-HMVi EXISTING \ i 21 6.80 22 21 OFF-HMV, EXISTING t l 22 4.33 23 24 ON-HMV, EXISTING s23 2.13 24 29 ON-HMV, EXISTING > i 24 2.70 24 26 ON-HMV, EXISTING • l 29 9.21 27 24 ON-HMV, EXISTING . 2 2k 6.24 27 28 ON-HMV EXISTING J2T 7.41 29 27 ON-HMV EXISTING I 2 28 2.12 30 29 ON-HMV EXISTING 929 1.T9 31 29 ON-HMV EXISTING 30 2.49 32 31 ON-HMV EXISTING I 2 31 3.28 33 31 ON-HMV EXISTING 5 * . ' • 32 18.22 34 20 OFF-HMV EXISTING 3 1 33 19.32 34 39 ON-HMV EXISTING & 3 34 3.20 36 37 OFF-HMV EXISTING 339 2.39 37 38 OFF-HMV .EXISTING 1 3 36 1.37 37 39 OFF-HMV , EXISTING 33T 1.92 40 34 OFF-HMV EXISTING 1 1 38 2.76 41 40 OFF-HMV .EXISTING 339 4.77 42 41 OFF-HMV EXISTING 3 1 • 0 4.26 43 40 OFF-HMV PROPOSED 4 1 140629.00 • 1 8.09 44 41 OFF-HMV .EXISTING 4 1 42 4.04 49 44 •FF-HMV .PROPOSED 4 1 133204.79 43 10.33 46 34 ON-HMV .EXISTING k 1 44 2.96 47 46 ON-HMV EXISTING 643 4.94 46 48 ON-HMV ,EXISTING 6 1 46 4.48 48 2 ON-HMV .EXISTING &Ul a* 57 For each stand there is a transportation cost representative of its location relative to the point of appraisal.. Since each stand is accessed by the closest road from among one of the possible road networks, processing across ail networks will assure a transportation cost estimate for each stand.. Not only will an estimate be generated, but that estimate will be based on the minimum route distance to the respective appraisal point. If stand access requires a proposed road to be developed, then the costs of road construction are distributed over the total volume accessed by that road. ,• Such costs reflect primary road development, and are assigned to the stands directly involved.. The initial base of stands was reduced prior to state variable analysis.. Stands which would not significantly contribute to the productive: capacity of the unit were eliminated.. Such stands included those less than ten acres (4 hectares) in size (213), those classified as "non-productive" (231), and those classified as "not sufficiently restocked" (12).. This left 1985 stands comprising 573, 840 acres (232, 217 hectares) as the basis for harvest planning within the Westlake PSYO. . For each of the 1985 stands, twenty initial state variables were generated. The. state variables represented present and future:volume and economic yields to be derived from each stand. Current volume per unit area and current net value per unit volume were two of the state variables of each stand.. Future volumes and values describing each stand at twenty year 58 intervals, from 40 to 200 years of age provided the remaining eighteen state variables. Volumes were derived from the BCFS VAC's.. Values were derived from a stand appraisal simulation». The appraisal involved estimation of the end product market values minus the related costs of making the wood available: to the mill. Appendix V displays the.appraisal report for the: mature stands of the Westlake PSYO.. The:contribution of each component to the derivation of stand value is itemized in the report.. Factor analysis was then performed on the initial 20 state variables. . Five orthogonal factors resulted which accounted for approximately 9955 of the information represented by the original variables. In other words, a four-fold reduction in the: state variable space only resulted in a 1% loss of information. Appendix VI presents the factor analysis results. . Two factors correlated with volume yield over time, while another two correlated with value over time. Each pair of factors could be interpreted to represent the rate of change.in yields, and the absolute range in yields over the time span. The remaining factor correlated with current volume and value yield. This reduced set of state variables was then used to derive stand groupings or timber classes. Prior to the aggregation process, stands were .pre-stratified into accessibility classes based on transportation and road development costs.. The rationale behind such a stratification was to demonstrate the impact of explicity accounting for stand access in cut schedule determination., Stands with similar access costs were deemed to have similar access characteristics. Accessibilty costs ranged 59 from $0.10/cunit ( $0.04/cubic metre) to $ 14.00/cunit ($4.94/cubic metre). Fourteen accessibility classes were established for the 1985 stands. Table 4 presents the distribution of the stands across the 14 classes.. Thus, stand accessibility provided the initial basis for timber class formation.. The cluster-dynamic programming approach was employed in reducing the original 1985 stands to 100 timber classes12. Cluster analysis was performed on the five state factors to determine stand aggregations within each accessibility strata. The. optimal number of timber classes within each strata considering all accessibility classes was found using dynamic programming., The determination of the number of timber classes within each strata was weighted by the area representation of each strata. The distribution of the ultimate number of timber classes across the accessibility classes is shown in Table 5. This data reduction process from 1985 stands to 100 timber classes resulted in a 23% error in aggregation. Cluster analysis was further employed to reduce the volume and value yield projections for the 100 timber classes to a smaller, more: manageable subset.„ Fifteen volume yield classes were generated with only a 2% loss in information.. Thirty economic yield classes were generated with a corresponding information loss of less than 1%. Tables of the:resulting yield 12A level of 100 classes reflects a Timber RAM restriction on the maximum number of timber classes allowed. . 60 Table 4. Stand Distribution Across Accessibility Classes % of Accessibility Class Access Cost ($/CCF) Stand Frequency TOtc Stai 1 0 - 1.00 145 7 2 1.01 - 2.00 181 9 3 2.01 - 2.50 114 6 4 2.51 - 3.00 153 8 5 3.01 - 3.50 142 7 6 3.51 - 4.00 130 7 7 4.01 - 4.50 92 5 8 4.51 - 5.00 165 8 9 5.01 - 6.00 180 9 10 6.01 - 6.60 138 7 11 6.61 - 7.00 184 9 12 7.01 - 8. 00 169 9 13 8.01 -11.00 167 8 14 11.01 14.00 25 1 TOTAL 1985 100 Table 5. Timber Class Distribution Across Accessibility Classes Intra-class Total Accessibility % Area Stand # of Timber Clustering Inter-class Error Class Representation Frequency Classes Formed Error (%) (Area-weighted %) 1 4 145 6 31.1 1.2 2 9 181 10 19.1 1.7 3 9 114 6 26.5 2.4 4 8 153 6 22.7 1.8 5 7 142 6 31.5 2.2 6 7 130 8 18.8 1.3 7 5 92 6 30.2 1.5 8 11 165 11 14.5 1.6 9 10 180 10 18.9 1.9 10 7 138 7 26.7 1.9 11 5 184 7 25.9 1.3 12 7 169 8 21.4 1.5 13 10 167 8 19.9 2.0 14 1 25 1 100.0 1.0 TOTAL 100 1,985 100 23.3 62 classes are shown in Appendix VII. The timber classes and yield classes thus formed ware then used in cut schedule determination for the Westlake PSYO. 63 6. „ ANALYSIS &ND DISCOSSION 6.1 Transportation Planning Fundamental road network information for the Westlake PSYO was generated from the transportation subsystem. .. Basic statistics on length of given road class, length of proposed road and other road network characteristics were identified. This data was used in the.transportation subsystem to determine optimal routing strategies, i.e. given a selected appraisal point, routings based on minimum distance were . identified for the entire unit. An example of the.optimal routings and distances pertaining to the 46 primary access road segments of the Westlake PSYO is outlined in Table 6.. Node 35 is specified as the appraisal node (sink) in the table. This appraisal location leads to an Isle Pierre mill.. So for example, in travelling from node 1 to the appraisal node the minimum distance is 49.00 miles (78.9 kilometres). The corresponding optimal routing strategy is sequentially decoded. The bracketed value.specifies the next node in the minimum route. Thus, from node 1 the optimal route is to travel to node;2, then to node 48, to node 46, to node 34, and finally to node 35, the:appraisal point. In this manner the optimal routings and distance are identified for the road network of the management unit. The nodes possessing large values (99999.00 and 9999) Table 6. Minimum Routing Distances and Policies ROAD NETWORK REPORT NODE OF APPRAISAL : 35 6 7 8 9 10 41.42 41.03 +4.25 48.65 51.68 C 51 I 51 I 71 I 71 ( 91 57.11 38.29 45.38 36.68 33.54 I 151 I 191 I 17J ( 20» I 341 20: 37.15 43.95 99999.00 99999.00 99999.00 99999.00 99999.00 99999.00 99999.00 99999.00 : ( 20) I 211 199991 (99991 199991 (9999) (9999) (9999) (9999) 19999) 30: 99999.00 99999.00 99999.00 15.32 0.0 99999.00 99999.00 99999.00 99999.00 17.23 (9999) (9999) (9999) ( 35) ( 0) (9999) (9999) 19999) (9999) I 341 40: 19.99 24.76 21.49 28.04 32.08 25.64 28.60 30.58 (40) ( 41) ( 40) ( 41) ( 44) I 34) ( 46) I 44) MINIMUM DISTANCE IN MILES, (AND ROUTING) TO NAP NODES: 12 3 4 5 0: 49.00 35.06 38.28 39.63 39.93 : (2) ( 48) ( 2) ( 3) I 3) 10: 50.81 56.89 50.04 56.67 44.38 : (9) I 11) ( 15) I 13) ( 17) 65 indicate the unit consists of one or more separate sub-networks. Travel between nodes of separate networks is impossible.. Hence, the large values indicate infeasible routings.. Minimum routings, distances and associated transportation costs were determined for each of the 1985 Westlake stands. A sample of the results can be found in Table 7.. For each stand there is a description of its qualitative characteristics, its geographic location (based on its visual centroid), the distance to the nearest access road (with a corresponding pointer), the minimum distance to the specified node of appraisal and the corresponding transportation cost. An additional feature of the analysis is the generation of road development costs for those proposed roads in the networlc, together with a proportioning of such costs over the volume from the stands involved. To examine the results in detail, focus is placed on one particular stand, 20057160. This stand is the 57th stand (057) located in Compartment 20, Region 60 of the Westlake PSYO. . The stand is a white spruce type, age class 8 (141-160 yrs.) and of good site. It has a volume yield of 4700 cubic feet per acre (329 cubic metres/hectare).. The nearest access road is road 46, being a distance of 1.39 miles (2.24 kilometres) from the centroid of the stand. . The distance from the stand to the specified Prince Seorge appraisal point is 37.85 miles (60.91 kilometres), which in turn results in a transportation cost of $3.33/cunit ($2.94/cubic metre) for the stand.. In perspective, the transportation costs for the stands in Compartment 20 as a whole ranged from a low of $5.70/cunit ($2.01/cubic metre) to a high of $9.10/cunit ($3.21/ cubic metre) with the average being Table 7. STAND ACCESS REPORT STANO NO. TTPE AGE SITE SLC USE CENTROID LOCATION 01 ST. TO NEAREST RD. RO. NO. 01 ST. TO NAP HAUL COST ROAD DEVEL OFMI IN LAT-LONG. INILESI INILESI U/CUNIT) COST It/CUNI 8002160 8 14 1 9331.17 12292.33 0.69 19 14.30 1.19 0.0 8003160 S 8 14 1 9339.43 12292.20 0.51 17 8.62 1.90 0.0 8036160 s 8 2 9 1 9327.89 12242.27 7.36 19 24.74 9.44 0.0 8069160 COTO 9331.43 12242.60 7.10 17 19.81 3.48 0.0 8033160 F 8 2 9 1 9328.63 12242.80 6.97 19 24.34 9.36 0.0 8001160 F 8 2 4 1 3332.93 12290.23 2.33 17 11.04 2.49 0.0 8064160 F 9331.77 12243.33 6.47 17 19.17 3.34 0.0 10001160 F 8 14 1 9339.98 12301.79 0.32 21 9.49 2.08 0.0 10029160 F 8 14 3 3339.41 12306.08 0.96 22 7.49 1.69 0.0 10098160 S 8 2 9 3 9341.48 12309.48 2.30 24 6.19 1.36 0.0 11034160 F 8 14 4 9346.46 12314.71 2.31 26 14.87 2.68 0.0 11013160 F 8 2 4 1 9339.77 12313.30 1.32 27 9.93 1.79 0.0 11043160 SF 8 2 9 1 9340.99 12312.62 0.66 25 6.37 1.19 0.0 12001160 S 8 111 9341.33 12329.99 2.34 31 22.16 3.99 0.0 12096160 s 5342.27 12329.41 3.39 91 29.21 4.18 0.0 14068160 SF 8 19 1 9333.79 12313.80 0.69 32 17.37 9.82 0.0 14038160 FS 8 14 1 9338.49 12312.73 2.40 27 11.09 t.00 0.0 14128160 s 8 19 2 9336.33 12319.60 1.90 27 13.76 2.48 0.0 14062160 SF 8 14 2 9300.00 12300.00 20.77 3 90.90 2.86 0.0 14122160 S 8 19 1 9336.41 12316.70 1.00 17 14.17 2.99 0.0 19110160 F 9336.96 12304.73 1.11 32 10.72 2.96 0.0 19034160 F 8 17 1 9330.80 12309.36 1.93 13 21.93 4.74 0.0 19I2T160 F 8 14 1 93 36 . 43 1 2 304 . 93 1.02 32 10.97 2.99 0.0 16013160 F 9337.38 12304.32 0.40 21 13.71 3.02 0.0 16073160 S 8 14 2 9339.39 12293.93 1.91 17 4.03 0.89 0.0 16049160 S 8 14 1 9339.46 12294.77 1.09 18 2.09 0.46 0.0 16046160 F 8 14 1 9337.41 12304.02 0.32 21 13.93 9.00 0.0 17093160 PL 8 16 1 9330.68 12256.46 1.71 19 14.89 9.28 0.0 17033160 FLS 5329.23 12254.38 0.97 19 16.47 3.62 0.0 17020160 PL 8 14 2 5331.66 12254.50 0.21 19 12.72 2.80 0.0 17001160 S 8 14 1 5332.46 12256.13 1.04 19 9.89 2.18 0.0 17002160 8 1 4.1 5330.66 12254.89 0.82 19 14.00 3.08 0.0 17040160 s 5329.80 12257.23 1.69 14 14.99 3.20 0.0 17019160 s 8 2 4 2 5330.00 12257.23 1.69 14 13.66 3.00 0.0 17031160 s 8 2 12 1 3329.77 12257.46 1.53 14 14.39 3.17 0.0 17032160 F 8 2 12 1 5329.63 12254.63 0.51 19 16.19 3.39 0.0 18039160 F 8 17 3 5327.06 12308.20 1.68 44 34.79 7.69 0.0 18101160 S 8 14 6 5324.73 12304.39 0. 72 9 21.22 4.67 0.0 18001160 s 8 19 3 5330.30 12305.33 1.42 13 18.81 4.14 0.0 18089160 F 8 14 2 3324.80 12306.60 0.63 9 22.00 4.84 0.0 18022160 s 8 17 1 5324.39 12304.63 1.05 9 21.99 4.74 0.0 18073160 F 8 14 1 5323.89 12306.80 0.82 9 22.29 4.90 0.0 18074160 S 8 14 1 5325.03 12303.80 0. 73 9 20.81 4.98 0.0 18040160 s 8 2 7 6 5325.50 12305.46 0.49 9 20.98 4.62 0.0 19033160 F 8 1 18 1 5321.05 12305.27 0.91 8 29.18 9.94 0.0 19014160 F 8 17 1 5321.05 12306.39 0.29 8 25.70 9.69 0.0 19001160 F 8 14 1 5325.25 12307.96 0.90 44 35.15 7.73 0.0 19049160 PLF 8 1 19 2 5300.00 12300.00 20.77 3 50.90 6.85 0.0 20097160 S S 1 19 1 5323.35 12316.46 1.39 46 37.85 8.33 0.0 20096160 F 8 2 15. 1 5322.05 12317.27 1.63 42 39.75 8.75 1.73 67 $7.67/cunit ($2.71/cubic matce). Detailed compartmental results can be found in Appendix VIII.. A comparison of harvesting cost with transportation cost exemplifies the significance of transportation in the economic evaluation of a stand. The harvesting cost for stand 057 was $7.80/cunit ($2.75/cubic metre).. With due consideration given to transportation cost, total cost more than doubles to $16. 13/cunit ($5. 