Forecasts of County-Level Land Uses 

Under Three Future Scenarios

A Technical Document Supporting

the Forest Service 2010 RPA Assessment

David N. Wear
August 2011 

Southern Research Station

200 W. T. Weaver Blvd.

Asheville, NC 28804

The Author: 

David N. Wear, Research Forester and Project Leader, Forest 

Economics and Policy Work Unit, U.S. Forest Service, Southern 

Research Station, Forestry Sciences Laboratory, 3041 Cornwallis Rd., 

Research Triangle Park, NC 27713.

Product Disclaimer

The use of trade or firm names in this publication is for reader information and does not 

imply endorsement by the U.S. Department of Agriculture of any product or service.
Forecasts of County-Level 

Land Uses Under Three 

Future Scenarios: 

A Technical Document 

Supporting the Forest Service 

2010 RPA Assessment

David N. Wear

iii

CONTENTS 

Page

List of Tables ................................................................................................................................................................................. iv

List of Figures .................................................................................................................................................................................. v

Introduction .................................................................................................................................................................................... 1

Forecasting Approach ...................................................................................................................................................................... 1

2010 RPA Assessment Scenarios ................................................................................................................................................... 10

Forecasts of Land Use Changes ..................................................................................................................................................... 14

Key Findings .................................................................................................................................................................................. 31

Literature Cited .............................................................................................................................................................................. 31

Appendix A .................................................................................................................................................................................... 33

Appendix B .................................................................................................................................................................................... 37

Appendix C .................................................................................................................................................................................... 39
iv

LIST OF TABLES

Page

Table 1—Definitions of land use categories in the NRI ..................................................................................................................3

Table C1—Forecasts of land uses for Scenario A1B in the United States by region, 1997-2060 .................................................39

Table C2—Forecasts of land uses for Scenario A2 in the United States by region, 1997-2060 ....................................................40

Table C3—Forecasts of land uses for Scenario B2 in the United States by region, 1997-2060 ....................................................41
v

 LIST OF FIGURES 

Page

Figure 1—Definition of the RPA Assessment Regions.................................................................................................................... 2

Figure 2—Concentration of five land uses (proportion of each county) on nonfederal land, 1997: (A) pasture, 

(B) crops, (C) forest, (D) range, (E) urban. (Source: NRI) .............................................................................................................. 4

Figure 3—Distribution of land uses within Assessment Regions, 1997. (Source: NRI) ................................................................. 9

Figure B1—Schematic of the U.S. Forest Assessment System. .................................................................................................... 11

Figure 4—Forecasted U.S. population for three RPA Scenarios (A1B, A2, B2), 2006-2060. ...................................................... 12

Figure 5—Forecasted per capita personal income for three RPA Scenarios (A1B, A2, B2), 2006-2060. .................................... 12

Figure 6—Forecasted change in population density (people per square mile), 1997-2060, for the A1B Scenario. 

Areas in green are forecasted to experience population declines over this period. ....................................................................... 13

Figure 7—Forecasted change in urban land uses for the United States from a base year of 1997, 2010-2060, by Scenario 

(numbers are for A1B Scenario). ................................................................................................................................................... 14

Figure 8—Forecasted urban area by region, 1997-2060, for the A1B scenario. ........................................................................... 14

Figure 9—Forecasted change in proportion of county in the urban land use, A1B Scenario, 1997-2060. ................................... 16

Figure 10—Forecasted change in the proportion of county in urban land use, B2 Scenario, 1997-2060. .................................... 17

Figure 11—Forecasted change in the areas of major nonfederal land uses, A1B Scenario, 2010-2060, compared 

to 1997. .......................................................................................................................................................................................... 18

Figure 12—Forecasted forest area by region, 1997-2060, A1B Scenario. .................................................................................... 19

Figure 13—Forecasted change in nonfederal forest area, 1997-2060, by RPA Scenario (A1B, A2, B2). .................................... 19

Figure 14—Forecasted change in proportion of county that is in forest use, A1B Scenario, 1997-2060. .................................... 20

Figure 15—Forecasted change in proportion of county that is in forest use, 1997-2060, B2 Scenario. ....................................... 21

Figure 16—Forecasted change in the area of cropland by RPA Scenario (A1B, A2, B2), 1997-2060. ........................................ 22

Figure 17—Forecasted change in proportion of county that is in cropland use, A1B Scenario, 1997-2060................................. 24

Figure 18—Forecasted change in proportion of county that is in cropland use, B2 Scenario, 1997-2060. .................................. 25

Figure 19—Forecasted change in the area of rangeland by RPA Scenario (A1B, A2, B2), 1997-2060. ...................................... 27

Figure 20—Forecasted change in proportion of county that is in cropland use, 1997-2060, A1B Scenario................................. 28

Figure 21—Forecasted change in proportion of county that is in cropland use, 1997-2060, B2 Scenario. ...................................29

Figure 22—Rural land use complexity index for nonfederal lands, 1997. .....................................................................................30

FORECASTING APPROACH

We forecast land use distributions at the county level for all 

counties in the coterminous United States using econometric 

models fit to historical data (Wear 2010). Separate models 

were estimated for each of four assessment regions (South, 

North, Rockies, and Pacific) with two exceptions. Texas 

and Oklahoma were split between regions, with the forested 

eastern portions of each State included in the South’s 

model and the remainder in the Rockies’ model. For 

model estimation then, Texas and Oklahoma counties were 

included with regions with most similar conditions, but for 

all reporting we aggregate all of Texas and Oklahoma into 

the South, consistent with the Forest Service Assessment 

Regions shown in figure 1.

Our land use models have two major components. In the first 

component, changes in county-level population and personal 

income are used to simulate future urbanization. The second 

component allocates rural land among competing uses. The 

econometric models developed by Wear (2010) were fit to 

land use change data from 1987 and 1997 to ensure that 

forecasted land use changes are generally consistent with 

observed urbanization intensities and rural land use changes. 

For the forecasts developed here, we hold constant the real 

rents of both agricultural and forest land uses—in effect 

assuming that the relative returns to these uses remains 

constant through the forecast period. We also examine where 

substitution between rural land uses might be concentrated 

in the United States under futures that alter the relative 

returns to forestry and agriculture. Details regarding the 

modeling approach are contained in Appendices A and B.

Observations of historical land uses were derived from the 

National Resource Inventory (NRI) survey of land uses 

conducted for the years 1987 and 1997. The NRI provides 

the only consistent, repeated, and exhaustive measure of 

non-Federal land uses in the United States, and 1997 is the 

last year for which detailed data are currently available. We 

Abstract

Accurately forecasting future forest conditions and the implications for 

ecosystem services depends on understanding land use dynamics. In 

support of the 2010 Renewable Resources Planning Act (RPA) Assessment, 

we forecast changes in land uses for the coterminous United States in 

response to three scenarios. Our land use models forecast urbanization 

in response to the population and economic projections defined by the 

scenarios and consequences for various rural land uses. Urban area is 

forecasted to expand by 1 to 1.4 million acres per year between 1997 and 

2060. Forest area is forecasted to decline by 24 to 37 million acres and 

cropland is forecasted to decline by 19 to 28 million acres over this period. 

About 90 percent of forecasted forest land losses are found in the Eastern 

United States with more than half in the South.

Keywords: Assessments, forecasting, land use. 

INTRODUCTION

The Forest and Rangeland Renewable Resources Planning 

Act (RPA) of 1974 mandates a periodic assessment of the 

condition and trends of the Nation’s renewable resources. 

