United States

Department of

Agriculture

Forest Service

Southern 

Research Station

Research Paper

SRS–52 

Simulations of Alternative Mechanical 

Thinning Treatment Programs on 

Western Timberland

Karen L. Abt, Jeffrey P. Prestemon, Kenneth E. Skog, 

R. James Barbour, Miles A. Hemstrom, 

and Robert J. Huggett, Jr.
Authors:

Karen L. Abt, Research Economist, and Jeffrey P. Prestemon, Research 

Forester, U.S. Department of Agriculture Forest Service, Southern Research 

Station, Research Triangle Park, NC 27709; Kenneth E. Skog, Supervisory 

Research Forester, U.S. Department of Agriculture Forest Service, Forest 

Products Laboratory, Madison, WI 53726; R. James Barbour, Program 

Manager, and Miles A. Hemstrom, Research Ecologist, U.S. Department 

of Agriculture Forest Service, Pacific Northwest Research Station, Portland, 

OR 97204; and Robert J. Huggett, Jr., Research Assistant Professor, North 

Carolina State University, Research Triangle Park, NC 27709.

April 2011

Southern Research Station

200 W.T. Weaver Blvd.

Asheville, NC 28804
Simulations of Alternative Mechanical Thinning 

Treatment Programs on Western Timberland

Karen L. Abt, Jeffrey P. Prestemon, Kenneth E. Skog, 

R. James Barbour, Miles A. Hemstrom,

and Robert J. Huggett, Jr.1

Abstract

We used the Economics of Biomass Removals model to evaluate the 

required treatment acreages, volumes removed, treatment costs and product 

revenues from national forest and other ownerships. We used three distinct 

treatment prescriptions to achieve two hazard reduction goals for treatable 

timberlands in the Western United States. The two hazard reduction goals 

were to maintain current hazard levels over 10 years, and to reduce hazard 

levels by 20 percent over 10 years. We also simulated one treatment prescription on national forest timberland only in order to evaluate the effect 

of requiring a minimum level of treatment in each State. These simulations 

show that costs and revenues differ by intensity of the hazard reduction goal 

and by treatment prescription. They show that uneven-aged treatment prescriptions tend to be more expensive than thin-from-below prescriptions for 

accomplishing hazard reduction goals. Uneven-aged prescriptions, however, 

yield greater timber product volumes and values for landowners, offsetting 

the higher costs. On the other hand, in situations without wood product 

markets, thin-from-below prescriptions could be a better option because of 

the lower net costs.

Keywords: Economics of Biomass Removals, fire hazard, mechanical

thinning, treatable timberland, treatment costs.

Introduction

Increasing fire, increasing fire suppression expenditures, 

and deteriorating forest health have contributed to interest 

in using mechanical thinning treatments as a forest 

management option. In addition, timber and biomass 

removals from treatments could be used to produce 

bioenergy or biofuels. The Economics of Biomass Removals 

(EBR) model was developed to address the market impacts 

of large-scale mechanical thinning treatment programs 

(Abt and Prestemon 2006, Huggett and others 2008, 

Prestemon and others 2008). In this report, we extend past 

analyses with an updated EBR model that assesses the 

costs and economic effects of three treatment prescriptions. 

This version of the model simulates the rate at which 

stand growth will cause transitions from one hazard 

class to another for all forest types. Inclusion of these 

forest-type specific growth transitions allows assessment 

of long-term impacts and provides new descriptions of 

a prescription’s effects on important policy outcomes, 

including estimates of jobs created and volumes of logging 

residuals. The objectives for this study were to simulate 

three mechanical thinning prescriptions on national forests 

and other timberlands in Western States (for the objective 

of maintaining or reducing fire hazard), and to summarize 

timber products and revenues, area treated, and jobs, as 

well as acres treated and acres remaining in high fire hazard 

status.

Research is needed to improve understanding of policy 

options available to land managers in fire prone regions 

of the United States. Assessments of mechanical thinning 

treatment programs currently implemented by the Forest 

Service, U.S. Department of Agriculture and other land 

management agencies have highlighted the need for 

agencies to come up with objective means of prioritizing 

these activities. Wildfire management is a complex task 

involving decisions on how to allocate scarce resources 

toward competing methods of improving forest health 

and reducing negative impacts. The EBR model provides 

a means of identifying priorities for mechanical thinning 

treatments. The model helps researchers sort through many 

of the complexities of mechanical thinning treatments 

because it provides objective information about the 

following: wildfire hazard recognizes the heterogeneity of 

fuels and forests across the landscape, the potential markets 

for timber products removed from forests upon treatment, 

priorities for treating wildland-urban interface and intermix 

areas, the costs of applying different treatment prescriptions, 

and the natural return to hazardous conditions that occurs 

following treatment. The remainder of this report outlines 

the essential elements of the EBR model and the simulations 

conducted to address current policy and land management 

concerns.

Methods

We used the EBR model to evaluate the effects of policy 

alternatives for reducing fire hazard with mechanical 

thinning treatments (Abt and Prestemon 2006, Prestemon 

and others 2008). With the model, we simulated markets, 

fire hazard, and growth transitions over time. Outputs from 

the model included jobs, as well as costs and revenues 

deriving from each of three treatment prescriptions. 

