Economic Valuation of Environmental Benefits and the Targeting of

Conservation Programs: The Case of the CRP. By Peter Feather, Daniel

Hellerstein, and LeRoy Hansen, Resource Economics Division, Economic

Research Service, U.S. Department of Agriculture. Agricultural Economic

Report No. 778.

Abstract

The range of environmental problems confronting agriculture has expanded in

recent years. As the largest program designed to mitigate the negative environmental effects of agriculture, the Conservation Reserve Program (CRP) has

broadened its initial focus on reductions in soil erosion to consider other landscape factors that may also be beneficial. For example, preserving habitats can

help protect wildlife, thus leading to more nature-viewing opportunities. This

report demonstrates how nonmarket valuation models can be used in targeting

conservation programs such as the CRP. 

Acknowledgments

With contributions from David Shank, Vince Breneman, Catherine Kascak, Tim

Osborn, and Gerald Whittaker. The authors acknowledge helpful comments

from: Mary Ahearn, Art Allen, Margot Anderson, William Anderson, Alex

Barbarika, Jackie Geoghegan, Ralph Heimlich, Andrew Laughland, Peter Parks,

John Reilly, and Marc Ribaudo.

1800 M Street, NW

Washington, DC 20036-5831 April 1999
Contents

Summary..............................................................................................................iii

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

Geography of Agricultural Land Use .................................................................2

Improving Agricultures Environmental Performance .......................................4

Economics of the CRP........................................................................................5

Estimating Nonmarket Benefits of the Conservation Reserve Program..............8

Background: Measuring the Value of Natural Resources ..................................8

Modeling CRP Impacts: A Stylized Framework ................................................9

Review of Prior Studies of the Recreational Value of the CRP .......................11

Features of the Nonmarket Benefit Models .....................................................13

Data Used in the Nonmarket Benefit Models ..................................................13

Freshwater-Based Recreation Results ..............................................................15

Pheasant-Hunting Recreation Results ..............................................................16

Wildlife-Viewing Results..................................................................................18

Discussion.........................................................................................................18

Applications of Nonmarket Valuation in Land Use Policy ................................20

The Relative Benefits of the EBI Targeting .....................................................20

The Impacts of Population................................................................................23

Using Economic Techniques To Improve Environmental Targeting ...............23

Future Research Directions.................................................................................30

Priorities in Environmental Benefits Related to Agriculture ...........................30

Other Environmental Benefits Recognized by the EBI ...................................31

Environmental Benefits Not Recognized by the EBI.......................................31

Improving the Technical and Theoretical Approaches.....................................32

Expected Advances Outside the Area of Economics .......................................32

The Future of Environmental Targeting ...........................................................32

References...........................................................................................................33

Appendix ADescription of the Water-Quality Valuation Model ....................39

Appendix BDescription of the Pheasant-Hunting Valuation Model ..............43

Appendix CDescription of the Wildlife-Viewing Valuation Model ...............48

Appendix DDescription of 15th CRP Signup Criteria ...................................55

ii Economic Research Service/USDA
Summary

Many conservation programs, such as USDAs Conservation Reserve Program,

improve environmental quality. Improving environmental quality leads to

enhanced ecosystem health, in general, and also augments the publics enjoyment of recreational activities. This report examines the effect of environmental

targeting of the CRP on the magnitude and type of environmental benefits and

outdoor recreational opportunities created by an agricultural land conservation

program.

Environmental targeting refers to the practice of directing program resources to

lands where the greatest environmental benefit is generated for a given expenditure, or alternatively, specific environmental goals are achieved for the least cost.

Ecosystem health and many outdoor activities, such as water-based recreation,

hunting, and nature viewing, are likely to have substantial value to individuals,

but these activities typically have no explicit price (or market value) associated

with them. In practice, therefore, environmental targeting presents the difficult

problem of how to value and aggregate the benefits derived from environmental

improvements.

Prior to 1990, CRP targeting was primarily based on soil erodibility. The current CRP targeting method is based on a broader range of environmental effects.

Central to the current targeting method is a land-scoring process known as the

Environmental Benefits Index (EBI). After the CRP signup period closes, each

parcel of land offered under the program is scored based on the EBI. Parcels

with the highest score are given priority for acceptance into the program. The

EBI includes physical characteristics of land (erodibility, soil leachability, proximity to waterbodies, etc.), and measures of locally affected populations (number of well-water users). 

What happens if alternative specifications of the current EBI are used to target

the CRP? This report uses nonmarket valuation models for three activities that

the CRP is likely to affectfreshwater-based recreation, wildlife viewing, and

pheasant huntingto demonstrate an alternative approach for targeting the CRP.

The results are as follows:

 Switching CRP targeting criteria from erodibility to the EBI approximately doubles the benefits of freshwater-based recreation and

wildlife viewing.

 CRP wildlife recreation benefits are significantly larger than freshwater-based recreation benefits. Based on the distribution of enrollment as of 1992, benefits include $348 million per year for wildlife

viewing, $80 million per year for pheasant hunting, and $36 million

per year for freshwater-based recreation.

 Natural resources that are near populated areas are likely to generate

larger recreational use benefits simply because they give residents relatively easy access to natural resources. Taking affected populations

into account when targeting CRP lands could increase the benefits of

several types of outdoor recreation.

Economic Research Service/USDA iii
 Valuation-based targeting of the CRP is feasible and might improve

its performance if public preferences are known and explicit. The

major advantage of a valuation-based targeting system is that it is

directed by public preferences. Although there will always be some

benefits that are difficult to quantify in monetary terms, developing a

valuation-based targeting system is feasible.

To fully implement valuation-based targeting of the CRP, more research is

required. Nonmarket benefit models would need to be estimated for all of the

primary nonmarket activities that are sensitive to the location and characteristics

of the CRP. These activities include:

 The remaining recreational uses significantly affected by the CRP,

 Public works and industrial operations that are affected by reduced

sediment loadings,

 Improved air quality,

 The public willingness to pay for the existence of wildlife augmented

by the CRP,

 Landscape amenities associated with the CRP, and 

 The effect of CRP on the quality of surface and ground water used for

drinking.

iv Economic Research Service/USDA
Economic Research Service/USDA 1

Introduction

The goal of maximizing the net benefits of rural land

conservation programs through an appropriately designed acreage selection process is continually growing in public importance. The Conservation Reserve

Program (CRP) provides an excellent example of an

evolving process to select appropriate acreage to conserve. At the programs outset, environmental quality

improvements were viewed as correlated with reducing soil erosion, a view that led to program rules that

restricted eligibility primarily to highly erodible lands.

In recent years, this view has evolved to recognize the

value of a broader set of attributes that characterize

our rural lands. To capture this complexity, a panel of

experts devised, and periodically modify, a system

that awards points to potential program acres based

upon multiple attributes of the land. Actual enrollment is limited to the acres with the highest scores.

This system, in which points represent the panels

assessment of the attributes importance to society, is

an example of a targeting mechanism.

This report will demonstrate how economics can help

target farm program acres so that the greatest net benefits are captured. This analysis measures the publics

willingness to pay (in dollar terms) for a variety of

environmental impacts. This approach uses nonmarket valuation techniques to quantify the environmental

impacts of the CRP. Specifically, the CRP converts

cropland into grasslands or forest lands, which can

enhance the natural environment in ways that people

care about. For example, soil erosion reductions from

the CRP can improve fishing opportunities, and habitat preservation can help protect endangered species.

Nonmarket valuation techniques offer a means of

measuring the dollar value of these enhancements.

This report will demonstrate how estimates of nonmarket values provide a far more robust set of information for the targeting of agricultural conservation

programs. This in turn, can lead to improvements that

strengthen program evaluations by facilitating the

comparisons of costs against a full range of benefits.

The range of benefits provided by the environment is

both broad and difficult to measure. The limited literature on the valuation of the environmental impacts of

the CRP is often characterized by large-scale regional

analysis and fails to account for the broad array of

natural resources affected by the CRP. This report

expands upon prior work by demonstrating how

recent improvements in economic valuation techniques provide a way in which environmental targeting mechanisms, such as those used to allocate the

CRP, can be more rigorously evaluated. In addition,

this analysis can be used to indicate modifications to

targeting mechanisms that may increase environmental benefits relative to program cost.

The analysis specifically examines how one form of

environmental targeting of the CRP can affect the

Nations enjoyment of outdoor recreation. Although

there are nonenvironmental1 and many other environmental effects of the CRP (aside from outdoor recreation), recreational activities are highly valued and

frequently involve market-based activities (such as

travel) from which dollar-based benefits can be

derived. For example, one-third of the U.S. population engage in wildlife viewing, one-quarter engage in

freshwater fishing, and over half visit a beach or

Economic Valuation of Environmental

Benefits and the Targeting 

of Conservation Programs

The Case of the CRP

Peter Feather, Daniel Hellerstein, and LeRoy Hansen

1Although this report focuses on nonmarket benefits, the

CRP has many nonenvironmental impacts (such as farm

income support) that may also be important when targeting

program acres. 
waterside (Cordell and others, 1998). The economic

impact of these activities can be substantial. For

example, Americans spent approximately $100 billion

on fishing, hunting, and wildlife-watching activities in

1996 (U.S. Dept. of the Interior, Fish and Wildlife

Service and U.S. Dept. of Commerce, Bureau of the

Census, 1997), and the net benefit of freshwater recreation has been estimated as $32 billion per year

(Mitchell and Carson, 1989). 

To fully capture the effects of the CRP on recreation,

we faced two practical considerations that dictate the

choice of modeling techniques. First, many of the

public benefits from the CRP result from changes in

land-use patterns that occur in their immediate surroundings. Furthermore, given the size and extent of

the CRP, changes in program acreage are likely to

affect local conditions in areas across the Nation.

Hence, this analysis uses spatially disaggregated

models that are applicable across a wide geographic

area to capture the impacts of variations in CRP

enrollment. Second, when estimating the benefits of

the CRP, one must account for the multiple-site

nature of rural recreation. This report introduces several new models that recognize this need. It also

takes advantage of new sources of survey data on

recreational choices and incorporates recent advances

in econometrics and geographic information systems. 

We focus on three activities that are thought to be significantly affected by enrolling environmentally sensitive lands into the CRP: water-based recreation,

pheasant hunting, and wildlife viewing. The data and

models used in this analysis are prototypes, and as

such, are not meant to deliver definitive policy analysis. With that caution in mind, we draw several conclusions from this limited analysis:

 Using an Environmental Benefits Index (an

EBI) can substantially increase environmental benefits of the CRP (relative to use

of erodibility-based criteria).

 The wildlife benefits of the CRP are larger

than the water-quality benefits. 

 Considering the proximity of environmental

impacts to human populations improves the

ability to target areas with the highest environmental benefits per dollar of program cost.

This report begins with an overview of how agriculture affects the Nations environment. We present a

short summary of programs and policies designed to

ameliorate the negative environmental effects of agriculture, followed by a longer description of the

Conservation Reserve Program, the largest such program. The main analysis of this report concludes with

a discussion of future research needs to provide information for improved targeting using economic criteria.

Geography of Agricultural Land Use

Agriculture is a resource-intensive industry, with over

half of the land in the contiguous 48 States and threefourths of freshwater withdrawals devoted to agricultural purposes. The broad extent of agriculture leads

to widespread environmental effects on surface- and

ground-water quality, air quality, fish and wildlife

habitats, species diversity, and land characteristics.

Box 1 summarizes these effects.

Agricultural lands are not necessarily located in

remote, sparsely populated areas. Approximately

one-half of the American population lives in a county

that is at least 25 percent agricultural, and over twothirds live in counties where agriculture comprises at

least 10 percent of the landscape (table 1). Even in

metropolitan (Butler and Beale, 1994) counties,

almost one-third of the population lives in counties

composed of at least 25 percent agricultural land. In

2 Economic Research Service/USDA

Table 1—Percentage of U.S. population living in counties with varying levels of farmland1

Percentage of county Percentage of U.S. population that is living in counties with at least—

that is farmland 10-percent 25-percent 50-percent 75-percent

Item (average) farmland farmland farmland farmland

All 50 70 46 23 9

Metro2 39 49 30 14 4

1Excludes Alaska and Hawaii.

2Metro counties defined by Butler and Beale, 1994.

Source: Data from the 1992 Agriculture Census and the 1990 United States Census of Population. 
Economic Research Service/USDA 3

Box 1Environmental Impacts of Agriculture

Impacts on Surface-Water Quality and Quantity

Agriculture is a primary source of nutrients in impaired surface waters; nutrients are the leading cause of

water-quality impairments in lakes and estuaries and the third leading cause in rivers (USEPA, 1995).

Siltation is one of the leading pollution problems in U.S. rivers and streams; and among the top four problems

in lakes and estuaries (USEPA, 1995).

The most frequently detected herbicides in surface waters include several triazines (atrazine, cyanazine, and

simazine), acetanilides (metolachlor and alachlor), and 2,4-D. These are among the highest in current agricultural use (USGS, 1997).

Impacts on Ground-Water Quality 

The drinking water of an estimated 50 million people in the United States comes from ground water that is

potentially contaminated by agricultural chemicals (Nielson and Lee, 1987). 

From its 1988-90 survey of drinking water wells, the EPA found nitrate in more than half of the 94,600 community water system wells and in almost 60 percent of the 10.5 million rural domestic wells. Levels exceed

minimum recommendations in 1.2 percent and 2.4 percent of the community and rural wells, respectively

(USEPA, 1992). 

Ground-water levels are declining from 6 inches to 5 feet annually beneath more than 14 million acres of irrigated land (Sloggett and Dickason, 1986). Ground-water overdrafts tend to permanently increase pumping

costs, lead to land subsidence which compacts the aquifer's structure, and can cause saltwater intrusion

(USDA/ERS, July 1997).

Impacts on Air Quality

Soil particulate and farm chemicals are carried in the air we breathe. The highest concentration of commonly

used agricultural herbicides, triazine and acetanilide, has been found in the areas where they are used most

frequently and in the highest amounts (Goolsby and others, 1993).

Impacts on Wildlife Habitat and Ecological Diversity 

Habitat loss associated with agricultural practices on over 400 million acres of cropland is the

primary factor depressing wildlife populations in North America. Modern farming methods brought about

dramatic reductions in many species, including cottontail rabbits and ring-necked pheasants (Risley and others, 1995; Wildlife Management Institute, 1995).

Annual wetland loss fell from the 458,000-acre average of the mid-1950's through the mid-1970's, to a

290,000-acre average between the mid-1970's and mid-1980's (U.S. Dept. of the Interior, 1994). Wetland losses often reduce biodiversity because many organisms depend on wetlands and riparian zones for feeding,

breeding, and shelter (NRC, 1995).

Agriculture is thought to affect the survival of 380 of the 663 species listed federally as threatened or endangered in the United States (USDA/ERS, July 1997).

Source: USDA, ERS.
fact, many State and local governments have developed programs that provide incentives to preserve

farmland near populated areas. The landscape amenities offered by some types of agricultural land use furnish open spaces and visual prospects that are increasingly valued by growing suburban populations

(American Farmland Trust, 1997).

Because such a large proportion of the U.S. population resides near agricultural land and because agriculture significantly affects the environment, the way

agricultural land is managed is likely to affect human

health, recreational activities, and general well-being.

The challenge of designing an environmental targeting

mechanism that brings the greatest benefits relative to

costs is not merely to identify agricultural land uses

causing the largest ecological impacts, but also to

consider how important these impacts are to the

American public. 

Improving Agricultures

Environmental Performance

Farmers and the Federal Government support a variety of actions that mitigate the potentially adverse

effects that agriculture may have on the environment

and on human health. Some of these actions include

adopting more environmentally benign practices, or

removing environmentally sensitive land from active

production. Most of these actions incorporate some

aspect of environmental targeting, defined by a focus

of effort and expense on selected areas. A few examples of these practices include:

Erosion reduction: Conservation tillage, reduced

tillage, and other crop residue management practices

help reduce soil erosion (Conservation Technology

Information Center) and improve habitat for some

wildlife populations (Best, 1995). 

Nutrient and animal waste management: Careful

planning of fertilizer application, constructing of

manure storage facilities, and other improvements can

limit surface-water runoff and ground-water infiltration of nitrates and other potentially harmful chemicals (Feather and Cooper, 1995; Glover, 1996; Letson

and Gollehon, 1996).

Irrigation efficiency and waste-water management:

Monitoring soil moisture, improving water application

4 Economic Research Service/USDA

Box 2USDA Programs That Encourage Farmers To Use 

Environmentally Benign Practices

Environmental Quality Incentives Program: Encourages farmers and ranchers to adopt practices that reduce environmental and resource problems. Producers who enter into 5- to 10-year contracts are offered technical assistance,

education, cost sharing, and incentive payments.

Wildlife Habitat Incentives Program: Provides cost sharing to landowners for developing habitat for upland

wildlife, wetland wildlife, threatened and endangered species, and fish and other types of wildlife.

Conservation Technical Assistance: Provides technical assistance to farmers for planning and implementing soil

and water conservation and water-quality practices.

Extension Education: Provides landowners and farm operators with information and recommendations on soil conservation and water-quality practices.

Wetland Compliance (Swampbuster): Makes landowners ineligible for any Federal assistance, loans, insurance, or

disaster payments for any year in which an annual crop is planted on converted wetlands.

Conservation Compliance: Requires producers who farm highly erodible land to implement a soil conservation

plan to remain eligible for certain farm program benefits.

Wetland Reserve Program: Provides easement payments and restoration cost-shares to landowners who return previously converted, or presently farmed, wetlands to wetland conditions.

Source: USDA, ERS.
technologies, and capturing wastewater can limit

salinization and related impacts on ground and surface

waters (Aillery and Gollehon, 1997).

Integrated pest management: Using scouting, spot

applications, and biological and cultural pest management may reduce damages from agricultural chemicals (Zalom and Fry, 1992).

Land retirement: Permanent and semi-permanent

retirement of cropland to more environmentally benign

land uses reduces erosion and creates habitats for

wildlife. Grass filter strips, wetland preserves, and cropland retirement are primary examples of land retirement.

USDA has initiated several programs that rely on education, financial assistance, and technical assistance to

encourage farmers to adopt environmentally benign

practices (see Box 2). Another way to achieve these

goals is through land retirement. Land retirement is

relatively easy to administer (in terms of monitoring

for compliance) and is more likely to produce anticipated improvements in environmental quality than

approaches that seek to modify agricultural production

practices (Young and Osborn, 1990). However, it can

require relatively large financial incentives to farmland owners.