The 2010 RPA Assessment provides a snapshot of current 

U.S. forest and rangeland conditions and trends on all 

ownerships, identifies drivers of change, and projects 50 

years into the future. Analyses of the status and trends for 

recreation, water, timber, wildlife (biodiversity) and range 

resources as well as land-use change, climate change, and 

urban forestry are included (USDA Forest Service 2001). 

Because land use patterns define the template upon which 

natural systems develop and affects the flow of all ecosystem 

services, forecasts of land use changes are a key element 

of the RPA Assessment. Forecasts of forest and nonforest 

uses are important inputs into analysis of forest conditions, 

wildlife habitat, carbon storage, and water demands, 

among others. This paper presents the land use forecasts 

associated with the three scenarios that frame the 2010 RPA 

Assessment.

Forecasts of County-Level Land Uses 

Under Three Future Scenarios: A Technical 

Document Supporting the Forest Service 

2010 RPA Assessment

David N. Wear
2

Forecasting Approach

uses dominate the South, the Northeast, the Lake States, 

and the Pacific Northwest. Cropland is concentrated in the 

Plains and Midwest, while rangeland is concentrated in the 

High Plains and intermountain West. Urban land, the least 

abundant land use, corresponds with the Nation’s cities, 

and the largest area of pastureland is found at the boundary 

between grassland and forest biomes from eastern Texas to 

northern Missouri.

Each of the four assessment regions has a distinctive 

distribution of non-Federal land uses (fig. 3). The Rockies 

is dominated by rangeland, while the South has the largest 

concentration of forests among regions. The North has the 

largest share of cropland, roughly equivalent to the area of 

forest land in the North.

used NRI county estimates of the areas of non-Federal land 

in pasture, cropland, forest, range, or urban uses (see table 

1), and define this as the total “mutable” land. All modeled 

land use change is within this land base; other land uses, 

including federal land, water area, enrolled Conservation 

Reserve Program lands, and utility corridors are held 

constant for all forecasts. 

The distributions of the five modeled land uses for nonFederal land in 1997 are shown in the five panels of figure 

2. Patterns of rural uses reflect biome boundaries (e.g., 

natural boundaries between grassland and forest land) and 

productivity determined by biophysical conditions along 

with comparative advantages for producing various goods 

and services determined by cost and return attributes. Forest 

Figure 1—Definition of the RPA Assessment Regions.
3

Forecasting Approach

Table 1—Definitions of land use categories in the NRI*

Forest land:

A land cover/use category that is at least 10 percent stocked by single stemmed forest trees of any size which will be at 

least 4 m (13 feet) tall at maturity. When viewed vertically, canopy cover is 25 percent or greater. Also included are areas 

bearing evidence of natural regeneration of tree cover (cutover forest or abandoned farmland) and not currently developed 

for nonforest use. For classification as forest land, an area must be at least 1 acre and 100 feet wide. 

Cropland:

A land cover/use category that includes areas used for the production of adapted crops for harvest. Two subcategories 

of cropland are recognized: cultivated and noncultivated. Cultivated cropland comprises land in row crops or close-grown 

crops and also other cultivated cropland, for example, hayland or pastureland that is in a rotation with row or close-grown 

crops. Noncultivated cropland includes permanent hayland and horticultural cropland.

Rangeland:

A land cover/use category on which the climax or potential plant cover is composed principally of native grasses, grasslike 

plants, forbs or shrubs suitable for grazing and browsing, and introduced forage species that are managed like rangeland. 

This would include areas where introduced hardy and persistent grasses, such as crested wheatgrass, are planted and 

such practices as deferred grazing, burning, chaining, and rotational grazing are used, with little or no chemicals or 

fertilizer being applied. Grasslands, savannas, many wetlands, some deserts, and tundra are considered to be rangeland. 

Certain communities of low forbs and shrubs, such as mesquite, chaparral, mountain shrub, and pinyon-juniper, are also 

included as rangeland.

Urban and built-up areas:

A land cover/use category consisting of residential, industrial, commercial, and institutional land; construction sites; public 

administrative sites; railroad yards; cemeteries; airports; golf courses; sanitary landfills; sewage treatment plants; water 

control structures and spillways; other land used for such purposes; small parks (< 10 acres) within urban and built-up 

areas; and highways, railroads, and other transportation facilities if they are surrounded by urban areas. Also included are 

tracts of < 10 acres that do not meet the above definition but are completely surrounded by Urban and Built-up land. Two 

size categories are recognized in the NRI: (1) areas 0.25 to 10 acres, and (2) areas > 10 acres.

Pastureland and Native Pasture:

A land cover/use category of land managed primarily for the production of introduced or native forage plants for livestock 

grazing. Pastureland may consist of a single species in a pure stand, a grass mixture or a grass-legume mixture. 

Management usually consists of cultural treatments—fertilization, weed control, reseeding, or renovation and control of 

grazing. (For the NRI, includes land that has a vegetative cover of grasses, legumes, and/or forbs, regardless of whether or 

not it is being grazed by livestock.)

*NRI = Natural Resource Inventory.
4

Forecasting Approach

0–0.200.21–0.400.41–0.600.61–0.800.81–1.0

Pasture

Figure 2—Concentration of five land uses (proportion of each county) on nonfederal land, 1997: (A) pasture, (B) crops, (C) forest, (D) range, (E) urban. (Source: NRI) (continued to next page)

(A)
5

Forecasting Approach

0–0.200.21–0.400.41–0.600.61–0.800.81–1.0

Crops

Figure 2 (continued)—Concentration of five land uses (proportion of each county) on nonfederal land, 1997: (A) pasture, (B) crops, (C) forest, (D) range, (E) urban. (Source: NRI) (continued to next page)

(B)
6

Forecasting Approach

Forest 0–0.20

0.21–0.400.41–0.600.61–0.800.81–1.0

Figure 2 (continued)—Concentration of five land uses (proportion of each county) on nonfederal land, 1997: (A) pasture, (B) crops, (C) forest, (D) range, (E) urban. (Source: NRI) (continued to next page)

(C)
7

Forecasting Approach

Range 0–0.20

0.21–0.400.41–0.600.61–0.800.81–1.0

Figure 2 (continued)—Concentration of five land uses (proportion of each county) on nonfederal land, 1997: (A) pasture, (B) crops, (C) forest, (D) range, (E) urban. (Source: NRI) (continued to next page)

(D)
8

Forecasting Approach

Urban 0–0.20

0.21–0.400.41–0.600.61–0.800.81–1.0

Figure 2 (continued)—Concentration of five land uses (proportion of each county) on nonfederal land, 1997: (A) pasture, (B) crops, (C) forest, (D) range, (E) urban. (Source: NRI) 

(E)
9

Forecasting Approach

Figure 3—Distribution of land uses within Assessment Regions, 1997. (Source: NRI)
10

2010 RPA Assessment Scenarios

2010 RPA ASSESSMENT SCENARIOS 

Future renewable resource conditions are influenced by 

a number of common driving forces such as population 

change, economic growth, and land use change. Three 

scenarios in the RPA Assessment are used to characterize 

the common demographic, socioeconomic and technological 

driving forces underlying changes in resource conditions 

and to evaluate the sensitivity of resource trends to a feasible 

future range of these driving forces. The use of scenarios 

links the underlying assumptions of the analyses of various 

resource conditions and uses, and frames the future 

uncertainty in these driving forces within the integrated 

modeling and analysis framework of the 2010 RPA 

Assessment (see text box). 

