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Estimating relative log prices of Douglas-fir through a financial analysis of the effects of wood density on lumber recovery and pulp yield.


Traditionally forest products markets have required logs with particular external properties such as diameter, length and knot size. However, markets are now beginning to include requirements for new internal properties, such as basic density and stiffness. Although markets have responded to these new requirements with prices that afford only limited incentive for producers to meet such demands, the new characteristics are valued by these markets and are considered key for competitive forest companies to stay in business. This paper presents a general methodology to estimate relative log prices of Douglas-fir when logs of different wood density classes are processed and converted into end products (lumber and pulp). Three log density classes were evaluated. For the lowest basic density class (300-399 kg/[m.sup.3]), net returns for pulp were about 28 percent lower than the middle class (400-499 kg/[m.sup.3]). The upper class (500-600 kg/[m.sup.3]) net return was 32 percent higher than the middle class. For conventional lumber log grades, the percentage differences between the middle density class and lower and upper classes were 9 and 4 percent, respectively. These results show that premium prices for logs can be established when internal properties, such as basic density, are specified.

Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is of considerable economic importance, especially for the forest products industries of the western United States, Canada, New Zealand, and some areas in Europe. It has been one of the most important raw material resources in the United States in recent years, and the wood is valued for high-quality building and construction materials, as well as for plywood and pulp. It is expected that international and U.S. markets will continue to demand Douglas-fir log products, especially in the high-quality structural lumber market (Schuler and Craig 2003).

Timber resources in the Pacific Northwest have gradually shifted from unmanaged old growth to intensively managed young growth. As younger stands are harvested, wood quality is negatively affected in comparison to old growth wood because of the presence of a higher proportion of juvenile wood, which in turn affects properties such as strength and dimensional stability (Gartner 2005). In addition, harvesting of younger trees increases the variability in product performance and affects log producers in their ability to meet the market demand of wood products.

Traditionally, tree species, log dimensions, and external quality characteristics such as branch size, sweep, scarring, and decay have been used to specify a particular log grade. However, markets are now beginning to include additional characteristics to specify the wood properties they require. For instance, consideration is now being given to such log properties as stiffness, strength, density, spiral grain, extractives content, and consumption of energy for processing (Andrews 2002, So et al. 2002, Young 2002). Corson (2001) noted, for example, that an integrated market-kraft pulp/ newsprint operation in New Zealand required its pulp logs to be separated into eight different log grades based on wood density and the process (mechanical pulping or kraft) for which they were destined.

Wood quality can be defined according to attributes that make wood valuable for a given use by society (Jozsa and Middleton 1994). Although it has been noted that the factors controlling wood quality can be confusing and are frequently contradictory (Anonymous 1965), optimally matching wood quality to markets can lead to improved product uniformity, productivity and profitability along the seedling to customer supply chain. For Douglas-fir, significant quality attributes for wood products include density, microfibril angle, fiber length, lignin content, ring width, knot size and distribution, grain angle and coarseness, color, etc. (Walker et al. 1993, Gartner 2005).

In general, density is one of the most important physical characteristics for wood products because it is an excellent predictor of strength, stiffness, hardness, and pulp yield (Megraw 1986). Accurately assessing density in real-time can be a challenge for log supply managers wanting to segregate logs into different product classes based on density. Variables such as stand age and height within a tree have been used in the past as substitutes for accurate measurements of density. For example, in the early 1990's the second author of this paper participated in value recovery audits of ten New Zealand logging crews supplying a range of log grades to many mills. One of the sawmills would only accept saw logs found at heights of less than 20 meters in a tree because of concerns about low-density wood occurring above this height (G. Murphy, unpublished).

