Projecting National Forest

Inventories for the 2000 RPA

Timber Assessment

Projecting National Forest

Inventories for the 2000 RPA

Timber Assessment

U.S. DEPARTMENT OF AGRICULTURE FOREST SERVICE

JOHN R. MILLS AND XIAOPING ZHOU

A Technical Document Supporting the 2000

USDA Forest Service RPA Assessment

A Technical Document Supporting the 2000

USDA Forest Service RPA Assessment
Authors John R. Mills and Xiaoping Zhou are research foresters, Forestry Sciences

Laboratory, Pacific Northwest Research Station, P.O. Box 3890, Portland, OR 97208.
Mills, John R.; Zhou, Xiaoping. 2003. Projecting national forest inventories for the

2000 RPA timber assessment. Gen. Tech. Rep. PNW-GTR-568. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research Station. 58 p.

National forest inventories were projected in a study that was part of the 2000 USDA

Forest Service Resource Planning Act (RPA) timber assessment. This paper includes

an overview of the status and structure of timber inventory of the National Forest

System and presents 50-year projections under several scenarios. To examine a

range of possible outcomes, results are shown for five removals scenarios that incorporate assumptions from both current and past studies of wood flows and harvesting

on national forests. In addition, two projections were developed to examine the effects

of volume reductions associated with large-scale disturbance events, such as fires,

insects, and disease. Projections were made by region and forest type by using the

aggregated timberland assessment system and plot-level inventory data with methods

consistent with procedures followed for private timberlands in the assessment. The

results of projected inventory volume differ across regions, but the total inventory of

both softwood and hardwood forest types is shown to increase in all scenarios. One

result is a shift in area to older age classes. Initially, 15 percent of the timberland is

classified as stands older than 150 years; under the base scenario with disturbance,

this area will increase to 32 percent by 2050. This shift means that in the future, a

larger share of U.S. timberland is projected to support mature and old forest conditions.

Keywords: National forests, timber supply, modeling, inventory projection, yield function,

seral stage, public policy.

Abstract

The USDA Forest Service conducts periodic assessments of the condition of all 

forest and rangeland resources in the Unites States under the authority of the Forest

Rangeland Renewable Resource Planning Act of 1974 (RPA 1974). The structure of

these periodic assessments allows for the synthesis and integration of the current

state of scientific knowledge and supports the development of mathematical models 

to project resources into the future. The 2000 RPA timber assessment is part of this

broader study, which includes all resources such as water, minerals, wildlife, and

recreation. The purpose of the timber assessment is to report the current status of the

resources, survey the prospective changes in the land and timber resource base, estimate the major determinants of trends in demand and supply, and examine the implications of these trends in making 50-year projections of the U.S. forest sector. The

results presented here document projections made for national forest timberland as

part of this assessment. 

National forests (fig. 1) support some 147 million acres of forest land, or about 20

percent of the 747 million acres of forested land in the United States (Smith et al.

2001). Approximately two-thirds of national forest area is classified as timberland,1

whereas 19 percent is in a class known as reserved2 forest land. The remaining 15 percent of forest land is less productive in the capacity to grow wood, but is not reserved

from harvest. The timber volume, or growing-stock inventory, on national forest timberland amounts to 30 percent of the U.S. total. Figure 2 illustrates the timberland area

and volume distribution among all four U.S. ownership groups: National Forest System

(NFS), other public, forest industry, and nonindustrial private forest (NIPF) owners.

Among these owners, the national forests are unique in that 77 percent of NFS timberland is in the mountainous regions of the West where 96 percent of the inventory

volume is found in species of trees considered softwoods. Overall, 88 percent of NFS

timberland inventory is softwood volume, whereas softwood fiber makes up about twothirds of the forest industry inventory. The other public inventory is just over half softwood volume. The largest timberland owner class, NIPF, is located in the Eastern

United States (89 percent) and is roughly two-thirds hardwood inventory volume. Many

NFS lands are located in terrain that is difficult to access and are governed with policies

aimed at producing a variety of public goods and only moderate levels of removals.3

Many acres of NFS timberland have never been accessed with roads and the timber

never harvested, creating the situation where NFS has, overall, a greater proportion of

older trees than do forests in other ownerships. Today, the NFS mandate continues to

differ from the other ownerships as it focuses on a broad spectrum of outputs and

issues surrounding the management of publicly owned forested ecosystems. 

1 Land capable of producing at least 20 cubic feet of industrial wood 

per acre per year and not withdrawn from timber production. 

2 Forested land under administrative or legal statute withdrawn from

timber use. 

3 The terms “harvest” and “removals” are often used as if they are 

interchangeable, but technically, they mean different things. Inventory 

is presented in units of net growing stock, it represents sound merchantable tree volume, and the term “removals” specifically refers to the 

amount of this net growing-stock volume removed by cutting trees. 

Removals volume is subtracted from the net inventory. In a technical 

sense, harvest volume describes what wood is available for processing 

after extraction from forests; this can include fiber from both sound and 

cull trees. Harvest also describes cutting trees (e.g., clearcut harvest, 

partial cut harvest, harvesting, etc.). 

1

Introduction
2

Figure 1—Distribution of national forests and parks.

Figure 2—U.S. timberland area and inventory volume by ownership (1997).
3

The intent of this paper is to examine the projections of national forest timberland

growth and volume by age and age-related seral stage and compare the results with

the other U.S. timberland ownerships. The projections were developed with approximately the same level of detail as those made for private timberlands. One exception

is that growth and yield regimes were not specifically identified for various management intensities to the extent they were for private lands in the South and Pacific

Northwest (for details, see Haynes, in press). The forest planning effort is undergoing

revision, so removals (or harvest) were not available from completed plans; to present

a range of possibilities, five removals scenarios were defined.4 Two of these scenarios

are based on assumptions made in previous assessments, two were derived by using

different approaches for the current assessment, and one is a no-harvest (zero cut)

option. Also, the impact of large-scale disturbances was examined by including in the

projections recent levels of fire, insects, and disease, and regeneration failures for two

of the scenarios. The purpose of using removals projected in past assessments is to

provide some perspective on how quickly the social and political forces shaping management policies are subject to change. The view of the future from 1989 was based

on a very different set of assumptions than those used today.

The model used for the inventory projections is known as the aggregate timberland

assessment system (ATLAS; Mills and Kincaid 1992). This model was first developed

for the 1989 timber assessment (Haynes 1990), and later updated for the 1993

assessment update (Haynes et al. 1995). In the assessment suite of models, ATLAS

provides the framework projecting inventories by incorporating assumptions regarding

forest growth and yield, timber management, harvesting, and changes in land use and

forest cover. (For an indepth explanation of the timber assessment modeling system,

see chapter 2 in Haynes, in press.) The modeling system has been adapted to project

changes in forest growth rates associated with changing temperatures and rainfall for

a national assessment examining the forest sector impacts of climate change (Joyce

et al. 1995, Mills and Haynes 1995, Mills et al. 2000). Other studies, in collaboration

with the U.S. Environmental Protection Agency, examined potential benefits consumers

of forest products might experience because of stricter air pollution standards, specifically lower ozone levels (Bentley and Horst 1997, 1998). On a smaller scale, ATLAS

has been used for specific state-level timber studies in both even-aged timber conditions of western Washington (Adams et al. 1992) and uneven-aged forests in Maine

(Gadzik et al. 1998). The model was recently modified to meet the needs of the 2000

RPA timber assessment through an update of all inventories, associated growth

parameters, and the expansion and integration of the available timber management

options. 

The projection system includes an inventory module, a management module, and a

harvest module. The inventory module contains descriptions of inventory in terms of

forest type, owner, site class (optional), management intensity (optional), and area 

4 Although some plan revisions are already completed, many forest or 

grassland plans are nearing the 15-year mark for revision mandated by 

the National Forest Management Act (NFMA 1976), and other forest or 

grassland plans are in the “maintenance” phase in which plans are 

amended. New planning regulations to guide the revisions are being 

evaluated internally and will soon be published for public comment 

(Zwight 2002). 

The Projection Model
4

Inventory Data

and volume by age class. The management module contains the parameters governing growth and yield, including the hardwood-softwood fiber mix, average diameter by

age, yield tables, regeneration stocking and density change variables, management

intensity shift variables, harvesting parameters, and area change parameters. The

harvest module contains future removals stratified by region, owner, and fiber type for

each projection period. 

This analysis is the first full implementation of ATLAS, or any age class model, with

national forest timberland data. The projections made for previous assessments were

based on simple growth-drain formulation, which satisfied the needs of the timber

assessment market model (TAMM; Adams and Haynes 1980, 1996). For the simulations of private timberland, TAMM is used to derive removal levels from future stumpage demand, which comes from the projection of the future final forest product

demand (lumber, plywood, oriented strandboard, pulp, paper, etc.). Removals are then

passed to ATLAS identified by stumpage supply region, private ownership, and fiber

type. When TAMM solves the market equilibrium in the private sector, fiber from national

forests is included in the solution as an exogenous input. Market forces are not influencing management and harvest on NFS timberland as they do in the private sector.

Thus, the process of finding the market solution does not need to interact with public

inventory levels or growth rates. 

The data were derived from the database assembled for the 2000 RPA assessment

(USDA Forest Service 1996).5 These data are based on a set of measurements from

permanent sample plots and compiled by researchers with the USDA Forest Service

Forest Inventory and Analysis (FIA) units. In the Eastern United States, the plots on

NFS lands were measured by FIA field crews, whereas the western data were collected

by both FIA units and NFS inventory specialists and contract crews. The data are

current for 1997; this means some previously measured plots were “updated” to

approximate values that existed in 1997, and some corrections were made where

documented errors existed. The compilation and analysis are consistent with the

approach used to develop RPA projections for private timberlands (see Haynes, in

press). The plot data are stratified by projection region, ownership, forest type, and

age class. The assembled data represent the initial inventory starting point. Many of

the required model growth and yield parameters are developed from the data. Details

of regional and forest type definitions can be found in appendix table 8. 

In the early 1990s, a change in data collection procedures on national forest lands

occurred in most of the West. A new plot and grid system was initiated by FIA to collect inventory under a system compatible with a national design. This means western

NFS data are now compatible with a system long in place on all private lands in the

West and with the system previously established across all ownerships in the East. A

drawback to the implementation of this system is that it imposes a new set of classification criteria on federal lands that were formerly under a map-based system of

inventory. This makes it difficult to compare the new numbers with those published by

using the older techniques. The tabular values presented for the West in 1991 and

1997 are not comparable in that they do not reflect real trends but result from the use 

5 Data are available on the Internet at http://www.fs.fed.us/pl/rpa/list.htm.
5

of different systems. Inventories for the East and North have been carried out for several cycles with greater consistency, so differences between the 1991 and 1997 values

for those regions reflect what are perceived as real changes in volume, growth, or 

timberland area. 

Forest land managed by the NFS is classified with a status of either nonreserved or

reserved. As shown in table 1, the nonreserved status is subdivided to define potential

uses. Public forests receive a wide range of use: some might be compatible on the

same piece of land, such as hiking, hunting, grazing, and timber harvesting, but others

are not compatible, such as timber harvesting and wilderness recreation. Timberland

is land with potential for growing crops of industrial wood, whereas other forest land,

owing to a combination of climate, topography, and soils, cannot support forests growing at the minimum commercial standard (see footnote 1). The uses for land in either

of these categories might include recreation, wildlife habitat, and domestic livestock

grazing, but it is assumed that commercial timber harvesting will occur only on timberland. The reserved category represents areas typically not available for commercial

harvest but set aside for specific use(s). Wilderness typically allows specific types of

recreation in association with preserved natural areas, and nonwilderness includes

lands serving to protect resources such as significant ecosystems, species habitats,

and municipal watersheds. These definitions are consistent with those used for private

lands; however, the definition of reserved lands would rarely be applicable to privately

owned land. Most of the reserved areas have not yet been inventoried for timber characteristics, but they contain a range from productive to nonproductive forests. For

details see Smith et al. (2001).

Status of Forest Land

Table 1—Status of forest land on national forests by region, 1997

Nonreserved Reserved

Total

Region forest land Timberland Other Nonwilderness Wilderness

Thousand acres

Pacific Northwest West 9,086.6 7,316.8 40.1 166.3 1,563.4

Pacific Northwest East 13,265.9 10,718.2 225.2 276.8 2,045.7

Pacific Southwest 16,747.7 10,086.0 2,928.3 378.8 3,354.6

Rocky Mountain North 30,968.7 23,955.9 910.2 67.1 6,035.5

Rocky Mountain South 41,256.1 19,537.3 14,850.9 309.1 6,558.8

North Central—Plains States 2,212.0 2,047.8 33.9 .0 130.3

North Central—Lake States 7,105.5 5,912.1 223.7 .0 969.7

Northeast 2,544.0 2,029.2 235.4 .0 279.4

South Central 6,870.2 6,457.2 11.3 .0 401.7

Southeast 5,469.9 4,594.3 52.4 1.1 822.1

Alaska 11,250.5 3,780.4 3,110.1 0.0 4,360.0

Total 146,777.1 96,435.2 22,621.5 1,199.2 26,521.2
6

The West plus Alaska accounts for 76 percent of all national forest timberland area

and 86 percent of the total national forest timber inventory volume (the West includes

the Pacific Northwest, Pacific Southwest, and Rocky Mountain regions; fig. 3). In contrast, the West supports roughly 14 percent of all private timberland area and 18 percent of the volume on private timberlands. Table 1 shows the Rocky Mountain South

region comprising 41 million acres of forest land, which is 28 percent of all NFS forest

land area (the most of any region), and 20 percent of NFS timberland. Owing to a

combination of factors that influence climate (elevation, latitude, prevailing winds, and

surrounding topography), roughly 36 percent of this region’s forest is in the “other”

less productive category, while another 17 percent is in the reserved category. This

represents the largest contribution to both categories (66 percent of “other” and 25

percent of all reserved). In contrast, the Rocky Mountain North region is one of the

most highly productive regions and second largest in terms of forest land area. About

77 percent of this region is timberland, which represents 25 percent of total NFS timberland, (the largest share of all regions), with just 3 percent falling into the “other”

category. The Rocky Mountain North region also holds 22 percent of all reserved area,

second to the Rocky Mountain South in terms of area in wilderness.