The three RPA scenarios are linked to globally 

consistent and well-documented scenarios used in the 

Intergovernmental Panel on Climate Change (IPCC) 4th 

Assessment (AR4) (IPCC 2007). The scenarios include a 

range of future global and U.S. socioeconomic and climate 

conditions likely to affect future U.S. resource conditions 

and trends (Nakicenovic and others 2000). The IPCC AR4 

scenario labels (A1B, A2, and B2) have been maintained in 

the 2010 RPA Assessment documentation for continuity. The 

IPCC AR4 global data were scaled to the U.S. national level 

and sub-national levels for the 2010 RPA Assessment. U.S. 

gross domestic product (GDP) and population projections 

used in AR4 analyses were updated, and U.S. population and 

disposable personal income data were then downscaled to 

the U.S. county level (Zarnoch and others 2010). While not 

a part of land use models, the climate output generated from 

several global circulation models (GCMs) for the scenarios 

were downscaled to the county scale (Coulson and others 

2010) and used in other components of the RPA Assessment.

Population and personal income projections for the 

three scenarios (A1B, A2, and B2) drive our forecasts 

of urbanization. The A1B population forecasts are based 

on 2004 Census projections for the entire United States, 

while A2 and B2 depart from these forecasts as described 

below. Zarnoch and others (2010) developed county-scale 

projections for each scenario based on forecasts from Woods 

and Poole’s (2007) spatial econometric/demographic model 

which are generally consistent with the A1B projection for 

2000-2030. County-level projections between 2030 and 

2060 were disaggregated by extending historical patterns of 

growth from the Woods and Poole projections (see Zarnoch 

and others 2010 for details). Projections for A2 and B2 

applied the same spatial pattern of population change, but 

were adjusted to yield county-level projections that add up 

to the national totals for the respective scenarios.

As shown in figures 4 and 5, A1B corresponds to mid range 

population growth and the highest per capita disposable 

personal income level of the three IPCC scenarios. Under 

this scenario, the United States population will be about 446 

million with per capita personal income around $80,000 

by 2060. Scenario A2 projects the highest population 

growth, reaching more than 500 million people by 2060, 

and the lowest projected per capita personal income, around 

$56,000. Scenario B2 projects the lowest population growth 

and mid level personal income, predicting a population of 

397 million people with per capita personal income around 

$60,000.

Population is not forecast to grow evenly across the US. 

Rather, most projected growth occurs around a number of 

existing urban centers (fig. 6). In addition, a large number 

of counties are expected to experience population declines 

(fig. 6 shows these counties in green for Scenario A1B). 

Population loss is forecasted to be especially high through 

the Great Plains and Corn Belt, within the Mississippi 

Alluvial Valley in the South, and in a band from northern 

Indiana to upstate New York.
11

2010 RPA Assessment Scenarios

The U.S. Forest Assessment System: 

Modeling for the 2010 RPA Assessment

Land use models represent one component of the U.S. Forest Assessment System (USFAS), a set of computer models 

designed to forecast alternative futures for the Nation’s forests. The USFAS provides a forward looking adjunct to the 

Nation’s Forest Inventory System implemented by the Forest Inventory and Analysis (FIA) Research Program of the U.S. 

Forest Service. The FIA system is a nationwide monitoring system of repeated inventories that provides for consistent 

tracking of inventories over time at a high level of detail. The USFAS accounts for changes driven by multiple drivers 

including biological, physical and human factors. These models address the influence of changing climate, market-driven 

timber harvesting, and land use changes along with changes driven by the natural succession of forest conditions.

Figure B1 shows a general schematic of this modeling system. The first column describes the input of data beginning with 

internally consistent combinations of social, economic, and technology forecasts defined as scenarios. The scenarios are 

linked to various General Circulation Models (climate models) to provide climate forecasts consistent with each scenario. 

Forest inventory data defines the starting conditions for all forested plots. 

The middle column of Figure B1 provides a general picture of the modeling framework. Future forest conditions are driven 

by biological dynamics—e.g., growth and mortality—which are affected by climate factors. In addition, human choices 

regarding allocations among land uses, disposal of forest land, timber harvesting, and forest management also affect 

changes in forests. The interplay of these factors yields the outputs described in the right column where forest projections 

are consistent with the flow of forest products and land uses. Changes in several other ecosystem services, including water 

and biodiversity, can also be derived from the forecasted changes in forest conditions and land uses. 

Figure B1 — Schematic of the U.S. Forest Assessment System.
12

2010 RPA Assessment Scenarios

Figure 4—Forecasted U.S. population for three RPA Scenarios (A1B, A2, B2), 2006-2060.

Figure 5—Forecasted per capita personal income for three RPA Scenarios (A1B, A2, B2), 2006-2060.
13

2010 RPA Assessment ScenariosFigure 6—Forecasted change in population density (people per square mile), 1997-2060, for the A1B Scenario. Areas in green are forecasted to experience population declines over this period.
14

Forecasts of Land Use Changes

FORECASTS OF LAND USE CHANGES

We forecasted urban land use changes in response to the 

population and income futures defined for each of the three 

scenarios (fig. 7). Scenario A1B, with an intermediate 

level of population growth but strong growth in personal 

income, yields the highest rate of urbanization: an increase 

of 86 million acres by 2060. Scenario B2, with the lowest 

income and lowest population growth has the lowest rate of 

urbanization (an increase of 59 million by 2060), and A2, 

with the highest population growth but intermediate income 

growth, yields an intermediate rate of urbanization (an 

increase of 75 million acres by 2060). Urban uses in 1997 

totaled 73 million acres, so forecasts show a rough doubling 

of urban area by 2060 with scenario A2 and increases of 

118 percent and 81 percent with scenarios A1B and B2, 

respectively (see appendix C for detailed forecast results).

The total area of urbanization is similar across the three 

scenarios until 2040, after which urbanization diverges 

among the scenarios (fig. 7). In the earlier years when the 

population differences across scenarios are quite small, 

urbanization is somewhat faster in scenario B2 because of its 

higher per capita income. After 2040, rates of urbanization 

are especially affected by income growth as urban growth 

for Scenario A1B (where population gains are intermediate 

but income growth is high) far exceeds the rate for Scenario 

A2 (where population growth is highest, but income growth 

is low). 

Urban growth varies by region (fig. 8). About 48 percent of 

urban growth forecasted for A1B is contained in the South 

(+42 million acres). The North gains about 27 million acres 

by 2060, while the Rockies and Pacific gain 11 and 8 million 

acres respectively. Urban area in the Rockies is projected to 

Figure 7—Forecasted change in urban land uses for the United States from a base year of 1997, 

2010-2060, by Scenario (numbers are for A1B Scenario).

Figure 8—Forecasted urban area by region, 1997-2060, for the A1B scenario.
15

Forecasts of Land Use Changes

Forest Land Uses 

Change in forest area varies substantially across the 

scenarios: A1B forecasts a loss of 38 million acres by 2060, 

A2 forecasts a loss of 32 million acres, and B2 forecasts a 

loss of 25 million acres. In terms of both area and percent, 

the South is forecasted to experience the greatest decline in 

forest area by 2060 (figs. 12 and 13). For the A1B Scenario, 

southern forests would decline by about 21 million acres 

(12 percent) between 1997 and 2060, while the North would 

lose about 12 million acres (8 percent), and the Rockies and 

Pacific would lose 1 million acres (4 percent) and 3 million 

acres (8 percent) respectively. Because the majority of forest 

land in the West is public, and therefore held fixed, the 

forecasted change in total (public and nonpublic) forest area 

for the western regions is < 1 percent.

