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It... a}... {I If? .lll 31¢”.II‘OI1IIIIIV’: 21.... -151)... 3......(lawuufiis. ..f ,. .21.... ...!i...i.t.i. iii... k3}...a5.w.te.}.i.l-k: .l .l I I l.l I b .1 KA-' ‘lr‘wliI-A} ..uilov" . v \I . . if. . ... ... ...»-..-1....-...,I...l 13..-... i-.- ...: - , I: II. ......VhtM.1‘!,.....:.....rb..;.u......»... 4.2...qu flat“... . . .2 ....Ir .. ...... ...u . are...» . . 1 .33...-1 o...s?o.......3l{ ......!07....3......,.a.n....wl...n.u€..u...o..11.t... z... .. ....V .1 II]... )u‘t... .l . ..nVl‘n... t ....le .l...!..no./I:nl.u I) .. .. 0.. n . . . I .r l .0 f 1 . ...V. ......f. . ...... . v v . t H? . . .r .. . .. . exit... - - . . - ...: a... .- ...II ,I'l'.$r / I,O’I ..ni . z .t ... ...: .01... E 19¢..iynlln:llu'trl. ...- , . .. .. - . - ......11|»h. Illlllllllllllllllllllllllllllllllllllllllllllllllllllll 3 1293 009141015 This is to certify that the thesis entitled THE IMPACT OF CUTTING METHOD ON NORTHERN HARDWOOD STUMPAGE PRICES presented by Paul J. Lewis has been accepted towards fulfillment of the requirements for MS degree in FOIEStry Major professor A t 10, 1 Date ugus 990 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution LIIRARY Michigan State University PLACE IN RETURN BOX to remove this checkout from your record. TO AVOID FINES return on or before date due. DATE DUE DATE DUE DATE DUE __l |l__r :LJE ‘ lljl | .4;le ___l fife—l __W___ MSU Is An Affirmative Action/Equal Opportunity Institution cmmms-pd THE IMPACT OF CUTTING METHOD ON NORTHERN HARDWOOD STUMPAGE PRICES By Paul J. Lewis A THESIS Submitted to Michigan State University in partial fulfillment of the require-outs for the degree of MASTER OF SCIENCE Department of Forestry 1990 ABSTRACT THE IMPACT OF CUTTING METHOD ON NORTHERN HARDWOOD STUMPAGE PRICES By Paul J. Lewis The impact of silvicultural cut method on northern hardwood stumpage prices was investigated. Timber sales from four National Forests in Michigan and Wisconsin were used to create a data base for the study. Using multiple regression techniques, a model of northern hardwood stumpage prices was developed for each National Forest. The results of the study indicate that stumpage prices are more sensitive to the species mix than to the harvesting method employed. In memory of William H. Lewis. iii ACKNOWLEDGEMENTS First, I would like to thank my graduate committee chairman, Dr. Carl Ramm. His guidance and confidence in my abilities helped me to complete this difficult task. I would also like to thank Dr. J. Michael Vasievich for giving me the opportunity to work with him and the U.S. Forest Service. It has been an enlightening experience. Thanks also to Dr. Jeffrey Vincent and Dr. Jonathan Haufler who provided valuable contributions with their suggestions and ideas. Finally, I would like to thank the staffs at the Huron-Manistee, the Hiawatha, the Nicolet, and the Ottawa National Forests for their assistance in collecting the data used for this thesis. Partial funding for this thesis was provided by the USDA Forest Service North Central Forest Experiment Station through Cooperative Agreement No. 23-89-12. iv TABLE List of tables ........... List of figures .......... Introduction ............. Problem definition .. Literature review ... Materials and methods .... The conceptual model Developing the data base Developing the empirical Tests for the best model Results and discussion Hiawatha National Forest Huron-Manistee National Forest Nicolet National Forest Ottawa National Forest Comparison of the models Reccomendations for future work Summary ........... Appendix 00.0.........OOOOOOOOOOOOOOO List of references 0 O O O O O C O O O 0 OF CONTENTS. 0 O O O O 10 10 13 20 51 58 38 42 Table Table Table Table Table Table Table Table Table Table Table Table 10: 11: 12: LIST OF TABLES Independent variables for predicting stumpage prices 1988) Predictor and identification variables selected from FSM-24SU (from Vasievich et al. A list of the variables used in the regression analyses Observed values for cut types Expected values for cut types Comparison of variable means using Fisher’s least significance difference (LSD) Analysis of variance and parameter test estimates for the dependent variable, STUMPAGE, Hiawatha National Forest Analysis of variance and parameter 0 .0000... ...........OOOOOOOOOOOOOOOO estimates for the dependent variable, STUMPAGE, Huron-Manistee National Forest Analysis of variance and parameter ...OOOOOOOOOOOOOIOOOOOOO estimates for the dependent variable, STUMPAGE, Analysis of variance and parameter Nicolet National Forest estimates for the dependent variable, STUMPAGE, Explanation of symbols used in the Appendix. Values of Hiawatha National Forest variables vi Ottawa National Forest I O O O O O O O O O O O .00......OOOOOOOOOOOOOOOOOO 12 21 24 24 26 41 44 48 Table Table Table Table Table Table Table 13: 14: 15: 16: 17: 18: 19: Hiawatha National Forest variables means and standard deviations .......... Values of Huron-Manistee National Forest variables 00.00000000000000000000000000. Huron-Manistee National Forest variables means and standard deviations .......... Values of Nicolet National Forest variables 0 D O 0 O O O O O O O O O O O O O O 0 O O O O O O O O O O O Nicolet National Forest variables means and standard deviations .......... Values of Ottawa National Forest variables O00.00.....OOOOOOOOOOOOOOOOCOO Ottawa National Forest variables means and standard deviations .......... vii 88 89 107 108 151 Figure Figure Figure Figure Figure Figure Sawtimber volume as a of total sale volume, Sawtimber volume as a of total sale volume, Sawtimber volume as a of total sale volume, Sawtimber volume as a of total sale volume, U.S. U.S. LlST OF FlGURES percentage Hiawatha NF ..... percentage Huron-Manistee NF percentage Nicolet NF ...... percentage Ottawa NF ....... Forest Service form FSM-2490 ..... Forest Service form FSH-2409.22 .. viii 68 69 INTRODUCTION PROBLEM DEFINITION In the Great Lakes region, northern hardwood: are an important component of the forest resource. Six million of Michigan’s 17.5 million acres of commercial forests are northern hardwoods. Placing an accurate monetary value on the timber is important in the timber selling process. If the timber is overpriced, it will go unsold. If it is undervalued, the seller loses money. Although much effort has been put into developing predictive models in recent years, some uncertainty still exists. One question which remains is how the method of cut affects the selling price of timber. The primary goal of this study was to identify what impact, if any, the method of cut has on the stumpage value of northern hardwood timber. Land managers can use this information to help make management decisions and to more accurately predict the selling price of their timber. Since timber sales from four National Forests in Michigan and Wisconsin were to be used as the source of data, another problem that needed to be addressed was the feasibility of combining data from different regions. LITERATURE REVIEW The northern hardwoods group is composed of a number of different species which grow in association in the Great Lakes Region. Northern hardwoods are most prevalent in Indiana, Michigan, Minnesota, Ohio, and Wisconsin. The species composition varies across the range but is typified by sugar maple (Acer saccharum’Marsh.), yellow birch (Betula alleghanensis Britton), American beech (Fbgus grandifblia Ehrh.), American basswood (Tilia americana L.) and eastern hemlock (Thugs canadensis (L.) Carr.). Other species which have local importance are balsam fir (Abies balsa-ea (L.) Mill.), American elm (Ulmus americanus L.), black ash (Fraxinus nigra Marsh.), red maple (Acer rubrum L.), northern red oak (Quercus rubra L.), eastern white pine (Pinus strobus L.), white ash (Fraxinus americsna L.) , and paper birch (Betula papyrifera Marsh.) (Tubbs et a1. 1983). This diverse mix of species provides for many possibilities in management. Several biological factors influence the selection of a silvicultural system will to harvest a stand of trees. The size of the trees, their age, their vigor, and the reproductive habits and shade tolerance of the desired species need to be considered. Other factors include the potential outbreak of insects or disease as well as the risk of fire (Burns 1983). There are two general methods used for harvesting timber. Even—aged methods - clearcutting, shelterwood, seedtree - are used when most of the stand needs to be removed in the first cut. The difference in age between h: be M a... trees in the regenerated stand usually does not exceed 20% of the rotation age (Burns 1983). Uneven-aged methods - individual tree selection, group selection, improvement cuts, commercial thinnings - target specific trees to be cut while leaving most of the stand intact. The percentage of the stand cut at any one time is usually no more than 20 to 30 x of the total stand basal area (Smith 1986). The highest valued products removed from northern hardwood stands - sawtimber and veneer - come from stands which have closed canopies and vigorous competition. Unfortunately, land managers are often tempted to clearcut stands, either for quick profit or due to a lack of knowledge about viable alternatives. One common assumption is that timber sales with low volume per acre (characteristic of uneven-aged cuts) attract fewer bidders, and therefore lower the stumpage value of the timber. Stumpage value is usually defined, and will be used here, as the value of timber standing on the stump. There have been numerous publications on the value of timber and how it can be derived. Guttenberg and Duerr (1949) and Duerr (1960) provided some of the earlier theoretical examinations of stumpage value with their concepts of conversion surplus and conversion value. Both measures are based on the difference between the end value of the products and the costs of producing the products. The difference is that the surplus measure accounts for only variable costs of production, while the value measure includes both fixed and variable costs. In practice, there are several factors which determine the amount a buyer is willing to pay for a particular stand of trees. They can be grouped into five broad categories: 1) general demand; 2) species present; 3) location of the sale; 4) site and sale characteristics; and 5) other factors. General demand is influenced by final demands for end products such as paper and lumber, the general business cycle, and existing supplies. The value of and demand for different species varies greatly and affects the sales price. The location of the sale dictates which mills might be able to utilize the wood, how many buyers will compete for the sale, and the general price zones. The sale method, either sealed bid or negotiated bid, the cutting strategy used, total volume, the time of year, and the-operability of the site are site/sale conditions which affect the final price. Other factors which might influence price include the landowner’s need for money and their knowledge of timber values, the bargaining skills of both parties involved, scaling errors, and the efficiency of the various processes involved. National Forest timber sales are unique in a number of ways. They are excellent study units because timber is scheduled for cutting regardless of market conditions, and because standardized procedures are used from sale to sale and forest to forest (Holley 1970). Also, the land manager does not have to be overly concerned about making a large profit, therefore, biological factors can be given more consideration than in the private sector. One drawback to National Forest Timber sales is that it may take several years to plan and carry out one sale. Before a sale is advertised, hearings are conducted and the public is given a chance to challenge the sale. Relatively few sales go unchallenged due to public concern about the environment and long-term productivity. Therefore, it is very important that each sale be carefully planned to reduce the chance of litigation. Silvicultural prescriptions must be completely justified in monetary and biological terms. Since, the Forest Service is mandated to use a multiple-use approach in managing National Forests, many decisions on how to cut a stand are based not only the needs of the desired tree species, but also on things such as wildlife or recreational needs. Even though the benefits to wildlife or recreation can be valued, any reference to timber value in this study is to the financial value of the timber. Inherent in the planning process is an accurate appraisal of the timber value. If the timber is priced too high, the sale will go unsold, and unsold sales waste time and money. Niccolucci (1989) found that between 1980 and 1985, approximately 20% of the timber offered for sale on National Forest lands went unsold. Huang and Buongiorno (1986) found similar results for the period of 1976 through 1980 on the Chequamegon National Forest in northern Wisconsin. On the other hand, the Forest Service by law can not sell timber below its fair market value (Buongiorno and Young 1984). The Forest Service uses two basic approaches to determine the fair market value of timber: residual value and transaction evidence (Weiner 1981). The residual value theory holds that timber has no use other than for raw wood material (Holley 1970). Its residual value is therefore the end value minus costs. Weiner (1981) gives the general equation for stumpage value (S): S = SP — (MC + LC + P&R) where SP is the product selling price, MC is the milling costs, LC is the logging cost, and P&R is profit and risk margin. ~ Residual value has been used by Hotvedt and Straka (1987) to analyze thinnings in southern pine plantations and by Darr (1973) to estimate stumpage value in the Pacific Northwest. This method requires predictions about end product use as well as the efficiency of the loggers and manufacturers. Many private companies are reluctant to provide information about their costs to outsiders. Costs can only be estimated, leading to a possible bias of the true stumpage value. The other widely accepted method of timber appraisal, transaction evidence, relies on past sales to predict present stumpage value. This method is replacing the [‘6 \‘e is tie i1 ,3: ~‘l' residual value method as the way to appraise timber on National Forest timber sales (McQuillan and Johnson-True 1988). The residual value method is more costly and time consuming, is very reliant on end product valuation, and may ignore the effect of competition that cause prices to deviate from the norm (Vasievich et al. 1988). The general approach to timber valuation by transaction evidence is to first gather quantitative data from a number of timber sales with similar characteristics, and then to use multiple regression to fit a predictive model. Multiple regression can quickly identify the variables which most affect the value of timber (Smith 1979). The calculations are relatively simple, and goodness of fit statistics can be used to measure a model’s accuracy and to set up confidence intervals. Several researchers have used transaction evidence models to predict stumpage values of National Forest Timber sales. Usually, high bid price or total bid price is modeled as a function of sale characteristics. Anderson (1969, 1976a, 1976b) found that average stand diameter, the proportion of the sale in sawtimber, and the current wholesale price of #2 dimension lumber were the best predictors of stumpage value in the southern pine region. Holley (1970) found that the number of bidders, the total sale volume, the proportion of the sale with grade B or better logs, and the current wholesale price of #2 dimension lumber were most important in predicting southern pine stumpage value. Buongiorno and Young (1984) found individual species volume to be the most important factor in predicting total high bid of northern hardwood timber sales. In developing their model, they ignored sales which received less than two bids. Their assumption was that a minimum of two bids was necessary for the high bid to represent the true market value of the timber. Young (1983) had previously shown that all else being equal, sales with only one bid paid significantly less than sales which received at least two bids. Buongiorno and Young assumed that potential bidders are knowledgeable about their competitors, which may not be true. Shaffer (1985) pointed out that some bidders do not really want to buy the timber but make a courtesy bid for information or public relations. None of the studies reviewed could directly estimate the effect of harvest method on stumpage value. In Montana, Jackson and McQuillan (1979) did model average stumpage price based on sale characteristics including the logging method and the reproduction method employed. However, citing a lack of information in the timber sale reports, they were able to use only imperfect approximations of several variables, such as percentage of the area tractor skidded for logging method and percentage of the area seedtree or clearcut for the reproduction method. They found that both of these variables significantly affected stumpage price. They also found tree size and average (*- (f volume per acre to be important. Transaction evidence models illustrate which variables are the most effective predictors of stumpage value in different regions. Southern forest sales and western forest sales are dominated by softwoods which are used for dimension lumber and plywood manufacturing. Tree size is the most consistent predictor of stumpage value because larger trees are used for veneer and as peeler logs, smaller trees for less valuable 2 x 4’3. Northern hardwood sales are more diverse, and the species mix is the most important factor in predicting stumpage price because individual species vary greatly in value. MATERIALS AND METHODS THE CONCEPTUAL MODEL The literature shows that it is feasible to predict a stand’s stumpage value based on stand and sale characteristics. It was decided that National Forest timber sales would be used to supply the data for the study because of their availability and standard format. Choosing the form of the model depends on the objectives of the researcher and the available data. Based on conclusions drawn from the literature, the decision was made to use a transaction evidence approach and multiple regression techniques to predict stumpage price. Vasievich et al. (1988) gave a strategy for developing a transaction evidence model. The process involves adding variables or groups of variables to the equation until all important price determinants have been included. The first decision is the choice of the dependent variable. Two possible choices are total sale price and average unit price. Both have drawbacks. Since total sale volume is a good predictor of total sale price, it may mask the effects of other variables. Average price per unit is more powerful, as it removes the effects of volume, but modelling average price generally produces low coefficients of determination (R2). 