Export Chip Prices as a Proxy 

for Nonsawtimber Prices in the 

Pacific Northwest

Gwenlyn M. Busby 1

United States 

Department of 

Agriculture

Forest Service

Pacific Northwest 

Research Station

Research Note

PNW-RN-554

December 2006

Abstract

Forest-land managers use price data and market analysis to form expectations and 

make informed management decisions. There is an abundance of price data for 

sawtimber, but for nonsawtimber, the availability of price data is limited. This 

constrains the ability of forest-land managers to form reasonable price expectations 

for stands that contain both sawtimber and nonsawtimber. In this paper, I show that 

export chip prices are a reasonable proxy for nonsawtimber prices in the Pacific 

Northwest. This conclusion is supported by evidence of arbitrage between the chip 

export market and three domestic markets in the Pacific Northwest. As to the chip 

export market in general, I observed increasing chip prices from 1968 through 

1995, a structural break in 1995 after which point we observe declining prices. 

I also found evidence of an inverse relationship between chip price and lumber 

production. 

Keywords: Wood chips, nonsawtimber, chip prices, residue prices. 

Introduction

Knowledge of nonsawtimber markets is increasingly relevant to forest-land management. Although forest managers and mill owners are well familiar with markets 

for sawtimber, less is known about nonsawtimber markets and price movements 

within these markets. Three factors have led to the increased importance of nonsawtimber product markets in the Pacific Northwest (PNW): (1) increased interest 

in reducing hazardous fuels, (2) increased interest in alternative energy sources, 

and (3) decreased average diameter of harvested trees. Fuel reductions often include 

the removal of small-diameter trees from stands that have little value as sawtimber. 

Therefore the value of nonsawtimber products will determine the economic feasi1 Gwenlyn M. Busby is a graduate student, Department of Forest Resources, Oregon State 

University, Corvallis, OR 97331.

Understanding nonsawtimber markets 

will be important in 

designing hazardous 

fuel treatments.
research Note PNW-Rn-554

bility of fuel reductions across the Western United States. Additionally, rising oil 

prices and increasing demand for alternative energy sources have piqued interest in 

the use of nonsawtimber products for biomass energy generation. 

There are parts of the West where much of the managed forest landscape is 

almost entirely second- and third-growth stands with a high proportion of smalldiameter trees. The decreasing average diameter of harvested stands is evidence of 

this shift (Haynes et al. 1995). In general, stands described as being small have an 

average diameter of about 8 inches and would produce few commercially valuable 

saw logs (4.5 to 11 inches small-end diameter). In spite of its increasing importance, 

relatively little attention has been paid to nonsawtimber markets in the PNW. 

Research in this area has been limited primarily by a lack of domestic price data.

The available nonsawtimber price data is for wood chips. There are two primary types of wood chips: sawmill residue and whole-tree chips. Historically, mill 

residue and nonsawtimber material have been viewed simply as a disposal problem 

(Hartman et al. 1975). Whole-tree chips are produced by a chipping machine that 

chips nonsawtimber material (less than 11 inches diameter at breast height). These 

chips are usually not as clean or uniform in size as mill residue chips. Although 

many stands in the PNW contain both sawtimber and nonsawtimber, the cost of 

chipping nonsawtimber material can be significant. Without information on domestic chip markets, it is difficult for forest managers to form price expectations. 

A detailed price series for wood chip exports from the PNW has been published for 

the last several decades (Warren 2005) by customs districts. The two that make up 

the PNW are the Columbia-Snake and the Seattle customs districts. 

The focus of the present analysis is on the usefulness of the wood chip export 

price series as a proxy for domestic nonsawtimber prices in the PNW. This paper 

uses the most recent price series data to update a similarly titled study (Haynes 

1999). The first section contains an analysis of the export price time-series: model 

specification and tests for trend and seasonality. The second section examines the 

relationship between the chip export price series and three domestic price series: 

fiber log prices from northeastern Oregon, chip and saw log domestic prices from 

the Puget Sound region, and utility log prices from northwestern Oregon. I examine 

the relationship between wood chip export prices and both PNW stumpage prices 

and lumber production in the third section. Finally, in the fourth section, we discuss 

the role of the Japanese chip export market on chip prices in the PNW. The paper 

concludes with a summary of the main findings and implications for forest-land 

managers.
Export Chip Prices as a Proxy for Nonsawtimber Prices in the Pacific Northwest