In terms of magnitude of cost and acreage, the largest

American land retirement program is the Conservation

Reserve Program (CRP). First authorized by Title XII

of the 1985 Food Security Act (USDA/ASCS, 1986),

the CRP pays for long-term idling of approximately

36 million acres (about 10 percent) of the Nations

cropland (see Box 3). The volunteer owner and/or

operator receives 50 percent of the cost of establishing

permanent perennial cover on the land and an annual

rental payment in return for leaving the land idle for

10 or 15 years. The original goals of the CRP were

(P.L. 99-198):

(1) Reducing soil erosion.

(2) Protecting soil productivity.

(3) Reducing sedimentation.

(4) Improving water quality.

(5) Improving fish and wildlife habitat.

(6) Curbing production of surplus

commodities. 

(7) Providing income support for farmers. 

The original (1986 to 1989) CRP contracts based eligibility, and thus acceptance, primarily on reductions

in soil erosion. The Food, Agriculture, Conservation,

and Trade Act of 1990 (P.L. 101-624) redirected the

enrollment selection to include a variety of factors

that explicitly considered water quality, soil erosion,

and other environmental concerns (USDA/ERS, 1994,

p. 177).

Economics of the CRP

The idling of millions of acres of cropland under the

CRP has affected virtually all citizens in some manner. The impacts of the CRP occur both on and off

the farm and affect public and private parties.

Potential costs and benefits can be divided into two

categories: private and public. Public costs and benefits occur primarily off the farm while private costs

and benefits occur primarily on the farm. The former

category captures off-site changes in water quality,

air quality, and wildlife habitat that accrue to society

in general. The latter category captures changes in

the welfare of agricultural producers themselves,

such as changes in income, production costs, and soil

productivity.

Table 2 summarizes previous estimates of the costs

and benefits of the CRP. These estimates were computed shortly after the program started. While not a 

Economic Research Service/USDA 5

Box 3The Conservation Reserve

Program

The Conservation Reserve Program is a longterm land retirement program designed to mitigate agriculture's adverse affects on the environment. When originally established under Title

XII, Subtitle D of the Food Security Act of 1985,

its purpose was to conserve and improve soil,

water, and wildlife resources by establishing

cover on highly erodible and other environmentally sensitive land through 10- and 15-year leases. The Food, Agriculture, Conservation, and

Trade Act of 1990 continued the program's

enrollment authority through 1995 and redirected

enrollment criteria to include factors other than

erodibility. The Federal Agricultural

Improvement and Reform Act of 1996 gave the

Secretary authority to conduct signups through

2002 with a 36.4-million-acre cap on enrollment.

Source: USDA, ERS.
complete accounting of all costs and benefits, they

illustrate the economic magnitude of the programs

effects. The two largest benefits are increases in the

value of market sales of farm commodities and reductions in commodity deficiency payments from the

Commodity Credit Corporation (CCC).2 These effects

are the result of higher market prices caused by the

idling of formerly cultivated farmland. Offsetting

these benefits are the two largest costs: direct CRP

costs and increased consumer food costs. At the

Federal Government level, the reduction in commodity payments is more than offset by the addition of the

CRPs costs. 

In addition to these effects on agricultural income and

government expenditures, other effects have been

quantified that largely occur in the public sector of the

economy, and primarily accrue to individuals living

off the farm. Of these, the largest estimated benefit is

from improved recreation resulting from the environmentally enhancing effects of the CRP. Links

between CRP lands and environmental improvements

are fairly well documented at the aggregate level (see

Box 4). For example, improved water quality leads to

increased enjoyment of water-based recreation activities while the improved species habitat provided by

the CRP results in better hunting and wildlife-viewing

opportunities. 

If the CRP, or other conservation programs, could be

targeted to provide more societal benefits for the same

costs, these programs would use resources more efficiently. Some efforts have already been made in this

direction. In the initial signup periods that occurred

between 1986 and 1989, selection of land into the

CRP depended primarily on erodibility criteria, which

were assumed to coincide with the first five (environ6 Economic Research Service/USDA

Table 2—Estimated costs and benefits of the Conservation Reserve Program1

Type of cost Benefit/ <Cost> Source

or benefit

Million dollars

Public:

Public works2 3,029 Ribaudo

Air quality3 548 Ribaudo and others; Huszar and

Piper

Recreation4 8,676 Ribaudo and others; John 

and others

Commodity Credit Corporation

cost savings 17,850 Young and Osborn

Increased food costs <18,950> Young and Osborn

Direct program costs <23,700> Young and Osborn

Private:

On-farm income5 20,300 Young and Osborn

Timber production6 5,400 Young and Osborn

Establish cover crops <1,600> Young and Osborn

Increased soil productivity 1,600 Ribaudo

Irrigation ditch maintenance 41 Ribaudo

Reduced industrial costs7 1,021 Ribaudo

1Costs and benefits for the entire program over a 10-year period discounted at a 4-percent rate. All estimates are based on the anticipated

enrollment of 45 million acres when the analyses were conducted unless otherwise noted.

2Includes cost savings associated with reduced maintenance on roadside ditches, navigation channels, water treatment facilities, municipal

water uses, flood damage, and water storage.

3Includes reduced health risks and cleaning costs associated with blowing dust.

4Includes sport-fishing, small-game hunting, nonconsumptive viewing, and waterfowl hunting. The latter two categories are based on the prevailing 34-million-acre CRP.

5Estimates vary from $9,200-$20,300 million.

6Estimates vary from $4,100-$5,400 million.

7Includes reduced costs associated with industrial uses, steam cooling, and flood damage.

Source: USDA, ERS.

2Income support through deficiency payments linked to crop

prices no longer exists, but did exist through 1996.
mental) goals listed on page 5. In signups since 1990,

acceptance criteria have been broadened with a combination of environmental indicators factored into the

bid process (USDA/ERS, 1997). It is believed that

these environmental indicators provide a more accurate and comprehensive prediction of land retirement

benefits than simply relying on erodibility. 

The question of where to place future CRP acreage to

obtain greater benefits can be answered by examining

the magnitude and location of these benefits.

Identifying and quantifying where large recreational

benefits could occur and targeting land retirement to

these areas would increase the outdoor recreation benefits of the CRP.

Economic Research Service/USDA 7

Box 4Environmental Benefits of the Conservation Reserve Program

Much of the land entering the CRP had previously been devoted to row crop production. Extensive row crop

production is known to be detrimental to many wildlife populations. By converting row crop lands into

grasslands, the CRP positively affects many wildlife species. Most of the species listed below benefit from

improved habitat and reproductive success.

The relationship between the CRP and water quality is less well understood, but appears to be significant.

Suspended sediment and nutrient run-off generated from farming have been cited as the most damaging nonpoint sources of harm to the U.S. environment (Smith and others, 1987). By retiring highly erodible croplands, it is assumed that the CRP creates large water-quality improvements by reducing soil erosion and nutrient run-off. Based on the original projection of a 45-million-acre CRP, Ribaudo and others (1990) estimate

that the program will reduce soil erosion by almost 750 million tons per year. This translates into large reductions in pollutants. Weitman (1994) estimated that nitrate loadings have declined by 90 percent, sediment and

herbicide loadings by 50 percent, and phosphorous loadings by as much as 30 percent in some U.S. agricultural regions as a result of the CRP.

Source: USDA, ERS.

Species Reference

Ring-necked pheasant Allen (1994), Anderson and David (1992a,b), Berthelsen (1989), 

Little and Hill (1993)

Non-game birds Campa and Winterstein (1992), Dunn and others (1993), Kimmel and 

others (1992), King (1991), Lauber (1991), Sample and Mossman 

(1990a,b) 

Raptors Evrard and others (1991)

Upland nesting waterfowl Berthelsen (1989), Kantrud (1993), Reynolds (1992)

Game birds Kimmel and others (1992), King (1991), Lauber (1991)

Neotropical migrant land birds Rodenhouse and others (1993)

Elk and deer Allen (1993), Newton and Beck (1993)

Eastern cottontail rabbit Allen (1994)
Estimating Nonmarket Benefits of the

Conservation Reserve Program

The basic goal of economic targeting of the CRP is to

retire lands that result in the greatest net social benefits. To accomplish this, knowledge of the benefits of

all activities that are influenced by the CRP is

required. Aggregated measures of benefits for the

whole Nation will not allow land to be targeted at a

desirable, field-level parcel. Instead, these benefits

need to be known at the local or micro level to

effectively target lands to retire. This requires using

models based on individual human preferences.

This report describes three models that account for

selected environmental effects associated with broad

changes in agricultural practices. We focus on how

enrolling cropland into the CRP has affected freshwater recreation, pheasant hunting, and wildlife viewing.

Due to differences in data availability, and in the

nature of each activity, different approaches are used

to analyze these three activities. However, in each

case, the goal is to estimate the value of natural

resource quality at a disaggregated level.

Environmental targeting, which leads to differential

impacts at the micro level, can then be investigated.

Before presenting the modeling results, we outline

the basic theory underlying the measurement of

amenity values of natural resources, as it pertains to

rural land uses (Ribaudo and Hellerstein, 1992).

Next, we review previous CRP valuation studies, and

then discuss how these studies could be improved

with better data and estimation techniques. The three

models featured in the study are then introduced,

along with a discussion of the data sources used. We

use the models to determine the recreational value

(from freshwater-based recreation, pheasant hunting,

and wildlife viewing) of the CRP as it presently

exists, and to determine the consequences of adopting

an alternative targeting mechanism.

Background: Measuring the 

Value of Natural Resources

The net economic benefit an individual receives from

consuming a market good (or service) is defined as

the excess, over and above the market price, that an

individual would pay to consume the good (or service). This net benefit is often referred to as a consumer surplus (Deaton and Muelbauer, 1980).

Although not directly measurable, methods of determining consumer surplus are well known in the case

of conventional goods (or services) traded in a market

with observable prices (see Box 5). Once the consumer surplus associated with consuming the good (or

service) is measured, valuation of quality changes can

be accomplished by comparing consumer surplus

before and after the change.

Valuing a change in natural resources, such as those

that may be affected by environmental targeting of the

CRP, is based on the same principle as for a good or

service that is sold in the marketplace. The main difference is that natural resources often lack a fully

developed market, hence they have no observable

price (Freeman, 1979). The lack of observable prices

complicates construction of measures such as consumer surplus that are based on an excess over

observable price criterion. To address these complications, analysts often employ the concept of the

total economic value an individual may derive from

a natural resource (Randall and Stoll, 1983). Total

economic value is essentially the same as net benefit,

but recognizes that the value derived from the quality

of the environment can be subdivided into two main

categories:

(1) Use valuethe value an individual

derives from directly using the resource. 

(2) Nonuse valuethe value given to the

existence of an environmental resource

even though it is not currently used. 

Use values are associated with activities such as

swimming, hunting, and viewing nature where the

individual comes into direct contact with the environment. These values also include commercial uses of

natural resources, such as fishing, and consumptive

uses, such as clean air and drinking water. Nonuse

values are less tangible since they arise from environmental preferences rather than direct use. Three categories of nonuse values are (Smith, 1996):

(1) Existence valuethe value derived from

knowing that the resource is maintained. 

(2) Bequest valuethe value the current

generation gains from knowing that

the resource is preserved for future 

generations.

8 Economic Research Service/USDA
(3) Option valuethe value of preserving the

resource so that the option of using it at

some future date is maintained. 

Table 3 lists approaches for obtaining these values.

Inferred approaches based on market behavior such as

averting expenditures, changes in production costs,

and revealed preference are commonly used to determine use values. The most commonly used method,

revealed preference, assumes that a relationship

between environmental quality and observable behavior exists. Presently, the only method that can be used

to recover nonuse values is contingent valuation.

Although contingent valuation has been criticized as

unreliable (Diamond and Hausman, 1994), it has been

upheld in the United States District Court of Appeals

(U.S. Dept. of the Interior, 1989) and approved for

use by Federal agencies performing benefit-cost

analysis (Arrow and others, 1993; United States Water

Resources Council, 1984). 

Modeling CRP Impacts: A Stylized Framework

To apply the techniques of nonmarket analysis to

environmental targeting of the CRP, we incorporate

the relationship between land retirement and the flow

of nonmarket benefits. This process involves several

steps. For example, figure 1 outlines the relationship

between the CRP and two potentially large use

value impacts: impacts on water quality and impacts

on wildlife. 

 Water Quality

In the case of water quality, retiring croplands

reduces soil erosion and runoff, which results

in lower levels of nutrients, sediments, and

pesticides entering water bodies. This

changes the biological conditions of the water

and directly affects what users of the water

body value. Anglers benefit from larger fish

populations, and boaters, swimmers, and nonEconomic Research Service/USDA 9

Box 5What is Consumer Surplus?

Measuring the contribution to human welfare due to the availability of a good, or service, is a common problem in

applied economics. One approach is to compute consumer surplus. Loosely speaking, consumer surplus is the amount of

money, above and beyond the market price, that a consumer would be willing to pay for a given good. If an individual

buys a good for $X, but would pay a maximum price of $X+$Y, then that individual's consumer surplus is $Y. Most individuals experience consumer surplus in almost every good they purchase. 

Policies that change prices, income, or the quality of goods can be evaluated in terms of how they affect individuals' consumer surplus. Simply put, desirable policies increase consumer surplus more than they increase other costs. 

To compute the consumer surplus for a good, the demand function for that good must be specified. For example, the figure below (a linear demand function), shows that at market price P', the quantity demanded will be Q'. The consumer

surplus is the area below the demand function and above the price.

Source: USDA, ERS.

Price

Demand and consumer surplus

Consumer surplus

P

Q

Demand

Quantity
contact recreationists benefit from clearer,

more aesthetically appealing water.

 Wildlife Habitat

The relationship is similar in the wildlife case.

Establishing grassland or forest cover creates

suitable habitat for birds, small game, and

large game. This, along with improvements

in water quality, increases wildlife populations. Hunters and wildlife viewers then benefit from these increased populations.

In both cases, there is a causal relationship between a

possible distribution of the CRP (as mediated through

a targeting mechanism) and nonmarket benefits.

More specifically, the size, placement, and management of CRP lands affect physical variables. These

decisions then change biological parameters, which

impact habitat quality and fish and wildlife populations. These altered environmental conditions ultimately are reflected in the values the public places on

the environment.

The analyses in this report focus on the values the

public places on the enhanced recreational activities.

This does not imply that CRP affects only recreational

values. However, recreational activities serve as good

examples, because they are often associated with

environmental amenities. Recreational activities tend

to be high-valued activities, so are very relevant when

addressing the value of environmental impacts. And

finally, recreational activities include market-based

activities, such as the travel costs associated with

obtaining access to the natural resource, where the

strength of individuals preferences is demonstrated in

dollar-based terms.

As environmental targeting evolves, determining the

changes in welfare caused by the ensuing changes in

land retirement patterns may involve directly estimating each of these steps in the causal relationships. In

the water-quality case, a physical model translating

changes in soil erosion into changes in observable

biological criteria such as water clarity and fish populations would be required. Similarly, the wildlife case

involves estimating changes in populations of wildlife

species resulting from habitat changes.

10 Economic Research Service/USDA

Table 3——Methods of valuing nonmarket goods

Method Value assessed Description 

Averting or defensive Use value Measuring expenditures made by individuals to reduce or negate 

expenditures pollution damages. Purchasing water filters or bottled water in 

response to polluted water are two examples (Bartik, 1988).

Changes in production Use value Inferring the cost of pollution by observing changes in firm profits,

costs input costs, or output prices due to changes in environmental quality.

Changes in output prices of marketable goods to consumers are one 

way to measure changes in environmental quality. It is also possible 

to measure this cost through changes in incomes of owners of factor 

inputs, or by changes in input expenditures (Freeman and Harrington, 

1990).

Revealed preference Use value Observing individual behavior and inferring the demand for environmental quality (Mendelsohn and Brown, 1983). Typically, recreational 

trips are used to measure the demand for environmental quality and 

the travel cost serves as the price. Conventional demand equations 

are then estimated to determine the value of environmental quality.

This is known as the travel cost approach.

Stated preference Total value Directly asking individuals their willingness to pay for a change in 

environmental quality (Mitchell and Carson, 1989; Randall and others, 

1983). Individuals are either asked to state their willingness to pay 

for a change in quality (open-ended format contingent valuation), 

or asked to vote yes or no to a set amount (referendum format 

contingent valuation), or asked to order scenarios involving varying 

prices and levels of environmental quality (conjoint contingent 

valuation analysis).

Source: USDA, ERS.
Ideally, these physical/biological models would be

readily available. Unfortunately, the data required to

estimate these relationships, and the knowledge of

exactly how physical and biological interactions

occur, are generally not available. Devising

approaches to overcome these deficiencies is the

major challenge facing the applied analyst. Often, an

approach is adopted where a set of environmental

indicators is used to represent the various physical

and biological impacts of some policy. Although not

ideal, this so-called reduced form approach allows

the analyst to partially abstract from the ideal biophysical models, while still incorporating available

biophysical information.

Underlying the use of environmental indicators is the

assumption that the link between physical effects and

what recreationists value can be approximated by

these indicators. In other words, since these indicators are proxies for the underlying environmental

quality ultimately valued by recreationists, individuals

are assumed to indirectly respond to them. For example, measures of the distribution of land types, such as

percent of land in cultivated crops or land in transitional wetlands, may be used as indicators of

the overall abundance of wildlife-viewing 

opportunities.

Review of Prior Studies of the 

Recreational Value of the CRP

When considering where to target the CRP, analysts

need to study how the CRP will affect recreational

activities. Several studies have provided some limited

economic analysis on this topic. Perhaps the best

known are ERS studies that give national estimates of

CRPs effect on freshwater fishing and small-game

hunting from the late 1980s (Ribaudo and others,

1990). The National Biological Survey in the early

1990s ( Allen, 1994; John, 1993, 1994) further analyzed the CRPs impact on the value of waterfowl

hunting and bird watching.

Tables 4-6 describe the important features of these

studies. These studies share a few common features:

 Most of these studies, except Ribaudo and

others (1990), employ land use measures as

environmental indicator variables. This

land use information is generated from

Economic Research Service/USDA 11

Figure 1

Links between CRP acreage and economic benefits 

Step 1: CRP acreage creates physical effects

 ̄

Water quality: Reductions in erosion decrease nutrient, Animal habitat: Creates beneficial grassland habitat 

pesticide, and sediment loadings. for animals.

Step 2: Physical effects translate into biological results

 ̄

Water quality: Decreases in loadings are beneficial Animal habitat: Populations of many wildlife species increase

for fish populations and improve the due to better habitat and water quality.

appearance of the water for recreational 

purposes.