10 11 There are five categories of price determinants which should be included as explanatory variables in any model of timber values. They are end product markets, effects of time, spatial effects, sale characteristics, and short-term supply, demand, and competition effects. End product market variables include the estimated value of the products to be made from the timber. These variables are most important for single species or single product sales. Effects of time variables (i.e. the Producer Price Index for hardwood lumber) are used to remove trends from the data due to inflation or seasonal fluctuation. Spatial effect variables would be used to account for hauling distance and price zones. Sale characteristics include the sale size, individual species volume, log grade, and terrain. Supply and demand variables explain volume available on the market at the time of sale and the amount of competition for the sale. Competition can not be known precisely in advance, but for this study that is not important. Since a descriptive rather than a predictive model was desired, information not known in advance could be used in the model. The complete list of possible explanatory variables suggested by Vasievich et al. (1988) is presented in Table 1. It was decided that a measure of the average unit price should be used as the dependent variable. The objective of the study was to determine the effect of ,I 4 p\ b VA rd r T1 p 3.1 c: 12 Table 1. Independent variables for predicting stumpage prices (from Vasievich et a1. 1988). End Product Marketg Average lumber or log prices by species (prior month) Average pulpwood delivered prices, by species (prior month) Housing starts (prior month or lagged several months Effect§_of Time Months from a base date Months from base squared Spatial Effectg Miles of paved road haul distance Miles of unpaved road haul distance Distance to nearest consuming mill Transportation distance and costs Mill capacity within a defined haul radius of the sale Average rate of timber removals as percent of inventory Price zone (dummy variable) Sale Characteristics Average skidding distance Harvesting method (dummy variable) Percent of volume logged by specific methods Mean tree diameter Trees per cord or thousand board feet (be) Volume per tree or per square foot of basal area Percent of sale volume in #1 logs Diameter of a tree of average basal area Average tree grade Volume or percent of volume in each grade Average log grade Defect percent Percent of total volume in pulpwood, sawtimber, and veneer Total sale volume Average volume per acre Size of timber sale in acres Short-term,SupplyL,Demand. and Competition Effects Number of mills buying timber from the area of the sale Number of potential buyers Number of bidders Volume available in all sales on the market at the same time Volume of damaged timber available on the market l3 cutting method on stumpage value and, as discussed previously, using total sale price might mask the effect. Table 1 was used as a guideline to select explanatory variables. The final form of the model would depend on which variables were available from the Forest Service sales records, although attempts would be made to insure that all five of the categories in Table 1 were represented in the model. DEVELOPING THE DATA BASE Timber sales data from four National Forests were used to develop a data base. The Forests were the Huron-Manistee, the Hiawatha, and the Ottawa National Forests in Michigan, and the Nicolet National Forest (NF) in Wisconsin. These Forests were chosen because of the large amount of northern hardwood timber sold and their proximity to Michigan. Information on sales from the period of 1980 to 1989 were found to be readily available for all Forests. Rejection criteria were established in advance to help decide which sales should be included in the data base. Since the northern hardwood sale characteristics were to be analyzed, sales had to be at least fifty percent northern hardwoods by volume. The species and species groups considered to contribute towards the fifty percent minimum were sugar maple, yellow birch, American beech, American elm, black ash, red maple, northern red oak, white ash, paper birch, and mixed hardwoods. Even though eastern 14 hemlock and eastern white pine are considered to be part of the northern hardwood group, they were not included because they can also be found in association with other conifers. Only those sales which were sold by sealed bid were included in the data base. Negotiated sales were not included because they might not reflect the true market value of the timber. Often, if a sale receives no bids when first offered for sale, it is reevaluated and sold by direct negotiation with a buyer. The Forest Service condenses most of the pertinent sale information into one form (FSM-2490). A supplement to this form, the Appraisal Summary - Transaction Evidence sheet (FSH 2409.22) was also used. An example of both forms may be found in the Appendix. A data base was created using selected variables from the forms. The variables (Table 2) were chosen using the list in Table 1 as a guideline. The complete list of variables and their values by Forest may be found in the Appendix. The dependent variable could be represented by either the average high bid or the average statistical high bid. The measures differ by what value is divided by total sale volume. Many sales have stipulations that require the buyer to build or improve permanent roads. The cost of these roads are estimated by the Forest Service and are credited to the buyer upon the successful completion of the roads. Potential buyers bid on the timber knowing they will receive 15 Table 2. Predictor and identification variables selected from timber sales records (FSH-2490 and FSH-2409.22). Forest Ranger District Sale name Quarter and fiscal year Acres in sale Bid date Salvage (Y or N) Contract number Termination date For individual species: Species code Product unit Volume Advertised rate High bid Statistical high bid Total sale volume in be Average high bid Average statistical high bid Number of bidders Small Business Administration (SBA) Class Total value of bid Separate values for sawlogs and pulpwood: Haul distance in miles Specified road construction in miles Temporary road construction in miles Purchaser credit limit Temporary road cost 16 the credit. The average high bid does not account for this credit; the statistical high bid does. The statistical high bid was therefore chosen as the dependent variable in the regression model. It is also representative of the average stumpage value of the timber and will be referred to in the model as STUMPAGE. The Ottawa NF had several deficit sales which, after consideration, were eliminated. Deficit sales are not to be confused with below-cost timber sales. Below-cost timber sales cost the government more (due to road development, administrative costs, etc.) than they receive in payment from the buyer, while deficit sales are those where the buyer takes less profit than usual (Rideout 1987). The deficit sales had negative statistical high bids, making it illogical to leave them in the data base. The costs of temporary roads, on the other hand, are borne completely by the buyer. The Forest Service usually employs several different cut methods for each sale. If only one cut method were used on each sale, the number of sales would increase tenfold. Therefore, each sale is subdivided into smaller units called purchase units or payment units. The payment units usually delineate the Compartment-Stand management subdivisions used by the Forest Service. Only one price is bid for each species-product in the sale, however, regardless of its frequency in different payment units. It is impossible, therefore, to explicitly calculate the effect of cut method 17 on stumpage price. It has been assumed that there is an effect and that the analysis will identify its magnitude and direction. The buyer must finish cutting all timber from one payment unit or group of units before starting cutting in another. In this way, the Forest Service can limit the amount of damage done if the buyer defaults on the sale. Default sales were not eliminated from the data set. It was assumed that the buyer fully expected to harvest all of the timber and therefore gave a bid based on their evaluation of the fair market value of the timber. A related requirement for the buyer is a performance bond equal to approximately ten percent of the sale value, which is held by a third party until cutting is complete. The bond is used to pay for damages in the case of buyer default. It is possible that the bond requirement influences stumpage prices. Usually only one cut method is used on a payment unit. This information - cut method for each payment unit - is included on form H-2430-9. The individual species volume by payment unit and the number of acres in each payment unit are recorded in the standard Forest Service timber sales contract (FS-2400-6T). The number of acres in sale on FSM-2490 was found to be an inaccurate measure of the acres actually cut. The acres in sale include areas which were not cut but were between two other areas being cut. The sum of the acres for each payment unit in a sale was therefore used as the value for total acreage cut. 8L OI E\ *4. u: U! 18 The cut methods used varied across the Forests, and not all methods were used on each Forest. The cut categories used were clearcuts (CC), shelterwood (SHELTER), seed tree (SEED), overstory removal (OR), selection (SELECT), improvement (IMPROVE), commercial thinning (THIN), and salvage (SALVAGE). Some methods were a combination of closely associated methods, for example SELECT included individual tree selection and group selection. Stand clearcutting and patch clearcutting were listed under CC, and SALVAGE contained stands salvaged because of mortality or sanitation. The volume harvested was tallied for each payment unit and aggregated under the appropriate cut method. The frequency of each cut method was determined for every sale as well. Some payment units had more than one cutting method. Three additional variables were created to account for that: EMIX, UNMIX, and MIX. CC, SHELTER, SEED, OR, and SALVAGE were considered to be even-aged silvicultural treatments while SELECT, IMPROVE, and THIN were considered to be uneven-aged methods. If the payment unit was cut by a mix of even-aged methods, volume cut was listed under EMIX. If all uneven-aged methods were used, volume was listed under UNMIX. If both even-aged and uneven-aged methods were used, the respective volume was listed under MIX. Two general indicators of cut method volume were also created: EVOL and UNVOL. EVOL was the sum of even-aged payment unit volume, UNVOL was the sum of uneven-aged payment unit volume. 19 The species and species-products being cut also varied across the National Forests. Twenty-five different species-products were found on Hiawatha NF sales, 33 on Nicolet NF sales, and 27 on Ottawa NF sales. The Huron-Manistee NF sales only had 9 species-products listed because, with the exception of aspen, no distinction was made between hardwoods (i.e. they were all listed as mixed hardwoods on the sales forms). Aspen was listed separately. Several new variables were created from the original ones. All volumes were converted to thousand board feet (be). This was necessary because pulp volume was reported in thousand cubic feet (Ccf) or cord volume on the sales forms. To ensure that the converted volumes were accurate, the total sale volume was recomputed and compared to the volume reported on FSM-2490. The conversion factors used were 1 be = 1.6 Ccf 1 be = 2 Cords The length of the sale in months was calculated by subtracting the bid date from the termination date to get the total length in days, and then dividing by 30 to get the number of months. Specified road costs per be were calculated by dividing total road cost by total sale volume. Temporary road costs were calculated in the same manner. Haul distance was weighted based on the amount of sawtimber and the amount of pulp in the sale, since they were usually appraised to different destinations. The percentage of total sale volume for each species-product was calculated as A pr- \- fi E1 in 20 a simple ratio. Overbid was calculated by subtracting the advertised rate from the high bid. Finally, a variable for the average volume harvested per acre (AVGVOL) was calculated. Anderson (1976a) did not find average volume per acre to be significant in explaining stumpage price variation, but Jackson and McQuillan (1979) did. A list of the variables can be found in Table 3. They are given with the units they are reported in along with a description of what the represent. DEVELOPING THE EMPIRICAL MODEL Once the list of potential regression variables was completed, it was time to choose the form of the model to be tested. As mentioned previously, a multiple regression model would be used. The general model form was P = b1X1n + b2X2n + + biXin + un where P is the dependent variable, stumpage value, b1 through b1 are unique regression coefficients corresponding to the independent variables X1 through Xi, and un is a random error term. A linear model form would be used because of its flexibility and ease of development and interpretation. An intercept term would be used in the model unless it could be shown not to be significantly different from zero. The question of regionality had to be answered prior to model testing. If the sales from different forests were too Table 3. Variable 21 A list of the variables used in the regression analyses. Units Description MONTHS ACRE PU BIDS ADVER STUMPAGE OVERBID VALUE FOBVAL HAUL SAWH PULPH SPEC$ SPEC TEMP$ TEMP TEMPM VOL AVGVOL EVOL UNVOL CC SHELT IMP THIN SELECT ROAD MIX UNMIX EMIX XEVOL XUNVOL %CC XSHELT XIMP %THIN XSELECT XROAD XMIX XUNMIX XEMIX #CC #SHELT #IMP Months Acres $/be $/be $/be $ $/be Miles Miles Miles $ $/be $ $/be Miles be be/acre be be be be be be be be be be be Contract length Total sale acreage Total number of payment units Number of bids received Forest Service advertised rate Statistical high bid for sale Difference between ADVER and STUMPAGE Total high bid Estimated timber selling price at the mill Weighted haul distance Estimated haul distance for sawtimber Estimated haul distance for pulpwood Specified road costs (purchaser credit limit) Specified road cost per unit of volume Estimated temporary road costs Temporary road cost per unit of volume Temporary road length Total sale volume Average volume per acre Volume cut by even-aged methods Volume cut by uneven-aged methods Volume cut from clearcut PU’s Volume cut from shelterwood PU’s Volume cut from improvement PU’s Volume cut from thin PU’s Volume cut from selection PU’s Volume cut from specified road PU’s Volume cut from mixed method PU’s Volume cut from mixed uneven-aged method PU’s Volume cut from mixed even-aged method PU’s EVOL/VOL (Percent of total sale volume) UNVOL/VOL CC/VOL SHELT/VOL IMP/VOL THIN/VOL SELECT/VOL ROAD/VOL MIX/VOL UNMIX/VOL EMIX/VOL Number of clearcut payment units Number of shelterwood payment units Number of improvement payment units 22 Table 3. (cont’d.). Variable Units Description #THIN Number of thin payment units #SELECT Number of selection payment units #ROAD Number of specified road payment units #MIX Number of mixed payment units #UNMIX Number of uneven-aged payment units #EMIX Number of even-aged payment units MHS be Mixed hardwood sawtimber RMS be Red maple sawtimber SMS be Sugar maple sawtimber YBS be Yellow birch sawtimber ES be Elm sawtimber AS be Aspen sawtimber BES be Beech sawtimber BC be Black cherry sawtimber PBS be Paper birch satimber MCS be Mixed conifer sawtimber PS be Pine sawtimber (red, white and jack) HS be Hemlock sawtimber SS be Spruce sawtimber RWS be Red and white pine sawtimber WPS be White pine sawtimber MHP be Mixed hardwood pulp AP be Aspen pulp BFP be Balsam fir pulp RWP be Red and white pine pulp MCP be Mixed conifer pulp SP be Spruce pulp HP be Hemlock pulp JPP be Jack pine pulp CP be Northern white-cedar pulp WPP be White pine pulp X(Species) Species volume/VOL (percent of total sale volume) N 0;. different, then separate stumpage price models should be used, or dummy variables for each forest should be included in a composite model. Differences between National Forests were tested using a two-way contingency table for differences in the probability of cut methods. Conover (1980) provides a detailed explanation on the use of contingency tables. The null hypothesis to be tested was: HO : All out types occur with equal probability on all four Forests. verses the alternate: H1 : At least one cut type does not occur with equal probability on all four Forests The test statistic is computed by first creating a contingency table with the National Forests as the rows and cut types as the columns (Table 4). The values in the cells (Oij) are the number of times a payment unit was cut by the particular cut method on the Forest. A table of expected values (Table 5) is then calculated and the test statistic T = Sum[(Oij - Eij)2/Eij] is compared to values of the chi-square distribution with (r-1)(c-1) degrees of freedom. The test statistic for the contingency table was T = 826. The value of X2 with 30 degrees of freedom at the 0.005 alpha level is 53.9. The excessively large value of the test statistic means that the null hypothesis must be rejected. The contingency table test showed that the different National Forests use at least one of the cut methods in different proportions. More than one cut method was used in 24 Table 4. Observed values for cut types. Huron- . Hiawatha Manistee Nicolet Ottawa Totals CC : 62 199 138 156 : 555 SHELT : 7 49 34 53 i 143 OR : O 69 14 23 : 106 SALV : 0 0 17 1 i 18 SELCT : 82 18 199 270 i 569 IMP : 53 4 236 141 : 434 THIN : 76 98 121 365 l 660 EMIX : 1 0 6 5 : 12 UNMIX : 18 O 39 65 l 122 MIX 1 34 80 25 43 l 182 ROADS : 10 18 39 91 l 158 Totals 343 535 868 1213 2959 Table 5. Expected values for out types. Huron- Hiawatha Manistee Nicolet Ottawa CC : 64 100 163 228 : SHELT : 17 26 42 59 : OR : 12 19 31 43 : SALV : 2 3 5 7 : SELCT 2 66 103 167 233 : IMP : 50 78 127 178 : THIN : 77 119 194 271 : EMIX : 1 2 4 5 : UNMIX : 14 22 36 50 : MIX 1 21 33 53 75 : ROADS : 18 29 46 65 : N 01 different proportions, based on the magnitude of the test statistic. Comparisons between the observed and expected values in Tables 4 and 5 confirm this. For example, the Huron-Manistee NF sales tend to contain more even-aged payment units and less uneven-aged payment units than expected. Also, neither the Hiawatha nor the Huron-Manistee NF’s had any salvage payment units, while the Nicolet NF had a total of 17. These differences probably reflect the differences between the National Forests’ species compositions, and not differences in timber management. It was assumed that cut types used were consistent over Ranger Districts within all of the Forests. Since some Ranger Districts had only a few sales, developing individual equations would not be prudent. Further evidence supporting the need for separation of the four National Forests came from a compariSon of the means of the potential regression variables by Forest. The least significance difference (LSD) method (Steele and Torrie 1980) was used to compare the means of selected variables by National Forest. Significance was deemed to occur at the 0.05 level. Table 6 shows the results of the analysis. While the Huron-Manistee NF averaged the fewest number of acres, volume, and payment units per sale, it had the highest average high bid and the greatest volume per acre removed. In contrast, sales on the Nicolet NF on the average were larger and worth more in total, yet per acre Table 6. Comparison of variable means using Fisher’s least significant difference (LSD) test. Variable STUMPAGE ($/be)t VALUE BIDS OVERBID MONTHS HAUL TEMP SPEC VOLUME ACRE PU (3) ($/be) (Miles (s/be (s/be (be) Hiawatha Huron- Manistee Nicolet 30.94 a 23.18 b 23821 bc 45037 a 2.62 a 2.77 a 7.40 a 7.67 a 34.4 c 51.3 a 34.8 b 76.7 a 0.60 b 0.25 c 1.07 b 2.86 a 824 c 1903 a 148 c 491 a 5.6 c 13.3 a Ottawa U‘U‘O‘WDOQODDP b Means followed by the same letter are not significantly different at the .05 level. 