Evaluating Forest Land

Development Effects on

Private Forestry in

Eastern Oregon

Jeffrey D. Kline and David L. Azuma

United States 

Department of 

Agriculture

Forest Service

Pacific Northwest

Research Station

Research Paper

PNW-RP-572

March 2007
Authors

Jeffrey D. Kline is a research forester, Forestry Sciences Laboratory, 3200 SW

Jefferson Way, Corvallis, OR 97331; David L. Azuma is a research forester,

Forestry Sciences Laboratory, 620 SW Main Street, Suite 400, Portland, OR 97208.

The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, water,

forage, wildlife, and recreation. Through forestry research, cooperation with the States and 

private forest owners, and management of the National Forests and national grasslands, it

strives—as directed by Congress—to provide increasingly greater service to a growing

Nation.

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public assistance program. (Not all prohibited bases apply to all programs.) Persons with 

disabilities who require alternative means for communication of program information (Braille,

large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice

and TDD).

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Independence Avenue, S.W. Washington, DC 20250-9410 or call (800) 795-3272 (voice) 

or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.
Abstract

Kline, Jeffrey D.; Azuma, David L. 2007. Evaluating forest land development 

effects on private forestry in eastern Oregon. Res. Pap. PNW-RP-572. Portland,

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 18 p.

Research suggests that forest land development can reduce the productivity of 

remaining forest land because private forest owners reduce their investments in 

forest management. We developed empirical models describing forest stocking,

thinning, harvest, and postharvest tree planting in eastern Oregon, as functions of

stand and site characteristics, ownership, and building densities. The models are

based on USDA Forest Service Forest Inventory and Analysis data gathered in 

eastern Oregon in 1987 and 1998, and data describing building densities gathered

by the Oregon Department of Forestry from aerial photographs taken over the same

period. We used the models to examine the potential effects of population growth

and development, as described by increasing building densities, on the likelihood

that private forest owners maintain forest stocking, precommercially thin, harvest,

and plant trees following harvest. Empirical results suggest that population growth

and development have had no measurable effect on these activities in eastern

Oregon during the period examined. Any development effects on private forest

management and investment so far are likely to be fairly localized. 

Keywords: Wildland-urban interface, nonindustrial private forest owners, 

urbanization. 
Summary

Previous research conducted in the Southern United States and in western Oregon 

suggests that forest land development can reduce the productivity of remaining 

forest land because private forest owners reduce their investments in forest management. We tested for these effects in eastern Oregon by developing empirical

models describing forest stocking, thinning, harvesting, and postharvest tree planting, as functions of stand and site characteristics, ownership, and building densities.

The models are based on USDA Forest Service Forest Inventory and Analysis data

gathered in eastern Oregon in 1987 and 1998, and data describing building densities gathered by the Oregon Department of Forestry from aerial photographs taken

over the same period. We used the models to examine the potential effects of recent

population growth and development, as described by increasing building densities,

on the likelihood that private forest owners maintain forest stocking, precommercial thin, harvest, and plant trees following harvest. We found forest land development in eastern Oregon to be uncorrelated with forest stocking, and the likelihood

of precommercially thinning, harvesting, and postharvest tree planting during the

period examined. The empirical results suggest that population growth and development have had no measurable effect on these activities among private forest

landowners in eastern Oregon during the period examined. These results differ

from those found in western Oregon, where increased development was found to 

be correlated with lower forest stocking and reduced likelihood of precommercial

thinning and postharvest tree planting. Our results likely owe to comparatively

lower rates of forest land development and lower inherent site productivity found

in eastern Oregon relative to that found for western Oregon. We suspect that any

development effects on private forest management and investment in eastern

Oregon so far are likely to be fairly localized. 
Contents

1 Introduction

2 Population Growth, Development, and Private Forestry

3 Modeling Stocking, Thinning, Harvest, and Planting

3 Forestry and Building Density Data

4 Empirical Modeling

5 Testing the Influence of Building Density on Private Forestry

6 Forest Stocking Levels

7 Likelihood of Precommercial Thinning

7 Likelihood of Harvesting

9 Likelihood of Tree Planting Following Harvest

11 Discussion

16 Conclusions and Policy Implications

17 Acknowledgments

17 Metric Equivalents

17 Literature Cited
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Introduction

Often cited research conducted in the Southern United States suggests that forest 

land development can reduce the productivity of remaining forest land because private forest owners reduce their investments in forest management and become less

likely to harvest timber (Barlow and others 1998, Munn and others 2002, Wear and

others 1999). Kline and others (2004) tested for these effects in western Oregon.

Their results suggested that population growth and development are correlated 

with reduced stocking levels, and with reduced rates of precommercial thinning 

and postharvest planting, but not with reduced rates of harvest. Declining forest

productivity resulting from development itself may not necessarily justify significant policy or management concern, and in fact, could simply reflect the workings

of efficient land markets shifting forest land to more highly valued developed uses.

However, changes in management could effect changes in the characteristics of

remaining forests, with resulting policy- and management-relevant economic and

ecological consequences. From an economic perspective, forest policymakers and

managers might want to anticipate how much timber likely will be supplied from

private forests in the future. From an ecological perspective, changes in management and harvesting can affect forests as ecological resources that provide wildlife

habitat and other public benefits. Related changes also can influence fuel loads and

corresponding wildfire risks in wildland-urban settings. Such possibilities might 

be of concern to policymakers and managers regarding future change. 