Step 3: Biological results affect consumer welfare

 ̄

Water quality: Improved appearance and smell of Animal habitat: Increased wildlife populations enhance 

water make it more desirable for hunting and wildlife-viewing activities through

contact recreation. Increased fish reduction in effort.

populations reduce angler effort.

Surface-water bodies are more 

visibly appealing for noncontact users.

Source: USDA, ERS.
12 Economic Research Service/USDA

Table 4—The 1990 ERS Freshwater Fishing Study 

Environmental indicator An “acceptable” water-quality (WQ) variable is defined for approximately 100 sub-State Wildlife 

Management Zones. This variable is based on sediment and chemical concentrations detected 

in surface waters at water-quality monitoring stations.

Econometric model Using 1980 FHWAR* data, the authors estimated a two-stage probability-of-participation/quantitygiven-participation model. Both stages use the WQ variable and personal characteristics as explanatory variables; the quantity stage also uses reported distance to most visited site as a price proxy.

Impact of CRP A set of models are used to link erosion (and chemical runoff) rates from CRP land to levels of 

in-stream loadings of pollutants.

Effect of impact By using multi-State data at the multi-State farm production region scale, in-stream loading changes 

are used to estimate a predicted WQ variable. With results from the econometric model, the analyst 

computes a new average quantity of trips (by multiplying the predicted probability of participation 

by the predicted number of trips).

Value of impact The difference between the new quantity and the observed quantity is multiplied by an average 

value per trip; where the average value is obtained from a review of the valuation literature.

*1980 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. Source: Ribaudo and others (1990).

Table 5—The 1990 ERS Small-Game Hunting Model

Environmental indicator Several land-use proxies for habitat variables (such as percent forest land) are computed at the 

State level.

Econometric model Using FHWAR* data, the authors estimated the probability of being a hunter, and the probability of 

being a small-game hunter; using the habitat variables and personal factors as explanatory variables.

Impact of CRP Adjust the various habitat measures, based on the enrollment of land into the CRP.

Effect of impact The probabilities of being a hunter, or of being a small-game hunter (given that one is a hunter) 

were predicted. Using average number of small-game hunting trips (assumed the same for all 

small-game hunters), a total number of small-game hunting trips is computed.

Value of impact The difference between the new quantity and the observed quantity of small-game hunting trips is 

multiplied by an average value per trip; where the average value is obtained from a review of the 

valuation literature.

*1980 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. Source: Ribaudo and others (1990).

Table 6—The 1992 NBS Waterfowl Hunting and Bird Watching Models 

Environmental indicator Several land-use proxies for habitat variables (such as percent forest land) are computed at the

Wildlife Management Zone level. To account for congestion, these were divided by the population of 

the area.

Econometric model Using FHWAR* data, the authors estimated a quality of experience/quantity of trip model. Both 

stages use the habitat measure, the distance to most preferred site, the distance to farthest site, 

and personal factors. In addition, the quantity model uses the predicted quality.

Impact of CRP Adjust the various habitat measures, based on the enrollment of land into the CRP.

Effect of impact For current participants, the quality measure is re-estimated, and then used to compute a predicted 

number of trips.

Value of impact Using reported distance as a proxy for price, a “marginal value” of the habitat variables is computed. More important, the demand curve is integrated to compute a consumer surplus.

*1980 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation. Source: Allen, 1994; John, 1993, 1994.
USDAs Natural Resources Conservation

Services National Resources Inventory

(NRI), and is commonly incorporated as

sub-State averages such as the percentage

of farmland in a Wildlife Management

Zone. Because Ribaudo (1989) was estimating water-quality benefits, his model

employed measures of water quality.

 All of the studies rely on the U.S. Fish and

Wildlife Services National Survey of

Fishing, Hunting, and Wildlife-Associated

Recreation (FHWAR) as a primary source

of data on individuals recreational activity

(i.e., behavioral data). This survey is one of

the few nationwide sources of data devoted

to outdoor recreation. Unfortunately, due to

confidentiality and other factors, the geographical specificity of the data is limited to

sub-State regions.

 In each study, the fishing and small game

models use per-trip values, obtained from

the travel cost literature, to impute changes

in value due to the CRP. The waterfowlhunting and wildlife-viewing models directly compute a consumer surplus from their

estimated demand curves.

 These studies used econometric methods

that were based on highly simplified statistical models and aggregated data. 

Features of the Nonmarket Benefit Models

These prior analyses of the nonmarket impacts of the

CRP relied on highly aggregated data and relatively

unsophisticated estimation methods. Measures of

resource availability were based on regional averages

instead of smaller more localized areas. Some of the

studies extrapolated benefit measures from related

studies rather than determining the benefits from

behavioral data. The imputation of trip prices was

often based on the most recent destination visited

rather than a consideration of the entire set of choices

made by the respondent. The econometric models

employed had limited scope and handled quality

changes at substitute sites in a simplified fashion.

In the context of environmental targeting, the final

point is crucial. That is, environmental targeting is

driven by the notion that careful placement and management of the CRP (or other similar programs) will

increase per acre (or per program dollar) benefits. An

all or nothing analysis of the entire CRP, which typifies the above models, is ill suited to examine the

more subtle and place-specific changes likely to arise

by variations in a targeting mechanism. 

The models constructed for this study are designed to

circumvent some of the shortcomings of prior models

and are formulated to allow a focus on the effects of

environmental targeting.3 The foremost change is an

improvement in the resolution of the geographical and

behavioral data; the models are better suited to deal

with substitute sites. Additionally, the range of activities analyzed is extended from earlier studies in the

case of freshwater-based recreation (as opposed to

freshwater fishing), and all wildlife viewing (as

opposed to bird-watching). The single activity model

(pheasant hunting) is also presented as an example of

an environmental amenity (the size of the pheasant

population) that is thought to be heavily affected by

the CRP. Each of the three separate models incorporates some of the improvements listed in Box 6.

Data Used in the Nonmarket Benefit Models

Examining the nonmarket impacts of environmental

targeting requires information about recreational

behavior and the natural resource base (see Box 7).

In this study, three primary databases are used: the

1995 National Survey of Recreation and the

Environment (NSRE), the 1991 National Survey of

Fishing, Hunting, and Wildlife-Associated Recreation

(FHWAR), and the 1992 National Resources

Inventory (NRI).

In the water-quality component of this study, behavioral information is derived from an ERS-funded component of the 1995 NSRE. The component of the

NSRE used in this analysis was undertaken specifically to collect data on water-based recreation. Four subState regions located in Washington, Nebraska,

Indiana, and Pennsylvania were sampled.

Respondents were asked to recall the number of trips

Economic Research Service/USDA 13

3Presently, much of the environmental valuation literature is

poorly suited for this type of problem. Most travel cost models focus on a single site or incorporate a relatively small

number of substitute sites that include no measures of site

quality. These models are good at examining demands in

localized areas, but less effective at considering changes at the

national level. Contingent valuation models often examine

global changes with minimal focus on geographic variability.
taken to wetlands, lakes, and rivers less than 100

miles from their residences within the last 12 months

in cases where water was an important reason for the

trip. The sample contains information on 1,510 persons evenly divided among the four areas. About 50

percent of the respondents participated in at least one

freshwater-based activity, with participants averaging

10 trips per year. 

The pheasant-hunting model and the wildlife-viewing

model use the 1991 FHWAR, which measures participation in wildlife-based outdoor recreational activities. A two-stage sampling design is used, with a

quarter of a million people asked screening questions

regarding their overall participation in hunting, fishing, and wildlife viewing. A followup survey, using

those judged most likely to be active participants, was

then conducted. Approximately 50,000 individuals

reported wildlife-associated recreation, with about

half reporting nonconsumptive activities (i.e., wildlife

viewing) and half reporting fishing and hunting activities. For purposes of the analysis, 5,851 individuals

sampled were identified as potential pheasant hunters

and more than 18,000 individuals sampled were identified as potential wildlife-viewers.

Unlike earlier studies that used FHWAR data, the version of the survey used here contains the ZIP Code

location of each respondents home. This information

helps construct a more accurate description of the

recreational amenities available to each respondent.

The result is a better identification of resource qualities affecting recreational behavior. One drawback is

the lack of precise information about the location of

14 Economic Research Service/USDA

Box 6Improvements in Nonmarket Benefit Models in This Study

Better measures of recreational choice.

The 1991 FHWAR survey contains more accurate demographic data. This version of the survey contains

more detailed information on residential location (i.e., ZIP Codes) than was previously available. This

aids in a more exact description of the resources available to each individual. Additionally, because the

survey was conducted after the CRP was in place, it reflects changes in behavior that occurred due to this

program. The ERS component of the 1995 National Survey of Recreation and the Environment was used

to measure freshwater recreation. This survey, although drawn from a limited geographical area, was

designed to highlight the relationship between land use and recreational behavior.

More accurate measures of landscape diversity.

Using sub-county location information in the NRI, in combination with geographical information system

techniques, estimates of relevant landscape features were derived throughout the United States at higher

resolution than otherwise possible. In addition to providing a more accurate description of an individual's

environment, this higher resolution permits local variations in the landscape to be accounted for.

Better estimation techniques.

Recent advances in estimation methods allow quality variations to directly influence consumer behavior.

In addition, these models are better able to simultaneously account for both the decision to engage in a

recreation activity and the intensity of participation.

Individual based benefits measures.

Economic theory dictates that marginal measures should be used to compute the impacts of changes in

an individual's choice set. In practice, this suggests using individual specific demand curves whenever

possible, rather than average trip values derived from studies of similar populations. Using estimators

that can exploit higher resolution data, the ability to obtain and use individual specific demand curves is

enhanced. 

Source: USDA, ERS.
the destination visited by each respondent. The only

information pertaining to destinations is the State visited and the distance traveled.4

We used the 1992 National Resources Inventory (1992

NRI) to describe the natural resource base in all three

studies. The 1992 NRI is the most recent of a series of

inventories conducted every 5 years by the U.S.

Department of Agricultures Natural Resources

Conservation Service (NRCS). The NRI contains

information on the status, condition, and trends of land,

soil, water and related resources on non-Federal land in

the United States. The survey is scientifically designed

and based on recognized statistical sampling methods.

To obtain a national sample of land use characteristics, NRCS samples more than 800,000 locations.

Each datum, or point, represents a homogenous area

of land. Because the location of these points is not

available due to confidentiality restrictions, the point

data are aggregated into larger areas which this analysis uses. These areas, termed NRI polygons are

constructed by aggregating points found in 14,414

nonoverlapping subcounty regions formed by the

intersection of county boundaries, NRCS Major Land

Resource Areas, and USGS hydrological units in the

continental United States.5

Freshwater-Based Recreation Results

The NRI, which describes the physical conditions

where recreation occurs, contains information on erosion levels but not on pollutant loadings. In terms of

the links between CRP acreage and economic benefits

highlighted in figure 1 (pg. 11), only the information

describing the physical effects of CRP acreage measured by soil erosion is available (Step 1). In the

absence of detailed data describing how those physical effects impact the health of the ecosystem (Step 2)

and ultimately affect consumer welfare (Step 3), it is

assumed that erosion influences recreational behavior

via unspecified biological and physical processes. 

Models of lake- and river-based recreation were estimated from the regional NSRE data described in the

preceding section. NRI polygons served as possible

sites that recreationists might visit, and are described

by average soil erosion and other physical data found

Economic Research Service/USDA 15

Box 7Data Requirements for Recreation Demand Models

Recreation demand models require information about recreational behavior and the natural resource base. In

this study, three principal sources of data are used:

(1) Data on the natural resource base from the National Resources Inventory

 More than 800,000 locations sampled nationwide.

 Extensive land use information.

 Soil and land cover information.

(2) Water-quality model behavioral data from the National Survey of Recreation and the Environment

 Sampled 1,500 respondents in four dispersed regions of the United States.

 Focused on water-based recreation.

 Information on up to nine locations visited over the past year.

(3) Pheasant-hunting and wildlife-viewing model data from the National Survey of Fishing, Hunting, and

Wildlife-Associated Recreation

 Extensive sample of more than 50,000 individuals nationwide.

 Sophisticated sampling and data collection techniques.

 Information on a wide variety of activities. 

Source: USDA, ERS

4An earlier version of the FHWAR contains slightly better

information on destination location. Instead of identifying

destination by State, a sub-State wildlife management zone

was identified.

5The average size of an NRI polygon is 132,365 acres; with

sizes ranging from 100 acres to over 3 million acres.
in the NRI (see Appendix A for details). The regional

models were then transferred to the entire United

States using benefit-transfer techniques. These techniques involve transferring the actual demand equation rather than a single point estimate as was done in

earlier studies. Table 7 shows the total annual consumer surplus estimates. The combined benefits of all

water-based recreation in lakes and rivers throughout

the United States are slightly over $37 billion per

year. Recreation occurring at lakes accounts for the

majority of these benefits. This estimate of the value

of water-based recreation is similar in magnitude to

estimates by Carson and Mitchell (1993). They calculated that the annual benefits of achieving a swimmable water-quality goal at all water bodies in the

United States range from $24 billion to $40 billion.

The CRPs contribution to these benefits is determined by calculating the difference in consumer surplus with and without the program. To predict consumer surplus without the program, subsequent erosion levels were predicted using the universal soil loss

equation found in the NRI. These new erosion estimates were then used in the recreation demand model

to predict consumer surplus without the CRP. The

difference between consumer surplus levels with

existing and no-CRP erosion rates appears in table 7.

Again, the majority of the benefits are to lake-based

recreation. The total annual contribution of CRP to

all freshwater-based recreation is approximately $35.4

million. This estimate is of the same magnitude as

Ribaudos (1989) estimate of $21.4 million, but larger

because Ribaudo considered only fishing whereas this

model considers all freshwater recreation. Compared

with the total consumer surplus estimates, the (1991)

CRPs contribution to benefits of water-based recreation is small. Total benefits are highest in the South

Eastern, Pacific/Mountain, and North Eastern regions.

Both total CRP benefits and benefits per acre are

highest in the South Eastern and North Eastern

regions. This is not surprising since these regions

contain a large portion of the U.S. population and a

large number of surface-water recreation sites.

Pheasant-Hunting Recreation Results

Biological evidence indicates that the CRP mostly

affects avian species (Allen, 1994). Ring-necked

pheasants, in particular, have been shown to be influenced by use of agricultural lands and the presence of

CRP lands (Basore and others, 1987; Hill, 1976; Jahn,

1988; Messick, and others, 1974, Minn. Dept of

Natural Resources, 1985; Warner, 1979 and 1984;

Warner and others, 1984). Furthermore, data from the

1991 FHWAR indicate that the pheasant is the most

popular upland game bird throughout the Midwest.

For these reasons, pheasant hunting, a component of

small-game hunting, is presented as a species-specific

special case.

Like the freshwater-based recreation model, an ideal

pheasant-hunting demand model would contain a component linking biological activity with human behavior.

Establishing a model linking CRP to pheasant popula16 Economic Research Service/USDA

Table 7—Freshwater-based recreation: Consumer surplus by region

Total consumer surplus2 Consumer surplus due to CRP3 Consumer surplus4

Region1 Lake River Lake River

- - - - - - - - - - - Million dollars per year - - - - - - - - - - - $/Acre/year

Pacific/Mountain 7,423.93 1,004.65 1.27 0.42 0.21

Northern Plains 685.66 95.71 2.13 0.34 0.28

Southern Plains 2,628.66 353.49 1.34 0.13 0.29

South Eastern 6,743.19 1,364.27 8.90 1.87 2.93

North Eastern 14,524.97 2,570.00 17.33 2.61 2.45

Total 32,006.41 5,388.12 30.98 5.37 1.07

1The Pacific/Mountain region contains WA, OR, CA, MT, ID, WY, NV, UT, CO, AZ, NM; the Northern Plains region contains ND, SD, NB, KS;

the Southern Plains region contains OK, TX; the South Eastern region contains AR, LA, MS, AL, GA, SC, FL, TN, NC, VA, KY, WV; the North

Eastern region contains MN, WI, MI, IA, MO, IL, IN, OH, PA, NY, VT, MD, DE, NJ, RI, CT, MA, NH, ME.

2Annual consumer surplus evaluated at estimated erosion levels resulting from acres in the CRP in 1992 (signups 1-11). Lake and river refer

to recreation occurring at lakes and rivers (respectively).

3This is the difference between total annual consumer surplus with CRP and the total without CRP. Erosion levels observed in 1982 were

used in place of observed erosion in the latter case.

4Consumer surplus attributable to the CRP on a per acre basis.

Source: USDA, ERS.
tions, which actual and potential hunters value, would

be desirable in this case. Unfortunately, the data

required to accomplish this are not available. However,

given the numerous studies documenting the improvements in pheasant habitat from CRP land, a reducedform model such as was used in the water-based recreation demand case is defensible. In this study, it is

assumed that hunters value pheasant populations, which

are directly affected by availability of CRP land. 

The model was estimated from the recreational data in

the FHWAR and the environmental data from the NRI

(see Appendix B for details). Because pheasant hunting is not significant across the Southern Plains and

South Eastern regions of the United States, this study

assumes that pheasant-hunting benefits in these areas

are insignificant. Therefore, this study calculates no

pheasant-hunting benefits in these regions.

Based on the population-weighting information (provided in the FHWAR survey), total consumer surplus

associated with pheasant hunting is estimated to be

$184 million annually (table 8). Dividing this total by

the number of trips observed in the survey shows that

the average value of a day spent pheasant hunting is

approximately $23. This result is consistent with

Walsh, Johnson, and McKean who found that, across

the Nation, the value of hunting upland game birds

averaged $27 per day. 

To determine (the 1991) CRPs contribution to the

benefits of pheasant hunting, the consumer surplus

associated with pheasant hunting without the CRP

must be predicted. The first step in doing this was to

determine the agricultural land use patterns that would

likely exist with no CRP. Following Osborn (1993), it

was assumed that any areas with CRP land would

have the land use observed when the 1982 NRI was

conducted (which was before the CRP was implemented). The difference between the predicted consumer surplus without CRP and the previous estimate

generates a consumer surplus of $80.3 million.

Dividing the regional estimates by CRP acreage gives

per acre benefits of $0.33 in the Pacific/Mountain

region, $3.00 in the Northern Plains region, and $6.24

in the North Eastern region.