Forest harvesting has become increasingly mechanized during the last few decades. Among other things, mechanization provides a platform for innovative measurement systems, which could lead to improved log segregation based on a wider range of wood properties. Segregation of logs, based on hand-held acoustic tools that measure stiffness, is already being used by some forest companies to improve the value of lumber recovery (Green and Ross 1997, Matheson et al. 2002). Fitting acoustic measuring systems to mechanized harvesters is currently being evaluated. Acuna and Murphy (2006b) have demonstrated that near infrared technology (NIR) could be used to assess wood density based on ejected chain saw chips as each stem was being cut into logs. The acoustic and NIR research efforts indicate that internal wood properties of logs are likely to be more commonly measured and specified by markets in the near future.

Despite many studies having reported that wood producers are sorting logs according to external and internal properties (Jappinen 2000, Matheson et al. 2002), there is little evidence to show markets are paying premium prices for logs of the same species with "desirable" internal characteristics, such as high density. However, it is recognized that logs with higher densities are desired by sawmills because of the larger proportion of highly graded lumber, and by pulpmills because of yield improvements when logs of a higher density are processed and converted into chips (Briggs 1994). It is also recognized that substantial differences in price between species can be found for logs with the same external description. For example, in NW Oregon there is currently a 30 to 40 percent difference between the prices for Douglas-fir and Western Hemlock (Tsuga heterophylla (Raf.) Sarg.) sawlogs of the same dimensions and external quality (Oregon Dept. of Forestry 2006). At least part of this difference is likely due to the market's perception of Hemlock's wood properties.

To estimate the effect of different wood characteristics on economic value, the best sources of information are product recovery studies in which volume and value of products are recorded. The economic importance of different wood properties varies with the products milled from the stems measured in a recovery study, the grading methods applied, and the price structure used (Aubry et al. 1998). Because product recovery studies are expensive and labor intensive, only a few examples can be found in the literature that relate to wood characteristics and their effects on product value for Douglas-fir (Lane et al. 1973, Fahey et al. 1991, Green and Ross 1997, Willits et al. 1997). One of the most extensive studies was sponsored by the Douglas-fir Stand Management Cooperative (Fahey et al. 1991). The usefulness of product recovery studies is dependent on the relationship between the two lumber grading systems (visual grading systems and mechanical or machine stress rated (MSR)) in use in the United States and the wood quality traits used to determine grading rules (Aubry et al. 1998).

Despite some studies having looked at the effect of different traits on lumber and veneer recovery (Aubry et al. 1998), and the effect of different silvicultural treatments on the quality and quantity of the final products (Sonne et al. 2004), none have estimated the premium price that markets would be willing to pay for logs of better internal characteristics, such as a higher wood density or stiffness. The research reported in this paper was aimed at estimating relative prices of Douglas-fir sawlogs and pulp logs with different wood densities.


Field site and tree data set

The data used in this study were collected from a dominant industrial Douglas-fir stand in the Pacific Northwest (Washington State). The site was selected based on logistics (location, and crew willingness to be studied) and the number of log grades being cut. Net stocked area of the stand was 12.18 ha (30.1 acres), with an average stand density of 273 stems per ha. It was on mainly flat ground with an access road through the middle, and was clearcut. The average tree volume was 2.35 [m.sup.3], and the average diameter at breast height (dbh) was 46 cm (Marshall 2005).

General procedure to estimate relative log prices

A set of relative prices for different log density grades was estimated by first determining the residual log values (revenues minus processing costs) and then applying the percent difference in residual values to the average market price for the equivalent, non-density-specific log grade. The particular sequence of steps needed to estimate the set of relative log prices was as follows:

1. Optimally buck the stems and separate the logs produced by grade. Each grade has exactly one market log price associated with it.

2. For each log grade, determine an average log based on external and internal characteristics (stem length, small diameter, juvenile wood percentage).

3. For the average log, calculate the residual value as a function of the end products obtained (lumber or pulp) for a range of log densities (300, 320, ..., 600 kg/[m.sup.3]). Divide the logs into three different density classes (300 to 399, 400 to 499, 500 to 600 kg/[m.sup.3]) and compute the average residual value for each class.

4. Calculate the percentage difference in residual value between average density classes. Assigning the current single log price (from step 1) to the middle density class (400 to 499 kg/[m.sup.3]). Multiply this log price by the percentage difference in residual value to estimate the relative log prices of the other two classes (300 to 399 and 500 to 600 kg/[m.sup.3]).