The composition of the inventory, in terms of timber volume, growth, and mortality, on

timberland varies with differences in topography, associated mean temperatures, and

rainfall patterns. Climate provides for growth potential, because forests are constantly

being shaped by the effects of disturbances such as harvesting, insect infestations,

disease, wildfires, and windstorms. These events create openings in stands or may

remove the entire stand, which then is either naturally or artificially regenerated or

replaced. Thus, climate influences the regional inventory, growth, and mortality rates

presented in table 2. 

The Pacific Northwest West region ranks fifth in timberland area, and it compares to

the much larger Rocky Mountain North region in terms of inventory volume and annual

growth while having just half the rate of mortality. A similar comparison can be made

Figure 3—National forest inventory projection regions used in Resource Planning Act timber assessment.

The Structure of

Timberland Inventory 
between the Pacific Northwest East and the Pacific Southwest regions, which are

nearly equal in timberland area. Lower rainfall and a shorter growing season contribute to lower growth rates, higher mortality, and hence less inventory in the interior

of Washington and Oregon than in California, where a large share of the timberland

is influenced by warmer and wetter maritime conditions. 

Reflecting the western distribution of national forests, 88 percent of the net growingstock inventory volume on national forest timberland is softwood6 fiber. This contrasts

with 41 percent softwood composition on 356 million acres of private timberland. 

The history of national forest management is reflected in the distribution of timberland

by age class. Because public and private forest management objectives differ, it is

likely that structural differences among ownerships will remain. 

On a regional basis, national forests in the North (Northeast and North Central regions)

have a higher concentration of hardwood species, with a hardwood fiber proportion of

68 percent, whereas the forests in the South (Southeast and South Central regions)

have a high pine component and nearly the opposite inventory fiber composition at 65

percent softwood. Conifers dominate the West and Alaska where softwood is 96 percent and 99 percent of volume, respectively. 

6 Softwood and hardwood fiber is associated with the types of forest 

products that trees in these classes can produce. Coniferous species 

are generally called softwoods, whereas deciduous broad-leaved species 

are generally referred to as hardwoods.

7

Table 2—Inventory, net annual growth, and mortality on national forest timberland by

region, 1997

Total Net annual Annual

Region Timberland inventory growth mortality

Thousand acres - - - - - - - - - -Million cubic feet- - - - - - - - -

Pacific Northwest West 7,316.7 52,878 843 209

Pacific Northwest East 10,718.2 23,993 322 265

Pacific Southwest 10,086.0 31,803 620 154

Rocky Mountain North 23,955.9 55,104 892 423

Rocky Mountain South 19,537.3 34,614 523 362

North Central—Plains States 2,047.8 2,181 48 14

North Central—Lake States 5,912.1 7,747 186 76

Northeast 2,029.2 4,478 82 34

South Central 6,457.2 11,645 336 63

Southeast 4,594.3 8,764 162 112

Alaska 3,780.4 18,909 89 124

Total 96,435.1 252,116 4,104 1,836
Age distribution of the inventory—Although many western tree species, principally

conifers, can live more than 400 years (Burns and Honkala 1990, Franklin and Dyrness

1973), as figure 4 would indicate, the majority of the forests in the United States are

relatively young. Roughly 62 percent of national forest timberland and 84 percent of

the timberland in private ownership supports stands of trees less than 100 years old.

Older forests occupy smaller portions of the landscape; stands of 150 years and older

account for 15 percent of national forest timberland and about 3 percent of private timberland. 

Seral stage of the inventory—Seral stage describes the ecological succession in

plant communities, typically ranging from a young or early stage to an old or late

stage. From a biological context, seral stage considers stand size, species composition, stand density or stocking, and the amount of dead and down material in the

stand. In context of the projections presented here, as stands grow older the average

tree size and stand volume increase. Older trees and older forests have typically

achieved a greater degree of succession than younger forests. The FIA correlates age

and tree size to a product class associated with the types of forest products a stand

could potentially produce. In table 3 we present a modified form of the FIA seral

stages based on size-product class (Powell et al. 1993) with five categories. These

categories are based on the assumption that stands in the South reach maturity at

younger ages than stands do in either the West or the North. The starting timberland

inventory is stratified by these seral stages in figure 5. The majority of mature and

older forests in the United States are found in the West, and smaller portions are

found in the South. As in the West and North regions, however, mature sawtimber is

the largest class in the South region. 

Forest types—Plots are aggregated and classified by forest type. Each type is a composite of many species that often occur together under similar conditions. Inventory

plots typically include measurements from more than one tree species. The FIA units

8

Figure 4—Timberland area distribution by stand age and ownership (all regions, 1997).
compute the contribution of stocking by individual species when assigning a forest

type to the plot. For projection purposes, it is assumed the plots within a forest type

can be assigned similar growth and yield characteristics. This understates the large

site variance among plots, but over a region we are seeking an answer that represents a set of averages. Up to 10 forest types were assigned to each region, and, in

general, they are the same as those assigned for the private ownership. For growth

modeling purposes, some forest types with few plots may be grouped with a type

exhibiting similar growth characteristics. 

9

Table 3—Seral stages based on age associated with forest products classes

West North South

Seral stage Softwood Hardwood Softwood Hardwood Softwood Hardwood

Stand age (years)

Seedlings 5 5 5 to 15 5 5 5

Saplings and poles 15 to 35 15 to 35 25 to 35 15 to 35 10 to 15 10 to 20

Young sawtimber 45 to 75 45 to 55 45 to 65 45 to 65 20 to 35 25 to 55

Mature sawtimber 85 to 135 65 to 135 75 to 135 75 to 135 40 to 75 60 to 75

Old mature 

sawtimber 145+ 145+ 145+ 145+ 80+ 80+

Source: Haynes, in press.

Figure 5—National forest area distribution by stand seral stage (1997).
The complete set of forest types recognized is presented by region in appendix table

8. The major forest types for each region are presented in table 4. It is interesting that

in most regions, 50 percent of the timberland area is composed of just one or two

forest types. For example, 50 percent of the inventory in the Pacific Northwest West

region is in the Douglas-fir (see table 30 for species names) forest type, and 50 percent of the Southeast is upland hardwood (a mix of upland site trees including white

and red oaks, hickory, the chestnut oak type, yellow-poplar, elm, maple, and black

walnut).

The projections of national forest timberland inventories rest on a set of assumptions.

These assumptions are associated with interpreting, compiling, and analyzing

resource data for the parameterization of the modeling framework.

Age classes—The timberland in the Northeast, South Central and Southeast regions

was stratified by 18 age classes. Because the western national forests generally support some of the oldest forests, they were stratified by using 26 age classes in an

effort to better identify potential late-seral stage conditions. A 10-year interval for age

class was used for all regions except for the Southern regions, where the inventory

was aggregated into 5-year classes. The FIA plot data did not contain an assigned

stand age for about 38 percent of the plots in the Northeast and some plots in the

South Central regions. Age class was assigned with a technique that considers volume and stocking in the same way ages were assigned to the private FIA plots in

these regions. All private timberland projected for the assessment was stratified in 18

age classes, by using 5-year intervals in the South and 10-year intervals elsewhere. 

Yield functions—Each inventory cell in ATLAS is projected relative to a base yield.

A set of base yield tables was derived for each forest type within each region from the

FIA plot data. This technique was developed in past assessments to produce aggregate growth yields (Mills 1990) for particular types when other sources were neither

available nor representative across a region. This use of empirical growth rates embodies

10

Modeling Assumptions

Table 4—Regional dominance of forest types on national forest timberland by

region, 1997 

Region Most dominant forest types by region 

Percentage of timberland

Pacific Northwest West Douglas-fir (50.2), fir and spruce (22.0)

Pacific Northwest East Ponderosa pine (33.6), Douglas-fir and larch (24.4)

Pacific Southwest True fir (28.0), mixed conifer (23.5)

Rocky Mountain North Douglas-fir (41.1), fir and spruce (25.6)

Rocky Mountain South Ponderosa pine (31.1), fir and spruce (25.3)

North Central—Plains States Oak and hickory (71.4), pines (12.2)

North Central—Lake States Aspen and birch (27.6), maple and beech (26.1)

Northeast Maple/beech/birch (51.1), oak and hickory (21.0)

South Central Upland hardwood (37.0), natural pine (30.2)

Southeast Upland hardwood (50.2), natural pine (22.3)
the effects of historical and recent management practices. This study used a series of

regressions conducted to predict net growth by age for each regional forest type. The

yields were then an accumulation of net annual growth; the following represents the

approach for a 10-year interval age classification:

Age class Net growth Yield

0 G0 Y0 = 0 

1 G1 Y1 = [(G0 + G1)/2]5 

2 G2 Y2 = [(G1 + G2)/2]10 + Y1

3 G3 Y3 = [(G2 + G3)/2]10 + Y2

4 G4 Y4 = [(G3 + G4)/2]10 + Y3

... ... ... 

Where 

Gac = net annual growth at age class ac, and

Yac = Yield at age class ac.

The variable Yac represents the net growing-stock volume at the age class midpoint

(e.g., at ac=0, age=5; at ac=1, age=15; and when ac=2, age=25; etc.). The general

model of the net growth (G) for age class (ac) is expressed as G = F(ac). The example of the net-growth function of the Douglas-fir forest type for the Pacific Northwest

West region for age classes greater than zero (ac>0) is:

Gac = 1002.8 + 41.5ac – 0.71 ac2 – 391.2Ln(ac) – 305.1e(1/ac), (ac>0). (1)

In regions with very few observations on national forests, yields were constructed with

the addition of FIA plots from private lands. Figures 6 and 7 compare yields developed

for private timberland on medium-productivity sites with the national forest all-site

yields. In the first case, the Douglas-fir yields were developed with two independent

data sets; the data were collected at different times, and no plots were in both sets. It

is interesting to note how the national forest growth rate begins faster than the mediumsite private lands, but then slows with age, crossing at roughly 110 years of age. The

Southern planted pine yield represents an overlapping situation where, because of

fewer observations, the NFS yields were developed with plots from both NFS and private lands. The Southern national forest values (fig. 7) were calculated with all timberland plots, whereas the private yield was derived from only timberland plots classified

as medium site productivity. It is interesting to note how well the medium site class

represents the overall average for national forest lands. The complete set of yields

developed for each region can be found in appendix tables 9 through 17. 

If the future is significantly different than the past, then this approach is subject to

bias; for instance, atmospheric pollution, climate change, and use of improved genotypes can compound errors associated with using empirical data to project stands forward (Adlard 1995). We normally handle these unknowns by making assumptions in

various scenarios. In the case of national forests, the largest effect on future inventories is likely to be the level of disturbance, namely insects, fire, and harvest.

Mortality—In a stand with differential tree growth, as canopy closure occurs, the 

slower growing trees become suppressed, accelerating their weakening and leaving

them less resistant to the effects of weather (drought and wind), insects, and disease

11
12

Figure 7—Planted pine yields in the South Central region, comparing national forest and medium-siteproductivity private ownerships.

Figure 6—Douglas-fir yields in the Pacific Northwest West region, comparing national forest and mediumsite-productivity private ownerships.
(Oliver and Larson 1990). So as forest stands age and average tree diameter increases,

the number of living trees in the stand typically decreases owing to mortality of trees

unable to compete for the limited resources (Davis and Johnson 1987). Oliver and

Larson (1990) identify two categories of mortality, “regular mortality” and “irregular

mortality.” On a landscape scale, regular mortality is part of forest succession, occurring as trees age and compete with each other for light and water. Meanwhile, irregular mortality is associated with disturbances such as fire, insect epidemics, or disease

that kill what are otherwise healthy trees. Mortality is measured in successive surveys

as the volume in the trees that have died over a fixed time interval. As shown in table

2, the most recent accounting for annual mortality indicates it is less than one percent

of standing inventory volume. 

The starting (1997) forest inventory data were compiled by FIA to represent net values

(live growing stock) for all volumes (growth, inventory, removals), and the projections

represent net values as well. Net growth is the total increment from growth (gross

growth) over a period, minus the volume of wood in trees that have died during that

period (see Smith et al. 2001). Using net values to calibrate the growth models is an

implicit approach to considering mortality. For example, the growth and yield relations

were developed from a cross section of field plots, many of which represent stands

that would be expected to have histories with various levels of mortality. For a few

plots, net growth was reported to be a negative value, meaning the volume of mortality exceeded the gross increment from growth. This would likely indicate a recent

disturbance to the stand. These plots were included in the process to calibrate net

inventory growth, so this background level of mortality is part of the projection. On a

landscape level, we assert that these projections reflect the average rate of historical

mortality. 

Disturbance—Oliver and Larson (1990) associate irregular mortality with disturbances that occur less frequently than regular mortality, kill or injure otherwise healthy

trees, and potentially leave entire stands damaged or destroyed. Depending on the

stage of stand development, when these disturbances are large enough to leave gaps

in the canopy, new vegetation may take root or surviving trees may grow into the available space. In managed stands, these gaps or areas are typically artificially regenerated. Past projections using ATLAS have not explicitly included stand-replacing events

that are large enough to be measured and associated with either catastrophic fires 

or large insect outbreaks. Stand-replacement disturbances are typically followed by

either natural or artificial regeneration, and the composition of the next forest potentially could be different than predisturbed conditions (e.g., the loss of a mixed pine-fir

stand replaced by planting ponderosa pine). From a modeling perspective, these

mortality occurrences can be treated like a form of harvest, where the entire stand is

removed, followed by regeneration. This acts to shift the local age-class distribution

toward younger stages. 