1 Research work accomplished through Research Joint Venture Agreement 

Number SRS-08-JV-11330143-088, “Modeling Dynamics in Support of 

National Resource Assessments,” September 2008.
 2

The EBR model is a two-stage mathematical program. The 

first stage uses a goal program to find the least expensive 

acres to treat, given a set of overriding priorities about the 

kinds of places needing treatment. The second stage uses 

a quadratic program to determine the market outcomes 

(prices, timber production, timber consumption, and wood 

transport) resulting from the removal of any timber products 

upon treatment. More details on the model are available in 

Prestemon and others (2008).

For these simulations, the EBR model used a starting 

budget and a treatment prescription, and simulated the 

treatment and market outcomes that resulted from that 

treatment program. The budget was then adjusted until the 

objective was met (within ± 0.3 percent ). For each of the 

Western States, the simulations provide hazard reduction, 

costs, timber products and revenues generated, and jobs 

created2

. We ran separate simulations for the two ownership 

categories in order to evaluate the outcomes from the 

different policies that would be necessary for the national 

forests as compared to other ownerships. A shortcoming of 

the current model is that the separate modeling of different 

ownership groups prevents the mathematical program from 

trading off treatment on national forests with treatments on 

other ownerships.

Before we could run the simulations, we completed the 

following steps: (1) defined treatable land base by owner, 

(2) defined level of aggregation over space, ownership and/

or management, (3) defined hazard level on the treatable 

land base, (4) developed and applied mechanical thinning 

treatment prescriptions, (5) estimated treatment costs, 

(6) estimated products from the treatment, (7) predicted 

hazard transition of all acres (treated and not treated), (8) 

defined modeling priorities, (9) defined mill capacities, (10) 

estimated transportation costs, (11) set replacement outcome 

for national forest harvest, (12) estimated jobs coefficients, 

and (13) calculated logging for biomass. These steps are 

discussed as follows:

1. Defined treatable land base by owner: For the 12 Western 

States, we used data from U.S. Department of Agriculture 

Forest Service Forest Inventory and Analysis (FIA) 2002 

Resources Planning Act (U.S. Forest Service 2002). We 

selected only plots classified as timberland (land capable 

of producing 20 cubic feet per acre per year). From 

that group of plots, we then excluded reserved areas 

(wilderness) and inventoried roadless areas. 

In addition to these administrative exclusions, we made 

two ecological exclusions. First, we removed the plots on 

the west side of the Cascade Mountains in Oregon and 

Washington from the treatable timberland base because 

these wet forest types are at lower risk of crown fire and 

catastrophic damage. We also excluded forest types which 

require intense stand replacement fires for regeneration, 

such as interior spruce-fir (Picea spp.-Abies spp.) and 

lodgepole pine (Pinus contorta Dougl.) stands across 

the rest of the West. However, we retained these stand 

replacement forest types in the wildland urban interface 

to ensure treatments would occur next to high-valued 

resources. 

For this analysis, we addressed two ownership groups—

national forests and other owners, which includes 

private and all non-national forest lands. Figure 1 

shows the breakdown of forest land into timberland 

and nontimberland for these owner groups, and figures 

2 (national forest) and 3 (other ownerships) show the 

treatable timberland base by State. 

2. Defined level of aggregation: The simulation can be run 

for different aggregations over space, ownership, and 

management—e.g., by State, forest type, or wildland 

urban interface designation. These aggregations are 

necessary when using expanded FIA plot data to ensure 

a low approximation error on both acres and volumes of 

timberland. For these simulations, we aggregated to the 

State and LANDFIRE map zone (2010) by owner (as 

previously described) and by wildland urban interface 

designation. LANDFIRE is a national program to develop 

fire and fuels data, and map zones are sub-State areas 

of similar fire and ecology that allow us to simplify our 

treatment prescriptions and regrowth parameters. The 

wildland urban interface (WUI) is the area where forests 

and human development meet, as defined in Radeloff 

and others (2005). The definition of WUI by Radeloff 

and others (2005) differs from the community specific 

definitions mandated under the Healthy Forest Restoration 

Act of 2003. WUI areas established under this act are not 

currently mapped for the entire Western United States, 

nor are they developed in a systematic manner as are the 

WUI areas provided by Radeloff and others (2005). As a 

result, WUI maps by Radeloff and others (2005) are the 

best available source for estimating the WUI area with 

high fire hazard over large areas such as those analyzed 

in this study. We multiplied the expanded plot results for 

each plot times the percent of forest area in the WUI in 

each Western county to develop estimates of timberland 

characteristics in the WUI. A final aggregation was done 

after we determined the following hazard levels. 

3. Defined hazard level on the treatable land base: Hazard 

was measured by a combination of stand-level torching 

index and crowning index, as shown in table 1. The use of 

torching and crowning index was based on Scott (1999) 

and Scott and Reinhardt (2001). The cutoff points for the 

hazard levels were consistent with levels used in Skog and 

others (2006), which were developed in conjunction with 

fire scientists and fuels experts. These levels put more 

weight on crowning index, as individual tree torching 2 Residue volumes can also be converted to potential electricity or biofuels. 
 3

was deemed less significant than the spreading potential 

measured by the crowning index. Initial in- and out-ofcondition acres are shown by State in figures 2 (national 

forest) and 3 (other ownerships).

4. Developed and applied mechanical thinning treatment 

prescriptions: The cut list and resulting hazard level for 

three types of mechanical thinning treatment prescriptions 

were developed from the plot characteristics. The three 

prescriptions were modeled separately, and included two 

uneven-age prescriptions based on regulation of stand 

density index to maintain an uneven-aged forest structure, 

and one even-aged prescription, based on regulation 

of basal area by thinning-from-below (TFB) (table 2). 