Optimal bucking procedure

One hundred stems were optimally bucked based on both external (e.g. small end diameter, length, knot size) and internal properties and on log price (Acuna and Murphy 2005). The stem description variables used in the bucking model were: length from the base of the stem, diameter, volume, quality, density. These were either assessed or predicted at intervals of 0.1 meter along the stems. Basic density was estimated for each 0.1-meter section of log by using a height-based regression equation obtained from approximately 400 wood samples of trees (Douglas-fir) located in the Coastal and Cascade Ranges of Oregon (Acuna and Murphy 2006a).

The same product specifications were applied to all 100 stems. Eight log grades, plus waste, were included in the analysis. Their prices are shown on Table 1. All sawlog grades included multiple lengths of 0.6 m. Pulp logs included multiple lengths of 0.1 m. A total of 113 lengths were included in the analysis. The log grades included were sawlogs, for export (log grades ES 1, ES2, ES3, ES4), and domestic markets (log grades DS1, DS2 and DS3), and pulpwood (log grade FIBER).

MSR and visual lumber grade recovery and net returns

No product recovery study was done. Instead the following steps were carried out. Total lumber yields were calculated for each log based on lumber recovery factor tables, which use log length and diameter as inputs (Hallock et al. 1979). The lumber volume in five MSR and visual lumber grade groups was then computed for each log using the grade recovery equations shown in Table 2 and an assumed taper of 21 mm per m (Fahey et al. 1991). One of the variables used by Fahey et al. (1991) is the percentage of juvenile wood (JWPC20). Since juvenile wood percentage measurements were not made for the 100-tree sample referred to above, a regression model, was developed using data supplied by OSU's Wood Science and Engineering Department. The model is as follows:

JWPC20 = 191.33 - 0.304 x (basic density), with basic density in kg/[m.sup.3].

Recovery of chips and sawdust were estimated using the equations presented in Table 3 (Fahey et al. 1991). Lumber, chip and sawdust yields were then multiplied by updated lumber prices from the Financial Evaluation of Ecosystem Management Activities (FEEMA) software (Chmelik et al. 1999) (Table 2), or chip and sawdust prices (Briggs and Fight 1992) to obtain total revenue for each log. The cost of processing logs into lumber was estimated (and then updated) using an equation developed by Briggs and Fight (1992). Net returns were calculated by subtracting processing costs from revenues.

Pulp logs

The net log value (NLV) per cubic meter of wood in the pulp yard was calculated following the methodology presented by Briggs and Fight (1992).

NLV per [m.sup.3] = BD x Y x (SP - NWC FC x (BDN / BD))

where BD = basic density of the actual log; Y= pulp process yield (ratio of dry metric ton of pulp per dry metric ton of wood); SP = selling price per metric ton of pulp; NWC = non-wood cost per metric ton of pulp (energy, chemicals, labor, etc.); FC = fixed cost per metric ton of pulp (overhead, depreciation, interest); BDN = "normal" basic density.

Inputs needed to estimate the NLV were obtained from different sources. The TD Bank Financial Group reports a pulp price of $630 per metric ton (TD Bank Financial Group 2004). Costs were obtained from Briggs and Fight (1992) and updated by using a consumer price index table (NWC = $143 per metric ton of pulp, FC = $94 per metric ton of pulp). Yield was assumed as 0.5, and the "normal" basic density was estimated as the species average (450 kg/[msup.3]) from Bowyer et al. (2003).


Of the 283 logs obtained through the optimal bucking procedure, 38 percent corresponded to pulp logs (log grade FIBER) and 33 percent to log grade ES1. The lowest percentages of logs (less than 2 percent) were associated with log grades ES2, ES4, and DS2. With the exception of the log grade DS3, all sawlogs had an average volume larger than 0.4 [m.sup.3]. Pulp logs had an average volume of 0.32 [m.sup.3].