27 volume and per be value was lower. The Ottawa NF and the Hiawatha NF had similar total volume and acreage in each sale, but the high bid for Ottawa NF sales averaged ten dollars higher than high bid for Hiawatha NF sales. There were other notable differences between National Forests. The average haul distance (HAUL) was significantly different between all Forests as was temporary road costs per be (TEMP). TEMP was much higher on the Huron-Manistee NF than on any of the other National Forests, but sales from that Forest had the smallest average specified road costs per be. This suggests that much more permanent road building was required (or desired) in the less developed areas of Michigan’s Upper Peninsula and northern Wisconsin. Competition for timber appeared to be similar across the region. The average number of bids per sale ranged from 2.6 to 2.9 with very small standard errors. Likewise, the average overbids per sale were fairly consistent, although more variable. Neither of these variables were significantly different across Forests. The means and standard deviations for each National Forest can be found in the Appendix. Finally, a generalization of Bartlett’s test for homogeneity (Morrison 1976) was used to determine the equality of the covariance matrices of the variables listed in Table 6. The null hypothesis tested was that the covariance matrices of the four National Forests are equal verses the alternative that at least two of the matrices are not dis th eq me be not equal. The test statistic is compared to a chi-square distribution with 0.5*(k—1)p(p+l) degrees of freedom, where k equals the number of covariance matrices and p is equal to the number of variables. The test statistic was found to equal 1098. The probability of a greater X2 is less than 0.0001. Therefore, the null hypothesis that the covariance matrices are all equal must be rejected. All of this suggests that there are basic differences between the National Forests represented in the study, particularly between the Huron—Manistee and the Nicolet. Regional models should therefore be used for predicting stumpage prices. Dummy variables for each Forests could be used, but that would reduce the error degrees of freedom and complicate interpretation of the results. The next step was to determine which variables should be used to predict the dependent variable, STUMPAGE. Scatter plots were used to examine the relationship between the independent variables and the dependent variable. None of the relationships appeared to be nonlinear, so transformations were not required. Simple correlation was then used to reduce the list of possible predictive variables. Several of the independent variables were highly correlated with each other, or were uncorrelated with the dependent variable. Aside from species volume and cut-method variables, the variables which seemed to be most important for all National Forests 29 included contract length (MONTHS), hauling distance (HAUL), a measure of competition for the sale (BIDS or OVERBID), the cost of specified roads per be (SPEC), and temporary road costs per be (TEMP). The alternative measures of sale competition were number of bidders and the amount of overbid or bid premium. OVERBID was chosen for two reasons. First, it had a higher correlation with the dependent variable, STUMPAGE. Secondly, bid premium accounts for incorrect appraisals of the timber value and cost allowances as well as the amount of competition (Schuster and Niccolucci 1989). The empirical model would have its own unique variables for species volume and cut method for each National Forest, such as total species volume and the percentage of sale volume for each species. The latter would probably be a better choice, because the magnitude of the variables would be approximately the same as the others being used. Total species volume ranged from zero be to over 1000 be, and large differences in magnitude within the covariance matrix can give unstable results in regression analysis. Also, because the sum of cut method volumes equals the sum of species volume, they both could not be used in the same regression. Because an intercept term was to be included in all regressions, the percentage of all species could not be used as this would create collinearity. STUMPAGE was first regressed only on species-product volume. The significant 30 species-products would then be expressed as percent of total volume in the regression of STUMPAGE on them and the other non-volume variables. Cut method could also be represented several alternative ways, either as percent volume, total volume, or the number of payment units cut by a particular method. The number of payment units had the least amount of difference between the smallest and largest values, so again, the smaller numbers would probably give better results. Since the objective was to determine how cut method affected stumpage value, it was important that a variable expressing cut method be included in the regression. Although it did not fit into any particular category in Table 1, the Small Business Administration (SBA) class of the successful high bidder was included in the regression in the form of dummy variables. The three classifications used were large business (L), small business (S), and no classification (N). It was thought that differences in company size might affect the high bid price because of profit structure, efficiency, or other reasons. Sale size in acres would also be included in the regression to determine the importance of the economy of scale, even though acreage did not correlate well with STUMPAGE. The one category suggested by Vasievich et a1. (1988), but not found on any of the sales forms, was a measure of end product markets. Some researchers have included a variable in their models that estimated the selling price of tl CI bl Pa Dc 31 the timber (e.g. McQuillan and Johnson-True 1988). For the Michigan National Forests, a reasonably good price guide exists in Timber-Mart North (1981 - 1989). This newsletter, published quarterly, gives a high, low, and average price for both stumpage and free-on-board (FOB) mill prices for timber in three price zones in Michigan. Most products are given three prices (low, average , high) if a market exists in that region. The average FOB mill price (FOBVAL) of the timber in each sale was estimated using the average price figure given in Timber Mart-North. Unfortunately, the price source for Wisconsin timber, the Wisconsin Forest Products Price Review (1985 - 1989) while providing both standing timber and FOB mill prices, has limited coverage. Many of the species-products did not have prices listed in certain periods of the year. Nonetheless, an attempt was made to develop an approximation of the average selling price of timber at the mill. TESTS FOR THE BEST MODEL Several methods were used to judge the best model. A priori expectations of the signs of the estimated regression coefficients were made so as to check the logic of the models. Logging costs such as haul distance and road building costs should decrease the average stumpage price paid. Longer sale periods and increased sale size (up to a point) should command higher bid prices. Large businesses might need more profit because of larger overheads than 32 small businesses, so an SBA(L) variable should have a positive coefficient. Higher-valued timber species should also cause an increase in the bid price. Even-aged logging methods (clearcut, shelterwood, overstory removals) should result in higher bid prices because stands are easier to harvest. More volume per acre is removed and less care is needed to protect the residual stand. Uneven-aged methods should then cause a decrease in bid price for the opposite reasons. More time is needed for harvesting operations because of the lower volume per acre and the greater amount of care required. The first statistical test would be an F-test using the ratio of the model mean square to the error mean square compared to the tabular value of F (Steele and Torrie 1980). This test is synonymous with testing that HozBi=B2=... =Bi=0 verses the alternate H1 : H0 is incorrect or, that the relation between the dependent and independent variables is zero. The 0.05 level of significance was selected for the F-test. Since the total sum of squares will not change, the model with the highest F-value would explain the most variation in the dependent variable. An adjusted R2 was used to evaluate models rather than the normal R2 (an adjusted R2 is corrected for small samples and a large number of predictor variables). It will generally increase as the F-value does, but it also is I‘E ac vi 33 related to the number of parameters in the model. The adjusted R2 was calculated as Add. R2 = 1 - {[(n - i)(1 - 82)] / (n - 19)} where n is the number of observations used to fit the model, p is the number of parameters estimated, and 1 = 1 if the model includes an intercept, i = 0 otherwise. Another measure of a model’s goodness-of-fit is the square root of the error mean square, also known as the standard error of the estimate (SEE). A smaller SEE indicates that a higher proportion of the variability in the dependent mean has been explained. When the SEE is divided by the mean of the dependent variable, the coefficient of variation (CV) is created. The CV gives a unitless measure of the model’s variation which can be used to compare it to the models from the other forests. The independent variables in the final medel also need to be tested for significance. A t-test was used to determine if the regression coefficients were significantly different from zero. The test would be judged significant at the 0.10 alpha level. A technique known as stepwise regression would be used to help select the final model. Although some researchers do not favor leaving model selection to the whims of a computer program, if the initial model has been well thought out, stepwise regression can be used to reduce large models to a more manageable size. In general, if a variable does not have a significant coefficient in the full model, it tl in th C0 “in 34 does not explain a significant proportion of the variation in the dependent variable and can be dropped from the model. However, it is possible that the effects of a group of variables is significant while individually their effect is too slight to be significant, or that the presence of one variable is masking the effect of another (Cramer 1972). Stepwise regression develops a sequence of regression models, starting with only one variable and adding or deleting variables at each step. Independent variables added earlier may deleted if they no longer provide useful information once a new variable or set of variables is added (Neter et a1. 1985). The usual assumptions of classical linear regression theory were made about the model (see Klienbaum et a1. 1988): 1) the errors are distributed normally and independently with constant variance and a mean of zero; 2) the model is properly specified, that is the errors do not contain information about the dependent variable that is not already included in the model; 3) the dependent variable (STUMPAGE) is distributed normally and independently; and 4) the independent variables are measured without error. The first two assumptions were checked by visual inspection of the residuals plots (see Chatterjee and Price 1977). Standardized residuals were plotted against the predicted values of the dependent variable. The standardized residuals have a zero mean and most should fall within the range of plus or minus 2 if the model is 35 correctly specified. Residuals that diverge from or converge toward the X axis for increasing values of the predicted variable indicate heteroscedastic error variances. These models may be corrected through weighted least squares. Linear plots or plots with distinct patterns indicate that an important variable has been left out of the model. Violations of the third assumption do not influence fitting of the least-squares model. However, while the usual parametric tests of hypotheses are robust, extreme departures of the dependent variable from normality can cause problems with statistical inference. Skewness and kurtosis can be used to judge how close a distribution is to normal. Stem and leaf diagrams and box and whisker plots can also be looked at to judge the severity of nonnormality. The Shapiro-Wilk test of normality was uSed to evaluate STUMPAGE (Conover 1980). The hypotheses tested by the Shapiro-Wilk test are: Ho : F(x) is a normal distribution function with unspecified mean and variance verses the alternative H1 : F(x) is nonnormal. The Shapiro-Wilk test can also be used to test the normality of the error terms. For samples sizes greater than 50, an approximation is used. The fourth assumption, that the independent variables were measured without errors, would be taken on faith. Completely outrageous values for an independent variable 36 would be investigated to determine if possibly there was an error in recording. Another assumption made was that simultaneous bias was not a problem with the models. Simultaneous bias can occur when the data are generated by a set of interdependent processes (Fomby et a1. 1988). Theoretically, the volume in a particular sale influences the supply of timber available which in turn influences demand. Demand then influences prices which in turn influences the amount of volume willing to be purchased. The explanatory variables are not independent of the disturbances, meaning that the dependent and independent variables need to be determined jointly. This would create a problem if the sales were very large or if the volume of timber being sold from all sources was very small. It was assumed that the timber market in the Great Lakes is large enough so that it would be nearly impossible for one sale to have a large impact on the market. Since the model is based on time series data, the possibility of autocorrelation exists. Autocorrelation can result in inefficiency and cause larger standard errors of the regression coefficients. Two ways to correct the problem are to include a lagged dependent variable in the model, or to detrend the data prior to doing regression analysis. The simplest method is to detrend the data. The models would still need to be tested for autocorrelation; the Durbin-Watson statistic (Kmenta 1971) would be an 37 appropriate choice. Kingsley and DeBald (1987) compared three methods of detrending hardwood stumpage prices in Pennsylvania. They used the Consumer’s Price Index (CPI) for all commodities, the Producer’s Price Index (PPI) for hardwood lumber, and the PPI for all lumber commodities. They concluded that using the PPI for hardwood lumber gave the best results. However, as there is more involved in logging than just the price of hardwood lumber, the CPI was thought to be a more appropriate measure of price changes. All price variables (STUMPAGE, FOBVAL, TEMP, SPEC, etc.) were then adjusted using.the CPI for the particular month in which the sale was made (U.S. Dept. of Labor 1980 - 1989). RESULTS AND DISCUSSION Model development was done using SAS statistical software (SAS Institute 1988). Four models were developed, one for each National Forest. STUMPAGE was regressed on OVERBID, HAUL, MONTHS, TEMP, SPEC, FOBVAL, AVGVOL, ACRE, SBA dummy variables and the appropriate variables for species volumes and cut-method. The models developed are presented individually for each Forest. A discussion of their implications follows. HIAWATHA NATIONAL FOREST The Hiawatha National Forest is located in Michigan’s Upper Peninsula and is split into two large areas, one in the eastern portion of the UP and one in the central portion of the UP. The Forest is supervised from Escanaba and has Ranger Districts in Rapid River, Manistique, Munising, Sault Ste. Marie, and St. Ignace. Sixty-two sales from the Hiawatha NF were found to meet the criteria established for the study. They were sold between July of 1980 and June of 1989 and ranged in size from just over 100 be to almost 6000 be total volume. Analysis of the normality of STUMPAGE showed it to have a large positive skewness (3.70), mainly because of three sales with much higher than average stumpage values. One sale was three standard deviations from the mean while the 38 39 other two were both outside of four standard deviations. Based on the amount of the bids, the amount of competition, and the buyers who bid on the sales, the logs from the three sales were probably used for