A unique combination of forestry and land use data enabled previous analysis

of forest land development effects on private forestry in western Oregon (Kline and

others 2004). Recent acquisition of similar data now enables such analysis for eastern Oregon—east of the crest of the Cascades. Using the methods of Kline and 

others (2004) in western Oregon, we developed empirical models describing forest

stocking, thinning, harvest, and postharvest tree planting in eastern Oregon as 

functions of stand and site characteristics, ownership, and building densities. The

models are based on USDA Forest Service Forest Inventory and Analysis data

gathered during 1987 and 1998 eastern Oregon inventories, and data describing

building densities gathered by the Oregon Department of Forestry. We use the

models to examine the potential effects of population growth and forest land development, as described by increasing building densities, on the likelihood that forest

owners maintain forest stocking, precommercially thin, harvest, and plant trees following harvest. Empirical results suggest that population growth and development

have had no measurable effect on private forest management and investment in

Changes in management could 

effect changes in 

the characteristics 

of remaining forests,

with resulting economic and ecological

consequences.

Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

1
eastern Oregon during the period examined. Any development effects on private

forest management and investment in eastern Oregon so far are likely to be fairly

localized. 

Population Growth, Development, and Private Forestry

Prevailing hypotheses suggest that forest land development can cause private 

forest lands to become less productive owing to (1) their gradual fragmentation into

smaller management units, (2) related changes in the characteristics and management objectives of newer more urban-minded forest owners, (3) potential conflicts

arising from conducting forestry in proximity to people, and (4) increasing uncertainty among remaining forest landowners about prospects for continued forestry 

in the future (for detailed discussion, see Kline and others 2004: 34-35). There also

is growing concern among policymakers and managers that locating homes on predominantly forested landscapes may carry unacceptable risks associated with wildfire. How private forest lands are managed may greatly influence wildfire risks

through changes in forest structure, stand density, fuel loads, and other factors.

Empirical studies that have tested for potential correlations between private forest

management and development include Barlow and others (1998), Munn and others

(2002), Wear and others (1999)—all conducted in the Southeastern United States—

and Kline and others (2004) conducted in western Oregon. 

Barlow and others (1998) and Munn and others (2002) combined data describing plot-level harvest activities, and stand and site characteristics in Mississippi and

Alabama (USA), with data describing human population densities and distances of

forest stands to urbanized areas. Both studies found the likelihood that forest owners harvest timber to be negatively correlated with population density and urban

proximity, suggesting potential impacts on stand density, age class, species composition, and successional stage. Wear and others (1999) used expert opinion to

identify the likelihood that forest lands in five Virginia (USA) counties were managed for commercial timber production. They combined this information with data

describing plot-level stand and site characteristics and population densities, and

found the likelihood that forest lands are managed for commercial timber production is negatively correlated with population densities. 

Building on these studies, Kline and others (2004) developed empirical models

describing forest stocking, precommercial thinning, harvesting, and postharvest tree

planting as functions of stand and site characteristics, ownership type, and building

densities—a proxy variable for forest land development. Their empirical results

suggest that increasing building densities have had no statistically significant effect

2

Research Paper PNW-RP-572
on the likelihood that private forest landowners in western Oregon harvested timber

from 1974 to 1994. However, their empirical results do suggest that increasing

building densities are correlated with reduced forest stocking, and reduced likelihood of precommercial thinning and postharvest tree planting. Taken together,

Kline and others (2004: 41) concluded that their results do support the general conclusion that population growth and forest land development may be reducing the

intensity with which private forest owners in western Oregon manage their forest

lands, but that so far only a small proportion of lands have been affected. Recent

acquisition of similar data describing forest management and building densities in

eastern Oregon enabled testing for development effects on private forestry there.

The potential presence of these effects in eastern Oregon is of growing policy and

management concern owing to rapid population growth and development in some

regions (e.g., Deschutes County) and to the persistent wildfire threats characteristic

of most east-side forests. 

Modeling Stocking, Thinning, Harvest, and Planting

We combined plot-level data describing forest conditions and management activities with aerial photopoint data depicting historical building densities to examine

the potential effects of population growth and forest land development on private

forest management in eastern Oregon. We used these data to estimate empirical

models describing forest stocking, precommercial thinning, harvesting, and postharvest tree planting as functions of stand and site characteristics, ownership, and

building densities. The models cannot directly address hypothetical management

effects regarding declining parcel size, changing owner characteristics, wildlandurban conflicts, and owners’ expectations about the future of forestry. However, 

the models can be used to test whether changes in forest management in locations

experiencing population growth and development are consistent with these hypotheses, by examining how forest stocking, precommercial thinning, harvesting, and

planting might differ as building densities increase. If population growth and development have reduced the intensity with which private forest landowners manage

their lands in eastern Oregon, we would expect lower forest stocking levels, and

less likelihood of thinning, harvesting, and postharvest tree planting in areas of

higher building densities.