70/cubic metre). Hence, it can be seen that analysis devoid of transportation cost could have serious management consequences. Another area of interest in the planning of harvests is the possible effects of wood flows to alternative appraisal locations. To examine the.effects on transportation costs of directing logs to another mill site an additional analysis was performed. A new node of appraisal to an alternative milling site (Isle Pierre) was selected with new routings, distances and transportation costs computed. Again turning attention to stand 057, the transport distance to the new appraisal point was 31.97 miles (51.45 kilometres) yielding a cost of $7.03/cunit ($2.48/cubic metre). A comparison of the log transport results from stand 057 to the.two alternative points of appraisal is shown in Figure 7. Appraisal point A represents the Prince George location, while appraisal point B represents the Isle Pierre location.. The results show that by hauling to the Isle Pierre. location there would be a savings of $1.30/cunit ($0.46/cubic metre) for stand 057.. on examining the results for the stands in Compartment 20 as a whole transportation costs ranged from $4.37/cunit ($1.54/cubic metre) to $7.77/cunit 89 69 ($2.71* /cubic metre) with the average being $6.35/cunit ($2.24/cubic metre). Detailed results for stand 057 can be found in Appendix IX. With all other things being equal, a manager contemplating log transport to several alternative mills can now assess the effects of transportation costs. In the above example it would appear to be much more: economical to transport wood from stands in Compartment 20 to the alternative appraisal location B. Thus for a specified point of appraisal, transportation costs based on the minimum route can be generated for any given stand within the . manage ment unit... Such transportation costings can then be incorporated with stand revenues and harvest cost estimates to provide a more, complete economic assessment of stand harvest value.. 6.2 Cut Scheduling Harvests were scheduled for the Westlake PSYO using the Timber RAM model.. The 100 timber classes formed from the: state variable subsystem were the forest units to be scheduled. Silvieultural treatments for each timber class consisted of simple clear-cutting strategies, with either natural regeneration or planting within five years of harvest.. Site preparation activities such as slash burning or drag scarification, as prescribed by management, were also included. The 30 economic yield projections and 15 volume yield 70 projections shown in Appendix VII were used to generate returns from the various harvesting alternatives. The timing of the first harvest for each timber class was the primary decision. All evaluations for the Westlake PSYU were: based on a 100-year conversion period, with a total planning horizon of 350 years. . Volume flow was constrained during the conversion period using seguential harvest control. The harvest in the:first decade was allowed to vary from -50% to +250% of the current harvest level of the unit.. Subseguent harvests were constrained to within 10% of the cut of the preceding decade. The. above basic harvest management parameters were used in the:following three sets of evaluations: 1) Volume Optimization - long term vs..short term 2) Economic Optimization - volume vs. value 3) Economic Optimization - with transportation vs. without 6.2.1 Case 1: Volume Optimization - Long Term vs.,Short Term Case 1 evaluated the implications of scheduling harvests for the maximization of long term (200 years) vs.,short term (30 years) volume production. Harvesting alternatives for mature stands allowed for clear-cutting anytime within the first six decades up to 200 years of age, at which time the:stand had to be cut.. For immature stands, clear-cutting was allowed during a sixty-year span from a first entry of either 20 years prior to 71 culmination of mean annual increment or 60 years of age.. The previously described sequential volume control constraints were employed. Two RAM runs were made. . The:objective of the first run was to maximize volume over a 200-year planning period., The cut scheduled in the first decade yielded 2.58 million cunits (7.31 million cubic metres). The corresponding net revenue generated during the first decade totalled 156.2 million dollars. The resulting long run sustained yield average was 1.83 million cunits (5. 18 million cubic metres) per decade for the Westlake PSYU. . This level can be viewed as representing the silvicultural potential for the Westlake under clear-cutting management seguences.. The objective of the second run was to maximize volume production over a 30-year planning period. The volume scheduled for harvest in the first decade totalled 2.88 million cunits (8.16 million cubic metres), with the corresponding net revenue being 171.2 million dollars. The long run sustained yield average was again approximately 1.83 million cunits (5.18 million cubic metres) per decade. A summary of the results can be found in Appendix X. In comparison, maximizing volume over a 30-year period (short term) vs. ra 200-year period (long term) generates an additional 300,000 cunits (849,510 cubic metres) during the first decade. In other words, an additional 30,000 cunits (84,95 1 cubic metres) can be harvested annually without appreciably sacrificing the long range productive capability of the management unit. This is eguivalent to an additional 1.5 72 million dollars per year in net revenue which could be generated. A comparison of the volume flows per decade is shown in Figure 8. The graph shows that during the first 40 years the scheduled harvest under short term volume: maximization is approximately 12% higher than the level for the long term run. However, from 50 to 100 years the harvest for the long term schedule more than compensates for the earlier deficiencies. Total harvest under long term volume maximization for the 200-year period is approximately 39.266 million cunits (111.19 million cubic metres). The total harvest under the short term run for the same period is approximately 38.422 million cunits (108.80 million cubic metres ).. Thus, the overall harvest is increased by approximately 844,000 cunits (2.39 million cubic metres) under long term volume maximization. Nevertheless, in both cases the perpetual sustained yield average stabilizes about a common harvest level as is shown for the post conversion period. . Figure 9 shows the effect on the species flow resulting from the stands available:for harvest in the first decade.for the two runs. The incremental volume for the short term maximization is primarily lodgepole. pine.. The species distribution is approximately 20% Douglas-fir (Pseudqtsuga Mfizissii (Mirb.). Franco), 11% white : spruce, 65% lodgepole pine with the balance primarily hardwood species. The distribution under long term volume: maximization is approximately 21% Douglas-fir, 18% white spruce, 57% lodgepole pine with the balance primarily hardwoods species. A complete.summary by Figure 8. Canparison of Volume Flow - Case 1 3ooo4 ^ 2500-4-O ^ 2000-f O ^ /3oo-t Conversion Period M x Long term volume max. • • Short term volume max. -I-Post Conversion Period Long run sustained yield average 5 10 TIME IN OECADCS rS 74 Figure 9. Comparison of Species Flow in Decade 1 - Case 1 (Ref. Appendix XE) *5oo 2.000 /BOO IOOO 5©o TTS U>1 \ FIR 5/* I SPRUCE /48* 2485 I PINE 0 Long term volume maximization Short term volume naximization 130 ISO m OTHER SPECIES 75 timber class of the species harvest for the two runs can be found in Appendix XI. In summary, these, runs indicate that the overall timber productivity for the Westlake PSYO should average approximately 183,000 cunits (518,201 cubic metres) per year.. Of this yearly production the approximate species distribution will be 60% lodgepone pine, 20% Douglas-fir, 15% spruce and 5% other species.. Further, maximizing the volume harvested over the next 30 years rather than a longer period will not adversely affect the long term productivity of the management unit.. Without the analytical capability of a system like TRACS, the insight provided above would be.difficult to obtain. 6.2.2 Case 2: Economic Optimization - Volume vs. . Value Case 2 evaluated the implications of scheduling harvests for the maximization of net revenue as opposed to maximization of long term volume production. The RAM run with the volume objective over 200 years, as described in Section 6.2.1, was used to represent the optimization of long term volume production. Another run, under the same conditions, was made with the objective of maximizing net revenue over 200 years. This run represented the optimization of value production.. A discount rate of 8% was used to reflect the present value of future revenue streams. Consequently, only revenues generated during the first 30 or so 76 years were of any significance. The net revenue generated during the first decade totalled 175.5 million dollars, in comparison with 156.2 million dollars for the long term volume production run.. The net revenue generated over the 200-year period was 200.4 million dollars. The corresponding net revenue under volume maximization was 179.3 million dollars. The volume scheduled for harvest in the first decade was 2.86 million cunits (8. ,10 million cubic metres), in comparison with 2.58 million cunits (7.31 million cubic metres). Once again the long run sustained yield average was approximately 1.83 million cunits (5.18 million cubic metres) per decade. A summary of the results can be found in Appendix XII. Figure 10 displays a comparison of volume flows per decade: for the two runs. . A similar pattern of volume harvests to that of Figure 9 is exhibited.. During the first five decades, harvest levels are higher under value maximization.. For the next five decades harvest levels are much lower than the levels shown for volume run.. The.excess inventory is liquidated much earlier under value maximization to capture:increased revenue. In contrast, the volume:maximization strategy rations out the excess inventory to generate Increased volume flow during the conversion period. Volume production stabilizes during the post conversion period around a common harvest level for both runs. A comparison of the species distribution of the harvest in the first decade is shown in Figure 11. The results are similar to those shown for short term volume maximization.. Differences in the. timber classes scheduled for harvest Figure 10. Comparison of Volume Flow - Case 2 3ax4 ^ eooo4 'SooA Conversion Period M « Volume maximization • • Value maximization X • Post Conversion Period Long run sustained yield average to 7"//W£- IN PEC A DCS 78 Figure 11. Comparison of Species Flow in Decade 1 - Case 2 20oo :a -J o /5oo tooo 5oo 723 I FIR I SPRUCE 1 PINE X> Volume maximization ^ Value maximization zap /a6 OTHER SPECIES 79 during the first decade are shown in Table: 8.. The. results generally support the early harvest of the higher valued stands under an economic objective.. There is an average of $11/acre ($27.18/hectare) incremental return in favor of the stands harvested under value optimization. Conversely, stands with greater volume yields per unit area are given priority under volume maximization. . Here the incremental return averages almost 12 cunits/acre (84 cubic metres/hectare) over those stands harvested under value optimization. Hence, these results indicate that the economic potential of the Westlake PSYO, based on the harvest from the first decade, is approximately 17.55 million dollars per year. Further, value-based harvest planning generates an additional 1.93 million dollars annually (during the first decade) over a volume-based strategy, without any significant difference;in the long range productivity of the unit. 6.2.3 Case 3: Economic Optimization - With Transportation vs. Without Case 3 evaluated the consguences of recognizing accessibility and transportation in the scheduling of management unit harvests. The Timber RAM run which maximized value production in Section 6.2.2 was compared with a previous run on the: Westlake PSYU performed under the BCFS CARP system.. The.generation of a Table 8. Differences in Timber Classes Scheduled for Harvest in Decade 1 - Case 2 Timber Major Age at Class Species Harvest 009 Spruce 120 021 Spruce 120 036 Pine/ 90 Pine-Spruce 063 Spruce 120 069 Cottonwood 150 081 Pine 100 083 Pine-Spruce 110 096 Pine/ 120 Pine-Spruce Total Average 116 002 Pine 90 006 Fir 90 010 Pine/ 90 Pine-Spruce 034 Fir 150 056 Spruce-Fir 130 076 Pine 110 Total Average 110 Volume Value Volume CCF/acre $/acre MCCF 46.12 65.80 6 46.12 65.80 72 32.11 57.02 248 46.12 65.80 155 70.19 18.65 4 45.62 57.08 140 11.30 48.69 2 58.62 53.65 78 705 44.53 54.06 28.40 62.65 14.11 76.60 28.40 58.43 22.38 73.71 47.10 66.17 55.44 55.54 32.63 65.52 :. Run Value Max. Run Acres MCCF Acres 135 1,570 7,731 3,355 50 3,073 144 1,337 17,395 270 9,495 4 252 350 12,321 3 133 105 2,225 21 379 753 24,805 81 harvest schedule for this CARP run was devoid of any consideration of stand location,. Stand valuation encompassed only those harvesting activities which resulted in the. logs being loaded at the landing. . No consideration was given to road development reguirements, proximity.to the existing road network or mill sites, hauling costs or stratification of the resulting timber classes into accessibility classes, as was incorporated in the TRACS system.. These differences not only affected the present and projected value of each stand, but also affected timber class formation. . In other words, stands for CARP were grouped together based on similarities in volume yields and value yields which included harvesting costs up to the landing. This differed from stand groupings for TRACS which were based on volume.yields and value yields which included delivered cost to the mill. The CARP run resulted in 89 timber classes and 24 economic yield classes, whereas the TRACS system resulted in 100 timber classes and 30 economic yield classes.. Other than the above differences resulting from recognition of accessibility and transportation, the parameters for both RAM runs were identical. The objective of each run was to maximize net revenue (with an 8% discount rate) over a 200-year planning period. . The conversion period for the old growth stands was specified to be 100 years, with a total scheduling horizon of 250 years.. Seguential volume control and regulation constraints were imposed. . The . cut for the first decade was loosely constrained to be within -50% and +250% of the current cut level for both runs. Each subsequent decade's cut had to be within 13% of the previous 10-year cut level.. The harvesting 82 alternatives for each timber class consisted of the timing of a simple clearcutting-regeneration sequence. For mature classes, clearcutting was allowed to take place within a sixty-year span starting from the first decade. For immature: classes, clearcutting was allowed within a sixty-year span starting from age 60 or 20 years prior to culmination of mean annual increment. Harvests became:mandatory at an age of 200 years.. The objective function value for the CARP-based run was approximately 267.2 million dollars, whereas the:value for the TRACS-based run was approximately 200.4 million dollars. Summary of the results can be found in Appendix