The two flexible stand density index (uneven-aged) 

prescriptions removed either more large trees (SDILarge) or more small trees (SDI-Small) using techniques 

developed in Shepperd (2007). These prescriptions were 

not allowed to remove more than 50 percent of initial 

basal area. This prevented some treated stands from 

reaching in-condition status, in spite of the thinning, 

and thus hazard was not eliminated even if the treatment 

was conducted. Lodgepole pine and spruce-fir stands in 

the WUI were thinned only with the TFB prescription, 

while the other forest types were evaluated for all three 

prescriptions. Similar prescriptions were evaluated in Skog 

and others (2006).

5. Estimated treatment costs: Gross treatment costs were 

estimated by adding a fixed cost ($200 per acre for all 

government and $100 per acre for all other owners) and a 

variable on-the-ground treatment cost. Variable treatment 

costs were derived from Arriagada and others (2008). 

The $200 per acre fixed cost was assumed to account for 

preparation, environmental analysis, and monitoring costs 

on government lands. We assumed the fixed costs for 

nongovernment timberlands were less, only $100 per acre 

because of the more extensive environmental requirements 

for government projects. 

Other

nontimberland

52%

Other

treatable

timberland

29%

Other

nontreatable

timberland

19%

Private

forest land

31%

Other public

forest land

20%

National forest

forest land

49%

National

forest

timberland

36%

National 

forest

nontimberland

36%

National forest

timberland

28%

Figure 1—All forest land in Western States by ownership and subset of national forest (right) and other owners (left) into 

nontimberland, nontreatable timberland, and treatable timberland.

0

2

4

6

8

10

AZ

CA

CO

ID

MT

NV

NM

OR

SD

UT

WA

WY

Million acres

Initial out-of-condition timberland acres

Initial in-condition timberland acres

State

Initial out-of-condition timberland acres

Initial in-condition timberland acres

10

8

6

4

2

0

Million acres

AZ

CA

CO

ID

MT

NV

NM

OR

SD

UT

WA

WY

State

Figure 2—Initial treatable timberland acres in- and out-of-condition 

on national forests for 12 Western States.

Figure 3—Initial treatable timberland acres in- and out-of-condition 

on other ownerships (non-national forest) for 12 Western States.
 4

6. Estimated products from thinnings: Products from thinned 

stands included sawlogs from ponderosa pine (Pinus

ponderosa Laws.), lodgepole pine, and other softwoods, 

and pulpwood (including all hardwoods), converted to 

chips. Chips were hauled to a mill if their value exceeded 

the cost of production and transportation.

7. Predicted transitions of all treated and not treated stands: 

An important element of the simulation model is the 

transition of stands (acres) from one hazard level to 

another as they grow. The transition matrix indicates how 

quickly stands change their in- or out-of-condition status. 

Three kinds of transitions are recognized: (1) transitions 

occurring through natural stand development without 

any treatment (pre-treatment transitions), (2) transitions 

that occur when a stand is treated (treatment transitions), 

and (3) transitions that happen in the years subsequent 

to treatment (post-treatment transitions). Pre-treatment 

transitions generally occur more slowly than posttreatment transitions. The current model does not account 

for transitions that occur due to natural disturbances like 

fire or insect outbreaks. 

Base rate pre-treatment and post-treatment transition 

matrices for each of our three prescriptions (SDI-Large, 

SDI-Small, and TFB) were derived from a sample of 

ponderosa pine-lodgepole pine stands in Colorado 

(Huggett and others 2008). To apply these transitions 

to other stands in other places in the West, we adjusted 

the pre- and post-hazard transition rates based on stand 

growth rates. These stand growth rates were obtained from 

LANDFIRE (2010) simulations and FIA volume growth 

rates (Smith and others 2004). Note that not all treated 

stands will move to in-condition status when treated 

because the treatment prescriptions will not remove more 

than 50 percent of basal area. The 50 percent limitation 

is based on consultation with Federal silviculturists, 

fuels specialists, and other technical specialists who have 

practical experience with the sorts of treatments that 

are typically implemented on Federal land given other 

ecological considerations and social constraints to Federal 

land management activities. These prescriptions are 

consistent with limits used in Skog and others (2006). 

8. Determined modeling priorities for treatment: The EBR 

model allows different treatment priorities to be used, 

including WUI status, treatment cost, forest type, and 

out-of-condition (hazard) level. In these simulations, WUI 

acres were treated first in order of their treatment cost. 

Non-WUI acres were treated next, and again, the least cost 

were treated first. 

Table 2—Mechanical thinning treatment prescriptions used in the simulations

Prescription 

acronym Description

Maximum percent 

of basal area 

removed

SDI-Large Uneven aged based on stand density index, 

flexibly adjusted to remove more large trees

50

SDI-Small Uneven aged based on stand density index, 

flexibly adjusted to remove more large trees

50

TFB Thin-from-below removing small trees until 

hazard goal is reached or BA limit is reached

25

SDI = Stand density index; TFB = Thinning from below.