veneer. A veneer mill located near the sales put in a bid on all three sales and was the successful high bidder for one of them. Dropping the three observations resulted in a much more normal distribution for STUMPAGE; skewness was reduced to 0.72 and the Shapiro-Wilk statistic became insignificant. However, the sales might contain information vital for the regression, so they were left in the data set for the first trial. If the three observations were truly different from the others, the regression would show it. Another reason to keep them in the data set was the desire to have as many observations as possible. The object was to create a descriptive rather than a predictive model. The first regression was done using percent species volume and the number of payment units cut by each cut method. Stepwise regression was used to help choose the best possible model. The model had an extremely high adjusted R2 value of 0.953. The equation was : STUMPAGE = 6.15 + 1.05(OVERBID) - 0.06(MONTHS) + 27.2(% MHS) + 60.8(% SMS) + 72.0(% RWS) - 1098.0(% PBS) + O.99(# THIN) - 2.45(# ROAD) One concern with the model was the magnitude of the coefficient for the percentage of paper birch sawtimber (% PBS). It is quite large compared to the other coefficients. Paper birch was only listed in one sale, so it is doubtful 40 that it truly has this large of an effect on stumpage value. For that reason, paper birch was dropped as a possible independent variable and the regression rerun. The model did not change very much; the results are presented in Table 7. The adjusted R-square for the model was 0.950 and the Durbin-Watson statistic was found not to be significant. There did not appear to be any obvious patterns in the residuals plot, however, there were four extreme outliers. Two of the outliers had standardized residuals with an absolute value of almost 4. One them was the Dukes sale, one of the sales with a very high value for STUMPAGE; another was the Lower Farm Hill sale. The other two outliers, the Lawson Road sale and the Lawson Road resale, were also identified as having unusually high values for stumpage. On the residuals plot, these two lay a distance from the main cluster of points and in the same general direction. According to Neter et a1. (1985), such points can "pull" the regression in that direction causing a distortion of the goodness-of—fit statistics. Another regression was then done, dropping the three sales with high stumpage value. The results were rather different from the model reported in Table 7. The adjusted R2 value was reduced to 0.826 and four more independent variables became significant at the 0.10 level. The model was: STUMPAGE = 8.09 + .68(OVERBID) - 0.06(MONTHS) + 34.03(% MHS) + 29.00(% SMS) + 66.48(% RWS) 41 Table 7. Analysis of variance and parameter estimates for the dependent variable, STUMPAGE, Hiawatha National Forest. Analysis of Variance ** Significant at the 0.05 level *** Significant at the 0.01 level Sum of Mean Source DF Sguares Squgre Fnglue Model 7 14779.0 2111.3 166.2 *** Error 54 683.3 12.7 Total 61 15462.3 Adjusted R2 = 0.950 Parameter Estimates Parameter Standard ngigblg Egtimgte Error INTERCEPT 6.12 1.37 *** OVERBID 1.06 0.10 *** MONTHS -0.06 0.02 ** X MHS 27.28 5.11 *** x SMS 60.33 5.92 *** _% RWS 52.34 23.49 ** # THIN 1.02 0.25 *** # ROAD -2.31 1.32 ** 42 + 27.45(% MCP) + 32.11(% RMS) + 36.02(% PS) + 0.62(# THIN) - 0.58(# CC) - 2.53 (# ROAD) The variables which became significant were the number of clearcut payment units (# CC) and the percent volume of three species-products, red maple sawtimber (% RMS), mixed pine sawtimber (% PS), and mixed conifer (% MCP). The estimated regression coefficients which changed the most from the first model to the reduced model were for the amount of overbid (OVERBID) and the percent of sugar maple sawtimber (% SMS). Reviewing the characteristics of the three deleted sales explains why this is so. They had higher than average overbids and were dominated by sugar maple sawtimber (from one-half to three quarters of the sale volume). HURON-MANISTEE NATIONAL FOREST The Huron-Manistee National Forest is located in the northern portion of Michigan’s Lower Peninsula. It is actually two forests, the Huron on the east side of the state and the Manistee on the west side, but it is administered by one supervisor’s office located in Cadillac. Ranger Districts include Harrisville, M10, and Tawas on the Huron side and Baldwin, Cadillac, Manistee, and White Cloud on the Manistee side. There were 99 sales initially included in the data base, but cut method information could not be found on four of them. Normality tests on STUMPAGE for the remaining 95 revealed one extreme outlier. The Van Gilder sale had the 43 highest bid received for any sale on the forest at $103.72 per be. Removing it caused the skewness of STUMPAGE to drop from 1.68 to 0.44. The Shapiro-Wilk test was still significant at the 0.05 level (probability < 0.0443), although it had improved from significance at the 0.0001 level. The Van Gilder sale was therefore not dropped from the data set. The first trial was done using percent species volume and number of payment units cut by each method. None of the cut methods were found to be significant. Volume by cut method was used next, with the same results: no significant cut method variables. Finally, the more general EVOL, UNVOL, and MIX were used, first as volume totals and then as a percentage of the total sale volume. MIX was not used as a percentage, however, because the three percentages would sum to one. Again, volume by cut method was not significant in explaining STUMPAGE. Six variables were significant for each model tested. They were OVERBID, TEMP, SPEC, HAUL, % MHS, and % AS. Their estimated regression coefficients changed very little between models, indicating that the models were not extremely sensitive to cut method. Table 8 gives the results of the regression. The adjusted R-square is very good (0.906). The standardized residuals were plotted against the predicted values of stumpage and showed no obvious patterns. None of the outliers were thought to be extreme, even though a few were outside of plus or minus two 44 Table 8. Analysis of variance and parameter estimates for the dependent variable, STUMPAGE, Huron-Manistee National Forest. Analysis of Variance Sum of Mean Source DF Squares Sgugre E Vglug Model 6 13814.0 2302.3 152.6 s** Error 88 1327.7 15.1 Total 94 15141.7 Adjusted R2 = 0.806 Parameter Estimates Parameter Standard ngiablg Estimate Error INTERCEPT 14.29 1.59 ##s OVERBID 1.01 0.05 **s TEMP -0.72 0.23 *s SPEC -0.87 0.17 st: HAUL -0.06 0.03 a X MHS 45.63 3.09 its x AS 43.28 8.91 *s* * Significant at the 0.10 level ** Significant at the 0.05 level *** Significant at the 0.01 level deviations. The residuals did not have first order autoregressive errors according to the Durbin-Watson statistic. NICOLET NATIONAL FOREST The Nicolet National Forest is located in the extreme northeastern portion of Wisconsin. It has headquarters in Rhinelander and Ranger Districts in Eagle River, Florence, Laona, and Lakewood. Sixty-six sales were found to meet the criteria set forth for the study. Normality testing of STUMPAGE showed it to be positively skewed (skewness = 1.54) with a Shapiro-Wilk statistic significant at the 0.0001 level. Unlike the Hiawatha NF and Huron-Manistee NF, the distortion of STUMPAGE’S distribution was not simply due to one or two high value sales. A check of the 95% and the 100% quantiles for each Forest, prior to removing the extreme outliers, revealed the problem. For the Hiawatha NF, the quantiles were found at $34.28 and $97.27 respectively, and $48.94 and $96.40 respectively for the Huron-Manistee NF. However, the 95% and 100% quantiles for the Nicolet NF were found at $52.17 and $59.87, so there is not an obvious distinction between extremely large values of STUMPAGE. Discarding the last few observations would make little sense, logically or theoretically. One solution to the problem would be to transform STUMPAGE so that it approached normality. This was done by 46 taking the logarithm (base 10) of each STUMPAGE value. LOG(STUMPAGE) was much closer to normal: skewness was reduced to 0.02 and the Shapiro-Wilk statistic was no longer significant. The drawback to using a transformed dependent variable is that it makes interpretation of the data more difficult. A less desirable option would be to delete all observations where STUMPAGE exceeded a certain level, in effect trimming off the tail of the distribution and removing the cause of the skewing. Of course, this would reduce the size of the data set which was relatively small to begin with. The third alternative would be to assume that the nonnormality of STUMPAGE was not severe enough to affect the regression results. All three alternative versions of the dependent variable were tried in regression equations. The same general group of variables was significant each time. However, deleting the six highest valued sales - alternative 2 - gave the worst results. The adjusted R-square was lowered by 15 points. LOG(STUMPAGE) gave more acceptable results, however, the non-transformed STUMPAGE gave a model with the highest R-square and lowest standard error of the estimate. EMIX and THIN were found to be significant when using the number of payment units as the independent variables, although they were not significant when percent volume by cut method or total volume by cut method were used. 47 Conversely, CC and SALVAGE were significant only when they were used as measures of volume. The final model, therefore, has a mix of cut method measures. Table 9 presents the results of the regression using the untransformed dependent variable and the full data set. The adjusted R-square value was 0.915. The standardized residuals plot showed no obvious patterns and no extreme outliers. The residuals themselves were normally distributed and did not display autocorrelation. OTTAWA NATIONAL FOREST The Ottawa National Forest is located in the northwestern corner of Michigan’s Upper Peninsula. It is contiguous with the Nicolet NF; the two are separated only by state boundaries. As might be expected, they share many of the same species in the same relative abundance. The results of the modelling however were somewhat different. The Ottawa NF is supervised from Ironwood and has Ranger Districts located in Bergland, Bessemer, Iron River, Kenton, Ontonagon, and Watersmeet. A high percentage of the sales offered on the forest consist primarily of northern hardwoods, as evidenced by a total of 130 sales found to be acceptable for the study.' Like the other Forests, STUMPAGE for the Ottawa NF was found to be positively skewed. Removing the highest values of STUMPAGE did not change the distribution appreciably so they were left in for the regression. If the values were 48 Table 9. Analysis of variance and parameter estimates for the dependent variable, STUMPAGE, Nicolet National Forest. Analysis of Variance Sum of Mean Source DF S es S V e Model 10 8981.6 898.2 68.8 *** Error 55 718.1 13.1 Total 65 9699.7 Adjusted R2 = 0.915 Parameter Estimates Parameter Standard V ' st' te Error INTERCEPT 6.88 1.62 as: OVERBID 0.95 0.07 as: SPEC -1.18 0.13 *t* x MHS 46.02 8.82 sea x SMS 73.42 6.48 *** % ES 65.48 6.84 sea 1 PS 32.91 8.67 *** # EMIX —4.36 1.58 as: # THIN 0.32 0.14 ts x CC 5.41 2.94 t x SALVAGE -15.79 4.59 at: * Significant at the 0.10 level ** Significant at the 0.05 level *** Significant at the 0.01 level 'R \ a 49 extreme, they should be obvious on the residual plots. As was done with the other National Forests, STUMPAGE was first regressed on the percent of volume by species-product, cut method counts, and the other usual variables. None of the cut method variables were significant at the 0.10 level. The significant variables were OVERBID, SPEC, MONTHS, % SMS, % YBS, % BCS, AND % SS. These same variables were significant when cut method volumes were used instead of cut method counts, but again, cut method was not significant. The final model tested included % EVOL and % UNVOL as independent variables in hopes that a very basic relationship between cut method and stumpage value might be shown. Only % EVOL was found to be significant at the 0.10 level. After reviewing the residuals, it was decided that three sales which had salvage volume should be dropped from the data set. The Ottawa NF had only one payment unit identified as being SALVAGE (Table 4), even though there were three salvage sales. The East King sale had only one payment unit, which was cut using a mix of salvage and selection cuts. Its standardized residual was over 5, large enough to be excluded. Ponozzo Lake had one payment unit listed as a commercial thinning. It too had a large standardized residual. The final salvage sale, Knucklehead Salvage, was also removed even though its standardized residual was within the acceptable range. Knucklehead Salvage had four payment units, all listed as being commercial thinnings. So, unlike the Nicolet NF which had 17 salvage payment units, the regression analysis for the Ottawa NF could not adjust STUMPAGE correctly for the salvaged sales. The model was then reestimated. The results of the final regression are shown in Table 10. The model has a reasonable adjusted R-square (0.844), 0.07 higher than the model which included the four discarded observations. The standard error of the estimate is slightly larger than the other National Forests’ models, although that is typical of larger samples. A plot of standardized residuals against predicted STUMPAGE did not exhibit any obvious patterns. The residuals were distributed normally and the Durbin-Watson did not test significant for autocorrelation. COMPARISON OF THE MODELS All of the models appear to be good predictors of STUMPAGE. No model had an adjusted R-square of less than 0.84, nor did any model violate any of the initial assumptions. The models shared some attributes, but were different with respect to the effect of cutting strategy on the high bid price. In all cases, OVERBID was highly significant in explaining the variation in STUMPAGE. The estimated regression coefficients for OVERBID ranged from 0.95 for the Nicolet NF to 1.06 for the Hiawatha NF. The Huron-Manistee NF and the Ottawa NF had coefficients of 1.01 and 1.04, 51 Table 10. Analysis of variance and parameter estimates for the dependent variable, National Forest. Analysis 0 Sum of Source ._DF Son 3 Model 8 38643. Error 118 6636. Total 126 45280. Adjusted R2 = 0.844 f Variance Mean 2 4830.4 9 56.2 1 Parameter Estimates STUMPAGE, Parameter Standard Variable Estimate Error INTERCEPT 8.18 2.71 ass OVERBID 1.04 0.13 ass MONTHS -0.13 0.06 ts SPEC -0.48 0.14 its x SMS 86.55 10.37 *ts x YBS 130.84 23.14 sat % BCS 259.39 66.87 tts x SS -117.37 35.59 #** x EVOL 7.12 2.78 as: ** Significant at the 0. *** Significant at the 0. 05 level 01 level Ottawa 85.9 *** res; inc: aff est thl re tn- 52 respectively. In general, you would expect a one dollar increase in the amount paid over the advertised rate to affect a one dollar increase in total price paid. The dollar cost of specified roads (SPEC) was significant for all models except the Hiawatha NF. The estimated regression coefficient had a negative sign, even though the buyer’s costs of permanent road building are reimbursed to a certain point. This could be indicative of two things: one, that the Forest Service underestimates the cost of road building, or two, timber buyers do not like the added burden of building a road. The latter more probable. Permanent roads take time away from logging and require close contact with the Forest Service to ensure that the road is being built to the proper specifications. This leads to longer and more involved sales. Longer sales negatively affected the predicted value of STUMPAGE, at least for the Hiawatha NF and the Ottawa NF. This could be due to uncertainty about future timber markets, or possibly it reflects a quantity discount (total volume and contract length are highly correlated). It might also be tied to the lost interest on the logger’s performance bond, which is not returned until the sale is completed. The magnitude of the effect is small, though, and many loggers received contract extensions. The cost of temporary road construction (TEMP) might be expected to have a significant negative impact on STUMPAGE, but it was significant only for the Huron-Manistee model. 53 The average value of TEMP for the Huron-Manistee was significantly higher than for the other Forests. It could be that loggers automatically deduct a set amount from every sale they bid on, regardless of the Forest Service estimate of road cost. In fact, if the estimated effect of TEMP from the Huron—Manistee NF model is subtracted from the mean of TEMP, the result is much closer to the range of the others. Haul distance (HAUL) might also be expected to negatively impact stumpage price, but it was only significant for the Huron-Manistee NF model, and it was only significant at the 0.0782 level. In their study, Jackson and McQuillan (1979) concluded that transportation costs are poor predictors of stumpage prices. These results appear to support their conclusions. As Buongiorno and Young (1984) discovered, individual species volume volumes were important predictors of STUMPAGE. Sugar maple sawtimber had a significant impact in all models, except the Huron—Manistee NF where all hardwoods are lumped together. Mixed hardwood sawtimber percentage did significantly affect STUMPAGE for the Huron—Manistee model, as well as the Nicolet NF and the Hiawatha NF. The mixed hardwoods category was not used very frequently for Ottawa NF sales, averaging less than 1 percent of total volume for all sales. Only one of the significant species volumes, spruce sawtimber, had a negative impact on STUMPAGE. That was for the Ottawa NF model and is probably indicative of a weak market for spruce. 