United States

Department of

Agriculture

Economic

Research

Service 

Technical

Bulletin

Number 1922 

September 2008

Regional Values for Policy Assessment

LeRoy Hansen and Marc Ribaudo

Economic Measures of Soil 

Conservation Benefits
www.ers.usda.gov

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Hansen, LeRoy

Economic measures of soil conservation benefits : regional values for 

policy assessment.

(Technical bulletin (United States. Dept. of Agriculture) ; no. 1922)

 1. Soil conservation—United States—Cost effectiveness.

 2. Soil erosion—Economic aspects—United States.

 I. Ribaudo, Marc. 

 II. United States. Dept. of Agriculture. Economic Research Service. 

III. Title.

S624.A1

Photo credit: Barge in river, BrandXPictures; boys fishing, Gary Kramer,

NRCS, USDA; all others, Lynn Betts, NRCS, USDA.

www.ers.usda.gov www.ers.usda.gov

Recommended citation format for this publication:

Hansen, LeRoy, and Marc Ribaudo. Economic Measures of Soil Conservation 

Benefits: Regional Values for Policy Assessment, TB-1922. U.S. Dept. of 

Agriculture, Econ. Res. Serv. September 2008.
United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Abstract

This report describes data and methodologies that the Economic Research Service has 

used to apply monetary values to changes in soil erosion. Values and methodology are 

clearly described so that analysts can apply the data to specifi c soil conservation projects. ERS has used the values to estimate soil conservation benefi ts of changes in farm 

programs and practices, but no analyses of farm programs or practices are provided here. 

The benefi t values are regional dollar-per-ton measures of 14 different categories of soil 

conservation benefi ts. There are other soil conservation benefi t categories beyond those 

reported here, so a full accounting of benefi ts is not possible. As a result, monetary values 

derived from applications of these data are likely to be lower-bound estimates of the 

benefi ts or costs of changes in soil erosion. The data are thought to be detailed enough for 

national and regional estimates, but lack precision for smaller scale estimates. 

Keywords: Soil conservation, benefi t analysis, soil conservation benefi ts, nonmarket 

value, soil erosion, water quality benefi ts, air quality benefi ts, soil productivity

About the Authors

LeRoy Hansen and Marc Ribaudo are agricultural economists with the Economic 

Research Service, USDA, in the Resource and Rural Economics Division.

LeRoy Hansen and Marc Ribaudo

Economic Measures of Soil 

Conservation Benefi ts

Regional Values for Policy Assessment

Technical

Bulletin

Number 1922

September 2008
ii

Economic Measures of Soil Conservation Benefi ts: Regional Values for Policy Assessment / TB-1922

Economic Research Service / USDA

Contents

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

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

Benefi t Types and Values and Their Applications . . . . . . . . . . . . . . . . . . . . 2

 Fundamentals of Per-Ton Benefi t Estimates. . . . . . . . . . . . . . . . . . . . . . 2

 Data Shortcomings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

The Benefi t Models: Economic Theory and Empirical Methodologies . . . . 8

 Travel Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

 Damage Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

 Replacement Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

 Averting Exenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

Model and Data Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

 Watershed Benefi t Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 Farm Production Region Benefi t Models . . . . . . . . . . . . . . . . . . . . . . 14

Soil Conservation Benefi ts: HUC- and County-Level Values. . . . . . . . . . 20 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Appendix table 1 – HUC Data

 Online only: www.ers.usda.gov/publications/tb1922/tb1922App1.xls

Appendix table 2 – County FIPS Data

 Online only: www.ers.usda.gov/publications/tb1922/tb1922App2.xls
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Summary

Annual conservation program expenditures have doubled to more than $5 

billion per year over the last decade. A major focus of these programs is on 

reducing soil erosion. This report describes the per-ton values of 14 types of 

soil conservation benefi ts. The values are derived from models that capture 

the cause-and-effect relationships between agricultural erosion and environmental benefi ts. Values and methodology are described so that analysts 

can apply the data to calculate regional and national benefi ts of specifi c soil 

conservation projects. Analysts can also use the per-ton benefi t estimates to 

determine where a 1-ton reduction in soil erosion might be most benefi cial. 

What Is the Issue?

Conservation programs best serve the public when their funding, design, and 

implementation maximize benefi ts relative to costs. Unlike the cost of soil 

conservation efforts, environmental benefi ts of decreasing soil erosion are not 

easy to measure. Information on the values of soil conservation benefi ts can 

aid in designing more cost-effective programs and evaluating accomplishments of programs, policies, and practices.