Comparing table 7 with table 8 indicates that, first, the

total consumer surplus estimate for freshwater-based

recreation is much larger than the total consumer surplus estimate for pheasant hunting. The popularity of

freshwater-based recreation compared with pheasant

hunting explains this. Freshwater-based recreation

includes a wide range of activities involving a substantial proportion of the population across the United

States. Pheasant hunting, on the other hand, is a single activity confined to a limited area. The results

also indicate that the gain in consumer surplus associated with the CRP is larger for pheasant hunting than

for freshwater-based recreation. This reflects the

large impact the CRP has had on pheasant populations

compared with the CRPs impact on freshwater quality. The CRP has had a tremendous positive impact on

pheasant populations (Allen, 1994), which play a critical role in the hunting experience. The effect on water

quality is less dramatic. This may explain why the

Economic Research Service/USDA 17

Table 8—Pheasant hunting: Consumer surplus levels by region

Region1 Total consumer surplus2 Consumer surplus Consumer surplus4

due to CRP3

- - - - - - - - Million dollars per year - - - - - - - - $/Acre/year

Pacific/Mountain 6.50 2.70 0.33

Northern Plains 58.36 26.69 3.00

Southern Plains N/A5 N/A5 N/A5

South Eastern N/A5 N/A5 N/A5

North Eastern 118.85 50.86 6.24

Total 183.77 80.28 2.36

1The Pacific/Mountain region contains WA, OR, CA, MT, ID, WY, NV, UT, CO, AZ, NM; the Northern Plains region contains ND, SD, NB, KS;

the Southern Plains region contains OK, TX; the South Eastern region contains AR, LA, MS, AL, GA, SC, FL, TN, NC, VA, KY, WV; the North

Eastern region contains MN, WI, MI, IA, MO, IL, IN, OH, PA, NY, VT, MD, DE, NJ, RI, CT, MA, NH, ME.

2Annual consumer surplus evaluated at 1992 CRP levels.

3This is the difference between total annual consumer surplus with the CRP and total without the CRP.

4Consumer surplus attributable to the CRP on a per acre basis.

5Negligible pheasant hunting occurs in these regions.

Source: USDA, ERS.
CRP-induced benefits of pheasant hunting are so much

larger than in the freshwater-based recreation case.

Wildlife-Viewing Results

Like the two preceding models, the impact of the CRP

on nonconsumptive, wildlife-oriented recreation

(wildlife viewing) would be best modeled in a multistep process, with changes in wildlife populations,

induced by the CRP, influencing public participation.

Again, the requisite biophysical models and behavioral data are not readily available. Hence, this study

assumes that some relationship exists between land

use and recreational trip taking.

In particular, a representative trip model is used.

This model estimates the total number of trips taken

to all locations as a function of indicators of landscape

characteristics. These indicators, derived from NRI

data, proxy for the size and health of wildlife populations (and other ecological attributes) at recreational

sites that may be available to an individual.

Specifically, measures of the level of CRP, cropland,

forest land, grassland (range and pasture), urbanization, and landscape diversity are used. 

As with the water-quality and pheasant models, a

reduced-form model is used to control for variations

in environmental characteristics. For each individual,

five distance zones are constructed (see appendix figure 1 in Appendix B). For each of these distance

zones, a weighted average of each of the several landscape characteristics is generated.6 In addition to the

zonal landscape characteristics variables, several personal characteristics were also included, such as sex,

race, education level, and household income. Lastly,

distance to most-visited site was used to construct a

proxy for trip price.

The three models presented in this study illustrate different means of accounting for variations in the price

and quality of recreational sites. The water-quality

model uses explicit information on the location of visited sites while the pheasant-hunting model uses ancillary information on bird populations to impute the

location of visited sites. The wildlife-viewing model

uses data that contain neither explicit site information

nor secondary information that can be used to impute 

a site choice. Therefore, a more complex econometric

model using the observed data to proxy for site choice

is needed.

To estimate welfare impacts, a benefits-transfer

approach is used.7 The predicted number of trips is

multiplied by a per trip value obtained from a contingent valuation question included in the FHWAR survey. Further description of the model and a detailed

discussion of results are included in Appendix C.

Table 9 shows the regional and national estimates of

consumer surplus values. As with the pheasant-hunting model, the population-weighting information provided in the FHWAR survey is used to derive a

change in consumer surplus due to the adoption of the

CRP. The value of this change, $348 million for the

entire Nation, is fairly close to the $380-million value

realized by the NBS estimates of bird-watching.

The largest total benefit and benefit per acre is in the

North Eastern region, followed by the Southern

Plains, Northern Plains, and South Eastern regions.

The model uses an anomalous negative benefit for

wildlife viewing in the Pacific/Mountain region associated with the distribution of CRP acres. One possible explanation is that the Pacific region contains little

CRP land in highly populated States (such as

California) where intensive recreation occurs, and

large amounts of CRP land in relatively unpopulated

States (such as Montana and Wyoming). This results

in the appearance that CRP is negatively correlated

with recreational activity.

Discussion 

The results of these three models indicate that CRP

acres enrolled as of 1992 have had a beneficial effect

on recreational activities. The largest effects are associated with increased wildlife-viewing recreation

($348 million), followed by pheasant hunting ($80

million), and freshwater recreation ($36 million). The

effects on a per acre basis are similar in magnitude.

The largest average per acre benefit is associated with

wildlife viewing ($10.02), followed by pheasant hunting ($2.36) and freshwater-based recreation ($1.07). 

18 Economic Research Service/USDA

6As described in Appendix C, to increase the flexibility of

the model, the parameter used to compute the weighted average is an estimable parameter.

7The coefficient on the proxy price could have been used to

directly estimate consumer surplus measures of the value of

these trips. However, the price coefficient was not stable,

resulting in coefficients with the incorrect sign in a few regions.
Although these models offer a number of improvements over prior research, they are subject to several

criticisms. In particular:

 Instead of specifying the complete physical,

biological, and behavioral relationships

between land retirement, environmental

quality, and recreational nonmarket benefits, these models use a less desirable

dependence upon environmental indicators.

This reduced-form approach is adopted due

to both a lack of knowledge about how

these interactions occur and a lack of data

required to represent them. This problem is

not uncommon in the valuation literature

and is likely to continue until comprehensive physical and biological models are

available. 

 The lack of exact destination location information in the FHWAR hindered further

refinement of the wildlife-viewing and

pheasant-hunting models. In nonmarket valuation models, recreational trips are

assumed to be associated with the environmental quality of the destination. Imprecise

knowledge of the location and environmental characteristics of destinations will thereby lessen the precision of the estimates. 

 Although not unique to the problem of

valuing the CRP, it can be difficult to separate the demand for CRP-influenced recreation from the demand for other goods. For

example, it is assumed that freshwater trips

were solely for recreation, hence the cost of

a trip can be attributed to the recreation

experience. The models do not separate

other enjoyable activities aside from recreation that may occur on a trip. This is a

common drawback found in most nonmarket valuation models.

Despite these problems, the models basically succeed

in identifying plausible relationships between land use

and recreational values based on observed behavior.

Additionally, the flexibility of the models offers the

advantage of improved accuracy. Being based on

behavioral and biophysical micro-data, one can apply

the models results to new scenarios relatively easily.

Thus, the impacts of changes in targeting mechanisms

can be potentially quantified by carefully applying the

models results.

We note that the per acre magnitude of these benefits

(~$13 per acre) is smaller than the average CRP rental

rate (~$50 per acre). Of course, these benefits are

only a subset of the positive impacts of the CRP;

hence they should not be used as a justification (or

critique) of program funding. In fact, the major use of

these findings is to provide a baseline for an analysis

of a simulated targeting mechanism conducted in the

next chapter.

Economic Research Service/USDA 19

Table 9—Wildlife viewing: Consumer surplus levels by region

Region1 Total consumer surplus2 Consumer surplus Consumer surplus4

due to CRP3

- - - - - - - - Million dollars per year - - - - - - - - $/Acre/year

Pacific/Mountain 1,385.31 -34.98 -4.27

Northern Plains 122.68 26.75 3.01

Southern Plains 315.25 62.35 12.14

South Eastern 1,260.52 4.89 1.33

North Eastern 3,616.74 288.70 35.44

Total 6,700.48 347.71 10.02

1The Pacific/Mountain region contains WA, OR, CA, MT, ID, WY, NV, UT, CO, AZ, NM; the Northern Plains region contains ND, SD, NB, KS;

the Southern Plains region contains OK, TX; the South Eastern region contains AR, LA, MS, AL, GA, SC, FL, TN, NC, VA, KY, WV; the North

Eastern region contains MN, WI, MI, IA, MO, IL, IN, OH, PA, NY, VT, MD, DE, NJ, RI, CT, MA, NH, ME.

2Annual consumer surplus evaluated at 1992 CRP levels.

3This is the difference between total annual consumer surplus with the CRP and total without the CRP.

4Consumer surplus attributable to the CRP on a per acre basis.

Source: USDA, ERS.
Applications of Nonmarket Valuation 

in Land Use Policy 

The results shown in the preceding chapter are useful

as components of a benefit-cost economic evaluation

of the program (see Box 8). In addition, the models

that generate these results can be used to compare

alternatives. Comparing alternative programs can be

accomplished by comparing the potential benefits

generated by each program. Of particular interest is

the comparison of different mechanisms for targeting

land retirement. Here, nonmarket valuation models

can be used to suggest where to retire land in order to

achieve greater recreational benefits.

Environmental targeting mechanisms are currently

used to select CRP acreage. This selection process is

based on an environmental benefits index (EBI).

The EBI translates several measures of environmental

quality into a single number that allows analysts to

compare different parcels of land with each other,

even if they have differing characteristics.

Constructing an EBI involves weighting primary

physical characteristics that describe the land offered

for enrollment (Osborn, 1997). Relative weights are

chosen to give equal consideration to the three primary components of the EBI: wildlife, water quality,

and soil erosion, with lesser weights given to the

remaining environmental components.8

The 1990 Farm Bill reauthorized the CRP when the

bulk of the CRP lands were already in place. Previous

acreage selection had been based primarily on erosion

and erosion potential, but USDA sought to increase the

benefits of land retirement by considering additional

factors. As a result, a more comprehensive environmental targeting mechanism (which considers more

than erosion) is currently used to select CRP acreage. 

Beginning in 1991, bids were ranked using an EBI.9

A recent version of the EBI, which was used for the

15th signup (March 1997), included six categories of

environmental characteristics and a cost factor.

(Appendix D describes the criteria used for the 15th

CRP signup.)

The EBI for the 15th signup weighted the wildlife,

water-quality, and soil erodibility factors equally.

Lesser weight was given to factors for 15-year tree,

shrub, and wetland retention; air quality; and conservation priority areas. The water-quality and wildlife

EBI components were composed of several subfactors

that comprised the total weight for that category. In

addition, a cost factor accounted for approximately

one-third of the total maximum score.

Analysts currently use an EBI that is similar, but not

identical, to the 15th signup EBI, to classify and rank

land offered for enrollment in the CRP during a general signup.10 With each signup, applicants submit

offers and the EBI scores each submission based on

its characteristics.11 Submissions are then ranked

nationally by EBI score, and the highest ranked submissions are accepted. Particularly environmentally

sensitive lands, such as riparian areas, may be accepted outside of general signup periods through a continuous signup.

The Relative Benefits of the EBI Targeting

To examine the benefits resulting from retiring lands

using an EBI as an environmental targeting mechanism, we generated a simulated distribution of CRP

acreage based on the 15th signup EBI. This simulated

CRP was constructed by calculating 15th signup

scores for 1992 National Resource Inventory (NRI)

points. NRI points with the highest EBI scores were

then selected for a hypothetical 34-million-acre

CRP (Osborn, 1993). The hypothetical selection was

limited to NRI records that were considered eligible

and likely to bid, based on partial budgeting and certain land uses (i.e., irrigated land was considered

unlikely to bid since CRP rental rates are dry-land

rates, which are generally lower than irrigated-land

rental rates). To retain comparability with the analysis

20 Economic Research Service/USDA

8An EBI score consists of the summation of several factor

and subfactor scores. Therefore, the range of scores that may

be granted (to a given factor) can be interpreted as an implicit

weight. For example, if factor A is assigned a score between 0

and 10, and factor B is assigned a score between 0 and 100,

then factor B has a 10 times larger implicit weight then factor A.

9Bids with acceptable rents employing certain practices (e.g.,

filter strips, grass waterways, windbreaks, etc.) were accepted

without the EBI stage.

10For example, following the 15th signup a number of refinements were made to the EBIs treatment of environmental

inputs.

11Actually, there is potential for some within-State

flexibilityalthough the total acreage awarded to a State is

based on this national EBI, a State-specific EBI can be used

to reallocate this total to different acres if the State is approved

prior to signup.
of the 1992 CRP, a 34-million-acre limit was maintained, and the constraint that CRP acreage could not

exceed 25 percent of the cropland in any county was

imposed.12

The subsequent redistribution of CRP lands resulted

in a slight decrease in acreage in three regions, and

increases in the North Eastern and South Eastern

regions (table 10). Using this new distribution of

CRP acreage, we used the previously described models to calculate the associated environmental benefits. 

Table 11 lists the changes in benefits, with a breakdown by region and by activity. For each activity, the

table reports the additional benefit that would be realized if a 34-million-acre CRP were selected using the

15th signup EBI, as compared with the 1992 distribution of CRP acres. 

The average benefit per acre differs across regions

(table 12). On average, retiring lands in the North

Eastern and South Eastern regions produces more

benefits (from the three activities we examined) than

retiring lands in other regions. This may indicate that

further increasing land retirement in these areas would

increase the overall benefits of the CRP.

In general, the 15th signup EBI criteria increase freshwater-based recreation and wildlife-viewing benefits,

and decrease pheasant-hunting benefits compared

with CRP acreage accepted prior to 1992.

Summarizing the results:

 Large increases in benefits result in all

regions for freshwater-based recreation and

wildlife viewing. Total water-based recreation benefits attributable to the CRP

increase from $36.35 million to $128.97

million, a difference of $92.62 million (a

255-percent increase). 

 The benefits of wildlife viewing increase

significantly. The contribution of the CRP

to the value of this activity increases from

$347.71 million to $634.99 million, a difference of $287.28 million (an 83-percent

increase). 

Economic Research Service/USDA 21

Box 8Three Uses of Nonmarket Valuation Models in Land-Use Programs

The nonmarket benefits attributable to changes in land use are sensitive to where the changes occur. Retiring

equal amounts of cropland in two different areas will likely result in different benefits. This difference, which

can be significant, depends on factors such as the characteristics of the surrounding population and the environmental quality of the retired lands. Models that recognize these factors can help explain why the benefits

vary. 

Three potential uses of nonmarket valuation models in the analysis of land-use policy are:

1. Program Evaluation: Every land use program generates benefits and costs. Often, programs are

required to be evaluated using benefit-cost analysis. Nonmarket valuation models provide a means of incorporating benefit estimates for otherwise unmeasurable impacts.

2. Program Selection: In cases where several programs, or variations of the same program, compete for

funding, these models can aid in the decision of which variation or program to implement.

3. Acreage Selection (targeting): The benefits of a change in land use are location specific. Nonmarket

valuation models can be used to identify where the largest benefits would occur. This information could be

used to improve the implementation and administration of a land-use program. 

Source: USDA, ERS.

12Another reasonable constraint would be to construct a simulated CRP with the same cost as the actual (1992) CRP.

However, constructing a simulation with the same acreage

allows the models to focus on how benefits change. CRPs

legislative constraint is acreage, not total dollars spent.
22 Economic Research Service/USDA

Table 10—Comparison of baseline and hypothetical CRP distributions

Region 1992 baseline CRP Hypothetical CRP

Million acres

Pacific/Mountain 8.196 7.966

Northern Plains 8.884 7.999

Southern Plains 5.136 4.975

South Eastern 3.678 4.290

North Eastern 8.146 8.810

Total 34.040 34.040

Source: USDA, ERS.

Table 11—Changes in benefits resulting from a hypothetical redistribution of the CRP1

Region2 Freshwater-based recreation3 Pheasant hunting Wildlife viewing 

Million dollars per year

Pacific/Mountain 2.61 -0.19 38.76

Northern Plains 5.76 -4.07 0.20

Southern Plains 2.45 N/A4 52.67

South Eastern 22.08 N/A4 143.32 

North Eastern 59.72 -5.78 52.51

Total 92.62 -10.05 287.28 

1Change in consumer surplus, in millions of dollars: the consumer surplus under the hypothetical 34-million-acre CRP distribution using the

15th EBI minus the consumer surplus under the baseline CRP.

2The Pacific/Mountain region contains WA, OR, CA, MT, ID, WY, NV, UT, CO, AZ, NM; the Northern Plains region contains ND, SD, NB, KS;

the Southern Plains region contains OK, TX; the South Eastern region contains AR, LA, MS, AL, GA, SC, FL, TN, NC, VA, KY, WV; the North

Eastern region contains MN, WI, MI, IA, MO, IL, IN, OH, PA, NY, VT, MD, DE, NJ, RI, CT, MA, NH, ME.

3Sum of lake recreation and river recreation benefits.

4Limited pheasant hunting occurs in these regions.

Source: USDA, ERS.

Table 12—Benefits per acre resulting from a hypothetical redistribution of the CRP1

Region2 Freshwater-based recreation3 Pheasant hunting Wildlife viewing 

Dollars/acre/year

Pacific/Mountain 0.54 0.31 0.47

Northern Plains 1.02 2.83 3.35

Southern Plains 0.79 N/A4 23.12

South Eastern 7.63 N/A4 34.55

North Eastern 9.04 5.12 38.73

Total average 3.79 2.06 18.65

1Average consumer surplus per acre of CRP under the hypothetical 34-million acre CRP distribution using the 15th EBI. Calculated as the

consumer surplus under the hypothetical minus the consumer surplus in the absence of the CRP, divided by the hypothetical CRP acreage.

These are the per acre benefits if a 34-million-acre CRP were distributed using the 15th EBI signup criterion.

2The Pacific/Mountain region contains WA, OR, CA, MT, ID, WY, NV, UT, CO, AZ, NM; the Northern Plains region contains ND, SD, NB, KS;

the Southern Plains region contains OK, TX; the South Eastern region contains AR, LA, MS, AL, GA, SC, FL, TN, NC, VA, KY, WV; the North

Eastern region contains MN, WI, MI, IA, MO, IL, IN, OH, PA, NY, VT, MD, DE, NJ, RI, CT, MA, NH, ME.

3Sum of lake recreation and river recreation benefits.

4Limited pheasant hunting occurs in these regions.