54 It is interesting to note that none of the pulpwood volumes had a significant impact on average stumpage value. This could be due to nearly constant pulp values over the last ten years. The FOB mill price reported by Timber Mart-North from 1981 to 1989 for pulp bought in the Upper Peninsula of Michigan averaged around $40 per cord with very little variation when adjusted for inflation by the Producer’s Price Index for lumber (U.S. Dept. of Labor 1981 - 1990). The main impetus of this study was to discover the impact of the method of cut on stumpage value. The answer would seem to be that the impact is Forest specific. None of the cut methods used had a significant impact on sales from the Huron-Manistee NF. This could be due in part to the large number of payment units which were clearcut, almost 40 percent. Uneven—aged cutting methods were used on less than one-quarter of the payment units. Because of the similarities in cut methods used, cut method was simply not very good at explaining differences in stumpage value, even when using the most general measures (such as UNVOL and EVOL). Only the percentage of the sale cut by even-aged methods was significant for the Ottawa NF model. It could be that the high value of sugar maple sawtimber outweighed any differences between cut methods, particularly for the uneven-aged methods. Sugar maple sawtimber averaged 16 percent of sales volume for all Ottawa NF sales. Loggers 0| 0| would be likely to ignore how the timber was to be cut, instead, their main concern would be the quality (lumber grade) and quantity of sugar maple. Even-aged methods had a higher correlation with intolerant hardwoods such as yellow birch and black cherry, which require clearcuts or shelterwoods to reproduce. As the percentage of even-aged volume increased, so to would the percentage of higher value hardwoods. The Hiawatha NF and the Nicolet NF models provide the most information about the interaction of cut method and stumpage value. This is because no one individual species-product dominates the composition of the average sale, as sugar maple does on Ottawa NF sales or as mixed hardwoods do on the Huron—Manistee. This can be seen in Figures 1, 2, 3, and 4 which display the percentage of sale volume in sawtimber by species for the Hiawatha NF, the Huron-Manistee, the Nicolet NF, and the Ottawa NF. The Nicolet NF model shows a sharp decrease in STUMPAGE as the percentage of salvaged volume increases. For every one percent of sale volume being salvaged, STUMPAGE decreases by $0.16. That is consistent with the risks of salvage sales. The timber being cut might not all be sound, something which can be determined only after harvesting. Although Vasievich et al. (1988) recommend separate models for salvage sales, leaving the observations in the data set expands the model’s applicability. Both the Hiawatha NF and the Nicolet NF models show an Hiawatha National Forest 56 t «>6 .4? <1" of}? if ' (0‘ 3 1? a 3 Q C! \o 0 ‘0 ~2‘ Q) Q W 20—1 4; *3 we “Ida ‘ . ‘0’,“ ‘2: <9 0°41” \ 9,,” , '7 \ V /a°" Lh um; Jo 6% " \ \\ fir r r f 1” r I f ' r :2 2 ~° ° awnm/x alas (010; 1° rushed Sawtimber volume by species 1366 of ercent P total sale volume. Hiawatha . Sawtimber volume as a Figure 1 5? k2 oounmcn (no.5! .05§~o> Once nodes no mwcaawncsu a me 0552; sonfiwabdm .N 0.2.8....— mflouam .3 0E20> canzsom E ....x. w .. s a. ..1 w . w a. \ aw. w... 0. . i 9" i\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ may «meson .95.qu 03m.c021co.51 8000* r0 10— imp rON ewmon egos I010) ,o iuaoied 58 Nicolet National Forest 6 Sawtimber volume by species 7%. \\\\\\K\\\ \\\\\\\\k\ ,\\\\\\\\\\V r T U r I In P 20'1' amnion eios (010; ,0 weaned total sale volume. Nicolet NF. Figure 3. Sawtimber volume as a percentage of Ottawa National Forest J06” S‘ 59 Sawtimber volume by species \\\k\\\ \\\\\\\\\\\\\R\\\\\\\\R\\\\\\ 20- V I “r T V V I V V Y r I I V V T T O O IO 0 p p ewnm ems (0101 lo iuemad age of rcent total sale volume, 0 tewa NF. Figure 4. Sawtimber volume as a 60 increase in STUMPAGE as the number of thinning payment units increase. This could imply: 1) that thinnings are more cost effective than other methods; 2) that the species cut in the thinnings are more valuable; 3) that the Forest Service deliberately lowers the advertised rate for thinnings to entice buyers to cut otherwise marginal timber. The latter is probably the closest to the truth. Commercial thinnings are generally used to remove trees from a stand to allow more room for the remaining trees, and the removals are not the biggest or best trees in the stand. Thinnings cover a wide range of timber types, though, and some sales did include pine plantations which are almost always cut by row thinning. In those cases, thinning probably represents a cost savings. The amount of clearcutting did significantly impact STUMPAGE in both the Nicolet NF model and the reduced Hiawatha NF model, however, in opposite directions. As the percentage of clearcut volume in the Nicolet NF sales increased, the stumpage price also increased. A sale which was completely clearcut would be expected to be worth $5.40 more per be than a sale without clearcuts. The Hiawatha NF sales showed a decrease in stumpage price as the number of clearcut units increased. Since the average sale had 6 payment units, a sale which was completely clearcut would be worth about $3.35 less per be than a sale with no clearcut payment units. Lewis and Ramm (1990) have shown that clearcut units in 61 general correspond with lower valued sawtimber and pulp products for Hiawatha NF sales. This could cause the slight negative impact seen in the Hiawatha model. If the same correspondence were assumed for NicolethF sales, it would suggest that clearcutting is more cost effective than other methods, otherwise a decrease would be expected. On the other hand, the examplar clearcut species, aspen, was more prevalent in Nicolet NF sales than it was in Hiawatha NF sales (20.7% by volume verses 7.7%). Although it is not more valuable than other species which are usually clearcut, the demand for aspen is generally good because of its versatility. Just two other cut method variables were found to have significant coefficients in the models. EMIX in the Nicolet NF model was significant in a negative direction. However, since only 6 of 855 payment units were cut with a mix of even-aged methods, this variable would probably not be applicable to a different set of data. The number of road units was found to negatively impact STUMPAGE in the Hiawatha model, but here too, there were a limited number of such sales. It is possible that all volume removed for roads (both temporary and permanent) was not reported separately in all sales. RECOMMENDATIONS FOR FUTURE WORK Several extensions of this study are possible. The economies of scale might play a greater part in determining 62 STUMPAGE than was shown by the models. Perhaps non-linear functions of the economies of scale variables are needed. For example, a non-linear function of logging costs could be used in a model. As mentioned previously, stumpage value can be defined as the difference between the timber’s selling price and total logging costs where total logging costs are made up of fixed and variable costs. Average fixed costs tend to decrease non-linearily as volume harvested increases. On the other hand, average variable costs tend to increase exponentially as volume increases because of the need for more logging equipment, worker overtime, etc. When plotted against total volume, average total costs appear as a U-shaped curve, increasing for low and high timber volumes (Randall 1987). Including variables to account for these costs might provide a better fit to the data. Also, even though total sale acreage was not found to be significant in the models, the logarithm (natural or base 10) of total acreage might be. Another variation on model development would be the pooling of the data from all four Forests to create one regression model. Although it was shown that differences exist between the variable means and their variances, there are more advanced regression techniques available to deal with these problems, such as generalized or weighted least squares (see for example Fomby et a1. 1988). Using these techniques on pooled data can result in lower independent variable standard errors. Even though the model might not 63 provide as good a fit to the data, the larger data base could provide more information than separate models. If, for instance, one cut method prevails on a Forest, it could be masking the effects of other cut methods. Also, the problem of extremely high values of stumpage distorting the fit of the regression would be reduced. One more area of research could include further exploration of zero bid sales. It is possible that the cut method(s) to be used on the sale was the reason why it received no bids. Many zero bid sales are reappraised and sold through direct negotiation. The difference between the advertised rate of the unsold sale and the negotiated selling price could be incorporated as an underbid variable. This would be synonymous with a negative overbid variable. Overbid was used in this study to account for both competition effects and improperly specified timber values or cost allowances, so it would be logical to extend it to measure underbid. A dummy variable should also be included in the regression signifying the sale as negotiated so as to capture any other variation not explained by underbid, such as a favored buyer. Finally, the study could be extended to include data from other National Forests in the Great Lakes Region such as the Superior National Forest in Minnesota or the Chequamegnon National Forest in Wisconsin. It might be possible to include sales from State Forests with some modifications. Jackson (1987) found significant differences 64 between the stumpage value paid for National Forest timber and for State Forest timber in Montana due in part to differences in sale size. He did mention that the National Forest sales tended to use more sophisticated and costly logging systems (i.e., skyline logging) and that they were scrutinized by outside agents more than locally conducted sales. SUMMARY The biggest problem to overcome was how to relate method of cut to stumpage values. The Forest Service calculates only one price for each species in a sale, even though the species might occur in several payment units which are cut using different silvicultural methods. Loggers wishing to purchase the timber bid one price for all timber of the same species. Several approaches were tried using different combinations of cut method variables, species volume variables, and sale characteristic variables. Multiple regression analysis was used to help choose the best models. It was determined that four separate models would be needed, one for each National Forest in the study. The mix of cut methods used was shown to be statistically different between the National Forests, as were many of the other variables to be used in the regression analysis. In the interest of model sensitivity to cutting method impact, four models were developed rather than using dummy variables to show membership to a National Forest. The models indicate that certain cut methods do affect the stumpage price paid. On the Hiawatha National Forest, one additional thinning payment unit increased the average stumpage price paid per be by $1.02, while one additional road unit decreased the average stumpage price paid by $2.52 65 66 per be. When sales dominated by high valued species were removed from the data set, an additional clearcut unit also decreased the average stumpage price paid per be by $0.59. On the Nicolet National Forest, one additional thinning payment unit increased the average stumpage price per be by $0.31. As the percentage of sale volume being clearcut increased, so too did the average stumpage price paid per be, on the order of $0.55 for every one percent increase in clearcut volume. The average stumpage price decreased by $1.58 for every one percent increase in volume being cut from salvage payment units. Likewise, an additional payment unit cut by a mix of even-aged methods (shelterwood, clearcut, overstory removal) caused a decrease in the average stumpage price paid of $4.36 per be. The Ottawa National Forest model provided only a general indication of the effect of cut method. A sale with 100% of the volume cut by even-aged methods had an increase in average stumpage price paid per be of $7.12. On the Huron-Manistee National Forest, none of the cut methods were found to be significant. The evidence suggests that the logging systems used in the Lake States are flexible. In most cases, the effects seem to be more related to species being cut than to the cost savings of a particular method. As an example, clearcuts induced opposite effects on Hiawatha NF sales and Nicolet NF sales. If the effect was related to costs of the particular method, the direction of the effects should be 67 the same. Increased commercial thinning did result in an increase of stumpage value for both Hiawatha NF sales and Nicolet NF sales, and it is probably related to lower minimum bid rates set by the Forest Service for the timber. Thinnings do not usually remove the best trees, so an increase in stumpage value would not be expected solely because of the species being cut. Shelterwood cuts, selection cuts, overstory removals, and improvement cuts did not significantly affect the stumpage price in any of the models. The difference between the high bid and the advertised rate was found to be one of the more important predictors of stumpage value. Sugar maple sawtimber percentage was also important on all but the Huron-Manistee NF sales. Sugar maple is one of the higher valued species harvested from the three northern forests, and also one of the more abundant. The cost of building roads, both temporary and permanent, was also significant in the models. Finally, the study does provide some insight into what sort of variables can used to determine the effect of cut method on stumpage value. It also shows where the information can be located (Forest Service forms used to record the data) in the event that other researchers might be interested in examining the same relationships in other regions. APPENDIX 68 .omemtzmm spam oua>uom umouom .m.: .m unawam (3 O. 32:26 :3 «3. u .21..st mm: was cad—n...“ 33.5 33. one: .32. 3.3mm. "2:; 33:26.. 2.3... ‘13.... \BN. .U .33.; so as .a 3 £00.08. 1.. :- lm r. .3. 3. 1... i 8 I3; J¢m°p Ii. 8:19....‘00. as. 3.0.60: ...-60.x sass . .3. :39: . m .2; ..m. .... ......Casa roll- I. a m... 00.. Comm and. m... 0.0. 3 .23... o. .16.. 30°... 0 O .33 .89 .... a. a .. ..I a... m. :— ‘UOOQ. .80.. 9. D .3... s». p. . 80... he salt Urn m .223... .. ... as: so so... do. saga... os.-U¢sp..0u as: as... puss-80 .9. slow so... a...- 3... Lek-Ill a ..Ou Cam 0.8195 88-, 08.0! m3) sea ...... 33.35; ‘5.“ \6n :2. ...... Isu- mum—22.5 as as us cm use. i I l 1 09s.! sass .....c ...-393a as. ...-s: .....e e... 1. .. : 2...... 2...... 33.1.3333: bldthi‘sd m. ‘06. Id: 3. mule. .- a )Hilldllflu—u m .00.... '13 '4: N m be!” m-Cmm ... .0 5.13.0 $2.83.. ea: . I I .smfimgvénnr a N ......qu 3a.... .83... . «Omen at ‘8‘. . _ «~38: SQ-Iusausl a< udguturau _ use. can...» 3 Esau- 69 05041 rosesr seavncs _ APPRAISAL summavs:‘r't’afxcmwsg‘cnom EVIDENCE 56.: New. {ix-"we: ~:---:;«ro sv Arr. Wildcat (Compartments 7 6 21) C. kolesar 02/24/83 P ' ~ - PM?" 0.1 000.836 S'tcl' (0.0.C3VSY I‘V‘ol." ‘ ilECO".).H_,\, VV (0'! c. - . PUNCH c“ L'M.‘ _ - _ __ n 19 30 U 3, .IO UC' A‘.=")‘D '0 ’OAUH MILES I‘M“ GO CONST (M-'.‘| 3°0- : ft“. no COS, 9“ 0002. .6 i E . ,. . ‘ e 0 2 S mu rmoo. 3/82 - l/ o -. Tz‘fi ‘AR .. W?! .. 3v 500: 7‘ 9m 90- cc 9' 1e ' - 20767.69) Market I Cull-(v Aduuument Am 3:" Once - 7. , 228-15.50) all-no o- t! Bu:- 5392.91)‘ 238.36) 30.68) mm (264 .32) Unum C ."9 \V-lh ‘03 1“! agtm Cr 0 (. Socc-l to “one Can fast Monuments «Wu, H n 20 13 8201' ...... .-,.;, (21672.59) Ind-sow. “wt-M :1.st - - (2183 .00) Aelumsnn to Lms 40 Ram ADV/(Inna In!!! no . a . as) .141 _ l-fl-cs'mlt'u' — ' ‘n (43— 30-48: (15503.68) AMNSED an utes' wu- . (13509 . 39) ‘9. 11¢ ustment 47. Tunes: Fromm. Value ' -‘~J-vs:-'«-o-:vr - « '.-v-.'o-us- QII..-'a"l' -- .- '1“ - M' "0‘ 1". n . '5 ..:- .--r--e-J'--\mg he ‘wm J‘J-l'v wee 9‘» l' "l' ' '-“"‘- ’- " 'ila"(11/OU Figure 6. U.S. Forest Service form FSH-2409.22 Table 11. Explantion of symbols used in the Appendix. Forest and District codes 4 Huron-Manistee 4 Huron) 5 M10 6 Tawas 7 Harrisville 24 Manistee) 1 Baldwin 2 Cadillac 3 Manistee 4 White Cloud A 6 Nicolet 2 Eagle River 3 Florence 4 Lakewood 5 Laona 7 Ottawa 1 Bergland 2 Bessemer 3 Iron River 4 Kenton 5 Octonogan 6 Watersmeet 10 Hiawatha 1 Rapid River 2 Manistique 3 Munising 4 Sault Ste. Marie 5 St. Ignace ‘1 p—v Table 11. (Cont’d.). An S following a sale name signifies that it was listed as a salvage sale. Small Large No classification SBA Class ZF‘U} All dollar values are actual and have not been adjusted by the Consumer’s Price Index. Table 12. Values of Hiawatha National Forest variables. Contract 810 Termination 884 Total Pat. 4 of Sale name Number Forest District Date Date Months Class Acres Units Bids NAME FOR DIST MONTHS SBA ACRE P0 8108 800 19498 1 4 30-Sep-87 31-Oct-90 37.8 N 158 8 1 Bear 18282 10 4 09-Jul-85 31-Oct-87 28.1 S 40 1 1 Borealis 19355 10 3 28-Jun-87 30-Apr-94 83.3 S 1088 10 4 Camp A 17540 10 3 03-May-84 30-Apr-88 24.2 S 251 5 8 Cat 8 House 18050 10 3 28-Jan-85 3i-Oct-91 82.2 L 1455 15 1 CCC Hardwoods 19280 10 5 12-May-87 31-Oct-92 88.8 S 485 13 2 Clark 18508 10 4 05-Dec-85 31-Uct-87 23.2 N 48 2 2 Corner 1532 10 2 24-Sep-80 31-Oct-85 82.1 N 395 3 2 County Line 20445 10 1 13-Jun-89 30-Apr-92 35.1 N 310 5 8 Dad’s Camp 19308 10 4 21-May-87 31-00t-90 42.0 N 100 3 3 Deer Creek 1803 10 3 29-Dec-81 31-00t-88 58.9 L 558 8 1 Dukes 18484 10 3 01-Nov-85 30-Apr-88 30.4 N 124 8 2 East Barrett 19413 1‘ 5 11-Aug-87 31-Oct-92 83.8 S 240 4 3 East Spur Hardwoods 18042 10 5 23-Apr-85 30-Apr-87 24.8 N 15 7 5 FH-13 Followup 18258 10 1 18-Jun-85 30-Apr-89 47.1 N 184 8 8 Flow Nest 1847 10 3 10-Jan-83 31-Oct-87 58.5 N 725 10 1 Gleason Lake East 19837 10 1 24-Nov-87 31-Oct-91 47.9 N 572 8 2 Hound Dog Hollow 19520 10 3 14-Oct-87 30-4pr-92 55.3 N 451 8 2 Johnson Creek Hardwoods 18813 10 1 04-Mar-88 31-Oct-90 58.? N 383 10 8 Johnson Lookout 1512 10 3 07-Jul-80 31-Oct-84 52.8 N 448 4 3 Johnson Lookout Resale 18290 10 3 09-Jul-85 30-Apr-87 22.0 N 158 2 2 Kenobo Lake 19811 10 1 17-N0v-87 31-Oct-92 80.3 N 925 8 8 Kinble Lake 1841 10 3 17-Dec-82 31-Oct-88 47.1 N 521 8 1 LaRock Hardwoods 19074 10 4 02-Dec-88 31-Oct-88 23.3 N 71 3 1 Lawson Road 18175 10 3 01-May-85 30-Apr-87 24.3 N 84 3 7 Lawson Road Resale 19330 10 3 12-Jun-87 30-Apr-88 10.8 L 49 2 9 Little Pole Lake 17573 10 1 25-May-84 31-0ct-90 78.3 L 924 20 2 Lost Luck Hardwoods 18882 10 3 01-Apr-88 31-Oct-88 31.5 N 188 3 8 Lower Farm Hill 1845 10 5 15-Dec-82 30-Apr-85 28.9 N 73 2 3 Maple Hill Nest 20084 10 5 05-Oct-88 31-Oct-92 49.8 S 189 4 4 Markey Lee 18829 10 4 09-Jul-88 31-Oct-88 28.2 N 88 2 3 McNearney Lake 19132 10 4 23-Dec-88 30-Apr-89 28.8 N 82 4 1 Mormon Creek Hardwoods 1 1838 10 1 25-Oct-82 31-0ct-83 12.4 N 50 1 1 lorlon Creek Hardwoods 11 19888 10 1 17-May-88 31-Oct-92 54.3 N 377 9 4 Mormon Creek Resale 18274 10 1 25-Jun-85 31-0ct-88 18.4 N 50 1 1 Nivation 1878 10 3 29-Jun-83 30-Apr-88 58.9 L 485 7 1 North Country Hardwoods 17488 10 5 09-Apr-84 30-Apr-88 49.4 S 251 4 1 Nugget 19777 10 5 09-Mar-88 31-Oct-93 88.7 N 587 14 2 Paradise 19314 10 3 22-May-87 30-Apr-90 3 .8 L 574 8 3 Pine Plains 19928 10 3 08-Jul-88 30-Apr-92 48.4 N 214 4 4 Play it Again 19553 10 4 29-Oct-87 31-Oct-91 48.8 N 154 3 3 Poplar Lake 19389 10 1 21-Jul-87 31-0ct-89 27.8 N 190 4 4 Porcupine 19383 10 1 30-Jun-37 31-Oct-90 40.8 N 333 7 5 Quarry Road Hardwoods 17831 10 5 29-Jun-84 31-Oct-88 28.5 S 27 9 3 Railbead Hardwoods 18805 10 3 03-Mar-88 31-Oct-88 8.1 N 188 1 3 Table 12. [Cont‘d.1. Contract 810 Termination 58A Total Fat. 4 of Sale name Number Forest District Date Date Months Class Acres Units Dids NAME FOR 0187 MONTHS SBA ACRE P0 8108 R.E.0. 