Forecasted forest losses are concentrated in a few subregions 

of the United States (figs. 14 and 15). Within the South 

Region, forest losses are especially concentrated in the 

Southern Appalachian Mountains and the Piedmont—

Northern Georgia, central North Carolina, and eastern 

Tennessee show especially high rates of forest loss. 

Elsewhere in the South, forest losses are concentrated along 

the coasts. In the North, forest losses are concentrated in a 

large area centered on Philadelphia and extending into New 

Jersey, Maryland, Delaware, and southern New York. Other 

smaller concentrations of forest loss in the North include 

an area stretching from Boston north into Vermont and the 

northern half of Michigan’s Lower Peninsula. In the Pacific 

Region, forest losses are concentrated in areas between 

Portland, OR and Seattle, WA and between San Francisco, 

CA and Reno, NV. In the Rockies, areas of forest losses are 

focused around Salt Lake City, UT and the Front Range of 

Colorado. 

Cropland Uses 

Forecasts of cropland loss range from 19 million acres 

for the B2 Scenario to about 28 million acres for the A1B 

Scenario (fig. 16). About 85 percent of cropland losses 

(24 million acres) are contained in the eastern assessment 

regions, with nearly an equal split between the South and 

the North. The largest region of cropland loss extends from 

southern Michigan southwest to the Lower Mississippi 

increase by the highest percentage (+153 percent), followed 

by the South (+140 percent), the Pacific (+109 percent), 

and the North (+110 percent). Thus, under A1B, all regions 

experience at least a doubling of urban area.

Within each region, urbanization is concentrated in some 

key areas (figs. 9 and 10). For the A1B scenario, the models 

forecast extensive development in three portions of the 

North: along the Atlantic seaboard in the northeast, between 

the lower peninsula of Michigan and the Ohio River, and 

between Minneapolis, MN and Chicago, IL. Additionally, 

the models forecast smaller areas of development in 

southern Missouri and in the New England states of 

Massachusetts, New Hampshire, and Vermont. Compared to 

A1B, the B2 scenario shows similar patterns of growth, but 

shrinks the total area experiencing development (fig. 10). 

A2 also has a similar pattern of growth with total change 

intermediate between A1B and B2.

In the South, development is projected to be especially 

strong in the Southern Appalachian Mountains and the 

adjacent Piedmont, roughly within a triangle formed by the 

cities of Raleigh, NC, Atlanta, GA, and Knoxville TN. Texas 

is forecasted to experience strong growth in a triangular area 

formed by the cities of Austin, Dallas, and Houston. Much 

of Florida is forecast to experience extensive development, 

especially along the Gulf and Atlantic coasts. Nashville, the 

Gulf Coast, northern Kentucky, and northern Virginia are 

also forecasted to grow strongly over this period. 

While growth is spread across fairly broad areas of the East, 

it is much more isolated in the West. The growth forecasted 

for the Rocky Mountain region is focused in four areas: 

Denver and the Front Range of Colorado, Albuquerque, 

NM, Las Vegas, NV and St. George, UT, and Salt Lake City, 

UT. In the Pacific Region, urban growth is focused within 

the Seattle-Portland region, the San Francisco Bay area, and 

southern California. 

Increases in urban land are reflected in declines for all 

other land uses (fig. 11). For A1B, forests are forecasted 

to decline by about 37 million acres (10 percent), cropland 

by 28 million acres (8 percent), and pasture by 9 million 

(8 percent) and rangeland by 12 million acres (3 percent) 

respectively. We explore the forecasts for each of these rural 

land uses across the three scenarios next.
16

Forecasts of Land Use Changes

Urban < -0.25

-0.25 to -0.10-0.10 to -0.03-0.03 to 0.03

0.03 to 0.100.10 to 0.25

> 0.25

Figure 9—Forecasted change in proportion of county in the urban land use, A1B Scenario, 1997-2060.
17

Forecasts of Land Use Changes Urban < -0.25 -0.25 to -0.10 -0.10 to -0.03 -0.03 to 0.03 0.03 to 0.10 0.10 to 0.25 > 0.25 Figure 10—Forecasted change in the proportion of county in urban land use, B2 Scenario, 1997-2060.
18

Forecasts of Land Use Changes

Figure 11—Forecasted change in the areas of major nonfederal land uses, A1B Scenario, 2010-

2060, compared to 1997.
19

Forecasts of Land Use Changes

Figure 12—Forecasted forest area by region, 1997-2060, A1B Scenario.

Figure 13—Forecasted change in nonfederal forest area, 1997-2060, by RPA Scenario (A1B, A2, B2).
20

Forecasts of Land Use Changes

Forest < -0.25

-0.25 to -0.10-0.10 to -0.03-0.03 to 0.03

0.03 to 0.100.10 to 0.25

> 0.25

Figure 14—Forecasted change in proportion of county that is in forest use, A1B Scenario, 1997-2060.
21

Forecasts of Land Use Changes Forest < -0.25 -0.25 to -0.10 -0.10 to -0.03 -0.03 to 0.03 0.03 to 0.10 0.10 to 0.25 > 0.25 Figure 15—Forecasted change in proportion of county that is in forest use, 1997-2060, B2 Scenario.
22

Forecasts of Land Use Changes

Figure 16—Forecasted change in the area of cropland by RPA Scenario (A1B, A2, B2), 1997-2060.
23

Forecasts of Land Use Changes

Alluvial Valley and includes much of western Kentucky, 

Indiana, and Ohio (figs. 17 and 18). Other focal areas 

for cropland loss are eastern North Carolina, the eastern 

seaboard between New York City, NY and Washington, 

DC, areas surrounding Chicago, IL and Minneapolis, MN 

the coastline between Houston, TX and New Orleans, LA, 

and southern Florida. In the West, cropland losses are much 

more limited and isolated with some notable declines in 

southern California, central Washington, and around Salt 

Lake City, UT.

Rangeland Uses

 Forecasts of rangeland losses extend from 8 million acres 

under the B2 Scenario to 12 million acres for the A1B 

Scenario (fig. 19). Roughly one half of rangeland losses 

occur in the Rockies region with the remainder in the South 

and Pacific regions. In the Rockies, rangeland losses are 

forecasted for Colorado and parts of Utah and Arizona (figs. 

20 and 21). In the South, nearly all of the rangeland losses 

are found in Texas, while most of the losses in the Pacific 

are in southern California. The very small area of rangeland 

found in the North is held fixed within the modeling 

framework (see appendix A).

Rural Land Use Flexibility 

Our three scenarios provide alternative realizations of the 

future based on different projections of population and 

economic growth. Future landscape patterns could also be 

shaped by a number of other factors, including bioenergy 

policies and technological developments that would favor 

either wood or other feedstocks in the production of liquid 

biofuels or the burning of wood for the generation of 

electricity. Climate policies that allow trading of the carbon 

sequestered on forest lands could also affect landowners’ 

land use and management decisions. These policy futures, 

and their interactions with changes in markets for wood 

products and crops could produce structural changes in rural 

land markets. 