The Forest Service keeps a record of the timberland area in need of reforestation

and identifies the cause.7 Figure 8 shows that until recently, the largest additions to

the national forest “reforestation needs” pool were due to harvest. Since 1987, as 

7 Data source is Reforestation and Timber Stand Improvement Needs Report (FS-2400-K) Table 1, as required by the 

National Forest Management Act of 1976, Sec.4d(1).

13
removal levels dropped, the additions from harvest fell, while at the same time additions from the area burned in fires increased. Although the year 2000 was dramatic in

terms of area burned, in 2001, fires also exceeded recent history,8 while at the same

time additions from insects and disease outbreaks rose to nearly equal the level of

additions from harvest. To address the issues associated with the redistribution of

acres affected by this irregular mortality, two projection scenarios were modified to

incorporate the explicit return of deforested area to the regeneration pool following

these losses. Although regeneration failures might not be classified as a form of mortality linked to a disturbance, they are included here because the area accounted for

is too large to ignore, being more acres than additions from insects and disease.

These scenarios incorporate and project additions to the reforestation/regeneration

pool at the average rate they occurred in the most recent 10 years (1992 through

2001). These disturbances require the model to regenerate an additional 140,000

acres annually. 

Modeling disturbance—The base case and zero cut scenarios were run with the

explicit addition of disturbance. The disturbance values were based on historical averages and calculated by region. In each 10-year projection period, a total of 1.4 million

acres was added to the regeneration pool. Several assumptions were made about

the forest types and age classes impacted. The disturbance was proportioned in each

region among the two most dominant forest types, those covering the largest area

(table 4). The predisposition of a stand to different disturbances changes with the

stand structure and type of disturbance, but generally increases with age (Oliver and

Larson 1990). Therefore, the impacts were distributed across stands at or greater than 

8 As this publication went to press, wildfires in 2002 had exceeded 

the area of Forest Service timberland burned in 2000.

14

Figure 8—Annual additions to the national forest reforestation needs pool, by cause.
the minimum harvest age class. The allocation was in proportion to existing area by

age class. As for a clearcut harvest, it was assumed that disturbed stands would be

managed for regeneration. Volume was removed from impacted acres, and the area

was returned to the regeneration pool. Unlike harvest volume, however, the volume

affected by disturbance was counted as mortality, and subtracted from growth to generate a new net growth value. Regeneration failures also were calculated by region

and projected for the two predominant forest types. The failed regeneration acres

were lagged, meaning they remained in the zero age class for an extra 5- or 10-year

period. Once in the pool, it was assumed that the action that preceded lands moving

there was irrelevant, and both harvested and disturbed acres were treated the same.

Lacking data on pre- and post-disturbance forest types, all regenerated stands

returned to the same forest type from which they originated. 

Management intensity—The NFS projections apply three management regimes that

differ by the approach to harvest. The first allows regeneration harvest; that is, a final

harvest or clearcut will occur in these stands over a range of ages followed typically

by the planting of seedlings. The second regime was developed to apply a partial harvest—this treatment removes a portion of volume to mimic a stand subject to multiple

entries. The third management option is labeled reserved, as these stands are projected forward in time but not harvested. The number of acres assigned to these regimes

was derived from a survey of national forest regional silviculturists coordinated by

Frank Burch (1999), which requested information on the amount of volume to be

removed by forest type, and the harvest method. Acreage estimates were derived 

by applying average per-acre harvest outputs by harvest method in each forest type. 

Likewise, when the projection begins, the number of acres treated depends on relative

available volume within a particular regime. Harvest is allowed to occur at younger

ages in the South than in the West, reflecting local customs based on the rate at

which stands mature into various product classes. Adding disturbance to the base

case scenario did not change the amount of volume removed in harvesting, as no

assumptions were made regarding the salvage of dead material. The disturbance was

applied before cutting, which lowered the volume available for cutting in the affected

forest types. 

Area change—The area of national forest timberland is assumed to remain unchanged

for the next 50 years. The model is flexible for implementing area and forest type

changes, but for public lands these transitions are occurring more slowly than on private lands where urbanization, agriculture, changing land ownership, and management objectives play a dynamic role in shaping timberlands. In the past 20 years, the

NFS land available for commercial timber projection has declined significantly, and

future changes in the “suitable” land base could have a marked impact on available

timberland. It is assumed that the national forest area base will be subject to relatively

small changes in the future, as land acquisitions and trades do occur, but not to the

extent as to significantly impact regional projections. 

Site class—National forest timberland covers a range of site productivity from 20 to

more than 225 cubic feet of net growth per acre per year as measured at the culmination of mean annual increment. Most NFS timberland in the Pacific Northwest West

produces over 120 cubic feet per acre per year, whereas other regions generally produce less. If the productivity of NFS timberland is ranked among other ownerships,

15
forest industry has the most productive lands, followed by NFS timberland, NIPF owners, and other government ownership.9 The poorest NFS sites are typically found on

lands classified in the nontimberland categories. 

Site productivity was a stratification variable for the projection of private inventories

in the South and Pacific Northwest where management regimes were customized for

three aggregate classes of site productivity (e.g., treatments were generally scheduled

at younger stand ages for high vs. low site productivity timberland). Because the projected NFS management regimes were not tailored specifically to site, this stratification would not improve or even change the outcome. 

Other parameters—Several other parameters are required for an ATLAS projection.

These contribute to the calibration of growth, the assignment of stocking upon regeneration, the proportion of hardwood and softwood fiber in the inventory, and the

descriptive variables such as average diameter by age. These empirical values were

derived from variables in the FIA plot data (see Mills 1990 for their relevance). 

Excluded timber and forest lands—Although presented in the tabulations, projections

did not include lands classified as timberland officially reserved (not to be confused

with the “reserved” management intensity class) or lands classified as nonproductive

forest land. These areas are important contributors to ecosystem diversity, provide

wildlife habitat, sequester carbon dioxide from the atmosphere, and provide utility for

human uses. Data are not yet available to project the forests in these areas. As data

become available, future projections will likely include these lands for various holistic

studies. 

The future levels of timber removals in national forests will be developed with tools 

of science and planning and with direction and input from the interactions among the

three branches of federal government, regional and local managers, and the American

public. During the 1990s, timber removals from national forests decreased dramatically from the higher levels mandated by Congress in the late 1980s. The de-emphasis on timber production has occurred as the management focus has broadened to

consider a variety of other resource issues including the preservation of threatened,

endangered, and sensitive wildlife species and complex questions relating to ecosystem health and integrity. To examine a range of possible outcomes, and address both

past and current trends, projections were made by using five removals scenarios. Two

scenarios represent the current best estimates by Forest Service personnel, two scenarios reflect assumptions made as part of the 1989 and 1993 timber assessments,

and the final projection assumes no commercial timber removals. The removal numbers were developed to provide input for the timber demand model used in the

assessment (TAMM), so the values were stratified by softwood and hardwood fiber

and aggregated to the RPA timber supply region.

Scenario one is labeled the “base case.” The removals were derived from compilations done at the Forest Service Washington Office (WO) specifically for use in the

2000 RPA timber assessment. These values are normally derived through the forest 

9 As calculated by using area and site productivity class and 

timberland area from the 1997 RPA database.

16

Projection Scenarios 
planning process, but at this time, many plans are 10 to 15 or more years old, and the

agency is in the process of overhauling the regulatory framework that will guide their

revision.10 This set of removals represents the current expectation based on the

guidelines of Forest Service policy. This scenario was run both with and without the

major disturbance regime. 

Scenario two is based on a survey of Forest Service regional silviculturists conducted

in 1999 by Burch (1999). These values were derived independently of the effort to

predict the removals used in scenario one. The silviculturists were asked to apply

their best professional judgment in making a two-decade projection of future removals.

Lacking completed forest plans, the regional analyses were predicated on two important assumptions: (1) volume outputs in full compliance with existing laws, regulations, plan direction, and agency policy; and (2) static budget and staffing levels for

preparing and administering timber sales and for the implementation of vegetation

management programs over the analysis period. Most respondents estimated that

the removals in the second decade would be the same as the first. The estimates

were made for the aggregate forest types that matched the FIA inventory data as

projected in the model. The removals were provided in board feet, which were converted to cubic feet. This conversion used FIA data to calculate a ratio by region and

fiber type (softwood or hardwood); values ranged from roughly 5.8 to 1.4 board feet

for 1 cubic foot. When projected, the removals provided for the second decade

(2010-2020) were held constant through 2050. 

Scenario three allocates the highest level of removals because it was originally the

base scenario for the 1989 RPA timber assessment, which was developed near the

end of the period when the Forest Service removals reached all-time highs. In that

assessment, the predicted removals covered the years of 1990 to 2040; however, this

analysis extends the later values to 2050. Public attitudes, values, and expectations

have changed in the short time since that assessment. It is difficult to judge if the

agency will ever return to a policy of these levels, so it might be safe to assume this

removal scenario represents an upper bound in considering the resulting projections. 

Scenario four follows the removal levels predicted for the 1993 RPA timber assessment update. By this time, harvesting was dramatically scaled back in the Pacific

Northwest to meet the requirements of the Northwest Forest Plan. Those values

reflected management considerations for wildlife habitat that dropped the projected

removals to almost half of the level assumed in the 1989 assessment. This level of

removals falls between 1989 RPA projection and the current RPA expectations.

Scenario five simply assumes no commercial removals, the zero cut scenario. It 

was developed to examine the NFS inventory structure after 50 years with no commercial harvesting and to highlight the ecosystem consequences that include a lack of

commercial-scale regeneration work. The result is a “forward movement” in the age

class structure. As stands age without replacement, a gap forms as there is a decline

in the area supporting stands in early-seral stages. Given what we know about natural

processes, this situation seems not only unrealistic, but as a management objective it 

10 Updated information can be found through links on the 

Forest Service Web site at http://www.fs.fed.us/.

17
would seem to be unachievable—stand-replacing events occur naturally. To examine

this, the scenario was run both without and with major stand-replacing disturbances,

and it was assumed disturbance would be followed by regeneration. 

Figure 9 shows the array of projected removals displayed with the historical removals

that occurred between 1952 and 1991.11 Strikingly apparent are the large gaps

between the projected removals levels for each successive assessment. In a relatively short time, an evolution in public thinking occurred that resulted in policies

greatly affecting our views of the future. Removal levels today are somewhere

near the levels of the base and Burch cases—a steep decline from the 1991 historical level and much steeper than the increase that came after 1952.

In our examination of the scenarios based on past assessments (scenarios three and

four), we are not projecting them from their inception in 1989 or 1993, but from the

inventory we have today (dated 1997). The level of removals assumed in those scenarios from 1989 or from 1993 forward did not occur, and it is likely the 1997 inventory

has grown larger than it was in the early 1990s. If we were to truly follow past assumptions, it would require the 1997 “initial” inventory to be estimated based on the 1989 or

1993 projections of anticipated removals. We also did not include disturbance in combination with the past assessment scenarios. The average rate of disturbance was

derived over a period when removals were significantly lower than those projected

under the third and fourth scenarios, and we assumed a correlation may exist between

disturbance and removals. Given the data presented here, that is difficult to judge,

however, as figure 8 shows that, for 1977 to 1987, second only to harvest, the biggest

reason for planting trees was regeneration failures, not natural disturbance. In this

short period, the graph shows that natural disturbance rose as cut levels and the

regeneration backlog declined (we acknowledge that “natural” disturbance is highly

influenced by weather, accidents, and arson12 ). Disturbance was also not added for

scenario two because it was similar to the base, which incorporated disturbance. 

The lowest base case removals are for the year 2010, where the 623 million cubic

feet (mcf) is just 30 percent of that projected by the 1989 assessment. Farther out, the

1989 assessment projection for softwood removals in 2040 rises to 1,935 mcf, whereas today, expectations are closer to 735 mcf. The 1993 assessment captured the legal

mandates of the early 1990s, when the softwood removals in 2010 were expected to

be 932 mcf, which is 27 percent higher than current projections. Today, the largest

reductions from the 1989 and 1993 assessment projections are found in the Rocky

Mountain, Pacific Northwest West, and Pacific Southwest regions. The softwood

removals in 1997 for Pacific Northwest West were just 25 percent of the level harvested in 1991. 

The inventory volume projected for softwood and hardwood forest types differs by

region, but in all scenarios, the total volume rises. Figure 10 shows a snapshot of the

projection results for 2050 by scenario and region. Tables 5 and 6 present details

11 In all scenarios, Alaska removals remain unchanged at 

the base case level.

12 Statistics available for 7 years from 1984 through 1990 

show that 89 percent of all wildfires had human origins, but 

50 percent of the area burned was by wildfire originating 

from lightning (USDA Forest Service 1992).

18

Projection Results
19

Figure 9—Historical and projected timber removals for national forest timberland.