Source: USDA, ERS.
 Pheasant-hunting benefits decline modestly.

Total benefits attributed to the CRP decline

from $80.27 million to $70.23 million, a

difference of $10.05 million (a 13-percent

decrease).

The benefits analyzed here are not comprehensive.

However, this examination of several nonmarket

effects indicates that a multi-objective EBI used as a

targeting mechanism (rather than criteria based primarily on erodibility) can increase CRPs environmental

benefits. The next section explores how targeting can

be further refined using nonmarket valuation models. 

The Impacts of Population

Improving the environment near heavily populated

areas results in more recreational benefits than the

same change in a sparsely populated area. Comparing

the benefits of a CRP based primarily on erosion criteria (the 1992 34-million-acre CRP) with the hypothetical CRP distribution based on the 15th signup

EBI demonstrates this. 

The 15th signup EBI includes population as part of

the surface-water, ground-water, and air-quality benefits. Table 13 shows that including population moves

CRP lands from less populous regions to more populous regions, leading to large increases in benefits.

Even regions that lost total CRP acreage had some

increase in benefits, a result likely due to CRP land 

being moved to more populous areas within the

region.13

Figures 2-4 further illustrate the impact of population.

Figure 2 shows the distribution of water-quality benefits resulting from the observed (1992) CRP. The

benefits appear most concentrated in the North

Eastern and South Eastern regions. Comparing this

map with the distribution of CRP lands, shown in figure 3 and in table 10, indicates that most of the benefits do not coincide with the location of the CRP. The

majority of the CRP acreage lies outside of these two

regions. The distribution of the U.S. population,

shown in figure 4, explains this phenomenon. Areas

that have both CRP acreage and dense populations

coincide with the high-benefit areas shown in figure

2. Considering population when choosing the size of

weights in an EBI is likely to improve recreational

benefits.14

Using Economic Techniques To

Improve Environmental Targeting

Development of an optimal environmental benefits

index requires a sound basis for determining the

weights applied to each of the many benefits produced

Economic Research Service/USDA 23

Table 13—Results of a redistribution of the CRP1

Region2 Change in acres3 Total change in benefits4 Population5

Million acres $ Million/year Million persons

Pacific/Mountain -0.230 41.18 51

Northern Plains -0.790 1.89 5 

Southern Plains -0.161 55.12 20

South Eastern 0.612 165.40 59

North Eastern 0.664 369.85 111 

1Comparison of the baseline CRP and the hypothetical CRP generated based on the 15th signup EBI.

2The Pacific/Mountain region contains WA, OR, CA, MT, ID, WY, NV, UT, CO, AZ, NM; the Northern Plains region contains ND, SD, NB, KS;

the Southern Plains region contains OK, TX; the South Eastern region contains AR, LA, MS, AL, GA, SC, FL, TN, NC, VA, KY, WV; the North

Eastern region contains MN, WI, MI, IA, MO, IL, IN, OH, PA, NY, VT, MD, DE, NJ, RI, CT, MA, NH, ME.

3Hypothetical CRP acres less actual CRP acres in million-acre units.

4Benefits from the hypothetical CRP distribution less benefits from the actual CRP distribution in million dollars.

5Population in millions of persons.

Source: USDA, ERS.

13See appendix table 5 in Appendix C for further evidence

on the net movement of CRP toward population centers.

14Moving CRP land may increase average rental rates, have

impacts on commodity production, or have other environmental impacts. Any of these may reduce net benefits. 
24 Economic Research Service/USDA

Key

No CRP

Low

Medium

Medium high

High

Source: USDA, ERS, based on NRCS 1992 National Resources Inventory data.

Figure 2

Average water-quality benefits from 1992 CRP by NRI polygon

Percent CRP lands, 1992

Figure 3

Percent of area in CRP

0

1 - 5

5 - 10

11 - 15

> 15

Source: USDA, ERS, based on NRCS 1992 National Resources Inventory data.

Economic

Research

Service

Economic

Research

Service
by the environment. Monetary valuation techniques

of nonmarket goods and services, which have been

developed expressly to compare what would otherwise be disparate impacts and consequences, is one

such basis. These economic techniques offer several

advantages, including objectivity, standardization of

measurement, and cost effectiveness:

 Objectivity: Policy prescriptions are formulated in a scientific fashion.

Replicability, empirical corroboration, and

transparency of design serve to minimize

political and other biases.

 Standardization of measurement: The

dollar-valued estimates, which are derived

from observations on individuals choices,

provide a clear measure of the strength of

peoples preferences. With these measures,

comparison of an array of program effects

is a straightforward financial exercise.

 Cost effectiveness: Economic models are

designed to answer what-if questions.

Mathematical optimization techniques,

when applied to economic models, can

automate the process of maximizing program benefits relative to program size

(where the size, as defined by acreage or

dollars, may be predetermined).

In short, using economic techniques to accomplish

environmental targeting involves replacing indirect

proxies (such as the EBI) with a direct procedure that

uses economic models, along with biophysical information, to determine where the net benefits of land

retirement are largest, and retire those lands first.

A Direct Procedure

The direct procedure employs a comprehensive measure of the environmental goods and services relevant

to individuals, and models how these goods and services influence their behavior. This direct procedure,

or CRP Valuation Function, incorporates two classes

of models:

(1) Biophysical models generate measures of environmental goods and services as functions of land

use patterns. 

Economic Research Service/USDA 25

Figure 4

U.S. population density, 1992

People per square kilometer

0 - 100

101 - 500

501 - 1000

> 1000

Source: USDA, ERS, based on data from the 1990 Census of Population.

Economic

Research

Service
(2) Economic models take this bundle (of environmental goods and services) and return a net benefit expressed in dollar terms. 

Roughly speaking, a direct CRP Valuation Function

might consist of models similar to those presented in

this analysis, but would account for a broader array of

environmental goods and services and would use

more accurate measures of biophysical impacts and

behavioral responses to these impacts.

For targeting purposes (that is, for ranking candidate

CRP acreage from greatest to least net benefit), a

direct CRP Valuation Function would generate a

schedule of the benefits of potential CRP acres (see

Box 9 for an outline of how this could be done).

Such a schedule could be used as a basis for optimal

environmental targeting. At its simplest, the acres

with the highest values (or highest relative to cost)

would be chosen. 

26 Economic Research Service/USDA

Box 9Using Economic Valuation Models To Rank CRP Acres

An Example of a CRP Valuation Function:

Assuming the requisite biophysical and economic models are available, a multi-step process could be used to rank candidate CRP acres:

1) Compute a baseline net benefit, using the current distribution of rural land uses. 

2) Generate a new total value by enrolling a single candidate acre into the CRP.

3) The value of entering this candidate acre into the CRP is computed as the difference between the baseline and new

values.

Note that one could test a given acre under several different management regimes (that is, under different cover

mixes).

4) Obtain a schedule of values by repeating this three-step process for all fields offered in CRP bids.

Due to non-linearities in the valuation function, the value of a given acre will often depend on how many other

nearby acres are in the CRP. To account for this may require updating the baseline as one repeats the first three

steps.

An Example of a Simulation Approach:

Valuation models can be used to aid the development of an EBI. In particular, a set of simulations can be used to compare alternative EBIs, and to suggest improvements in factors and subfactors, specifications, and weights. Much like the

analysis on pages 20-22, this approach relies on generating several hypothetical CRP distributions from differing factor

weights and then computing the benefits. The following outlines the necessary steps:

(1) Use existing data to estimate a baseline model (such as presented in chapter two) that links environmental characteristics to amenity values.

(2) For each of candidate EBIs, generate a simulated CRP. This requires knowledge of how the EBI scores of enrollable acres change as weight factors (scoring criteria) change. In general, acres with the highest (simulated) scores

are assigned to the (simulated) CRP. 

(3) Using this simulated CRP, generate a new distribution of environmental characteristics. For example, generate new

values for a "county-level %CRP" variable for use in a reduced-form model.

(4) Using these the new environmental characteristics, and the parameters generated in step 1, compute a set of net benefit numbers.

Each iteration of the above produces a set of net benefit numbers, which can be added up to generate a value that is a

function of the scoring criteria used in the candidate EBI. Using a broad set of candidate EBIs (with each EBI in this set

characterized by its own set of scores), a "surface of values" can be generated. This surface can be used for several purposes: such as quickly comparing alternative EBIs, or for evaluating the effect that changes in a factor score will have on

net benefits.

Source: USDA, ERS.
The direct CRP valuation function requires sophisticated biophysical models and exhaustive valuation

models. Although simplifications to this process that

retain most of the accuracy are possible,15 constructing a direct CRP Valuation Function is a demanding

task. The size of the problem is illustrated in table 14,

which lists all existing CRP-related national valuation

studies, organized around the factors of the 15th

signup EBI. In many cases, and especially for environmental characteristics with nonuse values, there

are no estimates of the benefits, or the estimates that

are available are ill-suited for use as inputs into the

design of an environmental targeting mechanism. 

A Practical Approach

Given these difficulties, it is worth considering simpler mechanisms. In particular, economic models can

help to construct an EBI that may provide more nonmarket benefits. As illustrated in table 15, EBIs and

economic valuation models have many similarities.

An EBI expresses the value of landscape variation as

changes in the factor and subfactor scores. Economic

models focus on how individuals value the change in

the quality of environmental amenities. Thus, to

quantify the relationship between an EBI and environmental amenities, a means of linking measures of

resource quality (used in the valuation models) and

factor and subfactor scores (used in the EBI) is

required. 

For example, a simulation approach can be used to

link economic models to a spectrum of possible

EBIs. The goal is to capture the effects of changes in

the landscape due to alternative EBIs. As detailed in

Box 9, this approach is an expansion of the methodology used in this analysis. Simple rules applied to

small-scale data (such as NRI records) are used to

simulate a land-use distribution under a proposed EBI;

reduced-form models are then used to compute the

recreational benefits of this simulated land-use distribution. Repeated over many different EBIs, the

effects of changes in factor scores (in terms of

changes in the value of recreational benefits) can be

observed.

In summary, whether the construction of a comprehensive direct procedure is the goal, or if one tackles

the more modest task of using valuation models to

improve an EBI, the need for accurate and complete

measures of amenity values is essential. The next

chapter discusses in greater detail these deficiencies

and the research directions they imply.

Economic Research Service/USDA 27

15For example, a literal implementation of the process

described in Box 9 requires solving a CRP Valuation Function

separately for each of the millions of potential CRP acres. This

can be simplified by using a set of representative acres, in

conjunction with weights measuring their geographic extent.
28 Economic Research Service/USDA

Table 14—CRP benefits within each EBI factor1

EBI factor and associated benefits Estimates in the literature Value Maximum EBI 

$ million/year Factor score 

Wildlife: Use value 

Small-game hunting2 Young and Osborn (1990) 443.8

Waterfowl hunting John (1993) 175.2

Wildlife viewing John (1994) 382.8

This analysis 347.0

Pheasant hunting This analysis 80.0

Big-game hunting None Unknown

Wildlife: Non-use value None Unknown 100

Water quality: Use value 

Sport fishing2 Ribaudo (1989) 21.4

Freshwater-based recreation This analysis 39.6

Saltwater-based recreation None Unknown

Ground-water quality None Unknown

Water quality: Non-use value None Unknown 100

Erosion: Use value 

Soil productivity2 Young and Osborn (1990) 227.5 

Ditch maintenance, municipal,

and industrial uses Ribaudo (1989) 125.1

Water storage, navigation, 

and flooding Ribaudo (1989) 122.2 

Erosion: Non-use value None Unknown 100

Air quality: Use value

Health/cleaning costs2 Ribaudo and others (1990) 51.1

Cleaning costs Hughes (1994)3 0.1

Sperow (1994)4 1.8

Carbon sequestering Parks and Hardie (1996) --5

Air quality: Non-use value None Unknown 25

Long term retention of trees,

shrubs and wetlands None Unknown 50

Conservation priority None Unknown 25 

1Estimates for benefits associated with 15th signup factors and scores. Dollar estimates are at the national level unless otherwise noted.

2In some cases, the original data was discounted over a longer time period. When this occurred, the figures were re-computed to reflect annual measures.

3For Colorado only.

4For New Mexico only.

5A rough net present value estimate of $65 billion is based on all CRP acreage being planted to forests.

Source: USDA, ERS.
Economic Research Service/USDA 29

Table 15—Similarities between the EBI and valuation models

EBI Valuation models 

Points are assigned to environmental categories, which Biophysical factors (or indirect measures of these

are specified as factors and subfactors (see Appendix D). biophysical factors) are used to model the quantity 

These points may vary in different parts of the country. of environmental amenities.

Land characteristics, as measured using the factors and Land characteristics, as measured by the independent 

subfactors, determine the points given a potential CRP variables included in valuation models, directly 

acre. influence the net benefit of a potential CRP acre.1

Acres with highest points, after factoring in costs, are Acres with highest dollar value, after accounting for costs, 

chosen. are chosen.

1For example, the points assigned to a landscape characteristic could be derived from its “marginal product:” the change in the quantity of an

environmental amenity given a change in the level of environmental characteristics, multiplied by the unit value of the amenity.

Source: USDA, ERS.
Future Research Directions

As the Nations largest user of land and water

resources, agriculture significantly affects the natural

environment. The Conservation Reserve Program is

one of the more important mechanisms for mitigating

the negative effects. The Federal Agriculture

Improvement and Reform Act of 1996 (FAIR) encourages the USDA to use the program to achieve the

greatest environmental benefits relative to costs.

Targeting the program will be important to its future

success.

This report describes how one can use natural

resource valuation models, which are driven by

observable public preferences, to select those program

acres that maximize environmental benefits relative to

cost. Using economics in environmental targeting

requires an understanding of the extent to which nonmarket activities are sensitive to the location of CRP

lands. The models developed here illustrate how

state of the art models allow one to compute subcounty-level estimates of environmental benefits, both

under current and alternative distributions of the CRP.

With results at this level of geographic disaggregation,

dollar-valued measures of the impacts of variations in

targeting mechanisms (such as changes in an EBI) can

be constructed, helping to shed light on critical environmental attributes.

This report focuses on three activities that the CRP

affects: freshwater-based recreation, wildlife viewing,

and pheasant hunting. Using currently available biophysical and economic data, we created several nonmarket valuation models that estimated how the net

benefit (as measured by consumer surplus) of these

activities is influenced by the current (circa 1992)

CRP. We also used these models to investigate the

environmental benefits of a simulated 34-million acre

CRP generated from a recently employed EBI. 

Although benefit estimates are not comprehensive, the

output from these models provides several insights,

including:

 The benefits associated with targeting CRP

lands using a multi-objective EBI are substantially higher than those associated with targeting CRP lands using eligibility criteria based on

erosion, as was done in early CRP signups.

 The CRP produces significantly larger benefits for wildlife viewing ($348 million per

year) and for pheasant hunting ($80 million

per year), than for freshwater-based recreation

($36 million per year). This difference is

observed under both the erosion and 15th

EBI signup criteria.

 Natural resources that are near populated

areas generate large benefits simply because

more people can easily enjoy the resources.

By favoring acres that generate positive

impacts for more people, explicitly taking

affected population into account when targeting CRP lands would increase the benefits of

several types of outdoor recreation.

Our results suggest that it is feasible to develop a targeting system based on economic valuation models.

However, to fully implement economic targeting of

the CRP, research efforts are needed to (1) increase

the number of environmental benefits that are evaluated; and (2) improve technical/theoretical approaches

used to estimate the benefit models. Priorities in each

of these areas are discussed below. Furthermore,

some expected advances outside of economics and

their importance to benefit estimation are also discussed to suggest additional directions that economic

analyses may continue to improve.

Priorities in Environmental Benefits

Related to Agriculture

To evaluate the CRP comprehensively, a number of

improvements in available data, in modeling physical

and economic relationships, and in statistical modeling

are necessary.

What is needed:

 Obtain better and more complete survey data

to determine all the benefits the CRP offers.

 Fully understand the impacts of the EBI.

 Incorporate new environmental goods and

services into the EBI.

 Refine the estimated relationships between

physical land characteristics and human wellbeing through advances in statistical and economic estimation techniques.

30 Economic Research Service/USDA
In addition, expanding the noneconomic knowledge

base, as regarding how to measure and predict environmental changes as land-use policies change, can be

expected to open new opportunities.

Other Environmental Benefits

Recognized by the EBI

The EBI used in the 15th signup contains numerous

factors that account for a range of environmental

goods and services. Comprehensive analysis of the

efficacy of the EBI requires some accounting for these

goods and services. These include:

Wildlife. The wildlife models of this report did not

incorporate a number of potentially important environmental goods and services, including:

 Other upland game species, such as quail

and grouse. A derivative of the pheasant

model, augmented by population estimates

of these species, could be used to analyze

these issues.

 Large game, such as deer and elk. A model

fashioned after the wildlife-viewing model

was estimated for large-game hunting, with

inconclusive results. Future improvements,

especially regarding resource availability

and site choice, are required.

 Waterfowl hunting and viewing. Waterfowl

impacts of CRP are geographically dispersed,

and are ill-suited to the local impact analysis used in this report. A proper analysis

requires construction of models that generate

changes in waterfowl populations at sites

throughout the Nation as the distribution of

CRP changes (nationwide).

 The effect of trees on wildlife. Although

the wildlife models include forest land variables, a better gauge should be developed

that more closely links forest qualities to

wildlife densities. 

 Threatened and endangered species.

Analysis of threatened and endangered

species requires consideration of nonuse

values, such as existence, option, and

bequest values (Boyle and Bishop, 1987;

Loomis and White, 1996).

 The effect of establishing native grass

mixes on lands.

Ground-water quality. Ground-water quality does

not directly affect recreational opportunities; however,

it does have impacts (such as bequest values) that may

be best quantified with nonmarket valuation techniques (Crutchfield, Cooper, and Hellerstein, 1997) or

avoidance costs (Nielsen and Lee, 1987).

Long-term soil productivity. Standard economic

theory suggests that long-term productivity impacts

should be captured in land prices. However, differences between social and private discount rates (due

to factors such as taxes or borrowing constraints), and

the tendency of single producers to overlook widespread productivity losses when making production

decisions, may lead to sub-optimal levels of soil protection. Public intervention may be justified to correct these problems (Boadway, 1979).

Wind erosion. To measure the benefit of reductions

in wind erosion, better models of the geographic distribution of changes in air quality, given changes in

land uses, are required. There is some promise that

estimates from traditional air-quality models can be

used (Huszar and Piper, 1986). 