What Does the Report Do?

Past research has generated per-ton soil conservation benefi t estimates for 14 

types of environmental benefi ts that are suitable for use in national analyses. 

The benefi t types can be placed in three general categories: 

• Twelve benefi t types refl ect soil conservation impacts on water quality 

and the subsequent impacts on industries, municipalities, and households.

• One benefi t type captures the effect of wind erosion reductions on household cleaning costs.

• One benefi t type has values of soil productivity preserved through reductions in wind and water erosion.

The report describes the development of each estimate, and provides some 

insight into regional variations in soil conservation benefi ts. The values 

can be viewed as prices that people, businesses, and government agencies 

would be willing to pay for a 1-ton reduction in soil erosion. For example, 

the reduction in municipal water-treatment costs due to a 1-ton reduction in 

erosion represents municipalities’ willingness to pay for that much reduced 

erosion.

The per-ton benefi t values are available on the ERS web site (www.ers.usda.

gov) in two databases. One provides per-ton benefi ts of soil erosion reduction 

for the 3,074 counties within the 48 contiguous States. The other provides 

per-ton benefi ts for the 2,111 8-digit Hydrologic Unit Code (HUC) watersheds within the contiguous States. While the benefi t categories in these data 

encompass many of the benefi ts of soil conservation, the categories do not 

measure every benefi t. For example, some people may value knowing that 

water quality is improved—even though they do not use the water––or that 

endangered species have an improved habitat, but estimates of these benefi ts 
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Economic Measures of Soil Conservation Benefi ts: Regional Values for Policy Assessment / TB-1922

Economic Research Service / USDA

are not available. As a result, applications of the available data will provide 

lower-bound estimates of total soil conservation benefi ts. 

How Was the Study Conducted?

The per-ton benefi t estimates are derived from models developed since 

the 1980s by ERS. The estimates are believed to be the best available for 

national analyses of soil conservation benefi ts, and the ERS data are updated 

as improved models become available. Four of the models generate marginal 

dollar-per-ton benefi t estimates; the others generate average per-ton estimates. Descriptions of the economic frameworks, data sources, and models 

supporting estimates within each of the 14 benefi t categories were synthesized from USDA published reports and peer-reviewed journal articles. All 

of the reported values were adjusted for infl ation by the Consumer Price 

Index, so that all values are in year 2000 dollars. The values can be directly 

applied to observed and potential changes in soil erosion. They can also be 

applied to nonagricultural changes in soil erosion, as long as the changes 

are appropriately calibrated. Although the data have county- and HUC-level 

values, the benefi t values are credible only when reported at national and 

multi-State levels. The model descriptions provide insights on how the 

benefi t values can best be applied and results interpreted. Values are reported 

by category, so users can choose those they feel are appropriate to their own 

applications. 
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Economic Measures of Soil Conservation Benefi ts: Regional Values for Policy Assessment / TB-1922

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Introduction

This report describes data and methodologies the Economic Research Service 

has used to assign monetary values to changes in soil erosion, and then 

explains how the results are best interpreted. The report presents estimates 

of per-ton values of 14 types of soil conservation benefi ts. The values are 

derived from models that capture the cause-and-effect relationships between 

erosion and the public’s willingness to pay to reduce erosion’s environmental 

impacts. There is one model for each type of benefi t. 

The data can be used to determine the value of soil conservation benefi ts of 

specifi c practices and programs. They can be applied directly to changes in 

fi eld erosion and, with appropriate modifi cation, to off-farm measures of soil 

erosion.

The benefi t categories in the data sets, while not comprehensive, are the most 

complete set of benefi t measures currently available for national assessments 

of soil conservation programs and practices. Many of these categories have 

been used to value soil conservation impacts of the Conservation Reserve 

Program (Hansen, 2007; Sullivan et al., 2004; Claassen et al., 2001, Ribaudo 

et al., 1989), the Environmental Quality Incentive Program (USDA, NRCS, 

2003), and the Conservation Security Program (USDA, NRCS, 2004). 

The values are also built into the Regional Environmental and Agriculture 

Programming Model (Johansson et al., 2007).