Figure 10—Initial and projected inventory for 2050 by region and scenario. PNWW is Pacific Northwest

West, PNWE is Pacific Northwest East, PSW is Pacific Southwest, RMN is Rocky Mountain North,

RMS is Rocky Mountain South, NC is North Central, NE is Northeast, SC is South Central, and SE is

Southeast. 
20

Table 5—Softwood inventory, annual removals, and net annual growth projected for national forest timberland under the base case with disturbance scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 5 5 5 4 5 4 5

Inventory 723 782 1,043 1,117 1,181 1,240 1,292

Net annual growth 17 14 12 11 11 10 10

North Central:

Removals 48 25 25 26 27 28 28

Inventory 3,216 3,646 4,558 5,039 5,438 5,749 6,038

Net annual growth 84 95 75 70 59 57 56

Southeast:

Removals 59 47 28 35 36 36 36

Inventory 2,826 2,991 4,067 4,539 4,759 4,885 4,985

Net annual growth 50 57 95 67 52 46 45

South Central:

Removals 169 139 77 93 94 93 93

Inventory 6,013 6,396 7,404 7,893 8,165 8,407 8,615

Net annual growth 174 192 148 126 119 117 111

Rocky Mountains:

Removals 389 130 142 172 183 193 200

Inventory 71,657 84,925 100,236 111,467 120,897 128,473 134,534

Net annual growth 1,285 1,273 1,317 1,174 990 836 719

Pacific Southwest:

Removals 314 96 108 132 139 145 148

Inventory 31,448 29,539 35,217 39,098 42,610 45,850 48,809

Net annual growth 463 616 508 491 472 448 427

Pacific Northwest West:

Removals 266 66 49 63 74 86 94

Inventory 33,621 51,399 61,909 69,432 76,266 82,401 88,008

Net annual growth 320 778 861 793 714 678 639

Pacific Northwest East:

Removals 330 72 68 87 103 120 130

Inventory 17,338 23,915 28,039 30,992 33,517 35,642 37,418

Net annual growth 269 320 335 308 282 264 249

Alaska:

Removals 99 51 29 29 29 29 29

Inventory 18,733 18,733 19,290 19,847 20,404 20,961 21,518

Net annual growth 85 85 85 85 85 85 85

United States:

Removals 1,679 629 531 641 690 735 792

Inventory 185,575 222,326 261,763 289,424 313,237 333,608 351,217

Net annual growth 2,747 3,430 3,437 3,125 2,784 2,542 2,342
21

Table 6—Hardwood inventory, annual removals, and net annual growth projected for national forest timberland under the base case with disturbance scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 22 17 8 10 10 11 11

Inventory 3,711 3,696 4,230 4,729 5,184 5,605 5,986

Net annual growth 69 60 56 53 50 46

North Central:

Removals 61 64 44 45 46 47 47

Inventory 5,228 6,282 7,919 8,893 9,559 10,134 10,657

Net annual growth 123 139 153 115 105 100 97

Southeast:

Removals 13 51 9 10 10 10 10

Inventory 5,565 5,773 7,293 8,025 8,478 8,768 8,967

Net annual growth 114 105 101 70 46 34 27

South Central:

Removals 36 56 24 29 30 30 30

Inventory 4,959 5,249 6,971 8,018 8,857 9,485 9,860

Net annual growth 147 144 138 125 105 80 59

West:

Removals 54 11 6 7 8 8 9

Inventory 6,178 8,613 10,592 12,211 13,522 14,746 15,969

Net annual growth 71 214 180 143 130 132 127

Alaska:

Removals N/A 0.4 0.3 0.3 0.3 0.3 0.3

Inventory N/A 176 21 252 290 328 366

Net annual growth N/A 4.1 4 4 4 4 4

United States:

Removals 186 199 91 102 104 106 106

Inventory 25,641 29,789 37,219 42,128 45,890 49,066 51,805

Net annual growth 543 674 636 511 443 400 361

N/A = not available.
regarding growth, removals, and inventory for the base case with disturbance scenario, and results from all other scenarios can be found in appendix tables 18 through

29. In total, the volume increase from 1997 to 2050 ranges from about 45 percent with

the 1989 assessment assumptions to 90 percent under the zero cut no-disturbance

scenario. This translates to increases that range from between 105 and 210 billion

cubic feet of additional timber (net growing stock). Of all the scenarios, the 1993

assessment projection falls roughly in the middle. It is interesting that the volume trajectories that match each other most closely are the base case without disturbance

and the zero cut case with disturbance. This means the losses from disturbance have

an aggregate impact on the inventory as large as the removals assumed for base

case. When disturbance is then added to the base case, the projection falls 3 percent

below the 1993 assessment scenario making the base with disturbance the second

lowest in terms of total projected volume in 2050. 

The results by region are uneven because of the independence of growth rates,

removals, and disturbance. Whereas growth and disturbance are tailored to the region,

removals differ independently in each scenario. For example, softwood removals are

highest under the 1989 assessment assumptions, and they range initially (1997) from

10 times the base case in the Pacific Northwest West to 52 percent above the base

case in the Northeast. By 2050, the difference in removals in the Pacific Northwest

West has narrowed to four times the base case, while strong softwood growth helps to

push inventory up by 40 percent. The biggest cap on inventory under the 1989 assumptions can be noted in the South Central region, where softwood removals begin 137

percent above the base case and gradually drop to 85 percent above the base; 

initially the inventory increases, but after 2010 the removals are high enough to push

inventory down, and by 2050 it is within 2 percent of the starting volume. 

The inventory outcome of the base (without disturbance) and the Burch projections

were very close, but their removals levels differ. The Burch removals remain constant

after the first 10 years, averaging 816 mcf per year; the base case removals tend to

increase over the projection, starting at 417 mcf per year and ending at 839 (averaging 700 mcf per year).13 In some regions there is a “balance” between these two projections, such as in the Pacific Northwest where the base case removals begin below

and then surpass the Burch estimates, producing nearly identical ending inventories.

In the Rocky Mountains and the South, however, the Burch removals exceed the

base case values in all periods, leading to lower ending inventories. In all, the Burch

softwood estimates are 7 percent greater than those in the base case, and hardwoods

are a notable 72 percent higher. Relative to timberland inventory, these differences

are small, as the Burch softwood projections end less than one-half of one percent

below the base case and the hardwoods are less by just 6 percent. 

Adding disturbance and regeneration failures to the projection reduces inventories 

in all regions, and those most affected had the highest regeneration needs. Figure 11

shows how disturbance further contributes to different outcomes by region. The Rocky

Mountain North region dominates all others in terms of both area affected and volume

lost to disturbances. The Pacific Southwest and Pacific Northwest West are next in

terms of significant amounts of volume lost; however, the area affected is relatively 

13 Values are U.S. totals excluding Alaska. 

22
23

Figure 11—Projection showing area affected by disturbance and subsequent timber volume lost by region.

PNWW is Pacific Northwest West, PNWE is Pacific Northwest East, PSW is Pacific Southwest, RMN is Rocky

Mountain North, RMS is Rocky Mountain South, NC is North Central, NE is Northeast, SC is South Central,

and SE is Southeast. 

smaller, which reflects the higher average volume per acre in these regions. By 2050,

the disturbances in Rocky Mountain North and Pacific Southwest reduced inventories

below the base by 15 and 11 percent, respectively. In these regions, the projected

(base with disturbance) softwood inventory closely matches the scenario that used

the highest (1989 assessment) removal assumptions, whereas the Pacific Northwest

inventory drops to match the scenario that used the 1993 assessment removals. In

the remaining regions, the disturbance impacts were smaller, and the results fall slightly

below the base. In all cases, however, the impacts were not enough to change the

overall trend of expanding inventory volume. 

As stands are projected forward in time, figures 12 through 14 show the inventory age

class structure generally shifting to the right. It also can be seen that the relationship

among age classes does not drastically change over time; the curves maintain an

outline of their initial (1997) shape, shrinking in proportion to the level of disturbance

and removals. Figure 12 illustrates the upper and lower bounds of the projections. All

age class distributions fall between the zero cut and 1989 RPA scenarios, and like the

zero cut scenario, several cross the 1989 RPA scenario with roughly 3.7 million acres

near the 95-year-old age class (when the East and West are combined). The zero cut

projection shifts the entire 1997 initial distribution 50 years to the right; accumulating

area in the 255+ age class and producing the highest amount of old stand conditions.
24

Figure 13—Western timberland area by age, showing the initial (1997) distribution and two projection 

scenarios that include disturbance.

Figure 12—Timberland area by age, showing the initial (1997) distribution and projections for the lowest and

highest removals.
25

Figure 14—Eastern timberland area by age, showing the initial (1997) distribution and two projection scenarios that include disturbance.

Because we assume no harvest and no disturbance, however, there is no area to fill

in the young age classes, leaving what should be considered an unrealistic gap in the

young seral stages. The 1989 RPA scenario also shifts the initial distribution into older

age classes, but removals flatten the distribution more than any other scenario. This

relatively high level of cutting is followed by regeneration, resulting in a future with the

largest area supporting younger stands. 

Initially, 15 percent of the timberland is classified with stands older than 150 years.

Under the base case projection, the area supporting stands older than 150 expands

by 18 million acres reaching 35 percent of the timberland by 2050. Because the

effects of fire, insects, and disease target older stands, adding disturbance reduces

area older than 150 by about 3 million acres, dropping the share to about 32 percent.

In the West, where 77 percent of NFS timberland resides, it can be seen from figure

13 how initially the bulk of the timberland, 54 percent, lies in the 80-year span between

age 60 and age 140. The seral stage defined as mature sawtimber lies in the middle

of this range. The base with disturbance projection pushes this bulge of acres into

older age classes, but at slightly lower levels as a result of removals and disturbance.

The largest projected reduction in older age classes is under the 1989 assessment

scenario; the relatively high level of western removals cuts largest number of acres

supporting older stands. Through regeneration and growth, this area is redistributed,

showing up as stands of trees in younger age classes. 

Eastern forests were initially (1997) younger, and they experienced more harvest in

ages less than 100 than did western forests. Figure 14 shows that without harvest, the

relatively low level of disturbance results in a significantly scant distribution of eastern
26

Figure 15—Timberland area distribution by product seral stage, showing initial (1997) conditions and

the base case with disturbance scenario in 2050.

forests under age of 50, whereas adding harvest significantly increases the representation of younger stands. The amount of area harvested and then regenerated is influenced by the initial distribution of inventory volume by age and the level of removals,

which together determine the amount of area that requires harvesting in order to

supply the volume requested. 

Figures 15 through 17 show the distributions of timberland in seral stages for the base

case with disturbance, the disturbance-only case, and the 1989 RPA assessment scenarios. Common among them is a shift in area to oldest stage (table 3). This means

that regardless of major region, an increasing share of the timberland is projected to

be composed of stands represented by the old mature sawtimber seral stage. At the

same time, there is a decline in area in both the young and mature sawtimber classes.

In the West and North (excluding Alaska), for example, roughly 20 percent of the timberland is initially in the oldest forest class, and when all scenarios are examined, by

2050, this class represents between 35 and 52 percent of the timberland. The smallest

gains in the West and North’s old mature sawtimber class occur under the 1989 RPA

assessment scenario (fig. 17). The higher removals target older stands and slow the

growth of this class, while reductions occur in the young and mature sawtimber classes.

Part of this loss of area in younger classes is from harvesting, but the majority is from

acres aging and shifting to the older seral stage. It can be seen that the area harvested

and moved to the seedlings and saplings and poles classes is smaller than the area

reduction occurring in young and mature sawtimber classes (recall the 1989 RPA scenario does not include explicit disturbance). The pole timber stage remains constant

under this scenario, bolstered in part by reforestation occurring early in the projection.

The difference between this and the disturbance-only scenario in the young seral
27

Figure 16—Timberland area distribution by product seral stage, showing initial (1997) conditions and the

disturbance only scenario in 2050.

Figure 17—Timberland area distribution by product seral stage, showing initial (1997) conditions and the

1989 RPA scenario in 2050.
stages suggests most of the decline in the old sawtimber stage is due to forests

aging. Under the base case scenarios, the old mature sawtimber stage increases to

represent 47 percent without, and 43 percent with disturbance. The reduction with disturbance results from the model parameters directing fire, insects, and disease to

“attack” older stands. 

After 2010, the trend in projected net growth is down (fig. 18), and the zero cut and

disturbance-only scenarios show the steepest decline in the growth rate. This downward trend reflects an inventory with an advancing average age, as the fastest rates

of growth occur before a stand reaches maturity. On average, conifers in the Western

States reach their highest growth rates near age 25 in coastal states and age 55 in

the Rocky Mountains; this is followed by a sharp drop and then a slow almost stable

period of decline as stands age.14 Increasing the softwood removals increases overall

net growth by regenerating a steady supply of acres that advance through the young

fast-growing classes. Lower removals act to create fewer numbers of young replacement stands. Perhaps less intuitive is the growth projection under disturbance where

the number of young stands increases after regeneration of disturbed areas, yet

growth is significantly lower. This is, in effect, an accounting issue. When harvest

occurs, the volume removed from inventory is put in the category called “removals,” 

14 Source: 1997 RPA database.

28

Figure 18—Projected net softwood growth by removal scenario.
29

Summary and

Discussion 

and subtracted from the inventory. The volume lost in a disturbance, however, is classified as mortality, and in addition to being subtracted from inventory, the mortality volume is subtracted from gross growth to obtain net growth. Had salvage of trees killed

in the disturbance scenario been assumed, it might reduce removals by substituting

dead wood for growing-stock trees; furthermore, salvage of mortality would not affect

the calculation of growth (see footnote 4). 

The management of national forests over the past 40 years reflects a rather dramatic

shift in public values relating to uses and outputs of forests. In these scenarios, the

difference between the removals projected under the 1989 and 1993 timber assessments shows how quickly expectations can change with the discovery and communication of new information—in this case, the connection between wildlife and their

habitat requirements in the Pacific Northwest West region where a debate focused on

protecting the northern spotted owl. Looking back to recent history—the 1960s—this

region supported annual softwood removals from national forests at roughly 550 mcf,

while softwood growth was just 197 mcf (USDA Forest Service 1973). What appears

to be out of balance simply reflects the inventory structure at the time: a large quantity

of older, slower growing high-volume stands were being replaced with young stands

that had not yet reached peak performance. A study of timber resources in the Pacific

Northwest, conducted in the early 1960s (USDA Forest Service 1963), reflects the

thinking among forestry professionals at the time. This study examined the timber situation across all ownerships and attempted to address the issue of sustainable harvest

levels by including 40-year projections. In calculating what was called “even flow,” it

was determined that the optimal forest age class situation would be one where trees

were cut at a maximum age of 80 years on national forests and harvested at younger

ages on private forests. This would lead to a stable situation where acres would be

evenly distributed by age, less than age 80, across all timberlands. In this scenario,

the projected timber potential (or output in terms of removals) for the Pacific Northwest

West national forests in 2000 was roughly 655 mcf per year, which would have composed 30 percent of the region’s total harvest. By 2000 it was expected that national

forest inventories would be reduced by 30 percent from 1960, and it was recognized

that there would be “a shift in the regional harvest toward a larger percentage of younggrowth timber as the liquidation of old growth progresses.” By 1989, many policies15

affecting outputs of timber from public lands had changed, resulting in a drop in the

emphasis on removals. Projections for the year 2000 were for removals of 460 mcf.