Priority areas. Conservation priority areas represent

regions that are likely to have special features that

yield extra benefits. Specifying and estimating nonmarket demands for these features allows the use of

their values (rather than a regional correction).

Other water-quality benefits. Additional waterquality impacts may be substantial, including impacts

to bays and estuaries, effects of coastal sediments on

private and public water uses, and impacts of erosion

on public works (such as dams and reservoirs).

Climate change. Significant organic matter buildup on

CRP lands can sequester atmospheric carbon dioxide.

Environmental Benefits Not 

Recognized by the EBI

The EBI includes factors that are presumed to directly

impact a number of environmental goods and services.

However, many of these environmental goods and

services were not considered when developing the

EBI. In particular, scenic and existence values of

rural landscapes were not considered.

Economic Research Service/USDA 31
The landscape variation imparted by the CRP may

positively affect the scenic values of rural landscapes.

This may be significant, especially in urbanized environments. Measuring such impacts is largely unexplored in the United States, though work in farmland

preservation (and European work on cultural amenities) may be applicable.

Existence values of wildlife species, as impacted by

the CRP, may also be quite large. Given that existence values may be held by a significant fraction of

the U.S. population, even small, per individual values

can yield large aggregate benefits. For example, the

abundance of a variety of nongame birds is likely to

have been increased by the CRP. Although some of

these species may be enjoyed by bird watchers, it is

possible that many people will value the fact that the

environment is more conducive to avian species in

general. In fact, there is evidence (Hagler Bailley,

1997) that many individuals hold non-negligible values for this existence value. 

Improving the Technical and 

Theoretical Approaches

Technical advances in both behavior modeling and

empirical approaches have been critical in allowing

current research to provide more location-specific

benefit estimates. Although research advances have

resulted from agency efforts (Feather, Hellerstein, and

Tomasi, 1997; and Hellerstein, 1992), advances have

also been based on work outside of USDA/ERS. 

The 1996 FHWAR survey will provide an immediate

improvement in estimating benefits. This survey contains information that will improve the identification

of sites visited by individuals. Since the location of a

visited site underlies our measures of environmental

characteristics consumed by a recreator, this will

improve our modeling capabilityboth in terms of

precision and scope. New sources of data will need to

continue to address this need for precise geographic

specificity. For example, due to the lack of precise

information about site choice, longer trips were not

analyzed (in either the water-quality or the wildlife

models). Although longer trips do not comprise the

bulk of trips taken, they may represent particularly

high-valued trips. With better information on the

location of long trips (as provided by the 1996

FHWAR), analysis of longer trips becomes feasible.

Expected Advances Outside the Area of Economics

Advances outside the area of economics are expected

to provide a much-improved measure of the

physical/environmental impacts of changes in agricultural land use. Improved computer capabilities will

facilitate complex analyses of very large data sets.

This includes applying improved geographic information systems (GIS) models to refine measures of environmental amenities. 

This report uses environmental indicators instead of

direct measures of biophysical impacts. The current

indicators, while useful, are fairly simple. We expect

richer sources of indicators to continue to develop,

especially as GIS tools mature. In addition, research

outside of economics is expected to continue to provide better models of environmental characteristics,

such as how water quality and wildlife populations

respond to changes in the CRP. 

The Future of Environmental Targeting

Environmental targeting, as mediated through mechanisms like the Environmental Benefits Index, will

always be a dynamic process. This report, which

focuses on the use of the EBI to allocate CRP lands,

illustrates the potential of economic analysis to provide objective assessments based on observable data.

Based on the results in this report, we believe that

economic valuation methods can contribute to the

development of more refined targeting measures.

Further research will provide more comprehensive

measures of the value of the CRPs environmental

impacts. Better measures of biophysical responses are

expected, and can affect direct measures of value

(such as changes in recreational behavior due to CRPinduced changes in wildlife populations) and indirect

measures (such as the existence value of rural wildlife

that the CRP may augment). 

Many of the prerequisites for future research are in

place. Data that will help expand the analysis of CRP

are becoming available, such as the 1996 FHWAR

and a national contingent-value analysis of the value

of Midwestern avian species. Biophysical models are

also increasing in scope, such as USGS models relating stream quality to land uses. Lastly, continual

improvements in the power of GIS data manipulations, and the refinement of comprehensive econometric models, should also help implement heretofore

prohibitively complex models. 

32 Economic Research Service/USDA
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U.S. Department of the Interior. State of Ohio vs.

Department of Interior, Case No. 86-1575. U.S.

District Court of Appeals (for District of Columbia),

July 14, 1989.

U.S. Department of the Interior, Fish and Wildlife

Service and U.S. Department of Commerce, Bureau

of the Census. 1991 National Survey of Fishing,

Hunting, and Wildlife-associated Recreation. 1993. 

U.S. Department of the Interior, Fish and Wildlife

Service and U.S. Department of Commerce, Bureau

of the Census. 1996 National Survey of Fishing,

Hunting, and Wildlife-associated Recreation. Nov.

1997.

U.S. Environmental Protection Agency (USEPA).

Environmental Investments: The Cost of a Clean

Environment, Report #EPA-230-11-90-083, Nov.

1990.

. Another Look: National Survey of

Pesticides in Drinking Water Wells, Phase II Report.

EPA 579/09-91-020, Jan. 1992.

, Office of Water. National Water Quality

Inventory: 1994 Report to Congress. EPA841-R-95-

005. Dec. 1995.

United States Geological Survey (USGS). Pesticides

in Surface Waters, U.S. Geological Survey Fact

Sheet FS-039-97, 1997.

United States Water Resources Council. Economic

and Environmental Principles for Water and Related

Land Implementation Studies, Washington, DC. 1984.

Waddington, D., K. Boyle, and J. Cooper. 1991 Net

Economic Values for Bass and Trout Fishing, Deer

Hunting, and Wildlife Watching. Division of Federal

Aid, U.S. Dept. of the Interior, Fish and Wildlife

Service, 1993.

Walsh, R.G., D.M. Johnson, and J.R. McKean.

Review of Outdoor Recreation Demand Studies With

Nonmarket Benefit Estimates, 1968-1988. Technical

Rpt. 54 Colorado State Water Resources Research

Institute, Colorado State University, Fort Collins, CO,

Dec. 1988.

Warner, R.E. Use of Cover by Pheasant Broods in

East-Central Illinois, Journal of Wildlife

Management, 43(2), 1979:334-46.

. Effects of Changing Agriculture on RingNecked Pheasant Brood Movements in Illinois, Journal of Wildlife Management, 48(3), 1984:1014-18.

Economic Research Service/USDA 37
Warner, R., and S.L. Etter. Farm Conservation

Measures to Benefit Wildlife, Especially Pheasant

Populations, in Transactions of the North American

Wildlife and Natural Resource Conference, Wildlife

Management Institute, Washington, DC, 1985:135-41.

. The Dynamics of Agriculture and RingNecked Pheasant Populations in the Corn Belt,

Journal of the World Pheasant Association, 11,

1986:76-89. 

Warner, R.E., S.L. Etter, G.B. Jocelyn, and J.A. Ellis.

Declining Survival of Ring-Necked Pheasant Chicks

in Illinois Agricultural Ecosystems, Journal of

Wildlife Management, 48(1), 1984:82-88.

Weitman, D. Water Quality Improvements and

Wetland Restoration, in When Conservation Reserve

Contracts Expire: Policy Options Conference

Proceedings. Arlington, VA. Soil Conservation

Society, Ankeny, Iowa, Feb. 10-11, 1994, pp. 20-22.

Wildlife Management Institute. How Much is

Enough?: A Regional Wildlife Habitat Needs

Assessment for the 1995 Farm Bill, compiled and

edited by Donald F. McKenzie and Terry Z. Riley of

the Wildlife Management Institute, Washington, DC,

Feb. 1995.

Young, C. E., and C.T. Osborn. The Conservation

Reserve Program: An Economic Assessment. AER626. U.S. Dept. Agr., Econ. Res. Serv., 1990.

Zalom, F., and W.E. Fry. Food, Crop Pests and the

Environment: The Need and Potential for Biologically

Intensive IPM. APS Press, St. Paul MN, 1992. 

38 Economic Research Service/USDA
Economic Research Service/USDA 39

Appendix ADescription of the WaterQuality Valuation Model

This section briefly describes the discrete-continuous

model used to value the nonmarket benefits of the

CRP.16 The survey data are from an ERS-sponsored

component of the 1992 National Survey of Recreation

and Environment in which 1,510 respondents were

asked to recall the number of trips taken to up to three

wetlands, three lakes, and three rivers less than 100

miles from their residences within the last 12 months

in cases where the presence of a water body was an

important reason for the trip. Trip destinations were

determined either by self-reported location names, or

by the self-reported distance and direction from the

respondents residence. Destinations themselves are

small, subcounty areas termed polygons found in the

1992 National Resources Inventory (NRI) database.

It is assumed that individuals face a choice set of all

NRI polygons within 100 miles of their residence

(approximated by the centroid of their resident ZIP

Code zone). Lake/wetland and river recreation are

modeled separately. Each polygon is described by

five variables:

1. Trip cost (TC) is the round-trip travel cost 

(distance*$0.35) plus the round-trip time cost

((personal income/2000)*0.333*distance/50). 

2. Percent forest (PF) is the percentage of the polygon in forest cover. 

3. Percent privately owned (PO) is the percentage

of land in the polygon that is privately owned. 

4. Erosion (ER) is the average 1992 NRI sheet and

rill erosion rate in tons per acre estimated using

the universal soil loss equation in each polygon. 

5. Log(Size) (M) is the natural logarithm of acres of

lake area (meters of river length) for the lake

(river) model in each polygon.

The first stage of the recreation demand model is a

random utility model (RUM) describing the choice of

destination on a recreational outing. The probability

that the k-th person visits the i-th destination is:

(A.1)

Pk(i)=exp{Vik+(1/μ)ln(Mi

)}/Sjexp{Vjk+(1/μ)ln(Mj)},

(A.2) Vik=b1*TCik +b2*PFi +b3*POi +b4*ERi

where b1,...,b4 and μ are parameters that are estimated

using maximum likelihood techniques. Results of this

stage of the estimation appear in appendix table 1.

The destination probabilities shown in equation (A.1)

are then used to compute expected trip costs E(TC)

and trip qualities E(Q) for each individual:

(A.3) E(TCi

) =SiPk(i)TCik,

(A.4) E(Qi

) =SiPk(i)Qk, 

where Qk = [PFk, POk, ERk, Mk]. Total trips are then

written as a function of income (Yi

), socioeconomic variables (Si

), expected costs, and expected

qualities:

(A.5) Ti = f(Yi

, Si, E(TCi), E(Qi)).

Equation (A.5) is estimated using a double-hurdle

count model. Separate sets of parameters explain the

decision to participate and the intensity of participation. Results appear in appendix table 2. Changes in

welfare resulting from a change in site quality are

computed by assuming that equation (A.5) is a

demand function. The welfare measure is the consumer surplus at the initial state minus the consumer

surplus at the final state. 

Using the model at the national level requires a

national data set of environmental quality and demographic information. The NRI supplies national environmental quality data while the U.S. Census supplies

national demographic information. A representative

individual was constructed in each of the 3,071

counties in the 48 conterminous States using the 1990

U.S. census. By assumption, this individual resides in

the geographic centroid of the county, has the average

income, age, gender, and education found in the county, and faces a recreational choice set of NRI poly16For more information, Feather and Hellerstein (1997)

describe the model and data in detail.
gons within 100 miles of the county centroid. 

Preliminary analysis of the consumer surplus measures in the study area showed large differences

between consumer surplus measures computed using

the survey data (W1) and consumer surplus measures

computed using the census data (W2). The former

measures (W1) are believed to be correct while the

latter measures (W2) are believed to be biased. This

bias is likely to result from the nonlinear nature of the

consumer surplus function. Since W2 is used as an

estimate of unknown W1 in the national analysis, a

calibration procedure was used to attempt to remove

this bias. First, both W1 and W2 were computed for

each county in the study area (where both survey and

census data are available). Next, the ratio W1/W2 is

regressed on county demographic information from

the census (see appendix table 3). This calibration

function, along with the estimated demand relationship equation (A.5) is used in each U.S. county to

determine the welfare changes for the representative

individual. In each county, W2 and W1/W2 are estimated from the census data. These are multiplied

together and then multiplied by county population to

arrive at a county welfare estimate.

For purposes of welfare estimation, several erosion

levels had to be predicted from the NRI data. Erosion 

was predicted for each of the proposed CRP scenarios

in the following manner. 

For each NRI point, erosion is estimated to be:

1. Observed 1992 NRI USLE erosion if the point is

either:

a. currently in the CRP and included in the proposed scenario;

b. currently not in the CRP and not included in the

proposed scenario.

2. Compute USLE=RK*LS*C*P using observed RK

and LS and the average C and P factors from pasture land in the county including the point if the

point is not currently in the CRP, but is included in

the proposed scenario.

3. Compute USLE=RK*LS*C*P using observed RK

and LS and the average C and P factors from highly erodible land in the county including the point if

the point is currently in the CRP, but is not included in the proposed scenario. 

40 Economic Research Service/USDA

Appendix table 1—Random utility models of lake and river recreation1

Parameters2 Lake recreation model3 River recreation model4

Trip cost (TC) -0.0834 -0.0992

(-108.1) (-90.0)

Percent forest (PF) -1.4271 -0.4545

(-18.4) (-5.0)

Percent privately owned (PO) -1.0778 -0.3101

(-19.3) (-4.5)

Erosion (ER) -0.1511 -0.1308

(-18.1) (-2.1)

Log(size) (M) 0.0141 0.1150

(5.5) (16.4)

1Random utility models based on water-oriented recreational activities at lakes and rivers. T-statistics for the null hypothesis that the parameter equals zero appear in parentheses.

2Trip cost is the round trip travel cost (distance*$0.35) plus the round trip time cost ((personal income/2000)*0.333*distance/50). Percent forest

is the percentage of the polygon in forest cover. Percent privately owned is the percentage of the polygon that is privately owned. Erosion is

the 1992 NRI sheet and rill erosion rate in tons per acre estimated using the USLE. Log(size) is the natural logarithm of acres of lake area

(meters of river length) for the lake (river) model.

3Estimated using a sample of 706 individuals averaging 9.78 lake-based trips per person. Most participants visited more than one location

over the year; the number of unique respondent/location pairs is 1,323.

4Estimated using a sample of 447 individuals averaging 10.81 river-based trips per person. Most participants visited more than one location

over the year; the number of unique respondent/location pairs is 772.

Source: Feather and Hellerstein, 1997.
Economic Research Service/USDA 41

Appendix table 2 — Double-hurdle Poisson models of lake- and river-based recreation1

Parameters2 Lake recreation model3 River recreation model4

Participation parameters4

Constant -0.2183 -0.7567

(-1.59) (-4.36)

Family income 0.0067 0.0035

(3.59) (1.49)

Age -0.0178 -0.0186

(-7.30) (-6.05)

Gender 0.3679 0.6567

(4.71) (6.79)

College 0.2191 0.0827

(2.51) (0.76)

Intensity parameters5

Constant 3.6353 6.2761

(37.28) (37.09)

E(Cost) -0.0214 -0.1044

(-5.30) (-20.65)

E(percent forest) -0.3466 0.8621

(-4.59) (12.98)

E(percent privately owned) -0.3784 1.1479

(-2.53) (8.29)

E(erosion) -0.0462 -0.0309

(-2.47) (-2.99)

E(size) -0.0413 -0.1927

(-4.21) (-16.1)

Family income -0.0021 -0.0057

(-3.70) (-9.85)

1Double-hurdle Poisson models of lake- and river-based recreation participation and intensity.

2Estimated using a sample of 1,510 survey respondents consisting of 706 participants and 804 nonparticipants.

3Estimated using a sample of 1,510 survey respondents consisting of 447 participants and 1,063 nonparticipants.

4Constant is a constant term. Family income is the respondent’s family income in dollars. Age is the respondent’s age in years. Gender

equals one if the respondent is male, zero otherwise. College equals one if the respondent has completed a college education.

5Constant is a constant term. E(Cost) is expected trip cost. E( percent forest) is expected percentage of land in forest cover. E(percent privately owned) is expected percentage of land privately owned. E(erosion) is expected erosion. E(size) is expected lake area (river length) for

lake (river) trips. Family income is the respondent’s family income in dollars.

Source: Feather and Hellerstein, 1997.
42 Economic Research Service/USDA

Appendix table 3—Calibration function estimates1

Variable2 River recreation3 Lake recreation4

Constant 1.2144 0.4970

(1.376) (1.633)

Permale 0.9956 0.3104

(0.625) (0.565)

Age -0.0361 -0.0081

(-2.849) (-1.863)

Highsc 0.3041 0.0588

(1.084) (0.607)

Inc20 -2.1149 -0.5982

(-2.766) (-2.268)

Income -0.0050 -0.0007

(-0.592) (-0.235)

Agege65 3.2893 0.5452

(2.935) (1.410)

R2 0.229 0.232

R2-Adjusted 0.189 0.193

1Least squares regression. Analysis is conducted in the study area. Dependent variable is the observed county calibration factor. The calibration factor is the average consumer surplus in each county from individual NSRE data divided by the consumer surplus of the representative

individual from the U.S. census. Sample size is 126.

2Constant is the constant term; Permale is the proportion of the county that is male; Age is the average age of persons in the county in years;

Highsc is the proportion of persons in the county who have graduated from high school; Inc20 in the proportion of households in the county who

have incomes less than $20,000 per year; Income is the median annual household income in the county in $1,000.00 dollar units; Agege65 is

the proportion of persons in the county who are 65 years old or older. R2 (R2-Adjusted) is the (adjusted) coefficient of determination. T-statistics for the null hypothesis that the parameter equals zero are in parentheses.

3For the river-recreation model.

4For the lake-recreation model.

Source: Feather and Hellerstein, 1997.
Appendix BDescription of the PheasantHunting Valuation Model

This analysis values changes in environmental benefits associated with changes in farmland use. The

empirical analysis estimates the demand for pheasant

hunting with a travel cost framework that combines a

random utility model with a travel cost demand model

(Feather, Hellerstein, and Tomasi; 1995). This

approach is similar to that discussed in Appendix A. 