The data are located on the ERS web site 

(www.ers.usda.gov/publications/tb1922/tb1922App1.xls and 

www.ers.usda.gov/publications/tb1922/tb1922App2.xls), so a user can easily 

access and apply the values and adjust them, as appropriate. Though the data 

do not have a comprehensive set of benefi t categories, they still offer a probable lower-bound estimate of the public’s willingness to pay for reductions in 

soil erosion and the subsequent impacts on environmental quality. However, 

estimates in each benefi t category have weaknesses due to limits on the precision of the economic models and the underlying biological, physical, and 

ecological process models available at the time the benefi ts were estimated.

The fi rst section of this report discusses each of the soil conservation benefi t 

categories, concepts behind their applications, and interpretation of results. 

The second section provides the technical background on how the per-ton 

benefi t estimates were derived. The economic reasoning, analytic approach, 

and primary data supporting each value are discussed.
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Benefi t Types and Values and 

Their Applications

Fundamentals of Per-Ton Benefi t Estimates

The ERS per-ton benefi t values can be found in HUC_MB (app. table 1, 

online only: www.ers.usda.gov/publications/tb1922/tb1922App1.xls). 

HUC_MB has 2,111 observations––one for each of the U.S. Geological 

Survey’s (USGS’s) 8-digit Hydrologic Unit Code (HUC) watersheds. Per-ton 

benefi t values can also be found in COUNTY_MB (app. table 2, online only: 

www.ers.usda.gov/publications/tb1922/tb1922App2.xls).

County_MB has 3,074 observations––one for each county of the 

contiguous States. Both datasets have the 14 benefi t categories (see box 

“Soil Conservation Benefi t Categories,” p. 4, for names and defi nitions). 

HUC_MB has the variable HUC, which has the 8-digit USGS code for each 

observation. COUNTY_MB has the variable county, which has the 5-digit 

State-county FIPS code. COUNTY_MB also has the location variables 

County_name, State_name, and State_abrv, which contain county names, 

State names, and abbreviated State names, respectively. Both HUC_MB and 

COUNTY_MB have the weight variables Water_weight, Soil_productivity_

weight, and wind_erosion_weight. Under special circumstances, these data 

may improve benefi t calculations.

Twelve of the benefi t categories are applicable to changes in water (sheet and 

rill) erosion only. One benefi t category, dust cleaning, is applicable only to 

changes in wind erosion. The benefi t category soil productivity is applicable 

to changes in both wind and water erosion. All benefi t estimates have been 

adjusted for infl ation, based on the Department of Labor’s Consumer Price 

Index for all urban consumers (CPI-U), to year 2000 dollars.

The per-ton benefi t values are conceptually similar to prices of market goods 

and services. Therefore, just as total revenue is equal to price times quantity 

(summed across all goods), total benefi ts are equal to the benefi t values 

times the changes in erosion. For example, suppose there is a 5-ton reduction in water erosion and a 2-ton reduction in wind erosion within a specifi c 

HUC (or county). Then, to estimate the value of the 5-ton reduction in water 

erosion, we would multiply each of the 12 water-related benefi t categories 

and soil productivity of that HUC (county) by 5 tons and sum all 13 values. 

To estimate the wind erosion benefi ts, we would multiply dust cleaning and 

soil productivity of that HUC (county) by 2 tons and sum the two values. The 

total benefi t of these erosion reductions is equal to the sum of the water and 

wind erosion benefi ts within the specifi c HUC (county). The benefi t categories are independent, so benefi ts are not double-counted.

In more general terms, the total benefi t (Total_benefi tsi

) of a change in water 

and wind erosion in HUC or county i can be expressed as: 

Total_benefi ti

 = 12

j 1

∑=

 (water_erosion_valuei,j * ΔWater_erosion_tonsi) 

+ soil_productityi

 *(ΔWater_erosion_tonsi + ΔWind_erosion_tonsi) (1)

+ dust_cleaningi

 * ΔWind_erosion_tonsi.
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The water-related benefi t categories––water_erosion_valuei,j , designated by 

the j subscripts––are the fi rst 12 of the 14 benefi t categories. (See box, “The 

Soil Conservation Benefi t Categories,” p. 4). The values of ΔWater_erosion_

tonsi

 and ΔWind_erosion_tonsi are the change in water and wind erosion 

that an analyst wishes to value. The i subscript indicates the relevant HUC or 

county. Keep in mind that Total_benefi tsi is not a total of all soil conservation 

benefi ts. It is a total of what can be estimated from the available data. 