By 1993 the projection fell to 114 mcf, and today it is estimated the national forest

removals in the Pacific Northwest West were roughly 39 mcf in 2000. Meanwhile,

in the 1960s, private timberlands in this region were projected to produce 1.2 billion

cubic feet per year in 2000, whereas the current assessment estimated 1.6 billion

cubic feet in 2000. Improvements in technology likely contribute to the ability to 

15 Policies regarding harvest from public land were shaped by 

changing public attitudes regarding the environment that were 

reflected by a series of laws passed by Congress, including

the 1964 Multiple Use and Sustainable Yield Act, the 1969 

National Environmental Management Act, the 1973 Endangered Species Act, the Renewable Resource Planning Act 

of 1974, the National Forest Management Act of 1976, and 

the Forest and Rangeland Renewable Resources Research 

Act of 1978.
support 33 percent more private removals; the intensification of stand management

leading to rotations at younger ages, and the improvements in processing technology

increase the utility of harvesting smaller logs. The management of public forests

reflects a changing set of values, and national forest timber harvested in 2000 was

roughly 2.2 percent of the region’s total harvest. 

The projections made for NFS timberlands today are consistent in both a modeling

and data perspective with projections made for private timberlands in the 2000 RPA

timber assessment. This is the first use of the new NFS Western plot-level inventory

data for regional projections. The results show a range of outcomes dependent on the

levels of removals and the amount of disturbance. In all cases, they show a future in

which removals and disturbance are exceeded by growth resulting in a significant

increase in the inventory volume on national forest timberland. The smallest increase

occurs under the assumptions incorporated into the 1989 assessment scenario, where

the total softwood inventory gains 43 percent and the hardwood volume increases by

56 percent (excluding Alaska). This scenario did not incorporate explicit disturbances;

however, if we apply the same reduction from disturbance that occurred in the base

scenario, the trend would still be upward, as the ending volume would be reduced by

roughly 10 percent. The highest inventory gains, 83 percent softwood and 94 percent

hardwood, occur when no harvesting and no major disturbances are modeled. This

“upper-bound” is unrealistic because, at the very least, forests evolved with major disturbances, and it is safe to assume disturbances will continue. The volumes projected

under the three remaining scenarios fall closer to the middle, including two versions

that explicitly project stand-replacing disturbance events. 

When compared to private lands, national forests have a greater proportion of stands

dispersed among the full range of age classes. This age class distribution implies that

national forests likely support a broader range of habitats for plant and animal species.

In 1997, about 39 percent of the national forest timberland had stands more than 100

years old, while this was true for just 5 percent of private timberland. The RPA projection (Haynes, in press) suggests this will increase to 15 percent on private lands, and

the base projection (with disturbance) shows an increase on national forests to 65 percent of timberland in age classes over 100 years. Although this might appear to be

disproportionate, by 2050 the combined projections show 110 million acres age 100

and greater, and about half (55 percent) are in national forests. Consequently, this

means a future with less area in the younger seral stages. Perhaps in addition to fiber

production, the role of private timberlands will be to fill this niche and provide the

ecosystem services associated with younger stands. Meanwhile, the public timberlands on national forests will continue to play multiple roles in meeting the needs of 

an increasingly diverse society. 

Thanks to Frank Burch of the Washington Office for his valuable input and comments,

including a compiled survey of regional silviculturists and statistics on disturbance and

regeneration needs on national forest lands. Also, thanks to researchers and editors

from the PNW Forestry Sciences Lab: Richard Haynes, manager of the Human and

Natural Resources Interactions Program, for his review and valuable input, and Judy

Mikowski and Gail Russell for editorial assistance. And thanks to those who reviewed

and made comments to improve the manuscript. 

30

Acknowledgments
When you know: Multiply by: To find:

Acres 0.4047 Hectares

Cubic feet 0.02832 Cubic meters

Cubic feet per acre 0.06997 Cubic meters per hectare

Adams, D.; Alig, R.; Anderson, J. [et al.]. 1992. Future prospects for western

Washington’s timber supply. Institute of Forest Resources Contribution 74. Seattle,

WA: University of Washington, College of Forest Resources. 201 p.

Adams, D.M.; Haynes, R.W. 1980. The softwood timber assessment market model:

structure, projections, and policy simulations. Forest Science. Monograph No. 22.

62 p.

Adams, D.M.; Haynes, R.W. 1996. The 1993 timber assessment market model:

structure, projections, and policy simulations. Gen. Tech. Rep. PNW-GTR-368.

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

Research Station. 58 p. 

Adlard, P.G. 1995. Myth and reality in growth estimation. Forest Ecology and

Management. 71(3): 171-176.

Bentley, J.T.; Horst, R.L., Jr. 1997. Forestry sector benefits of alternative ozone 

scenarios. Prepared for U.S. EPA under EPA contract 68-D3-0030. Princeton, NJ:

Mathtech, Inc. 14 p. [plus appendix].

Bentley, J.T.; Horst, R.L., Jr. 1998. Commercial forestry benefits of alternative emission controls for the NOX SIP call. Prepared for U.S. EPA under EPA contract 68-

D98-113. Princeton, NJ: Mathtech, Inc. 13 p. [plus appendix].

Burch, F. 1999. Two decades of national forest harvest assumptions: regional silviculturists survey results by region and forest type. Unpublished report. [No pagination]. On file with: John Mills, Portland Forestry Sciences Laboratory, Pacific

Northwest Research Station, P.O. Box 3890, Portland, OR 97208-3890. 

Burns, R.M.; Honkala, B.H., tech. eds. 1990. Silvics of North America. Agric. Handb.

654. Washington, DC: U.S. Department of Agriculture. 1552 p. 2 vols.

Davis, L.S.; Johnson, K.N. 1987. Forest management. New York: McGraw-Hill, Inc.

790 p.

Forest Rangeland Renewable Resources Planning Act of 1974 [RPA]; Act of 

August 17, 1974; 16 U.S.C. 1601 (note).

Franklin, J.F.; Dyrness, C.T. 1973. Natural vegetation of Oregon and Washington.

Gen. Tech. Rep. PNW-8. Portland, OR: U.S. Department of Agriculture, Forest

Service, Pacific Northwest Forest and Range Experiment Station. 417 p.

Gadzik, C.J.; Blanck, J.H.; Caldwell, L.E. 1998. Timber supply outlook for Maine:

1995—2045. Augusta, ME: Department of Conservation, Maine Forest Service. 

39 p.

Hansen, M.H.; Frieswyk, T.; Glover, J.F.; Kelly, J.F. 1992. The eastwide forest

inventory data base: users manual. Gen. Tech. Rep. NC-151. St. Paul, MN: U.S.

Department of Agriculture, Forest Service, North Central Forest Experiment

Station. 48 p.

31

Metric Equivalents

References
Haynes, R.W., tech. coord. 1990. An analysis of the timber situation in the United 

States: 1989-2040. Gen. Tech. Rep. RM-199. Fort Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station.

286 p. 

Haynes, R.W., tech. coord. [In press]. An analysis of the timber situation in the

United States: 1952-2050. Gen. Tech. Rep. PNW-GTR-560. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research Station. 

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

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

Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station.

66 p.

Joyce, L.A.; Mills, J.R.; Heath, L.S. [et al.]. 1995. Forest sector impacts from 

changes in forest productivity under climate change. Journal of Biogeography. 

22: 703-713. 

Mills, J.R. 1990. Developing ATLAS growth parameters from forest inventory plots. 

In: LaBau, V.; Cunia, T., eds. State-of-the-art methodology of forest inventory: a

symposium proceedings. Gen. Tech. Rep. PNW-GTR-263. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research Station:

112-118.

Mills, J.R.; Alig, R.J.; Haynes, R.W. 2000. Modeling climate change impacts on the

forest sector. In: Joyce, L.A.; Birdsey, R., tech. eds. The impact of climate change

on America’s forests: a technical document supporting the 2000 USDA Forest

Service RPA Assessment. Gen. Tech. Rep. RMRS-GTR-59. Fort Collins, CO:

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station:

69-78.

Mills, J.R.; Haynes, R.W. 1995. Influence of climate change on supply and demand

for timber. In: Joyce, L.A., ed. Productively of America’s forest and climate change.

Gen. Tech. Rep. RM-GTR-271. Fort Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Forest and Range Experiment Station: 46-55. 

Mills, J.R.; Kincaid, J.C. 1992. The aggregate timberland assessment system—

ATLAS: a comprehensive timber projection model. Gen. Tech. Rep. PNW-GTR-281.

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

Research Station. 160 p.

National Forest Management Act of 1976 [NFMA]; Act of October 22, 1976; 16 

U.S.C. 1600.

Oliver, C.D.; Larson, B.C. 1990. Forest stand dynamics. New York: McGraw-Hill, Inc.

467 p.

Powell, D.S.; Faulkner, J.L.; Darr, D.R. [et al.]. 1993. Forest resources of the United

States, 1992. Gen. Tech. Rep. RM-234. Fort Collins, CO: U.S. Department of

Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station.

132 p. [plus map] [revised, June 1994].

Smith, W.B.; Vissage, J.S.; Darr, D.R.; Sheffield, R. 2001. Forest resources of the

United States, 1997. Gen. Tech. Rep. GTR-NC-219. St. Paul, MN: U.S. Department

of Agriculture, Forest Service, North Central Research Station. 190 p.

32
U.S. Department of Agriculture, Forest Service. 1963. Timber trends in western

Oregon and western Washington. Res. Pap. PNW-5. Portland, OR: U.S. Department

of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment

Station. 154 p.

U.S. Department of Agriculture, Forest Service. 1973. The outlook for timber in the

United States. For. Res. Report 20. Washington, DC. 367 p.

U.S. Department of Agriculture, Forest Service. 1992. 1984-1990 forest fire statistics. Washington, DC: Fire and Aviation Management, State and Private Forestry.

[No pagination]. 

U.S. Department of Agriculture, Forest Service. 1996. The 1997 national RPA forest

data base: data dictionary and contributor’s guide. 65 p. http://fia.fs.fed.us/rpa.htm.

(25 September 2002).

Zwight, D. 2002. Personal communication. Assistant Director, Ecosystem Management

Coordination, National Forest System, U.S. Department of Agriculture, Sidney R.

Yates Federal Building, 201 14th Street SW at Independence Ave. SW,

Washington, DC 20250. 

33
Table 7—National forest assessment (RPA) regions 

RPA

region Region name States

0 Pacific Northwest West Western Oregon, western Washington

1 Pacific Northwest East Eastern Oregon, eastern Washington

2 Pacific Southwest California, Hawaii 

3 Rocky Mountain North Idaho, Montana

4 Rocky Mountain South Arizona, Colorado, Nevada, New Mexico,

western South Dakota, Utah,Wyoming

5 North Central—Plains States Illinois, Indiana, Iowa, Kansas, Missouri, 

Nebraska, Ohio

6 North Central—Lake States Michigan, Minnesota, Wisconsin, North 

Dakota, eastern South Dakota

7 Northeast Connecticut, Delaware, Maine, Maryland,

Massachusetts, New Hampshire,

New Jersey, New York, Pennsylvania, 

Rhode Island, Vermont, West Virginia

8 South Central Alabama, Arkansas, Kentucky, Louisiana,

Mississippi, Oklahoma, Tennessee, 

Texas

9 Southeast Florida, Georgia, North Carolina, South 

Carolina, Virginia

10 Alaska Alaska (not projected with ATLAS)

34

Appendix
Table 8—Forest types projected for national forests by RPA regions 

RPA

region Region name States

0 Pacific Northwest West Douglas-fir, western hemlock, fir and 

spruce, pine, red alder, hardwood mix, 

nonstocked

1 Pacific Northwest East Ponderosa pine, Douglas-fir and larch, 

true fir, lodgepole pine, hardwood, 

nonstocked

2 Pacific Southwest True fir, Douglas-fir, ponderosa pine, 

mixed conifer, pinyon/juniper, chaparral,

redwood, hardwood, nonstocked 

3 Rocky Mountain—North Douglas-fir, fir and spruce, lodgepole pine, 

ponderosa pine, hardwood, high 

elevation, nonstocked

4 Rocky Mountain—South Douglas-fir, fir and spruce, lodgepole pine, 

ponderosa pine, hardwood, pinyon/ 

juniper, high-elevation softwoods, 

nonstocked

5 North Central—Plains States Oak and hickory, pines, oak-pine, lowland hardwood, maple and beech, 

nonstocked

6 North Central—Lake States Jack pine, red pine, white pine, spruce 

and balsam fir, swamp conifer, oak and 

hickory, lowland hardwood, maple and 

beech, aspen and birch, nonstocked

7 Northeast White/red/jack pine, spruce and balsam 

fir, loblolly/shortleaf/oak/gum/cypress, 

oak-pine, oak and hickory, elm/ash/red 

maple, maple/beech/birch, aspen/birch,

nonstocked

8 South Central Planted pine, natural pine, oak-pine, 

upland hardwood, lowland hardwood, 

nonstocked

9 Southeast Planted pine, natural pine, oak-pine, 

upland hardwood, lowland hardwood,

nonstocked

10 Alaska Western hemlock, hemlock-spruce, 

spruce (not modeled)