XIII. Hence, ignoring transportation costs resulted in a 33% overstatement of the economic potential of the management unit over the:planning horizon. A comparison of the volume flow resulting from each run is shown in Figure 12. The CARP run, lacking transportation considerations, resulted in an 18% greater harvest level during the. first 80 years. This harvest volume increment was realized to the detriment of the post conversion harvest level as shown in the figure. The long run sustained yield average for the CARP run was 1.76 million cunits (4.98 million cubic metres) as compared with 1.83 million cunits (5.18 million cubic metres) for the.TRACS run. Examination of the results for the first decade reveals that the net revenue from the CARP run is 162.6 million dollars, as compared with 119.4 million dollars from the TRACS run. The :corresponding volume harvested in the first decade is 3.4 million cunits (9.6 million cubic metres) for the CARP run versus 2.9 million cunits (8.2 million cubic metres) Figure 12. Comparison of Volume Flow - Case 3 Z9CO-+ Conversion Period „ Value maximization * x with transportation • • Value maximization without transportation X—— X. \ H 5 77Af£* /V P£CAP£S Post Conversion Period Long run sustained yield average IS 84 for the TRACS ran. Hence, by not reflecting transportation considerations the scheduling not only generates more net revenue than actually exists but also harvests more volume than is currently accessible. Figure 13 presents a comparison of the species flow resulting from the stands available for harvest in the first decade. The CARP run reveals that there is more Douglas-fir and spruce, and less lodgepole pine available than shown for the TRACS run. Specifically, the distribution for the CARP run is 32% Douglas-fir, 27% spruce, 36% lodgepole pine with the remainder other species. The distribution for the TRACS run is 22% Douglas-fir, 13% spruce, 61% lodgepole pine with the remainder other species.. The effect of reflecting accessibility and transportation considerations can be further evidenced by examining the results within one particular sub-unit, Compartment 20, Region 60 of the Westlake PSYU. Table 9 lists the stands available for harvest in the first decade. A description of the major species, age, soil-landform class, area and volume yield are given for each stand., In addition, two positional attributes are identified. The first attribute is the distance from the stand centroid to the road network.. This attribute serves as an indicator of accessibility.. The second attribute, distance:from the stand to the:appraisal location, serves as an indicator of transportation reguirements. The results from the TRACS run showed that from the list of candidate stands, both the.distance to a primary access road and distance to the appraisal point are more favorable than for the 85 Figure 13. Comparison of Species Flow in Decade 1 - Case 3 &ooo 1995 /5oo //Q3 UJ -J o /ooo \ Boo I FIR 917 4/3 \ SPRUCE / / I 1 PINE Maximization with transportation Majcimization without transportation /93 OTHER SPECIES Table 9. Comparison of Stands Harvestable in Decade 1 within Compartment 20 Soil- Transportation Without Transportation  Stand Species Age Land Dist. Dist. to Dist. Dist. to No. (Years) Class Acres Volume to road Appraisal Acres Volume bo road Appraisal (M cunits) (miles) (miles) (M cunits) (miles) (miles) 20007 PI 90 17 215 7.2 0.5 24.6 20013 PI 90 17 43 1.5 0.2 24.9 20018 PI 70 18 498 15.9 0.4 30.5 20025 PI 70 18 140 4.5 0.5 31.1 20029 PI 90 8 39 1.4 0.2 24.3 20033 S 90 8 23 1.2 0.2 24.7 20050 PI 70 8 68 2.2 0.1 25.5 20056 F 150 15 19 1.1 1.6 33.7 20057 S 150 15 17 0.8 1.4 32.0 20058 F 130 15 632 28.1 0.2 30.8 20059 PIS 130 15 346 17.0 3.5 32.5 20061 PIS 110 15 37 1.3 2.0 28.9 20064 F 110 15 320 13.9 3.0 27.7 20066 S 90 15 330 17.1 1.6 28.7 20075 PIS 130 15 272 13.3 3.6 30.7 20076 S 130 15 26 1.7 3.7 29.9 20077 F 130 15 52 2.3 0.9 28.7 20078 PI 110 15 45 3.0 0.8 32.9 45 3.0 0.8 32.9 20080 F 110 15 40 1.9 0.4 27 .6 40 1.9 0.4 27.6 20081 S 90 15 17 0.9 2.5 29.6 20086 S 130 7 42 0.9 2.0 28.9 20090 F 110 7 104 4.5 3.4 28.5 20092 F 90 7 14 0.6 3.9 31.0 20099 S 130 7 10 0.2 0.2 5.7 20103 PI 70 7 220 7.0 0.2 25.4 20116 F 110 7 44 2.6 2.5 27.2 20117 S 110 7 21 1.3 0.1 27.6 20128 S 130 7 216 4.8 0.3 27 .0 20129 PIS 130 7 109 5.3 3.5 6.7 20130 PI 110 7 89 5.9 0.6 25.7 20137 PI 70 7 175 5.6 0.6 25.8 TOTAL 1595 57.3 2713 122.6 AVERAGE - weighted by volume 0.4 27.6 2.0 28.9 87 CARP run.. Under the TRACS run, each stand is within a mile (1.6 kilometres) of the main road network.. The average across all stands, weighted by volume, is 0.4 miles (0.6 kilometres).. In comparison, stands up to 4 miles (6.4 kilometres) away from the road network are selected for harvest in the CARP run, the average distance being 2 miles (3.2 kilometres)... The:stands scheduled for harvest in the first decade across the. entire unit were.on the average approximately 40% closer to the road network under the TRACS system. The table also shows that scheduling harvests without considering transportation, results in a greater average distance from the stands to the appraisal location. The. stands selected from the CARP run averaged 1.3 miles (2.1 kilometres) more than for the TRACS run, with the average haul distance being 28.9 miles (46.5 kilometres) and 27.6 miles (44.4 kilometres) respectively.. Hence, accessibility anl transportation considerations have significant impact on the scheduling of stands for harvest.„ The volume, accessible and the value yield are both overstated for the Westlake PSYU in the absence of proper accounting of stand location.. Stand location in relation to both the road network and the appraisal point or mill site must be integrated in management unit harvest planning.. 88 7. . CONCLUSIONS Planning models never provide the total answer.. They do, however, allow evaluation of alternatives. . They provide management with a quantitative framework for exploring the consequences of proposed actions. The improved analytical capability provided by models reduces uncertainty in the planning environment.. Theiresult is better decision-making.. A continued supply of timber is critical to the well-being of both the industrial firm and the province as a whole. Harvest planning concerns the:control of timber flows over time to meet supply needs. This control encompasses the quantitative aspects of when, what and where, relative to the volume and value yields to be derived from forest stands. This thesis has presented TRACS, a Transportation Analysis-Cut Scheduling system designed for harvest planning at the management unit level. TRACS is an extension to existing harvest planning tools. The system integrates accessibility and transportation into the silvicultural and economic aspects of scheduling timber harvests for a management unit.. TRACS begins with a physical resource inventory, from which timber yields and silvicultural potential can be determined. Next, a transportation modelling subsystem relates the road network to primary stand access and log hauling reguirements. Alternative stand-to-mill flows can be evaluated, with optimal routing strategies identified. An economic valuation is then performed for each stand based on delivered wood costs to the mill and end-product pricing of lumber and chips. At this 89 point, the management unit inventory contains both volume and value information yielding an improved reflection of the timber resource. Data analysis techniques of factor and cluster analysis were incorporated with dynamic programming to generate stand aggregations or timber classes. These timber classes, homogeneous in respect to volume and value responses, were a more appropriate delineation for management unit planning. Projections of volume and value yields corresponding to the timber class components were also formed.. The Timber RAH model was then used to schedule the:timber classes for harvest.. The reporting features of TRACS finally transformed the results into understandable graphs and tables which related the havests back to the original stands. The utility of the TRACS system has been demonstrated on a actual British Columbia forest management unit.. The analyses presented have evaluated alternative volume flow and value flow strategies. The silvicultural and economic potential of the management unit has been identified. In addition, the consequences of excluding transportation considerations in harvest planning have been shown to be significant. Relevant harvest planning results can only be achieved through explicit recognition of accessibility and transportation reguirements.. The TRACS system has been developed to facilitate such heeds. The net result is a means for improved management unit harvest planning. 90 BIBLIOGRAPHY I. . BCFS , 1975.. Forest Resource Planning In British Columbia.. Brief sub. to Royal Comm. on For. Res. by B.C. For.Serv., Nelson B.C. 31 pp.. 2.. Bare, B..B. and E. L. Norman 1969. An Evaluation Of Integer Programming In Forest Production Scheduling Problems. Purdue Univ., Agric. .Expt. .Sta. ,Res. . Bull..847 7 pp. 3., Chappelle, D. E., M. Hang and R. C. Miley..1976.. Evaluation Of Timber RAM As A Forest Management Planning Model. . J..of For.. 5 p..288-293 4... Clutter, J. „ L • 1968. Max-Million - A Computerized Forest Management Planning System. Sch. For..Res., Oniv. Ga.. 6 1 pp. 5. Craig, G. A. 1979. OSFS Tries Economic Analysis In Planning Timber Sale Levels. For. Ind. 8, p. .30-31 6.. Curtis, F. H..1962.. Linear Programming The Management Of A Forest Property. J. of For. .60, p. ,611-616 7.. Dijkstra, E. W. 1959 A Note On Two Problems In Connexion With Graphs, Numerische Mathematik, 1, p. 269-271 8.. Dreyfus, S..1969. An Appraisal Of Some Shortest Path Algorithms, J. ORSA, 17, p. 395-412 9.. Elmagharaby, S. .E. , 1970. . Some Network Models In Management Science. Lecture Notes In 0. R. and Math..Systems, 29, Edited By M. Beckmann and H..P. .Kungi, Springer-Verlag, New York 10. Eisner, G.H., M. R..Travis, and P.,H.,Kourtz 1975. Dynamic Programming Subroutines Based On The Dijkstra Algorithm For Finding Minimum Cost Paths In Directed Networks.. Information Report FF-X-51, 16 pp., II. . Fowler, K. S..1978. Toward A More. Integrated Regional Timber Model. . For. . Sci. . 24 (4) p. .434-443 12.. Gower, J. C..1967.. A Comparison Of Some Methods Of Cluster Analysis. Biometrics 23, p..623-637 13. . Haley, D. . 1975. . Regulation Of The Rate Of Timber Harvesting In British Columbia.„Policy Background Paper, Royal Comm..On For. Resources, Victoria, B.C. 40 pp. 91 14.. Hanzlik, E. J. 1922 Determination Of The Annual Cut On A Sustained Yield Basis For Virgin American Forests. .J. of For. . 20(6) 15. Hennes, L. C. , M. J. Irving and D. I.,Navon 1971., Forest Control Regulation ... a Comparison Of Traditional Methods And Alternatives. .USDA For. ,Serv. .Res. . Note PSW-231 10 pp. 16.. Herrick, 0. W..1976.. Key Indicators Of Successful Logging Jobs In The Northeast, N. E. .For. .Exp. .. Sta. , Upper Darby, Pa..USDA For. .Serv. .Res. ,Pap. , NE-352 5 pp.. 17.. Hrubes, R. .J. .and D. I. Navon 1976. . Application Of Linear Programming To Downward Sloping Demand Problems In Timber Production..USDA For. Serv..Res. Note PSW-315 6 pp. 18.. Johnson, K. N. . 1976. Optimizing Timber Sales During The Conversion Period. Can. . J. of For..Res..6 6 pp. 19.. Kidd, W. .E., E. F. .Thompson, and P. H. J Hoepner. 1966. . Forest Regulation By Linear Programming - a Case Study. .J. .of For. .64 p. .611-613 23.. Leak, W..B. .1964. Estimating Maximum Allowable Timber Yields By Linear Programming. USDA For.,Ser.,Paper NE-17 9 pp. . 21. Littschwager, J. M..and T. H.Tcheng 1967.. Solution Of A Large Scale Forest Scheduling Problem By Linear Programming Decompostion. J. of For..62 p..644-649 22. Loucks, D.,P. 1964.. The Development Of An Optimal Program For Sustained Yield Management..J..of For.,62 p..485-490 23. . Mass - Management Sciences Staff. 1974. , Analysis Of Computer Support Systems For Multifunctional Planning - Report 1. USDA PSW For. and Range Expt. Sta. , Berkley, Calif. 216 pp.,. 24.. Navon, D..I., 1971. Timber RAM... a Long Range Planning Method For Commercial Timber Lands Under Multiple - Use Management..USDA For. Serv. .Res. .Pap. PSW-70 22 pp. 25.. Navon, D. I. .1975. . Short Run And Long Run Models For Planning Forest Transportation. .Proc. .Soc. Amer. . For. . Systems Analysis Working Group. , Athens, -Ga. .p. .300-312 92 26. Ddendahl, W. F. 1975.. Transportation Analysis And Modelling For Forest Resource Management..Proc., Soc. . Amer. . For. Systems Analysis Working Group. Athens, Sa. p. 233-242 27.. Veldman, D. J. 1967.. Fortran Programming For The Behavioral Sciences, Holt, Rinehart and Winston, New York 28.. Walker, J. L. .1974. . ECHO - An Economic Harvest Optimization Model. Proc. ,West. .For. . Conf., Spokane 23 pp., 29.. Ward, J. H. 1963 Hierarchical Grouping To Optimize An Objective Function, Amer. Stat. Assoc. J.. 58 p. , 236-244 33.. Ware, G. 0..and J.l..Clutter. 1971.. A Mathematical Programming System For The Management Of Industrial Forests. . For. , Sci. 1 7 (4) p. . 428-445 31. Weintraub, A..and D..Navon 1976., A Forest Management Planning Model Integrating Silvicultural And Transportation Activities. . Man..Sci..22 (12) p..1299-1309 32. . Weisz, R. N„ and R. Carder. . 1975. Development Of Land Ose Planning And Transportation Planning Systems For National Forest Management: A Status Overview..Proc. Soc. Amer. For..Systems Analysis Working Group. Athens, 3a. p. 87-104 33. Williams D. H.. 1976.. Integrating Stand Forest Models For Decision Analysis..Ph. D..Thesis, Faculty Of Forestry, Dniv.,of Brit. Col. 175 pp. . 34.. Williams, D. H..and M. Yamada. 1976., Cluster Analysis For Land Management Models, Can. .J. For. . Res. 6 p. 532-538 35.. Williams, D. .H., J. .C. McPhalen, S. M. Smith, M. . M. Yamada and G. ,G. Young 1975. . Computer Assisted Resource Planning: An overview of The CARP Project..Onpub.. Rep., B.C. For. Serv. 30 pp. 93 Dijkstra»s Shortest Route Algorithm The.algorithm considers the nodes and arcs of a network to be a member of one of three possible sets at any given instance: 1) SET I = Sw, + SAJ This is the set of permanently labelled nodes and arcs. . The set includes all those: nodes, S^, , and arcs, SA, , which are a part of a known minimum path. Nodes and their corresponding arcs will be added to this set in ascending order of path length from the source. 