Table 1—Hazard definitions based on index thresholds

Plots are classified as 

Torching index 

threshold

Crowning index 

threshold

mph

Out-of-condition (hazard 

exists) if

TI < 25, and CI < 25

or if TI ≥ 25, and CI < 40

In-condition (hazard does not 

exist) if

TI ≥ 25, and CI ≥ 25

or if TI < 25, and CI ≥ 40

TI = Torching index; CI = Crowning index. 
 5

9. Defined mill capacity by State: Mill capacity for 

sawtimber volumes was fixed throughout the simulations 

at current levels (Spelter and Alderman 2003, 2005), 

while mill capacities for merchantable materials other 

than sawtimber were based on average removal levels of 

these materials are reported by Smith and others (2004). In 

the modeling, stated capacities could not be exceeded by 

more than 40 percent because this is the limit that could 

be accommodated by adding additional shifts to existing 

mills. Instead when volumes removed in a State exceeded 

shift-adjusted local mill capacity products were shipped to 

other mills in the West, the rest of the United States, or the 

rest of the world, consistent with current prohibitions on 

the export of softwood logs removed from Federal lands in 

the Western United States. 

10. Estimated transportation costs: For these simulations, 

transportation costs were $1 per thousand board feet per 

mile for sawlogs and $0.005 per cubic foot per mile for 

pulpwood. Distances to sawmills and pulp mills were 

estimated as the county-level weighted average distance 

from the county’s FIA plots to the nearest five sawmills 

and pulp mill, respectively. Countywide average distances 

were used because we are not simulating individual plot 

treatments, but expanded plots, which represent many 

different stands within the county, each of which would 

have a different distance to each mill type (Prestemon and 

others 2008).

11. Set replacement outcome for national forest harvests: 

For this simulation, products from treatments were 

assumed to replace regular (nontreatment related) harvests 

on national forests if product quantities are less than 

base-level national forest harvests; otherwise, they add to 

regular harvests. Market forces were allowed to act so that 

treatment harvests will partially replace regular timber 

harvests occurring outside of national forests on the rest of 

the timberlands in a State.

12. Estimate job coefficients: Jobs were calculated using 

averages from Hjerpe and Kim (2008), where direct jobs 

derive from the actual expenditure of treatment money, 

and total jobs derive from actual expenditures plus the 

indirect effects of those businesses spending money and 

the induced effects of the employees of those businesses 

spending money. We assumed that the $200 per acre 

administrative cost to the government-owned treatments 

resulted in six direct jobs, and an additional five indirect 

and induced jobs, for a total of 11 jobs per million dollars 

of expenditure (e.g., for every 5,000 acres treated on 

government administered lands, a total of 11 jobs were 

produced). The treatment prescriptions were assumed to 

be accomplished by logging contractors, and the private 

administrative cost of $100 per acre as well as the on-theground treatment costs for all owners, were assumed to 

result in four direct jobs, and an additional 13.2 indirect 

and induced jobs, for a total of 17.2 jobs per million 

dollars of expenditure. These jobs estimates exclude the 

effects of treatments from regular harvests on national 

forests or the effects of treatments outside of national 

forests, thus we would expect that some timber harvesting 

jobs would be displaced by the thinning treatment jobs. 

13. Estimated logging residues: A West-wide average 

of 0.30 units of logging residues were assumed to be 

produced from each plot, half of which was assumed 

recovered and removed from the site. The recovered 

volumes are reported.

Once all of the above decisions and data manipulation were 

completed, we simulated the policy alternatives by solving 

the EBR mathematical programming model. We used an 

iterative approach which started with an initial budget for 

treatments, and then applied that budget to the model as a 

constraint. After a simulated 10 years of treatments given 

the budget, acres out-of-condition were compared to initial 

conditions to determine whether the budget was sufficient 

to achieve either a 0 percent or 20 percent reduction in 

hazardous acres, within our ± 0.3 percent tolerance range. 

If it was insufficient, the budget was adjusted upward and 

the simulation re-run. Likewise, if hazard levels were below 

the target reduction amounts, then the budget was adjusted 

downward and re-run. This process was repeated until 

convergence at either 0 percent or 20 percent reduction in 

hazardous acres.

Results and Discussion

We simulated a total of 13 different hazardous mechanical 

thinning treatment policies. Separate simulations were 

run for two ownerships (national forest and other), three 

treatment prescriptions (SDI-Large, SDI-Small and TFB), 

and two hazard reduction goals (keep hazardous acres 

constant, and reduce hazardous acres by 20 percent). 

This resulted in 12 separate simulations. In addition, we 

conducted one simulation for national forest treatable 

timberland using the SDI-Large prescription which required 

a minimum level of treatment in each State.

The two hazard reduction goals evaluated were:

(1) Maintain current conditions: treat enough timberland 

over the 12 States, within a ± 0.3 percent tolerance range, 

such that the area of treatable timberland out-of-condition 

did not change (i.e., remained approximately constant) by 

the end of a 10-year program of treatments; or, (2) Improve 

conditions by 20 percent: treat enough timberland over the 

12 States, within a ± 0.3 percent tolerance range, to reduce 

the area of treatable timberland out-of-condition by 20 

percent by the end of a 10-year program of treatments.