Equation 1 can be estimated for all HUCs or counties, when erosion estimates are available. To provide reliable estimates, the HUC-level values of 

equation 1 must then be aggregated to larger watersheds, such as the U.S. 

Geological Survey’s 12 2-digit hydrologic drainage basins. And the countylevel values must be aggregated to multicounty regions that are larger than 

States, such as the U.S. Department of Agriculture’s 10 Farm Production 

Regions (FPRs) and the Economic Research Service’s 9 Farm Resource 

Regions (FRRs). In general, benefi ts estimated for smaller geographic 

regions will be less reliable, given that 11 of the benefi t values are multiState––specifi cally, FPR-level––averages. Note that three benefi t values are 

estimated by HUC and, as a result, are likely to provide reliable estimates for 

HUCs and larger regions.

Equation 1 is relevant when HUC- or county-level estimates of erosion are 

available. But when erosion changes are reported on a larger geographic 

scale, mean-value estimates of water_erosion_valuei

, soil_productivity and 

dust_cleaning suited to the geographic scale will need to be calculated. That 

is, suppose the analyst has access to FRR-level estimates of changes in soil 

erosion. Then, in order to estimate the value of the changes in erosion, the 

analyst needs FRR-level values of water_erosion_valuei

, soil_productivity, 

and dust_cleaning. A linear average of the per-ton values is not likely to be 

appropriate because erosion levels are higher in some counties and HUCs, 

and it is reasonable to assume that erosion changes are more likely to occur 

where erosion is greater. To facilitate calculations based on this assumption, 

Water_weighti

 is set equal to the total water erosion in the county or HUC i. 

The weighted mean value of water-erosion benefi t j for region k, weighted_

mean_water_erosion_valuek,j is:

weighted_mean_water_erosion_valuek,j = 

The counties i=1 through N lie in region k. And weighted mean values of 

soil_productivity and dust_cleaning (weighted_mean_soil_productivityk

and weighted_mean_dust_cleaningk

, respectively) are similarly calculated, 

but using the weights Soil_productivity_weighti and Dust_cleaning_weighti

, 

respectively, instead of Water_weighti

. 

Then total benefi t (inasmuch as the data allow) for region k (Total_benefi tsk

) 

of a change in water and wind erosion in region k is expressed as:

(2)

N

i 1

∑=

water_erosion_valuei,j * Water_weighti

N

i 1

∑=

Water_weighti
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The Soil Conservation Benefi t Categories

 Consumer/producer surplus Level of Range of values Year

Categories gain due to aggregation ($/ton) estimated

Reservoir services Less sediment in reservoirs HUC 0 to $1.38 2007

Navigation Shipping industry avoidance 

 of damages from groundings HUC 0 to $5.00 2002

Water-based 

 recreation Cleaner fresh water for recreation HUC 0 to $8.81 1997

Irrigation ditches Reduced cost of removing sediment and

and channels aquatic plants from irrigation channels FPR $0.01 to $1.02 2007

Road drainage 

ditches Less damage to and fl ooding of roads FPR $0.20 1986

Municipal water Lower sediment removal costs

treatment for water-treatment plants FPR $0.04 to $1.45 1989

Flood damages Reduced fl ooding and damage 

 from fl ooding FPR $0.10 to $0.77 1986

Marine fi sheries Improved catch rates for marine

 commercial fi sheries FPR 0 to $0.93 1986

Freshwater fi sheries Improved catch rates for

 freshwater commercial fi sheries FPR 0 to $0.12 1986

Marine recreational Increased catch rates for

fi shing marine recreational fi shing FPR 0 to $1.57 1986

Municipal & Reduced damages from salts and

industrial water use minerals dissolved from sediment FPR $0.07 to $1.47 1986

Steam powerplants Reduced plant growth on

 heat exchangers FPR $0.04 to $1.05 1986

Soil productivity Reduced losses in

 soil productivity FPR $0.37 to $1.21 1990

Dust cleaning Decrease in cleaning due to 

 reduced wind-borne particulates FPR 0 to $1.14 1990

HUCs are watersheds defi ned by USGS’s 8-digit hydrologic unit codes; FPRs are USDA’s multi-state Farm Production Regions.

All dollar values are adjusted to year 2000.
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1To the extent that nutrient and sediment effects on benefi ts are correlated, 

the reported dollar-per-ton benefi t estimates will include effects of nutrients.