Forestry and Building Density Data

Plot-level forestry data were obtained from the USDA Forest Service Forest 

Inventory and Analysis Program’s regular inventories of forest land in western

The potential 

presence of these

effects in eastern

Oregon is of growing

policy and management concern owing

to rapid population

growth and development in some

regions.

Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

3
Oregon (Azuma and others 2004). The inventories consist of periodic nationwide

assessments of nonfederal land in the United States as authorized by the Forest and

Rangeland Renewable Resources Research Act of 1974. Inventory data were gathered in eastern Oregon by using photointerpretation and ground-truthing on a systematic sampling of nearly 4,378 field plots. The inventories gathered detailed

information regarding forest characteristics, as well as information about any precommercial thinning, harvesting, and tree planting observed from one inventory to

the next as well as forest stocking. Eastern Oregon inventories are conducted on

roughly a 10-year cycle. Data used for this study were gathered during the 1987

and 1998 inventories. The 1998 inventory indicated that forest land made up 35

pecent of all land in eastern Oregon (Azuma and others 2004: 12) with about 3.4

million acres under private ownership (Campbell and others 2003: 15). 

Substantial portions of private lands in eastern Oregon are in nonforest uses,

including range (52 percent) and intensive agriculture (22 percent), as well as a

small proportion (3 percent) in urban and low-density residential (Lettman 2004:

13). To describe forest land development in eastern Oregon, the Oregon Department

of Forestry gathered building density data consisting of the numbers of buildings

on nonfederal land observed in the 640-acre vicinity of sample points located on

aerial photographs taken in 1975, 1986, and 2001 (Lettman 2004). With about

13,103 sample points, the data provide nearly 40,000 observations of building densities varying in space and time. Building densities for 1987 and 1998 were estimated by interpolating between 1986 and 2001 values, to roughly coincide with 

the 1987 and 1998 Forest Inventory and Analysis data. Cross-referencing between

building density sample points and forest inventory field plots enables analysis of

building densities and the forest stocking, thinning, harvest, and planting variables. 

Empirical Modeling

Two types of data make up the dependent variables available for study: (1) discrete 

data consisting of dummy variables describing evidence of whether or not precommercial thinning, harvesting, or postharvest tree planting activities occurred from

one forest inventory to the next; and (2) continuous data describing forest stocking

at each inventory. The potential effects of various factors on each dependent variable can be analyzed by using the structural model

yi

*

= β ́xi + εi (1)

4

Research Paper PNW-RP-572
where x is a vector of explanatory variables describing factors hypothesized to

influence each dependent variable y, β is a vector of estimated coefficients, ε is an

error term, and i = 1, . . . , n identifying individual observations in the sample. 

Precommercial thinning, harvesting, and postharvest tree planting activities

occurring between the two successive forest inventories are described by discrete

data, and we define the probability that evidence of each activity was observed 

as yi

*. Although yi* is unobservable, observed evidence of each activity can be

described as a vector of binary variables yi defined by

yi = 1 if yi

* > 0, 0 otherwise . (2) 

For example, when examining the likelihood of harvesting, yi equals 1 for plots

observed as harvested since the previous inventory and 0 if harvesting was not

observed. We assume that error in the model is distributed normally and use the

binary variable yi to estimate a probit model. The model describes the likelihood

that evidence of a harvest was observed from the 1987 to the 1998 forest inventory

and is described as

P(yi = 1) = Φ(β ́xi) (3) 

where xi are explanatory variables and β ́ are estimated coefficients. Two other 

probit models describe the likelihood that evidence of precommercial thinning and

postharvest tree planting were observed. Forest stocking described by a continuous

dependent variable—average basal area per acre of sample plots—can be examined

by using ordinary least squares regression. 

Testing the Influence of Building Density 

on Private Forestry

Several factors reasonably can be expected to influence forest stocking, thinning, 

harvesting, and postharvest tree planting on private forest lands. We analyzed each

of the dependent variables by considering as many possible factors as feasible and

using the best data available. The first empirical model describes forest stocking, 

as represented by basal area. The remaining three empirical models describe the

likelihood that private forest owners precommercially thinned, harvested, and

planted trees following harvest. In each case, the empirical analyses closely follow

econometric model specifications reported in Kline and others (2004). Explanatory

variables tested in each of the four models are described in table 1. 

Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

5
Forest Stocking Levels

Stocking may not necessarily be treated as a choice variable by private forest owners, but less intensive management could lead to lower stocking on private forest

stands. We examined the basal area—our proxy for stocking—on sample plots

observed during the 1998 inventory and whether basal area varied by building densities. Changes in procedures from the 1987 to 1998 inventories prevent us from

pooling 1987 and 1998 data, because the 1987 inventory described stand age—a

key explanatory variable in forest stocking models—at the plot level, whereas the

1998 inventory data described stand age at the subplot or “condition class” level.

We examined 1998 basal area because it would have been subject to greater levels

of forest land development than 1987 basal area. We estimated an ordinary least

squares regression model describing average basal area per acre as a function of

stand age, site index, nonindustrial private ownership, and building density. The

model describes how average basal area might have varied by building density

after accounting for stand and site characteristics. Although past management practices, such as thinning, likely affect current stocking levels, including explanatory

variables describing past activities was not possible because such data were not

available for prior years. We restricted the analysis to even-aged stands owing to

difficulties posed by modeling basal areas of uneven-aged stands. Also, because

6

Research Paper PNW-RP-572

Table 1—Descriptions of explanatory variables tested in the empirical models

Variable Description

AGE Age of plot stand in years at current forest inventory 

occasion.