Next, we discuss the results of the simulations without 

minimum State treatment constraints for each of the owner 

groups, followed by a short discussion of the constrained 

simulation. 
 6

National Forest Treatable Timberland

Simulations (Without Minimum

Treatment Constraints)

Just over half of the treatable timberland in the 12 Western 

States is managed as national forest land. As well, 56 

percent of the out-of-condition treatable timberland 

falls under national forest management. From the three 

prescriptions and two hazard reduction goals simulations, 

the average annual area treated ranges from 620,000 acres 

(0 percent reduction using TFB) to 2,250,000 acres (20 

percent reduction using SDI-Large) (table 3). The difference 

in area treated derives from both the hazard reduction goal 

and from differences in growth rates on treated stands. 

The average annual treated area by State is shown in 

figure 4. Note that initial in- and out-of-condition acres are 

identical for all national forest simulations and that final 

acres for each hazard reduction goal are within tolerances 

noted above for the hazard reduction goal. Over the 10 years 

of the simulations, the total acres treated comprises from 

18 to 66 percent of all treatable timberland, with the higher 

acreages treated under the 20 percent hazard reduction goal.

Table 4 provides the average annual volumes removed for 

sawlogs and chips, as well as estimates of gross biomass and 

jobs resulting from the treatments. The 20 percent reduction 

simulations removed more than three times the volume 

of sawlogs and chips than did the 0 percent reduction 

simulations. In addition, the SDI-Large prescription 

removed much more volume than the TFB prescription for 

both hazard reduction goals. 

The number of jobs produced was highest with the SDILarge and lowest with TFB. More jobs are created in 

the SDI-Large treatment because more acres needed to 

be treated, and more acres needed to be treated because 

regrowth on treated stands was higher on the plots selected 

as least-cost under the SDI-Large treatments. Applying the 

SDI-Large prescription to reduce hazard by 20 percent on 

national forest treatable timberland contributed nearly 9,000 

direct and more than 15,000 indirect and induced jobs per 

year, while TFB contributed 5,800 direct and 10,000 indirect 

jobs. To only keep hazard constant on treatable timberland, 

60 to 65 percent fewer jobs per year were produced (table 

4). Again, because some of these harvests would have 

occurred anyway, not all of these jobs are new jobs. Jobs 

are estimated from the expenditures made to accomplish the 

treatments.

The SDI-Large treatment cost more to implement than the 

SDI-Small and the TFB for both hazard reduction goals 

(table 5). The SDI-Small prescription had the smallest net 

0

1

2

3

4

5

AZ

CA

CO

ID

MT

NV

NM

OR

SD

UT

WA

WY

Million acres

State

Final out-of-condition timberland acres:

Unconstrained

Initial out-of-condition timberland acres

Final out-of-condition timberland

acres: State-minimum constrained

Figure 4—Initial and final treatable timberland acres out-of-condition 

on national forests in 12 Western States, under unconstrained and Stateminimum-constrained simulations, for the stand density index-large 

prescription with an objective to reduce the West-wide area out-ofcondition by 20 percent in 10 years.

Table 3—Acreage simulation results for national forest treatable timberlands by hazard reduction goal 

and treatment prescription

Hazard reduction goal 

Treatment 

prescription

Treated area

(annual average)

Initial out-ofconditionFinal out-ofconditionInitial inconditionFinal incondition

- - - - - - - - - - - - - - - - - - - - - - Million acres - - - - - - - - - - - - - - - - - - - - - -

SDI-Large 0.83 21.51 21.63 19.43 19.31

SDI-Small 0.68 21.51 21.69 19.43 19.25

Keep hazardous 

acres at current level

TFB 0.62 21.51 21.68 19.43 19.26

SDI-Large 2.25 21.51 17.36 19.43 23.58

SDI-Small 1.89 21.51 17.21 19.43 23.73

Reduce hazardous 

acres by 20 percent

TFB 1.63 21.51 17.20 19.43 23.74

SDI = Stand density index; TFB = Thinning from below.
 7

cost (gross treatment revenues less gross treatment costs), 

while TFB had the lowest total revenue, gross cost and cost 

per acre. To reduce the area of timberland out-of-condition 

by 20 percent in 10 years would cost $1.25 to $1.93 billion 

per year for national forest treatments. In simulations where 

hazard level was kept constant, total treatment costs were 40 

to 70 percent lower than the cost of achieving a 20 percent 

reduction in hazardous acres.

The value of timber products removed was highest with the 

SDI-Large and lowest with TFB. Note that for Federal lands 

the revenues were not returned to the agency, but would 

accrue to the U.S. Treasury directly unless stewardship 

contracting authorities were used to implement treatments. 

Our simulations were based on timber sale authorities, so 

reducing hazard by 20 percent, generated products from 

national forest timberland treatments that could be sold and 

would potentially have an annual stumpage value ranging 

from $0.14 to 0.89 billion. In simulations where hazard was 

kept constant, values of timber products were about 40 to 75 

percent lower (table 5).

Under stewardship contracting authorities there would be 

a potential to use some or all of these revenues to offset 

treatment costs. Even if revenues were returned to the U.S. 