Figure 1 shows that the proportion of logs associated with rough green lumber, chippable product, and sawdust resulting from the log conversion process remained relatively constant for most saw log grades; almost 60 percent for rough green lumber, 30 percent for chippable volume, and 10 percent for sawdust. ES4 and DS3 were the exceptions; proportions of rough green lumber and chippable product ranged between 45 and 50 percent for these log grades.


Product volumes associated with visual and MSR grades are presented in Figure 2a for a basic density of 350 kg/[m.sup.3] in Figure 2b for a density of 550 kg/[m.sup.3]. When a density of 350 kg/[m.sup.3] was used, most volume was concentrated on the visual grade No. 3 (50%) as well as on machine stress grade 1450f 1.3E (32%). The highest valued grades (2100f 1.8E and 1650f 1.5E) only represented 0.1 and 12 percent of the total volume, respectively. Proportions and absolute values changed when a density of 550 kg/[m.sup.3] was used to compute the total volume in each lumber grade. MSR grades 2100f 1.8E, 1650f 1.5E and 1450f 1.3E accounted for 2, 24, and 32 percent of the total volume, respectively. Visual grades No. 3 and Economy accounted for 30 and 12 percent, respectively. For a basic density of 550 kg/[m.sup.3] the volume was distributed 60:40 for MSR and visual grades, whereas the opposite distribution was observed for a density of 350 kg/[m.sup.3]


Gross returns ($ per log) are presented in Figure 3a for a basic density of 350 kg/[m.sup.3] in Figure 3b for a density of 550 kg/[m.sup.3]. When a density of 350 kg/[m.sup.3] was used, highest returns were concentrated on visual grade No. 3 (39%) and MSR grade 1450f 1.3E (41%). The highest valued grades (2100f 1.8E and 1650f 1.5E) only represented 0.1 and 17 percent of the total return, respectively. When a density of 550 kg/[m.sup.3] was used the total gross return was distributed across a number of lumber grades. MSR grades 2100f 1.8E, 1650f 1.5E and 1450f 1.3E represented 3, 32, and 36 percent of the total return, respectively. Similarly, visual grades No. 3 and Economy represented 23 and 5 percent, respectively. For a basic density of 550 kg/[m.sup.3] the total gross return was distributed 70:30 for MSR and visual grades, whereas the distribution was 56:44 for a density of 350 kg/[m.sup.3]. These returns will be sensitive to the lumber prices used in the analyses.


Figure 4 shows the net return for basic densities ranging between 300 kg [m.sup.-3] and 600 kg/[m.sup.3]. For log grade ES2, the net return for the average log varied between $87 and $104. Net return peaked at 540 kg/[m.sup.3] and then fell slightly due to diminished volumes of lumber grades 1450f 1.3E and Economy. Net returns for log grades ES3, DS1 and DS3 followed the same tendency, with returns ranging from $60 to $71, $26 to $30, and $13 to 60 respectively. For each of the saw log grades there was an 18 to 20 percent difference in net return due to basic density. Net returns for log grade FIBER (pulplogs) varied from $17 to $40; a difference of 140 percent. This indicates that pulp, as an end product, is more sensitive to variations in basic density of logs than lumber.


In the next step, the range of basic densities were grouped under three different classes, from 300 to 399 kg/[m.sup.3] (lower class), 400 to 499 kg/[m.sup.3] (middle class), and 500 to 600 kg/[m.sup.3] (upper class), and an average net return calculated for each class (Table 4). For the saw log grades there was a 9 percent drop in net returns for the lower class and a 4 percent increase for the upper class compared with the middle density class. Basic density increased the net return up to a certain point; beyond that, its marginal effect was less important than for lower densities.

For log grade FIBER, considerable marginal gains were found between the lower and upper density classes. Lower density class net return was 28 percent less and upper density class was 31 percent more than that of the middle density class. These differences highlight the effect of basic density on the net return and the importance of a proper segregation of logs as soon as possible in the supply chain from the forest to the pulp mill.