AGE2 The variable AGE squared.

BASAL AREA t Basal area (square feet per acre) of the plot stand at current

forest inventory occasion.

BASAL AREA t-1 Basal area (square feet per acre) of the plot stand at 

preceding forest inventory occasion.

SITE INDEX Site index of the plot.

SLOPE Percentage slope at the plot.

DISTANCE TO ROAD Distance of the plot to the nearest road in meters (100s).

NONINDUSTRIAL Variable equals 1 if nonindustrial private owned; 0 otherwise.

BUILDING DENSITYt Number of buildings within a 640-acre circle (Lettman 

2004) surrounding plot at forest inventory occasion.

BUILDING DENSITYt-1 Number of buildings within a 640-acre circle (Lettman 

2004) surrounding plot at preceding forest inventory 

occasion.

Note: Unless otherwise noted, all variables are from USDA Forest Service Forest Inventory and 

Analysis data (Azuma and others 2004).
Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

7

few plots comprised hardwoods, we restricted the model to conifer stands alone.

These restrictions reduced the basal area sample size to 268 observations when

combined with other explanatory variable data. 

The estimated model is quite weak, with an adjusted R2 of just 0.01 (F = 1.44,

df = 5, P = 0.201) (table 2). Alternative specifications of the basal area model were

tested—several versions including dummy variables to account for different forest

types, for example—but these consistently failed to improve model fit. The weak

empirical results suggest that just describing a significant portion of the variation 

in the basal areas of eastern Oregon forest inventory plots is challenging much less

showing any statistically significant negative correlation between basal area and

building densities. We did find stand age to have a statistically significant positive

but diminishing influence on average basal area per acre, as would be expected.

The estimated coefficient for nonindustrial private ownership is negative, consistent

with lower forest stocking on these lands, but its statistical significance is rather

weak (P = 0.16). The estimated BUILDING DENSITYt coefficient is negative, but

is not statistically significant (P > 0.75), suggesting that forest stocking did not

vary by building densely in eastern Oregon in 1998. 

Likelihood of Precommercial Thinning

Forest inventory data indicate that just 3 percent of sample forest plots were precommercially thinned from 1987 to 1998. We estimated a probit model describing

the likelihood that forest owners precommercially thinned, as a function of basal

area, site index, slope, road distance, ownership, and building density. These inventory data provided 415 observations when combined with other explanatory variable

data of which 20 observations represent forest stands that were precommercially

thinned. The resulting estimated model is quite weak, with a chi-square value 

of 6.59 (df = 6, P = 0.360) (table 2). Only the negative estimated coefficient for

SLOPE was found to be statistically significant (P < 0.10), suggesting lower incidence of precommercial thinning on forest stands located on steeper slopes, consistent with likely higher thinning costs on steeper slopes. The estimated BUILDING

DENSITYt-1 coefficient is negative but statistically insignificant (P > 0.35), suggesting that the likelihood of precommercial thinning on private forest land did 

not vary by building density in eastern Oregon. 

Likelihood of Harvesting

Forest inventory data for 1987 to 1998 indicate that 52 percent of sample plots 

retained for analysis (after combining with other explanatory variable data) had

experienced some type of harvest more significant than firewood cutting or other
Table 2—Estimated coefficients of the ordinary least squares basal area model and precommercial thinning, harvest, and

postharvest tree planting probit models for eastern Oregon

Basal area Precommercial thinning Harvesting Postharvest planting

Estimated Estimated Estimated Estimated

Variable coefficient t-statistic coefficient t-statistic coefficient t-statistic coefficient t-statistic

Intercept 28.496 0.43 -1.460** -2.287 -0.156 -0.44 -1.567 -1.559

AGE 2.507** 2.11 — — — — — —

AGE2 -0.016* -1.65 — — — — — —BASAL AREA t — — — — — — -0.014** -2.04BASAL AREA t-1 — — -0.001 -0.27 0.006*** 5.12 — —SITE INDEX -0.224 -0.32 0.001 0.14 0.001 0.25 0.012 1.09

SLOPE — — -0.016* -1.90 -0.012*** -2.85 -0.017 -1.07

DISTANCE TO ROAD — — -0.007 -0.19 0.009 0.46 -0.254 -1.34

NONINDUSTRIAL -33.199 -1.40 0.148 0.64 -0.207 -1.55 — —

BUILDING DENSITY t -0.331 -0.26 — — — — — —

BUILDING DENSITY t-1 — — -0.070 -0.91 -0.001 -0.25 -0.040 -0.46

Summary statistics N = 268 N = 415 N = 415 N = 207

Adj. R2 = 0.01 Log-L = -76.86 Log-L = -265.63 Log-L = -32.21

F = 1.44 X 2 = 6.59 X 2 = 43.18 X 2 = 15.69

df = 5, 262; P = 0.201 df = 6; P = 0.360 df = 6; P < 0.001 df = 5, P = 0.008

Note: The

*

,

**

, and

***

indicate that the probability of the t-statistic exceeding the critical t-value is greater than 90 percent, 95 percent, and 99 percent,

respectively. Explanatory variables are defined in table 1. Basal area model describes 1998 inventory basal area. Precommercial thinning, harvesting, and

postharvest planting models describe activities occurring from 1987 to 1998 inventory.