Water_weighti, Soil_productivity_weighti, and Dust_cleaning_weighti are 

based on the USDA’s 1997 National Resources Inventory (NRI), the most 

recent data that provide a means of generating county- and HUC-level 

erosion estimates (USDA, NRCS, 2000). While erosion is likely to have 

changed since 1997, we know that if erosion changes were proportionately 

equal across counties and HUCs, the weights would not change. But if 

changes are not proportional, as is likely the case, then the reliability of the 

weights may be decreased. Note that 11 of the 14 variables are FPR-level 

averages, so that when a region k is an FPR, weights will not affect benefi t 

estimates. The weights in these data are updated as better erosion estimates 

become available. 

Data Shortcomings

While the data and equations 1, 2, and 3 make it relatively easy to value 14 

soil conservation benefi ts, interpretation of results requires an understanding 

of the data’s shortcomings. The four most important are:

1. The values in each soil conservation benefi t category are average 

regional values, which do not capture intraregional variations in 

values. Values in 11 of the benefi t categories of the ERS data have been 

generated by models that provide values for each of the 10 FPRs (fi g. 

1). As a result, the per-ton benefi t values do not vary across counties or 

HUCs within the same FPR. The actual value of a 1-ton reduction in 

erosion is likely to vary across HUCs and counties within each region. 

Because this variation is not captured, the estimated HUC and county 

values might be equal to, greater than, or less than the actual values. 

However, as we aggregate HUC- and county-level estimates, the standard 

error around benefi t estimates is likely to fall.

 In contrast, three of the soil conservation benefi t categories—reservoir,

shipping, and recreation––have values that are taken from studies that 

 generate HUC-level values. The county-level estimates are based on 

the HUC/county overlaps and are expected to provide fairly reasonable 

estimates. But neither the HUC- nor county-level estimates capture the 

variation in values within HUCs or counties. In other words, these per-ton 

benefi t estimates do not capture fi eld-to-fi eld variations in soil conservation benefi ts.

 The actual value of a 1-ton reduction in erosion depends both on physical 

factors––the quantity of sediment that reaches a stream or lake and the 

subsequent ecological impact— and economic factors, the willingness 

of fi rms and individuals to pay to prevent or eliminate the ecological 

impacts.1 The per-ton benefi t values embody these relationships, or more 

precisely, the average value of these relationships. When evaluating 

changes in farm programs and practices, consideration of fi eld-to-fi eld 

variation is important when erosion changes occur on acreage with 

greater, or less than, average physical or economic impacts. For example, 

Total_benefi ti

 = 12

j 1

∑=

(weighted_mean_water_erosion_valuek,j * ΔWater_erosion_tonsi)

+ weighted_mean_soil_productivityk

 * (ΔWater_erosion_tonsk + ΔWind_erosion_tonsk)

+ weighted_mean_dust_cleaningk * (ΔWater_erosion_tonsk)

(3)
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analyses of programs that target land with specifi c characteristics, such 

as riparian buffers, could produce biased results. A 1-ton reduction in 

erosion by riparian buffers is likely to have greater water quality impacts 

than a 1-ton reduction elsewhere in the region (HUC, county, etc.) 

(Khanna et al., 2003). Consequently, a program that reduces relatively 

more erosion on riparian lands will likely have greater-than-average water 

quality impacts per ton of erosion. The benefi t values are still useful, but 

conclusions drawn from such analyses must include the caveat that the 

estimated benefi ts might be biased downward. 

2. Not all soil conservation benefi ts are included. A further shortcoming 

of the benefi t estimates derived from the 14 soil conservation benefi t 

categories is that the estimates do not include all soil conservation 

benefi ts. The impacts on wetlands, endangered species, and most coastal 

recreational activities, as well as people’s willingness to pay simply 

to know that water quality is improved, are examples of conservation 

benefi ts that have not been modeled. Finally, to a smaller degree, benefi t 

estimates are likely to be biased downward because 13 of the models are 

built on theoretical frameworks (the replacement cost, damage function, 

and averting-behavior frameworks) that cannot capture full willingness to 

pay (Ribaudo and Hellerstein, 1992).

3. Benefi t values are designed to be applied to farmland erosion. The 

per-ton values are applicable to changes in erosion on agricultural lands. 