2) SET II = 5NZ * SA1 This is the set of temporarily labelled nodes and arcs. The set includes all those nodes, S„Zr and arcs, SAZ. , which are candidates for inclusion in Set I. All nodes in SJIJZ are connected to at least one node in Sv/ . Further, each node in S^ has one and only one arc in SAl leading to it. 3) SET III = s*, + SA3 This is the set of unlabelled nodes and arcs.. The set includes all those nodes, S^, , and arcs SA3 which have not been rejected. Initially, all nodes and arcs are unlabelled and members of Set III, i.e. S^ = [i| i=1,2,.. . ,n} and S^a = [a(i,j) | iSS^g and j&S^j}.. The algorithm then proceeds through the following steps: 94 Step 1) The source node, 1, is put in node set I, (i.e. S^, = [1}) and given a permanent label value of zero. Step 2) Consider all arcs connecting the node just transferred to S^,, with any of the other nodes in or S^3. Two possibilities arise in this temporary labelling process: Case 1: i&Sv/ , j&S^ , a(i,j)&S/>3 If any of the new nodes, j, to be considered are in Svt, then check to see if the corresponding new arc, a(i,j) yields a shorter path distance from the source to node j than the previous arc. If arc a(i,j) yields a shorter distance, then place a(i,j) in SAl and reject the: previous arc in S4t. If however, a(i,j) yields an equal or longer path, then reject a(i,j).. Case 2: i&S*,, , j&Sv3, a(i,j)&S*3 If any of the new nodes, j, is in Syyj place node j in St/z, and place the corresponding arc, a(i,j) in SAi. Step 3) Restricting consideration of arcs to 5AI and to s4i/ every node j in S^/z has one and only one path connecting to the source node, 1. Associated with each path is a distance. The node j (j&S^) having the shortest distance from the source is transferred from S^ to SN1, with the corresponding arc a(i,j) (i&S/v/, j&S vz.) transferred to S,,, . . This step is the permanent labelling process. Step 4) If all nodes (or the specified sink) have: been transferred to SM,, then stop.. Otherwise, go to step 2 and continue processing., If the shortest path from the source to all other nodes of an N node network is desired, then the iterative minimization must be executed exactly N-1 times. During the procedure N(N-1) elementary operations are needed to assign temporary labels, with an additional N(N-1)/2 comparisons necessary to assign permanent labels. A further (N-1)2 comparisons are needed for updating and indexing the node list. Hence, a total of approximately 3N2 elementary operations are required.. On this basis Dreyfus (1969) states that Dijkstra's algorithm is the 95 most efficient around. Eisner, et al. (1975) offers further support in assessing Dijkstra's algorithm as superior to two other algorithms investigated. 96 a£PENDIX_II Land Classes Of The Westlake PSYO* (Source: BCFS - Prince George, 1974) Land, Class I Parent Material: Sandy loam and loam textured colluvium and/or till deposits overlying basic bedrock. Loam and clay loam textured glacial till. Soil Series: a mixture of Cluculz and Twain.. Topography: Very steeply sloping and strongly rolling or very hilly. Drainage: Ranges from imperfectly to rapidly drained.. Comments: Liable to damage by skidding and erosion. Susceptible to frost heaving.. Soils often shallow and rocky with moisture limitations to regeneration. LandClass II Parent Material: Sandy loam and loam textured colluvium and/or till deposits overlying basic bedrock.. Loam and clay loam textured glacial till. Soil Series: a mixture of 60% Oona and 40% Twain. Topography: Very steeply sloping and strongly rolling.. Drainage: Moderately well to rapidly drained. Comments: Liable to damage by skidding and erosion.. Land :class; III Parent Material: Ablation till deposits or gravelly outwash and valley train deposits overlain with 97 loamy sand, sand and sandy loam textured capping. Soil Series: A mixture of 60% Cobb and 40% Ramsey. Topography: Gently to moderately rolling. , Drainage: Ranges from imperfect to rapid.. Comments: Soil moisture limitations to regeneration. Fertility sometimes low. i^£d_Class_IV Parent Material: Loam and clay loam textured glacial till deposits; intermittent surface modification with sandy loam textures.. Rolling and hilly drumlinized till plain land forms.. These may be combined with gravelly outwash and valley train deposits overlain with loamy sand and sandy loam textured capping. . Soil Series: Deserter or mainly Deserter., Topography: Sometimes steeply sloping and hilly, usually rolling and hilly. Drainage: Mostly imperfectly to well drained with rapid drainage.on the gravelly outwash deposits.. Comments: Susceptible to some frost heaving.. Land Class V Parent Material: Heavy clay textured glacio-lacustrine deposits.. Some silt loam to silty loam textured glacio-lacustrine deposits. Soil Series: Pineview or 80% Pineview and 20% Berman. Topography: Ondulating to strongly rolling. , Drainage: Ranges from imperfect to moderately well. Comments: Susceptible to frost heaving. Logging may increase compaction and erosion and cause stream siltation. Land.Class VI Parent Material: Sphagnic moss, sedge, and associated hydrophytic vegetation. 98 Soil Series: A mixture of Chief and Moxley.. Topography: Depressional to nearly lavel or gently undulating. Drainage: Very poor. . Comments: Filled in areas of lakes and ponds often supporting black spruce. . Land _Class _VII Parent Material: Mainly clay textured glacio-lacustrine deposits.. Some variable textured fluvial deposits and silt loam to silty clay loam textured glacio-lacustrine.deposits. Soil Series: Mainly Vanierhoof, with some Stellako, Berman and Bednesti. Topography: Ranges from nearly level to strongly rolling.. Drainage: Ranges from imperfectly to rapid, with the majority moderately well to well drained.. Comments: Susceptible to frost heaving. . Logging results in loss of soil structure, increased compaction and arosion yielding stream sedimentation. . Land Class VIII Parent Material: Variable textured fluvial deposits.. Small inclusions of sphagnic moss, sedge and associated hydrophytic vegetation. , Soil Series: Mainly Stellako, with some Moxley and Chief. Topography: Nearly level to undulating. Drainage: Ranges from very poor to rapid. , Comments: Logging may cause stream sedimentation.. Land Class IX Parent Material: Loam and clay loam textured glacial till deposits.. Rolling, hilly, strongly to very steeply sloping till plain land forms between approximately 3500 to 4500 feet elevation. . Also sandy loam textured colluvium and/or till deposits overlying basic bedrock.. 99 Soil Series: A mixture of 70% Twain ana 30% Oona. . Topography: Very steeply sloping. Drainage: Moderately well. Comments: Frost heaving and generally poor climatic conditions for growth. . Land Class X Parent Material: Sandy loam and loamy sand textured ablation till deposits; clay textured glacio-lacustrine deposits; some loam and clay loam textured glacial till deposits; some inclusion of sphagnic moss, sedge and associated hydrophytic vegetation.„. Soil Series: & mixture of Crystal, Cobb, Deserter; Crystal, Moxley and Chief; Crystal and deserter; Beaverly. Topography: Ranges from gently undulating to strongly rolling. Drainage: Ranges from very poor to well drained.. Comments: The areas of non-organic origin are stable and robust. . Land Class ^XI Parent Material: Gravelly outwash and valley train overlain with loamy sand, sand and sandy loam textured capping. Silt loam to silty clay loam textured glacio-lacustrine deposits. Gravelly and sandy esker deposits with variable interstratified loamy sand, sand and sandy loam. Sandy outwash and deltaic deposits.. Soil Series: A mixture of Mix, Berman, Roaring, Giscombe, Mapes, Sax ton and Deserter. . Topography: Gently undulating to gently rolling., Drainage: Ranges from moderately well to rapidly drained. Comments: Generally stable, logging on the.fine textured glacio-lacustrine deposits results in some erosion and stream siltation. 100 Land Class XII Parent Material: Sandy loam and loam textured colluvium and/or till deposits overlying acidic bedrock. Soil Series: A mixture of Decker, Deserters and Ormond., Topography: Hilly to very hilly. Drainage: Ranges from well to rapidly drained. Comments: Shallow and rocky soils.. Significant soil loss can occur as a result of skidding and erosion. Land Class XIII Parent Material: Silt loam to silty clay loam textured glacio-lacustrine deposits., Heavy clay textured glacio-lacustrine deposits. Soil Series: A mixture of Berman, Pineview, Siscome and Fraser. Topography: Gently undulating to moderately rolling.. Drainage: Ranges from poorly to well drained. Comments: Susceptible to frost heaving.. Stream siltation may occur after logging on the steeper slopes. Land -Class XIV Parent Material: Gravelly and sandy esker deposits with variable inter-stratified loamy sand, sand and sandy loam. Some inclusion of sedge and associated hydrophytic vegetation. Soil Series: A mixture of Roaring and Chief.. Topography: Ranges from nearly level to strongly rolling. Drainage: Rapid on mineral soils, very poor on organic. Comments: Mineral soils droughty and of low fertility.. Land Class XV Parent Material: Loam and clay loam textured glacial till deposits; intermittent surface modification 10 1 with sandy loam textures.. Rolling and hilly drumlinized till plain land form. Some beach deposits of loamy sand and sandy textures. Soil Series: Mainly Barrett with some Kluck and Crystal.. Topography: Ranges from undulating to hilly.. Drainage: Ranges from imperfectly to rapidly drained. Comments: Generally stable and robust. Land Class XVI Parent Material: Loam to clay loam textured glacial till deposits; intermittent surface modification with sandy loam textures.. Steep land till land forms. Sandy loam and loam textured colluvium and/or till deposits overlying basic bedrock.. Soil Series: A mixture of Telegraph and Drmond. . Topography: Strongly rolling and hilly. Drainage: Ranges from moderately well to rapidly drained. Comments: Climatic conditions for growth are poor.. kand _C1ass_XVII Parent Material: Gravel and sand esker and kame deposits; hummocky.. Soil Series: A mixture of Morice, Guniza and Ramsey.. Topography: Gently undulating to moderately rolling. Drainage: Ranges from rapid to well drained. . Comments: High probability of damage from slash burning. . Land Class XVIII Parent Material: Sandy outwash and valley terrace deposits overlain with finer sands and loamy sands. Some depositional clay strata. Soil Series: Mainly Cottonwood, with some Blackwater. Topography: Ranges from gently undulating to strongly 102 rolling. Drainage: Rapid to well drained, but moderately well to imperfectly drained where clay strata occur. Comments: High probability of damage from slash burning.. Land Class XIX Parent Material: Sandy outwash and valley terrace deposits overlain with finer sands and loamy sands. Some.depositional clay strata.. Silt loam to silty clay loam textured glacio-lacustrine deposits. Soil Series: A mixture of Blackwater, Beaverly, Bednesti and Cottonwood. Topography: Ranges from gently undulating to strongly rolling. Drainage: Ranges from imperfectly to rapidly drained. . Comments: Broadcast burning acceptable:on the:lacustrine deposits. Otherwise a high probability of damage from slash burning.. APPEMDIX_III Prescribed Stand Treatments For The Westlake PSYU TREATMENT SEQUENCES LC/GT Method of Falling Tree Extracted As Extracted By Season Site Prep. Regen. Method Next Crop Number Species Either H = hand T = tree length M = Mech F = Full tree S = Spruce + F P = Pine + F D = Decid. All Snip, saw L = Log or length feller-buncher C = Clean log S = Skidder W = Winter D = Drag N - Natural Scarify Regen. Species C = Cat S = Summer B = Broadcast burn W = Windrow N = No treatment (N*) P = Plant Note: When there are optional operations, the frequency of occurrence is given as a percent. Link operations are obligatory sequences. N* - If not cleanly logged, knock down slash with chain. Land Class and Growth Type Method of Felling Tree Extracted As Extracted By I All H (Twain) ' II All = I III All M IV All M 7 H 3 V S H V P+D M VI No logging VII S H T C 6 C S 4 F S (fert. problem) F S 8 C 2 T C 5 S 5 F S 6 T C 4 T S 6 C 4 Season Site Regeneration Subsequent Preparation Method Crop W N N PI (F) S 7 — N* W 3 D W 5 D S 5 N N N PI PI W B S (F) W 8 B+W S 2 D — W 7 B s 3 W+B P S (F) N PI (S) S (F) Land Class Method Tree and of Extracted Extracted Season Site Regeneration Subsequent Growth Type Felling As By Preparation Method Crop VII P M T F S 7 C 3 W 7 B+W 6 S 3 D 4 --S (F) PI (S) VIII D H (Cottonw.) (20 ac) C 7 S 3 W N Residual Cot. VIII S (+P) H C 5 S 5 W B (Brush prob.) IX All = I & II (Twain) Topography Important X All = III Wide fluctuations in X. X may need more cat and more winter than III XI All = X = III XII All H S 5 C 5 W N D if slope % < 20% PI XIII = V (2 sections) XIV All H 7 M 3 D 7 N 3 N PI Land Class Method Tree and of Extracted Extracted Season Site Regeneration Subsequent Growth Type Felling As By Preparation Method Crop XV All M F S S 6 N N PI W 4 D XVI = I XVII = XIV XVIII All M F S S 7 N N PI W 3 D XIV = VII (2 way) APPENDIX IV Management Reports On Stands Of The Westlake PSYO REGION CONPT. STANO NO. SOIL-LAND USE CLASS POT. USE TliiER SPP. ••«••• ••••*• *••**»•*• **»»»«**» ••*»*««*« *••••«*• **••**•*••* 60 16 14065160 4 OEFERRED NONE PL 60 16 14066160 4 DEFERRED NONE S 60 16 1406T160 4 DEFERRED NONE PLS 60 16 14068160 5 FORESTRY NONE SF 60 16 14069160 5 FORESTRY NONE FS 60 14 14070160 5 FORESTRY NONE PLS 60 14 14071160 5 FORESTRY NONE PL 60 14 14072160 5 FORESTRY NONE PL 60 14 14073160 5 FORESTRY NONE S 60 14 14074160 5 FORESTRY NONE PL 60 14 14075160 5 FORESTRY NONE PL 60 14 14076160 5 FORESTRY NONE S 60 14 14077160 5 FORESTRY NONE DEC ID 60 14 14078160 5 FORESTRY NONE PLS 60 14 14079160 5 FORESTRY NONE FS 60 14 14080160 5 FORE STRY NONE S 60 14 14081160 5 FORESTRY NONE NP 60 14 14082160 5 UNGULATE NONE F 60 14 14083160 5 UNGULATE NONE PL 60 14 14084160 5 UNGULATE NONE PL 60 14 14085160 5 UNGULATE NONE DEC ID 60 14 14086160 5 UNGULATE NONE PL 60 14 14087160 5 UNGULATE NONE PLS 60 14 14088160 5 UNGULATE NONE LOGGED 60 14 14089160 6 FORESTRY NONE S 60 14 14090160 6 FORESTRY NONE PL 60 14 14091160 6 FORESTRY NONE S CLASS EXP. REGEN. STOCKING SITE ACREAGE VOL.IMCFI •**•• **•••*•• •»•» ••••••••• PLISFI G 16 2154 SFB N 14 616 PLISFI N 14 2228 SF G 151 4700 FSPL N 132 3700 PLS N 140 4000 PLFCSI H 132 0 PLIAFI P 1876 140 S P 11 60 P 186 300 PLISFI G 1428 1828 SPL P 37 285 N 65 1150 PLISFI G 75 3019 FISI N 47 2681 S N 42 731 163 0 FIPLSI N 86 2750 PLIAFI P 80 140 PLIASI P 6 300 P 6 616 PLISFI P 86 830 5 PLISFI G 37 3019 P 10 0 T S G 131 5200 2 PL N 113 550 2 N 13 875 REGION CONPT. STANO NO. E. HARV. YR. ****** •••••• ••*••*••• *****•**•*•• 40 14 14065160 601 60 14 14066160 601 60 14 14067160 601 60 14 14068160 600 60 14 14069160 600 60 14 14070160 600 60 14 140TU60 601 60 14 140T2160 601 60 14 14073160 601 60 14 14074160 601 60 14 1407S160 601 60 14 14076160 601 60 