While agriculture affects many wildlife species, this

study looks at pheasants for two reasons. First, it is a

very popular game bird. Data from the 1991 National

Survey of Fishing, Hunting, and Wildlife-Associated

Recreation (FHWAR) indicate that the pheasant is the

most popular upland game bird throughout the

Midwest. Second, pheasants are sensitive to changes

in uses of agricultural lands. The continued specialization in agriculture and increased use of insecticides

and herbicides have cost pheasants cover and food

sources, thereby reducing nesting success and chick

survival (Basore, Best, and Wooley, 1987; Hill, 1976;

Jahn, 1988; Messick and others, 1974; Minn. Dept of

Natural Resources, 1985; Warner, 1979 and 1984;

Warner and others, 1984). Thus, pheasant populations

have trended downward. For example, pheasant populations in South Dakota fell from an estimated 16

million in the mid-1940s to less than 2 million by

1986 (S.D. Dept. of Game, Fish, and Parks, 1988).

This study includes those States with the historically

most suitable environment for pheasants, which are

the Lake States (Wisconsin, Michigan, and

Minnesota), the Corn Belt States (Iowa, Illinois, Ohio,

Missouri, and Indiana), the Northern Plains States (the

Dakotas, Nebraska, and Kansas), and Montana.

Ideally, we would like to estimate the travel cost

demand model as a function of pheasant populations

and use a biological model to quantify pheasant populations as a function of agricultural practices.

However, biological studies on pheasants attempt to

track habitat preferences of pheasants or the impact of

habitat changes at single stages in their life cycle.

While these studies indicate important environments

for nesting, brood habitat, and winter cover and food

supply, they do not model pheasant populations as a

function of habitat. To overcome this lack of a biological model, we employ a reduced-form model,

which is a combined biological-behavioral model.

The reduced-form model includes the critical habitat

variables that must be determinants of pheasant populations. Coefficients on the habitat variables represent

both the biological and the subsequent behavioral

responses.

Behavioral data used in this analysis come from the

1991 national FHWAR survey. To ensure that our

sample included all potential pheasant hunters, we

included those who indicated that they had hunted any

species at least once in any of the past 10 years or

thought they might hunt in the survey year. There is a

total of 5,834 observations on potential hunters in the

relevant States. The ZIP Codes of respondents were

obtained for analytical purposes. Specifically, the latitude and longitude of the ZIP Codes served as an

approximate geographic location of respondents

residences.

For each individual, a set of possible destination

sites was constructed. Sites were defined by a geometric division of the surrounding land into semi-circular zones, with each of these zones treated as a possible destination site. To be representative of potential sites, zones were large enough so that environmental quality might differ across sites yet small

enough to ensure that environmental quality did not

vary significantly within a site. For these reasons and

because pheasants move a few miles throughout the

year as habitat needs change, sites are defined in 25-

mile increments around each ZIP Code center (Warner

and Etter, 1985). The closest site is the area within 25

miles of the ZIP Code centroid. The next closest sites

are those beyond 25 miles but within 50 miles. There

are three sites defined in the 25- to 50-mile range, five

sites in the 50- to 75-mile range, and seven sites in the

75- to 100-mile range. All sites are of equal area. A

total of 16 sites is defined for each respondent (appendix figure 1).

A Geographic Information System (GIS) was

employed to obtain statistically representative measures of relevant site-quality characteristics for each

site. Specifically, the average shifted histogram

(ASH) technique was used to estimate environmental

characteristics across geographic locations based on

observed environmental quality measures (Scott and

Whittaker, 1996). The ASH technique generates a

surface of data on characteristics at all grid points.

The grid scale used in this analysis was approximately

3.9 miles. The environmental characteristics of each

site are assumed to be represented by the characteristics of that sites central grid point.

Economic Research Service/USDA 43
44 Economic Research Service/USDA

As with the water-quality model, observed environmental quality measures are taken from the 1992

National Resource Inventory (NRI). Also, the 1990

Census of Population provides the population and size

of each census tract from which population densities

are determined. The latitude and longitude of each

NRI centroid and census tract are used to identify the

geographic locations of these environmental quality

measures.

The specifics on the distance traveled and State visited reported by FHWAR survey respondents are

enough detail to identify the site visited for approximately 70 percent of the observations. When more

than one site fit the distance/State criteria, an allocation heuristic based on the potential site with the highest pheasant population is used to designate the visited

site. Pheasant population estimates are obtained from

the North American Breeding Bird Survey (BBS).

The BBS is a national survey that attempts to obtain

counts of all bird species observed along designated

routes on scheduled days (Bart and others, 1995).

The BBS data is processed in a GIS model as are the

other environmental characteristics. 

Many environmental characteristics have affected

pheasant populations over time but not all affect

pheasant populations across space. Those pheasant

habitat characteristics that tend to vary across sites

include: hay and small grain crops (oats, barley, and

wheat), which provide marginal nesting cover (HAYGRN); corn and soybeans, which provide feed but

poor early season cover (CRNSOY); pastures, which

tend to be lesser disturbed than tilled soils and thus

are a marginal nesting and feed source (GRASS); forest cover, which does not provide good habitat (FOREST); cropland not included in other variables, which

tends to be a better source of habitat than excluded

nonagricultural land uses (OTHCROP); and undisturbed cover, which provides good nesting cover,

insects for newly hatched chicks, and winter cover

(CRP) (Jahn, 1988; Kimmel and others, 1992; USDA,

1989; Warner and Etter, 1986). The percentage of all

land in each of these uses is derived for each NRI

polygon, converted to a geographic resource measure

based on the latitude and longitude of the NRI polygon, and used in the GIS model to produce the site

measures as outlined above.

As in Appendix A, Feather, Hellerstein, and Tomasi

(1995) developed the modeling approach applied here.

The first step in this approach uses a Random Utility

Model (RUM) to model site selection. Important factors affecting site selection are the environmental

amenities of the sites, the effect of crowding on the

quality of recreational hunting at each site, and the

travel cost to each site. Therefore, the independent

variables of the RUM are:

1. TC represents the travel cost to the site.17

2. lnCRP is the natural log of the portion of a sites

acres in CRP. The natural logarithm is taken to

account for diminishing returns. To avoid a ln(0),

0.0001 is added to all CRP values.

3. HAYGRN is the portion of a sites acres in hay

and small grain. The small grains included are

oats, barley, and wheat as recommended in conversations with wildlife biologists. 

Appendix figure 1

Delineation of Sites Around ZIP Code Centroids

Source: USDA, ERS.

17Travel cost includes both the time cost and mileage cost.

Time cost is based on the opportunity cost of time multiplied

by the estimated travel time. The opportunity cost of time is

set at one-third the hourly wage, and the hourly wage equals

annual income/2000 hours per year. Travel time is estimated

by dividing the distance traveled by an average speed of 42

mph where 42 mph is the average rate of speed of respondents

in a recent recreation survey (Feather, Hellerstein, and Tomasi,

1995). Mileage costs are set at the American Automobile

Associations estimated $0.30 per mile.
4. CRNSOY is the portion of the sites acres in corn

and soybeans.

5. GRASS is the portion of acres in pasture.

6. FOREST is the portion of acres in forest.

7. OTHCROP is the portion of farmland in crops

other than CRP, HAYGRN, CRNSOY, or GRASS.

8. HAYGRNSQ is HAYGRN squared.

9. CRNSOYSQ is CRNSOY squared.

10. FORESTSQ is FOREST squared.

11. POP is the sites human population density.

Following equation A.2, the systematic component of

utility is given as:

(B.1) Vik =b1*TCik +b2*lnCRPi +b3*HAYGRNi

+ b4*CRNSOYi +b5*GRASSi + b6*FORESTi

+b7*OTHCROPi +b8*HAYGRNSQi + 

b9*CRNSOYSQi +b10*FORESTSQi + b11*POPi

All coefficients of the RUM are significant at the 99-

percent confidence level and are of the expected sign

(appendix table 4). The correct sign and statistical

significance of the coefficients on lnCRP provide

strong statistical support for the hypothesis that CRP

acreage is critical to pheasant habitat. These results

are used to estimate the expected travel costs, E(TC),

and the expected site quality, E(Q), of the representative site visited from each ZIP Code. The quality vector, Q, includes lnCRP, HAYGRN, CRNSOY,

GRASS, FOREST, OTHCROP, HAYGRNSQ, CRNSOYSQ, FORESTSQ, and POP. 

With the number of trips an individual takes designated as T and the individuals socioeconomic characteristics and income contained in the vector S and variable Y, the participation model is specified in equation

A.5. The participation model is estimated as a

Poisson count data model because the dependent variable is a nonnegative integer (Creel and Loomis,

1990; Englin and Shonkwiler, 1995; Hellerstein,

1992). The parameters of the participation model are

consistent with prior expectations. Coefficients are of

expected sign, and all are significant at the 90-percent

level (appendix table 4). 

Both of the estimated models are directly employed to

measure the change in consumer surplus to pheasant

hunters due to a change in agricultural land use. The

focus here is on changes in CRP acreage although the

estimated models can also be applied to changes in

acreage of corn/soybeans, hay/small grain, and pasture

land. 

To evaluate the change in consumer surplus resulting

from a change in CRP enrollment, new NRI land use

measures are derived with a simulation model that

identifies those NRI observations that would be in the

CRP under the new conditions and returns any NRI

observations leaving the CRP to its use reported in the

1982 NRI (Osborn, 1993). The resulting new NRI

land-use measures are used in the GIS to generate

new land-use measures at grid points. 

As with the original data, these simulated measures of

land uses and populations at each sites center grid

point are used to characterize the site. Using the

RUM model, the expected travel cost, E(TC), and

expected site characteristics, E(Q), are derived for

each ZIP Code. Then, for each observation, the individuals E(TC), E(Q), income, and personal characteristics are used in the participation model to determine

the consumer surplus. The change in an individuals

consumer surplus associated with the change in CRP

enrollment is the difference in consumer surplus

before and after the change. 

Economic Research Service/USDA 45
46 Economic Research Service/USDA

Appendix table 4—Empirical results

Variables Random utility model1 Poisson model1

Constant -1.97

(4.18)

COST -0.148 -0.0424

(114) (-4.57)

ln(CRP) 0.237 0.0713

(11.1) (1.73)

HAYGRN 0.0645 0.0773

(9.40) (5.35)

CRNSOY 0.0884 0.0559

(18.2) (5.78)

GRASS 0.0458 0.0184

(16.7) (3.86)

FOREST -0.0448 -0.0433

(5.51) (4.89)

OTHCROP 0.0647 0.0139

(17.5) (2.21)

HAYGRNSQ -0.000345 -0.00129

(3.26) (6.60)

CRNSOYSQ -0.000558

(11.9)

FORESTSQ 0.000928 0.000875

(6.43) (7.04)

POP -0.00121 -0.00266

(11.2) (5.50)

MALE 1.83

(17.0)

RURAL -0.127

(1.84)

AGE -0.0186

(8.39)

ED12 0.305

(3.35)

ED16 0.194

(2.82)

INCOME 9.48*10-11

(8.70)

WEIGHT -0.000925

(10.6)

WEIGHTSQ 6.60*10-8

(4.79)

See note at end of table. —Continued
Economic Research Service/USDA 47

Appendix table 4—Empirical results—Continued

Constant is a constant term;

COST is the travel cost = ((1/3 INCOME/2000 hours/year)/42mph + $0.30) * distance traveled;

ln(CRP) is the natural logarithm of the portion of acres in the CRP;

HAYGRN is the portion of acres in hay, wheat, barley, and oats;

CRNSOY is the portion of acres in corn and soybeans;

GRASS is the portion of acres in pasture;

FOREST is the portion of acres in forest;

OTHCROP is the portion of farmland in crops other than CRP, HAYGRN, CRNSOY, or GRASS;

HAYGRNSQ is HAYGRN squared;

CRNSOYSQ is CRNSOY squared;

FORESTSQ is FOREST squared;

POP is the population density measured in people per square mile;

MALE is a zero-one dummy variable equal to one when the respondent is male;

RURAL is a zero-one dummy variable equal to one when the respondent resides in a rural community;

AGE is the age of the respondent;

ED12 is a zero-one dummy variable equal to one when the respondent has completed high school but not college.

ED16 is a zero-one dummy variable equal to one when the respondent has completed college;

INCOME is annual household income.

WEIGHT is the sample weight of the observation;

WEIGHTSQ is WEIGHT squared.

1t-statistics for the null hypothesis that the parameter equals zero appear in parentheses.

Source: USDA, ERS.
Appendix CDescription of the WildlifeViewing Valuation Model

In addition to pheasant populations, changes in the

Conservation Reserve Program (CRP) may affect a

variety of other wildlife populations, with resulting

impacts on public participation in nonconsumptive

wildlife-based outdoor recreation (wildlife viewing).

To study this possible relationship, FWHAR data

were used to model how the CRP influences the publics participation in wildlife viewing.

The nonconsumptive wildlife-associated recreation

component of FHWAR data contains approximately

26,000 observations. This analysis uses the number

of trips taken for the primary purpose of viewing

wildlife, and focuses on the trips taken within the general vicinity of the individuals home, which roughly

translates to all trips taken within approximately 100

miles of the individuals residence.

The size and extent of the FHWAR database are the

primary features motivating its use. However, counterbalancing these positive features is the paucity of

information on the recreational sites visited by respondents. As with the pheasant and water-oriented recreation models, site-specific information is very important, since landscape characteristics (that is, the extent

of CRP) are likely to influence recreational behavior. 

In contrast to the pheasant model, which used ancillary information (the Breeding Bird Survey) to designate visited sites, the available data could not be used

to impute which (of several possible sites) the respondent visited. Thus, rather than select a particular site,

the model examines how the aggregate trip-taking

behavior of an individual is a function of the aggregated characteristics of all the sites available to that

individual.

Briefly, the analysis involves the following steps:

1) Create landscape characteristics variables

defined at a number of semi-circular zones

around each respondent (LC), which are then

aggregated into circular-distance-band, aggregated landscape characteristic variables (Z).

2) For each individual, extract visitation (Q), and

socioeconomic (X), data from the 1991

FHWAR.18

3) Using the distance to most frequently visited

site as a dependent variable, estimate a representative trip price (P). 

4) Regress total number of trips against X, P, and Z.

5) Using coefficients from step 4, estimate predicted

number of trips (and consumer surplus) under

several scenarios.

The following sections provide greater detail on each

of these steps.

Imputing Landscape Characteristics

Following the procedure used in the pheasant study,

the ASH (Scott and Whittaker, 1996) technique is used

to create a variety of landscape characteristics from

National Resource Inventory (NRI) data. Several

broad measures of land use as proxies for wildlife

habitat (and potential populations) are created:

1) percentCRP. The percent of the land (in the subcounty region) that is in CRP.

2) percentCROP.

3) percentFOREST.

4) percentGRASS (rangeland and pasture).

5) RUC: Rural-Urban Continuum code (0 being

most urban, 9 being most rural).

6) DIVERSITY: Landscape diversity, with higher

values of DIVERSITY indicating a more variegated landscape (based on the interspersion of water

bodies, forest land, grassland, and cropland).

48 Economic Research Service/USDA

18This requires knowledge of a key piece of information: the

individuals residence. Since public-use releases of FHWAR

do not contain this information (due to privacy concerns),

analysis of the data necessitated using raw data at U.S. Census

facilities.
For each FHWAR respondent, 19 zones are defined.

These zones are equivalent to those 16 zones generated for the pheasant study, but with the inner zone

divided into 4 components.19

To simplify the model (and avoid problems with missing data), the landscape characteristics (LC) of the 19

zones are aggregated into 5 distance-band-aggregated measures (Z). To account for the possibility that

landscape heterogeneity may be important, we use a

constant elasticity of substitution functional form to

compute an aggregate measure.

Specifically, this measure is defined as:

(C.1) 

where: 

a is a parameter to be estimated,

Jk is the number of zones in the k-th distance

band (that is, the 62-mile distance band four has 5

zones),

LCjk is the value of the characteristic in the j zone

of band k,

and

Zk is the aggregated measure of the land characteristic of the k-th band.

Note that when

a = 0 : Maximum matters

a = 1 : Sum (or average) matters

a >>1 : Variations in characteristics do not matter

Given the K=5 distance-zones, and six characteristics

(listed above), this yields 30 separate distance-bandaggregated landscape characteristic (Z) variables.20

Individual Data

Data on nonconsumptive wildlife-associated recreation were obtained from the 1991 FHWAR. For each

surveyed individual, the number of visits to non-distant sites was extracted. Operationally, this involved

several steps:

1) Trips to ones own State, and to all States for

which the most visited location was within 100

miles of the residents home, were summed to

obtain total non-distant trips (for all potential

wildlife viewers).21

2) Using information on past participation, and on

current plans, observations on individuals who

were not likely to be potential wildlife viewers

were dropped.22

3) Several socioeconomic variables were extracted

for each individual, including male, caucasian,

Economic Research Service/USDA 49

19These four zones are defined as zone 1 being within 12

miles of the own ZIP Code centroid; and zones 2 through 4

between 12 and 25 miles of the centroid.

20The five distance zones could be further aggregated into an

overall measure by using an endogenous distance decay.

Although this yields a more parsimoniously specified model, it

also complicates estimation.

21This focus on trips to non-distant sites is necessitated by

modeling concerns; such as the large number of sites one

would have to include in order to account for far away trips.

However, note that trips to these non-distant sites account

for over 90 percent of nonconsumptive wildlife-oriented trips. 

22The following table contains the percent of observations in

four categories.

0 Trips >0 Trips

Excluded observation 18 percent 7 percent

Included observation 45 percent 30 percent

Ideally, the excluded-observation / >0 trips category should

contain 0 percent (since individuals who took a trip should not

be a priori excluded). Conversely, the included-observation /

0 trips category may contain a large percentage, since it is

possible for potential participants to choose 0 trips in any

given season. Overall, approximately 85 percent of all trips

were accounted for by individuals retained in the sample. 

Z

J

k LC

j

k

= jk

æ

è

ç ö

ø

÷ =

S

1

1 ( ) /a

a
rural residence, high school graduate, college

graduate and household income. All but household income are dummy (0/1) variables.

Household income is an approximation based on

the center of seven broad ranges (in the $0 to

>$75,000 interval).

4) Individual weights were also obtained for each

observation. These are demographic weights,

computed by the FHWAR survey designers, that

are used when creating population level

predictions.

Constructing an Imputed Price 

For several reasons, it is desirable to include an

explicit price information in the analysis. First, if

explicit price information can be obtained (say, a

representative price), then welfare estimates using

consumer surplus may be readily computed. Second,

including such price information should improve the

models performance.

The problem is, as with landscape characteristics, the

paucity of knowledge about which sites were visited

implies a lack of explicit price information; a problem

that is exacerbated when individuals took zero trips.

As a substitute, an imputed representative price can

be used. 