35
Table 9—Yield tables for the Pacific Northwest West region by forest type

Age Western Fir and Hardwood

class Douglas-fir hemlock spruce Pine Red alder mix

Cubic feet per acre

5 0 0 0 0 0 0 

15 535 482 117 405 708 5 

25 2,985 2,631 890 1,873 3,282 486 

35 5,600 5,013 2,080 3,099 5,442 1,632 

45 7,927 7,385 3,412 4,173 7,342 3,089 

55 9,981 9,699 4,772 5,135 9,050 4,704 

65 11,804 11,935 6,103 6,008 10,605 6,393 

75 13,432 14,086 7,371 6,807 12,034 8,103 

85 14,893 16,146 8,560 7,542 13,355 9,802 

95 16,213 18,113 9,661 8,223 14,582 11,467 

105 17,411 19,983 10,670 8,854 15,725 13,083 

115 18,503 21,757 11,588 9,441 16,792 14,640 

125 19,503 23,432 12,421 9,988 17,791 16,133 

135 20,422 25,008 13,174 10,499 18,727 17,558 

145 21,272 26,484 13,855 10,975 19,605 18,913 

155 22,060 27,860 14,474 11,420 20,429 20,200 

165 22,794 29,135 15,042 11,836 21,203 20,200 

175 23,482 30,310 15,568 12,224 21,930 20,200 

185 24,128 31,384 16,066 12,587 22,613 20,200 

195 24,740 32,356 16,548 12,925 23,254 20,200 

205 25,320 33,227 17,027 13,240 23,855 20,200 

215 25,875 33,997 17,517 13,532 24,419 20,200 

225 26,408 34,664 18,032 13,804 24,947 20,200 

235 26,922 35,230 18,585 14,056 25,440 20,200 

245 27,421 35,694 19,191 14,289 25,901 20,200 

255 27,909 36,055 19,865 14,503 26,331 20,200 

36
Table 10—Yield tables for the Pacific Northwest East region by forest type

Age Ponderosa Douglas-fir Lodgepole

class pine and larch True fir pine Hardwood

Cubic feet per acre

5 0 0 0 0 0 

15 59 54 0 95 32 

25 309 682 217 583 261 

35 583 1,701 745 1,168 585 

45 878 2,639 1,392 1,722 891 

55 1,190 3,456 2,063 2,234 1,166 

65 1,515 4,157 2,711 2,706 1,413 

75 1,848 4,755 3,308 3,142 1,631 

85 2,187 5,264 3,840 3,544 1,825 

95 2,527 5,697 4,302 3,916 1,996 

105 2,864 6,065 4,691 4,261 2,145 

115 3,195 6,379 5,010 4,580 2,275 

125 3,515 6,648 5,264 4,876 2,388 

135 3,822 6,881 5,459 5,150 2,483 

145 4,110 7,085 5,602 5,403 2,563 

155 4,376 7,269 5,704 5,637 2,628 

165 4,616 7,438 5,773 5,854 2,680 

175 4,826 7,599 5,822 6,053 2,718 

185 5,003 7,758 5,860 6,237 2,744 

195 5,142 7,922 5,901 6,405 2,758 

205 5,239 8,095 5,956 6,559 2,763 

215 5,292 8,283 6,037 6,699 2,763 

225 5,306 8,491 6,159 6,825 2,763 

235 5,306 8,725 6,333 6,939 2,763 

245 5,306 8,988 6,574 7,041 2,763 

255 5,306 9,285 6,895 7,132 2,763 

37
Table 11—Yield tables for the Pacific Southwest region by forest type

Douglas-fir

Age Pinyon and Ponderosa Mixed

class juniper True fir Hardwood redwood pine conifer

Cubic feet per acre

5 0 0 0 0 0 0 

15 1 28 0 58 5 29 

25 11 196 15 397 66 159 

35 26 463 370 832 205 349 

45 47 840 1,090 1,250 415 636 

55 73 1,343 1,826 1,660 689 1,028 

65 103 1,954 2,493 2,076 1,024 1,523 

75 137 2,652 3,072 2,510 1,411 2,114 

85 173 3,419 3,564 2,970 1,845 2,793 

95 211 4,239 3,979 3,461 2,321 3,548 

105 251 5,099 4,328 3,987 2,832 4,368 

115 291 5,989 4,620 4,552 3,372 5,240 

125 332 6,899 4,868 5,156 3,935 6,152 

135 372 7,822 5,082 5,801 4,516 7,090 

145 410 8,750 5,269 6,487 5,107 8,041 

155 447 9,678 5,440 7,214 5,703 8,990 

165 481 10,600 5,600 7,980 6,299 9,924 

175 511 11,511 5,758 8,785 6,887 10,828 

185 537 12,408 5,919 9,627 7,463 11,687 

195 558 13,286 6,089 10,505 8,020 12,488 

205 574 14,143 6,273 11,417 8,551 13,216 

215 584 14,974 6,478 12,360 9,052 13,856 

225 587 15,778 6,706 13,334 9,515 14,394 

235 587 16,551 6,961 14,335 9,936 14,814 

245 587 17,291 7,249 15,361 10,307 15,102 

255 587 17,997 7,572 16,410 10,624 15,242 

38
Table 12—Yield tables for the Rocky Mountain North region by forest type

Age Ponderosa Lodgepole High-elevation

class Douglas-fir pine Fir-spruce pine Hardwood softwoods

Cubic feet per acre

5 0 0 0 0 0 0 

15 16 19 0 3 20 27 

25 281 266 260 227 132 153 

35 816 740 880 737 309 312 

45 1,442 1,278 1,626 1,348 589 499 

55 2,085 1,816 2,390 1,983 960 713 

65 2,705 2,318 3,118 2,603 1,334 951 

75 3,283 2,769 3,782 3,189 1,660 1,210 

85 3,806 3,159 4,365 3,730 1,915 1,486 

95 4,268 3,483 4,862 4,220 2,093 1,778 

105 4,667 3,742 5,271 4,658 2,201 2,082 

115 5,003 3,938 5,596 5,048 2,253 2,395 

125 5,280 4,075 5,842 5,392 2,272 2,715 

135 5,500 4,158 6,018 5,697 2,276 3,039 

145 5,669 4,193 6,133 5,969 2,276 3,363 

155 5,794 4,199 6,199 6,215 2,276 3,686 

165 5,879 4,199 6,227 6,444 2,276 4,004 

175 5,934 4,199 6,233 6,665 2,276 4,315 

185 5,964 4,199 6,233 6,888 2,276 4,615 

195 5,979 4,199 6,233 7,122 2,276 4,902 

205 5,987 4,199 6,233 7,376 2,276 5,172 

215 5,996 4,199 6,233 7,663 2,276 5,424 

225 6,015 4,199 6,233 7,992 2,276 5,654 

235 6,053 4,199 6,233 8,374 2,276 5,860 

245 6,119 4,199 6,233 8,820 2,276 6,038 

255 6,223 4,199 6,233 9,342 2,276 6,185 

39
Table 13—Yield tables for the Rocky Mountain South region by forest type

Age Ponderosa Lodgepole Pinyon High-elevation

class Douglas-fir pine Fir-spruce pine Hardwood juniper softwoods

Cubic feet per acre

5 0 0 0 0 0 0 0 

15 23 0 0 0 0 3 9 

25 219 63 122 71 90 26 61 

35 559 231 396 262 324 62 138 

45 946 460 708 529 643 108 230 

55 1,342 719 1,028 836 1,010 159 333 

65 1,726 990 1,345 1,160 1,402 213 444 

75 2,088 1,263 1,654 1,488 1,808 270 561 

85 2,422 1,532 1,951 1,810 2,215 328 684 

95 2,726 1,793 2,235 2,120 2,617 386 810 

105 2,999 2,043 2,504 2,410 3,008 444 940 

115 3,244 2,280 2,758 2,677 3,384 500 1,073 

125 3,463 2,503 2,995 2,917 3,741 554 1,208 

135 3,658 2,713 3,216 3,127 4,075 606 1,346 

145 3,833 2,909 3,420 3,305 4,385 654 1,485 

155 3,992 3,093 3,607 3,448 4,667 699 1,626 

165 4,140 3,264 3,776 3,553 4,920 739 1,768 

175 4,282 3,424 3,928 3,621 5,142 774 1,911 

185 4,422 3,575 4,062 3,648 5,332 804 2,055 

195 4,567 3,717 4,178 3,652 5,489 829 2,200 

205 4,721 3,853 4,276 3,652 5,610 846 2,345 

215 4,890 3,983 4,356 3,652 5,695 859 2,491 

225 5,080 4,110 4,417 3,652 5,744 861 2,637 

235 5,296 4,236 4,460 3,652 5,759 861 2,783 

245 5,545 4,362 4,485 3,652 5,759 861 2,929 

255 5,833 4,490 4,493 3,652 5,759 861 3,076 

40
Table 14—Yield tables for the North Central Plains States region by 

forest type

Age Oak and Lowland Maple and

class Pine Oak-pine hickory hardwood beech

Cubic feet per acre

5 0 0 0 0 0 

15 116 65 30 0 13 

25 575 406 186 83 117 

35 1,000 828 391 312 306 

45 1,366 1,239 615 644 546 

55 1,682 1,618 844 1,043 821 

65 1,956 1,959 1,070 1,488 1,123 

75 2,192 2,259 1,289 1,964 1,443 

85 2,397 2,515 1,497 2,456 1,778 

95 2,574 2,727 1,691 2,956 2,123 

105 2,724 2,893 1,869 3,454 2,474 

115 2,851 3,014 2,030 3,941 2,821 

125 2,958 3,088 2,171 4,410 3,151 

135 3,044 3,115 2,292 4,855 3,466 

145 3,112 3,117 2,392 5,236 3,766 

155 3,163 3,117 2,471 5,516 4,041 

165 3,199 3,117 2,526 5,696 4,281 

175 3,219 3,117 2,559 5,761 4,466 

185 3,226 3,117 2,569 5,761 4,566 

195 3,226 3,117 2,569 5,761 4,591 

205 3,226 3,117 2,569 5,761 4,591 

215 3,226 3,117 2,569 5,761 4,591 

225 3,226 3,117 2,569 5,761 4,591 

235 3,226 3,117 2,569 5,761 4,591 

245 3,226 3,117 2,569 5,761 4,591 

255 3,226 3,117 2,569 5,761 4,591 

41
Table 15—Yield tables for the North Central Lake States region by forest type

Age Jack Spruce and Swamp Oak and Lowland Maple and Aspenclass pine Red pine White pine balsam fir conifer hickory hardwood beech and birch

Cubic feet per acre

5 0 0 0 0 0 0 0 0 0 15 25 94 182 43 25 58 35 62 42 25 179 688 871 332 156 313 189 351 307 35 401 1,496 1,488 730 315 608 360 688 674 45 641 2,271 2,049 1,103 468 928 535 1,036 1,041 55 890 2,988 2,567 1,439 611 1,267 712 1,385 1,388 65 1,144 3,646 3,057 1,738 745 1,620 890 1,733 1,708 75 1,400 4,250 3,532 2,003 870 1,986 1,070 2,076 1,996 85 1,657 4,804 4,007 2,237 987 2,361 1,250 2,414 2,251 95 1,908 5,312 4,496 2,442 1,097 2,745 1,431 2,743 2,472 105 2,142 5,777 4,976 2,622 1,200 3,133 1,613 3,064 2,658 115 2,325 6,203 5,421 2,778 1,297 3,521 1,798 3,373 2,807 125 2,465 6,593 5,836 2,912 1,389 3,895 1,990 3,671 2,920 135 2,596 6,950 6,221 3,025 1,475 4,217 2,188 3,956 2,996 145 2,716 7,274 6,576 3,119 1,556 4,389 2,385 4,227 3,035 155 2,827 7,569 6,901 3,195 1,633 4,420 2,583 4,482 3,045 165 2,928 7,837 7,196 3,254 1,705 4,420 2,770 4,720 3,045 175 3,018 8,078 7,461 3,297 1,773 4,420 2,938 4,940 3,045 185 3,099 8,294 7,696 3,325 1,837 4,420 3,085 5141 3,045 195 3,169 8,486 7,901 3,339 1,897 4,420 3,213 5,322 3,045 205 3,230 8,655 8,076 3,342 1,954 4,420 3,320 5,482 3,045 215 3,281 8,803 8,221 3,342 2,008 4,420 3,408 5,619 3,045 225 3,321 8,931 8,336 3,342 2,058 4,420 3,475 5,732 3,045 235 3,352 9,039 8,421 3,342 2,105 4,420 3,523 5,820 3,045 245 3,372 9,127 8,476 3,342 2,150 4,420 3,550 5,882 3,045 255 3,380 9,198 8,496 3,342 2,191 4,420 3,559 5,917 3,045 

42
Table 16—Yield tables for the Northeast region by forest type

Loblolly/

shortleaf/ Maple/

Age Jack/red/ Spruce and oak/gum/ Oak and Elm/ash/ beech/ Aspen/class white pine balsam fir cypress Oak-pine hickory red maple birch birch

Cubic feet per acre

5 0 0 0 0 0 0 0 0 15 429 164 1,009 522 779 436 400 184 2 5 777 416 1,773 1,013 1,368 880 830 483 35 1,113 738 2,443 1,473 1,934 1,388 1,280 835 45 1,438 1,099 3,021 1,902 2,477 1,894 1,702 1,210 55 1,751 1,466 3,505 2,300 2,996 2,367 2,095 1,607 65 2,034 1,807 3,896 2,668 3,492 2,806 2,460 2,025 75 2,300 2,133 4,193 3,004 3,965 3,211 2,796 2,466 85 2,550 2,443 ,4398 3,309 4,415 3,582 3,103 2,929 95 2,783 2,738 4,509 3,584 4,842 3,820 3,382 3,414 105 3,001 3,017 4,528 3,828 5,246 4,224 3,632 3,921 115 3,202 3,281 4,528 4,040 5,626 4,494 3,854 4,450 125 3,389 3,532 4,528 4,222 5,983 4,731 4,047 5,001 135 3,561 3,768 4,528 4,373 6,317 4,34 4,211 5,574 145 3,718 3,990 4,528 4,493 6,628 5,103 4,347 6,170 155 3,861 4,197 4,528 4,581 6,916 5,238 4,455 6,787 165 4,030 4,436 4,528 4,639 7,180 5,340 4,534 7,427 175 4,189 4,666 4,528 4,667 7,421 5,408 4,584 8,089