Treasury, policymakers may be interested in understanding 

the total net cost of treatment alternatives. For the 20 percent 

hazard reduction goal, the net cost of treatments—treatment 

costs minus revenue from harvest—was highest for the TFB 

prescription, followed closely by the SDI-Large prescription, 

and, notably, lowest for the SDI-Small prescription (table 

5). Because the revenues obtained from treatments were 

assumed to be replacing harvest that already would have 

occurred on national forests, only a part of the revenue will 

Table 4—Volume and job simulation results for national forest treatable timberlands by hazard reduction goal 

and treatment prescription

Hazard reduction goal

Treatment 

prescription

Sawlog 

volumes 

removed

Merchantable 

chip volumes 

removed

Gross biomass 

available

Direct

jobs

Indirect and 

induced jobs

- - - - - Million cubic feet - - - - - 

Million ovendry tons

SDI-Large 211 91 183 3,064 5,340

SDI-Small 110 72 162 2,468 4,303

Keep hazardous 

acres at current level

TFB 16 23 142 2,033 3,559

SDI-Large 697 289 212 8,756 15,232

SDI-Small 378 245 187 7,318 12,732

Reduce hazardous 

acres by 20 percent

TFB 68 127 162 5,755 10,044

SDI = Stand density index; TFB = Thinning from below. 

Table 5—Cost and revenue simulation results for national forest treatable timberlands by hazard reduction 

goal and treatment prescription

Hazard reduction goal 

Treatment 

prescription

Gross 

revenues

Gross 

treatment 

costs

Net 

treatment 

costs

Average 

gross 

treatment 

cost

Average 

net 

treatment 

cost

- - Annual average in million dollars - - - - - - Dollars per acre - - - - 

SDI-Large 390 670 280 805 337

SDI-Small 239 539 300 798 441

Keep hazardous

acres at current level

TFB 45 438 393 706 634

SDI-Large 890 1,927 1,037 857 461

SDI-Small 808 1,610 802 853 424

Reduce hazardous 

acres by 20 percent

TFB 139 1,253 1,114 771 683

SDI = Stand density index; TFB = Thinning from below.
 8

be additional to what would have been received without the 

fire hazard treatments. The average gross treatment cost per 

acre was slightly higher for the SDI-Large prescription and 

lowest for TFB, ranging from about $706 per acre to $857 

per acre, depending on the prescription and hazard reduction 

goal (table 5). These costs excluded the potential revenues 

obtainable by selling timber products from treatments. Net 

costs of treatment per acre, which include these revenues, 

were also lowest for the SDI-Large prescriptions, resulting 

in part from the smaller acreages treated to meet the goals, 

as well as moderate treatment costs and revenues (table 5).

Other Ownerships (Non-National Forest)

Treatable Timberland Simulations (Without

Minimum Treatment Constraints)

Other ownerships include Federal lands managed by 

the U.S. Department of the Interior, State and county 

managed forest lands, and all privately owned forest land. 

With 48 percent of treatable timberland and 44 percent of 

out-of-condition acres, the actions of these owners have 

considerable influence on overall fire hazard throughout 

the West. For the private and non-Federal government (i.e., 

State, county, and local government) landowners, much 

of the required treatment will be accomplished only if the 

treatments pay for themselves through thinning revenues, or 

if some type of subsidy program is utilized. The analysis of 

a subsidy program is beyond the scope of this study, thus the 

simulations run for non-national forests provide total costs 

and revenues for treatment programs but do not specify how 

the programs would be administered or funded.

Average annual acres treated in the six simulations ranged 

from 0.64 million (0 percent reduction using TFB) to 

1.99 million acres (20 percent reduction using SDI-Large) 

(table 6). Using the SDI-Small prescription required that 

only 14 percent more acres were treated than under TFB 

under both hazard reduction goals, while SDI-Large 

required an additional 17 to 22 percent increase in treated 

acres. The increase in acres treated in the SDI treatments is 

needed to account for increased growth on the treatments, 

which results in treated stands transitioning to out-ofcondition faster on the SDI-Small and faster still on the 

SDI-Large treatments.

As with national forests, initial in- and out-of-condition 

acres were identical for these simulations, and final acres 

for each hazard reduction goal were within tolerances noted 

above for the hazard reduction goal. Over the 10 years of the 

simulations, the total area of non-national forest treated area 

comprised from 19 to 58 percent of all treatable timberland, 

with the higher acreages treated under the 20 percent hazard 

reduction goal.

These ownerships had treatment responses similar to 

the national forests, with the 20 percent goal treatments 

removing substantially more volume, and the SDI-Large 

removing more sawlog and chip volumes than the other 

two prescriptions (table 7). And as in the national forest 

simulations, higher logging residues were obtained from the 

TFB prescription. Jobs produced also follow trends similar 

to the national forests for both the treatments and hazard 

reduction goals.

The trends of the treatment costs and revenues were similar 

for both ownership groups, with, generally, the more intense 

and extensive treatments showed higher gross revenues and 

higher gross costs (table 8). Net treatment cost per acre 

also differed slightly for the other owners, though these 

differences were small. 

National Forest Treatable Timberland

Simulation (SDI-Large Only; With

Minimum Treatment Constraints)

We conducted an additional simulation where we set a 

minimum treatment level for national forest treatable 

timberland in each State using the SDI-Large treatment 

prescription. In this simulation, we required that the total 

spending on treatment each year on national forests in each 

Western State be at least half of that State’s remaining 

treatment costs relative to West-wide remaining treatment 

costs on the national forests. For example, if California had 

20 percent of the treatable hazardous timberland on national 

forests in the Western United States in year t, then the model 

required that at least half of the 20 percent (or 10 percent) 

of hazardous national forest timberland treated in year t

West-wide had to be in California. This simulation treated 

only national forest land, and used only the SDI-Large 

prescription. Beginning and ending values of timberland 

area out-of-condition are shown in figure 3. 