14 14077160 60 60 14 14078160 601 60 14 14079160 601 60 14 14080160 601 60 14 14081160 60 14 14082160 601 60 14 14083160 601 60 14 14084160 601 60 14 14085160 60 60 14 14086160 601 60 14 14087160 601 60 14 14088160 60 60 14 14089160 600 60 14 14090160 601 60 14 14091160 60 L. HARV. VR. SEASON OF HARVEST OPERATION TYPE SITE PREPARATION REGENERATION • ••*•*•*•••• •••••••••••**»*•, •••••*••*••»*« •*••*•****••*••• ***•«••*•„• 60 SUMMER FULL TREE SKIO NONE NAT 80 SUM. OR WIN. FULL TREE SKIO NONE NAT 60 SUMMER FULL TREE SKIO NONE NAT 20 WINTER FULL TREE CLEAN LOG NONE NAT 20 WINTER LOP AND SCATTER NONE NAT 20 WINTER FULL TREE SKIO DRAG SCARIFY NAT 100 SUN. OR WIN. FULL TREE SKIO 0RA6 SCARIFY NAT 80 WINTER FULL TREE SKIO DRAG SCARIFY NAT 100 WINTER FULL TREE SKID COMPLETE SLASHBURN PLT 60 WINTER FULL TREE SKIO DRAG SCARIFY NAT 60 WINTER FULL TREE SKIO ORAG SCARIFY NAT 100 WINTER SELECTION CLEAN LOG NONE NAT NONE PROTECTION FOREST NONE 40 WINTER FULL TREE SKIO ORAG SCARIFY NAT 60 SUM. OR WIN. FULL TREE SKIO NONE NAT 80 WINTER SELECTION CLEAN LOG NONE NAT NONE NONE NONE 40 WINTER FULL TREE CLEAN LOG NONE NAT 80 WINTER FULL TREE SKIO ORAG SCARIFY NAT 60 WINTER FULL TREE CLEAN LOG NONE NAT NONE PROTECTION FOREST NONE 60 WINTER FULL TREE SKIO ORAG SCARIFY NAT 40 WINTER FULL TREE SKIO ORAG SCARIFY NAT NONE PROTECTION FOREST NONE 40 WINTER SELECTION FULL TREE NONE NAT 80 WINTER FULL TREE SKIO NONE NAT NONE PROTECTION FOREST NONE SITE . LOGPRICE HARVEST FORESTRY ROAOINC . STA NO f C—TYPE SL AGE USE SITE VOL AREA . SEAS OP PREP REG SPECIES . /HCF COST/MCF COST/MCF COST/MCF . COORDINATES 14038160 B 4 6 F N 325 6 M LAS NAT SF 14099160 F 4 6 F M 249 20 S FTS DS NAT FS 14060160 LOGGED 4 F G 1047 • F TS NAT PLFS 14061160 NP 4 F 212 14062160 SF 4 8 D G 520 21 H LAS NAT SF 14063160 PLS 4 6 0 G 400 284 S FTS NAT PLISt 14064160 PL 4 5 0 G 355 449 S FTS NAT PLISFI 14065160 PL 4 4 D G 215 16 s FTS NAT PLISFI 14066160 S 4 5 D M 61 14 • FTS NAT SFB 14067160 PLS 4 5 D H 222 14 s FTS NAT PLISFI 14068160 SF 5 8 F G 470 151 H FCL NAT SF 14069160 FS 5 7 F n 370 132 M LAS NAT FSPL 14070160 PLS 5 4 F N 400 140 H FTS OS NAT PLS 14071160 PL 5 1 F N 132 • FTS DS NAT PLFISI 14072160 PL 5 2 F P 14 1874 W FTS OS NAT PLIAFI 14073160 S 5 2 F P 6 11 M FTS CSB PLT S 14074160 PL 5 3 f P 30 186 M FTS DS NAT 14075160 PL 5 4 F G 182 1428 H FTS OS NAT PLISFI 14076160 S 5 4 F P 28 37 H SCL NAT SPL 14077160 OECIO 5 4 F N 115 69 PRT 14078160 PLS 3 5 F G 301 75 H FTS DS NAT PLISFI 14079160 FS 5 9 F N 268 47 • FTS NAT FISI 14080160 S 5 6 F n 73 42 M SCL NAT S 14081160 NP 9 F 163 14082160 • F 9 7 U N 275 86 M FCL NAT FIPLS) 14083160 PL 5 2 U p 14 80 H FTS DS NAT PLIAFI 14084160 PL 5 3 u p 30 6 H FCL NAT PLIASI 14085160 DEC ID S 3 U p 41 6 PRT 14086160 PL 5 4 u p 83 86 M FTS OS NAT PLISFI 14087160 PLS 5 5 U G 301 37 M FTS OS NAT PLISFI 14088160 LOGGED 5 u P 10 PUT 14089160 S 6 7 F G 520 131 H SFT NAT S 14090160 PL 6 2 F N 59 119 H FTS NAT PL 14091160 S 6 F N 87 13 PRT 14092160 PL 6 3 F N 139 7 S FTS NAT PL 14093160 S 6 F N 347 10 M FTS NAT SIPLI 14094160 B 6 6 F N 344 5 M LAS NAT BS 14095160 NP 6 F 356 14096160 PL 7 1 F N 8 H FTS NAT PL 14097160 PL 7 2 F P 14 21 H FTS NAT PL 14098160 OECIO 7 3 F P 80 15 H FTS NAT APL 14099160 LOGGED 7 F G 112 H FTS NAT PLISFI 14100160 NP 7 F 10 14101160 F 7 7 U N 275 19 H LAS NAT FS 14102160 PL 7 5 u G 290 86 M FTS NAT PLIAS) 14103160 PL 7 1 U H 67 M FTS NAT PL 14104160 PL 7 2 u P 14 248 M FTS NAT PL 14105160 OECIO 7 3 u P 80 26 H FTS NAT APL 14106160 PL 7 4 u G 215 15 M FTS DS NAT PLIFI 14107160 LOGGED 7 u G 81 H FTS NAT PLISFI 14108160 NP 7 u 45 14109160 PL 7 5 G G 290 8 M FTS NAT PLIASI 14110160 PL 7 G P 14 27 M FTS NAT PL 14111160 OECIO 7 3 G N 40 13 PRT 14112160 PL 7 G G 121 9 H FTS OS NAT PL 14113160 DEC ID 7 3 G P 80 18 PRT APPENDIX_V Stand Economics Report On Mature Stands WESTLAKE PSYU - STAND APPRAISAL FOR STATE VARIABLE SUBSYSTEM PACE 35 «»*»••*»»#••**»••»»•***«»•*»*•••*•*•*«**«»*»*»•»*•***«****•**•*********»***** STAND ECONOMICS REPORT STANO NO. AGE SITE G-TYPE VOLUME STOCK. CCF/AC PCT. SELL PR. t/CCF F-B COST SKID COST AREA COST t/CCF t/CCF t/CCF HARV.COST t/CCF HAUL COST t/CCF RO OEV. t/CCF NET VALUE t/CCF t/ACRE 24139160 12 26035160 12 24069155 12 24131160 12 26155160 12 24095160 12 240T4160 12 240TS160 12 24015155 12 25006160 12 25061160 12 25007160 12 25057160 12 25056160 12 25064160 12 25043160 12 25065160 12 25075160 12 25074160 12 25067160 12 39003160 12 43009160 12 43004160 12 43010160 12 43001160 12 53002155 12 53013155 12 53007155 12 76008155 12 76003155 12 77006155 12 77044155 12 77045155 12 77038155 12 77037155 12 77005155 12 9043160 14 9007160 14 9023160 14 9038160 14 9029160 14 6001160 14 6011160 14 8004160 14 8035160 14 8066160 14 8021160 14 10069160 14 10130160 14 10067160 14 P! 2 P 2 DEC 2 P 2 2 2 36.00 37.50 38.60 44.00 36.00 36.00 36.00 44.00 44.00 43.00 44.00 43.00 31.90 36.00 36.00 37.30 11.00 21.50 11.00 21.50 40.00 32.00 36.00 36.00 36.00 36.00 13.00 13.00 35.60 35.60 40.00 35.60 40.00 40.00 35.60 35.60 50.50 8.00 8.00 44.00 44.00 52.00 29.00 64.10 53.40 24.60 26.20 47.00 44.00 47.00 0.95 0.95 l.Oi 1.14 0.93 0.93 0.95 1.08 1.16 0.75 0.77 I.11 0.81 0.93 0.95 0.95 0.51 0.90 0.51 0.90 0.73 0.56 0.93 0.93 0.93 0.61 0.60 0.60 0.62 0.62 0.70 0.62 0.70 0. 70 0.62 0.94 0.92 0.19 0.19 1.12 1.62 0. 95 0. 53 1.03 1.30 0.B1 0. 64 1. LI 1.12 1.11 70.03 75. 52 87.45 59.17 59.17 59.17 70.03 73.76 70.03 69.59 74.55 74.55 75.52 59. 17 70.03 75.52 71.55 77.61 71.55 77.61 70.03 69.59 59.17 59.17 59.17 69.59 71.55 71.55 69.59 69.59 70.03 69. 59 70.03 70.03 69. 59 70.03 75.52 75.52 75.52 87.45 87.45 75.52 75.52 65.20 67.12 32.36 67.12 75.52 87.45 75.52 2.55 3.48 2.86 2.59 2.59 2.59 2.55 2.94 2.55 2.55 2.64 2.64 3.03 2.59 3.49 3.03 4.83 4.66 4.83 4.66 2.87 2.55 3.56 2.59 3.18 2.55 4.55 4.55 2.55 3.50 2.59 3.50 3.51 3.51 3.50 2.55 3.39 6.42 6.42 2.86 2.86 4.30 3.48 2.74 2.55 2.56 2.87 4.34 2.86 4.34 4.05 5.73 3.22 4.78 4.19 4.19 4.05 4.47 4.05 5.45 3.66 5.00 4.66 4.19 4.90 4.66 5.03 4.91 5.03 4.91 5.13 4.05 6.24 4.19 8.59 4.05 5.03 5.03 4.05 4.90 4.19 4.90 5.03 5.03 4.90 4.05 5.40 6.69 6.69 4.17 4.17 6.69 5.40 4.67 5.28 5.43 4.67 6. 16 3.48 6.69 0.38 0.63 0.43 0.24 0. 38 0.55 0.55 3.45 0.45 0.70 0.31 0.70 0.43 0.38 1.13 0.36 3.69 1.89 3.69 1.89 0.59 0.62 0.75 0.55 0.47 0.38 3.13 3. 13 0. 38 1.14 0.34 1.14 1.02 1.02 1.14 0.38 0.47 3.5S 3.58 3.65 0.65 0.55 0.32 0. 37 0.54 1.17 0.90 0.64 0.24 0.61 6.98 9.84 6.51 7.61 7.16 7.33 7.15 7.86 7.05 8.70 6.61 8.34 8.12 7.16 9.53 8.05 13.55 11.46 13.55 11.46 8.58 7.22 10.55 7.33 12.23 6.98 12.70 12.70 6.99 9.55 7. 12 9.55 9.55 9.55 9.55 6.99 9.26 16.69 16.69 7.68 7.68 11.54 9.69 7.78 8.37 9. 15 8.45 11.15 6.58 11.64 0.50 5.09 7.98 0.84 0.54 0.63 0.56 5.33 6.89 6.05 6.12 6.76 7.85 7.81 7.90 6.99 7.90 7.67 8.41 7.91 2.71 6.75 6.59 6.77 6.78 8.58 8.32 8.61 10.32 10.31 9.73 8.54 9.99 9.95 8.57 9.51 2.63 1.63 1.57 1.26 1.26 2. 13 2.10 4.02 5.74 5. 52 4. 16 0.92 0.39 1.43 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.73 1.73 1.73 0.0 0.0 0.0 1.73 0.0 0.0 1.73 0.0 0.0 0.0 0.0 0.0 0.0 0. 0 0.0 0.0 0.0 0.0 0.0 0.0 0. 0 0.0 62.54 60.59 72.96 50.72 51.47 51.22 62.32 60.57 56.09 54.84 61.82 59.45 59.56 44.20 52.61 60.47 50.11 58.48 49.59 58.24 58.73 55.61 42.03 45.07 40.16 52.29 48.80 48.51 52.28 49.73 53.17 49.77 50.49 50.53 49.74 53.53 63.63 57.20 57.27 7B.5I 78.51 61.84 63.72 53.40 53.01 17.68 54.51 63.45 80.48 62.45 2251.58 2272.16 2816.26 2231.50 1852.92 1843.76 2243.51 2665.21 2467.82 2358.05 2720.02 2556.23 1899.84 1591.30 1893.79 2267.80 551.16 1257.27 545.52 1252.12 2349.29 1779.63 1513.20 1622.53 1445.90 1882.35 634.34 630.64 1861.12 1770.34 2126.98 1771.73 2019.46 2021.31 1770.57 1905.59 3213.48 457.62 458.12 3454.49 3454.36 3215.89 1867.90 3422.76 2830.87 435.03 1428.03 2982.25 3541 .04 2935.26 WESTLAKE PSYU - STAND APPRAISAL FOR STATE VARIABLE SUBSYSTEM PAGE 36 STAND ECONOMICS REPORT STAND NO. AGE SITE G-TYPE VOLUME STOCK. SELL PR. F-B COST SKID COST AREA COST HARV.CUST HAUL COST RO DEV. NET VALUE CCF/AC PCT. I/CCF $/CCF J/CCF $/CCF $/CCF t/CCF »/CCF $/CCF S/ACRE 10030160 14 2 F 68.00 1.73 87.45 3.28 3.73 0.35 7.36 1.70 0.0 78.39 10059160 14 2 S 47.00 1. a 75.52 4.J4 6.69 0.61 11.64 1.39 0.0 62.49 10109160 14 2 F 44.00 1.12 87.45 2.86 4.17 0.65 7.68 0.26 0.0 79.51 10042160 14 2 S 47.00 1.11 75.52 4.34 6.69 0.61 11.64 1.16 0.0 62.72 10002160 14 I PLS 24.00 0.58 71.55 2.94 4.81 0.98 8.74 2.92 0.0 59.90 10140160 14 2 F 44.00 1.12 87.45 2.86 3.48 0.24 6.58 0. 31 0.0 80.56 10003160 14 2 SF 50.00 1.30 77.61 3.26 4.66 0.47 8.40 2.71 0.0 66.50 10018160 14 4 I PLS 24.00 0.58 71.55 2.94 4.81 0.98 8. 74 3.01 0.0 59.80 11062160 14 I S 56.00 1.02 75.52 4.28 6.69 0.48 11.45 2.53 0.0 61.54 11015160 14 I FS 56.00 0.91 86.34 3.34 3.93 0.42 7.69 2.38 0.0 76.28 11014160 14 , I PLS 49.00 1.18 71.55 2.81 4.81 0.48 8.11 1.23 0.0 62.22 11044160 14 . I PLS 49.00 1.18 71.55 2.58 5.43 0.58 8.60 1.04 0.0 61.91 12057140 14 I PLS 49.00 0.81 71.74 2.58 3.89 0.40 6.88 3.96 0.0 60.90 12002160 14 , I S 45.00 1.06 75.52 4.36 5.40 0.90 10.67 3.91 0.0 60.95 12058160 14 , » S 35.00 0.81 75.52 2.99 4.35 0.56 7.90 4.37 0.0 63.24 14032160 14 I FS 44.50 0.75 86.34 2.88 3.65 0.24 6.77 1.92 0.0 77.65 14089160 14 I S 52.00 0.95 75.52 3.78 9.33 0.32 13.43 2.22 0.0 59.87 14001160 14 i I s 8.00 0.19 75. 52 6.42 5.40 5.08 16.90 3.66 0.0 54.96 14040160 14 ; I SF 40.00 1.04 77.61 3.29 4.66 0.59 8.55 2.12 0.0 66.95 14101160 14 ; I F 27.50 0.70 87.45 2.86 4.10 1.10 8.06 2.86 0.0 76.53 14022160 14 ; ! SF 42.00 1.09 77.61 2.93 4.33 0.25 7.51 1.88 0.0 68.22 14039160 14 < I PLS 49.00 1.18 71.55 2.81 4.81 0.48 8.11 1.20 0.0 62.24 14069160 14 i t FS 37.00 0.81 81.29 2.88 4.41 0.77 8.07 2.37 0.0 70.B3 14123160 14 i ! F 27.20 0.69 87.45 4.69 3.77 1.49 9.95 2.83 0.0 74.67 14082160 14 2 ! F 27.50 0.70 87.45 2.86 4.17 1.04 8.07 3.44 0.0 75.93 15128160 14 1 PLS 57.60 0.95 71.74 2.79 4.81 0.41 8.02 3.33 0.0 60.40 15013160 14 1 S 15.00 0.27 75.52 5.25 6.69 1.91 13.85 3.95 0.0 57.72 15129160 14 2 ! S 35.00 0.83 75.52 3.44 5.40 0.68 9.52 2.99 0.0 63.01 15035160 14 2 ' S 39.00 0.92 75.52 4.43 6.16 0.78 11.37 3.01 0.0 61.14 15058160 14 2 ! S 47.40 1.12 75.52 4.34 6.16 0.64 11.14 4.90 0.0 59.49 16075160 14 1 S 52.00 0.95 75.52 3.38 5.40 0.45 9.24 0.89 0.0 65.38 16027160 14 1 PLS 48.70 0.80 71.74 3.04 8.19 0.35 11.57 0.94 0.0 59.22 16 0 74160 14 1 PLS 49.00 0.81 71.74 2.81 4.81 0.48 8.11 0. 79 0.0 62.84 16047160 14 1 PLS 49.00 0.81 71.74 2.81 4.81 0.48 8.11 1.00 0.0 62.63 16048160 14 2 > F 44.30 1.12 87.45 3.33 3.73 0.54 7.59 1.26 0.0 78.60 17003160 14 1 PL 64.10 1.01 6 5.20 2.74 4.67 0.37 7.78 2.51 0.0 54.91 18057160 14 1 F 68.00 1.34 87.45 2.86 4.10 0.44 7.41 4.06 0.0 75.99 18056160 14 I S 45.00 1.06 75.52 4.36 6.16 0.67 11.19 4.47 0.0 59.85 18090160 14 I F 27.00 0.69 87.45 3.41 3.73 0.88 8.02 4.93 0.0 74.50 19047160 14 1 PLS 32.30 0.5) 71.74 2.58 3.89 0.43 6.91 6.85 0.0 57.98 19034160 14 1 F 44.40 0.87 87. 45 2.86 2.99 0.37 6.23 5.75 0.0 75.48 19046160 14 1 S 36.00 0.65 75.52 2.99 4.35 0.38 7.72 6.85 0.0 60.95 19015160 14 2 F 32.90 0. 84 87.45 2.86 4.10 0.92 7.88 7.47 0.0 72.10 19066160 14 1 PLS 28.00 0.67 71.55 3.65 4.76 1.45 9.86 6.85 0.0 54.84 20111160 14 1 PLF 57.60 0.95 74.55 2.64 4.68 0.52 7.84 6. 54 0.0 60.17 20076160 14 1 S 65.50 1. 19 75.52 2.99 4.35 0.21 7.55 7.90 0.0 60.07 20045160 14 2 S 22.00 0.52 75.52 4.83 6.69 1.23 12. 75 7.67 0.07 55.04 20058160 14 2 F 44.50 1.1) 87. 45 2.86 2.99 0. 31 6.16 8.08 0.0 73.21 20128160 14 2 S 22.20 0.52 75.52 4.82 6.16 1.36 12. 34 7.28 0.0 55.90 20059160 14 2 PLS 49.00 1. 18 71.55 2.58 3.89 0.28 6.76 8.47 0.0 56.32 5330.70 2936.87 3498.45 2947.73 1437.53 3544.58 3325.12 1435.30 3446.29 4271.54 3048.58 3033.74 2983.94 2742.72 2213.51 3455.55 3113.13 439.70 2677.89 2104.64 2865.23 3049.93 2621.48 2030.97 2088.20 3478.78 865.84 2205.36 2384.59 2819.61 3400.00 2884.21 3079.25 3069.00 3458.32 3519.76 5167.03 2693.44 2011.62 1855.37 3351.11 2194.21 2372.18 1535.45 3465.97 3934.78 1210.81 3257.98 1241.00 2759.84 WESTLAKE PSVU - STAND APPRAISAL FOR STATE VARIABLE SUBSYSTEM PAGE 37 »********+****•*•**•********************************************************************************************************»**»*:* STAND ECONOMICS REPORT STAND NO. AGE SITE G-TYPE VOLUME STOCK. SELL PR. F-B COST SKID COST AREA COST CCF/AC PCT. t/CCF t/CCF */CCF t/CCF 20086160 16 2 s 22.20 0.92 75.92 4.82 6.16 1.36 20079160 14 2 PLS 49.00 1.18 71.55 2.58 3.89 0.28 20099160 14 2 S 22.20 0.52 75. 52 4.82 6. 16 1.36 20077160 14 2 F 44.50 1.13 87.45 2.86 2.99 0.31 20129160 14 2 PLS 49.00 1.18 71.55 2.58 5.27 0.62 21009199 14 1 F 61.99 1.22 87.45 3.29 3.73 0.38 21099199 14 1 F 61.90 1.22 87.45 2.86 4.10 0.46 21001199 14 1 F 61.90 1.22 87.45 3.29 3.73 0.38 21024160 14 1 PL 26.00 0.42 65.20 2.55 3.77 0.53 21044199 14 1 F 61.90 1.22 87.45 2.86 2.99 0.27 21026160 14 1 FS 92.00 0.88 86.34 2.88 3.14 0.26 21038160 14 1 SPL 92.00 0.91 73.76 4.12 5.00 0.78 21023160 14 1 PLS 60.00 0.99 71.74 2.58 3.89 0.23 21029159 14 1 SPL 69.40 1.15 73.76 3.71 3.86 0.35 21012160 14 1 PLS 60.00 0.9? 71.74 2.58 3.89 0.23 21049160 14 t S 69.00 1.18 75.92 4.23 5.40 0.63 21098160 14 2 SF 69.00 1.69 77.61 3.64 3.46 0.35 21099160 14 J S 69.00 1.53 79.92 3.85 4.05 0.35 21093160 14 3 PLS 19.00 0.81 71.95 3.99 4. 76 2.14 22086160 14 1 SPL 92.00 0.96 73.76 4.36 6.20 0.85 22032159 14 1 PL 93.40 0. 86 69.20 2.76 4.67 0.44 22094160 14 1 SPL 92.00 0.96 73.76 4.36 6.20 0.85 22011199 14 1 F 44.40 0.87 87.49 3.32 3.73 0.53 22018160 14 1 S 92.00 0.99 79. 32 3.38 5.40 0.45 22009160 14 1 FS 43.00 0.73 86.34 3.37 3.93 0.55 22012199 14 1 S 92.00 0.98 79.92 3.46 5.40 0.74 22006160 14 1 PLS 49.00 0.81 71.74 2.81 4.81 0.48 22008199 14 1 PLF 93.40 0.88 74.55 2.82 4.25 0.44 22004199 14 1 PL . 93.40 0.86 69.20 2.76 4.67 0.44 22024160 14 2 SF 47.00 1.22 77.61 4.97 5.72 0.61 22119160 14 2 FPL 48.00 1.04 82.29 2.85 3.18 0.28 22093160 14 2 S 47.00 1.11 79.52 4.34 6.16 0.64 22038160 14 2 F 27.00 0.69 87.45 2.86 4.17 1.06 22114160 14 2 FPL 37.00 0.89 82.29 4.59 4.01 1.10 22067160 14 2 F 62.00 1.98 87. 45 2.86 4.10 0.49 22102160 14 2 SPL 92.00 9.74 70.09 4.36 6.20 0.85 22009155 14 3 PL 10.80 0.50 70.28 3.20 4.67 2.19 22034159 14 3 S 10.80 0.41 75.52 3.82 5.40 2.19 22009155 14 3 PLS 10.80 0.46 71.55 3.25 4.81 2.19 23039199 14 1 PL 64.10 1.03 65.20 2.93 7.99 0.26 23012160 14 1 PLS 63.00 1.04 71.74 3.25 4.76 0.65 23099160 14 1 F 44.00 0.87 87.45 2.86 4.10 0.69 23017160 14 1 S 95.00 1.09 75.52 3.38 5.40 0.43 23043160 14 1 F 44.00 0.87 87.45 3.79 6.38 0.38 23049155 14 1 S 52.10 0.95 75.52 2.99 4.35 0.38 23070160 14 1 s 65.90 1.19 75.52 4.23 6.16 0.47 23004155 14 1 PL 64.10 1.03 65.20 2.74 4.67 0.37 23030155 14 1 PL 64.10 1.03 65.20 2.93 7.99 0.26 23168160 14 1 F 44.00 0.87 87.45 2.86 2.99 0. 31 23030160 14 1 S 15.00 0.27 75.52 4.35 9.33 1.12 HARV.COST t/CCF HAUL COST t/CCF RO DEV. t/CCF NET VALUE t/CCF t/ACRE 12.34 6.76 12.34 6. 