43
Table 17—Yield tables for the South Central and Southeast regions by 

forest type 

Age Planted Natural Upland Lowland

class pine pine Oak-pine hardwood hardwood

Cubic feet per acre

5 0 0 0 0 0 

10 331 273 195 167 140 

15 1,167 525 397 303 284 

20 2,067 863 628 483 467 

25 2,782 1,222 848 666 649 

30 3,211 1,554 1,104 860 830 

35 3,360 1,875 1,384 1,091 1,049 

40 3,439 2,177 1,675 1,348 1,318 

45 3,517 2,462 1,950 1,630 1,582 

50 3,595 2,736 2,202 1,901 1,830 

55 3,675 2,978 2,450 2,164 2,091 

60 3,755 3,200 2,710 2,414 2,374 

65 3,835 3,407 2,923 2,652 2,664 

70 3,915 3,614 3,127 2,880 2,940 

75 3,995 3,782 3,352 3,082 3,180 

80 4,075 3,960 3,539 3,278 3,400 

85 4,155 4,138 3,707 3,465 3,677 

90 4,235 4,281 3,891 3,632 3,986 

44
Table 18—Softwood removals, inventory, and growth on national forests, 1991 and 1997 with projections to

2050 under the base case scenario without disturbance

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 5 5 5 4 5 4 5

Inventory 723 782 1,044 1,118 1,184 1,243 1,297

Net annual growth 17 14 12 11 11 10 10

North Central:

Removals 48 24.6 24.9 26.3 27.0 27.8 28.2

Inventory 3,216 3,646 4,560 5,046 5,451 5,766 6,058

Net annual growth 84 94.9 75.6 70.1 59.5 57.2 56.7

Southeast:

Removals 59 47.3 28.3 34.9 35.5 35.6 35.5

Inventory 2,826 2,991 4,116 4,627 4,879 5,030 5,147

Net annual growth 50 57.2 99.3 70.3 54.5 48.6 46.5

South Central:

Removals 169 139.4 77.2 93.0 93.9 93.4 93.0

Inventory 6,013 6,396 7,501 8,054 8,383 8,672 8,908

Net annual growth 174 192 155.0 132.4 124.5 120.6 113.6

Rocky Mountains:

Removals 389 130 141.9 172.2 183.3 193.3 199.5

Inventory 71,657 84,925 103,127 117,191 129,510 139,863 148,486

Net annual growth 1,285 1,272.9 1,597.1 1,465.0 1,273.1 1,101.4 954.8

Pacific Southwest:

Removals 314 95.6 107.5 132.0 138.7 145.2 148.1

Inventory 31,448 29,539 36,765 42,055 47,197 52,244 57,137

Net annual growth 463 616.2 642.0 648.2 648.3 638.5 628.2

Pacific Northwest West:

Removals 266 65.8 48.7 62.6 74.4 86.2 93.5

Inventory 33,621 51,399 62,799 71,103 78,727 85,640 92,011

Net annual growth 320 777.8 898.3 848.4 781.9 739.9 692.1

Pacific Northwest East:

Removals 330 71.6 68.3 86.8 103.3 119.8 130.4

Inventory 17,338 23,915 28,039 30,992 33,517 35,642 37,418

Net annual growth 269 319.8 376.8 351.6 327.0 306.0 288.1

Alaska:

Removals 99 50.5 29.3 29.3 29.3 29.3 29.3

Inventory 18,733 18,733 19,290 19,847 20,404 20,961 21,518

Net annual growth 85 85 85 85 85 85 85

United States:

Removals 1,679 629.3 530.6 641.4 690.0 735.0 762.2

Inventory 185,575 222,326 267,241 300,033 329,252 355,061 377,980

Net annual growth 2,747 3,430 3,941.3 3,682.1 3,364.4 3,107.3 2,874.5

45
Table 19—Hardwood removals, inventory, and growth on national forests, 1991 and 1997 with projections 

to 2050 under the base case scenario without disturbance

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 22 16.5 8.2 9.7 10.1 10.8 10.5

Inventory 3,711 3,696 4,237 4,743 5,203 5,629 6,015

Net annual growth 88 68.5 60.3 56.6 54.0 50.5 46.6

North Central:

Removals 61 64.1 44.0 44.9 45.7 46.6 46.7

Inventory 5,228 6,282 7,932 8,925 9,609 10,200 10,741

Net annual growth 123 139 164.5 136.6 112 104.2 100.3

Southeast:

Removals 13 51.1 8.5 10.3 10.1 9.9 9.8

Inventory 5,565 5,773 7,397 8,213 8,743 9,094 9,338

Net annual growth 114 104.6 110.1 77.7 52.9 38.9 31.0

South Central:

Removals 36 55.5 23.7 29.0 29.5 29.8 30.1

Inventory 4,959 5,249 7,073 8,213 9,152 9,867 10,304

Net annual growth 147 144.3 146.9 134.4 114.7 87.6 63.5

West:

Removals 54 11.1 6.3 7.4 8.3 8.4 9.0

Inventory 6,178 8,613 10,688 12,401 13,809 15,131 16,446

Net annual growth 71 213.6 189.0 152.0 140.3 140.9 138.3

Alaska:

Removals N/A 0.4 0.3 0.3 0.3 0.3 0.3

Inventory N/A 176 214 252 290 328 366

Net annual growth N/A 4.1 4.1 4.1 4.1 4.1 4.1

United States:

Removals 186 198.7 92.5 98.9 102 105 105.7

Inventory 25,641 29,789 37,541 42,747 46,806 50,249 53,210

Net annual growth 543 674.1 674.9 561.4 478.0 426.2 383.8

N/A = not available.

46
Table 20—Softwood removals, inventory, and growth on national forests, 1991 and 1997 with projections 

to 2050 under the Burch scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 5 5 2 2 2 2 2 

Inventory 723 782 1,076 1,174 1,260 1,339 1,411

Net annual growth 17 14 12 11 10 9 9 

North Central:

Removals 48 25 24 25 25 25 25 

Inventory 3,216 3,646 4,285 4,755 5,184 5,539 5,882 

Net annual growth 84 9,439 72 70 61 59 58 

Southeast:

Removals 59 47 45 46 46 46 46 

Inventory 2,826 2,991 3,875 4,285 4,496 4,627 4,733 

Net annual growth 50 57 101 75 62 58 56 

South Central:

Removals 169 139 108 107 107 107 107 

inventory 6,013 6,396 7,097 7,507 7,794 8,069 8,312 

Net annual growth 174 192 161 140 135 134 129 

Rocky Mountains:

Removals 389 130 298 299 299 299 299 

Inventory 71,657 84,925 100,725 113,177 124,214 133,555 

Net annual growth 1,285 1,273 1,582 1,455 1,280 1,123 992 

Pacific Southwest:

Removals 314 96 48 48 47 48 47

Inventory 31,448 29,539 37,279 43,289 49,382 55,484 61,494 

Net annual growth 463 616 646 655 660 652 641 

Pacific Northwest West:

Removals 266 66 55 55 55 55 55 

Inventory 33,621 51,399 62,611 70,885 78,608 85,736 92,416 

Net annual growth 320 778 940 873 792 753 709

Pacific Northwest East:

Removals 330 72 66 66 66 66 66 

Inventory 17,338 23,915 27,924 30,958 33,747 36,299 38,631 

Net annual growth 269 320 376 352 328 305 284 

Alaska:

Removals 99 51 29 29 29 29 29 

Inventory 18,733 18,733 19,290 19,847 20,404 20,961 21,518 

Net annual growth 85 85 85 85 85 85 85 

United States:

Removals 1,679 629 675 675 676 676 676 

Inventory 185,575 222,326 264,162 295,877 325,089 351,609 375,804 

Net annual growth 2,747 12,774 3,975 3,716 3,412 3,178 2,964 

47
Table 21—Hardwood removals, inventory, and growth on national forests, 1991 and 1997 with projections 

to 2050 under the Burch scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 22 17 2 2 2 2 2

Inventory 3,711 3,696 4,282 4,819 5,318 5,785 6,209

Net annual growth 88 69 60 56 53 49 45

North Central:

Removals 61 64 62 62 62 61 61 

Inventory 5,228 6,282 7,671 8,465 8,971 9,415 9,826 

Net annual growth 123 136 152 114 107 103 102

Southeast:

Removals 13 51 21 21 20 20 20 

Inventory 5,565 5,773 7,237 7,964 8,436 8,748 8,969 

Net annual growth 114 105 111 81 58 46 39 

South Central:

Removals 36 56 59 60 60 60 61 

Inventory 4,959 5,249 6,568 7,413 8,135 8,711 9,094 

Net annual growth 147 144 149 140 127 108 92 

West:

Removals 54 11 24 24 24 23 24 

Inventory 6,178 8,613 10,467 11,985 13,202 14,345 15,503

Net annual growth 71 214 187 149 137 140 138 

Alaska:

Removals N/A 0 0 0 0 0 0 

Inventory N/A 176 214 252 290 328 366 

Net annual growth N/A 4.1 4 4 4 4 4 

United States:

Removals 186 199 168 168 168 167 168 

Inventory 25,641 29,789 36,439 40,898 44,352 47,332 49,967 

Net annual growth 543 671 663 544 486 449 421

N/A = not available.

48
Table 22—Softwood removals, inventory, and growth on national forests, 1991 and 1997 with projections 

to 2050 under the 1989 RPA scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 5 5 8 8 9 9 8 

Inventory 723 782 1,012 1,054 1,086 1,112 1,138 

Net annual growth 17 14 12 12 11 11 10 

North Central:

Removals 48 25 41 44 48 48 48 

Inventory 3,216 3,646 4,331 4,624 4,829 4,950 5,080 

Net annual growth 84 95 73 69 60 60 62 

Southeast:

Removals 59 47 49 51 53 53 53 

Inventory 2,826 2,991 3,830 4,209 4,382 4,474 4,548 

Net annual growth 50 57 102 76 64 61 60 

South Central:

Removals 169 139 161 166 172 172 172 

Inventory 6,013 6,396 6,545 6,542 6,457 6,413 6,396 

Net annual growth 174 192 171 159 164 170 170 

Rocky Mountains:

Removals 389 130 571 595 618 618 618 

Inventory 71,657 84,925 97,221 106,681 114,617 120,963 126,141 

Net annual growth 1,285 1,273 1,560 1,440 1,290 1,164 1,069 

Pacific Southwest:

Removals 314 96 257 261 264 265 264 

Inventory 31,448 29,539 34,478 38,091 41,627 45,086 48,461 

Net annual growth 463 616 621 617 613 604 598 

Pacific Northwest West:

Removals 266 66 455 452 449 449 449

Inventory 33,621 51,399 57,594 62,019 66,106 69,856 73,413

Net annual growth 320 778 907 873 830 811 792

Pacific Northwest East:

Removals 330 72 295 294 293 293 293

Inventory 17,338 23,915 24,831 25,450 25,962 26,445 26,939 

Net annual growth 269 320 361 346 341 341 344

Alaska:

Removals 99 51 29 29 29 29 29

Inventory 18,733 18,733 19,290 19,847 20,404 20,961 21,518

Net annual growth 85 85 85 85 85 85 85

United States:

Removals 1,679 629 1,866 1,900 1,934 1,936 1,935

Inventory 185,575 222,326 249,132 268,517 285,470 300,260 313,634

Net annual growth 2,747 3,430 3,890 3,676 3,458 3,308 3,191

49
Table 23—Hardwood removals, inventory, and growth on national forests, 1991 and 1997 with projections 

to 2050 under the 1989 RPA scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 22 17 15 16 13 13 13 

Inventory 3,711 3,696 4,152 4,600 5,021 5,435 5,812

Net annual growth 88 69 61 58 56 52 49

North Central:

Removals 61 64 55 58 60 60 60

Inventory 5,228 6,282 7,733 8,590 9,133 9,590 10,015

Net annual growth 123 139 153 115 107 103 102

Southeast:

Removals 13 51 30 32 34 33 33

Inventory 5,565 5,773 7,144 7,793 8,187 8,425 8,589

Net annual growth 114 105 112 83 63 52 48

South Central:

Removals 36 56 96 104 112 111 112

Inventory 4,959 5,249 6,258 6,768 7,127 7,360 7,489

Net annual growth 147 144 152 146 140 129 121

West:

Removals 54 11 48 49 51 50 51

Inventory 6,178 8,613 10,144 11,433 12,428 13,368 14,351

Net annual growth 71 214 188 152 143 150 146

Alaska:

Removals N/A 0.4 0.3 0.3 0.3 0.3 0.3

Inventory N/A 176 214 252 290 328 366

Net annual growth N/A 4.1 4 4 4 4 4

United States:

Removals 186 199 244 258 270 268 269

Inventory 25,641 29,789 35,645 39,436 42,186 44,506 46,622

Net annual growth 543 674 670 557 512 489 469 

N/A = not available.