18128 10 3 24-00t-83 30-Apr-89 87.2 N 858 9 1 Samson 11 17849 10 4 10-Jul-84 31-0ct-85 15.9 S 80 1 2 Sand Lilly 19803 10 3 12-Nov-87 30-Apr-94 78.? S 894 10 2 Satago 19829 10 5 18-N0v-87 31-0ct-90 35.9 S 88 3 3 Silver 18852 10 3 25-Jul-88 30-Apr-92 70.2 N 428 4 3 Sky’s the Limit 20205 10 3 13-Dec-88 30-Apr-93 53.3 N 473 7 2 South Sugarbush 1580 10 3 30-Mar-81 31-00t-82 19.3 S 181 1 2 Spinulous 19118 10 4 18-Dec-88 30-Apr-89 28.8 S 51 2 2 Spring South 20108 10 5 19-0ct-88 31-0ct-93 81.3 N 151 5 2 Steam Engine Run 19878 10 3 17-May-88 30-Apr-89 11.8 N 442 5 3 Stillman 20197 10 3 09-Dec-88 30-Apr-93 53.4 N 473 7 3 Three Cedar Murphy 20213 10 2 19-Dec-88 31-Oct-94 71.4 N 720 20 3 Twenty-four Hardwoods 1831 10 5 28-Aug-82 30-Apr-84 20.4 N 13 5 2 Upper Farm Hill 1883? 10 5 17-Jul-88 31-Oct-88 27.9 S 35 3 3 Vertz 19348 10 5 17-Jun-87 31-Oct-90 41.1 S 88 2 2 Hanna 19450 10 4 29-Jul-87 31-Oct-89 27.5 N 42 1 1 Hanna Two 20015 10 4 20-Sep-88 31-Oct-91 37.9 N 80 2 2 Table 12. lCont‘d.). Adv. Stat Total 508 CPI Total Saw Pulp Spec Sale name Rate Hi bid Overbid Value Value 11982: Haul Haul Haul Road NAME ADVER STUMPAGE 0788810 VALUE FOBVAL 1001 HAUL SANH PULPH SPECS $7be S/be 3/be 3 S/be Miles Miles Miles 3 0CD 10.3 10.38 0.00 9302 88.53 115.0 24.8 41 24 0 Dear 14.37 18.75 2.38 2355 98.81 107.8 38.8 49 33 0 Borealis 4.39 11.04 8.85 47824 85.70 113.5 47.0 9 53 350 Camp A 4.88 14.28 9.80 13202 98.18 103.4 51.1 18 55 0 Cat 8 House 8.71 11.74 3.03 89590 92.98 105.5 47.3 12 52 33808 CCC Hardwoods 9.17 12.93 3.78 17433 87.10 113.1 23.9 13 28 0 Clark 11.98 13.02 1.04 2287 92.43 109.3 23.4 37 21 0 Corner 11.18 20.08 8.88 31935 N/A 84.0 40.0 40 40 4977 County Line 11.71 22.32 10.81 28882 95.40 124.1 35.2 37 35 5997 Dad's Camp 3.71 4.83 1.12 3575 78.88 113.1 24.3 40 24 0 Deer Creek 1.97 3.11 1.14 7570 84.17 94.0 49.0 49 49 9348 Dukes 50.95 88.49 15.54 35993 125.01 109.0 17.2 1 42 0 East Barrett 12.48 18.25 5.79 11089 91.12 114.4 20.7 20 21 0 East Spur Hardwoods 17.85 29.14 11.49 3744 98.30 108.9 20.4 21 20 0 FH-13 Followup 8.23 9.81 3.38 12248 89.24 107.8 31.2 33 31 12319 Plow Hest 1.25 1.32 0.07 2299 83.88 97.8 47.1 10 51 5292 Gleason Lake East 13.07 20.84 7.57 37798 85.02 115.4 38.2 40 38 0 Hound Dog Hollow 21.13 23.95 2.82 50012 94.00 115.3 41.1 7 48 0 Johnson Creek Hardwoods 8.81 12.84 3.83 24537 88.94 108.8 38.1 38 38 8358 Johnson Lookout 1.87 8.70 7.03 15514 N/A 82.7 44.2 44 44 0 Johnson Lookout Resale 5.33 11.28 5.93 8332 90.54 107.8 44.4 8 5 0 Kenobo Lake 14.84 21.19 8.35 57191 88.18 115.4 38.3 40 38 0 Kimble Lake 8.88 8.59 1.93 18812 88.87 7.8 45.1 22 50 3187 LaRock Hardwoods 13.91 14.79 0.88 4000 81.78 110.5 22.3 38 20 0 Lawson Road 51.81 87.32 35.51 30000 134.08 107.3 13.0 3 44 D Lawson Road Resale 58.81 110.88 52.05 27388 127.24 113.5 13.0 3 44 0 Little Pole Lake 1.28 5.48 4.18 28925 95.01 103.4 34.3 38 3 24932 Lost Luck Hardwoods 8.87 15.10 8.23 11419 90.57 108.8 50.3 12 58 835 Lower Farm Hill 10.35 17.78 7.41 8483 100.04 97.8 22.8 22 23 0 Maple Hill Hest 11.74 19.85 8.11 7433 98.59 120.2 22.3 49 14 0 Markey Lee 10.42 12.22 1.80 3422 84.71 109.5 14.8 30 14 0 McNearney Lake 13.27 14.24 0.97 4293 88.48 110.5 25.8 3‘ 22 0 Mormon Creek Hardwoods 1 13.58 21.54 7.98 4025 89.29 98.2 27.3 29 27 0 Mormon Creek Hardwoods 11 8.01 14.83 8.82 17183 88.35 117.5 3 .1 35 34 9308 Mormon Creek Resale 14.49 15.48 0.97 3007 89.88 107.8 27.3 29 27 0 Nivation 1.93 1.93 0.00 3215 91.70 99.5 44.7 10 51 8382 North Country Hardwoods 10.89 12.10 1.21 9980 101.82 103.1 14.2 18 13 0 Nugget 8.83 13.48 4.83 20118 88.43 118.5 21.0 50 18 0 Paradise 12.33 18.18 3.85 29598 89.92 113.1 87.5 23 79 0 Pine Plains 7.98 18.49 10.53 18083 88.28 118.5 58.8 9 83 0 Play it Again 3.44 8.05 2.81 3595 79.79 115.3 22.0 0 22 0 Poplar Lake 4.73 8.89 3.98 7430 83.18 113.8 31.3 34 '1 8579 Porcupine 18.83 21.48 4.85 23797 89.88 113.5 27.4 29 27 0 Quarry Road Hardwoods 13.02 18.87 5.85 4000 105.28 103.7 15.1 20 12 0 Railhead Hardwoods 10.03 12.57 2.54 8251 89.85 108.8 58.4 4 83 D 75 Table 12. (Cont’d.). Adv. Stat Total 808 CPI Total Saw Pulp Spec Sale name Rate Hi bid Overbid Value Value 11982: Haul Haul Haul Road NAME ADVER STUMPAGE OVERBID VALUE EUHVAL 1001 HAJL SAkH PULPH SPECS S/be S/be Sibe 3 S/Hbf Miles Miles Miles 3 R.E.0. 2.29 2.38 0.09 4577 94.85 101.0 51.1 3 58 0 Samson II 7.07 1 .11 4.04 3592 88.10 104.1 12.0 0 12 0 Sand Lilly 18.89 17.12 0.43 79177 91.85 115.4 58.4 17 83 0 Satago 12.35 17.45 5.10 2793 88.'5 115.4 20.8 20 21 0 Silver 4.85 10.13 5.28 28197 87.48 109.5 48.5 10 53 0 Sky’s the Limit 18.53 19.22 2.89 31880 95.80 120.5 41.3 9 50 2400 South Sugarbush 10.02 11.83 1.81 4500 82.75 88.5 43.0 43 43 0 Spinulous 5.90 10.20 4.30 1581 72.00 110.5 15.0 0 15 0 Spring South 8.77 13.17 8.40 8917 89.39 120.2 24.4 54 19 0 Steam Engine Run 15.98 17.32 1.34 38098 89.30 117.5 44.8 10 51 7708 Stillman 11.58 13.53 1.97 30980 88.75 120.5 49.8 12 53 4874 Three Cedar Murphy 5.39 13.14 7.75 50889 87.88 120.5 55.8 28 59 18888 Twenty-four Hardwoods 14.71 22.00 7.29 3001 91.58 97.7 19.2 21 18 0 Upper Farm Hill 18.45 37.53 19.08 3878 107.51 109.5 20.8 20 21 0 Vertz 13.25 18.43 3.18 3089 93.11 113.5 18.1 28 13 0 Hanna 9.74 18.88 8.92 2807 88.21 113.8 34.8 48 32 0 Hanna Two 13.50 15.42 1.92 4109 95.81 119.8 38.5 51 35 0 78 Table 12. (Cont’d.1. Avg. Temp Avg. Temp Total Avg. Even Uneven Clear Shelt imp Sale name Spec Road Temp Miles Volume Volume Volume Volume Volume Volume Volume NAME SPEC TEMPS TEMP TEMPM VOL AVGVOL EVOL UNVOL CC SHELT 1MP $/be S S/be Miles M51 be/acre be M87 851 th Mtf BCD 0.00 ‘50 0.28 0.5 898 5.87 804 292 808 0 0 Bear 0.00 0 0.00 0.0 141 3.52 0 141 ' 0 0 Borealis 0.08 2289 0.53 1.0 4313 4.05 931 3040 342 590 2395 Camp A 0.00 889 0.72 1.0 928 3.89 514 411 0 0 Cat 8 Mouse 5.71 2289 0.39 1.0 5928 4.07 1375 4550 890 281 2154 CCC Hardwoods 0.00 850 0.83 0.5 1349 2.90 535 139 535 0 0 Clark 0.00 225 1.29 0.4 174 3.79 0 174 0 0 0 Corner 3.13 1148 0.72 0.8 1592 4.03 293 1299 292 0 0 County Line 4.84 3888 2.99 1.4 1293 4.17 0 1293 0 0 0 Dad’s Camp 0.00 400 0.54 0.8 740 7.40 740 0 742 0 0 Deer Creek 3.84 955 0.39 1.0 2437 4.37 873 925 878 0 0 Dukes 0.00 388 0.88 0.0 541 4.37 0 541 0 0 0 East Barrett 0.00 0 0.00 0.0 807 2.53 189 438 189 U 0 East Spur Hardwoods 0.00 0 0.00 0.0 128 8.57 0 128 0 0 0 EH-13 Followup 9.87 1189 0.92 1.0 1274 8.92 738 537 888 0 0 Flow Nest 3.05 447 0.28 0.0 1738 2.40 9 1274 0 0 444 Gleason Lake East 0.00 2958 1.82 0.3 1831 3.20 0 1831 0 0 0 Hound Dog Hollow 0.00 200 0.10 0.0 2089 4.83 0 1750 0 0 843 Johnson Creek Hardwoods 4.30 1809 0.83 0.9 1941 5.07 281 1880 274 0 727 Johnson Lookout 0.00 0 0.00 0.0 1783 4.00 0 1783 0 0 0 Johnson Lookout Resale 0.00 801 1.07 0.5 582 3.80 0 582 0 0 582 Henobo Lake 0.00 3378 1.25 1.0 2899 2.92 0 2248 0 0 0 Kimble Lake 1.82 525 0.27 0.7 1958 3.78 382 1199 384 0 0 Lanck Hardwoods 0.00 200 0.74 0.4 271 3.81 0 271 0 0 0 Lawson Road 0.00 0 0.00 0.0 344 5.37 0 344 0 U U Lawson Road Resale 0.00 0 0.00 0.0 248 5.05 0 248 0 0 0 Little Pole Lake 5.08 1753 0.38 2.1 4930 5.34 2289 2881 2014 142 422 Lost Luck Hardwoods 1.10 294 0.39 0.0 758 4.58 0 383 0 0 51 Lower Farm Hill 0.00 85 0.23 0.1 385 5.00 0 385 U 0 0 Maple Hill Vest 0.00 0 0.00 0.0 375 2.22 0 375 0 0 0 Markey Lee 0.00 0 0.00 0.0 280 4.24 0 280 0 0 0 McNearney Lake 0.00 300 1.00 0.8 302 3.88 0 332 0 0 0 Mormon Creek Hardwoods 1 0.00 400 2.14 0.0 187 3.74 0 187 0 0 187 Mormon Creek Hardwoods 11 8.03 2743 2.37 0.2 1159 3.07 281 898 254 0 0 Mormon Creek Resale 0.00 0 0.00 0.0 194 3.89 0 194 0 0 195 Nivation 3.83 505 0.30 0.3 1887 3.59 221 0 217 0 0 North Country Hardwoods 0.00 429 0.52 0.2 823 3.28 204 819 204 0 341 Nugget 0.00 850 0.43 0.8 1495 2.84 0 1094 0 0 1025 Paradise 0.00 804 0.33 0.0 1829 3.19 0 1829 0 0 1027 Pine Plains 0.00 755 0.77 1.2 977 4.57 0 585 0 0 0 Play it Again 0.00 750 1.28 1.5 594 3.88 0 594 0 0 0 Poplar Lake 10.03 1338 1.56 0.5 855 4.50 14 841 0 0 0 Porcupine 0.00 ‘245 2.03 0.5 1108 2.82 322 787 0 322 0 Quarry Road Hardwoods 0.00 808 2.83 0.5 214 7.94 214 0 215 0 0 Railhead Hardwoods 0.00 0 0.00 0.0 857 3.49 0 857 0 0 0 Table 12. (Cont’d.1. Avg. Temp Avg. Temp Total Avg. Even Uneven Clear Shelt imp Sale name Spec Road Temp Miles Volume Volume Volume Volume Volume Volume Volume NAME SPEC TEMPS TEMP TEMPM VUL AVGVUL EVOL UNVOL CC SHELT lMP $7807 3 37801 Miles 887 be/acre 'be 851 M01 851 M08 8.8.0. 0.00 1397 0.73 1.0 1927 2.94 945 982 703 243 0 Samson 11 0.00 0 0.00 0.0 323 5.3‘ 0 323 0 0 0 Sand Lilly 0.00 200 0.04 0.3 4825 5.17 928 1908 927 0 513 Satago 0.00 0 0.00 0.0 180 2.42 0 180 U U 182 Silver 0.00 175 0.07 0.2 2588 8.04 0 1220 0 0 737 Sky’s the Limit 1.45 450 0.27 2.1 1858 3.50 0 1088 0 0 0 South Sugarbush 0.00 0 0.00 0.0 392 2.17 0 392 0 0 0 Spinulous 0.00 250 1.81 0.5 155 3.04 0 155 0 0 0 Spring South 0.00 0 0.00 0.0 877 4.48 481 197 482 0 0 Steam Engine Run 3.70 0 0.00 0.0 2085 4.72 34 789 0 0 771 Stillman 2.13 32‘ 0.14 0.0 2288 4.84 495 1358 479 18 1358 Three Cedar Murphy 4.38 3081 0.79 0.8 3859 5.38 718 '143 893 0 143 Twenty-four Hardwoods 0.00 0 0.00 0.0 138 10.49 138 0 138 0 0 Upper Farm Hill 0.00 700 7.14 0.8 98 2.80 0 98 0 0 0 Vertz 0.00 0 0.00 0.0 188 2.85 0 188 0 0 0 Hanna 0.00 250 1.48 0.5 189 4.01 0 189 0 0 0 Vanna Two 0.00 0 0.00 0.0 287 3.33 0 287 0 0 0 Table 12. lCont’d.1. Even Uneven Clear % Vol % Vol %EVOL %UNVOL 87. “Q 27 D—‘ O—‘ C'CQCQCJ‘ ab ('2 rev—o N COOC-‘CJCNoc-CC-C‘C'O‘. , .— u x ' B 100. 4% 32.8% .0% 21. 55. 23. 39. .0% 18. .0% 100. 8% 8% 2% 8% 4% 0% ' .8% .0% .8% .0% .8% .5% .0% .0% .5% .0% .0% .0% .5% .0% .0% .0% .0% .0% .0% .0% .0% .0% .0% .5% .0% .3% .8% .0% .0% .0% .0% .8% .0% 0% 100. 70. 44 78. 10. 100. .8% 81 100. 0. 38. 100. 72. 100. .2% .3% 42 73 100. 83 85. 100. 100. 83. 81 100. 100. 100. 54 48. 100. 100. 100. 100. 100. 77 0% 5% .4% 8% 3% 0% 0% 0% 0% 0% 2% 0% 0% .8% 5% 0% '3 3% .2% 0% 0% 0% .0% 0% 0% 0% 0% 0% 0% .5% 100. .0% 75. 73. 100. 57.- 100. 0% 2% ceascorCH fixb‘rflfl')‘8 Shelt imp % Vol % Vol %CC %SHELT %lMP .—‘ C LC .5 N NA >—- .... C ' .9% C - 3. .9% ”.0% .8% .0% .3% .0% 100. .7% .0% 3.9% .0% .0% .0% .0% .0% .1% .0% .0% 0% .0% .0% .0% .0% .0% .9% .0% .0% .0% .0% .0% .0% .9% .0% .0% .8% .0% .0% .0% .0% .0% .0% C: a. ...4 NOC‘CCCCCHCC-CCOOCCCOC‘GOGI‘chc (‘3 C: it C b. -~3 coccxmc: flflflfli')! CZ. C'C: )0 . % CCC ”bib! .0% .0% .0% .0% .01 .0% .0% .0% .3% .0% .0% .91 .0% .0% .0% .0% .0% .0% .0% .0% .0% .01 .0% .01 .0% .0% .0% .1% .01 (J‘ (a: OMQOCC'QCOCCOG‘aCOCC N- t. L‘ A C. ‘3 ’ H C COflCK‘CC‘CZ-CC~CO . h .0% .5% .0% .3% .0% .0% .J% .0% q o D .0% .0% .0% .0% .0% .8% .0% .4% .b .0% .0% .8% VA .0% .0% .0% .8% .7% m 0 f3 .0% .0% .0% .0% .0% 1.0% .0% .4% .8% '.2% .0% .0% .0% .0% .0% Thin Select Road Mixed Unmix Emix Sale name Volume Volume Volume Volume Volume Volume % Vol NAME THlN SELECT ROAD Mlx UNMlX EMlX M87 M08 be M87 M81 881' 8CD 292 0 0 0 0 0 Bear 0 141 0 0 0 0 Borealis 0 0 0 344 854 0 Camp A 0 412 0 0 0 0 Cat 8 Mouse 2398 0 150 0 0 252 CCC Hardwoods 0 139 0 879 0 0 Clark 174 0 0 0 0 0 Corner 0 1297 0 0 0 0 County Line 741 558 0 0 0 0 Dad’s Camp 0 0 0 0 0 0 Deer Creek 283 850 0 839 0 0 Dukes 0 542 0 0 0 0 East Barrett 0 438 0 0 0 0 East Spur Hardwoods 0 128 0 0 0 0 FH-l3 Followup 148 0 51 0 390 0 Flow Vest 0 298 9 448 538 0 Gleason Lake East 1835 0 0 0 0 0 Hound Dog Hollow 910 0 0 339 0 0 Johnson Creek Hardwoods 523 0 8 0 408 0 Johnson Lookout 0 1782 0 0 0 0 Johnson Lookout Resale 0 0 0 0 0 0 Kenobo Lake 2254 0 0 452 0 0 Kimble Lake 0 1202 0 378 0 0 LaRock Hardwoods 273 0 0 0 0 0 Lawson Road 0 344 0 0 0 0 Lawson Road Resale 0 248 0 0 0 0 Little Pole Lake 0 2239 111 0 0 0 Lost Luck Hardwoods 0 0 0 394 312 0 Lower Parm Hill 0 385 0 0 0 0 Maple Hill Vest 0 378 0 0 0 0 Markey Lee 280 0 0 0 0 0 McNearney Lake 304 0 0 0 0 0 Mormon Creek Hardwoods l 0 0 0 0 0 0 Mormon Creek Hardwoods 11 114 788 8 0 0 0 Mormon Creek Resale 0 0 0 0 0 0 Nivation 0 0 4 1448 0 0 North Country Hardwoods 0 82 0 0 213 0 Nugget 0 0 0 402 73 0 Paradise 0 0 0 0 804 0 Pine Plains 587 0 0 413 0 0 Play it Again 598 0 0 0 0 0 Poplar Lake 438 0 14 0 408 0 Porcupine 790 0 0 0 0 0 Quarry Road Hardwoods 0 0 0 0 0 0 Railhead Hardwoods 0 0 0 0 857 0 0. 0% C )0 CCCCCCCCI‘WC‘D—‘CCCCCCACCC‘ .0% .0% CCCCC .0% Table 12. {Cont’d.1. Sale name NAME 8.8.0. Samson 11 Sand Lilly Satago Silver Sky’s the Limit South Sugarbush Spinulous Spring South Steam Engine Run Stillman Three Cedar Murphy Twenty-four Hardwoods Upper Farm Hill Vertz Vanna Vanna Two Thin Select Road THIN SELECT ROAD 595 323 520 392 198 occocach-Accococ-ooc» 79 Mixed Unmix Emix Mix 0 0 1052 0 1388 591 UNMlX 881% 383 0 809 0 0 (’1 Us <2? C‘ OCC‘OCSJQCOOG c'oc-cc'oocccococ-occ Even 100 CCC Uneven Clear Volume Volume Volume Volume Volume Volume % Vol % Vol % Vol %EVOL %UNVUL .1% 50 .0% 100. ' .0% 41 .0% 100 .0% 47 .0% 84 .0% 100 .0% 100 .0% 29. .8% 38. .8% 59 .5% 81 .0% 0. .0% 100. .0% 100. . % 100 .0% 100. .9% 0% .2% .0% .2% .4% .0% .0% 0% 9% .2% .5% 0% my a 0% .0% 0% %CC Shelt imp % Vol % Vol 38.5% 12.8% 0.0% 0.0% 20.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 71.2% 0.0% 0.0% 0.0% 20.9% 0.7% 18.0% 0.0% 99.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% %SHELT %lMP 100 .0% .0% .1% .0% '.3% .0% .0% .0% .0% .0% .4% .7% .0% .0% .0% .0% .0% 80 Table 12. (Cont d.J. Thin Select Road Mixed Unmix Emix 4 of 4 of 4 of 4 of 4 of Sale name % Vol % Vol % Vol % Vol % Vol % Vol Clear Shelter Imp Thin Select NAME %THlN %SELECT %RUAD %MlX %0NM1X %EM1X ACC tSHELT 41MP 41818 tSELECT 8CD 32.8% 0.0% 0.0% 0.0% 0.0% 0.0% 8 0 0 2 0 Bear 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 1 Borealis 0.0% 0.0% 0.0% 8.0% 15.2 0.0% 1 1 8 0 0 Camp A 0.0% 44.5% 0.0% 0.0% 0.0% 0.0% 3 0 0 0 2 Cat 8 Mouse 40.4% 0.0% 2.5% 0.0% 0.0% 4.3% 1 1 4 7 0 CCC Hardwoods 0.0% 10.3% 0.0% 50.4% 0.0% 0.0% 2 0 0 0 3 Clark 99.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 2 0 Corner 0.0% 81.5% 0.0% 0.0% 0.0% 0.0% 1 0 0 0 2 County Line 57.3% 43.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 3 2 Dad’s Camp 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3 0 0 0 0 Deer Creek 11.8% 28.7% 0.0% 34.4% 0.0% 0.0% 4 0 0 1 2 Dukes 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 8 East Barrett 0.0% 72.2% 0.0% 0.0% 0.0% 0.0% 1 0 0 0 3 East Spur Hardwoods 0.0% 99.8% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 7 FH-13 Followup 11.5% 0.0% 4.0% 0.0% 30.8% 0.3 2 0 0 1 0 Flow Vest 0.0% 17.0% 0.5% 25.8% 30.8% 0.0% 0 0 2 0 3 Gleason Lake East 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 8 0 Hound Dog Hollow 43.8% 0.0% 0.0% 18.2% 0.0% 0.0% 0 0 3 2 0 Johnson Creek Hardwoods 28.9% 0.0% 0.3% 0.0% 21.0% 0.0% 1 0 3 4 0 Johnson Lookout 0.0% 99.9% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 4 Johnson Lookout Resale 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 2 0 0 Kenobo Lake 83.5% 0.0% 0.0% 18.8% 0.0% 0.0% 0 ’ 0 0 7 0 Kimble Lake 0.0% 81.4% 0.0% 19.2% 0.0% 0.0% 2 0 0 0 3 LaRock Hardwoods 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 3 0 Lawson Road 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 4 Lawson Road Resale 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 2 Little Pole Lake 0.0% 45.4% 2.3% 0.0% 0.0% 0.0% 8 1 3 0 9 Lost Luck Hardwoods 0.0% 0.0% 0.0% 52.1% 41.2% 0.0% 0 0 1 0 0 Lower Farm Bill 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 2 Maple Hill Vest 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 4 Markey Lee 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 2 0 McNearney Lake 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 4 0 Mormon Creek Hardwoods I 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 1 0 0 Mormon Creek Hardwoods 11 9.8% 87.8% 0.7% 0.0% 0.0% 0.0% 2 0 0 1 5 Mormon Creek Resale 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 1 0 0 Nivation 0.0% 0.0% 0.2% 88.8% 0.0% 0.0% 1 0 0 0 0 North Country Hardwoods 0.0% 7.5% 0.0% 0.0% 25.9% 0.0% 1 0 1 0 1 Nugget 0.0% 0.0% 0.0% 28.9% 4.9% 0.0% 0 0 10 0 0 Paradise 0.0% 0.0% 0.0% 0.0% 44.0% 0.0% 0 0 4 0 0 Pine Plains 58.0% 0.0% 0.0% 42.3% 0.0% 0.0% 0 0 0 3 0 Play it Again 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 3 0 Poplar Lake 51.0% 0.0% 1.8% 0.0% 47.5% 0.0% 0 0 0 2 0 Porcupine 71.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0 2 0 5 0 Quarry Road Hardwoods 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 9 0 0 3 0 Railhead Hardwoods 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0 0 0 0 0 81 Table 12. {Cont’d.1. Thin Select Road Mixed Unmix Emix 4 of 4 of 4 of 4 of 4 of Sale name % Vol % Vol % Vol % Vol % Vol % Vol Clear Shelter lmp Thin Select NAME %THIN %SELECT %ROAD %MlX %UNMlX %EMlX 4CC 4SHELT 41MP 47810 tSELECT R.E.0. 0.0% 30.9% 0.0% 0.0% 19.9% 0.0% 3 1 0 0 3 Samson 11 0.0% 99.9% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 1 Sand Lilly 12.7% 0.0% 0.0% 22.7% 17.5% 0.0% 2 0 1 4 0 Satago 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 3 0 0 Silver 19.9% 0.0% 0.0% 52.9% 0.0% 0.0% 0 0 1 l 0 Sky’s the Limit 0.0% 31.4% 0.0% 35.7% 33.2% 0.0% 0 0 0 0 3 South Sugarbush 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 1 Spinulous 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 2 0 Spring South 0. % 29.2% 0.0% 0.0% 0.0% 0.0% 2 0 0 0 3 Steam Engine Run 0.0% 0.0% 1.8% 81.5% 0.0% 0.0% 0 0 2 0 0 Stillman 0.0% 0.0 0.0% 19.2% 0.0% 0.0% 1 1 4 0 0 Three Cedar Murphy 43.1% 34.9% 0.7% 0.0% 0.0% 0.0% 3 0 1 8 7 Twenty-four Hardwoods 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 5 0 0 0 0 Upper Farm Hill 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 3 Verta 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 0 2 Vanna 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 1 0 Vanna Two 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 0 2 0 Table 12. 1Comt'd.1. 