16 8.47 7.40 7.42 7.40 6.84 6.12 6.28 9.91 6.71 7.91 6.71 10.25 7.49 8.29 10.89 11.42 7.87 11.42 7.39 9.24 T.89 9.60 8.11 7.51 7.87 10.40 6.32 11.15 8.09 9.70 7.45 11.42 10.06 11.42 10.25 11.18 8.66 7.65 9.21 10.56 7. 72 10. 85 7.78 11. 18 6.16 14.80 7.67 8.08 7.00 7.64 8.07 7.66 7.18 7.69 7.28 6.98 7.97 8.61 8.53 6.98 7.78 8.38 8.09 7.78 8.29 3.94 4.17 3.96 5.03 2.74 2.05 6.65 2.50 3.24 4.32 3.61 5.62 4.67 3.26 6.61 5.55 5.45 4.72 4.35 3.56 4.29 4.60 6.34 3.24 6.64 6.02 6.85 4.56 4.26 6.81 6.69 0.07 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.73 1.73 1.73 0.0 1.73 1.73 1.73 1.73 1.73 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 55.44 56.71 56.18 73.65 55.01 72.39 72.84 72.36 51.08 74.39 70.36 53.51 34.77 58.86 55.52 55.16 69.34 37.79 90.64 98.41 93.16 96.36 74.83 63.94 76.44 99.27 61.13 63.80 93.01 63.60 70.36 39.70 76.10 65.98 74.45 53.23 55.50 59.75 57.74 49.73 58.48 73.46 63.07 70.25 61.78 57.82 52.86 49. 76 74.48 54.03 1290.81 2778.84 1247.23 3277.34 2699.92 4480.80 4508.88 4479.28 1328.11 4602.07 3658.77 2782.48 3286.27 3849.71 3331.09 3585.11 3921.88 3753.99 962.16 1869.11 2838.49 1803.69 3322.98 3303.97 3287.06 1896.74 2999.44 3406.95 2830.78 2989.23 3377.10 2806.06 2054.66 2441.44 4615.77 1703.30 599.42 645.31 623.63 3187.82 3684.23 3232.45 3468.79 3090.99 3218.97 3758.49 3388.11 3189.81 3277.15 810.49 WESTLAKE PSYU - STAND APPRAISAL FOR STATE VARIABLE SUBSYSTEM PACE 38 ********************************************************************************************************************************** STAND NO. ASE SITE G-TYPE VOLUME STOCK CCF/AC PCT. 23152160 14 I S 25.00 0.45 23020155 14 2 PL 26.20 0.64 23003160 14 2 S 45.00 1.06 230*5155 14 2 ACON 18.50 0.56 23149160 14 2 SF 44.00 1.14 23150160 14 2 PL 26.00 0.63 24038155 14 1 PL 64.00 1.03 24106160 14 1 SPL 50.00 0. 88 24003160 14 1 S 45.00 0.82 24075155 14 1 F 61.90 1.22 24088160 14 1 PLF 58.00 0.95 24002160 14 1 PLF 61.00 1.00 24137160 14 1 SPL 52.00 0.91 24112160 14 1 FS 44.50 0.75 24138160 14 1 PLF 58.00 0.95 24073160 14 1 PLF 58.00 0.95 24013160 14 2 FS 44.50 0.98 24037155 14 2 F 40.00 1.02 24032160 14 2 FS 35.00 0.77 24130160 14 2 FS 45.00 0.99 24072160 14 2 SPL 45.00 1.05 25040160 14 1 FS 44.00 0.75 25004160 14 1 PLF 58.00 0.95 25005160 14 1 SPL 56.00 0.98 25059160 14 1 PL 22.60 0.36 25022160 14 1 S 56.00 1.02 25054160 14 1 PLS 32.00 0.53 25053160 14 1 S 36.00 0.65 25073160 14 2 PLS 28.00 0.67 39002160 14 1 SF 47.00 0.91 43008160 14 1 PLS 49.00 0.81 53001155 14 1 SPL 65.00 1.14 53006155 14 3 PLS 11.00 0.47 53012155 14 3 PLS 11.00 0.47 76007155 14 1 S 65.00 1.18 76002155 14 1 S 22.00 0.40 76006155 14 1 PLS 64.00 1.05 77019155 14 1 PLS 64.10 1.06 77002155 14 I PL 32.00 0.52 77043155 14 1 PLS 64.00 1.05 77004155 14 1 SPL 50.60 0.89 77036155 14 1 SPL 65.40 1.15 77003155 14 1 PLS 64.00 1.05 77034155 14 2 PL 30.00 0. 73 77035155 14 2 PLS 64.00 1.54 9037160 16 3 F 19.50 0.6S 8054160 16 1 PL 58.00 0.B9 8020160 16 1 PL 58.00 0.89 8063160 16 1 S 49.00 0.87 8019160 16 1 S 49.00 0.87 STAND ECONOMICS REPORT SELL PR. F-B COST SKID COST AREA CD t/CCF t/CCF t/CCF t/CCF 75.52 2.99 4.35 0.55 67. 12 2.87 4.67 0.90 75.52 4.36 5.40 0.93 64.72 2.58 3.48 1.07 77.61 2.93 3.76 0.31 67.12 2.55 3.77 0.53 65.20 2.55 3.77 0.26 73.76 2.93 4.75 0.21 75.52 4.36 5.40 0.90 87.45 2.86 2.99 0.27 74.55 2.64 3.40 0.34 74.55 3.19 4.26 0.67 73.76 2.93 4.16 0.26 86.34 2.88 3.65 0.24 74.55 2.64 3.40 0.24 74.55 2.64 3.40 0.34 81.29 3.37 3.93 0.53 87. 45 2.86 2.99 0.41 81.29 3.41 3.93 0.58 81.29 2.88 3.65 9.24 70.09 2.93 4.16 0.44 86.34 2.88 3.14 0.31 74.55 2.64 4.68 0.52 73.76 4.10 5.74 0.54 65.20 2.55 3.77 0.60 75.52 4.28 6.16 0.54 71.74 2.58 3.89 0.43 75. 52 2.99 4.35 0.38 71.55 3.65 4.76 1.45 77.61 3.27 4.66 0.50 71.74 2.58 3.89 0.40 73.76 2.93 4.16 0.21 71.55 4.75 4. 76 3.69 71.55 4.75 4.76 3.69 75.52 2.99 4.35 0.21 75.52 4.83 5.40 1.85 71.74 2.58 3.89 0.21 71.74 2.58 3.89 0.21 65.20 2.55 3.77 0.43 71. 74 3.25 4. 76 0.63 73. 76 2.93 4.16 0.27 73.76 4.04 5.00 0.62 71.74 2.58 3.89 0.21 67.12 3.53 4.64 1.35 71.55 3.25 4.76 0.63 87.45 2.86 4.09 1.47. 70.79 2.91 7.64 0.29 70.79 2.73 4.43 0.41 75.52 4.2 5 6.64 0.58 75.52 3.35 5.51 0.48 RV.COST HAUL COST RD DEV. NET VALUE t/CCF t/CCF t/CCF t/CCF t/ACRE 7.88 5.66 0.0 61.98 1549.47 8.45 4.26 0.0 54.41 1425.62 10.67 3.24 0.0 61.61 2772.64 7.13 6.01 0.0 51.58 954.32 7.00 5.59 0.0 65.02 2861.02 6.84 6.89 0.0 53.39 1388.06 6.58 6.46 0.0 52.16 3338.26 7.89 6.30 7.54 52.03 2601.31 10.67 3.89 0.0 60.96 2743.24 6.12 6.49 0.0 74.84 4632.42 6.38 5.23 0.0 62.94 3650.42 8.12 4.92 0.0 61.51 3751.93 7.35 4.82 0.0 61.59 3202.56 6.77 0.84 0.0 78.73 3503.70 6.27 1.05 0.0 67.23 3899.19 6.38 5.38 0.0 62.79 3641.69 7.83 0.81 0.0 72.65 3232.84 6.27 7.57 0.0 73.61 2944.28 8.01 0.91 0.0 72.37 2533.08 6.77 4.45 0.0 70.07 3153.12 7.53 3.91 0.0 58.65 2639.47 6.33 7.29 0.0 72.72 3199.77 7.84 6.40 0.0 60.32 3498.32 10.37 6.83 0.0 56.56 3167.43 6.92 7.60 0.0 50.68 1145.42 10.98 6.76 0.0 57.78 3235.77 6.91 8.04 0.0 56.79 1817.23 7.72 6.35 0.0 61.45 2212.24 9.86 8.03 0.0 53.66 1S02.47 8.44 2.69 0.0 66.49 3124.85 6.88 3.95 0.0 60.90 2984.21 7.30 8.64 1.73 56.09 3645.92 13.21 8.63 1.73 47.97 527.71 13.21 8.33 1.73 48.28 531.08 7.55 10.34 0.0 57.63 3745.76 12.07 10.31 0.0 53.13 1168.95 6.69 10.01 0.0 55.04 3522.58 6.69 9.74 0.0 55.31 3545.24 6. 75 9.29 0.0 49.17 1573.35 8.64 9.89 0.0 53.21 3405.13 7.36 9.75 0.0 56.65 2866.66 9.67 9.88 0.0 54.21 3545.57 6.69 8.67 0.0 56.37 3607.96 9.53 9. 56 0.0 48.03 1440.83 8.64 9.45 0.0 53.46 3421.31 8.42 1.19 0.0 77.84 1517.98 10.84 3.51 0.0 56.44 3273.45 7.57 3.24 0.0 59.98 3478.92 11.48 3.57 0.0 60.47 2962.90 9.34 1.83 0.0 64.35 3153.12 WESTLAKE PSYU - STAND APPRAISAL FOR STATE VARIABLE SUBSYSTEM PAGE 19 «»»•*••»*•**••»**»#»••»***•*•»••****»*****•**»«**»»« STANO ECONONICS REPORT STAND NO. AGE SITE G-TYPE VOLUME STOCK. CCF/AC PCT. SELL PR. */CCF F-B COST SKID COST AREA COST J/CCF */CCF J/CCF HARV.COST S/CCF HAUL COST */CCF RD DEV. 3/CCF NET VALUE i/CCF »/ACRE •OMUO 16 aoosiso 16 8034160 16 S065160 16 6033160 16 8001160 16 8064160 16 10001160 16 10029160 16 10038160 16 11034160 16 11013160 16 11043160 16 12001160 16 12056160 16 14068160 16 14038160 16 14128160 16 14062160 16 14122160 16 15110160 16 15034160 16 15127160 16 16013160 16 16073160 16 16045160 16 16046160 16 17053160 16 17033160 16 17020160 16 17001160 16 17002160 16 17040160 16 17019160 16 17031160 16 17032160 16 18055160 16 18101160 16 18001160 16 18089160 16 18022160 16 18073160 16 18074160 16 18040160 16 19033160 16 19014160 16 19001160 16 19045160 16 20057160 16 20056160 16 COTO 64.90 49.00 44.80 51.50 38.40 38.40 38.40 38.00 38.00 47.00 57.00 40.00 55.00 47.00 47.00 47.00 52.00 49.00 52.00 49.00 38.40 44.00 38.40 38.40 30.00 30.00 38.00 44.50 49.20 44.50 70.30 64.90 49.20 55.00 49.20 38.40 38.40 49.00 47.00 38.00 29.00 50.00 57.00 2 0.00 46.50 38.00 38.00 57.00 47.00 59.00 1.00 0.87 1.02 1.61 0.93 0. 93 0.93 0. 73 0.73 1.07 1.10 0.96 1.39 0.83 0.83 0.89 0.83 0.87 0.98 0.87 0.74 0.85 0.74 0.76 0.53 0.53 0.73 0.68 0.79 0.68 1.2V 1.09 1.12 1.26 1.12 0.93 0.74 0.87 0.83 0. 73 0.31 0.97 1.01 0.46 0.90 0.73 0. 73 0.91 0.83 1.42 70.79 75.52 75.52 32.36 85.37 85.37 85.37 87.17 87.17 73.52 87.17 85.37 77.61 75.52 75. 52 75. 33 88.03 75.52 75.33 75. 52 87. 17 87.17 87.17 87. 17 75.52 75.52 87.17 70. 79 72.82 70.79 75.52 70. 79 75.52 75.52 75.52 85.37 87.17 73.52 75.52 87. 17 75.52 87.17 75.52 75.52 87. 17 87.17 87. 17 74. 55 75.52 85. 37 2.72 3.35 4.29 2.65 2.86 3.33 2.86 3.34 3.14 4.27 3.28 3.33 3.95 4.27 2.96 4.01 3.34 4.25 3.23 4.25 4.45 2.86 3.33 4.45 3.42 3.42 3.34 2.97 3.28 2.76 3.31 2.72 4.25 3.33 4.25 4.43 2.86 3.35 4.27 3.34 4.53 3.30 3.33 4.84 2.86 2.86 3.34 2.64 2.96 2.86 4.43 5. 51 6.64 6.27 4.09 3.80 4.09 3.80 3.80 6.64 3.80 3.80 5.42 5.34 4.55 5.42 3.69 5.34 4.41 5.34 3.47 4.09 3.80 3.47 5.51 5.51 1.80 7.64 4.27 4.41 5.51 4.43 5.06 5.51 5.06 3.47 4.09 5.51 6.64 3.80 6. 16 3. 80 5.51 6.16 1.20 4.09 3.80 3.21 4.55 3.20 0.16 0.48 0.64 0.56 0.75 9.62 0.75 0.62 0.62 0.61 0.41 0.59 0.52 0.86 0.42 0.51 0.45 0.93 0.45 0.81 1.45 0.69 0.62 1.45 0.79 0.79 0.62 0.38 1.11 0.51 9.14 0.16 l.ll 0.43 1.13 1.45 0.79 0.48 0.61 0.62 1.04 0.47 0.41 1.51 0. 36 0.80 3.62 0.24 0.29 0.23 7.52 9.34 11.57 9.47 7.70 7.75 7.70 7.76 7.76 11.52 7.50 7.72 9.89 10.47 7.93 10.04 7.48 10.42 8.10 10.42 9.36 7.64 7.75 9.36 9.72 9.72 7.76 10.99 8.68 7.72 9.15 7.52 10.44 9.27 10.44 9.36 7.74 9.34 11.52 7.76 11.74 7.57 9.25 12.52 6.42 7. 75 7. 76 6.09 7.80 6.29 3.19 1.90 5.44 3.48 5.36 2.43 3.34 2.08 1.63 1.36 2.68 1.79 1.15 3.99 4.18 3.82 2.00 2.48 2.86 2.55 2.36 4.74 2.33 3.02 0.89 0.46 3.00 3.28 3.62 2.80 2.18 3.08 3.20 3.00 3.17 3.55 7.65 4.67 4.14 4.84 4.74 4.90 4.58 4.62 5.54 5.65 7.73 6.85 8.33 8.75 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.73 60.09 64.29 58.51 19.41 72.32 75.20 74.34 77.34 77.78 62.64 77.00 73.87 66.57 61.06 63.41 61.46 78.55 62.62 64.38 62.55 73.45 74.79 77.10 74.79 64.91 65.34 76.42 56.53 60.52 60.27 64. 19 60.20 61.88 63.24 61.91 72.46 71.78 61.51 59.86 74.57 59.04 74.71 61.69 58.38 75.21 73.77 71.68 61.61 59.39 68.60 3899.75 3190.04 2621.10 999.52 2777.19 2887.63 2854.65 2938.77 2955.67 2944.01 4388.93 3034.81 3661.39 2869.64 2980.28 2888.73 4084.81 3068.42 3347.96 3064.81 2897.18 3290.94 2960.33 2 871.95 1947.40 1960.16 2903.77 2515.45 2977.43 2682.05 4312.54 3906.90 3044.45 3478.44 3046.21 2782.39 2756.51 3014.19 2813.44 2833.85 1712.13 3735.32 3516.24 1167.68 3497.31 2803.14 2721.91 3511.94 2791.35 4047.40 WESTLAKE PSYU - STAND APPRAISAL FOR STATE VARIABLE SUBSYSTEM PAGE 40 STAND ECONOMICS REPORT STAND NO. AGE SITE G-TYPE VOLUME STOCK. CCF/AC PCT. SELL PR. t/CCF F-B COST SKIO COST AREA COST */CCF $/CCF l/CCF HARV.COST */CCF HAUL COST S/CCF RO DEV. S/CCF NET VALUE t/CCF t/ACRE 21011155 16 21053155 16 21024155 16 21037160 16 21023155 16 21054155 16 21028155 16 21006160 16 21011160 16 21001160 16 21048160 16 21023160 16 22052160 16 22003155 16 22023160 16 22001155 16 22002159 16 22029155 16 22030159 16 22031159 16 22118160 16 22004160 16 22062199 16 22006199 16 29139160 16 23 029199 16 23019199 16 29069160 16 23167160 16 23094160 16 23001199 16 23092160 16 23046199 16 23002199 16 23069155 16 23003155 16 23044155 23066155 23043155 16 23038155 16 23016160 16 23002160 16 23018155 16 23148160 16 23001160 16 23011160 16 24001155 24025160 24005155 16 24014155 16 16 16 16 16 COTD SP 46.20 46.20 70.30 42.00 46.20 70.30 46.20 47.00 49.00 47.00 42.00 49.00 49.00. 58.00 47.00 48.00 66.20 66.20 49.00 53.00 47.00 64.00 7.70 7.70 38.00 58.00 64.90 47.00 38.00 66.00 49.00 60.00 68.70 38.40 68.70 64.90 49.00 44.80 66.20 38.40 4 7.00 23.00 38.40 38.00 32.00 23.00 46.50 49.00 54.60 39. 70 0.89 0.89 1.2t 0.74 0.89 1.24 0.89 1.07 1.12 1.07 0.96 1.12 0.95 0.89 0.89 0.85 1.28 1.28 0.87 0.82 1.19 1.33 0.28 0.23 0. 73 3.89 1.00 0.83 0.73 1.28 0.87 1.16 2.83 0.74 1.33 1.00 0.87 0.79 1.28 0.93 1.07 0.55 0.93 0. 96 1. 15 0.73 0.93 0.85 I.OS 0. 77 87.17 87.17 75.52 75.52 87.17 75.52 87. 17 75.52 75.52 75.52 75. 52 75.52 87.17 70.79 75.33 75. 52 87.17 87.17 75.52 70.79 77.61 82.41 75.52 75. 52 87.17 70. 79 70.79 75. 52 87.17 87. 17 75.52 87. 17 32.36 87.17 87.17 70.79 75.52 75.52 87. 17 85.37 75.52 85.37 85.37 77.61 75.52 87.45 87. 17 76. 18 87. 17 87. 17 2.86 2.86 3.78 4.32 3.91 4.13 3.91 3.75 2.96 3.75 4.32 2.96 2. 86 2.73 4.31 3.35 3.27 3.27 3.35 2.74 2.91 3.31 5.05 4.00 2.86 2.91 2.72 4.27 2.86 2.86 3.35 2.86 2.65 3.33 2.86 2.72 2.96 2.96 2.86 3.81 3.35 4.79 3.33 2.91 4.47 4.79 4.35 3.26 4.29 2.86 3.20 4.09 4.25 5.34 2.90 6.64 2.90 9.03 4.55 9.03 5.34 4.55 4.09 4.43 5.42 5.51 3.80 3.80 5.51 4.43 3.54 3.69 9.03 5.51 3.20 7.64 4.43 6.16 3.20 4.09 5.51 4.09 4.65 3.80 3.20 4.43 4.55 4.55 3.20 5.87 5.51 3.66 3.80 3.54 5.34 3.66 4.39 4.84 4.39 3.20 0.43 0.62 0.34 0.97 0.52 0.43 0.49 0.36 0.28 0.36 0.97 0.28 0.62 0.41 0.61 0.49 0.36 0.36 0.48 0.45 0.29 0.37 2.IS 3.07 0.52 0.29 0.36 0.64 0.36 0.46 0.48 0.50 0.29 0.62 0.29 0.36 0.40 0.44 0.30 0.44 0.50 1.77 0.62 0.36 1.27 1.77 0.58 0.48 0.50 0.50 6.49 7.57 8.37 10.63 7.33 11.20 7.30 13.13 7.79 13.13 10.63 7.79 7.57 7.57 10.04 9.35 7.42 7.42 9.34 7.62 6.74 7.38 16.26 12.58 6.58 10.84 7.52 11.08 6.42 7.41 9.34 7.46 7.59 7.75 6.35 7.52 7.91 7.95 6.36 10.12 9.36 10.22 7.75 6.81 11.08 10.22 9.32 8.59 9. 17 6.56 7.09 6.99 6.98 8.14 7.15 6.98 6.99 7.95 7.99 7.75 8.31 8.41 6.03 4.59 3.63 4.63 5.23 5.39 5.19 5.28 4.92 4.49 5.03 4.72 3.83 4.49 4.42 6.91 3.76 6.18 5.44 5.76 5.77 5.18 5.75 4.47 5.55 5.78 5.68 5.58 3.01 4.53 5.53 5.37 3.57 3.33 6. 81 5.30 6.81 6.73 0.0 0.0 0. 0 1. 73 0.0 0.0 0.0 0.0 1.73 0.0 1.73 1.73 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 73.59 72.61 60.16 55.02 72.68 57.34 72.88 54.44 58.00 56.64 54.85 57.59 73.57 58.64 61.65 61.54 74.52 74.36 60.99 57.90 65.95 70.55 54.23 58.22 76.76 55.47 58.86 57.53 76.99 73.58 60.74 73.95 19.00 74.25 75.07 58.80 62.05 61.79 T5.13 69.67 63.15 70.62 72.10 65.44 60.87 73.90 71.04 62.29 71.18 73.88 3399.79 3354.65 4229.55 2310.80 3358.04 4030.88 3367.19 2558.69 2842.08 2568.11 2303.70 2821.86 3604.92 3400.97 2897.62 2953.79 4933.33 4922.36 2988.74 3068.66 3099.50 4514.97 417.58 448.31 2916.97 3217.07 3819.71 2704.03 2925.44 4856.35 2976.33 4437.17 1305.55 2851.04 5157.47 3816.42 3040.53 2 768.09 4973.61 2675.24 2968.06 1624.21 2768.69 2486.58 1947.89 1699.73 3303.30 3052.18 3886.63 2932.97 WESTLAKE PSYU - STAND APPRAISAL FOR STATE VARIABLE SUBSYSTEM p»Gc *. *••»••**»•*••••••*•»••*»******•****»*»*»»*»*»***»»»***••*»*******«*»•**«****** STAND ECONOMICS REPORT STAND NO. AGE SITE G-TYPE VOLUME STOCK. SELL PR. F-B COST SKIO COST AREA COST HARV.COST HAUL COST RD DEV. CCF/AC PIT. t/CCF */:CF t/CCF t/CCF t/CCF t/CCF t/CCF 2*931160 16 1 FS 62.00 0. 99 88.03 3.32 3.69 0.38 7.39 5.41 0.0 2*081155 16 1 F 46.50 0.90 87.17 2.86 3.20 0.36 6.42 6. 52 0. 0 24012160 16 1 SF 49.00 0.92 75.33 3.24 4.41 0.49 8.13 5.46 0.0 2*136160 16 1 PLF 46.50 0.74 74.55 2.64 3.21 0.29 6. 14 0.91 0.0 2*089155 16 1 F 43.60 0.84 87.17 2.86 4.09 0.66 7.61 6.97 0.0 2*023155 16 1 F 46.50 0.9) 87.17 2.86 3.20 0.42 6.49 7.07 0.0 2*087155 16 1 F 52.00 1.09 87. 17 2.86 3.20 0.32 6.38 6.72 0.0 2*098155 16 1 F 38.00 0.73 87.17 2.86 4.09 0.75 7.71 7.13 0.0 2*036155 16 1 F 52.00 1.09 87.17 2.86 3.20 0.32 6.38 6.40 0.0 2*057155 16 1 F 49.00 0.95 87. 17 2.86 3.20 0.34 6.40 7.04 0.0 2*062 1 60 16 2 FS 46.50 0.97 82.41 4.45 4.49 0.58 9.52 0. 84 0.0 2*026155 16 2 F 59.00 1.42 85.37 2.86 3.20 0.23 6.29 8.78 1.73 2*068155 16 2 F 52.00 1.25 85.37 2.86 3.20 0.32 6.38 7.70 0.0 2*105160 16 2 FS 46.50 0. 97 82.41 2.88 3.45 0.23 6.55 0. 91 0.0 2*15*160 16 2 FS 46.50 0.97 82.41 2.88 2.97 0.29 6.14 0.85 0.0 2*071160 16 2 SPL 54.00 1.21 73.50 2.91 3.92 9.37 7.20 3.95 0.0 2*111160 16 2 FS 46.50 0.97 B2.41 2.88 3.45 0.23 6.55 0.87 0.0 2*09*160 16 2 SPL 47.00 1.05 73.50 2.91 3.92 0.42 7.26 3.85 0.0 2*001160 16 9 FS 25.00 0.75 84.60 4.82 3.73 1.53 10.18 4.46 0.0 25039160 16 1 FS 49.00 0.78 88.03 2.88 2.97 0.28 6. 