8

Research Paper PNW-RP-572
incidental harvest. We examined the likelihood that forest owners harvested

between the inventory years of 1987 to 1998, and whether harvesting varied by

building densities. Conversion of forest land to residential uses often involves harvesting existing forest stands prior to new construction. However, such harvests 

are not explicitly reported in Forest Inventory and Analysis Program inventories,

because they occur on plots considered to have converted to nonforest uses, and the

inventories do not report forestry data for such plots. Also, the potential number of

plots experiencing preurban-conversion harvesting likely is small, because the actual total number of forest plots converting to urban uses have been few. For these

reasons, we restricted our analysis to inventory plots on which forest use continued. 

We estimated a probit model describing the likelihood of any harvest activity

more significant than firewood cutting or other incidental harvest. The resulting

estimated model is relatively strong with a chi-square value of 43.18 (df = 6, P <

0.001), but yielded only two estimated coefficients that were statistically significant

at a high level of confidence (table 2). We found basal area to have had a statistically significant positive influence (P < 0.01) and slope to have a statistically significant negative influence (P < 0.01) on the likelihood of harvest activity. These

results are consistent with greater harvest activity among higher stocked stands and

lower activity on steeper slopes, which likely increase harvest costs. The estimated

negative coefficient for NONINDUSTRIAL suggests that harvest activity has been

lower among nonindustrial private forest owners; however, its statistical significance is somewhat weak (P = 0.12). The estimated BUILDING DENSITYt-1 coefficient is negative but statistically insignificant (P > 0.80) suggesting that harvest

likelihood on private forest land did not vary by building density in eastern Oregon. 

Likelihood of Tree Planting Following Harvest

Forest inventory data indicate that just 10 of the harvested sample plots retained 

for analysis in the harvest model had been planted with trees by the 1998 inventory.

Oregon’s Forest Practices Act has evolved over time but generally requires reforestation following harvest on lands identified as timberland—those lands capable

of annual wood production of at least 20 cubic feet per acre at culmination of mean

annual increment (Oregon Department of Forestry 2006). Rules do allow natural

regeneration in place of planting, as well as other exemptions. Artificial reforestation such as tree planting generally must be completed within 2 years, and harvested stands generally must be brought up to minimum stocking standards within 6

years (Oregon Department of Forestry 2006). Forest inventory data indicate that 90

percent of harvested sample plots retained for analysis in the harvest model were

Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

9
10

Research Paper PNW-RP-572

classified as timberland, and these included the 10 plots on which tree planting 

had been observed. Although no tree planting had been recorded on sample plots

retained for analysis that were classified as “other forest”—forest lands that do not

meet timberland criteria—those plots were retained for analysis in the tree-planting

model as owners could have opted to plant trees on those stands. 

We estimated a probit model describing the likelihood that forest owners 

planted trees following harvest between the inventory years of 1987 and 1998. 

Data were restricted to plots on which harvesting had occurred since 1987. Basal

area observed at the current (postharvest) inventory, rather than the preceding (preharvest) inventory, was used as a proxy for stocking following harvest and prior to

planting. Basal area measurements include only trees having a minimum 2.5 centimeters diameter at breast height—recently planted seedlings generally would not

be included. On recently harvested stands, inventory-observed basal areas would

indicate the amount of postharvest re-stocking needed. The resulting estimated

model is fairly strong with a chi-square value of 15.69 (df = 5, P = 0.008) (table 2).

However, only the estimated negative coefficient for BASAL AREA was found to

be statistically significant at a high level of confidence (P < 0.05), consistent with

our expectation that residual basal area following harvest would indicate restocking

needs. The variable NONINDUSTRIAL was omitted from model estimation

because no nonindustrial private-owned inventory plots included in the sample had

been planted following harvest by the 1998 inventory. The estimated coefficient for

DISTANCE TO ROAD is negative but its statistical significance is rather weak (P

= 0.18). The estimated BUILDING DENSITYt-1 coefficient is negative but statistically insignificant (P > 0.80) suggesting that postharvest planting likelihood on 

private forest land in eastern Oregon did not vary by building density. 

The fairly small number of sample plots recorded as planted likely confounds

model estimation. Given the fairly large number of sample plots that were harvested between the 1987 and 1998 inventories, the relatively low number of plots

recorded as planted by the 1998 inventory may seem surprising given Oregon’s

reforestation requirements. Stands that still meet minimum stocking standards even

after harvest are not subject to reforestation requirements, but it is not possible to

identify such stands because stocking standards depend on the sizes of residual

trees and include stems per acre and basal area criteria. Conceivably, harvested but

still unplanted sample plots included in the tree planting analysis either were (1)

not subject to reforestation requirements because they already met minimum stocking standards, (2) harvested just prior to the 1998 inventory and still within their

reforestation compliance period based on either artificial or natural regeneration, 
or (3) potentially out of compliance with reforestation requirements. Another possible explanation is that some forest landowners were anticipating changing their

land use either to agriculture or development, which leads to a waiving of reforestation requirements, although at least in the case of development this explanation

seems unlikely given relatively low rates of development on eastern Oregon forest

lands (e.g., Kline and others, in press). Still one other possible explanation is that 

in some cases, the Forest Inventory and Analysis data gatherers may have missed

observing existing evidence of postharvest tree planting. The relative merit of any

of these possible explanations cannot be known with certainty. 