Lastly, current log prices in each log grade were arbitrarily assigned to the middle class. Then, based on the reduction and increment percentages shown in Table 4 relative log prices for the lower and upper density classes were estimated (Table 5).

Discussion and conclusions

This study shows that it is possible to estimate relative log prices of Douglas-fir based on wood density, even though markets do not yet pay a premium for wood density. This can be done by first calculating the net return of end products (lumber and pulp) obtained from the logs and then adjusting current log prices within each grade based on the differences in net return due to density.

Including new log properties in a bucking system may reduce the total value recovered by the forest owner unless appropriate premiums are paid for these properties. Therefore, it is essential to elaborate methodologies that allow companies to estimate the relative price that markets should be willing to pay for logs with different internal characteristics, such as basic density. Previous studies have shown that the requirement of minimum levels of basic density for log grades (without premium prices for additional properties) can reduce the total value by as much as 40 percent (Acuna and Murphy 2005).

The authors are unaware of any studies that have tried to make an estimation of the premium price that markets would be willing to pay for logs with better internal characteristics, such as a higher wood density. They are also unaware of any studies that have analyzed the economic effects of optimally bucking logs based on prices differentiated according to an internal wood characteristic.

There are limitations associated with this study which could have affected the results and our conclusions: only one stand was used, one set of market conditions were evaluated, the market was supply constrained, the equation to convert basic density to percent of juvenile wood was built from a small sample, only lumber, chips, sawdust and pulp were considered as end products to calculate net returns, equations used to calculate the proportion of lumber in each grade as well as the proportion of sawlogs available to be sawn were extracted from a previous study (Fahey et al. 1991), arbitrary density classes were used, and a density function was used, rather than actual density, to calculate the density at each segment of a stem.

Results obtained showed that pulp as an end product was more sensitive to variations in basic density of logs than lumber. However, the real effect of density is also determined by selling prices and processing costs. With lower selling prices and higher costs, the effect of basic density may be neutralized and lower net returns expected. For the lower basic density class, net returns (and log prices) for pulp were about 28 percent lower than the middle class, whereas the upper class net return was 32 percent higher than the middle class. For sawlog grades, the percentage differences between lower and upper class with the middle class were on average 9 and 4, respectively.

The next steps in this research should be the analysis of the effect of density on revenue in demand-constrained markets, as well as an assessment of the impact of additional wood properties in increasingly competitive markets. The study of new approaches to estimate premium prices for logs based on internal wood characteristics will allow for validation of the results presented here and facilitate the development of a standard methodology for estimating log price premiums.

Finally, although the methodology presented in this study is subject to limitations and based on some assumptions, the results presented give an indication of the potential impacts of new market requirements and how these can affect companies' decision-making in the future. In the meantime, wood producers should expect the evolution of markets for new requirements and be prepared for such changes.

Literature cited

Acuna, M.A. and G.E. Murphy. 2005. Optimal bucking of Douglas-fir taking into consideration external properties and wood density. New Zealand J. of Forestry Sci. 35(2): 139-152.

-- and --. 2006a. Geospatial and within tree variation of wood density and spiral grain in Douglas-fir. Forest Prod. J. 56(4):81-85.

-- and --. 2006b. Use of near infrared spectroscopy and multivariate analysis to predict wood density of Douglas-fir from chain saw chips. For. Prod. J. 56(11/12):67-72.

Andrews, M. 2002. Wood quality measurement--son et lumiere. New Zealand J. For. 47(3):19-21.

Anonymous. 1965. Western wood density survey. Report Number 1. USDA Forest Research Paper FPL-27.58 pp.

Aubry, C.L., W.T. Adams, and T.D. Fahey. 1998. Determination of relative economic weights for multitrait selection in coastal Douglas-fir. Can. J. Forest Res. 28:1164-1170.

Bowyer, J.L., R. Shmulsky, and J.G. Haygreen. 2003. Forest Products and Wood Science: An Introduction. Fourth ed. Iowa State University Press. Ames, Iowa 554 pp.