12 6.32 0.0 25052160 16 1 PLF 57.00 0.91 74.55 2.64 3.21 0.24 6.09 6.78 0.0 25003160 16 2 FPL 52.00 1.07 83.29 2.85 4.11 0.58 7.55 6.65 0.0 25016160 16 2 FS 38.00 0.79 82.41 2.88 4.06 0.80 7.73 6.90 0.0 39001160 16 1 F 57.00 1.10 87.17 3.28 3.80 0.41 7.50 2.78 0.0 46012160 16 1 SPL 49.00 0.85 76.18 3.26 4.84 0.48 8.59 5.61 0.0 53010155 16 2 S 42.50 0.97 75.52 4.32 5.34 0.96 10.61 8.33 1.73 76005155 16 1 F 38.00 0.73 87.17 2.86 3.20 0.36 6.42 10. 10 0.0 77001155 16 1 PLS 61.00 0. 98 72.82 2.58 3.67 0.22 6.48 8.99 0.0 770*7155 16 2 F 46.50 1.12 85.37 2.86 3.20 0.36 6.42 8.43 0.0 77033155 16 2 SF 49.00 1.24 77.61 4.00 4.41 0.33 9.23 9.32 0.0 77055155 16 2 F 46.50 1.12 85. 37 2.86 3.20 0. 36 6. 42 8.63 0.0 77066155 16 2 F 46.50 1.12 85.37 2.86 3.20 0.36 6.42 8.41 0.0 10005160 18 1 SPL 10.00 0.17 73.76 3.72 4.64 2.36 10.72 1.91 0.0 10072160 18 2 PL 10.00 0.2) 70.28 2.55 4.66 3.02 10.24 1.47 0.0 10032160 18 2 SPL 10.00 0.22 73.76 3.72 4. 64 2.36 10. 72 1.61 0. 0 EXECUTION TERMINATED 12:04:01 T •23.706 RC«0 $13.47 NET VALUE t/CCF t/ACRE 75.23 74.23 61.74 67.50 72.59 73.61 74.07 72.34 74.39 73.73 72.06 68.57 71.30 74.95 75.43 62.34 74.99 62.39 69.96 75.59 61.69 69.10 67.78 75.89 61.98 54.85 70.65 57.35 70.52 59.05 70.32 70.55 61.13 58.57 61.43 4664.48 3451.68 3025.28 3138.65 3164.87 3423.03 3851.41 2748.79 3868.07 3612.90 3350.65 4045.65 3707.43 3485.36 3507.56 3366.49 3487.03 2932.21 174B.96 3704.04 3516.27 3593.07 2575.78 4382.73 3036.87 2331.09 2684.64 3498.49 3279.38 2893.60 3270.06 3280.45 611.30 585.73 614.34 tSIG APPENDIX VI Factor Analysis Results '— - FACTOR ANALYSIS - REVISED JAN. 8, 1975 _JtME. PROGRAM WILL ATTEMPT TO ACQUIRE 2 PAGE(S 1 OF MEMORY TO PUN THIS PROBLEM . .... - - -•••••FACTOR ANALYSIS ON 20 TYPE ISLAND STATE VARIABLES OF THE WESTLAKE psru *•••* INPUT FORMAT - -IA4,10X,F5.2,10X,F8.2,18X,9( IX, F5.2lt7X.9llX .F6.2I1 < OUTPUT FORMAT IA4.10X.8F8.3) NUMBER OF VARIABLES .. 20 MAX. ITERATIONS FOR COPMUNALITIFS 1 MAX. ITERATIONS FOR ROTATION 50 MAXIMUM NUMBER OF FACTORS TO BE EXTRACTED 8 LOWER LIMIT ON EIGENVALUES 0.10000 UPPER LIMIT ON REFERENCE AXIS CCRRELATIONS 0.95000 THE CORRELATION MATRIX IS FORMEC .- - - — •— DIAGONAL ELEMENTS ARE UNALTERED VARIHAX ROTATION IS PERFORMED VARIABLE NAMES ARE REAO IN CASE IDENTIFICATION IS REAO WITH EACH CASE NUMBER OF CASES 1985 ST.DEV. OF SUM VARIABLE MEAN ST.DEV. "VARTANCE THE MEAN MINIMUM MAXIMUM T OBSEWATTONS " 1 VOL NOW 23.637 18.129 328.670 0.40691 0.0 70.303 1985.0 46919. 2 VAL NOW 1347.7 1184.5 0.140299E*07 26.586 -51.850 5330.7 1985.0 o.26752E*o7 3 VOL340 7.4104 4.7313 22.3854 U.13619 0.33030 30.15J 1985.0 14710. 4 VOLS60 16.041 7.6736 58.8847 0. 17223 1.4000 46. 730 1985.0 31841. 5 V0L380 24.C55 9.6 387 92.9038 6.21634 2.8003 64.000 1985.3" 47749. 6 V0LB100 30.481 11.011 121.241 3.24714 4.2003 71.2 20 1985.0 60504. 7 V0LS120 34.584 11.915 141.966 0. 26743 5.1700 72.50J 1905.0 68648. 8 V0L»140 37.194 12.535 157.135 0.28136 5.713J 71.793 19P5. 0 7J830. 9 V0L3163 36.591 12.915 166.ROb 0.28988 5 .9800 75.26J 1985.0 76604. 10 VOLaiSO 35.341 13.138 172.602 0.29488 6.0800 77. OJJ 1985.0 78091. 11 VOL3200 39.476 13.211 174.520 0.29651 6.3833 7 7. 5 83 1985.0 78 363. 12 VAL340 46.340 17.453 304.594 0.39172 0.0 76.95J 1985.0 91985. 13 VAL360 48.406 15.428 238.038 0.34629 2.4703 78.37J 1985.0 96085. 14 VAL380 51.737 14.658 21<..853 0.32903 8.3933 79.170 1985.0 0.10270F*06 15 VALSIOO 52.611 14.102 216.151 0.32999 9.8100 79.850 1985.0 0.10443E»06 16 VALS120 53.756 14.627 213.940 0.32830 9.9403 8 0. 2 70 1985.0 0.10670E»06 17 VAL3140 53. 582 14.501 210.286 0.3i548 10.033 80.5t>J 1985.0 0. 10636F»06 18 VAL3160 55.575 14.372 206.557 0. 3*258 10.060 80.65J 1985.0 O.U032F»06 19 VALS180 55.6e6 14.417 207.850 0.32359 10.393 80. 5 10 1985.0 0.11354E+36 CORRELATION MATRIX HITH INITIAL COMMUNAL ITY ESTIMATES ON THE DIAGONAL 1 VOL NOW 2 VAL NOW 3 V0L340 4 V0L360 5 VOL380 6 VOL 310 0 7 VOLS 120 8 VOL«140 1 VOL NOW 1.0000 > 2 VAL NON 0.56134 1.0300 ' 3 VOL340 0.70019 0.74511 1.0000 4 V0L860 0.73942 0.15220 0.95301 1.0000 5 VOL380 0.72223 0.72201 0.88170 0.97927 1.0000 6 VOL3100 0.68139 0.67404 0.80335 0.93124 0.984 76 1.0000 7 VOL3120 0.66034 0.64806 0.75660 0.89694 3.96556 0.99552 1.0000 8 V0L3140 0.64777 0.63122 0. 72504 0.87242 0.94923 0.98732 0.99770 1.0000 9 V0L3160 0.64366 0.62845 0.70922 0.85 792 0.93843 0.98041 0.99405 0.99896 10 V0L3180 0.63948 0.62789 0.70005 0.84641 3.92860 0.97332 0.98935 0.99623 11 VOL3200 0.63734 0.62760 0.69240 0.8361 7 0.91853 0.96451 0.98257 0.99155 12 VAL340 0.44926 0.58170 0.56655 0.58483 0.59311 0.58782 0.56947 0.55045 13 VAL360 0.42065 0.57957 0.52952 0.53121 0.52012 0.49671 0.46932 0.44514 14 VAL380 0.37204 0.53754 0.45870 0.44026 0.43428 0.41928 0.39801 0.37787 15 VAL3100 0.37204 0.53580 0.45436 0.44593 0.45192 0.45160 0.43458 0.41536 16 VAL3120 0.34585 0.51455 0.42496 0.40240 0.40320 0.39913 0.38181 0.36248 17 VAL3140 0.33935 0.51140 0.43832 0.42710 0.42050 0.40390 0.37908 0.35592 18 VAL3160 0.33768 0.50107 6.41503 6.41352 0.42176 0.41996 0.4024 7 0.38313 19 VAL3160 0.32992 0.49630 0.41145 0.40489 0.41115 0.40830 0.39058 0.37123 20 VAL3200 0.33015 0.49683 0.41095 0.404 24 0.41U46 0.40769 0.39011 0.37091 9 VOL3160 10 VOL3180 11 V0L3233 12 VAL340 13 VAL360 14 VAL380 15 VAL3100 16 VAL3120 9 V0L8160 1.0000 10 VOL3180 0.99905 1.0000 11 VOL8200 0.9S616 0.59894 1.0000 12 VAL340 0.54723 0.54775 0.54247 1.0000 13 VAL360 0.44028 0.44066 0.43595 0.93441 1.0000 16 VAL380 0.37755 0.38143 0.37925 0.91010 0.96855 1.0000 15 VAL3100 0.41521 0.41963 6.41687 0.90450 0.94086 6.96298 1.0000 16 VAL3120 0.36243 0.36743 0.36486 0.88951 0.94109 0.98298 0.97629 1.0000 17 VAL3140 0.35297 0.35531 0.35147 0.88700 0.97792 0.98599 0.94903 0.96819 18 VAL3160 0.38243 0.38546 0.38155 0.90604 0.96193 0.98982 C.96850 0.98087 19 VAL3180 0.37384 0.3 7464 0.37137 0.90436 0.96358 0.99219 0.96874 0.98252 20 VAL3200 0.37063 0.3 7456 0.37143 0.90405 0.96361 0.99221 0.96855 0.98260 17 VAL3140 18 VAL3160 19 VAL3180 20 VAL3200 17 VAL3140 1.0000 18 VAL3160 0.58591 1.0000 19 VAL3180 0.58859 0.59871 1 .0000 20 VAL3200 0.98857 C.59860 0.99992 1.0000 sun OF SQUARES OF OFF DIAGONAL ELEMENTS' 91.689 MEAN OF SQUAPFS OF OFF DIAGONAL ELF ME M S = 0.24129 SQUARE ROOT OF MEAN OF SQUARES OF OFF CI AGONAL ELEMENTS' 0.49121 EIGENVALUES 13.353 4.8831 0.99859 0.44807 0.11590 0.81387E-01 0.42399E-01 0. 32316E-31 0.15498E-01 0.13468E-01 0.76567E-02 0.36242E-02 0.29308E-02 0. 11B56E-02 0.36499E-03 0.13261E-33 3.61443E-04 3.23237F-J4 0.11182F-04 3.32 787P-35 CUMULATIVE PROPORTION OF TOTAL VARIANCE 0.66767 0.91182 0.S6175 0.98415 0.98995 0.99402 0.99614 3.99775 0.99853 0.99920 0.99959 0.99977 0.99991 0.99997 0.99999 1.0000 1.0000 1.0000 I.OOOO 1 .0000 PER CENT OF TOTAL VARIANCE ACCOUNTED FOR BY EACH FACTOR 66.76684 24.41530 4.99293 2.24034 0.57949 0.40693 0.21200 0.161S8 0.07749 0.06734 6.03828 0.01812 6.01465 0.00593 0.00182 0.OJU51 0.00031 0.03312 0.00006 3.00332 TINE FOR INITIAL FACTOR-LOAD 1NGS-MATR I » IS 0.9036E-01 SECONDS '  TIME FOR ACCURACY CHECK IS 0.14E-01 SECONDS. Faann BOUNDS FHR FICFNVA. UFS  0.37567E-04 0.18819E-04 0.14133E-05 0.65505E-06 0.4U68E-06 ERROR BOUNDS FOR EIGENVECTORS 0.88703E-05 0.96892E-05 0.51346E-05 0.39441E-C5 0.24787E-05 VARIABLE OP IG I r. At COMMLNALITV EST IMATEO CCHMUNAL1TY FINAL COMMUNAL 1TY 1 VOL NOW 2 VAL NOW 1.0000 l.COOO 1.0000 1.0000 u.99126 0.99027 3 V0L340 4 VOL960 5 VOL380 1.0000 l.OCOO l.CCOO 1.0000 1.0000 1.0000 0.98504 0.99583 0.99173 6 VOLB100 7 VOL a120 8 VOL8140 1.0000 l.CCOO 1.0000 1.0000 1.0000 1.0000 0.99373 0.99746 0.99950 9 VOLS 160 10 voLaieo 11 VOLa200 l.COOO l.COOO 1.0000 1.0000 1.0000 1.0000 0.99938 0.99665 0.99032 12 VAL840 13 VALa60 14 VALaeo l.CCOO 1.0000 1.0000 1.0000 1.0000 1.0000 0.99574 0.97270 0.99097 15 VALalOO 16 VALai20 17 VAL3140 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.95786 0.97938 0.98372 18 VAL3160 19 VALaiao 20 VALa200 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.99409 0.99666 0.99668 SUM OF COMMUNAL ITIES 20.000 20.000 19.799 MEAN COMMUNAL I TV 1.0000 1.0000 0.98995 p—• J MATRIX OF RESIDUALS WITH UNIQUENESSES ON THE DIAGONAL 1 VOL NOW 2 VAL NOW 3 V0L340 4 VOL360 5 VOLiB 0 6 vOLaioo 7 V0Lai20 8 V0L3140 1 VOL NOH 0.E7174E-02 2 VAL NCW -0.91295E-02 0.97333E -02 3 VOL340 -0.35534E-02 0.27814E -02 0.14964E- 01 • V0L360 O.21908E-O2 -0.20159E -02 -0.70986E- 02 0.41677E- 02 5 V0L380 0.33746E-02 -6.2B839E -02 -0.10628E- 01 0.51995E- 02 0.82735E -02 6 V0L3100 0.28821E-02 -0.237C4E -02 -0.73530E- 02 0.27575E- 02 0.59740E -02 0.62680E- 02 ' V0L3120 0.160B6E-02 -0.12977E -02 -0.34454E- 02 0.89682E- 03 0.28231E -02 0.37083P- 02 0.25378E- 02 S V0L3140 0.57583E-03 -0.49829E -03 -0.44 167E- 03 -0.96217E- 04 0.23999E -03 0.76830E- 03 0.75542E- 63 3.4950TE- 03 9 VOL3160 -0.86503E-03 0.76016E -03 0.22815E- 02 -0.10083E- 02 -0.18933E -02 -0.17807E- 02 -0.90764E- 03 -0.31695E- 04 10 VOLS180 -0.266C6E-02 0.23713E -02 0.56B18E- 02 -0.24798E- 02 -0.46165E -02 -0. 44308 E- 02 -0.25737E- 02 -0.59011E- 03 11 VOL3200 -0.62044E-02 0.36B06E -02 0.88062E- 02 -0.35335E- 02 -0.72878E -02 -0.74902E- 02 -0.46286E- 02 -0.12340E- 02 12 VAL340 -0.1171BE-02 0.84226E -03 0.58352E- 02 -0.2506 OE- 02 -0.31741E -02 -0.17698E- 02 -0.75978E- 03 -0.21454E- 03 13 VAL360 -0.32913E-03 0.10206E -02 -0.10528E- 01 0.39277E- 02 0.40560E -02 0.12831E- 02 0.33243E- 03 0.36636E- 03 14 VAL380 0.S8578E-O3 -0.13457E -02 0.16386E- 02 -0.18351E- 03 -0.95029E -03 -0.29491E-02 -0.l9S64f!- ti -6. 30764E- 03 15 VAL3100 -0.26389E-02 0.13569E -02 0.64685E- 02 -0.18035E- 02 -0.30800E -02 -0.23340E- 03 -0.13928E- 03 -0.79373E- 03 16 VAL3120 -0.59319E-03 -0.66988E -04 0.71187E- 02 -0.30779E- 02 -0.38106E -02 -0. 14455E- 02 -0.92232E- 04 0.13000E- 03 17 VAL3140 0.27643F-03 0.79160E -03 -6. e6748E- 02" 6.2763 7E-02 0.43482E -02 0.24230E- 02 0.91475E- 03 0.28867E- 03 ' 18 VAL3160 0.23719E-02 -0.19416E -02 -0.16765E- 02 0.10502E- 02 0. 18554E -02 0.17578E- 02 0.10693E- 02 0.36952E- 03 19 VAL3160 0.16393E-02 -0.12883E -02 -0.44208E- 03 0.14951E- 03 0.73969E -03 0.77025E- 03 0.46679E- 03 0.13560E- 03 20 VAL3200 0.14593E-02 -0.11126E -02 -0.27316E- 03 0.96285E- 04 0.54278E -03 0.56578E- 03 0.J3557F- 63 0. 11035E- 03 9 V0L3160 10 V0L31 80 11 V0L3200 12 VAL340 13 VAL360 14 VAL 3 80 15 VAL8100 16 VAL3120 9 VOL3160 0.6201SE-03 10 V0L3180 0.125UE-02 0.33483E -02 11 V0L3200 0.19608E-02 0.55886E -02 0.96840E-02 12 VAL340 0.50547E-03 0.13548F-02 0.18987E-02 0.42621E-02 13 VAL360 -0.31036E-03 -0.78137E -03 -0.54873E-03 -0.10082E-01 0.27299E-01 14 VAL380 0.10221E-02 0.17191E -02 0.28835E-02 0.79175E-03 -0.19754E-02 0.90317E- 02 19 VAL3100 -0.59869E-03 0.31433E -04 0.16374E-03 0.4249 3E-02 -0.11358E-01 -0.90035E- 02 0.42141E-01 16 VAL3120 0.31492E-03 0.10607E -02 0. 10644E-02 0.63927E-02 -0.12980E-01 -0.12903E- 03 0.105 35E-01 0.20625E-01 17 VAL3140 -0.60348E-03 -0.13096E -02 -0.16332E-02 -0.59421E-02 0.1530OE-01 -0. 29686 E-03 -6.17065f-61 -6. U327E-01 18 VAL3160 -0.31338E-03 -0.15534E -02 -0.26963E-02 0.2715 7E-04 -0.280 75E-02 -0.12059E- 03 -0.58688E-02 -0.43844E-02 19 VAL3180 -0.10749E-03 -0.5592 IE -03 -0.10381E-02 0.18968E-04 -0.17267E-02 0.80526E- 03 -0.62411E-02 -0.42151E-02 20 VAL3200 -0.56243E-04 -C.40027E -0 3 -0. 76 08 7E-03 0.11253E-04 -0.15377E-02 0.84897E- 03 -0.643336-02 -0.4i816E-02 17 VAL3140 18 VAL3160 19 VAL3180 20 VAL3200 IT VAL3140 0.16284E-01 0.33255E-03 0.U655E-O2 0.U843E-02 18 VAL3160 JI.59091E-02 0.34282E-02 0.33186E-02 19 VAL3180 0.33350E-02 0.32544E-02 20 VAL3200 0.33249E-02 FACTOR-LOAOINGS MATRIX BEFORE ROTATION VARIABLE FACTOR 1 1 VOL NOW 2 VAL NOW 3 V0L34Q 4 V0L360 5 VOL380 6 V0L3100 0.68814 0.77691 C.78140 0.e4C09 0.86155 0.85693 -0.34108 -0.17475 -0.32948 -0.43995 -0.47837 -0.49319 -0.57664 -0.56269 -0.28778 -0.14138 0.26138 0.19868 -0.42557 -0.2 7b54 -0.76976E-03 -0.14351 0.12286 -0.32329E-01 -0.2378OE-01 0.67334E-02 0.44416F-01 0. 74704E -02 D.46521E-02 0.45682E-02 7 VOL.1120 0.E4072 -0.5L841 0.17626 0.32675E-01 0.50682E-02 8 VOLJ140 0.62412 -0. 52245 0.20413 -0.753B4E-01 0.46892E-02 9 V0L3160 C.62C18 -0.51961 0.21464 0.10273 0.77856E-J2 10 V0Lai80 C.61895 -0.51231 0.22091 0.12063 0.12450E-01 11 VOLa200 0.81366 -0.5107C 0.22115 0. 13521 0.16<!19E-01 , 12 VALa40 C.88098 0.34944 0.574156-01 -0.41089E-01 -0.30417 13 VAL360 0.E5565 0.46086 -0.19613E-01 -0.64189E-01 -0.69493E-01 < 14 VALiSO 0.61855 0.56565 -0.39091E-02 -0.3098 7E-0i! 0.30B75E-01 15 VALaiOO 0.82445 0.J2393 0.36006E-J1 0.33432E-01 0.32755E-01 16 VAL8120 0.7S645 0.57860 0.19805E-01 0.30497E-01 0.73902E-01 17 VALS140 0.80074 0.5ei65 0.77114E-0i -0.2964 7E-01 0.57235E-01 19 V*Lai60 0.81124 0.57462 0.5e669t-01 0.29bl6E-01 0.38203E-01 19 VAL3180 0.80539 0.58553 0.54986fc-01 0.24539E-01 0.39298E-01 20 VALa200 0.60519 0.58576 0.544B7E-01 0.2590BE-01 0.397O8E-01 SUM OF SQUARED FACTOR-LOADINGS DIVIDED BY SUM OF COMMUNAL I TIES • -- — ... 0.67445 0.24663 0.50436E-01 0.22631E-01 0.5853 7E -02 ORTHOGONAL ROTATION ITERATION SIMPLICITY CRITERION 0 -0.45S74 1 -7.7960  2 -7.8022 3 -7.8022 TINE FOR ROTATION IS 0.2832E-01 SECONDS ROTATED FACTOR-LOADINGS MATRIX FACTOR 1 2 3 4 5 VARIABLE 1 VOL NOW -0.16961 0.5C079 -0.83842 0.91678E-01 -0.19123E-01 2 VAL NOW -0.35260 0.45265 -0.80010 0.14449 0.40995E-02 3 V0La40 -0.26161 0.62007 -0.34573 0.64230 -0.49246E-02 4 VOL360 -0.23314 0. 76903 -0.30849 0.4 7179 -0.33852E-01 5 VOLaSO -0.23223 0.68344 -0.24841 0.30768 -0.28889F-01 6 VOLS 100 -0.22939 0.53800 -0.18417 0.16391 -0.21920E-01 T V0Lai20 -0.21224 C.95875 -0.16042 0.84817E-01 -0.16746E-01 8 V0L3140 -0.19338 0.56859 -0.15015 0.34750E-01 -0.13857E-01 9 VOLS 160 -0.19392 0.96913 -0.14987 0.53295E-02 -0.88033E-02 10 VOLaiBO -0.19917 0.96663 -0.14968 -O.13B34E-01 -0.29037E-02 11 VOLa200 -0.l56?e 0.96293 -0.15328 -U.28031E-01 0.20333E-02 12 VAL«40 -0.65019 0.37109 -0.12528 0.98471E-01 -0.33138 13 VALS60 14 VALaSO J5_VALalOO 16 VALai20 17 VALai40 18 VALai60 -0.92716 -0.56779 -0.945C8 -C.56690 -C.56873 -0.57501 0.24412 0.17367 0.22129 6. 16214 0.15427 0. 16604 -0.15058 -0.13478 -0. 12004 -0.11843 -0.10662 -0.69319E-01 0.14569 0.77270E-01 0.34225E-01 0.36848E-01 0.95281E-01 0.24090E-01 19 VAiaieo 20 VALa200 -0.57870 -C.57672 -0.9785 7E-01 0.73705E-02 0.11385E-01 0.53010E-01 0.32146E-01 0.16705E-01 17344 17304 -0.87268E-01 -0.88123E-01 0.28373E-01 0.27257E-01 0.17601E-0.18117E-01 01 SUM OF SQUARED FACTOR-LOADINGS OIVIDEO BY SUM OF COMMUNAL ITIES 0.44108 0.41428 0.9544bE-01 0.42735E-01 0.64530E-02 ho MATRIX OF CORRELATIONS OF FACTORS WITH VARIABLES. VARIABLES ARE REORDERED ACCORDING TO HIGHEST CORRELATION WITH A FACTOR. FACTOR 1 VARIABLE 12 VAL340 13_VAL360 15 VAL3100 16 VAL3120 16 VALaSO -0.85019 ^0.92716 -6.94508 -C.S6690 -0.56779 0.37109 0.24412 0.22129 0.16214 0.17367 -0.12528 -0.15058 -0.12004 -0.11843 -0.134/8 0.98471E-01 0.14569 0.34225E-01 0.36848E-01 0.7727OE-O1 -0.33138 -0.97857E-01 0. H385F-01 0.53010F-01 0.73705E-02 17 VAL3140 18 VAL3160 19 VAL8180 20 VAL3200 9 VOL3160 -C.96873 -0.57501 jHO.S7870_ -0.5 78 72 ******** -0.19 392 0.15427 0.18604 _0.17344 6.17304 •*••*•** 0.96913 -0. 