Export Price Data: Model Specification, Trend, 

and Seasonality

Export chip market prices have been reported since 1968 in the U.S. Forest Service publication Production, Prices, Employment, and Trade in Northwest Forest 

Industries. Quarterly wood chip export prices for the period 1968:12 to 2004:4 from 

the PNW were calculated as the volume weighted average of chip prices from the 

Columbia-Snake and Seattle customs districts. The original values for the time 

series data are shown in figure 1.3

Figure 1 indicates a shift in the underlying determinants of chip prices between 

the third and fourth quarter of 1995. This break occurs soon after the northern 

spotted owl (Strix occidentalis caurina) controversy of the early 1990s. A possible 

explanation for such a break is the changing composition of harvested material 

for lumber production during this period. Suppose that before the early 1990s the 

2 Quarterly prices are noted as year:quarter. For example, the second quarter of 1999 would be 

written 1999:2.

3 For all statistical analysis, I used the natural log of price.

0

20

40

60

80

100

120

140

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Export chip price (1982 dollars per short ton)

Figure 1—Export chip prices, Pacific Northwest (Seattle and Columbia-Snake customs district), 1968 

to 2004. Source: Warren, various years.
research Note PNW-Rn-554

forest industry was producing lumber from large-diameter trees, without much 

taper, which generated small amounts of wood chips per board foot of lumber. And 

suppose that following the listing of the spotted owl, only smaller diameter, tapered 

trees, which generate more wood chips per board foot of lumber, were harvested. 

This would increase the supply of wood chips in the market and depress chip prices. 

To test to see if there has been such a shift in the chip market, I divide the sample 

into two groups–1968:1 to 1995:3 and 1995:4 to 2004:4–and use the Augmented 

Dickey-Fuller (ADF) unit root test for stationarity.

Testing for stationarity is the first step in most time-series analyses. Stationarity ensures that the mean, variance, and covariance of the stochastic process 

generating the dependent variable do not change over time. The ADF test statistic 

indicates that the first group in the time-series is stationary at the 1-percent level of 

significance. With a test statistic of -3.026 and a 10-percent critical value of -3.203, I 

cannot reject non-stationarity of the second group. The absence of stationarity may 

be due to seasonality or a trend within data. First I tested for seasonality.

Seasonality refers to regular, seasonal fluctuations. For example, market 

demand for air conditioners exhibits seasonality because there is a peak in demand 

every summer. To test for seasonality in the export price data, it is necessary to 

quantify the seasonal fluctuations in the data. To this end, I first plotted the sample 

autocorrelation function (not shown). If the data exhibit quarterly seasonality, 

I would expect some degree of correlation between data points separated by four 

quarters. That is, I would expect to see correlation between price in time t and price 

in time t+4. Thus, seasonality can be identified by the presence of regular peaks 

in the autocorrelation function. The autocorrelation functions for the two periods 

analyzed: 1968:1 to 1995:3 and 1993:4 to 2004:4 did not exhibit regular peaks; 

therefore, I concluded that the price series is unaffected by seasonal fluctuations.

Table 1—Seasonal adjustments

	 	 Columbia-Snake	 Seattle	 	 Pacific Northwest

Quarter	 customs district		 customs district		 average

First 1.000 1.002 1.001

Second 0.996 0.998 0.997

Third 1.004 0.996 1.002

Fourth 0.999 1.003 1.000

To further test for seasonality, I calculated seasonal adjustments for the four 

quarters, following Haynes (1999). These adjustments, described in table 1, capture 

the deviation in the specified quarter’s price from the average price for all quarters. 

Because all adjustments were close to one, I concluded that chip prices are not 
Export Chip Prices as a Proxy for Nonsawtimber Prices in the Pacific Northwest

affected by seasonal fluctuations. The absence of seasonality implies that both 

the yearly average and the quarterly average produce equivalent estimates and no 

seasonal adjustments are necessary.