Briggs, D.G. 1994. Forest products measurements and conversion factors: With special emphasis on the U.S. Pacific Northwest. College of Forest Resources, University of Washington. Seattle, Washington. 154 pp.

-- and R.D. Fight. 1992. Assessing the effects of silvicultural practices on product quality and value of coast Douglas-fir trees. Forest Prod. J. 42(1):40-46.

Chmelik, J.T., R.D. Fight, and R.J. Barbour. 1999. Softwood lumber prices for evaluation of small-diameter timber stands in the intermountain west. Research Note FPL-RN-0270. USDA Forest Service, Forest Product Laboratory. Madison, Wisconsin.

Corson, S.R. 2001. Optimal allocation and use of today's trees for wood and paper products--the New Zealand experience. Presentation to the Markus Wallenberg Prize Ceremony, Stockholm, Sweden. 2 October. 18 pp.

Fahey, T.D., J.M. Cahill, T.A. Snellgrove, and L.S. Heath. 1991. Lumber and veneer recovery from intensively managed young growth Douglas-fir. Res. Pap. PNW-RP-437. USDA Forest Serv. Pacific Northwest Research Station. Portland, Oregon. 25 pp.

Gartner, B.L. 2005. Assessing wood characteristics and wood quality in intensively managed plantations. J. of Forestry 100(2):75-77.

Green, D.W. and R. Ross. 1997. Linking log quality with product perfomance. Role of wood production in ecosystem management. In: Proceedings of the Sustainable Forestry Working Group at the IUFRO All Division 5 Conference, Pullman, Washington. July 1997. pp. 53-58.

Hallock, H., P. Steele, and R. Selin. 1979. Comparing lumber yields from board-foot and cubically scaled logs. Research Paper FPL 324.USDA Forest Service, Forest Products Laboratory, Madison, Wisconsin.

Jappinen, A. 2000. Automatic sorting of sawlogs by grade. PhD thesis. Department of Forest Management and Products, Swedish University of Agricultural. Uppsala, Sweden.

Jozsa, L.A. and G.R. Middleton. 1994. A discussion of wood quality attributes and their practical implications. Special Publ. No. SP-34. Forintek Canada Corp., Vancouver, B.C.

Lane, P.H., R.O. Woodfin, J.W. Henley, and M.E. Plank. 1973. Lumber recovery from old-growth coast Douglas-fir. Res. Pap. RP-PNW-154. USDA Forest Service, Pacific Northwest Forest and Range Experiment Station. Portland, Oregon.

Marshall, H. 2005. An investigation of factors affecting the optimal output log distribution from mechanical harvesting and processing systems. Ph.D Thesis, Oregon State University. Corvallis, Oregon. 200 PP.

Matheson, A.C., L.D. Ross, D.J. Spencer, B. Joe, and J. Ilic. 2002.2002. Acoustic segregation of Pinus radiata logs according to stiffness. Ann. Sci. 59:471-477.

Megraw, R.A. 1986. Douglas-fir wood properties, In: Douglas-fir: Stand management for the future. C. Oliver, D. Hanley, and J. Johnson (eds.). Inst. of For. Res. Contrib. 55. College of Forest Resources, University of Washington, Seattle. Washington. pp.81-96 Oregon Dept. of Forestry. 2006. Log price information, forests/timber sales/logpage.shtml, accessed 7 June 2006.

Schuler, A. and A. Craig. 2003. Demographics, the housing market, and demand for building materials. Forest Prod. J. 53(5):8-17.

So, C.L., L.H. Groom, T.G. Rials, R. Snell, S. Kelley, and T. Meglen. 2002. Rapid assessment of the fundamental property variation of wood. In: Proceedings of the 11th Biennial Southern Silvicultural Research Conference. K.W. Outcalt (ed.). General Technical Report SRS-48. USDA Forest Service, Southern Research Station, Asheville, North Carolina. 622 pp.

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The authors are, respectively, Graduate Research Assistant and Professor, Department of Forest Engineering, Oregon State University, Corvallis, Oregon (, glen.murphy( This paper was received for publication in January 2006. Article No. 10157.