While marginal demand changes can be effectively modeled 

using econometric specifications such as the one developed 

here, structural changes could exceed the information 

content of these models. Rather than attempt to model the 

impacts of these types of structural changes—i.e., changes 

in rural land rents that far exceed historical precedent—

we use an index of land use complexity to indicate where 

future land use changes might be especially sensitive to 

changes in these markets. We posit that the potential for 

broad scale land use changes would likely be concentrated 

in areas where current land use is highly diverse. Since the 

marginal returns to alternative uses in areas with diverse 

land use patterns are likely to be similar (thereby explaining 

the diversity of land use choices within the area), small 

differences in land rents could result in land use switching. 

We construct a measure of land use diversity to map the 

distribution of these complex landscapes.

Our measure of diversity or flexibility starts with the premise 

that land use switching is most likely in places where current 

land uses vary within a county. The potential for switching 

between crop uses and forests is likely to be greatest 

where both crop and forest uses currently coexist. This 

correspondence between diversity and substitutability could 

reflect both biophysical factors such as soil productivity and 

demand factors such as access to markets. In either case, 

the probability of land use conversions may correlate with a 

measure of this complexity. 

Our rural land use complexity index has two important 

elements: the proportion of land within a county that is 

rural and the diversity of rural land uses (see appendix B for 

details). The index incorporates three land use aggregates: 

(1) undeveloped rural uses, equal to the sum of forest and 

range uses, (2) cropland, and (3) pastureland. The index 

ranges between 0 and 1 and reaches its maximum when the 

entire county is rural and there is an equal split between the 

three use classes—cropland, pasture, and native. Minimum 

values occur where counties have no rural land or where 

only one land use dominates the rural area.

A map of the rural land use complexity index (fig. 22) 

shows that complexity is unevenly distributed and that a 

few large areas of high complexity are found across the 

United States. The area with the greatest concentration of 

complexity includes most of Missouri, western Kentucky, 

central and western Tennessee and northern Mississippi. 

Another especially complex rural landscape is contained 

between prairie and northern mixed forest (Laurentian) 

ecological provinces in Minnesota and Wisconsin. The 

Great Plains are generally moderately diverse but rural 

lands are highly complex in the Cross-Timbers zone in 

east-central Oklahoma and Texas. Much of southern Idaho 

and parts of eastern Washington also have high complexity 

values. In the North smaller areas of high complexity are 

observed in upstate New York and southeastern Ohio. In the 

South, a high degree of complexity occurs in south central 

Florida, southwestern Georgia to southeastern Alabama, 

and southern Louisiana. These highly complex landscapes 

provide one measure of where policy shifts could have 

immediate effects on land use switching.
24

Forecasts of Land Use Changes

Cropland < -0.25

-0.25 to -0.10-0.10 to -0.03-0.03 to 0.03

0.03 to 0.100.10 to 0.25

> 0.25

Figure 17—Forecasted change in proportion of county that is in cropland use, A1B Scenario, 1997-2060.
25

Forecasts of Land Use Changes

Cropland < -0.25

-0.25 to -0.10-0.10 to -0.03-0.03 to 0.03

0.03 to 0.100.10 to 0.25

> 0.25

Figure 18—Forecasted change in proportion of county that is in cropland use, B2 Scenario, 1997-2060.
26

Forecasts of Land Use Changes

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27

Forecasts of Land Use Changes

Figure 19—Forecasted change in the area of rangeland by RPA Scenario (A1B, A2, B2), 1997-2060.
28

Forecasts of Land Use Changes

Rangeland < -0.25

-0.25 to -0.10-0.10 to -0.03-0.03 to 0.03

0.03 to 0.100.10 to 0.25

> 0.25

Figure 20—Forecasted change in proportion of county that is in cropland use, 1997-2060, A1B Scenario.
29

Forecasts of Land Use Changes

Rangeland < -0.25

-0.25 to -0.10-0.10 to -0.03-0.03 to 0.03

0.03 to 0.100.10 to 0.25

> 0.25

Figure 21—Forecasted change in proportion of county that is in cropland use, 1997-2060, B2 Scenario.
30

Forecasts of Land Use Changes

Rural diversity 0–0.20

0.21–0.400.41–0.600.61–0.800.81–0.900.91–1.0

Figure 22—Rural land use complexity index for nonfederal lands, 1997.
31

Key Findings

KEY FINDINGS

•	 Urbanization. Between 60 and 86 million acres of rural 

land are forecasted to be developed between 1997 and 

2060, at a rate of 1 to 1.4 million acres per year. With this 

development comes loss of rural land uses and forests 

are the greatest source of newly developed land over this 

period. 

•	 Forest losses. Between 24 and 38 million acres of forests 

are forecasted to be converted to other uses between 1997 

and 2060. More than half of the forecasted forest losses 

occur in the South and more than 90 percent occur in the 

Eastern United States.

•	 Cropland losses. Cropland losses are forecasted to range 

between 19 and 28 million acres and would be focused 

primarily in the Midwest and Mid Atlantic States.

•	 Rangeland losses. Rangeland losses are forecast to range 

between 8 and 11 million acres and would be focused in 

Colorado, Nevada, southern California, and central Texas. 

•	 Rural land use flexibility. Rural land use complexity 

is highest in a few areas including Missouri, central 

Kentucky, the cross-timber region of Oklahoma and Texas, 

a zone in central Minnesota and Wisconsin, and southern 

Idaho. Land uses in these areas may be especially variable 

in response to new policies and structural changes in 

markets that influence returns to rural land uses.

LITERATURE CITED

Coulson, D.P.; Joyce, L.A.; Price, D.T. 2010. Climate scenarios 

for the conterminous United States at the county spatial scale 

using SRES scenarios A1B and A2 and PRISM climatology. Fort 

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

Rocky Mountain Research Station. http://www.fs.fed.us/rm/

data_archive/dataaccess/US_ClimateScenarios_county_A1B_

A2_PRISM.shtml. [Date accessed: March 25, 2011].

Intergovernmental Panel on Climate Change [IPCC]. 2007. Climate 

change 2007, Synthesis Report. 107 p. http://www.ipcc.ch/

publications_and_data/publications_ipcc_fourth_assessment_

report_synthesis_report.htm. [Date accessed: March 25, 2011].

Nakicenovic, N.; Alcamo, J.; Davis, G. [and others]. 2000. Special 

report on emissions scenarios: A special report of working 

group III of the Intergovernmental Panel on Climate Change. 

Cambridge, UK: Cambridge University Press. 599 p. http://

www.grida.no/climate/ipcc/emission/index.htm. [Date accessed: 

March 25, 2011].

Rudis, V.A. 1999. Ecological subregion codes by county, 

conterminous United States. Gen. Tech. Rep. SRS–36. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station. 95 p.

U.S. Department of Agriculture Forest Service. 2001. 2000 RPA 

assessment of forest and rangelands. FS-687. Washington, DC: 

U.S. Department of Agriculture, Forest Service. 78 p.

Wear, D.N. 2010. Forecasting land uses for alternative futures. 

Draft manuscript. On file with: David Wear at U.S. Department 

of Agriculture Forest Service, Research Triangle Park Forestry 

Sciences Laboratory, 3041 Cornwallis Road, Research Triangle 

Park, NC 27713. 57 p.

Woods and Poole Economics. 2007. Complete Economic Data 

Source (CEDDS) Technical documentation. Washington, DC: 

Woods and Poole Economics Inc. 101 p.