Discussion 

The empirical results suggest that increasing building densities have had no statistically significant effect on forest stocking and the likelihood that private forest

owners precommercially thinned, harvested timber, and planted trees following 

harvest in eastern Oregon from 1987 to 1998. These results differ from those found

in western Oregon by using similar methods. Kline and others (2004) found that

private forest landowners in western Oregon tended to have lower forest stocking,

and showed lower likelihood of precommercial thinning and postharvest tree planting as building densities increased. Although Kline and others (2004) found harvest

rates to be unaffected by building densities, they suggested that these forest land

development effects may not yet be observable in western Oregon. From our examination of forestry and building density data from eastern Oregon, it would appear

that forest land development effects on stocking, thinning, harvest, and postharvest

planting either are not prevalent in the region or are not yet observable from available Forest Inventory and Analysis data. 

A relatively small proportion of all forest land in eastern Oregon currently is

located in places where significant residential and other development has taken

place. Of sample plots evaluated in this analysis, only 5 percent of plots were 

located in areas having building densities over 10 buildings per square mile in

1998. Although some locations in eastern Oregon—Deschutes County, for example

—have been experiencing significant population growth and development in recent

years, most eastern Oregon forest land remains sparsely populated. Also, from

1975 to 2001, more than twice as much eastern Oregon rangeland was converted 

to low-density residential and urban uses as forest land (Lettman 2004: 16). Such

conversions also have been more prevalent on agricultural lands. These factors

would tend to limit opportunities for observing development effects on private

forestry in eastern Oregon. 

A relatively small

proportion of all 

forest land in eastern

Oregon currently is

located in places

where significant 

residential and other

development has

taken place.

Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

11
In addition to finding little empirical evidence of forest land development

effects on private forest management, our analysis also yielded generally weak

empirical results describing forest stocking and management activities. Typically,

indicators of forest management intensity, such as stocking, precommercial thinning, harvesting, and postharvest planting are assumed to be functions of stand 

and site characteristics as well as forest owners’ management objectives. Kline and

others (2004) generally found robust relationships between these same indicators

and stand age, basal area, site index, and slope in western Oregon (table 3). Our

models for eastern Oregon generally are empirically weaker than those of Kline

and others (2004) in terms of their ability to predict forest management indicators,

and also have fewer statistically significant variable coefficients. Only stand age is

statistically significant in the eastern Oregon basal area model, only basal area and

slope are statistically significant in the harvesting model, and only basal area is 

statistically significant in the postharvest tree planting model (table 3). The statistical significance of those estimated coefficients for each of these variables certainly

is reasonable in each respective model. The poor statistical significance of other

candidate explanatory variables could owe to the much smaller sample sizes available for analysis compared to those available to Kline and others (2004). 

However, the characteristics of eastern forest lands examined in this paper 

also differ from those of western forest lands examined by Kline and others (2004).

These differences conceivably could influence our ability to find robust empirical

models describing private forest management. For example, lower average site

indices—and thus lower inherent site productivity—on eastern Oregon forest lands

examined compared to those found on western Oregon forest lands (table 4) may

inspire less intensive private management among eastern Oregon forest landowners

generally. Timberland accounts for 70 percent of the 14.9 million acres of forest

land in eastern Oregon versus 90 percent of the 15.3 million acres of forest land in

western Oregon (Campbell and others 2002, 2003). Forest stocking rates (basal

areas) found on eastern Oregon forest lands examined also tended to be lower than

those found on western Oregon forest lands (table 4), possibly providing less

incentive for precommercial thinning. Similarly, postharvest forest stocking rates

(basal areas) on eastern Oregon forest lands examined tended to be higher than

postharvest stocking rates on western Oregon forest lands (table 4), providing less

incentive for postharvest tree planting. Lastly, lower average building densities

found on eastern Oregon forest lands examined compared to those found on western Oregon forest lands (table 4) provide fewer opportunities to observe the potential effects of forest land development on private forestry in eastern Oregon. 

12

Research Paper PNW-RP-572
Ta

ble

3

—

C

o

m

p

aris

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n

of w

e

stern

Ore

g

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aria

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statistic

ally

sig

nific

a

nt P( <

0.10)

Basal area Precom

mercial thin

ning Harvesting Postharvest planting

Western

Eastern

Western

Eastern

Western

Eastern

Western

Eastern

Variable Oregon

Oregon

Oregon

Oregon

Oregon

Oregon

Oregon

Oregon

A

G

E

+

+

A

G

E2 - -

B

ASA

L

A

R

E

A t - -

B

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L

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C

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B

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Y t -

B

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NSIT

Y t-1 - -

Note: Presence of a “+” or “-” indicates sign of estimated coefficients for explanatory variables that generally were found to be statistically significant

(P

< 0.10) in estimated models; absence of a sign indicates the estimated coefficients generally were not found to be statistically significant. Explanatory

variables are defined in table 1. Western Oregon model information from

Kline and others (2004); eastern Oregon model information from table 2.

Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

13
Table 4—Comparison of mean values of explanatory variables tested in western and eastern Oregon models describing basal

area, and precommercial thinning, harvest, and postharvest planting likelihood

Basal area Precommercial thinning Harvesting Postharvest planting

Western Eastern Western Eastern Western Eastern Western Eastern

Variable Oregon Oregon Oregon Oregon Oregon Oregon Oregon Oregon

N = 901 N = 268 N = 1,563 N = 415 N = 1,551 N = 415 N = 416 N = 207

AGE 37.1 57.2 — — — — — —BASAL AREA t — — — — — — 66.9 76.8

BASAL AREA t-1 — — 107.0 65.8 106.8 65.8 — —

SITE INDEX 114.4 89.0 111.7 88.4 111.7 88.4 112.3 89.2

SLOPE — — 30.1 18.1 30.1 18.1 27.0 16.0

DISTANCE TO ROAD — — 3.1 2.6 3.1 2.6 2.9 2.7

NONINDUSTRIAL 0.26 0.34 0.35 0.41 0.35 0.41 0.38 0.33

BUILDING DENSITY t 4.9 2.1 — — — — — —

BUILDING DENSITY t-1 — — 6.6 3.3 6.6 3.3 7.0 2.6

Note: Table compares explanatory variable mean values from those western Oregon models reported in Kline and others (2004) that are most

similar to the eastern Oregon model in their econometric specification and estimation. Explanatory variables are defined in table 1.

14

Research Paper PNW-RP-572
The relative lack of statistically significant variable coefficients in eastern

Oregon models also could mean that private forest landowners there respond to

narrower ranges of stand and site characteristics than those found to influence

western Oregon owners. One reason for this could be that in eastern Oregon, forests

generally might be managed less intensely by private forest landowners than in

western Oregon. Timberland, with its greater inherent potential site productivity, 

is where we would expect the most intensive forest management. But private

landowners own just 26 percent of the timberland in eastern Oregon versus 74 

percent owned by public agencies such as the national forests and the Bureau of

Land Management. Of that private timberland, 30 percent is owned by nonindustrial private owners (Azuma and others 2004: 14). As is common among nonindustrial private forest landowners in western Oregon (Johnson and others 1999: 24),

nonindustrial private forest landowners in eastern Oregon tend to manage their

lands according to multiple objectives, including aesthetics and recreation along

with timber production among their management goals. Campbell and others

(2003: 15) estimated that less than half (43 percent) of nonindustrial private forest

land in eastern Oregon is managed with “income from timber” as the most important benefit perceived by owners. Many owners cited other benefits as most important to them, including aesthetic enjoyment, potential increases in land value, and

recreation. Owners of these lands likely respond to other factors in addition to or in

place of stand and site characteristics when making forest management decisions. 

There are other likely factors as well that may confound empirical analysis of

forest management activities in eastern Oregon. From 1980 to 1994, much of eastern Oregon experienced an outbreak of western spruce budworm (Choristoneura

occidentalis), which by 1987 had damaged 1.1 million acres of private forest land

(Azuma and Overhulser 2006). Extensive salvage harvesting of damaged stands,

combined with a general slowdown in national forest harvests during the late 1980s

and a corresponding increase in harvests on private forest lands, led to unusually

high harvest rates over the period examined (1987 to 1998). Also, in contrast to

western Oregon forests where tree growth can be limited by light, leading to a

greater prevalence of precommercial thinning, tree growth in eastern Oregon forests

tends to be limited more by lack of water such that few private landowners there

see much value in precommercial thinning. More private forest landowners also

tend toward multiaged management, now and then harvesting just a few large trees

rather than clearcutting. Lastly, given the greater propensity of wildfire in eastern

Oregon, there is the possibility that some owners conduct certain management

activities in response to recent fires or to reduce future wildfire threat. All of these

Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

15
factors would tend to confound our ability to describe forest management activities

in terms of stand and site characteristics alone. An alternative explanation is simply

that private forest management in eastern Oregon may not lend itself to easy

description by using Forest Inventory and Analysis data. The relative validity of

any of these explanations cannot be known with certainty.

Conclusions and Policy Implications

Forest land development to date does not appear to have affected the ways in which

private forest landowners in eastern Oregon have managed their forest lands based

on the data and period examined. This result differs from results found in studies

conducted in the Southeastern United States (Barlow and others 1998, Munn and

others 2002, Wear and others 1999) and in western Oregon (Kline and others 2004),

which all suggest that the intensity of private forest management may decline with

increasing forest land development. Prevailing hypotheses suggest that increasing

population densities reduce the productivity of private forest lands through parcelization, changing forest owner characteristics, forest-urban conflicts, and reducing

owners’ expectations regarding the future productive potential of their forest lands.