The imputed representative price is based on the

FHWARs distance to the most frequently visited site

variable. This distance variable is converted into a

travel cost, using average cost per mile information,

and a simple time cost (based on a fraction of household income). This travel cost is then used as the

dependent variable in a sample selection model. The

use of this predicted price offers two advantages: as a

control for potential simultaneity between price and

quantity of trips, and to impute a price for the

(many) individuals who consumed zero trips.

To predict this price, a sample selection model is

used:

(C.2)

where:

The first equation is a Probit on whether the individual took any trips, with Z and X used as regressors.

The g coefficients from this Probit estimator are used

to compute a Mills ratio. This Mills ratio, along with

the Z and X variables, are regressed against the log of

observed price (using observations with non-zero

trips) in a standard semi-log OLS. Lastly, the predicted values of g, bc, and bg are used with equation

C.2.ii to impute a price for all observations (including

individuals who took zero trips).

The Demand Estimator

Using the X (socioeconomic), P (imputed price), and

Z (aggregated landscape characteristics) variables, a

representative trip demand curve is estimated. 

To clarify, lacking good information on where people

went on their wildlife-associated trips, rather than

selecting a visited site (using ancillary information)

and estimating a RUM model (viz., the pheasant

model), the wildlife-viewing model focuses on the

total number of wildlife-associated trips within a few

hours drive of an individuals residence. Hence, the

use of the aggregated landscape characteristic (Z)

variables to estimate total trips is best interpreted as

arising from a reduced-form model of the site-selection problem solved by an individual recreator. That

is, the reduced-form model combines trips to all sites

into a total number of trips, and uses aggregated

landscape characteristics to explain the total number

of trips taken. Thus, the determinants of a set of chosen trips (to unknown-to-the-analyst sites) are represented by these aggregated characteristics.

To control for the prevalence of zero trips, a doublehurdle Poisson estimator is used to estimate the representative trip demand curve.

(C.3) 

where:

50 Economic Research Service/USDA

[ ]

[ ]

i ob Participant;

ii E v

) Pr ( , )

) (,)

(,)

g cg

b b cb fcg

c g

c c b

=

>= +

F

F ln(Price); , |Q 0 v

c = X and Z variables

`g = Predicted value of g from step 1

f = Normal pdf

F = Normal cdf

( )

Pr ( ; | , ) ! ( )

Pr ( | , ) ( )

ob q q e

q

e

ob q e e e

q

> =

æ

è

ç ö

ø

÷ -

= = +-

-

-

- --

0 1

0 1

l g l

l g

l

g

l lg
l is the quantity parameter: l =exp(Rb)

g is the participation parameter: g=exp(St)

b and t are parameters to estimate

R are factors that influence the number of trips;

including P (price information), X (socioeconomic factors), and Z (aggregated landscape characteristics)

S are factors that influence participation (a subset

of X is used)

Note that, along with the imputed price term (P), the

use of distance zone variables allows cost differences

to affect demand. 

Summarizing the Model

The model to be estimated is:

Z = Z(LC, a)

P = P(X2,Z; g)

q = F(X1,X2,Z,P,W;b1,b2,bp,bz)

where:

q = Number of trips. 

X1= Individual specific variables that influence

probability of participation; typically socioeconomic variables.

X2 = Individual specific variables that influence

quantity of trips; typically socioeconomic

variables. Note that X1 and X2 may contain

the same variables.

Z = Aggregated landscape characteristic variables, for z different variables and k=1..K

bands. These will be a function of the 19

LC variables and a (the CES aggregation

parameter). 

P = The imputed price of a trip. Based on a sample

selection model with the observed distance to

favorite site as the dependent variable, and X2

and Z as the independent variables.23

Z() = The distance-band landscape aggregation

variables (equation i).

P() = The sample selection imputed price model

(equation ii).

F() = The hurdle Poisson model (equation C.3).

W = Population weight correction factor.24

and 

b1,b2,bp,bza,g, = Parameters to be estimated.

Although simultaneous estimation of the above would

be optimal, operational difficulties dictate a multistage model, to wit:

Economic Research Service/USDA 51

23The observed price term is computed as the sum of an outof-pocket cost and a time cost:

P= [0.3 * DIST] + [ WAGE * 0.33 (DIST/50) ]

Where:

DIS: Distance to site (in miles)

0.3: Approximate per mile cost of using a car

WAGE: Imputed wage rate = Household income divided by

2040.

DIST/50: Time required to travel DISTANCE

0.33: Fraction of travel time that is onerous. The assumption is that recreational travel is not as unpleasant as

work, hence should not be valued at the wage rate

(Shaw, 1992).

Note that the following is assumed:

1) The WAGE rate assumes that the trip taker is the sole wage

earner in the household; and freely chooses to work 2,040 hours.

2) Out-of-pocket costs (0.3 * DIST) assume a group size of one

(no cost sharing, and no variation in fuel economy, depreciation

rates, etc. 3) An average speed of 50 miles per hour.

24When using the FHWAR weights to scale up to the population, the desired equivalence between observed and predicted (using the baseline data) number of trips need not

hold. There are several ways of addressing this inconsistency;

including ex-post calibration, weighted estimation, or inclusion of the weight as a correction factor. Though all of these

are problematic, the use of the weight as a correction factor

involves the fewest ad hoc assumptions.
1) Using a grid search, select a candidate value of

a. 

a) For each candidate value of a, the Zk variables are generated. 

b) Given Z, P is imputed.

c) Given Z and P, estimate F(×).

d) Record the log-likelihood from c.

2) Reiterate step 1 for different values of a.

3) Given a set of coefficient vectors (one vector for

each value of a) choose the one with the best log-likelihood. The b coefficients associated with this best loglikelihood are the estimated parameters of the model.

Since it might be expected that recreational behavior

may vary across the country, this model was applied

separately to the five sub-national regions: the West,

Northern Plains, Southern Plains, North, and the

South.25

Constructing Alternative Scenarios

Total recreational trips under different allocations of the

CRP are estimated under the three scenarios discussed

in the body of the paper: 1992 CRP (signups 1 to 11),

a no-CRP scenario, and a 15th EBI scenario.

Appendix table 5 lists the percentCRP (and

percentCROP) perceived by the FHWAR sample

under these scenarios. Since the FHWAR sample is

not uniformly distributed geographically, these percentages will differ from the actual landscape distribution in the regions.26

Across scenarios, the LC variables for each observation will be different. The impacts of these changes

are examined by recomputing the predicted number of

trips, using equation C.1 to recompute the aggregated

landscape characteristics (Z), equation C.2 to recompute X and P, and the estimated coefficients from the

model (equation C.3) to generate new predictions of

trip demand. 

Some Results

Screening information (on past wildlife-associated

recreation) was used to classify approximately twothirds of the sample as being potentially interested in

nonconsumptive wildlife-associated recreation; the

remaining one-third of the sample was classified as

uninterested and was not included in the estimation.

About one-half of the potentially interested individuals (one-third of the sample) actually took at least one

trip (appendix table 6 gives further details). Note that

the average reported trip value is based on a contingent valuation question asked of everyone who took a

nonconsumptive, wildlife-oriented trip (Waddington

and others, 1993). 

The canonical estimator for this model, as described

above, is based on a double-hurdle Poisson model

and an imputed price. Given the large number of

52 Economic Research Service/USDA

26These perceived values are derived in the following

manner. First, for each respondent, compute an average

percentCRP (and average percentCROP) in the 19 zones (in

the approximately 100-mile band surrounding his/her residence). Second, average these 100-mile-band averages.

Appendix table 5—Perceived percentCRP and percentCROP

1991 CRP 15th EBI No CRP

(34 million NRI acres)

Region percentCRP percentCROP percentCRP percentCROP percentCRP percentCROP

West 1.2 9.9 1.1 9.9 0 11.2 

Northern Plains 4.5 52.3 4.3 52.4 0 56.9 

Southern Plains 0.9 19.2 1.4 18.6 0 20.0 

North 1.1 28.9 1.6 27.8 0 29.9 

South 0.9 14.6 1.3 13.6 0 15.4 

Source: USDA, ERS.

25The five regions consist of:

1) West: CA, WA, OR, MT, ID, WY, NV, UT, CO, AZ,

NM.

2) Northern Plains: SD, ND, NE, KS.

3) Southern Plains: OK, TX.

4) North: MN, WI, MI, IA, MO, IL, IN, OH, ME, VT,

NH, CT, RI, MA, NY, PA, MD, NJ, DE, DC.

5) South: AR, LA, MS, AL, GA, SC, FL, KY, TN, WV,

VA, NC.
variables, appendix table 7 lists some of the more

important variables. We also list the sum of the bz

coefficients for each landscape characteristic, which

can be interpreted as the effect given a uniform

change in landscape characteristics.

Note that the coefficients are best interpreted as the

percent change given a unit change in the variable.

The probability variables range from 0 to 100; the

RUC (rural-urban continuum code) ranges from 0 to

9, and the diversity variable ranges from 1 to 4.

These coefficients are somewhat difficult to interpret,

as they show no strong pattern. PercentCRP seems to

be more often positive then negative, with the exception of the Southern Plains.

As a measure of model quality, the correlation

between the weighted observed and weighted predicted number of trips (based on the original scenario)

can be used in lieu of an R-square statistic.

Ideally, the coefficient on the imputed price could be

used to generate consumer surplus values.

Unfortunately, the imputed price coefficients are often

positive (or negative but very small in magnitude),

which yields impossible (or implausible) consumer

surplus values. It would appear that the distribution

of quality sites obscures the price relationship.27

However, since the imputed price does allow extra

information (the distance to last site data) to be

incorporated, we will retain the results with the understanding that the imputed price is to be interpreted

loosely.28

Instead of directly computing consumer surplus, we

use a benefits transfer value. In particular, the average per day value of wildlife watching is used as a

proxy for per-trip value. Although several sources for

such a value exist, the self-reported value from the

FHWAR is most appropriate for this exercise. The

regional averages of these values are used to report

the consumer surplus of wildlife-viewing trips

under the three scenarios.

Economic Research Service/USDA 53

Goodness of fit: Observed and predicted trips

Correlation: Correlation:

individual trips weighted trips 

West 0.18 0.40* 

Northern Plains 0.31 0.15

Southern Plains 0.43 0.41

North 0.24 0.18

South 0.19 0.17

*When a large outlier was not removed from the West, the weighted correlation was 0.81.

27If the distribution of site quality varies over the population

(with some individuals living close to better sites, while others

must travel long distances to attain better sites), then the

imputed price should be correlated with number of trips. That

is, better quality sites nearby should yield more trips to closer

sites; hence a negative sign on the imputed price coefficient.

On the other hand, if the shape of the distribution of site quality is similar across the population (say, increasing with respect

to distance), but with some individuals having better allaround choices (say, the slope of the distance/quality relationship varies across individuals), then high prices may be associated with high number of trips. That is, individuals who can

pay a high price for a fabulous site may take more trips than

individuals who choose a closer mediocre site instead of a

farther out slightly better than mediocre site.

28A number of other specifications were attempted, including

models without imputed price terms, and models that used the

simple Poisson model. The results from these models were

qualitatively similar to the double-hurdle, imputed price

model.

Appendix table 6—Regional summary of participation in nonconsumptive wildlife-associated recreation

Number Number Number of Average number Average Average reported

of observations retained participants of trips distance trip value

West 5,561 3,391 1,624 9.33 (16.7) 22 (33) 30 

Northern Plains 2,075 1,679 659 11 (23) 13 (18) 25 

Southern Plains 992 785 270 8.9 (19) 25 (44) 31 

North 9,827 7,878 3,122 13 (23) 14 (30) 32 

South 6,451 4,699 1,547 10 (19) 15 (19) 31 

Source: USDA, ERS.
54 Economic Research Service/USDA

Appendix table 7—Some coefficients from the double-hurdle model with imputed price 

(t-stats in parentheses)

West Northern Plains Southern Plains North South 

Aggregation parameter

a 0.47 2.33 1.5 4.7 0.47 

Some probability stage

coefficients 

INCOME -2.13e-7 -1.58e6 4.14e-6 -1.02 1.22e-6

(-.19) (-0.82) (1.5) (-1.3) (1.0) 

OWN_CRP -0.019 -0.052 0.13 0.042 -0.047

(-12.1) (-2.3) (3.4) (4.1) (-1.66)

Some quantity stage 

coefficients

Income -9.34e-6 5.13e-6 6.1e-6 -9.5 -5.0e-6

(-10.7) (8.4) (4.4) (-15.5) (-6.5) 

CRP0 1 0.352 0.023 -1.17 0.011 0.89

(12.2) (0.61) (-5.8) (1.17) (17.4)

CRP02 -0.234 -0.002 0.30 0.00053 -0.65

(-9.9) (-0.44) (5.7) (7.7) (-15.4) 

CRP03 0.0077 0.0010 -0.13 -0.00066 0.15

(0.86) (0.40) (-4.7) (-9.0) (8.3)

CRP04 0.061 0.0013 -0.004 -4.31 0.016

(12.8) (2.1) (-0.42) (-0.5) (1.9)

CRP05 -0.028 -8.06e-5 0.037 1.6e-5 -0.003

(-8.3) (-0.4) (-5.60) (10.0) (-0.056)

Price 0.029 -0.0093 -0.002 0.133 0.027

(4.8) (-0.90) (-0.34) (11.9) (3.14) 

Summation of landscape

characteristic coefficients

S percentCRP 0.16 0.02 -0.96 0.01 0. 40

S percentCROP 0.57 .10 0.02 -0.01 -0.001

S percentForest 0.15 .27 -0.04 0.003 0.008

S percentGrass 0.005 .03 -0.01 -0.03 0.02

S percentDiversity -0.31 -2.58 .37 -0.02 1.6 

S RUC 0.11 0.05831 0.29 0.06 -0.003 

Log likelihood 17671 10733 3239 59802 20316 

Source: USDA, ERS.
Appendix DDescription of 15th CRP

Signup Criteria

Appendix figure 2 illustrates the weighting of the EBI

factors.

Wildlife (100 points maximum; formula =

(A/50)*(A+B+C+D+E+F)

A. Cover Factor (0-50 points): Points are awarded

depending on which of the 40 possible cover types

are expected to become established. 

B. Endangered Species Area (0-15 points): If cover to

be established is within the known present range

of a listed, proposed, or candidate species and is

expected to benefit that species then 15 points are

awarded; otherwise 0 points are awarded.

C. Wetland Proximity (0-10 points): If semi or permanently flooded wetlands are within 1 mile of

the site, 5 points. If wetlands within or adjacent to

the bid area equal to more than 5 percent of the

contract area, 10 points.

D. Adjacent to Protected Areas (0-10 points): If protected wildlife habitat within 1 mile of the site, 5

points. If protected wildlife habitat adjacent to the

site, 10 points.

E. Contract Size (0-5 points): If the site is equal to

the State average, or up to twice the area of average, 2 points. If the site is more than twice the

State average, 5 points.

F. Upland/Wetland Ratio (0-10 points): This subfactor is based on the ratio of the acreage of uplands

offered to the acreage of restored wetlands offered.

If the ratio is less than 1:1 or greater than 10:1, 1

point. Ratios in the range of 1:1 to 3:1 or 6:1 to

10:1 get 5 points. Ratios between 3:1 and 6:1

receive 10 points.

Water Quality (100 points maximum; formula =

A+B+C+D)

A. Priority Area (0-30 points): 30 points are awarded

to acreage located in areas identified by State and

local water quality plans, State-identified wellhead

recharge areas, areas with coastal nonpoint pollution control programs developed under the Coastal

Zone Act Reauthorization Amendments, or areas

identified in plans developed in accordance with

the Clean Water Act.

B. Ground-Water Quality (0-20 points): Points are

awarded based on the average leaching potential

(rounded to the nearest whole number) and by

county population. The actual number of points

for a given score is taken from a State/county

table.

C. Surface-Water Quality (0-40 points): Points are

awarded based on the average sheet and rill erosion index for all acres being offered, the distance

to waterbodies of the nearest portion of the tract,

and county population. The actual number of

points for a given score is taken from a

State/county table.

D. Cropped Wetland (0-10 points): If least 10 percent

of the land enrolled meets the cropped wetland

eligibility criterion, 10 points are awarded.

Erosion (100 points maximum)

Erodibility Index (0-100 points): The higher of the

weighted average erodibility index for sheet and rill

erosion (using the USLE), or erodibility indicator for

wind erosion rounded to the nearest whole number is

used to determine the overall erodibility index.

Indices below 8 receive no points. Indices from 8 to

19 receive 25 to 80 points (= 25 + 5*(index-8)). A

score of 20 (21) receives 90 (100) points.

Long-Term Retention of Trees, Shrubs, and

Wetlands (50 points maximum)

Estimated Retention Period (0-50 points): Cover types

such as trees and restored wetlands that are likely to

remain after the contract has expired are awarded

points. Partial tree plantings are prorated to the nearest whole point. Hardwood tree, long leaf pine, and

white cedar plantings receive 50 points. Softwood

trees receive 40 points. Restored wetlands with semipermanent and permanently (temporarily and seasonally) flooded wetlands in the field receive 25 points. 

Air Quality (25 points maximum)

Air-Quality Component (0-25 points): The ZIP Code

and wind erodibility index (EI) of the tract (provided

by NRCS) is used to determine a score based on numEconomic Research Service/USDA 55
bers from an ERS county look-up table (weighted by

population).

Conservation Priority Area (25 points maximum)

Conservation Priority Area Component (0-25 points):

If the bid is located within an approved CRP State

Conservation Priority Area, it receives 25 points (if

the resource concern for which the CPA is selected

would be addressed by enrolling the land).

Cost Component (200 points maximum; formula =

A+B)

A. Rental Factor (0-190 points): The following formula based on the per acre bid is used: rental factor = 190 - 190*(bid amount/165).

B. Cost-Share Factor (0-10 points): If cost-share

assistance is not provided, 10 points are awarded. 

56 Economic Research Service/USDA

Appendix figure 2

Scoring criteria for CRP eligibility for enrollment (signup #15)

Wildlife

Water quality

Erodibility index

Long-term tree,

shrub, and wetland retention

Air quality

Conservation

priority area

Contract cost

Note: The above is not to scale.

Source: USDA, ERS.

Cover factor

50

Proximity Proximity Upland/ Endangered Contract

to to wetland species area area

wetlands protected ratio 15 5

10 areas 10

10

Priority area Ground-water Surface-water quality Cropped

30 quality 40 wetland

20 10

100

50

25

25

Bid factor

190

Cost

share

factor

10