However, the benefi t values can also be applied to nonagricultural 

erosion, if properly calibrated. The calibration must be based on an 

equivalence of the environmental quality impacts of agricultural erosion 

Mountain

Pacific

Corn Belt

Southern

Plains

Southeast

Northeast

Appalachia

Northern

Plains

Delta

States

Lake States

Figure 1

USDA’s Farm Production Regions
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and erosion from the nonagricultural source. For example, if, in a given 

region, a 1-ton reduction in agricultural erosion has the same water quality 

impact as a 2-ton reduction in erosion at construction sites (measured by 

the quantity of sediment reaching a stream), then the water quality benefi t 

of a 1-ton reduction in erosion at construction sites is half the value of a 

1-ton reduction in agricultural erosion. 

4. Values have been adjusted for infl ation, but other time-related factors 

may be relevant. Ten of the 14 benefi t values were estimated more 

than 20 years ago, 1 was estimated in the late 1990s, and the remaining 

3 were estimated within the last 6 years. Over time, the benefi t values 

might have changed, but the size and direction of change in any one of 

the benefi t values are unknown. For example, municipal water treatment 

costs are likely to increase with increases in the population served, but 

advances in water treatment technology are likely to lower treatment 

cost. Increases in populations might tend to increase the total willingness 

to pay for improvements in environmental quality, but increases in the 

availability of substitute activities and goods that come with increases in 

populations might decrease willingness to pay. Increases in incomes can 

raise the value of surrounding amenities, but improved transportation can 

make alternative sites comparable substitutes. These and other factors 

might affect some or all of the benefi t values discussed here. At this point, 

there appears to be no means of capturing the net effect of these factors. 

Lacking evidence to suggest otherwise, we assume, after adjusting for 

infl ation, that the benefi t values have not changed over time. We do know 

that, over time, infl ation decreases the real purchasing power of the dollar. 

We therefore have adjusted all benefi t values for infl ation, based on the 

Department of Labor’s Consumer Price Index for all urban consumers 

(CPI-U), to year 2000 dollars.
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The Benefi t Models: Economic Theory 

and Empirical Methodologies 

Four theoretical frameworks ––travel cost, damage function, replacement 

cost, and averting expenditures––underlie the 14 soil conservation benefi t 

models. (For a detailed discussion of these and other methods, see Lew et al., 

2001.) All are indirect means of estimating environmental benefi ts. 

Travel Cost

This method uses expenditure and trip data to estimate the demand for a 

recreation activity where environmental quality is one of the determinants 

of demand. Changes in consumer surplus associated with changes in environmental quality can be derived from the estimated demand function. The 

approach requires data on respondents’ recreational activities and travel costs 

(including the cost of time) and the environmental quality of recreation sites 

the person visited, as well as potential substitute sites.

Damage Function

This approach applies to businesses that use an environmental input, such as 

water. It is based on the assumption that the loss in welfare due to a decrease 

in environmental quality is approximately equal to the value of the loss in 

revenue or increase in costs. The approach is thought to provide conservative 

benefi t estimates, fi rst, because it implicitly assumes that no remedial actions 

are taken and, second, because market effects are not considered (Freeman, 

1993). However, in the case of a single-product fi rm, the damage function 

approach will not underestimate the change in welfare, as long as the change 

in environmental quality does not change the quality or quantity of the fi rm’s 

output (Ribaudo and Hellerstein, 1992).

Replacement Cost

This method assumes that the loss in welfare due to a change in environmental quality is approximately equal to the expenditures made to replace, 

repair, or restore goods and capital assets. Like the damage function 

approach, the replacement cost approach is believed to provide a conservative benefi t estimate because, fi rst, if there are no expenditures, the approach 

sets the value of the damages equal to zero. Second, as with the damage function approach, the replacement cost method assumes that no remedial action 

is taken. And third, the approach ignores the cost of reduced performance 

before the good is replaced. 

Averting Expenditures

This approach assumes that the loss in welfare due to a change in environmental quality is approximately equal to the change in expenditures made to 

counteract the change in quality of the environmental asset. The approach 

assumes that marginal changes in defensive expenditures leave the quality 

of the environmental good(s) unchanged (changes in expenditures are a 

perfect substitute for changes in environmental quality). However, because 

it is commonly accepted that this assumption does not hold, the avertingexpenditures approach is believed to provide conservative benefi t estimates 
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(Freeman, 1993; Ribaudo, 1989). In practice, it can be diffi cult to isolate the 

portion of expenditures that is attributable to averting activities (Winpenny, 

1991).