I specified the time series model with a linear time-trend (to achieve stationarity) and a first-order autoregressive term (AR(1)). Higher order autoregressive models were tested, but based on the Akaike information criterion (AIC)4 and Schwarz 

criterion (SC),5 both of which measure goodness of fit, I concluded the AR(1) model 

provided the best fit. The model is specified as follows:

= + β + β + ε 1 −1 2 c t t y t y [1]

where yt is chip export price in time t, c is the intercept, t is the time-trend, yt-1 is 

the previous quarter’s chip price and ε is the error term. Here t is chosen to equal 

one in the first period and increases by one in each successive period. The linear 

time-trend coefficient captures trend, or constant changes in each time quarter. The 

AR(1) term captures the degree of correlation between quarterly prices. By including an AR(1) term, I was able to estimate the influence of last quarter’s price on this 

quarter’s price. I also included a dummy variable for three observations capturing 

the recession of the early 1980s (1980:2, 1980:3, 1980:4).

I used a Chow test (Chow 1960) to test whether the estimated coefficients were 

the same before 1995:3 as they were after. Results of the Chow test indicated that 

the structural break is significant at the 1-percent level, and a structural change 

did occur between 1995:3 and 1995:4. To control for the structural break, I divided 

the sample, at the break point, into two groups and estimated the model allowing 

slope and intercept to differ across groups. The results of the model estimation are 

described in table 2.

All of the independent variables are significant at the 10-percent level or better. 

The time-trend coefficient is 0.017 for group 1, indicating a positive price trend, 

and -0.0038 for group 2, indicating a negative price trend. Haynes (1999) reported 

a positive long-term price trend using data through 1997. However, with the addition of recent data, the opposite for chip prices can be seen after 1995:3. The AR(1) 

coefficient is 0.6416 for group 1 and 0.51 for group 2. This implies that up until 

1996:3 a 1-percent increase in the previous quarter’s chip price was associated with 

4 The AIC minimizes ln(SSE/T)+2K/T, where SSE is sum of squared errors, T is sample size, 

and K is the number of regressors (Kennedy 2003).

5 The SC minimizes ln(SSE/T)+[K*ln(T)]/T, where SSE is sum of squared errors, T is sample 

size, and K is the number of regressors (Kennedy 2003).

Nonsawtimber prices 

have been declining 

since the late 1990s.
research Note PNW-Rn-554

a 0.6416 percent increase in the present quarter’s chip price. After 1996:3, a 1-percent increase in the previous quarter’s chip price was associated with a 0.51 percent 

increase in the present quarter’s chip price. Although the correlation between 

quarterly prices has declined since 1995:3, it remains relatively high.

Arbitrage Between Domestic and Export Markets

The model developed in the previous section describes our basic understanding of 

chip export price patterns since 1968, but to determine if these prices are a reasonable proxy for domestic prices, we need to know the degree to which domestic 

and export markets arbitrage. Market arbitrage describes a situation where the two 

seemingly distinct chip markets—the domestic chip market and the export chip 

market—are in fact one. Suppose the price for chips is higher in one market than in 

the other, then a trader could buy chips in the low-price market and re-sell it in the 

high-price market. Traders will engage in, and profit from, buying low and selling 

high until chip prices are equalized and a single market has been established. This 

process of price equalization is termed market arbitrage.

If domestic and export markets arbitrage, we can infer the existence of a single 

chip market and conclude that chip export prices provide a reasonable proxy for 

domestic prices. Next I used domestic price data from Oregon and the Puget Sound 

region to test for market arbitrage between domestic markets and the export market 

(Columbia-Snake and Seattle customs districts). Similar price movements provide 

evidence of arbitrage between export markets and three domestic markets: northeastern Oregon fiber log markets, and Puget Sound chip and saw log markets.

Northeastern Oregon Fiber Log Prices

Fiber log prices in northeastern Oregon were obtained from Oregon State University Extension, in La Grande, Oregon. Individual fiber logs are usually 5+ inches in 

Table 2—Model estimation

	 	 Group 1	 	 Group 2

Variables	 1968:1-1995:3	 1995:4-2004:4

 

Constant 1.4329*** 2.5164***

Time 0.0017*** -0.0038*

AR(1) 0.6416*** 0.5100***

Recession dummy 0.1293**  —

— = There was no recession in the second time period, hence no dummy. 

Significance level: *** = 1 percent, ** = 5 percent, * = 10 percent.
Export Chip Prices as a Proxy for Nonsawtimber Prices in the Pacific Northwest



diameter, and stands average at least 8 inches in diameter. Annual fiber log prices 

for the period 1994 to 2004 are shown in figure 2. These prices represent the milldelivered price, not the price at the time of transaction. 