We compared this simulation to the unconstrained 

simulation for the same prescription (SDI-Large), also 

reducing national forest hazardous acres West-wide by 

20 percent. We found the following: 

1. Treated area by State is less variable over time when the 

constraint is imposed. When the constraint is included, the 

variance in treated acres from year to year drops by 

43 percent. 

2. Treatment acreages are lower in California, Colorado, 

Oregon, and Wyoming, and increased in Idaho, Montana, 

New Mexico, and Utah when the constraint is imposed.

3. The total cost of the constrained treatment program is 

slighty lower when the constraint is imposed; this occurs 

because the requirement to treat a minimum acreage in 

each State overrode the requirement to treat WUI first. 

This trade-off resulted in cheaper, non-WUI timberland 

in some States getting substituted for WUI timberland in 

other States. 
 9

Table 7—Volume and job simulation results for other ownerships (non-national forest) treatable timberlands by 

hazard reduction goal and treatment prescription

Hazard reduction goal 

Treatment 

prescription

Sawlog 

volumes 

removed

Merchantable 

chip volumes 

removed

Gross biomass 

available

Direct 

jobs

Indirect and 

induced jobs

- - - - Million cubic feet - - - - 

Million oven-dry 

tons

SDI-Large 220 96 221 2,749 4,721

SDI-Small 121 79 196 2,280 3,917

Keep hazardous 

acres at current level

TFB 12 25 321 1,811 3,119

SDI-Large 521 280 247 6,789 11,645

SDI-Small 282 222 221 5,524 9,477

Reduce hazardous 

acres by 20 percent

TFB 44 111 351 4,469 7,679

SDI = Stand density index; TFB = Thinning from below.

Table 6—Acreage simulation results for other ownerships (non-national forest) treatable timberlands by 

hazard reduction goal and treatment prescription

Hazard reduction goal

Treatment 

prescription

Treated area

(annual average)

Initial out-ofconditionFinal out-ofconditionInitial inconditionFinal incondition

- - - - - - - - - - - - - - - - - - - - Million acres - - - - - - - - - - - - - - - - - - - - 

SDI-Large 0.86 16.75 16.86 17.31 17.20

SDI-Small 0.73 16.75 16.86 17.31 17.21

Keep hazardous 

acres at current level

TFB 0.64 16.75 16.86 17.31 17.21

SDI-Large 1.99 16.75 13.45 17.31 20.61

SDI-Small 1.63 16.75 13.50 17.31 20.57

Reduce hazardous 

acres by 20 percent

TFB 1.44 16.75 13.41 17.31 20.66

SDI = Stand density index; TFB = Thinning from below.

Table 8—Cost and revenue simulation results for other ownerships (non-national forest) treatable timberlands by 

hazard reduction goal and treatment prescription

Hazard reduction goal 

Treatment 

prescription

Gross

revenues

Gross 

treatment 

costs

Net 

treatment 

costs

Average 

gross 

treatment 

cost

Average 

net 

treatment 

cost

 - - - - Annual average in million dollars - - - - - - - Dollars per acre - - - 

SDI-Large 398 630 232 729 270

SDI-Small 237 522 285 715 390

Keep hazardous acres 

at current level

TFB 33 412 379 640 592

SDI-Large 714 1,564 850 786 427

SDI-Small 543 1,272 729 778 447

Reduce hazardous 

acres by 20 percent

TFB 112 1,023 911 710 633

SDI = Stand density index; TFB = Thinning from below.
 10

Conclusions

Federal land management agencies are interested in reducing 

overall wildfire hazard. Mechanical thinning treatments are 

one means to help achieve this goal. In this report, we used 

the EBR model to evaluate mechanical thinning treatments 

as a tool to accomplish this goal on national forest and 

other timberland. We also estimated economic benefits to 

the geographic areas where the work is done. The model 

considered the ability of wood product markets to absorb 

materials removed during fire hazard reduction treatments. It 

also considered stand growth, which allows tracking of how 

untreated stands grow out-of-condition and how long treated 

stands stay in-condition. 

The EBR model simulates mechanical thinning treatments, 

resulting fire hazard levels, growth on treated and untreated 

stands, and market equilibrium solutions. We conducted 

13 simulations for this analysis—six simulations (for three 

prescriptions and two owners) that maintain hazard at the 

current level; six simulations (for three prescriptions and 

two owners) that reduce hazard by 20 percent; and one 

simulation for one prescription on national forests that 

required reducing hazardous acres by 20 percent but also 

establishing a minimum treatment amount for each State.

Of the three prescriptions, the TFB had the lowest gross 

cost per acre, but this prescription also resulted in the lowest 

revenues from the lowest level of removals. The unevenaged prescription that removes more large trees (SDI-Large) 

was the most expensive to implement, but it also brought in 

the highest revenues and provided more jobs.

Keeping hazard levels constant on treatable timberland 

in the Western United States will require large, costly 

programs. The costs and revenues will vary depending on 

where and how the treatments are implemented, with higher 

costs and revenues attributing to the more intense removals, 

which also require treating more acres to keep hazard levels 

constant because regrowth is higher on stands treated with 

more intense treatments. Reducing hazard on treatable 

timberland by 20 percent over 10 years would require even 

larger programs, would result in larger volumes of wood 

products, and would treat more acres. 