50
Table 24—Softwood removals, inventory, and growth on national forests, 1991 and 1997 with projections 

to 2050 under the 1993 RPA scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 5 5 6 5 6 5 6 

Inventory 723 782 1,033 1,100 1,161 1,215 1,267 

Net annual growth 17 14 13 12 11 11 10 

North Central:

Removals 48 25 36 39 42 42 42 

Inventory 3,216 3,646 4,377 4,730 5,004 5,187 5,370 

Net annual growth 84 95 74 70 60 60 61 

Southeast:

Removals 59 47 48 50 51 47 52 

Inventory 2,826 2,991 3,812 4,193 4,373 4,487 4,574 

Net annual growth 50 57 102 76 65 61 60 

South Central:

Removals 169 139 128 131 134 124 136 

Inventory 6,013 6,396 6,844 7,099 7,237 7,404 7,547 

Net annual growth 174 192 165 149 148 148 147 

Rocky Mountains:

Removals 389 130 315 333 353 353 353 

Inventory 71,657 84,925 100,619 112,851 123,489 132,308 139,662

Net annual growth 1,285 1,273 1,582 1,455 1,281 1,126 999 

Pacific Southwest:

Removals 314 96 93 91 91 91 91 

Inventory 31,448 29,539 36,683 42,188 47,762 53,330 58,811 

Net annual growth 463 616 641 647 650 642 633 

Pacific Northwest West:

Removals 266 66 99 99 98 101 98 

Inventory 33,621 51,399 61,985 69,914 77,364 84,264 90,726 

Net annual growth 320 778 905 861 801 760 714 

Pacific Northwest East:

Removals 330 72 142 141 140 140 140 

Inventory 17,338 23,915 26,898 29,127 31,166 33,037 34,764 

Net annual growth 269 320 371 350 332 317 304 

Alaska:

Removals 99 51 29 29 29 29 29 

Inventory 18,733 18,733 19,290 19,847 20,404 20,961 21,518 

Net annual growth 85 85 85 85 85 85 85 

United States:

Removals 1,679 629 895 917 944 932 947 

Inventory 185,575 222,326 261,541 291,049 317,960 342,193 364,239 

Net annual growth 2,747 3,430 3,936 3,704 3,433 3,209 3,012

51
Table 25—Hardwood removals, inventory, and growth on national forests, 1991 and 1997 with projections 

to 2050 under the 1993 RPA scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 22 17 23 25 25 26 25

Inventory 3,711 3,696 4,061 4,439 4,780 5,099 5,391

Net annual growth 88 69 62 59 58 56 52

North Central:

Removals 61 64 80 84 88 88 87

Inventory 5,228 6,282 7,395 7,962 8,213 8,396 8,577

Net annual growth 123 139 149 112 106 105 108

Southeast:

Removals 13 51 14 15 15 20 14

Inventory 5,565 5,773 7,302 8,080 8,590 8,912 9,138

Net annual growth 114 105 126 92 67 52 45

South Central:

Removals 36 56 39 41 42 55 43

Inventory 4,959 5,249 6,871 7,908 8,772 9,397 9,777 

Net annual growth 147 144 149 138 120 97 74 

West:

Removals 54 11 41 42 43 40 43

Inventory 6,178 8,613 10,226 11,614 12,715 13,755 14,862

Net annual growth 71 214 190 156 144 152 151 

Alaska:

Removals N/A 0.4 0.3 0.3 0.3 0.3 0.3

Inventory N/A 176 214 252 290 328 366

Net annual growth N/A 4 4 4 4 4 4 

United States:

Removals 186 199 197 207 213 228 213 

Inventory 25,641 29,789 36,069 40,255 43,360 45,887 48,111 

Net annual growth 543 674 680 562 499 466 433 

N/A = not available.

52
Table 26—Softwood removals, inventory, and growth on national forests, 1991 and 1997 with projections to

2050 under the zero cut scenario without disturbance

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 5 5 — — — — — 

Inventory 723 782 1,098 1,215 1,318 1,411 1,495 

Net annual growth 17 14 12 11 10 9 8 

North Central:

Removals 48 25 — — — — — 

Inventory 3,216 3,646 4,898 5,671 6,347 6,914 7,427 

Net annual growth 84 95 80 72 59 52 49 

Southeast:

Removals 59 47 — — — — — 

Inventory 2,826 2,991 4,405 5,183 5,667 5,987 6,224 

Net annual growth 50 57 97 62 39 27 21 

South Central:

Removals 169 139 — — — — — 

Inventory 6,013 6,396 8,251 9,468 10,402 11,142 11,655 

Net annual growth 174 192 141 106 85 65 40 

Rocky Mountains:

Removals 389 130 — — — — — 

Inventory 71,657 84,925 104,735 120,410 134,478 146,541 157,005 

Net annual growth 1,285 1,273 1,608 1,474 1,251 1,103 916 

Pacific Southwest:

Removals 314 96 — — — — —

Inventory 31,448 29,539 37,920 44,472 51,134 57,818 64,408 

Net annual growth 463 616 651 664 671 663 651 

Pacific Northwest West:

Removals 266 66 — — — — — 

Inventory 33,621 51,399 63,307 72,122 80,361 87,973 95,097 

Net annual growth 320 778 897 845 775 729 675 

Pacific Northwest East:

Removals 330 72 — — — — — 

Inventory 17,338 23,915 28,819 32,547 35,996 39,150 42,018 

Net annual growth 269 320 381 354 324 295 268 

Alaska:

Removals 99 51 29 29 29 29 29 

Inventory 18,733 18,733 19,290 19,847 20,404 20,961 21,518 

Net annual growth 85 85 85 85 85 85 85 

United States:

Removals 1,679 629 29 29 29 29 29 

Inventory 185,575 222,326 272,723 310,935 346,107 377,897 406,847 

Net annual growth 2,747 3,430 3,952 3,672 3,298 3,026 2,712

53
Table 27—Hardwood removals, inventory, and growth on national forests, 1991 and 1997 with projections 

to 2050 under the zero cut scenario without disturbance

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 22 17 — — — — — 

Inventory 3,711 3,696 4,331 4,914 5,456 5,961 6,420 

Net annual growth 88 69 60 55 52 47 43 

North Central:

Removals 61 64 — — — — — 

Inventory 5,228 6,282 8,520 10,025 11,200 12,243 13,193 

Net annual growth 123 139 163 122 107 98 89 

Southeast:

Removals 13 51 — — — — — 

Inventory 5,565 5,773 7,486 8,383 8,981 9,381 9,661 

Net annual growth 114 105 110 76 49 33 24 

South Central:

Removals 36 56 — — — — — 

Inventory 4,959 5,249 7,303 8,659 9,805 10,687 11,234 

Net annual growth 147 144 144 128 105 72 42 

West:

Removals 54 11 — — — — — 

Inventory 6,178 8,613 10,757 12,538 14,025 15,430 16,825 

Net annual growth 71 214 189 152 141 140 138 

Alaska:

Removals N/A 0.4 0.3 0.3 0.3 0.3 0.3

Inventory N/A 176 214 252 290 328 366 

Net annual growth N/A 4.1 4 4 4 4 4 

United States:

Removals 186 199 0.3 0.3 0.3 0.3 0.3

Inventory 25,641 29,789 38,611 44,771 49,757 54,030 57,699 

Net annual growth 543 674 669 537 457 395 341 

N/A = not available.

54
Table 28—Softwood removals, inventory, growth on national forests, 1991 and 1997 with projections to 

2050 under the zero cut with disturbance scenario 

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 5 5 — — — — — 

Inventory 723 782 1,097 1,214 1,316 1,409 1,492 

Net annual growth 17 14 12 11 10 9 8 

North Central:

Removals 48 25 — — — — — 

Inventory 3,216 3,646 4,896 5,663 6,336 6,900 7,411 

Net annual growth 84 95 79 71 58 52 48 

Southeast:

Removals 59 47 — — — — —

Inventory 2,826 2,991 4,356 5,094 5,544 5,837 6,055 

Net annual growth 50 57 93 58 36 25 20 

South Central:

Removals 169 139 — — — — — 

Inventory 6,013 6,396 8,152 9,303 10,175 10,863 11,337 

Net annual growth 174 192 134 100 79 60 37 

Rocky Mountains:

Removals 389 130 — — — — —

Inventory 71,657 84,925 101,843 114,684 125,927 135,302 143,081 

Net annual growth 1,285 1,273 1,328 1,183 988 819 681 

Pacific Southwest:

Removals 314 96 — — — — —

Inventory 1,448 29,539 36,360 41,463 46,415 51,174 55,676 

Net annual growth 463 616 515 500 484 458 432 

Pacific Northwest West:

Removals 266 66 — — — — — 

Inventory 33,621 51,399 62,417 70,448 77,897 84,726 91,082 

Net annual growth 320 778 819 766 697 650 602 

Pacific Northwest East:

Removals 330 72 — — — — — 

Inventory 17,338 23,915 28,371 31,669 34,668 37,364 39,783 

Net annual growth 269 320 339 309 278 250 224 

Alaska:

Removals 99 51 29 29 29 29 29 

Inventory 18,733 18,733 19,290 19,847 20,404 20,961 21,518

Net annual growth 85 85 85 85 85 85 85

United States:

Removals 1,679 629 29 29 29 29 29 

Inventory 185,575 222,326 266,782 299,385 328,682 354,536 377,435 

Net annual growth 2,747 3,430 3,403 3,083 2,715 2,407 2,138

55
Table 29—Hardwood removals, inventory, growth on national forests, 1991 and 1997 with projections to

2050 under the zero cut with disturbance scenario

Projections

Region 1991 1997 2010 2020 2030 2040 2050

Million cubic feet

Northeast:

Removals 22 17 — — — — — 

Inventory 3,711 3,696 4,325 4,902 5,439 5,939 6,394 

Net annual growth 88 69 59 55 52 47 43 

North Central:

Removals 61 64 — — — — — 

Inventory 5,228 6,282 8,508 9,993 11,152 12,180 13,116 

Net annual growth 123 139 161 120 106 96 88 

Southeast:

Removals 13 51 — — — — — 

Inventory 5,565 5,773 7,384 8,197 8,719 9,060 9,296 

Net annual growth 114 105 101 67 42 28 21 

South Central:

Removals 36 56 — — — — — 

Inventory 4,959 5,249 7,204 8,468 9,520 10,318 10,805 

Net annual growth 147 144 135 119 95 65 37 

West:

Removals 54 11 — — — — — 

Inventory 6,178 8,613 10,690 12,344 13,731 15,031 16,327

Net annual growth 71 214 175 143 130 131 126 

Alaska:

Removals N/A 0.4 0.3 0.3 0.3 0.3 0.3 

Inventory N/A 176 214 252 290 328 366 

Net annual growth N/A 4.1 4 4 4 4 4 

United States:

Removals 186.0 199 0.3 0.3 0.3 0.3 0.3 

Inventory 25,641.0 29,789 38,325 44,156 48,851 52,856 56,304 

Net annual growth 543.0 674 635 508 428 371 318 

N/A = not available.

56
Table 30—List of scientific names for common tree species of the United States

Common name Scientific name

Alpine fir (subalpine fir) Abies lasiocarpa (Hook.) Nutt.

Apache pine Pinus engelmannii Carr.

Arizona pine Pinus ponderosa var. arizonica (Engelm.) Shaw

Ash Fraxinus spp.

Aspen Populus spp.

Balsam fir Abies balsamea (L.) Mill.

Balsam poplar Populus balsamifera L.

Basswood Tilia spp.

Beech Fagus spp.

Birch Betula spp.

Blackgum (black tupelo) Nyssa sylvatica Marsh. var. sylvatica

Black walnut Juglans nigra L.

Chestnut oak Quercus muehlenbergii Engelm.

Chihuahua pine Pinus (associates with Apache pine)

Colorado blue spruce Picea pungens Engelm.

Cottonwood Populus spp.

Cypress Taxodium spp.

Douglas-fir Pseudotsuga menziesii (Mirb.) Franco

Eastern white pine Pinus strobus L.

Elm Ulmus spp.

Engelmann spruce Picea engelmannii Parry ex Engelm.

Grand fir Abies grandis (Dougl. ex D. Don) Lindl.

Gray birch (yellow birch) Betula alleghaniensis Britton

Gum Liquidambar spp.

Hackberry Celtis occidentalis L.

Hemlock Tsuga spp.

Hickory Carya spp.

Incense cedar Libocedrus decurrens Torr.

Jack pine Pinus banksiana Lamb.

Jeffrey pine Pinus jeffreyi Grev. & Balf.

Juniper Juniperus spp.

Larch Larix spp.

Limber pine Pinus flexilis James

Loblolly pine Pinus taeda L.

Lodgepole pine Pinus contorta Dougl. ex Loud.

Longleaf pine Pinus palustris Mill.

Maple Acer spp.

Mountain hemlock Tsuga mertensiana (Bong.) Carr.

Oak Quercus spp.

Paper birch Betula papyrifera Marsh.

Pinyon Pinus edulis Engelm.

Ponderosa pine Pinus ponderosa Dougl. ex Laws 

Red alder Alnus rubra Bong.

Red pine Pinus resinosa Ait.

Redwood Sequoia sempervirens (D. Don) Endl.

Scrub oak Quercus laevis Walt.

57
Table 30—List of scientific names for common tree species of the United States

(continued)

Common name Scientific name

Shortleaf pine Pinus echinata Mill.

Silver fir (Pacific silver fir) Abies amabilis Dougl. ex Forbes

Sitka spruce Picea sitchensis (Bong.) Carr.

Slash pine Pinus elliottii Engelm.

Sugar pine Pinus lambertiana Dougl.

Sweetgum Liquidambar styraciflua L.

Sycamore Platanus occidentalis L.

Tamarack Larix laricina (Du Roi) K. Koch

Tanoak Lithocarpus densiflorus (Hook. & Arn.) Rehd.

True firs Abies spp.

Tupelo Nyssa spp.

Western hemlock Tsuga heterophylla (Raf.) Sarg. 

Western redcedar Thuja plicata Donn ex D. Don

Western white pine Pinus monticola Dougl. ex D. Don

White cedar Chamaecyparis thyoides (L.) B.S.P.

White fir Abies concolor (Gord. & Glend.) Lindl. ex Hildebr.

White pine See western white pine

Willow Salix spp.

Yellow-poplar Liriodendron tulipifera L.

58
The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color,

national origin, sex, religion, age, disability, political beliefs, sexual

orientation, or marital or family status. (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).

To file a complaint of discrimination, write USDA, Director, Office of

Civil Rights, Room 326 W, Whitten Building, 1400 Independence

Avenue SW, Washington, DC 20250-9410 or call (202) 720-5964

(voice and TDD). USDA is an equal opportunity provider and

employer.
UNITED STATES DEPARTMENT OF AGRICULTURE

FOREST SERVICE

PACIFIC NORTHWEST RESEARCH STATION

GENERAL TECHNICAL REPORT

PNW-GTR-568

January 2003