The estimation of a regression model using fiber log data is prohibited by the 

small number of observations in this domestic price series. The trend lines fitted to 

the fiber log price series and to the export chip price series appear to be quite similar. For the period 1993 to 2004 there is a 52-percent decrease in fiber log prices 

and a 40-percent decrease in real export chip prices, both along the trend line. This 

coincidence of price movement is evidence of arbitrage between the domestic and 

export market. If we removed the transportation and processing cost differences 

and controlled for quality differences, we would expect the domestic and export 

prices to converge.

Puget Sound Chip and Saw Log Prices

Annual data from the Puget Sound on Douglas-fir (Pseudotsuga menziesii (Mirb.) 

Franco) chip and saw logs for the period 1994 to 2004 were taken from Log Lines. 

The Puget Sound region includes Clallam, Island, Jefferson (except coastal area 

south of Hoh River), King, Kitsap, Lewis, Mason, Pierce, San Juan, Skagit, Snohomish, Thurston, and Whatcom Counties. Chip and saw logs are generally 5 to 

7 inches in diameter, on the small end, and 12 to 40 feet in length and yield both 

0

20

40

60

80

100

120

140

160

1992 1994 1996 1998 2000 2002 2004 2006

Year

Fiber log price (1982 dollars per 

thousand board feet)

Figure 2—Fiber log price northeastern Oregon, 1993-2004. Source: Oregon State University Extension 

Service 2006.
research Note PNW-Rn-554



sawtimber and nonsawtimber products: two 2 by 4s and wood chips. Prices for 

chip and saw logs are reported in dollars per thousand board feet ($/MBF) and are 

shown in figure 3. 

Again, the estimation of regression model is prohibited by the small number 

of observations for the domestic price series. Fitting a trend line indicates a 

slight downward trend. For the period 1994 to 2004, real chip and saw log prices 

decreased 13 percent while real export chip prices decreased 38 percent. This 

discrepancy might be due to the nature of the log. Recall that although a portion of 

chip and saw logs is used to produce wood chips, the remainder is used as sawtimber. For the period 2001 to 2004, Log Lines data indicate No. 2 Douglas-fir prices 

increased from $352/MBF to $367/MBF. Increasing sawtimber prices would moderate declining chip prices. Therefore, controlling for sawtimber price movements, 

domestic chip and saw log prices in the Puget Sound region provide additional 

evidence of market arbitrage.

Oregon Utility Log Prices

Quarterly price data for utility logs from 1977:1 to 2004:4 were obtained from the 

Oregon Department of Forestry (fig. 4). Utility logs are similar to chip and saw logs 

in character and use; in general, utility logs do not meet the minimum requirements 

for peelers and sawmill grades, and most of the log is used to produce chips. Visual 

0

50

100

150

200

250

300

350

400

450

1992 1994 1996 1998 2000 2002 2004 2006

Year

Chip and sawlog price (1982 dollars per 

thousand board feet)

Figure 3—Domestic chip and saw log price, Puget Sound region, 1994-2005. Source: Log Lines, 

various years. 
Export Chip Prices as a Proxy for Nonsawtimber Prices in the Pacific Northwest



examination of the utility log price series reveals both similarities and differences 

between its pattern and that of the chip export price pattern.

Utility log prices and chip export prices exhibit similar patterns after 1989. 

From 1989 through 1995:3, we see increasing prices, and after 1995:3, we observe 

falling prices. These movements are consistent with chip export price observations. 

The utility log prices, however, appear more similar to a step function than the 

relatively gradual chip price movements. With a larger data set, the structural break 

observed in the export price series in late 1995 also appears in this domestic series. 

Before 1989, however, domestic utility log prices and chip export prices exhibit 

opposite trends. The reason for this discrepancy is unclear. Nonetheless, the coincidence of price movement after 1989 provides evidence of arbitrage with the chip 

export market.

Related Markets

In this section, I discuss the connections between chip price and three related markets. Understanding the interrelationships between markets is important in order to 

determine how, and by how much, changes in one market will affect related markets. This discussion is not intended to be an analysis of chip price determinants, 

but provides an overview of market linkages. I explore how the supply of mill 

0

20

40

60

80

100

120

140

160

180

1975 1980 1985 1990 1995 2000 2005 2010

Year

Utility log price (1982 dollars per thousand board 

feet)

Figure 4—Utility log prices, Oregon Department of Forestry, 1997 to 2005. Source: 

Oregon Department of Forestry 2006. 
research Note PNW-Rn-554

10

residue from softwood lumber production could affect chip prices. Then I explored 

the relationship between stumpage price movements and chip price movements. Finally, I examined the relationship between Japanese export demand and chip price.