Wooldridge, J.M. 2002. Econometric analysis of cross section and 

panel data. Cambridge, MA: MIT Press. 752 p.

Zarnoch, S.J.; Cordell, H.K.; Betz, C.J.; Langner, L. 2010. 

Projecting county-level populations under three future scenarios: 

a technical document supporting the Forest Service 2010 RPA 

Assessment. e-Gen. Tech. Rep. SRS–128. Asheville, NC: U.S. 

Department of Agriculture Forest Service, Southern Research 

Station. 8 p.
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33

Appendix A

Population and income are expected to be positively 

associated with the area of urban uses—in effect they proxy 

for an urban land use rent. To model changes in the area of 

urban land use, we difference equation 2:

U U pop pop pop pop 

it   t−1 1 it i − + t−1 2 it − + it− 

2

1

2 β β 

( ) ( ) 

+ − ( ) ( ) 

U U pop pop pop pop inc in it   t−1 1 it i − + t−1 2 it − + it− it − 2

1

2 β β β3 ( ) ( ) ( cit it − 

+ − 

1)

( ) ( ) ( )

* ε

 (3)

Differencing causes observed and unobserved fixed 

attributes of the county to fall out of the change equation 

(see Wooldridge 2002). Change therefore relies strictly on 

time-varying variables that are forecast to change between 

periods. Other time-varying variables such as rents accruing 

to crop or timber uses are excluded from this model by 

assumption—i.e., that urban rents completely dominate all 

rural rents in the area of the county affected by the shift in 

demand. We posit that this urban growth difference equation 

may differ across subregions of the United States, due in 

part to the effects of topography and climate on the spatial 

agglomeration of uses (e.g., mountainous areas and flat areas 

may reveal different development patterns determined in part 

by topographic features). We therefore estimated separate 

models for broad regions and within each regional model 

we allowed for differences in coefficients by ecological 

provinces (Rudis 1999) by interacting dummy variables for 

the ecological provinces with each independent variable.

To address changes in rural land uses we considered three 

different models with a progression of complexity. The first 

model simply allocates development among rural uses based 

on proportion of occurrence—Rent Neutral Urbanization 

Model. The second model allows the allocation of newly 

developed land to be skewed from the observed proportions 

and influenced by the rents accruing to the rural uses 

based on historical evidence—Rent-Biased Urbanization 

Model. The third model allows additionally for substitution 

between rural uses in response to changes in rents—Rural 

Substitution Model.

Appendix A—Land Use Change Models

This appendix provides documentation of the land use 

models used to generate forecasts for this report. Wear 

(2010) provides details on the modeling approach. 

For each county in the coterminous United States, we 

model the urbanization process and changes in four rural 

uses: forest, crops, range, and pasture. The dataset used 

for model estimations is a panel of observed land uses in 

2 years (1987 and 1997), the most recent comprehensive 

dataset available from the NRI land use inventory. Models 

were applied to what we define as the variable or mutable 

land base: nonfederal land classified as developed, crops, 

pasture, range, or forests. All other land in the county was 

held fixed in its current use. A two-stage modeling approach 

first defines urban-rural allocations and then allocates land 

among the four rural land uses.

We assume that the demand for urban uses dominates all 

other land uses. That is, we expect that the amount of urban 

land use is determined by demand factors that influence 

urban land rents and is unaffected by competition with any 

other land use. Consider the following reduced form model: 

U f    Y Z, , X (1)

where: 

U = the area in urban use

U f    Y Z, , = a vector of time-varying variables from the RPA X

scenarios, including the population contained in the 

county (pop), and the real per capita disposable income 

for the county (inc). These variables change within each 

RPA scenario

U f U f   Y Z, ,  Y Z and X, , X = vectors of observed and unobserved timeinvariant variables respectively, and describe the land 

quality attributes of the county—for example soil 

productivity, access to markets, etc. 

A linear specification of equation 1 is:

Uit   β β pop pop it   β β it inc Z it  i Xi i  t δ α ε 0 1 2

2

3

 β β pop pop it   β β it inc Z it  i Xi i  t δ α ε 0 1 2

2

3 (2)
34

Appendix A

P P t t = − −1   U U t t − −1 + −   C C t t−1 + −   F F t t−1 ( ) (5.3)

where: 

δ’s are defined by equations 4.1 and 4.2

pc and pf = variables that proxy for rents accruing to crop 

and forest uses respectively

β’s = estimated coefficients with the expected signs 

indicated (e.g., we expect the crop rent coefficient to be 

positive in equation 5.1 because higher prices would reduce 

the loss of cropland to urban uses). 

The Rent-Biased Urbanization and Rural Substitution 

models were only applied to eastern regions and rangeland 

uses were held constant—i.e., the mutable land base is 

restricted to urban, cropland, forest, and pasture uses. 

Rural Substitution Model

A third formulation allows for rural land uses to change 

in response to changes in rural land rent determinants in 

addition to urbanization. Changes to relative rents could lead 

to rural land use switching irrespective of population/income 

changes. Consider the equations for current amounts of 

forest and cropland uses similar to equation (2):

F p t   φ φ f t   φ φ fc p U c t fu t f   δ α Z Xf U  ε , , 0 ff

F p t   φ φ f t   φ φ fc p U c t fu t f   δ α Z Xf U  ε , , 0 ff

 (6.1)

Ct   cf f p t c   c c p U t cu t   c c Z X  U γ γ γ γ δ α ε 0 , ,

Ct   cf f p t c   c c p U t cu t   c c Z X  U γ γ γ γ δ α ε 0 , , (6.2)

Here we assume that the areas of land in forest and cropland 

are determined by the time-varying rents accruing to wood 

products and crops (p’s) and vectors of observed and 

unobserved fixed attributes that influence the suitability of 

land for various uses (Z and Y respectively). Pasture area (P) 

is defined as a residual land use. Rental values for forest and 

crop uses and the area of urban use are considered timevarying. To account for the urbanization dynamic in the 

Rent-Biased Urbanization Model, we substitute equations 

(5.1) and (5.2) for urban change terms in equations (7.1) and 

(7.2) as follows:

C C t t   c c   c c P P t cf ft cu cf f P P t f t 



  + −   −1 α β β δ φ , , −1 φ 

C C t t   c c   c c P P t cf ft cu cf f P P t f t cc c P t 

  + − 

  −1 α β β δ φ , , −1 φ  , −  

 Pc t, −  F

*

1 ε (7.1)

Rent Neutral Urbanization Model

This model assumes that changes in rural land uses are 

driven exclusively by urbanization and that the probability 

of a rural use being converted to a developed use is defined 

by the observed proportion of that land use within the 

county. Because urban rents dominate rents for rural land 

uses, developers are indifferent to opportunity or conversion 

costs of agricultural and forest land uses. In equation form, 

the changes in cropland (C), forest (F) and pasture (P) uses 

indexed by time period (t) are:

C C C

A U t t U U t

t

− = − t t C U − − − = −

−

1 −

1

1

1 ( ) , δ (4.1)

F F F

A U t t U U t

t

− = − t t F U − − − = −

−

1 −

1

1

1 ( ) , δ (4.2)

P P t t = − −1   U U t t − −1   C U F U ( ) , , δ δ (4.3)

where 

Total area, A = Ut

 + Ct + Ft + Pt. 

Rent-Biased Urbanization Model

For this model, we continue to assume that urbanization 

exclusively determines changes in rural land uses. However, 

in this model we allow the rents accruing to different rural 

land uses to influence these changes—i.e., to allow for 

disproportional change among rural uses. 