4 of 4 of 4 of 4 of Mixed Red Sugar Yellow Elm Black Sale name Roads Mix Unmix Emix Hdws Maple Maple Birch Elm Aspen Beech Cherry NAME 4ROAD 4MlK 40NM11 4EMlx MHS RMS SMS YBS ES AS 888 BC BCD 0 0 0 0 24 0 0 0 0 0 0 0 Bear 0 0 0 0 15 0 18 0 0 0 0 0 Borealis 0 1 1 0 0 350 90 32 0 39 28 0 Camp A 0 0 0 0 0 0 43 0 0 0 0 0 Cat A Mouse 1 0 0 1 94 143 91 28 0 0 12 0 CCC Hardwoods 0 8 0 0 9 0 111 0 0 42 0 0 Clark 0 0 0 0 0 0 28 0 0 0 0 0 Corner 0 0 0 0 381 0 0 0 0 32 0 0 County Line 0 0 0 0 0 108 0 0 0 0 0 20 Dad's Camp 0 0 0 0 0 0 0 0 0 12 0 0 Deer Creek 0 1 0 0 0 14 44 25 0 88 78 0 Dukes 0 0 0 0 0 0 291 37 0 0 0 0 East Barrett 0 0 0 0 54 0 85 0 0 30 0 0 East Spur Hardwoods 0 0 0 0 50 0 0 0 0 0 0 0 FH-13 Followup 1 0 2 0 24 54 0 0 0 29 0 0 Flow Vest 1 2 2 0 32 22 85 18 0 0 20 0 Gleason Lake East 0 0 0 0 150 0 0 0 0 0 0 0 Hound Dog Hollow 0 1 0 0 9 25 39 18 0 0 0 0 Johnson Creek Hardwoods 1 0 1 0 89 0 0 0 0 0 0 0 Johnson Lookout 0 0 0 0 0 58 80 52 20 0 53 0 Johnson Lookout Resale 0 0 0 0 0 11 28 25 0 0 19 0 Kenobo Lake 0 1 0 0 399 0 0 0 ' 0 0 0 0 Kimble Lake 0 1 0 0 0 19 118 48 0 30 84 0 LaRock Hardwoods 0 0 0 0 0 0 0 0 0 0 0 0 Lawson Road 0 0 0 0 0 0 245 15 0 0 0 0 Lawson Road Resale 0 0 0 0 0 0 173 14 0 0 0 0 Little Pole Lake 1 0 0 0 84 75 207 21 0 155 0 0 Lost Luck Hardwoods 0 1 1 0 0 78 15 12 0 0 23 0 Lower Farm Hill 0 0 0 0 0 0 112 0 0 18 0 0 Maple Hill Vest 0 0 0 0 35 0 33 0 0 21 0 0 Markey Lee 0 0 0 0 0 0 11 0 0 0 0 0 McNearney Lake 0 0 0 0 0 33 19 18 0 0 0 0 Mormon Creek Hardwoods 1 0 0 0 0 10 0 13 0 0 0 0 0 Mormon Creek Hardwoods 11 1 0 0 0 88 0 0 0 0 0 0 0 Mormon Creek Resale 0 0 0 0 10 0 13 0 0 0 0 0 Nivation 1 5 0 0 38 20 99 25 0 27 29 0 North Country Hardwoods 0 0 1 0 47 0 99 0 0 152 0 0 Nugget 0 3 1 0 82 0 133 0 0 28 0 0 Paradise 0 0 2 0 19 142 148 31 0 0 37 0 Pine Plains 0 1 0 0 0 113 0 0 0 0 0 0 Play it Again 0 0 0 0 0 0 0 0 0 0 0 0 Poplar Lake 1 0 1 0 87 0 0 0 0 0 0 0 Porcupine 0 0 0 0 181 0 0 0 0 0 0 U Quarry Road Hardwoods 0 0 0 0 82 0 0 0 0 0 0 0 Railhead Hardwoods 0 0 1 0 19 11 35 0 0 0 0 0 83 Table 12. lCont’d.). 4 of 4 of 4 of 4 of Mixed Red Sugar Yellow Elm Black Sale name Roads Mix Unmix Emix Hdws Maple Maple Birch Elm Aspen Beech Cherry NAME 4ROAD 481K 4UNMlK 4EMIX MHS RMS SMS YBS ES AS BBS BC 8.8.0. 0 0 2 0 19 43 23 42 0 0 0 0 Samson 11 0 0 0 0 0 0 0 0 0 0 0 0 Sand Lilly 0 2 1 0 87 188 128 18 0 0 52 0 Satago 0 0 0 0 32 0 0 0 0 3 0 0 Silver 0 2 0 0 0 100 104 14 0 15 35 0 Sky’s the Limit 0 2 2 0 43 75 199 0 0 0 38 0 South Sugarbush 0 0 0 0 14 9 14 17 0 0 0 0 Spinulous 0 0 0 0 0 0 0 0 0 0 0 0 Spring South 0 0 0 0 28 0 0 0 0 80 0 0 Steam Engine Run 1 2 0 0 149 30 148 0 0 0 0 0 Stillman 0 1 0 0 81 15 95 0 0 0 0 0 Three Cedar Murphy 1 0 0 0 0 111 0 0 0 48 143 0 Twenty-four Hardwoods 0 0 0 0 5t 0 0 0 0 0 0 0 Upper Farm Hill 0 0 0 0 40 0 0 0 0 0 0 0 Vertz 0 0 0 0 45 0 0 0 0 0 0 0 Vanna 0 0 0 0 18 0 14 0 0 0 0 0 Vanna Two 0 0 0 0 59 0 0 0 0 0 0 0 Table 12. (Cont d.1. Paper Mixed Sale name Birch Con. NAME PBS MCS Pine PS 84 Hem- lock HS SS Red A Vnite Mixed Spructh pin Pine Hows RVS VPS MHP Aspen AP EalsamRed a Mixed Spruce SP 800 Bear Borealis Camp A Cat 4 Mouse CCC Hardwoods Clark Corner County Line Dad’s Camp Deer Creek 1 Dukes East Barrett East Spur Hardwoods FH-l3 Followup Flow Vest Gleason Lake East Hound Dog Hollow Johnson Creek Hardwoods Johnson Lookout Johnson Lookout Resale Kemobo Lake Kimble Lake LaRock Hardwoods Lawson Road Lawson Road Resale Little Pole Lake Lost Luck Hardwoods Lower Farm Hill Maple Hill Vest Markey Lee McNearney Lake Mormon Creek Hardwoods 1 Mormon Creek Hardwoods 11 Mormon Creek Resale Nivation North Country Hardwoods Nugget Paradise Pine Plains Play it Again Poplar Lake Porcupine Quarry Road Hardwoods Railhead Hardwoods (’i on» ...—a CD meaccooo—ccooooooohocooooocooo-I-ccoc-oc—owoccoc: N (’1 COGOQC‘OOC'OOOGOCCQCOOOCDOQCOCOoomcccOOQGGO-“COQW ooooooooocwowoccocoooowooocrc:ooococcocococccoc OCOOK‘DC-CC p—- *6 283 (a: ...—H C'CI'CD“ H OGOHocc‘c-ccc-rbooooccoocom OOC‘COCDOCWCOOCOOCOCOOOOCCOCCCDCQCOOOC‘CO‘DDCCCOCO 489 108 3177 420 3388 749 148 847 973 470 1100 201 394 78 832 1331 1380 1007 1288 1320 430 2172 985 184 84 81 2897 800 179 257 289 208 140 857 147 928 341 1248 1424 881 559 874 783 122 382 125 215 58 81 211 25 259 549 42 107 83 48 208 151 80 425 739 35 28 28 237 115 13 30 35 82 118 Fir Vh pin Con. BFP RVP MCP be 801 M01 0 19 0 0 0 0 88 43 0 218 33 0 323 877 0 0 0 174 0 0 0 0 0 180 190 0 0 0 0 0 32 137 0 13 0 0 0 0 3 0 0 0 238 118 48 185 0 0 307 0 0 88 815 0 0 80 232 19 13 0 18 0 0 0 0 48 38 21 0 0 48 0 0 0 0 0 0 0 225 0 0 18 0 0 0 0 18 2 0 0 0 0 0 0 0 0 0 0 0 0 100 122 0 0 0 80 0 0 0 0 47 18 0 0 28 0 0 91 0 0 0 0 0 11 0 0 0 0 108 0 0 11 70 12 0 w ‘0 H c: - N t\. Table 12. (Cont’d.1. Sale name NAME Paper Mixed Pine PS 85 Hem- lock HS SS Red 8 Vnite Mixed Spructh pin Pine RVS VPS MHP AP BalsamRed 4 Mixed Hdws Aspen Fir Vh pin Con. BFP RVP MCP R.E.0. Samson 11 Sand Lilly Satago Silver Sky's the Limit South Sugarbush Spinulous Spring South Steam Engine Run Stillman Three Cedar Murphy Twenty-four Hardwoods Upper Farm Hill Verts Vanna Vanna Two Birch Con. PBS MCS M87 M07 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CQGOCOOQQGOOOOOOO ccccooeoecooooccm p—m ocoooocowocoooocw COONOHC~©C'OC>°¢ cooccoc‘ocooccooco 998 323 2831 85 2103 1285 243 155 337 1712 1833 2885 71 53 143 128 192 129 177 158 444 11 18 109 OOOC‘GE 708 o N -——- N (A: CNCCCCOC 808 N. m N .a- N. c; u c: Gd c c..- Fir BBS (\1 N lock HS [C CCC. Red 8 Spruceknite SS CCCCCCCCOCCCCCCOC (A- Q CI? CC. C t \. O N ...A A cc» 0:: (I: r—o c»: ‘ -, Q ‘ ' H cow (.3 rec 4- O‘NOCQ w‘amp—ov—o wm-omcc —\‘!(.A:C CCMNIQOCP D—‘ N “H (I‘D c O té (aces-u Mixed Unmix Volume Volume Volume 81% I CCGC‘CCU‘CCO Nv N Um e—d C N r—e N t». N (R:- ‘ .-<:4-<:<:<:»o<=-&~<=<: N CC cr CC‘C’C’CX‘O 152 161 664 CCC no bdrm; CCWCCCC‘CC‘N-a Cu; (7! (A) .1... (A: N- 0‘ —. C- N 3 ...; “COOCCMCGCC'CQO‘QCoce-‘c‘ccccc ... (J‘ H C»: N ...—d C)" (3 Even Uneven Clear 08 % Vol % Vol % Vol % Vol %EVOL %UNVOL %CC %OR 5.0% 95.0% 5.0% 0.0% 0.0% 100.0% 0.0% 0.0% 11.2% 62.9% 11.3% 0.0% 100.0% 0.0% 0.0% 0.0% 1.4% 93.'% 0.0% 0.0% 18.2% 81.8% 17.5 0. % 54.2% 45.8 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 39.7% 48.9% 39.8% 0.0% 8.6% 31.4% 7.6% 0.0% 3.0% 97.0% 2.9% 0.0% 3 .0% 62.0% 34.8% 0.0% 65.3% 34.7% 63.7% 0.0% 34.5% 38.4% 26.0% 0.0% 2.6% 97.4% 2.6% 0.0% 36.4% 63.6% 22.2% 0.0% 0.0% 100.0% 0.0% 0.0% 0.5% 99.5% 0.0% 0.0% 18.2% 81.8% 18.3% 0.0% 0.0% 41.6% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 14.7% 70.1% 12.8% 0.0% 19.4% 9.1% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 4.4% 95.6% 4.3% 0.0% 7.8% 92.2% 7.3% 0.0% 1.9% 73.4% 0.0% 0.0% 33.5% 66.5% 32.7% 0.0% 30.2% 69.8% 30.2% 0.0% 1.2% 87.5% 0.0% 0.0% 8.7% 91.3% 8.7% 0.0% 0.0% 100.0% 0.0% 0.0% 16.4% 83.6% 16.4% 0.0% 0.4% 99.6% 0.0% 0.0% 0.6% 99.4% 0.0% 0.0% 37.6% 52.0% 35.8% 0.0% 59.8% 34.2% 25.0% 3.0% 16.9% 63.0% 7. % 1.1% 4.0% 96.0% 0.0% 0.0% 0.3% 99.7% 0.0% 0.0% 2.4% 37.6% 0.0% 0.0% 0.6% 99.4% 0.0% 0.'% Table 18. [Contld.l Sale Name NAME Perch Lake Perch Tower Pickle Pond Picnic Table Pine Creek Poncho Ponozzo Lake - S Pothole River Bend Rookery R.J. R.J. 42 Santa Fe Section 18 Section 28 Section 28 - II Sidnaw Branch Silver Bullet Silver Creek Silver Lake Ski Pole Skoglund Creek Slapjack South Dinky Sparkle III Sparkle 1V Stambaugh Pit Storm Sudden Lake Sullivan Creek Tenderfoot East Tenderfoot Vest Tote Creek Tradition Creek U.S. 2 Vebstur Vellington Vhitetail Vildcat V011 Imp Thin Road 138 Salv Volume Volume Volume Volume IMP 193 ccoowoccc THiN ROAD (7! “cz-a-oczo H p—o NH mm-coowco-hcoc:occoocooc‘a‘joooowoooo (1" H D—‘ woe:- GANG SALV v—atx CCOCOCc-CCOCCOC‘QCQCOONL‘DOOGOOCOCOCCCOOCOOO Mixed MIX Unmix Emix Volume Volume Volume UNMIX EMIX OOOOOCC 470 260 C CCCC‘COOC-C V CCOC‘QOOCCOCOOOCOG-OU‘C’OOGOOOOOCC Even % Vol 0.0% 70.0% 0.0% 4.2% 40.6% 0.0% 27.5% 0.0% 81.0% 41.3% 64.4% 38.2% 0.0% 0.0% 0.0% 28.6% 55.3% 81.7% 0.0% 54.0% 68.2% 100.0% 13.2% 21.2% 7.7% 0.0% 17.6% 48.5% 0.0% 43.9% 1.7% 0.0% 76.9% 1.0% 4.2% 22.1% 0.0% 3.1% 93.8% Uneven Clear % Vol %EVOL %0NVOL 97. 99. 30. 100. 95. 59. 100. .5% 100. .0% 58. 35. 45. 100. 95. .0% 45. 44. 18. 100. 46. .0% .0% 86. 78. 92. 100. 82. .5% 100. 56. 98. 100. 23. 99. 95. .9% 100. 96. .2% On 51 ‘P 8% 0% 0% 8% 2 0% 0% 7% 6% 2% 0% 9% 0% 3% 0% 0% 8% 8% 3% 0% 4% 0% 1% 3% 0% 0% 8% 0% 9% 0R % Vcl % Vol %CC %OR 0.0% 0.0% 0.0% 0.0% 70.1% 0.0% 0.0% 0. % 4.2% 0.0% 39.3% 0.0% 0.0% 0.0% 2'.9% 0.0% 0.0% 0.0% 0.0% 0.0% 41.3% 0.0% 64.4% 0.0% 31.0% 2.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 54.9% 0.0% 35.0% 45.0% 0.0% 0.0% 5‘.9% 0.0% l‘.0% 0.0% 100.0% 0.0% 0.0% 0.0% 17.7% 0.0% 0.0% 0.0% 0.0% 0.0% 17.6% 0.0% 46.9% 0.0% 0.0% 0.0% 44.2% 0.0% 0.0% 0.0% 0. % 0.0% 14.'% 56.0% 0.9% 0.0% 0.0% 0.0% 19.0% 0.0% 0.0% 0.0% 0.0% 0.0% 36.7% 0.0% Table 18. lCont‘d.l Shelt Select Imp Thin Road Salv Mixed Unmix Emir 4 of 4 of Sale Name % Vol % Vol % Vol % Vol % Vol % Vol % Vol % Vol % Vol Clear OR NAME %SHELT %SELECT %1MP %THIN %ROAD %SALV %MIX %UNMIX %EMIX FCC 438 Admin 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.1% 0.0% 0 0 Adrian Creek 10.4% 71.4% 0.0% 0.0% 2.1% 0.0% 11.4% 0.0% 0.0% 0 1 Aldridge Creek 0.0% 0.0% 0.0% 8.5% 1.7% 0.0% 47.0% 20.1% 22.7% 0 0 Augustine 0.0% 0.0% 29.3% 69.0% 1.8% 0.0% 0.0% 0.0% 0.0% 0 0 Basswood Ridge 0.0% 90.5% 0.0% 9.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Big King 0.0% 28.1% 0.0% 0.0% 0.0% 0.0% 10.8% 61.2% 0.0% 0 0 Birch 59.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Bonifas Creek 29.8% 0.0% 0.0% 67.6% 2.6% 0.0% 0.0% 0.0% 0.0% 0 0 Broken Bridge 0.0% 87.4% 0.0% 12.4% 0.4% 0.0% 0.0% 0.0% 0.0% 0 0 Buck 0.0% 0.0% 0.0% 33.5% 0.0% 0.0% 0.0% 0.0% 0.0% 1 3 Bullseye 14.7% 51 8% 0.0% 33.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Bullwinkle 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Canyon Falls 0.0% 0.0% 0.0% 86.3 3.2% 0.0% 0.0% 0.0% 0.0% l 0 China Road 0.0% 26.3% 0.0% 30.2 0.0% 0.0% 0.0% 0.0% 0.0% 3 0 Chipmunk 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Clear Lake 0.0% 33.8% 0.0% '3.5% 0.0% 0.0% 0.0% 20.8% 0.0% 1 0 Compartment 85 0.0% 0.0% 0.0% 99.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Cookout 0.0% 0.0% 80.3% 0.0% 0.0% 0.0% 0.0% 19.9% 0.0% 0 0 Coontail Camp 0.0% 80.0% 0.0% 0.0% 1.8% 0.0% 0.0% 0.0% 0.0% 1 0 Copps Tower 0.0% 0.0% 0.0% 66.1% 5.0% 0.0% 0.0% 16.3% 0.0% 2 0 County 527 0.0% 0.0% 62.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Coupe 0.0% 99.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Curry Lake 0.0% 0.0% 0.0% 98.1% 2.1% 0.0% 0.0% 0.0% 0.0% 0 0 Deer Fly 57.8% 4.8% 0.0% 1.2% 0.0% 0.0% 0.0% 0.0% 0.0% 5 1 Defiance Creek 0.0% 74.0% 0.0% 20.3% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Divide 0.0% 14.8% 0.0% 46.1% 0.0% 0.0% 0.0% 0.0% 0.0% 7 0 Eagle Lake 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 East Dolph 0.0% 0.0% 27.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3 1 East End 0.0% 0.0% 0.0% 90.9% 3.0% 0.0% 0.0% 4.7% 0.0% 1 0 East Irish 0.0% 39.5% 0.0% 5.2% 0.4% 0.0% 31.3% 23.6% 0.0% 0 0 East King - S 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 99.9% 0.0% 0.0% 0 0 East Perch 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 East Prickett 10.1% 32.1% 31.0% 0.0% 1.4% 0.0% 0.0% 0.0% 0.0% 1 1 Eastern Divide 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Edna Creek 11 0.0% 0.0% 0.0% 90.9% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Elbow 0.0% 26.0% 48.9% 0.0% 5.8% 0.0% 0.0% 16.6% 0.0% 0 1 Elmwood North 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Farce Creek 0.0% 0.0% 85.0% 15.3% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Finnegan 0.0% 0.0% 9.7% 23.4% 1.7% 0.0% 0.0% 53.2% 0.0% 1 0 Fisher Hardwood 0.0% 99.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Fisher Lake 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% U 0 Fourth Lake 0.0% 78.7% 0.0% 0.0% 0.0% 0.0% 0.0% 21.5% 0.0% 0 0 011118 0.0% 16.6% 63.4% 0.0% 1.1% 0.0% 0.0% 5.2% 0.0% 2 0 Green Beanie 0.0% 62.1% 0.0% 11.0% 1.8% 0.0% 0.0% 25.1% 0.0% 0 0 Grizzly Bear 1.0% 12.3% 12.8% 25.0% 1.3% 0.0% 0.0% 0.0% 0.0% 4 4 140 Table 18. 1Cont’d.1 Shelt Select Imp Thin Road Salv Mixed Unmix Emix 4 of 4 of Sale Name % Vol % Vol % Vol % Vol % Vol % Vol % Vol % Vol % Vol Clear OR NAME %SHELT %SELECT %IMP %THIN %ROAD %SALV %MIX %UHMIK %LMIK 4CC 40R Hartley Landing 0.0% 13.1% 0.0% 32.1% 0.0% 0.0% 0.0% 49.9% 0.0% 1 0 Hartley Landing Resale 0.0% 21.1% 0.0% 43.3% 0.0% 0.0% 0.0% 36.2% 0.0% 0 0 Hayfield 0.0% 62.7% 0.0% 0.0% 0.0% 0.0% 25.8% 0.0% 0.0% 1 0 Haystack 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 38.0% 0.0% 62.2% 0 0 Hemlock Lake 0.0% 0.0% 30.1% 39.3% 1.5% 0.0% 0.0% 29.3% 0.0% 0 0 Hideout II 0.0% 53.1% 0.0% 28.7% 0.7% 0.0% 0.0% 0.0% 0.0% 2 0 Hilltop 1982 54.2% 10.9% 0.0% 34.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Hilltop lClisch) 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Hilltop (Corey) 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Honeysuckle 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Jacknife 0.0% 12.3% 19.0% 0.0% 0.0% 0.0% 11.4% 17.6% 0.0% 4 0 Jackson 0.0% 0.0% 0.0% 55.0% 1.0% 0.0% 12.6% 23.8% 0.0% 3 0 James Lake 0.0% 0.0% 0.0% 97.1% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Jumbo East 0.0% 0.0% 0.0% 62.0% 3.2% 0.0% 0.0% 0.0% 0.0% 4 0 Kallio 0.0% 0.0% 24.5% 0.0% 1.7% 0.0% 0.0% 10.2 0.0% 5 0 Kenton Heights 8.6% 0.0% 0.0% 38.4% 0.0% 0.0% 27.1% 0.0% 0.0% 1 0 Ketchum Lake 0.0% 97.6% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Kits Creek 6.3% 13.1% 41.7% 8.7% 7.8% 0.0% 0.0% 0.0% 0.0% 2 0 Knucklehead Salvage - S 0.0% 0.0% 0.0% 99.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Lambert Creek 0.0% 99.5% 0.0% 0.0% 0.5% 0.0% 0.0% 0.0% 0.0% 0 0 Little Giant 0.0% 15.6% 50.6% 15.7% 0.0% 0.0% 0.0% 0.0% 0.0% 2 0 Little Giant Hardwoods 0.0% 0.0% 0.0% 42.0% 0.0% 0.0% 58.0% 0.0% 0.0% 0 0 Lone Volf 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Lower Dam 0.0% 0.9% 0.0% 56.4% 1.9% 4.3% 23.7% 0.0% 0.0% 1 0 Lucky Shot 19.5% 0.0% 0.0% 9.1% 0.0% 0.0% 71.6% 0.0% 0.0% 0 0 L.A.R. 0.0% 10.9% 0.0% 74.2% 0.0% 0.0% 0.0% 14.8% 0.0% 0 0 Madelyn Lake 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Marsh Creek South 0.0% 95.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 McRae Creek 0.0% 0.0% 0.0% 92.1% 0.5% 0.0% 0.0% 0.0% 0.0% 1 0 Merry Pete 0.0% 20.5% 52.9% 0.0% 1.9% 0.0% 24.8% 0.0% 0.0% 0 0 Mink Lake 0.0% 0.0% 0.0% 47.2% 0.8% 0.0% 0.0% 19.3% 0.0% 4 0 Mitigwaki Creek 0.0% 70.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2 0 Montombo 0.0% 22.1% 0.0% 49.6% 1.1% 0.0% 11.4% 15.8% 0.0% 0 0 North Grade 0.0% 0.0% 0.0% 91.4% 0.0% 0.0% 0.0% 0.0% 0.0% 2 0 North McAllister 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Old Farm 0.0% 0.0% 0.0% 83.6% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Osprey 0.0% 0.0% 90.3% 0.0% 0.4% 0.0% 0.0% 9.3% 0.0% 0 0 Otter Lake 0.0% 99.6% 0.0% 0.0% 0.6% 0.0% 0.0% 0.0% 0.0% 0 0 Paint Springs 0.0% 23.3% 0.0% 14.0% 1.9% 0.0% 10.5% 14.7% 0.0% 4 0 Paw Lake 28.9% 0.0% 0.0% 34.2% 2.9% 0.0% 6.0% 0.0% 0.0% 3 1 Peckerwood 8.6% 0.0% 63.1% 0.0% 1.4% 0.0% 18.1% 0.0% 0.0% 2 1 Pelton 4.0% 0.0% 2.5% 93.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Pelton Vest 0.0% 72.4% 18.0% 9.1% 0.2% 0.0% 0.0% 0.0% 0.0% 0 0 Pendleton Creek 0.0% 97.8% 0.0% 0.0% 2.4% 0.0% 0.0% 0.0% 0.0% 0 0 Perch Corner 0.0% 0.0% 0.0% 56.9% 0.7% 0.0% 0.0% 42.5% 0.0% 0 0 141 Table 18. 1Comt’d.1 Shelt Select Imp Thin Road Salv Mixed Unmix Emix 4 of 4 of Sale Name % Vol % Vol % Vol % Vol % Vol % Vol % Vol % Vol % Vol Clear OR NAME %SHELT %SELECT %IMP %TBIN %ROAD %SALV %MIK %UNMIX %EMIX 400 408 Perch Lake 0.0% 8.9% 0.0% 57.4% 2.2% 0.0% 0.0% 31.5% 0.0% 0 0 Perch Tower 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Pickle Pond 0.0% 0.0% 14.1% 16.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3 0 Picnic Table 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 99.6% 0.0% 0 0 Pine Creek 0.0% 0.0% 8.8% 87.1% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Poncho 0.0% 0.0% 0.0% 59.3% 1.6% 0.0% 0.0% 0.0% 0.0% 6 0 Ponozzo Lake - S 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Pothole 0.0% 0.0% 0.0% 41.0% 0.8% 0.0% 0.0% 31.6% 0.0% 2 0 River Bend 0.0% 0.0% 0.0% 99.8% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Rookery 81.0% 0.0% 0.0% 0.0% 0.0% 0.0% 19.0% 0.0% 0.0% 0 0 R.J. 0.0% 25.8% 9.3% 23.7% 0.0% 0.0% 0.0% 0.0% 0.0% 4 0 R.J. 42 0.0% 15.7% 15.3% 4.8% 0.0% 0.0% 0.0% 0.0% 0.0% 2 0 Santa Fe 3.9% 2.1% 0.0% 43.1% 0.7% 0.0% 16.6% 0.0% 0.0% 4 1 Section 18 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Section 28 0.0% 96.0% 0.0% 0.0% 0.0% 0.0% 4.1% 0.0% 0.0% 0 0 Section 28 - II 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Sidnaw Branch 28.7% 20.2% 0.0% 0.0% 0.0% 0.0% 26.4% 24.9% 0.0% 0 0 Silver Bullet 0.0% 0.0% 0.0% 36.3% 0.6% 0.0% 0.0% 8.4% 0.0% 13 0 Silver Creek 0.0% 11.8% 0.0% 0.0% 1.9% 6.5% 0.0% 0.0% 0.0% 2 3 Silver Lake 0.0% 0.0% 0.0% 0.0% 0.0% 4.0% 96.2% 0.0% 0.0% 0 0 Ski Pole 0.0% 18.4% 0.0% 27.5% 1.1% 0.0% 0.0% 0.0% 0.0% 2 0 Skoglund Creek 56.4% 0.0% 0.0% 0.0% 0.0% 0.0% 31.8% 0.0% 10.7% 1 0 Slapjack 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 6 0 South Dinky 13.2% 0.0% 35.2% 51.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Sparkle III 0.0% 0.0% 0.0% 78.8% 3.6% 0.0% 0.0% 0.0% 0.0% l 0 Sparkle IV 7.7% 12.0% 10.9% 14.0% 0.0% 0.0% 0.0% 55.6% 0.0% 0 0 Stambaugh Pit 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Storm 0.0% 64.0% 13.0% 5.5% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Sudden Lake 0.0% 1.3% 11.2% 39.0% 1.4% 0.0% 0.0% 0.0% 0.0% 7 D Sullivan Creek 0.0% 1.8% 82.3% 16.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Tenderfoot East 0.0% 0.0% 0.0% 56.2% 0.0% 0.0% 0.0% 0.0% 0.0% 2 0 Tenderfoot Vest 0.0% 0.0% 0.0% 98.4% 1.9% 0.0% 0.0% 0.0% 0.0% 0 0 Tote Creek 0.0% 72.5% 0.0% 0.0% 0.0% 0.0% 0.0% 27.6% 0.0% 0 0 Tradition Creek 6.1% 0.0% 0.0% 23.2% 0.0% 0.0% 0.0% 0.0% 0.0% 2 5 0.8. 2 0.0% 99.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1 0 Vebstur 0.0% 2.2% 93.8% 0.0% 4.3% 0.0% 0.0% 0.0% 0.0% 0 0 Vellington 0.0% 0.0% 0.0% 78.0% 3.1% 0.0% 0.0% 0.0% 0.0% l 0 Vhitetail 0.0% 58.1% 0.0% 41.9% 0.0% 0.0% 0.0% 0.0% 0.0% 0 0 Vildcat 0.0% 0.0% 0.0% 96.8% 3.2% 0.0% 0.0% 0.0% 0.0% 0 0 V611 57.1% 0.0% 0.0% 6.2% 0.0% 0.0% 0.0% 0.0% 0.0% 5 0 142 Table 18. [Cont’d.l 4 of 4 of 4 of 4 of 4 of 4 of 4 of 4 of 4 of Mixed Red Sugar Sale Name Shelt Select Imp Thin Road Salv Mix Unmix Emix Hdws Maple Maple NAME 4SHELT 4SELECT 4IMP 4THIN 4ROAD 4SALV 4MIX 4UNMIX 48812 MHS RMS SMS 861 861 861 Admin 0 0 0 4 0 0 0 0 0 0 0 52 Adrian Creek 1 8 0 0 l 0 1 0 0 0 40 186 Aldridge Creek 0 0 0 1 3 0 3 1 1 0 79 109 Augustine 0 0 2 4 1 0 0 0 0 0 0 74 Basswood Ridge 0 10 0 1 0 0 0 0 0 51 0 74 Big King 0 3 0 0 0 0 1 5 0 0 33 129 Birch 3 0 0 0 0 0 0 0 0 6 0 0 Bonifas Creek 2 0 0 3 1 0 0 0 0 0 0 157 Broken Bridge 0 16 0 3 1 0 0 0 0 19 27 174 Buck 0 0 0 2 0 0 0 0 0 0 63 214 Bullseye l 4 0 4 0 0 0 0 0 0 15 79 Bullwinkle 0 4 0 0 0 0 0 0 0 0 14 62 Canyon Falls 0 0 0 4 1 0 0 0 0 0 0 89 China Road 0 2 0 3 0 0 0 0 0 0 17 129 Chipmunk 0 0 2 0 0 0 0 0 0 0 20 14 Clear Lake 0 2 0 3 0 0 0 l 0 0 22 96 Compartment 85 0 0 0 2 0 0 0 0 0 0 0 43 Cookout 0 0 7 0 0 0 0 2 0 0 19 298 Coontail Camp 0 4 0 0 1 0 0 0 0 0 8 121 Copps Tower 0 0 0 9 4 0 0 2 0 0 0 94 County 527 0 0 3 0 0 0 0 0 0 6 0 45 Coupe 0 2 0 0 0 0 0 ' 0 0 0 10 135 Curry Lake 0 0 0 9 2 0 0 0 0 0 0 167 Beer Fly 6 l 0 1 0 0 0 0 0 0 25 178 Defiance Creek 0 8 0 2 0 0 0 0 0 13 25 121 Divide 0 2 0 6 0 0 0 0 0 0 0 258 Eagle Lake 0 0 0 l0 0 0 0 0 0 0 22 336 East Dolph 0 0 2 0 0 0 0 0 0 31 51 0 East End 0 0 0 14 3 0 0 1 0 0 45 254 East Irish 0 5 0 1 1 0 4 3 0 0 92 1167 East King - S 0 0 0 0 0 0 1 0 0 0 0 4 East Perch 0 5 0 0 0 0 0 0 0 0 0 410 East Prickett 1 3 5 0 2 0 0 0 0 0 57 310 Eastern Divide 0 0 0 3 0 0 0 0 0 35 0 26 Edna Creek 11 0 0 0 6 0 0 0 0 0 0 0 41 Elbow 0 2 3 0 1 0 0 l 0 43 0 142 Elmwood North 0 9 0 0 0 0 0 0 0 79 0 162 Farce Creek 0 0 9 2 0 0 0 0 0 0 175 174 Finnegan 0 0 3 6 2 0 0 8 0 0 18 548 Fisher Hardwood 0 l 0 0 0 0 0 0 0 0 0 15 Fisher Lake 0 12 0 0 0 0 0 0 0 18 0 102 Fourth Lake 0 5 0 0 0 0 0 l 0 56 12 131 Gillie 0 3 14 0 2 0 0 1 0 59 0 193 Green Beanie 0 7 0 2 1 0 0 3 0 10 0 537 Grizzly Bear 1 l 2 7 l 0 0 0 0 0 62 651 143 Table 18. (Coot’d.) 4 of 4 of 4 of 4 of 4 of 4 of 4 of 4 of 4 of Mixed Red Sugar Sale Name Shelt Select Imp Thin Road Salv Mix Unmix Emix Hdws Maple Maple MAME 4SHELT 4SELECT 4IMP 4THIN 4ROAD 4SALV 4M1! 