Averting expenditures occur before losses are incurred. For example, 

suppose forest lands are cleared in order to create or expand downhill ski 

slopes. Without the tree canopy and the ecology of a forest fl oor, runoff from 

summer rains swells streams and increases downstream fl ood frequency and 

levels. In response, individuals living in the fl ood plain raise the foundations 

of their houses. The cost of raising foundations is an averting expenditure 

and represents part of what people would be willing to pay, in advance, to 

have prevented the environmental impacts of the ski slopes. Others might 

move away, or stay and deal with the additional losses; all would be willing 

to pay to prevent impacts, but their willingness to pay is not captured by the 

averting-expenditures approach. 
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Model and Data Descriptions

All of the benefi t models are reduced-form models in that the per-ton benefi t 

estimates embody a complex set of physical processes, linking changes in 

erosion on agricultural lands to environmental quality and the economic 

values that individuals, fi rms, and the public sector place on changes in environmental quality. For example, the per-ton estimates related to water quality 

capture water’s effect on soils and nutrients in fi elds and on their movements 

to waterways, the subsequent changes in water quality and ecology, the 

effects that these changes have on water users (individuals, fi rms, and the 

public sector), and the values individuals place on changes in these effects 

(fi g. 2).

Of the 14 models described in this report, three models, estimated since 

1997, generate dollar-per-ton benefi t values for each of the 2,111 8-digit 

hydrologic unit code (HUC) watersheds of the contiguous States. Each of 

these models estimates the value of reductions in water erosion. The sum 

of the per-ton values, by HUC, ranges from zero to $14.38. The other 11 

models, most of them estimated in the 1980s, generate benefi t values by the 

multi-state Farm Production Regions (FPRs) (fi g. 1). One benefi t category—

soil productivity benefi ts—accounts for changes in both water and wind 

erosion. Ten are applicable only to water erosion. The remaining benefi t category is applicable to changes in wind erosion. The sums of the per-ton water 

erosion and soil productivity benefi ts by FPR range from $1.46 to $7.12 per 

ton. The sums of the wind erosion and productivity benefi t values within the 

FPR range from $0.41 to $1.54 per ton (table 1). 

Watershed Benefi t Models

The three watershed studies apply very different approaches. However, 

they are similar in that each begins by estimating values of soil conservation impacts at sites and then aggregates across sites to generate HUC-level 

estimates. 

Table 1

Benefi t estimates ($/ton) produced by models that generate estimates for Farm Production Regions1

Farm Irrigation Road Municipal 

Production ditches drainage water Flood Marine Freshwater

Region2 and canals ditches treatment damages fi sheries fi sheries 

Appalachia 0.01 0.2 0.05 0.26 0.01 0.02

Corn Belt 0.01 0.2 0.18 0.10 0.00 0.01

Delta States 0.12 0.2 0.04 0.71 0.02 0.12 

Lake States 0.03 0.2 0.32 0.50 0.00 0.12

Mountain 0.54 0.2 1.06 0.21 0.00 0.00

N. Plains 0.12 0.2 0.22 0.13 0.00 0.00

Northeast 0.01 0.2 0.27 0.77 0.93 0.00

Pacifi c 1.02 0.2 0.47 0.33 0.42 0.00 

S. Plains 0.22 0.2 1.45 0.27 0.14 0.03 

Southeast 0.16 0.2 0.31 0.53 0.00 0.00

—continued 1Values refl ect a 1-year reduction in erosion.

2See fi gure 1 for the location of each FPR.
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Reservoir Services 

As sediment accumulates in reservoirs, the quantity and quality of reservoir 

services are reduced. For example, reservoir sediment can reduce the quality 

of beaches, shoreline boating, water reserves for power generation, capacity 

for holding fl ood waters, and the quality of spawning grounds. An increase 

in erosion can increase the rate that sediment settles in a reservoir and, as 

a result, leave the sediment level higher and service lower in subsequent 

years. Conversely, reducing erosion will reduce the rate that sediment settles 

in a reservoir and leave future reservoir service levels higher (Hansen and 

Hellerstein, 2007). Dredging a reservoir restores services, so dredging expenditures can be assumed to represent a restoration (replacement) cost.

The parameters of the benefi ts model are estimated by applying the replacement cost method and assuming that reservoir owners/managers dredge 

reservoirs at the optimal time, when marginal benefi ts equal marginal costs. 