This is a simple extension of the Rent Neutral Urbanization 

Model where we allow the relative values of crop and forest 

uses to influence the effects of urbanization on rural land. 

Equations 4.1–6.1 are modified to allow the change in rural 

uses to be affected by forest and crop rent proxies.

C C p p t t   c c   c c cf f C U 

 

− + −

−1 α β β δ ,

( ) ( ) ( )

 (5.1)

F F p p t t   f f   c c ff f F U 

 

− − 

−1 α β β δ ,

() () ( )

 (5.2)
35

Appendix A

F F t t   f f   c c P P t ff ft fu fc c P P t c t ff f P t 

  + − 

  −1 α β β δ γ −1  γ , ,  , −  

 Pf t, −  C

*

1 ε

 

c P P t ff ft fu fc c P P t c t ff f P t 



 + − 

  

β δ γ −1  γ , ,  , −  

 Pf t, −  C

*

1 ε (7.2)

P P t t = − −1   U U t t − −1 + −   F F t t−1 + −   C C t t−1 ( )

P P t t = − −1   U U t t − −1 + −   F F t t−1 + −   C C t t−1 ( ) (7.3)

For the Rent Neutral Urbanization Model, we estimated 

equation (3) using weighted least squares, weighted by the 

square root of the area of the county to account for nonconstant variances. For the Rent-Biased Urbanization and 

Rural Substitution Models we applied a weighted Seemingly 

Unrelated Estimation approach to also account for cross 

equation correlations. The Rent-Biased Urbanization 

Model requires joint estimation of equations 3, 5.1, and 5.2 

while the Rural Substitution Model requires estimation of 

equations 3, 7.1, and 7.2. Coefficient estimates are described 

in Wear (2010).

Forecasting Algorithm

These models are designed to forecast change in the areas of 

urban, forest, and crop uses with pasture use as a residual. 

Because areas in any land use are not constrained to be 

positive by the structure of these equations, nonegativity 

constraints and “adding-up” rules need to be applied to 

ensure logical forecasts. For the forecasts developed for this 

report we adopted the rent neutral urbanization models for 

the North, Pacific, and Rocky Mountain Regions. Including 

rural rent variables in the models for these regions added 

little information to the forecasts. Models for the South 

showed strong improvements in explanatory power using 

the Rural Substitution model. This form of the model was 

applied to the South but forecasts described here hold the 

relative rents of the crop and forest land uses constant over 

time.
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37

Appendix B

Appendix B—Rural Land Use 

Complexity Index

We construct a complexity index as a gauge of the potential 

for land use change among rural land uses within a 

county. At one end of this spectrum are counties that are 

dominated by native land uses such as forest or range, 

where inherent productivity or economic demands preclude 

active agricultural management. At the other end of this 

spectrum are lands that are dominated by crops, where 

soil productivity and markets favor intensive agricultural 

production. Between these two extremes, intensive and 

extensive agricultural land uses coexist with native cover in 

varying quantities. We hypothesize that land use changes are 

more likely to occur in these middle zones where land use 

complexity (LUC) is high, because this is where returns to 

alternative uses are likely to be comparable and where small 

variations in relative returns could cause changes in land 

allocations among uses.

To construct a land use complexity index, we utilize a 

standard diversity formula from information theory. The 

entropy or complexity of a system (land within a county) 

with n possible States (land uses) is defined as:

where

pi

 = the observed proportion of land use i, the sum of these 

proportions is 1 (100 percent)

k = a scaling parameter.

If a county has only one rural land use, then one of the pi

’s is 

equal to one and the logarithm of one is zero (we apply the 

rule that the logarithm of zero, in the limit, is equal to one), so 

the complexity index is equal to zero. The highest value of the 

LUC is achieved where land uses are equal across all classes. 

This occurs where pi is equal to 1/n—where no single land 

use represents a majority of the county. We define the scaling 

factor (k) equal to so that 

LUC ranges from zero (least complex) to one (maximum 

complexity).

We implement the complexity index by defining the 

proportion of land use across three use classes: cropland, 

pasture, and native, defined as the sum of range and forests 

in the county so n is equal to three. We further modify the 

definition of LUC to account for the proportion of the county 

that is in a rural use (R) as follows:

 

So the range of LUC now depends on the availability of 

rural land. LUC is at a maximum where all rural land uses 

occur in equal proportion and where the rural proportion of 

the county (R) is equal to one. LUC is at a minimum either 

where one rural land use occurs (pi

 =1) or where there is no LUC = -k∑ rural land (R=0).

n

i=1

p ln p i i

1/[∑ 1/n ln{1/n}] = 1/ln{1 i=1 /n} n

LUC = -Rk∑

n

i=1

p ln p i i
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39

Appendix C

Appendix C—Data Tables

Table C1Ñ Forecasts of land uses for Scenario A1B in the United States by region, 

1997Ð 2060a

Land use category 

Subregion Year Urban Cropland Forest Pasture Range Total area 

thousand acres 

North 

1997 28,929 142,190 149,747 36,063 86 357,015 

2010 33,445 140,183 147,763 35,538 86 357,015 

2020 37,590 138,350 145,928 35,060 86 357,015 

2030 41,316 136,706 144,267 34,640 86 357,015 

2040 45,488 134,857 142,413 34,171 86 357,015 

2050 50,172 132,771 140,338 33,648 86 357,015 

2060 55,441 130,417 138,007 33,064 86 357,015 

Pacific 

1997 6,997 19,770 38,433 4,115 32,983 102,298 

2010 8,736 19,414 37,736 4,030 32,382 102,298 

2020 9,880 19,182 37,262 3,975 31,999 102,298 

2030 10,958 18,962 36,813 3,923 31,642 102,298 

2040 12,109 18,727 36,331 3,870 31,261 102,298 

2050 13,339 18,476 35,818 3,814 30,850 102,298 

2060 14,662 18,206 35,267 3,756 30,406 102,298 

Rockies 

1997 6,851 123,385 28,744 15,596 256,332 430,907 

2010 9,138 122,728 28,484 15,467 255,091 430,907 

2020 10,694 122,267 28,302 15,382 254,261 430,907 

2030 12,154 121,845 28,127 15,303 253,477 430,907 

2040 13,727 121,386 27,952 15,218 252,623 430,907 

2050 15,475 120,874 27,761 15,128 251,669 430,907 

2060 17,375 120,299 27,556 15,032 250,645 430,907 

South 

1997 29,879 84,292 175,812 61,191 111,854 463,029 

2010 38,368 81,736 171,837 60,109 110,979 463,029 

2020 44,923 79,842 168,482 59,285 110,497 463,029 

2030 50,770 78,213 165,481 58,497 110,068 463,029 

2040 57,083 76,462 162,178 57,701 109,606 463,029 

2050 63,966 74,563 158,544 56,831 109,124 463,029 

2060 71,630 72,498 154,434 55,863 108,604 463,029 

Total 

1997 72,656 369,637 392,736 116,965 401,255 1,353,249 

2010 89,687 364,061 385,820 115,144 398,538 1,353,249 

2020 103,087 359,641 379,974 113,702 396,843 1,353,249 

2030 115,198 355,726 374,688 112,363 395,273 1,353,249 

2040 128,407 351,432 368,874 110,960 393,576 1,353,249 

2050 142,952 346,684 362,461 109,421 391,729 1,353,249 

2060 159,108 341,420 355,264 107,715 389,741 1,353,249 

a

Total area refers to the Ò mutableÓ area defined by the sum of nonfederal urban, cropland, pasture, and 

range uses. 
40

Appendix C

Table C2Ñ Forecasts of land uses for Scenario A2 in the United States by region, 1997Ð 2060a