4UNMIX 4EMIX MHS HMS SMS be be be Hartley Landing 0 2 0 2 0 0 0 3 D 0 11 58 Hartley Landing Resale 0 2 0 2 0 0 0 1 0 11 0 34 Hayfield 0 5 0 0 0 0 3 0 0 0 12 90 Haystack D 0 0 0 0 0 0 D 2 D 19 31 Hemlock Lake 0 0 2 2 1 D 0 2 0 D 0 160 Hideout II 0 5 0 2 1 0 0 0 0 0 29 140 Hilltop 1982 2 1 0 1 0 D D 0 0 D 10 133 Hilltop (Clisch) 0 0 0 3 0 0 0 0 0 D 13 47 Hilltop (Corey) 0 0 0 3 D 0 0 0 0 0 13 47 Honeysuckle 0 0 0 3 0 0 0 0 D D D 53 Jacknife 0 1 l 0 0 0 1 2 0 0 0 216 Jackson 0 0 0 5 1 0 1 2 0 0 27 274 James Lake 0 0 0 10 D 0 0 D 0 0 D 268 Jumbo East 0 0 D 10 6 0 0 0 0 0 13 292 Kallio 0 0 3 0 l 0 0 1 0 115 0 157 Kenton Heights 1 0 0 3 0 0 l 0 D 0 13 72 Ketchum Lake 0 8 D D 0 D 0 0 0 35 D 135 Hits Creek 1 2 6 l 2 0 0 D 0 0 63 201 Knucklehead Salvage - S D 0 0 4 0 D 0 0 0 0 0 27 Lambert Creek 0 6 0 D l D D D 0 0 86 0 Little Giant 0 2 7 2 0 0 0 ‘0 0 38 0 138 Little Giant Hardwoods 0 0 0 1 0 0 1 0 0 0 6 48 Lone Holt 0 0 8 0 D 0 0 0 0 38 0 438 Lower Dam 0 1 0 5 4 0 2 0 0 0 25 316 Lucky Shot 1 0 0 1 0 0 4 D D 11 0 242 L.A.R. 0 1 0 5 0 0 0 l 0 0 0 66 Madelyn Lake 0 7 D 0 0 0 0 0 0 0 9 100 Marsh Creek South 0 7 D 0 0 0 0 0 0 D 6 77 McRae Creek 0 0 0 8 1 D 0 0 0 0 D 102 Merry Pete 0 2 4 0 l D 1 0 0 22 0 242 Mink Lake 0 0 0 6 1 0 0 2 0 0 10 82 Mitigwaki Creek 0 7 0 D 0 D 0 0 0 27 30 74 Montombo 0 2 0 6 2 0 1 1 0 0 36 384 North Grade 0 0 0 7 D 0 0 0 0 0 45 193 North McAllister D 5 0 D 0 0 0 0 D 0 52 363 Old Farm 0 0 0 6 0 0 D 0 0 34 D 90 Osprey 0 0 11 D l 0 0 1 0 28 0 219 Otter Lake 0 16 0 0 2 D 0 0 0 30 38 304 Paint Springs 0 2 0 3 1 0 l l D 9 0 93 Paw Lake 5 0 0 7 4 0 1 0 0 0 35 208 Peckerwood 2 0 9 0 2 0 3 D D 54 0 872 Pelton 1 0 l 13 0 0 0 0 0 0 0 117 Pelton lest 0 2 1 1 1 0 0 0 0 D 0 55 Pendleton Creek 0 6 0 0 1 8 0 0 D 38 0 102 Perch Corner 0 0 0 5 1 0 0 3 0 0 50 233 144 Table 18. (Cont’d.) 4 of 4 of 4 of 4 of 4 of 4 of 4 of 4 of 4 of Mixed Red Sugar Sale Mame Shelt Select Imp Thin Road Salv Mix Unmix Emix Hdws Maple Maple MAME 4SHELT 4SELECT 4IMP 4THIM 4ROAD 4SALV 4MIX 4UNMIX 4EMIX MHS HMS SMS be be be Perch Lake 0 2 D 6 1 D 0 4 0 0 15 575 Perch Tower 0 0 0 13 0 0 0 0 0 0 11 239 Pickle Pond 0 0 1 1 D 0 0 D D 0 0 49 Picnic Table 0 0 0 0 0 D 0 l D 0 0 19 Pine Creek 0 0 1 7 0 0 0 0 0 0 87 183 Poncho 0 0 0 15 3 0 D D 0 U 50 284 Ponozzo Lake - S 0 0 D 1 D 0 0 0 D 0 0 0 Pothole 0 0 0 4 1 0 0 2 0 38 0 187 River Bend 0 0 0 2 0 0 0 D 0 0 0 44 Rookery 3 0 D 0 0 0 l 0 l 43 43 111 R.J. 0 3 1 4 D 0 D 0 0 0 70 269 R.J. 42 0 2 1 1 0 D 0 D D 10 95 24 Santa Fe 1 1 D 7 1 0 1 D 0 0 18 205 Section 18 0 11 0 0 0 0 0 0 0 0 29 426 Section 28 0 9 D 0 0 0 1 0 0 0 11 249 Section 28 - II 0 2 D 0 0 0 0 0 0 0 0 39 Sidnaw Branch 3 2 D 0 0 0 2 2 0 0 59 472 Silver Bullet D 0 D 11 l 0 0 1 0 0 D 163 Silver Creek 0 l 0 0 1 0 D 0 0 0 30 173 Silver Lake 0 0 0 0 0 l 3 0 0 0 0 98 Ski Pole 0 1 0 1 1 0 D g 0 0 0 9 67 Skoglund Creek 6 0 0 0 0 0 3 0 l 0 69 85 Slapjack 0 0 0 0 D 0 0 0 0 15 0 94 South Dinky l D 1 2 0 0 0 0 0 30 0 47 Sparkle III 0 0 0 8 1 0 0 0 0 0 12 248 Sparkle IV 1 1 l l 0 D 0 4 0 25 0 129 Stambaugh Pit 0 0 0 1 D 0 0 0 0 0 0 10 Storm 0 6 1 l 0 0 0 0 0 0 14 310 Sudden Lake 0 1 l 5 1 0 0 0 0 0 20 147 Sullivan Creek 0 1 12 2 0 0 0 0 0 29 0 955 Tenderfoot East 0 0 0 3 D 0 0 0 0 14 0 29 Tenderfoot Meat 0 D D 5 l D D 0 0 28 0 70 Tote Creek 0 5 0 0 0 0 0 2 0 0 15 478 Tradition Creek 1 0 0 2 D 0 0 D D 79 0 371 U.S. 2 0 9 0 0 0 0 0 D 0 0 15 200 Mebstur 0 1 16 D 3 0 0 0 0 96 0 448 Mellington 0 0 0 8 2 0 0 0 0 9 0 125 Vhitetail 0 l D 1 0 0 0 0 0 0 0 64 Vildcat 0 D 0 5 1 0 0 0 0 0 11 119 Volt 9 0 0 1 0 0 0 0 0 D 100 1000 145 Table 18. (Cont’d.) YellowBass- Red Nhite Black Paper Mixed Balsam Hem- Red 2 Sale Name Birch Mood Elm Aspen Oak Ash CherryBirch Con. Fir lock SpruceNhite NAME YBS BAS ES AS 80 ASH BC PBS MCS BFS HS SS RNS Admin 0 D D I 0 0 0 0 D 0 D 7 0 Adrian Creek 114 0 D 0 0 0 0 15 D D 11 109 10 Aldridge Creek 46 0 32 D 0 13 0 0 D 0 0 14 0 Augustine l3 0 105 D 0 0 0 D 0 0 0 11 0 Basswood Ridge 28 0 0 0 0 0 0 0 13 0 0 0 0 Big King 28 D 6 28 0 0 23 0 D 0 D 2 0 Birch 0 0 0 14 0 0 0 0 ID 0 D D 0 Bonifas Creek 19 0 98 34 0 0 0 0 0 D 0 22 0 Broken Bridge 39 14 0 49 D 0 0 0 0 D 0 0 0 Buck 52 37 0 35 0 51 0 0 0 0 D 0 0 Bullseye 32 0 0 19 0 D 0 0 0 D 0 9 0 Bullwinkle 22 0 D 9 0 0 26 0 D 0 0 45 0 Canyon Falls 22 15 84 19 0 0 0 0 D 0 0 4 0 China Road 10 0 24 139 0 0 0 0 D 0 0 7 D Chipmunk 20 0 0 0 0 0 0 0 0 0 0 0 0 Clear Lake 40 0 0 83 0 0 0 44 0 D 0 14 0 Compartment B5 8 D 0 0 0 0 0 0 0 0 0 4 0 Cookout 23 17 D 22 0 0 0 0 8 0 0 D 0 Coontail Camp 20 D 0 0 0 D 0 0 0 0 0 17 29 Copps Tower 37 43 0 19 0 19 0 0 D 0 0 31 0 County 527 0 D 0 0 0 0 0 0 0 0 0 D D Coupe 38 D 99 6 0 0 39 0 D 0 0 22 0 Curry Lake 31 D 0 10 0 D 0 0 D 0 0 11 0 Deer Fly 130 0 D 50 0 0 D 0 12 0 0 74 0 Defiance Creek 22 48 0 48 0 0 0 0 0 0 0 15 0 Divide 22 37 0 179 0 D 0 0 0 0 0 163 199 Eagle Lake 45 D 0 16 0 0 0 0 D D 0 25 0 East Dolph 0 0 0 0 0 0 0 0 5 0 0 0 0 East End 55 15 37 85 0 17 0 0 D 0 0 25 0 East Irish 472 154 48 67 0 0 0 0 0 0 86 0 24 East Hing - S 0 D 37 0 0 0 0 0 0 0 0 3 0 East Perch 70 D 0 0 0 D 0 D D D 0 5 0 East Prickett 82 71 0 67 0 0 0 0 D 0 D 13 7 Eastern Divide 13 0 0 D 0 0 0 D 22 0 0 D 0 Edna Creek II 12 0 25 0 0 0 0 0 D 0 D 0 0 Elbow 39 9 0 0 0 0 0 0 9 0 0 0 0 Elmwood North 33 D 0 0 0 0 0 D 22 0 D 0 0 Farce Creek 126 38 D 0 0 D 0 0 D 0 D 79 0 Finnegan 109 96 D 238 D 32 0 0 0 0 0 0 0 Fisher Hardwood 0 0 22 11 0 D 0 0 0 0 0 0 0 Fisher Lake 10 D 0 0 0 0 0 0 D 0 D 0 0 Fourth Lake 50 D 0 l7 0 D 0 D 0 D D 42 D Gillis 88 0 0 0 0 0 0 0 D 0 0 0 0 Green Beanie 86 D 0 0 0 0 0 0 36 0 U D 0 Grizzly Bear 195 73 36 177 0 0 0 0 D D 21 0 D 146 Table 18. (Cont’d.7 YellowBass- Red ihite Black Paper Mixed Balsam Hem- Red 8 Sale Name Birch Hood Elm Aspen Oak Ash CherryBirch Con. Fir lock SpruceNhite NAHE YBS HAS ES AS 80 A88 BC PBS MCS BFS HS SS 888 Hartley Landing 31 0 41 43 0 0 0 D 0 O 0 4 0 Hartley Landing Resale 16 D 0 0 0 O 0 D 3 0 0 D 0 Hayfield 17 D 94 12 0 O 0 D O 0 O O O Haystack 13 18 19 8 O 0 0 13 D 0 O 38 0 Hemlock Lake 11 0 0 O 0 0 0 0 0 O 0 O 0 Hideout II 27 21 0 58 O 0 D 0 22 0 D D D Hilltop 1982 92 16 21 0 0 0 0 0 0 8 0 5 0 Hilltop (Clisch) 0 0 0 11 0 0 D 0 0 0 D D 2 Hilltop (Corey) O 0 D 11 0 0 0 0 D 0 O 0 2 Honeysuckle 17 0 21 D 0 0 O D D 0 0 O D Jacknife 20 17 O 254 O 0 0 0 D 8 D 15 D Jackson 113 16 55 32 0 19 15 D 0 O D 41 0 James Lake 23 18 180 5 0 0 0 0 O 0 0 0 O Jumbo East 43 0 O 130 0 0 0 11 D 16 0 45 10 Kallio D 0 0 D D D 0 O 130 0 O 0 0 Kenton Heights 0 O O 40 0 0 0 21 D D 0 30 31 Ketchum Lake 25 0 O 0 0 D O O 7 0 O O 0 Kits Creek 110 0 0 16 0 O O D O 0 D 33 D Knucklehead Salvage - S 10 14 122 7 O D 0 O 0 O 0 8 0 Lambert Creek 0 0 105 14 D D 26 9 D 0 0 0 0 Little Giant 37 0 O 0 O D O 0 0 0 0 O 0 Little Giant Hardwoods 13 0 D 25 0 0 0 0 .0 0 0 23 0 Lone Iolf 45 O D 0 0 0 0 O O 0 0 0 0 Lower Dam 75 56 41 28 O 0 0 0 0 0 O 14 0 Lucky Shot 12 0 0 0 O O 0 0 9 0 0 D 0 L.A.R. 16 D 90 90 0 0 0 0 D D 0 U D Madelyn Lake 20 O 0 0 0 D 0 D O 0 0 52 D Marsh Creek South 25 O 48 115 0 0 0 0 D 12 D 31 O McRae Creek 0 0 202 7 0 0 17 0 0 0 O 11 0 Merry Pete 33 0 0 0 0 O 0 0 ID 0 0 O 8 Mink Lake 37 0 101 92 0 0 0 0 O 0 O 7 O Mitigwaki Creek 48 O 0 59 O 0 O O O 0 0 37 14 Montombo 59 360 126 178 0 103 O 0 0 D 0 16 0 North Grade 63 0 D 31 0 6 0 D O 0 O 3 U North McAllister 66 D 346 0 0 0 22 0 0 O D 16 0 Old Farm 17 0 0 0 D O 0 O O O O 9 D Osprey 65 0 O 0 0 0 0 0 O O D O U Otter Lake 56 74 0 48 O O D D O 0 D 19 8 Paint Springs 0 0 0 0 0 0 0 D 16 U D D 11 Paw Lake 65 0 O 63 0 D D 15 0 186 O 143 137 Peckerwood 247 O 0 O O O D O 97 O 0 U D Pelton 58 17 D 11 0 0 U U U D U 39 O Pelton Nest 25 U 10 28 U D O U D U O O U Pendleton Creek 10 D O O O D U U 19 U U U U Perch Corner 48 O O D O O O C O U U 26 D 147 Table 18. [Cont’d.} YellowHass- Red White Blacs Paper Mixed Balsam Hen- Red 2 Sale Name Birch Nood Elm Aspen Oak Ash CherryBirch Con. Fir lock SpruceNhite NAME YBS HAS ES AS 80 ASH BC PBS MCS BPS HS SS RNS Perch Lake 74 18 210 67 D 0 O U 0 12 D 27 U Perch Tower 68 0 12 0 0 0 O D D D D D D Pickle Pond 8 0 0 O U 0 D D 10 0 U U 0 Picnic Table 0 O 7 0 D 13 D 0 D D O u U Pine Creek 79 15 0 49 0 28 O D U D 0 U 0 Poncho 113.1 8 0 357 0 15 O D U U D 47 U Ponozzo Lake - S O O 75 O O O O 8 D O D O O Pothole 27 D O O D 0 D 0 O O U D 15 River Bend 0 0 12 O 0 D D O U 0 D U 11 Rookery 127 D 0 3O 8 0 0 D 0 8 O 28 U R.J. 77 51 5O 87 0 14 O D 0 30 U 23 U R.J. 42 31 D 34 0 0 il 0 U U D O U 8 Santa Fe 2 17 28 132 O 16 D D O O O 15 U Section 18 41 79 D 0 3 23 0 D 0 O 17 U U Section 28 19 12 142 16 0 D 21 D D D O O 8 Section 28 - 11 O 0 O 0 0 O 8 U D D D U U Sidnaw Branch 230 28 0 14 O D 0 O 18 U U 125 31 Silver Bullet 10 2 D 278 O O 0 O O O D 3' 42 Silver Creek 74 39 D 144 D 18 O D 17 O D 80 19 Silver Lake 9 8 68 0 U U 0 O O O O o 6 Ski Pole 18 O D 3 O 0 O 10 D D O 46 24 Skoglund Creek 111 0 D 39 O D D 27 '0 O D 2 158 Slapjack 11 0 O O 0 D O O 15 O 0 U 0 South Dinky 21 10 O 17 0 8 O D U D D O O Sparkle Ill 77 29 89 188 0 21 0 13 O O U 6 O Sparkle IV 12 D D D D 0 0 0 U 0 O u U Stambaugh Fit 0 D 68 O 0 U 0 D O D D D 8 Storm 52 10 D 43 D D O O 0 D 29 10 U Sudden Lake 16 11 5 118 U 0 O D D 0 0 7 O Sullivan Creek 233 0 0 O O 0 U D D U 0 O U Tenderfoot East 10 0 0 O D O U D O b O O 8 Tenderfoot Nest D 0 0 0 0 O O U 0 D U D D Tote Creek 70 0 O D D O U D D D D 5 8 Tradition Creek 115 D 0 D D D D 0 19 U D 0 U U.S. 2 59 D 173 19 D 8 32 D U 0 0 16 D Nebstur 146 O 0 D 0 0 0 D 17 0 0 U U Nellington 32 62 0 82 0 0 0 D D 0 0 0 0 Whitetail 0 D 0 D 0 D D 0 O O O O D Nildcat 32 47 22 5 D O 0 O O O O O O 8011 153 D 17 218 0 0 0 U 0 35 0 131 188 148 Table 18. lCont’d.) Jack White N. wh.Mixed Baisan Hired N. wh. Hen- Sale Nane Pine Pine Cedar Hows Aspen Fir Pine Con. Cedar Spruce lock NAHE JPS NPS CS NHP AP BFP PP MCP CP SP HP Admin 8 8 8 323 4 5 8 8 8 4 8 Adrian Creek 8 8 8 697 8 8 14 8 12 52 62 Aldridge Creek 8 8 8 558 3 8 1 8 8 5 33 Augustine 8 8 8 318 8 28 8 8 8 13 8 Basswood Ridge 8 8 8 1443 194 8 8 4 8 8 8 Big King 8 8 8 691 48 8 8 8 8 1 8 Birch 8 8 8 378 152 39 22 8 8 8 8 Bonifas Creek 8 8 8 564 67 18 8 8 8 6 8 Broken Bridge 8 8 8 988 128 8 8 8 8 18 8 Buck 8 8 0 232 37 8 8 8 8 8 8 Bullseye 8 8 8 322 46 6 8 8 8 8 49 Bullwinkle 8 8 8 565 13 8 8 8 8 18 8 Canyon Falls 8 8 8 267 42 12 8 8 8 6 8 China Road 8 8 8 335 228 15 8 8 8 ll 8 Chipmunk 8 8 8 68 8 6 8 8 8 8 8 Clear Lake 8 8 8 628 54 8 8 8 8 14 8 Compartment 85 8 8 8 72 3 8 8 8 D 3 8 Cookout 8 8 8 764 3 8 8 4 8 8 8 Coontail Camp 8 8 8 484 154 8 9 8 8 9 8 Copps Tower 8 8 8 1198 246 8 8 U 8 16 8 County 527 8 8 8 239 73 8 8 8 8 8 8 Coupe 8 8 8 216 15 8 8 8 .8 12 8 Curry Lake 8 8 8 288 18 13 8 8 8 8 8 Deer Fly 8 0 8 657 54 164 8 45 8 41 8 Defiance Creek 8 8 8 685 88 8 8 8 8 2 8 DiVide 8 8 8 1244 333 8 182 8 8 164 8 Eagle Lake 8 8 8 692 84 52 8 8 8 8 25 East Dolph 8 8 0 478 489 8 8 43 8 8 8 East End 8 8 8 1256 293 8 8 8 8 16 8 East lrish 8 8 25 736 '29 117 8 8 189 8 175 East King - S O 8 8 23 8 8 8 8 8 1 8 East Perch 8 8 8 488 0 8 8 17 8 8 8 East Prickett 8 8 8 685 349 8 19 11 8 8 8 Eastern Divrde 8 8 8 538 52 8 8 4 8 8 8 Edna Creek 11 O 8 8 173 68 8 8 8 8 8 8 Elbow 8 8 8 481 41 8 8 11 8 8 8 Elmwood North 8 8 8 1662 17 8 8 6 8 8 t Farce Creek 8 8 8 627 8 8 8 8 8 47 8 Finnegan 8 8 8 1441 785 8 8 8 8 8 8 Fisher Hardwood 8 8 8 31 17 8 8 8 8 8 8 Fisner Lake 8 8 8 1125 147 8 8 8 8 8 8 Fourth Lake 8 8 8 524 78 8 8 8 8 11 8 Gillis 8 8 8 1471 485 8 8 8 8 8 8 Green Beanie 8 8 8 2811 117 8 8 15 8 8 8 Grizzly Bear 8 8 0 1812 527 56 8 8 8 8 22 149 Table 18. (Cont’d.l Jack White N. wh.Mixed Balsam Mixed N. wh. Hen- Sale Nane Pine Pine Cedar Hdws Aspen Fir Pine Con. Cedar Spruce lock NAME JPS WPS CS MHP AP BFP PP CP CP SP HP Hartley Landing 8 8 8 283 113 8 8 8 8 13 8 Hartley Landing Resale 8 8 8 169 12’ 8 8 18 8 8 8 Hayfield 8 8 8 149 51 8 8 8 8 8 8 Haystack 8 8 8 156 13 53 8 8 8 28 8 Hemlock Lake 8 8 8 858 125 8 8 8 8 8 8 Hideout II 0 8 8 473 115 13 8 8 8 9 0 Hilltop 1982 8 8 8 181 4 21 8 8 8 8 8 Hilltop (Clischl 8 8 8 225 42 8 19 0 8 8 19 Hilltop (Corey) 8 8 8 ‘25 42 O 19 8 8 0 19 Honeysuckle 8 8 8 146 8 8 0 8 8 8 28 Jacknife 8 8 U 853 398 53 8 8 8 24 8 Jackson 8 8 8 1286 183 8 8 8 8 25 8 James Lake 8 8 8 373 28 8 8 8 8 8 8 Jumbo East 8 8 8 898 517 196 163 8 8 73 8 Kallio 8 8 8 955 481 8 8 296 8 8 8 Kenton Heights 8 8 8 436 111 8 27 8 12 23 38 Ketchum Lake 8 8 8 1864 197 8 8 8 8 8 8 Hits Creek 8 8 8 433 34 33 8 8 8 13 8 Knucklehead Salvage - S 8 8 8 189 14 4 8 8 8 8 8 Lambert Creek 8 8 8 347 65 8 8 8 8 8 8 Little Giant 8 8 8 1149 825 8 8 8 8 8 8 Little Giant Hardwoods 8 8 8 175 59 8 8 8 '8 37 8 Lone Wolf 8 8 8 583 31 8 8 8 8 8 8 Lower Dan 0 8 13 461 67 78 8 8 27 13 8 Lucky Shot 8 8 8 476 34 8 8 27 8 8 8 L.A.H. 8 8 8 277 153 8 8 11 8 8 8 Madelyn Lake 8 8 8 374 8 18 8 8 8 15 8 Marsh Creek South 8 8 8 539 141 58 8 8 8 13 8 McRae Creek 8 8 8 172 72 8 8 8 8 16 8 Merry Pete 8 8 8 833 28 8 8 5 8 8 8 Mink Lake 8 8 8 388 317 92 8 8 8 8 8 Mitigwaki Creek 8 8 8 712 186 18 11 8 8 13 8 Montonbo 8 8 8 1185 389 53 8 8 8 17 56 North Grade 8 8 8 294 61 8 8 8 8 3 8 North McAllister 8 8 8 422 59 8 8 8 8 3 8 Old Farm 8 8 8 715 384 8 8 8 8 4 8 Osprey 8 8 8 1375 627 8 8 8 8 8 8 Otter Lake 8 8 8 1845 182 8 8 8 8 8 8 Paint Springs 8 8 8 873 387 8 8 58 8 8 8 Paw Lake 8 8 8 1815 172 583 73 8 55 114 8 Peckerwood 8 8 8 2876 163 8 8 151 8 C 8 Pelton 8 8 8 1888 3 8 8 8 8 28 8 Pelton West 8 8 8 236 51 8 8 8 8 t 8 Pendleton Creek 8 8 8 597 162 8 8 4 8 8 8 Perch Corner 8 8 8 924 38 8 8 8 8 14 t Table 18. (Cont‘d.1 Jack White N. wh.Mixed Balsam Mixed N. wn. Hem- Sale Name Pine Pine Cedar Hdws Aspen Fir Pine Con. Cedar Spruce lock NAHE JPS WPS CS MHP AP BFP PP MCP CP SP HP Perch Lake 8 8 8 1858 61 46 8 8 8 8 8 Perch Tower 8 8 0 138 8 8 8 8 8 8 9 Pickle Pond 8 8 8 468 455 8 8 25 8 8 8 Picnic Table 8 8 8 61 8 8 8 8 8 8 8 Pine Creek 8 8 8 526 95 8 8 8 8 1 8 Poncho 8 8 8 1822 788 8 8 8 8 31 8 Ponozzo Lake - S 8 8 8 43 8 8 8 8 8 8 8 Pothole 8 8 8 962 476 8 8 28 8 8 8 River Bend 8 8 8 188 37 8 61 8 8 8 8 Rookery 8 248 8 583 49 216 31 8 8 17 8 R.J. 8 8 8 452 178 72 8 8 8 2 8 R.J. 42 8 8 8 265 192 7 8 8 8 8 1 Santa Fe 8 8 8 798 622 8 8 8 8 18 8 Section 18 8 8 8 678 8 8 8 8 8 8 51 Section 28 8 8 8 269 38 8 8 8 8 8 8 Section 28 - II 8 8 8 45 18 8 8 8 8 8 8 Sidnaw Branch 8 8 8 738 57 8 14 8 28 51 39 Silver Bullet 184 8 8 1328 458 55 547 8 8 48 8 Silver Creek 8 8 8 399 328 8 8 35 8 33 8 Silver Lake 8 8 8 118 8 8 8 8 8 8 8 Ski Pole 8 8 8 494 18 64 7 8 ’16 14 17 Skoglund Creek 8 8 8 689 186 8 153 8 8 6 7 Slapyack 8 8 8 517 452 8 8 18 8 8 8 South Dinky 8 8 8 587 58 8 8 8 8 8 8 Sparkle Ill 8 8 8 519 411 84 8 U 8 16 8 Sparkle IV 8 8 8 648 138 8 8 8 8 8 8 Stambaugh Pit 8 8 8 l 8 8 8 8 8 8 8 Storm 8 8 8 417 146 8 8 8 8 8 78 Sudden Lake 8 8 8 396 244 13 8 8 8 8 D Sullivan Creek 8 8 8 1683 248 U 8 8 8 8 8 Tenderfoot East 8 8 8 212 177 8 8 8 8 8 8 Tenderfoot West 8 8 8 247 141 8 8 8 8 8 8 Tote Creek 8 8 8 434 8 12 8 8 8 6 18 Tradition Creek 8 8 8 678 392 8 8 9 8 8 8 U.S. 2 8 8 8 382 28 8 8 8 8 6 8 Webstur 8 8 8 1283 54 8 8 22 8 8 8 Wellington 8 8 8 771 187 8 8 8 8 8 8 Whitetail 8 8 8 267 35 8 8 8 8 8 8 Wildcat 8 8 8 137 8 8 8 8 8 8 8 Wolf 8 8 8 823 655 335 46 8 4) 189 8 Table 19. Ottawa National Forest variable means and standard deviations. Standard Standard Variable Mean DeViation Variaoie Mean Deviation MONTHS 48.8 15.6 488 1.19 1.95 ACRE 378.2 232.7 48R 8.18 8.78 P8 9.2 5.3 438817 8.48 1.26 8185 2.7 1.9 4991887 2.86 ..28 A8988 $21.73 $14.58 418k 1.85 2.82 STUMPAGE 329.93 328.31 42818 2.79 3.44 OVERBID 38.19 38.58 4RtA8 8.62 1.83 VALUE 333.387 347.819 4SALV 8.81 8.89 FOBVAL 3181.48 39.47 4811 8.33 8.83 HAUL 13.6 7.1 4UN811 8.58 1.16 SAvh 15.7 7.3 41811 8.84 8.23 PULPH 12.6 8.3 MHS 18.2 28.7 SPECS 35.588 8.284 RMS 18.3 27.8 SPEC 34.12 35.5‘ SMS 198.2 196.7 TEMPS 3945 31.815 YBS 47.5 61.1 TEMP 31.82 31.14 HAS 13.4 38.2 TEMPM 8.9 1.3 BS 2 .5 51.1 VOLUME 1285.8 845.4 AS 36.9 63.5 AVGVOL 3.2 1.2 RC 8.8 8.3 EVOL 288.7 494.9 ASH 3.3 11.6 UNVOL 864.2 675.8 BC 1.7 6.5 CC 1? .5 322.7 PBS 1.4 5.5 OR 26.9 32.6 MCS 4.3 15.3 SHELT 66.8 257.3 888 1.9 18.3 SELECT 288.5 474.8 HS 1.3 8.3 188 154.7 448.3 SS 16.2 29.4 THIN ‘33.9 434.6 899 6.7 27.2 ROAD 11.1 28.2 JPS 8.8 9.1 SALV 1.2 9.8 WPS 1.9 21.8 811 59.5 157.1 CS 8.3 2.5 UNMlA 89.4 221.6 MHP 613.9 448.7 8811 4.7 31.6 AP 154.3 132.5 48781 19.8% 26.73 BFP 28.6 61.9 %UNVOL 76.43 29.2% PP 16.2 52.7 %CC 12.8% 18.83 MCP 6.7 38.8 %OR 1.8% 8.5% C? 2.2 11.6 %SHELT 4.3% 13.7% SP 11.5 26.3 %SELECT 24.8% 36.5% HP 5.5 19.9 ALMP 18.23 ‘3.63 %THlN 33.2% 36.63 SROAD 8.7% 1.3% %SALV 8.1% 8.8% XMIK 5.7% 16.33 %UNMIK 6.5% 15.1% %EMlK 8.73 5.83 LIST OF REFERENCES LIST OF REFERENCES Anderson, W.C. 1969. Pine sawtimber behavior in South Carolina. USDA Forest Service Research Paper SO-42. Southern Forest Experiment Station, New Orleans, La. 12 p. Anderson, W.C. 1976a. A method for appraising multiple-product sales of southern pine stumpage. USDA Forest Service Research Paper SO-126. Southern Forest Experiment Station, New Orleans, La. 9 p. Anderson, W.C. 1976b. Appraising southern pine pulpwood stumpage. USDA Forest Service Research Paper SO-129. Southern Forest Experiment Station, New Orleans, La. 7 p. Buongiorno, J., and T. Young. 1984. Statistical appraisal of timber with an application to the Chequamegon National Forest. Northern Journal of Applied Forestry 1(4):72-76. Burns, R.M. 1983. Silvicultural systems for the major forest types of the United States. USDA Forest Service Agricultural Handbook No. 445. Washington, D.C. 191 p. Chatterjee, S. and B. Price. 1977. Regression analysis by example. John Wiley & Son, New York. 228 p. Cramer, E.M. 1972. 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