Softwood Lumber Markets

Lumber markets and chip markets are closely related because wood chips are a 

byproduct of lumber production. When lumber production increases, the supply of 

wood chips also increases and you would expect to see chip prices fall. Similarly, 

when lumber production decreases, the supply of wood chips also decreases and 

you would expect to see chip prices increase.

Because sawmill residue is a primary source of wood chips, you would expect 

there to be an inverse relationship between lumber production and chip prices. 

About 45 to 50 percent of every log that enters a sawmill ends up as chips (see 

Hartmann et al. 1975, tables II-7 and II-11). Therefore, the more lumber that is 

produced, the more chips there are on the market, and the more likely prices are to 

fall. Lumber production for western Oregon and western Washington are shown in 

figure 5. There appears to be a close relationship between lumber production and 

chip export prices. Periods of increasing lumber production are associated with 

0

2

4

6

8

10

12

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Lumber (million board feet)

Figure 5—Lumber production, 1968 to 2004. Source: Western Wood Products Association, 

various years. 
Export Chip Prices as a Proxy for Nonsawtimber Prices in the Pacific Northwest

11

falling chip prices, and periods of decreasing lumber production are associated with 

increasing chip prices. This inverse relationship indicates that sawmill residue is 

the dominant supplier of wood chips to that market. This observation is relevant 

for land managers interested in whole-log chipping—whether for fuel reduction 

or biomass energy production: whole-log chips will have to compete in the chip 

market with supplies of sawmill residue, which is generally a cleaner and higher 

quality product. 

Stumpage Markets

To gain further insight into the factors influencing chip price, I used a second-order 

vector autoregression (VAR) model to explore the relationship between domestic 

stumpage prices and chip export prices. A VAR model is used to estimate the 

interdependence between multiple time series. Here I estimated how closely chip 

prices and stumpage prices move together. I estimated this interdependence for 

three quarterly stumpage price series: Oregon Department of Forestry (ODF 2006) 

delivered log prices, average stumpage price of timber sold on state land in western 

Washington (USDA FS, various years), and average stumpage price of timber sold 

on state land in western Oregon (USDA FS, various years). For each stumpage price 

series, the second-order VAR model consists of an equation explaining stumpage 

price based on two of its own lagged prices and two lagged chip price observations. 

Higher and lower order models were considered, but the second-order model had 

the best fit.

Western Oregon and Washington stumpage prices include 108 observations, 

and ODF delivered log prices include 112 observations. The model estimates for the 

VAR model indicate there is no statistically significant relationship between ODF 

delivered log prices and export chip prices. I observed a similar lack of relationship 

between western Oregon stumpage prices and export chip prices. The VAR estimates did, however, indicate a statistically significant relationship between western 

Washington stumpage prices and export chip prices.

The observed significant relationship between western Washington stumpage 

prices and export chip prices may be due to the composition of Washington stumpage, which is characterized by a higher proportion of hemlock (Tsuga spp.) and fir 

(Abies spp.) than Oregon stumpage. The market price for hemlock and fir is less 

than the market for Douglas-fir; therefore, they are more responsive to changes 

in the chip market—they are more (less) likely to be chipped given an increase 

(decrease) in chip price.
research Note PNW-Rn-554

12

Export Markets

Given chip market arbitrage between domestic and export markets, price movements in domestic chip markets are tied to demand shifts in export markets. More 

wood chips from the Pacific Northwest are exported to Japan than to any other 

country; between 1975 and 2004, the majority of total chip exports were sent to 

Japan (Warren various issues). Figure 6 shows annual Japanese chip and particle 

imports from 1975 to 2004. The Japanese export demand appears robust from 1975 

through 1995 and generally trends up.

One example of the connection between the Japanese imports and Pacific 

Northwest exports can be seen in the period 1985 to 1995. During this period, there 

were sustained increases in Japanese imports, and chip export prices in the Pacific 

Northwest were rising. Increases in Japanese demand would create upward pressure 

on prices. This correlation does not imply causation, but may provide land managers with some insight into related markets.