Land use category 

Subregion Year Urban Cropland Forest Pasture Range Total area 

thousand acres 

North 

1997 28,929 142,190 149,747 36,063 86 357,015 

2010 32,704 140,549 148,053 35,623 86 357,015 

2020 36,105 139,072 146,524 35,228 86 357,015 

2030 39,140 137,762 145,143 34,884 86 357,015 

2040 42,375 136,363 143,673 34,518 86 357,015 

2050 46,182 134,710 141,945 34,091 86 357,015 

2060 50,687 132,750 139,897 33,595 86 357,015 

Pacific 

1997 6,997 19,770 38,433 4,115 32,983 102,298 

2010 8,741 19,409 37,735 4,029 32,384 102,298 

2020 9,858 19,178 37,270 3,974 32,017 102,298 

2030 10,930 18,955 36,821 3,923 31,669 102,298 

2040 12,081 18,715 36,336 3,869 31,297 102,298 

2050 13,397 18,440 35,782 3,809 30,870 102,298 

2060 14,928 18,119 35,137 3,742 30,372 102,298 

Rockies 

1997 6,851 123,385 28,744 15,596 256,332 430,907 

2010 8,981 122,805 28,491 15,476 255,155 430,907 

2020 10,383 122,419 28,316 15,400 254,388 430,907 

2030 11,706 122,071 28,149 15,329 253,652 430,907 

2040 13,107 121,709 27,981 15,254 252,856 430,907 

2050 14,644 121,297 27,798 15,177 251,991 430,907 

2060 16,500 120,802 27,578 15,085 250,942 430,907 

South 

1997 29,879 84,292 175,812 61,191 111,854 463,029 

2010 37,852 81,753 172,413 60,489 110,993 463,029 

2020 43,710 79,989 169,675 59,576 110,549 463,029 

2030 48,709 78,651 167,252 58,748 110,140 463,029 

2040 53,837 77,287 164,747 57,913 109,716 463,029 

2050 59,699 75,747 161,861 56,953 109,240 463,029 

2060 66,452 73,975 158,498 55,889 108,686 463,029 

Total 

1997 72,656 369,637 392,736 116,965 401,255 1,353,249 

2010 88,278 364,516 386,692 115,617 398,618 1,353,249 

2020 100,056 360,658 381,785 114,178 397,040 1,353,249 

2030 110,485 357,439 377,365 112,884 395,547 1,353,249 

2040 121,400 354,074 372,737 111,554 393,955 1,353,249 

2050 133,922 350,194 367,386 110,030 392,187 1,353,249 

2060 148,567 345,646 361,110 108,311 390,086 1,353,249 

a

Total area refers to the Ò mutableÓ area defined by the sum of nonfederal urban, cropland, pasture, and range 

uses. 
41

Appendix C

Table C3Ñ Forecasts of land uses for Scenario B2 in the United States by region, 1997Ð 2060a

Land use category 

Subregion Year Urban Cropland Forest Pasture Range 

Total 

area 

thousand acres 

North 

1997 28,929 142,190 149,747 36,063 86 357,015 

2010 34,555 139,669 147,290 35,416 86 357,015 

2020 37,662 138,310 145,906 35,051 86 357,015 

2030 39,237 137,630 145,199 34,863 86 357,015 

2040 41,135 136,789 144,367 34,638 86 357,015 

2050 43,695 135,652 143,238 34,344 86 357,015 

2060 45,437 134,901 142,450 34,142 86 357,015 

Pacific 

1997 6,997 19,770 38,433 4,115 32,983 102,298 

2010 8,859 19,393 37,687 4,025 32,334 102,298 

2020 9,860 19,186 37,270 3,976 32,006 102,298 

2030 10,528 19,045 36,985 3,942 31,797 102,298 

2040 11,158 18,913 36,716 3,911 31,601 102,298 

2050 11,911 18,755 36,397 3,876 31,359 102,298 

2060 12,590 18,608 36,108 3,844 31,147 102,298 

Rockies 

1997 6,851 123,385 28,744 15,596 256,332 430,907 

2010 9,411 122,617 28,462 15,451 254,966 430,907 

2020 10,699 122,260 28,304 15,382 254,263 430,907 

2030 11,536 122,060 28,192 15,337 253,783 430,907 

2040 12,424 121,828 28,084 15,291 253,281 430,907 

2050 13,512 121,529 27,960 15,235 252,671 430,907 

2060 14,438 121,312 27,846 15,190 252,121 430,907 

South 

1997 29,879 84,292 175,812 61,191 111,854 463,029 

2010 40,288 80,986 170,996 60,315 110,915 463,029 

2020 45,768 79,338 168,435 59,459 110,499 463,029 

2030 48,739 78,604 167,025 58,892 110,242 463,029 

2040 52,043 77,754 165,351 58,365 109,987 463,029 

2050 56,234 76,644 163,196 57,743 109,683 463,029 

2060 59,318 75,864 161,660 57,232 109,426 463,029 

Total 

1997 72,656 369,637 392,736 116,965 401,255 1,353,249 

2010 93,113 362,665 384,435 115,207 398,301 1,353,249 

2020 103,989 359,094 379,915 113,868 396,854 1,353,249 

2030 110,040 357,339 377,401 113,034 395,908 1,353,249 

2040 116,760 355,284 374,518 112,205 394,955 1,353,249 

2050 125,352 352,580 370,791 111,198 393,799 1,353,249 

2060 131,783 350,685 368,064 110,408 392,780 1,353,249 

a

Total area refers to the Ò mutableÓ area defined by the sum of nonfederal urban, cropland, pasture, and range 

uses. 

Wear, David N. 2011. Forecasts of county-level land uses under three future scenarios: a technical 

document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. SRS-141. 

Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 41 p.

Accurately forecasting future forest conditions and the implications for ecosystem services depends on 

understanding land use dynamics. In support of the 2010 Renewable Resources Planning Act (RPA) 

Assessment, we forecast changes in land uses for the coterminous United States in response to three 

scenarios. Our land use models forecast urbanization in response to the population and economic 

projections defined by the scenarios and consequences for various rural land uses. Urban area is 

forecasted to expand by 1 to 1.4 million acres per year between 1997 and 2060. Forest area is forecasted 

to decline by 24 to 37 million acres and cropland is forecasted to decline by 19 to 28 million acres over 

this period. About 90 percent of forecasted forest land losses are found in the Eastern United States with 

more than half in the South.

Keywords: Assessments, forecasting, land use. 

The Forest Service, United States Department of Agriculture 

(USDA), is dedicated to the principle of multiple use management 

of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, 

cooperation with the States and private forest owners, and 

management of the National Forests and National Grasslands, it strives—as 

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

Nation.

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income is derived from any public assistance program. (Not all prohibited bases 

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should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write to USDA, Director, Office of Civil 

Rights, 1400 Independence Avenue, SW, Washington, D.C. 20250-9410, or call 

(800) 795-3272 (voice) or (202) 720-6382 ( TDD). USDA is an equal opportunity 

provider and employer.
United States Department of Agriculture

Forest Service

Southern Research Station

General Technical Report SRS-141

August 2011