We are unable to find empirical results in eastern Oregon that are consistent with

these hypotheses by using the data examined. Possible explanations for these contrary results include lower levels of forest land development, less intensive forest

management (e.g., lower rates of precommercial thinning), and unusually high 

levels of salvage harvesting in eastern Oregon relative to locations where forest

land development effects on forestry were found. Because 74 percent of the most

productive forest lands in eastern Oregon—those classified as timberland—are

under public and especially federal ownership (Azuma and others 2004: 14), we

suspect that future timber production in eastern Oregon likely will be influenced

more by federal forest policy and management than by forest land development.

Potential ecological effects are uncertain and remain to be evaluated. 

Our results should not imply that forest policymakers and managers should 

not remain vigilant to possible forestry effects resulting from forest land development in eastern Oregon. Significant population growth and development often can

be unforeseen, and can occur rapidly in places where it was little expected. If such

growth were to occur in eastern Oregon, more significant forestry effects might 

be found in the future. Still, we have to consider the likelihood of such events in

eastern Oregon and also consider what types of land are most likely to be affected.

Although previous research did find some effect of forest land development on 

private forestry in western Oregon (Kline and others 2004), additional research

16

Forest land development to date does

not appear to have

affected the ways in

which private forest

landowners in eastern Oregon have

managed their forest

lands. 

Research Paper PNW-RP-572
suggested that those effects in the future could be relatively modest owing to relatively low rates of projected forest land development over a large proportion of private forest land and the relative isolation of many private forest lands from faster

growing population centers (Kline and Alig 2005: 717). Existing projections of

potential future development in eastern Oregon tend to suggest relatively modest

rates of development as well over a broad range of private forest lands (Kline 

and others, in press). This implies too that future development effects on private

forestry in eastern Oregon could be relatively modest or at least highly localized 

to those few regions that do experience significant population growth and development in sufficient proximity to forest lands in coming years. 

Acknowledgments

We thank Mike Cloughsey, Gary Lettman, Venkatarao Nagubadi, and Ralph Alig 

for helpful comments.

Metric Equivalents

When you know: Multiply by: To find:

Acres 0.405 Hectares

Square feet .093 Square meters

Square miles 2.590 Square kilometers

Cubic feet .028 Cubic meters

Literature Cited

Azuma, D.L.; Dunham, P.A.; Hiserote, B.A.; Veneklase, C.F. 2004. Timber 

resource statistics for eastern Oregon, 1999. Resour. Bull. PNW-RB-238.

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific

Northwest Research Station. 42 p.

Azuma, D.L.; Overhulser, D.L. 2006. Impacts of a western spruce budworm 

outbreak on private lands in eastern Oregon, 1980-1994. 19 p. Unpublished

document. On file with: David Azuma, USDA Forest Service, Pacific

Northwest Research Station, Forestry Sciences Laboratory, P.O. Box 3890,

Portland, OR 97208. 

Barlow, S.A.; Munn, I.A.; Cleaves, D.A.; Evans, D.L. 1998. The effect of urban 

sprawl on timber harvesting. Journal of Forestry. 96: 10-14.

Evaluating Forest Land Development Effects on Private Forestry in Eastern Oregon

17
Campbell, S.; Azuma, D.; Weyermann, D. 2002. Forests of western Oregon: an 

overview. Gen. Tech. Rep. PNW-GTR-525. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station. 27 p.

Campbell, S.; Azuma, D.; Weyermann, D. 2003. Forests of eastern Oregon: an 

overview. Gen. Tech. Rep. PNW-GTR-578. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station. 31 p.

Johnson, R.L.; Alig, R.J.; Kline, J.D.; Moulton, R.; Rickenbach, M. 1999.

Management of non-industrial private forest lands: survey results from western

Oregon and Washington owners. Research Contrib. 28. Corvallis, OR: Forest

Research Laboratory, Oregon State University. 39 p. 

Kline, J.D.; Alig, R.J. 2005. Forest land development and private forestry with 

examples from Oregon. Forest Policy and Economics. 7(5): 709-720.

Kline, J.D.; Azuma, D.L.; Alig, R.J. 2004. Population growth, urban expansion, 

and private forestry in western Oregon. Forest Science. 50(1): 33-43. 

Kline, J.D.; Moses, A.; Lettman, G.J.; Azuma, D.L. [In press]. Modeling forest 

and rangeland development in rural locations, with examples from eastern

Oregon. Landscape and Urban Planning.

Lettman, G.J. 2004. Forests, farms and people: land use change on non-federal 

land in eastern Oregon, 1975-2001. Salem, OR: Oregon Department of

Forestry. 42 p.

Munn, I.A.; Barlow, S.A.; Evans, D.L.; Cleaves, D. 2002. Urbanization’s

impact on timber harvesting in the south central United States. Journal of

Environmental Management. 64: 65-76.

Oregon Department of Forestry. 2006. Oregon Department of Forestry Forest 

Practice Administrative Rules and Forest Practices Act: Chapter 629 Forest

Practices Administration. Salem, OR. 93 p. 

Wear, D.N.; Liu, R.; Foreman, J.M.; Sheffield, R.M. 1999. Effects of population 

growth on timber management and inventories in Virginia. Forest Ecology and

Management. 118: 107-115. 

18

Research Paper PNW-RP-572
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