Maurici A. Acuna* Glen E. Murphy*

* Forest Products Society Member.

[c]Forest Products Society 2007. Forest Prod. J. 57(3):60-65.
Table 1.--Log grades and prices used to optimally buck the stems.

Log grade (a) Market prices Log grade Market prices

 ($/[m.sup.3]) ($/[m.sup.3])

ES1 149 DS1 104
ES2 132 DS2 97
ES3 125 DS3 77
ES4 93 FIBER 22

(a) ES = export saw log, DS = domestic saw log, FIBER = pulp log
(Marshall 2005).

Table 2.--Lumber prices and model coefficients for predicting MSR and
visual grade recovery percentages.

MSR and visual grades Model (a) price (b)


MSR 2100f 1.8E 18.69 x exp(2.962 x LLAD + 0.025 441
 x JWPC20 - 2.95811 x LLA
 [D.sup.2] - 0.000783 x JWPC
MSR 1650f 1.5E 38.1 x exp(0.79 x LLAD - 0.702 x 398
 LLA[D.sup.2] - 0.000 105 x JWPC
MSR 1450f 1.3E Obtained by subtraction 338
Visual No. 3 0.93 x exp(3.415 x LLAD - 0.761 x 232
 LLA[D.sup.2]) + 0.003 x JWPC
Visual Economy 2.93 x exp(1.105 x LLAD - 0.0106 123
 x JWPC20)

(a) Model coefficients (Fahey et al. 1991): LLAD = large limb average
diameter (average of largest limb in each log quadrant in inches);
JWPC20 = percentage of juvenile wood corresponding to 20-year annual
rings from the pith (cubic meters of juvenile wood divided by cubic
meters of log volume).

(b) Lumber prices obtained from Chmelik et al. (1999).

Table 3.--Cubic recovery percent equations for rough green lumber,
sawdust, and chips (Fahey et al. 1991).

Product Equation [R.sup.2] SEE

Rough green lumber 71.83 -452/D (a)-0.149 taper 0.32 10.07
Sawdust 9.6 - 51 /D - 0.023 taper 0.23 1.41
Chips By subtraction: 100 -
 (RG lumber + sawdust)

(a) D = small end diameter in millimeters. Taper is in millimeters per

Table 4.--Net return ($ per log) by basic density classes and
log grades. Percentage differences between the middle class
and lower or upper class are shown in parentheses.

 Lower class Middle class Upper class
 (300 to 399 kg/ (400 to 499 kg/ (500 to 600 kg/
Log grade [m.sup.3]) [m.sup.3]) [m.sup.3])


ES1 75.1 (-8.7) 82.3 85.4(+3.7)
ES2 91.1 (-8.7) 99.7 103.4 (+3.7)
ES3 63.5(-8.6) 69.4 71.9(+3.6)
ES4 25.3 (-8.6) 27.7 28.7(+3.7)
DS1 26.8(-9.0) 29.5 30.6(+3.8)
DS2 67.9(-9.1) 74.7 77.6(+3.9)
DS3 13.8 (-8.7) 15.2 15.7 (+3.7)
FIBER 19.6 (-28.3) 27.3 35.8 (+31.2)

Table 5.--Log prices ($ per [m.sup.3]) by basic density classes and
log grades.

 Lower class Middle class Upper class
 (300 to 399 kg/ (400 to 499 kg/ (500 to 600 kg/
Log grade [m.sup.3]) [m.sup.3]) [m.sup.3])


ESI 136.0 149.0 154.5
ES2 120.3 131.7 136.5
ES3 114.7 125.4 129.9
ES4 85.2 93.2 96.6
DS1 94.2 103.5 107.4
DS2 88.0 96.8 100.5
DS3 70.0 76.6 79.4
FIBER 15.7 21.9 28.7
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Author:Acuna, Mauricio A.; Murphy, Glen E.
Publication:Forest Products Journal
Date:Mar 1, 2007
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