Note that the exchange rates influence chip export market performance. In 

Japan, price signals for wood chips are displayed in terms of yen, not dollars. 

Therefore the exchange rate has important implications on the chip market. To 

illustrate, in the period 1984 to 1996, the yen increased 28 percent, relative to the 

dollar. During this period, the real cost of U.S. wood chips in Japan also decreased 

by 28 percent. In response to lower prices, Japanese demand increased as indicated 

by the steep increase in Japanese imports between 1984 and 1996. 

0

200

400

600

800

1000

1200

1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

Chip and particle imports (million cubic 

feet)

Figure 6—Japanese chip and particle imports, 1975 to 2004. Source: FAOSTAT 2006.
Export Chip Prices as a Proxy for Nonsawtimber Prices in the Pacific Northwest

13

Concluding Remarks

Although there is no absolute measure of how well export chip prices serve as 

proxy for domestic nonsawtimber prices in the PNW, we find evidence in support 

of its usefulness. This conclusion is based on evidence of arbitrage between chip 

export markets and fiber log markets in northeastern Oregon, chip and saw log markets in the Puget Sound, and utility log markets in Oregon since 1989. In general, 

the chip export market exhibits a long-term upward trend from 1968:1 to 1995:3 and 

a downward trend from 1995:4 to 2004:4, an inverse relationship to lumber production, and a positive relationship to Japanese export volume.

Given continued increases in lumber production and no significant increases 

in Japanese wood chip import demand, land managers can expect chip prices to 

continue declining. This expectation, which will form price forecasts used by land 

managers, is contrary to that found in Haynes (1999), which analyzed data through 

1997 and found a long-term upward price trend. Stands throughout the PNW 

contain a mix of both sawtimber and nonsawtimber, and the potential value of 

nonsawtimber material will depend on chip prices. In the PNW, land managers can 

look to export chip prices and related markets to gain insight into domestic nonsawtimber markets. This insight is valuable to forest-land managers who are trying to 

form expectations about future price movements and make informed decisions in a 

dynamic market environment.

Literature Cited

Chow, G.C. 1960. Tests of equality between sets of coefficients in two linear 

regressions. Econometrica. 28: 591–605.

FAOSTAT. 2006. Japan: chip and particle imports. <http://faostat.fao.org>. 

(May 15).

Hartman, D.A.; Atkinson, W.A.; Bryant, B.S.; Woodfin, R.O. 1975. Conversion 

factors for the Pacific Northwest forest industry. Seattle, WA: Institute of Forest 

Products, College of Forest Resources, University of Washington. 112 p.

Haynes, R. 1999. Chip prices as a proxy for non-sawtimber prices in the Pacific 

Northwest. Res. Note PNW-RN-537. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 25 p.

Haynes, R.W.; Adams, D.M.; Mills, J.R. 1995. The 1993 RPA timber assessment 

update. Gen. Tech. Rep. RM-GTR-259. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment 

Station. 66 p.

Export chip prices 

are a good proxy for 

nonsawtimber prices.
research Note PNW-Rn-554

14

Kennedy, P. 2003. A guide to econometrics. 5th ed. Cambridge, MA: The MIT 

Press. 500 p. 

Log Lines. [Various years]. Log price reporting service. Mount Vernon, WA.

Oregon Department of Forestry. 2006. Data for Douglas-fir utility logs. <http://

www.oregon.gov/ODF/STATE_FORESTS/TIMBER_SALES/logpage.shtml (June 

2006).>

Oregon State University Extension Service. [Various years]. Blue Mountain 

Renewable Resources Newsletter. La Grande, OR.

U.S. Department of Agriculture, Forest Service [USDA FS]. [Various years].

Timber cut and sold reports. On file with: Pacific Northwest Region, 333 SW 

First Avenue, Portland, OR 97208. 

Warren, D. [Various years]. Production, prices, employment, and trade in 

Northwest forest industries, all quarters. Resour. Bull. PNW-RB. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station.

Western Wood Products Association. [Various years]. Statistical yearbook of the 

western lumber industry. Portland, OR.

Metric Equivalents

When you know:	 Multiply by:	 To find:

Inches (in) 2.54 Centimeters

Feet (ft) .3048 Meters
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