10662 -0.B9319E-01 _-0. 87268E-01 -6.88123E-01 0.95281E-01 0.24090E-01 0.283 73E-01 0.2 7257E-01 0.32146E-01 0.16705E-01 0.17601E-01 0.18117E-01 -0.14987 0.53295E-02 -0.88033E-02 8 VOL3140 10 VOL3180 11 V0La200 T VOL3120 6 VOL8100 5 V0L380 -0.19338 -C.15517 -0.19698 -0.21224 -0.22939 -0.23223 0.56859 0.56663 0.96293 0.55875 0.53800 0.B8344 -0.15015 -0.14968 -0.15328 -0.16042 -0.18417 -0.24841 0.34750E-01 -0.13834E-01 -0.28031E-01 6.84817E-01 0.16391 0.30788 -0. 13857E-01 -0.29O37F-02 0.20333E-02 -0.16746E-01 -0.21920E-01 -0.28889E-01 4 V0L360 -0.23314 2 VAL MOM -0.35260_ 1 VOL NOW -0.16961 3 V0L340 -0.26161 0.78903 **•*•*•• 0.45265 0.50079 0.62007 -0.30849 •**•••** ^0.80010 -0.83842 ******** -0.34573 0.47179 0.14449 0.91678E-01 ******** 0.64230 -0.33B52E-01 0.40995E-02 -0.19123E-01 -0.49246E-02 *»»»»*** ******** ******** SUM OF SQUARED F ACTOR-LCADIMGS DIVIDED BY SUM OF COMMUNAL IT IES 0.44108 0.41428 0.95448E-01 0.42735E-01 0.64530E-02 REGRESSION COEFFICIENTS FOR FACTOR SCORES FACTOR 1 VARIABLE 1 VOL NON 0.65893E-01 -0.83960E-01 -0.75377 -0.34574 -0.15961 2 VAL NOW 0.27321E-01 -0.11401 -0.68219 -0.18956 0.92376E-01 3 V0La40 0.36172E-01 -0.57135E-01 0.80654E-01 1.065 7 0.2BB71 4 V0L360 0.22988F-01 0.17539 0.14050 0.43151 0.13599 5 voLaso 0.58785E-01 -0.73450E-CI 0.29777E-01 0.55866 -0.15694 6 VOLS100 -0.13566E-01 0.12190 0.85045E-01 0.59506E-03 0.18325 7 VOLal20 0.52214E-01 0.36319 0.19740 -0.24686 -0.5004 1E-01 8 V0L3140 0.0 0.0 0.0 0.0 0.0 9 VOL «)160 0.0 0.0 0.0 0.0 0.0 10 VOLaiBO C.39076E-01 0.66511 0.19681 -0.98837 0.43771 11 VOL 3200 ""o.o o.c 0.0 0.0 0.0 12 VAL340 -0.48789F-01 -0.3C8P5E-C1 0.77869E-02 -0. 10910 -2.62 73 13 VAL360 -0.10215 -0.60369E-01 0.14283E-01 0.11488 -0.61065 14 VAL380 -0.13259 -0.11205E-01 0.15862E-01 -0.39411E-02 0.27798 15 VAL3100 -0. 12774 -0.56356E-C2 0.12177E-01 -0.64786E-01 0.28056 16 VAL3120 -0.14376 -0.2t654F-01 0.72592E-02 -0.17-.18E-01 0.63 82 9 . 17 VAL3140 -0.13589 -0.1721CF-C1 0.510641-01 0.73321F-O1 0.48543 18 VAL3160 -0.14030 0.56296E-02 0.41283E-01 -0.77385E-01 0.33670 19 VAL3160 -0.2782S -0. 5128 7E-01 0.64936E-01 -0.6334&E-01 0.66941 NO ON 127 APPENDIX VII Volume And Value Yield Classes From Cluster Analysis AGE WEtGHTED VOLUME YTEIO CLASSES IN YRS. IN CCF/ACRE i i- inj* ^ ^ • ' »-»v »- ___——— > 1 1 _____ ___ SI3S3S1 1 - - -- • - 4 5 tssriassssss 6 ssssssssiBtr 7 8 SS StSSS—'SSSSSCSSSSSSSS 9 SSSSSS8SS9 10 sazvsscssi 20 J 0.92 0.00 0.11 0.0 0.18 0.02 2.27 0.36 0.00 0.00 •0! 12.69 6.59 4.34 1.51 2. 10 5. 16 17.78 2.70 8.06 10.88 60! 22.63 15.54 9.98 5.63 5.01 12.05 29.56 6.49 18.47 27.40 80: 29.55 24.57 15.44 tl.*8 7.96 19. 13 37.66 10.55 28.22 41.65 lOOt 34.20 32.24 19.78 16.65 10.37 25.20 42.89 13.97 35.99 52.26 120: 37.02 37.02 22.75 19.74 12.22 29.05 46.12 16.68 41.10 58.62 140! —3-B.T4 40.02 24. T6 21.76 13.43 31. 49 48.07 18.43 44.43 62.59 160: 39.63 41.65 25.79 22.99 14.03 32.82 49.24 19.33 46.35 64.68 180J 40.09 42.52 26.34 23. 73 14.31 33.55 50.01 19.89 47.32 65.45 200! 40.23 42.64 26.53 23.97 14.43 33. 70 50.20 20. 13 47.55 65.19 ho 00 * AGE IN YRS. WEIGHTED IN VOLUME YIELD CLASSES CCF/ACRE > — 11 13 14 15 20: 0.0 0.28 1.10 5. 75 2.43 40: 9.30 21.23 8.63 ?8.74 20.29 60S 23.11 46.70" 16.70 43.75 35.73 80: 35.71 63.96 23.12 53.65 46.43 100: 45.62 71.18 27.73 59.40 53.61 120: 51.60 ~3d.7l 62.91 58.02 1*0: 55.32 71.61 32.71 64.83 60.73 160: 57.28 68.77 34.03 66.11 62.12 180: 58.13 65.09 34.90 " 67. 07 62.87 200: 58.01 60.01 35.33 66.74 63.16 EXECUTION T-11.37 OR TERMINATED »17 $3.36. $3.45T SSTG t AGE IN YRS. WEIGHTED ECONOMIC YIELD IN t/CCF CLASSES 1 2 ttllltltltktimflflslEHBltre 3 4 5 6 7 8 t==««r MS: exeats 9 10 • SltUHUII 20: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 40t 5E.77 •5.07 8.09 37.63 6.65 34.52 53.54 62.06 71.93 56.94 601 59.82 55.51 15.06 46.94 71.80 46.63 53.98 6 3.70 73.79 59.36 BOi 62.08 57.07 18.20 57.28 75.81 14.26 55.87 64. sr- '4.89 59.3V loot 63.23 5S.79 19.79 54.75 77.39 51.88 56.93 6 5.43 75.70 61.43 1201 63.96 59.34 20.50 56.63 78.19 56.28 57.69 6 5.80 76.18 61.68 140i 63.65 59.62 21.15 60.26 78.70 58.05 58.68 66. 54 76. 73 62.59 160t 64.66 62.44 21.57 60.90 78.75 58.88 60.85 66.40 75.81 63.03 leot 65.27 «1.96 21.90 61.19 79.01 59.03 60.97 66.77 76.66 62.73 ZOOt €5.47 ti. 14 22.1 5 61.41 79.21 59.25 61.15 67.00 76.80 63. 10 (jO O AGE IN YRS. WEIGHTED ECONOMIC YIELD IN S/CCF CLASSES f 11 -SCBCSS-CS3CS1 12 I3CSZSS 13 14 15 16 17 18 19 3:s::=z3is 20 20! 0.6 0.(3 0.0 0.0 0.0 0.0 0.0 0.0 U.U U.U AO! 23.85 •2.44 13.78 38.88 6.76 51.77 0.0 50.64 17.82 11. 70 60: 71.99 51.57 38.53 43.31 12.78 50.10 60.78 48.41 18.42 34.82 80: 75.46 55.76 54.53 56.30 15.70 55.18 68.77 53. 52 18.57 51. 53 100! 76.68 «8.28 50.23 50.34 17.19 57.08 71.47 55.18 18.62 47.26 120: 77.64 58.75 57.71 58.11 17.82 57.75 72.74 5 5.90 18.64 54.82 140: 78.15 56.66 58.57 58.45 18.44 55.20 73.53 53.46 18.63 55. BZ 160: 78.31 60.38 58.98 56. 86 18.84 59.42 73.90 57.62 18.65 56.26 ISO: 78.63 60.33 59.30 59.08 19.14 59.26 74. 27 5 7.50 18.64 56.58 200: 78.86 tC. 51 59.53 59.26 19.38 59.44 74.55 57.67 18.62 56. 80 1 AGE WEIGHTED ECONOMIC YIELD CLASSES IN YRS. IN t/CCF > : . 21 •tsttssstsssss 22 23 essss»* = 24 25 26 27 28 29 = == 3 3 = 30 sszassxzaau 20: 0.0 0.0 0.0 0.0 6.0 0.0 0.0 0.0 0.0 0.0 AO: 16.74 50. CI 70.15 25.02 4.10 66.26 6.06 45.91 S.78 52. 35 60: 66.92 47.87 71.40 37.16 10.31 67.71 36.82 44.84 45.14 54.23 SO: 70.54 72.26 49.04 13. IS 68.60 45. T6 49.29 52.00 b4. 84 1001 72.01 •3.14 72.91 45.21 14.65 69.14 47.56 51.27 54.59 54.63 120: 72.52 53.65 73.29 51.38 15.27 69.88 49.82 52.17 55.98 56,35 140: 73. 23 !2.45 73.58 52.11. 15.68 70.31 50.55 50.63 56.72 56.91 160: 73.26 56.06 73.16 52. 50 16.27 70.25 51.00 5 3.62 57.17 57.93 180: 73.50 55.83 73.57 52.79 16.57 70.48 51.25 53. 71 57.45 57.33 200: EXECUTICr. T-11.83 CR 73.66 TERMINATEC -130 $3.80, 55.57 S3.86T 73.67 53.00 16.80 70.64 51.40 53.86 5 7.64 57. 52 *SIG 133 APPENDIX -VIII Transportation Economics By Compartment For The:Westlake PSYO COMPARTMENT REPORT ARTMENT REGION • OF * OF AVERAGE HAUL MIN. HAUL MAX. HAUL AVERAGE STANDS UNLOCATABLE STANDS COST ($/CCF» COST <$/CCFI COST U/CCF) DEV. COST 9 60 38 0 1.63 0.83 2.63 0.0 6 60 17 1 2.03 1.83 2.38 0.0 8 60 65 5 3.04 0.86 5.88 0.0 10 60 133 0 1.69 0.10 4.22 0.0 11 60 64 0 2.09 0.77 2.75 0.0 14 60 100 17 2.86 1.20 4.40 0.0 15 60 126 1 2.80 1.57 5.00 0.0 16 60 62 4 1.06 0.30 3.25 0.0 18 60 92 0 4.82 3.38 7.91 0.0 19 60 59 24 6.85 4.38 8.06 0.0 20 60 113 17 7.67 5.70 9. 10 0.07 21 55 58 28 6.98 6.25 7.69 0.0 22 60 123 1 4.48 2.05 6.71 0.0 23 60 151 8 4.47 2.85 6.93 0.21 23 55 64 5 5.25 4.26 6.42 0.0 24 60 140 1 4.22 0.03 6.45 1.21 24 55 84 2 7.05 6.20 8.90 0.25 12 60 95 0 4.10 2.13 6.73 0.0 17 60 54 3 2.51 1.36 3.62 0.0 22 55 70 15 4.72 2.88 6.84 0.0 25 60 68 3 6.76 6.05 8.41 0.0 39 60 15 0 2.91 2.69 3.06 0.0 44 60 12 0 6.59 6.43 6.77 0.0 76 55 11 0 10.13 9.96 10.34 0.0 77 55 64 0 9.35 8.21 10.31 0.01 21 60 65 0 7.99 6.99 8.97 1.04 40 60 4 0 3.34 3.29 3.35 0.0 43 60 12 0 6.42 3.95 6.89 0. 0 46 60 10 0 5.94 5.61 6.17 0.0 53 55 12 0 8.49 8.32 8.64 1.73 27 55 4 0 10.45 10.31 10.56 0.0 135 APPENDIX IX Transportation Analysis Results For An Isle Pierre Appraisal Point - Stand 057 WESTLAKE PSYU - TRANSPORTATION NETWORK ANALYSIS 'A6E 42 TYPE ISLAND REPORT STAND NO. TYPE AGE SITE SLC USE CENTR0IO LOCATION IN LAT-LONG. OIST. TO NEAREST RD. INILESI RD. NO. OIST. TO NAP INILESI HAUL COST IS/CUNITI ROAD DEVELOP RENT COST It/CUNITI •002160 8003160 •034160 •065160 COTD •033160 •001160 8064160 10001160 10029160 10098160 11034160 11013160 11043160 12001160 12096160 14068160 1403*160 141201*0 14062160 141221*0 19110160 190341*0 1S12T160 16013160 160T3160 16049160 1604*160 17033160 17033160 17020160 17001160 17002160 17040160 17019160 17031160 17032 1 60 18055160 18101160 18001160 18089160 18022160 18073160 19074160 18040160 19033160 19014160 19001160 19045160 I 300*7.60 1 •owiu a TT TT 5331.17 12252.33 0.69 19 si.ia 11.26 0.0 5335.43 12252.20 0.51 IT 45.)0 9.96 0.0 5327.89 12242.27 7.36 19 61.41 13.51 0.0 5331.43 12242.60 7.10 IT 92.40 11.99 0.0 5328.63 12242.80 6.97 19 41.02 13.42 0.0 5332.93 12250.23 2.3) 17 47.71 10.90 0.0 5331.77 12243.33 6.47 IT 51.85 11.41 0.0 5339.98 12301.75 0.32 21 39.85 a. 77 0.0 5339.41 12306.08 0.96 22 7.4) 1.6) 0.0 5341.48 12305.48 2.30 24 6.19 1.3* 0.0 5346.46 12314.71 2.31 2* 14.87 2.60 0.0 5339.77 12313.30 1.32 2T 9.93 1.79 0.0 5340.59 12312.62 0.66 29 6.37 1.19 0.0 5341.33 12325.59 2.34 31 22.16 3.99 0.0 5342.27 12)25.41 3.39 31 23.21 4.18 0.0 5333.75 12313.80 0.69 32 20.69 4.99 0.0 3338.45 12312.73 2.40 27 11.09 2.00 0.0 9336.33 12315.60 1.50 27 13.76 2.48 0.0 5300.00 12300.00 20.77 3 60.41 19.29 0.0 5336.41 12316.70 1.00 27 14.17 2.99 0.0 5336.96 12304.73 1.11 32 28.18 6.20 0.0 5930.80 12)09.36 1.53 13 98.20 12.30 0.0 9336.43 12304.53 1.02 32 28.15 6.19 0.0 9337.38 12304.32 0.40 21 44.11 9.70 0.0 5339.35 12233.9) 1.51 IT 40.T0 8.9* 0.0 53)9.46 12254.77 1.09 18 30.77 a. 93 0.0 9337.41 12304.02 0.32 21 44.03 9.69 0.0 5330.48 12256.46 1.71 19 91.97 11.34 0.0 9329.23 12254.38 0.57 19 93.19 11.69 0.0 5331.66 12254.50 0.21 19 49.40 10.87 0.0 5)32.46 12256.19 1.04 19 46.97 10.24 0.0 5330.66 12254.89 0.82 19 90.67 11.19 0.0 5)29.80 12257.2) 1.69 1* 91.23 11.27 0.0 5330.00 12257.2) 1.69 14 90.33 11.07 0.0 5)29.77 12257.46 1.53 14 91.07 11.23 0.0 5329.6) 12254.6) 0.51 15 52.02 11.62 0.0 5327.06 12)08.20 1.68 44 28.71 6.32 0.0 5)24.7) 12)04.39 0.72 9 51.47 11.32 0.0 5330.30 12)05.33 1.42 13 99.49 12.21 0.0 5324.80 12306.60 0.63 9 92.29 11.49 0.0 5324.39 12304.63 1.05 9 91.80 11.40 0.0 5325.89 12306.80 0.82 9 92.90 11.99 0.0 5325.03 12303.80 0.73 9 91.06 11.23 0.0 5325.90 12305.46 0.49 9 51.2) 11.27 0.0 5321.05 12305.27 0.51 • 42.89 9.43 0.0 5321.05 12306.39 0.25 8 41.85 9.21 0.0 5325.25 12307.96 0.50 44 29.10 6.40 0.0 5300.00 12300.00 20.77 1 1Q 60.41 31.97 D.29 7.0) O.Q 0.0 1 5323.35 12316.46 5*22.05 I23l?.2» 1 • 1.63 42 33.71 7.42 l.TS ' OJ 137 APPENDIX X Summary Of Cut Scheduling Results - Case 1 Long_Term Short Term Objective maximize volume over 200 yrs. maximize volume over 30 yrs. Objective value at 30 years: 6,978.3 MCCF 7,802. 3 MCCF Objective value at 200 years: 39,265. 6 MCCF 38,422.1 MCCF Long run sustained yield average: 1,831.9 MCCF/decade 1,831.9 MCCF/decade Volume harvested in decade 1: 2,575.0 MCCF 2,879. ,1 MCCF Net revenue in decade 1: $106.3 MM $116.5 MM 138 APPENDIX_XI Species Harvest By Timber Class - Case. 1 SPECIES BREAKDOWN OF HARVEST IN OECAOE 1 - CASE It LONG TERN TIMBER CLASS F C H B s CV PW PL PV L CT 0 NB BI 1 m»m*M mm A ISMIUO1 PA VOLUME IN N C C F 7 0.33 0.0 0.0 0.27 7.87 0.0 0.0 3.94 0.0 0.0 0.03 0.0 0.0 0.13 0.30 0.0 9 18.28 0.0 0.0 2.90 154.43 0.0 0.0 32.13 0.0 0.0 0.70 0.0 0.0 1.97 6. 01 0.0 14 17.69 0.0 0.0 0.84 5.74 0.0 0.0 5.27 0.0 0.0 0.09 0.0 0.0 1.84 1.40 0.0 1* 7.IS 0.0 0.0 O.OS 0.37 0.0 0.0 0.35 0.0 0.0 0.0 0.0 0.0 0.19 0.13 0.0 21 30.98 0.0 0.0 1.61 83.61 0.0 0.0 18.73 0.0 0.0 0.43 0.0 0.0 1.32 3.67 0.0 30 22.06 0.0 0.0 3.51 40.39 0.0 0.0 370.15 0.0 0.0 2.49 0.0 0.0 1.99 7.03 0.0 3* 20.12 0.0 0.0 4.18 38.52 0.0 0.0 282.43 0.0 0.0 2.66 0.0 0.0 2.24 3.81 0.0 49 10.32 0.0 0.0 0.52 6.04 0.0 0.0 126.38 0.0 0.0 0.26 0.0 0.0 0.44 1.2* 0.0 94 27.99 0.0 0.0 1.70 18.51 0.0 0.0 185.97 0.0 0.0 1.19 0.0 0.0 2.79 3.29 0.0 63 96.96 0.0 0.0 3.41 60.44 0.0 0.0 26.79 0.0 0.0 1.10 0.0 0.0 2.40 4.11 0.0 47 13.24 0.0 0.0 0.28 3.91 0.0 0.0 81.92 0.0 0.0 0.10 0.0 0.0 0.8* 0.07 0.0 64 84.32 0.0 0.0 0.58 4.39 0.0 0.0 4.14 0.0 0.0 0.0 0.0 0.0 1.74 1.99 0.0 69 0.0 0.0 0.0 0.0 0.14 0.0 0.0 0.0 0.0 0.0 3.37 0.0 0.0 0.0 •.0 0.0 79 99.68 0.0 0.0 0.74 12.43 0.0 0.0 6.08 0.0 0.0 0.02 0.0 0.0 2.02 2.90 0.0 •1 19.29 0.0 0.0 0.49 6.17 0.0 0.0 112.19 0.0 0.0 0.24 0.0 0.0 1.44 1.3* 0.0 •3 0.14 0.0 0.0 0.04 0.28 0.0 0.0 1.06 0.0 0.0 0.03 0.0 0.0 0.02 0.09 0.0 91.09 0.0 0.0 0.63 4.7C 0.0 0.0 4.49 0.0 0.0 0.0 0.0 0.0 1.88 1.47 0.0 «* 32.76 0.0 0.0 9.01 69.69 0.0 0.0 421.97 0.0 0.0 4.99 0.0 0.0 4.11 11.91 0.0 TOTALS* 408.09 0.0 0.0 30.32 913.59 0.0 0.0 1684.01 0.0 0.0 19.99 0.0 0.0 27.1* M.T* 0.0 X l 20.7 0.0 0.0 1.0 17.9 0.0 0.0 57.4 0.0 0.0 0.7 0.0 0.0 0.4 0.0 «3 SPECIES BREAKDOWN Of HARVEST IN OECAOE 1 - CASE 11 SHORT TERN TIMBER CLASS T 1* 14 30 34 3* 49 34 9* 43 47 43 49 TS M •1 33 •3 92 94 TOTALS* I l 0.3) 11.69 T.13 22.06 2.60 20.12 10.32 21.99 49.31 96.34 13.24 •4.32 0.0 95.68 12.09 19.29 0.14 91.09 95.82 32.76 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 775.15 0.0 20.2 0.0 0.0 0.0 0.0 O.O 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.27 0.84 0.05 3.51 0.02 4.18 0.52 1.70 1.41 3.41 0.28 0.58 0.0 0.74 0.92 0.45 0.04 0.63 6.18 9.01 7.87 5.74 0.37 40.39 0.13 38.52 6.04 18.51 40.64 60.44 3.91 4.35 0.14 12.43 16.56 6.17 0.28 4.7C 91.59 65.69 CV PM VOLUME IN 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 PL MCCF 3.94 5.27 0.39 370.15 0.13 282.43 126.33 189.97 12.69 26.79 81.92 4.16 0.0 6.03 9.49 112.19 1.06 4.49 829.23 421.97 PV aaa a 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 34.74 424.47 0.0 0.0 2484.65 0.0 0.9 11.1 0.0 0.0 64.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 o.o : 0.0 0.0 0.0 0.0 0.0 0.0 CT ••••••1 0.03 0.09 0.0 2.43 0.0 2.66 0.26 1.13 0.39 L.IO 0.10 0.0 3.37 0.02 0.05 0.24 0.03 0.0 3.32 6.93 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MB 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Bl 0.13 1.34 0.15 1.95 0.09 2.24 0.43 2.79 3.63 2.40 0.09 1.74 0. 0 2.02 1. M 1.64 0. 02 1. M 4.35 4.11 0.30 1.40 0.13 7.03 0.09 S.31 1.29 3.29 2.49 4.11 0.37 1.99 0.0 2.30 1.00 1.39 0.05 1.47 12.99 11.99 PA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 22.63 0.0 0.6 0.0 0.0 32.23 40.39 0.0 0.0 0.3 1.4 0.0 141 APPENDIX_XII Summary Of Cut Scheduling Results - Case 2 Volume Value Objective: maximize volume over 200 yrs. maximize value over 200 yrs. Objective value at 200 years: $179.3 MM $200.4 MM Volume.production at 200 years: 39, 265. 6 MCCF 38,385. 3 MCCF Long run sustained yield average: 1,831.9 MCCF/decade 1,831.9 MCZF/decade Volume harvested in decade 1: 2,575.0 MCCF 2,864. 3 MCCF Net revenue in decade 1: $106.3 MM $119.4 MM 142 APPENDIX XIII Summary Of Cut Scheduling Results - Case 3 TRACS CARP Objective maximize value over 200 yrs. maximize value over 200 yrs. Objective value at 200 years: $200.4 MM $267.2 MM Volume production at 200 years: 38,385. 3 MCCF 40,4 71. 2 MCCF Long run sustained yield average: 1,831.9 MCCF/decade 1,755.7 MCCF/decade Volume harvested in decade 1: 2,864.3 MCCF 3,442. 4 MCCF Net revenue in decade 1: $1 19.4 MM $162.6 MM 

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