To enhance accuracy and usefulness of this research, future 

modeling efforts should include developing new methods 

for measuring both hazard and values at risk; expanding the 

data and methods to include nontimberland (i.e., woodlands, 

grasslands and other forest land); developing new transition 

estimates to more accurately quantify how fire hazard 

changes over time for both untreated and treated stands; 

expanding the modeling to include prescribed fire treatments 

and wildfire as a fuel treatment; adjusting the unevenaged treatment simulations to more closely match local 

management objectives or policy constraints; developing 

methods to account for transitions that occur due to other 

natural disturbances such as windthrow or insect and disease 

outbreaks; and incorporating expansion or contraction of 

traditional and/or bioenergy wood using capacity.

Literature Cited

Abt, K.L.; Prestemon, J.P. 2006. Timber markets and fire-related fuel 

treatments in the Western U.S. Natural Resource Modeling. 19(1):15-43.

Arriagada, R.A.; Cubbage, F.W.; Abt, K.L.; Huggett, R.J., Jr. 2008. 

Estimating harvest costs for fuel treatments in the West. Forest Products 

Journal. 58(7-8): 24-30. 

Hjerpe, E.E.; Kim, Y-S. 2008. Economic impacts of southwestern national 

forest fuels reductions. Journal of Forestry. 106(6): 311-316.

Huggett, R.J., Jr.; Abt, K.L.; Shepperd, W. 2008. Efficacy of mechanical 

fuel treatments for reducing wildfire hazard. Forest Policy and 

Economics. 10(6): 408-414.

Landfire. 2010. Vegetation Dynamics Models. http://www.landfire.gov/

NationalProductDescriptions24.php. [Date accessed: July 8, 2010].

Prestemon, J.P.; Abt, K.L.; Huggett, R.J., Jr. 2008. Market impacts of a 

multiyear mechanical fuel treatment program in the U.S. Forest Policy 

and Economics. 10(6): 386-399.

Radeloff, V.C.; Hammer, R.B.; Stewart, S.I. [and others]. 2005. The 

wildland urban interface in the United States. Ecological Applications. 

15: 799-805.

Scott, J.H. 1999. NEXUS: a system for assessing crown fire hazard. Fire 

Management Notes. 59: 20-24. 

Scott, J.H.; Reinhardt, E.D. 2001. Assessing crown fire potential by linking 

models of surface and crown fire behavior. Res. Pap. RMRS-RP-29. 

Fort Collins, CO: U.S. Department of Agriculture Forest Service, Rocky 

Mountain Research Station. 59 p.

Shepperd, W.D. 2007. SDI–Flex: a new technique of allocating growing 

stock for developing treatment prescriptions in uneven-aged forest 

stands. In: Restoring fire adapted ecosystems: Proceedings of the 2005 

National Silviculture Workshop. Gen. Tech. Rep. PSW-GTR-203. 

Albany, CA: U.S. Department of Agriculture Forest Service, Pacific 

Southwest Research Station: 171-180.

Skog, K.E.; Barbour, R.J.; Abt, K.L. [and others]. 2006. Evaluation of 

silvicultural treatments and biomass use for reducing fire hazard in 

western states. Res. Pap. FPL-RP-634. Madison, WI: U.S. Department of 

Agriculture Forest Service, Forest Products Lab. 29 p.

Smith, B.R.; Miles, P.D.; Vissage, J.S.; Pugh, S.A. 2004. Forest resources 

of the United States, 2002. Gen. Tech. Rep. NC-GTR-241. St. Paul, MN: 

U.S. Department of Agriculture Forest Service, North Central Forest 

Experiment Station. 137 p.

Spelter, H.; Alderman, M. 2003. Profile 2003: softwood sawmills in the 

United States and Canada. Res. Pap. FPL-RP-608. Madison, WI: U.S. 

Department of Agriculture Forest Service, Forest Products Lab. 79 p.

Spelter, H.; Alderman, M. 2005. Profile 2005: softwood sawmills in the 

United States and Canada. Res. Pap. FPL-RP-630. Madison, WI: U.S. 

Department of Agriculture Forest Service, Forest Products Lab. 85 p.


Abt, Karen L.; Prestemon, Jeffrey P.; Skog, Kenneth E. [and others]. 2011. 

Simulations of alternative mechanical thinning treatment programs on western 

timberland. Res. Pap. SRS–52. Asheville, NC: U.S. Department of Agriculture Forest 

Service, Southern Research Station. 10 p.

We used the Economics of Biomass Removals model to evaluate the required treatment 

acreages, volumes removed, treatment costs and product revenues from national forest 

and other ownerships. We used three distinct treatment prescriptions to achieve two 

hazard reduction goals for treatable timberlands in the Western United States. The 

two hazard reduction goals were to maintain current hazard levels over 10 years, and 

to reduce hazard levels by 20 percent over 10 years. We also simulated one treatment 

prescription on national forest timberland only in order to evaluate the effect of requiring 

a minimum level of treatment in each State. These simulations show that costs and 

revenues differ by intensity of the hazard reduction goal and by treatment prescription. 

They show that uneven-aged treatment prescriptions tend to be more expensive than 

thin-from-below prescriptions for accomplishing hazard reduction goals. Uneven-aged 

prescriptions, however, yield greater timber product volumes and values for landowners, 

offsetting the higher costs. On the other hand, in situations without wood product 

markets, thin-from-below prescriptions could be a better option because of the lower net 

costs.

Keywords: Economics of Biomass Removals, fire hazard, mechanical thinning, treatable 

timberland, treatment costs.
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