Dredging costs are assumed to be a function of reservoir characteristics and 

the quantity of sediment dredged. The reservoir benefi ts model by Hansen 

and Hellerstein (2007) is estimated using public and private reports of 

dredging costs. They also estimate a sedimentation model, linking changes in 

erosion to changes in reservoir sedimentation, and couple it with the benefi t 

model so benefi ts can be linked to changes in erosion. Given the historical 

nature of the dredging data, Hansen and Hellerstein assume the decisions 

to dredge were based on erosion rates similar to those observed in the 1982 

National Resources Inventory (NRI). The NRI contains 800,000 statistically 

based sample points on U.S. non-Federal range, crop, pasture, and forest 

lands (USDA, SCS, 1984).

The reservoir benefi ts model and sedimentation model are used to generate 

reservoir-level marginal benefi t estimates. The model estimates account for 

the multiyear impacts that a one-time reduction in soil erosion will have. 

With the marginal benefi t estimates, we can value the increase in present and 

Table 1

Benefi t estimates ($/ton) produced by models that generate estimates for

Farm Production Regions1—continued 

Farm Marine Municipal and Steam Total Total

Production recreational industrial power- Soil Dust water- windRegion2 fi shing use plants productivity cleaning related related

Appalachia 0.01 0.43 0.92 0.57 0.00 2.47 0.57

Corn Belt 0.00 0.21 1.05 1.01 0.00 2.77 1.01

Delta States 0.02 0.68 0.44 0.43 0.00 2.76 0.43

Lake States 0.00 1.36 0.94 1.21 0.00 4.68 1.21

Mountain 0.00 0.21 0.29 0.26 0.60 3.37 0.86

N. Plains 0.00 0.07 0.33 0.41 0.64 1.46 1.05

Northeast 1.57 1.45 0.66 1.27 0.00 7.12 1.27

Pacifi c 0.49 0.17 0.04 0.40 1.14 3.54 1.54

S. Plains 0.41 0.28 0.24 0.37 0.38 3.61 0.75

Southeast 0.00 0.48 0.42 0.41 0.00 2.51 0.41

1Values refl ect a 1-year reduction in erosion. 

2See fi gure 1 for the location of each FPR.
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Estimates of erosion changes are based on:

 1) rainfall erosivity

 2) soil erodibility

 3) slope characteristics

 4) crop management

 5) conservation practices

Changes in Erosion Are Estimated 

Factors that affect the amount of eroded soil that 

reaches a waterway:

 1) distance to waterway

 2) slope of the land to the waterway

 3) cover on the land 

Movement of Soil to Waterway Are 

Implicitly Captured

Benefit values are measures of changes in:

 1) consumer surplus

 2) producer surplus

 3) government costs 

Benefit Values Are Measured

Sediment’s impacts on physical and biological resources:

 1) water looks ‘dirty’

 2) beaches become muddy

 3) sediment settles in reservoirs and shipping lanes

 4) sediment in flood waters increases flood damages

 5) sediment decreases the quality of fish habitat

Sediment’s Impacts on Physical and Biological 

Resources Are Implicitly Captured

Environmental amenities that are affected 

 1) swimming, boating, and recreational fishing

 2) commercial fishing

 3) navigation

 4) water storage

Physical and Biological Effects on Environmental 

Amenities Are Implicitly Captured

Figure 2

Reduced-form models implicity capture the links 

between changes in erosion and benefit values
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future reservoir services resulting from a marginal reduction in agricultural 

erosion. 

The HUC-level marginal benefi t values for reservoir services are derived 

from the reservoir-level marginal benefi t estimates. Values are calculated in 

four steps. First, the marginal benefi t of a 1-percent reduction in the erosion 

rate is calculated for each of the more than 70,000 reservoirs in the United 

States. Second, the marginal benefi ts are summed across the reservoirs within 

each HUC. Third, the change in erosion (number of tons) associated with a 

1-percent change in the erosion rate on agricultural lands is calculated for 

each HUC. Finally, each HUC-level marginal benefi t estimate is converted 

to a per-ton estimate by dividing the benefi t estimate by the number of tons 

represented by a 1-percent change in the erosion rate. 

The estimates of marginal reservoir benefi ts vary widely across HUCs. In 

163 HUCs, marginal benefi ts equal zero. These HUCs appear to have no 

reservoirs affected by agriculture. In the remaining watersheds, per-ton soil 

conservation benefi ts are as high as $1.38.

Navigation Industry 

Sediment buildup in shipping channels and harbors delays water traffi c 

and damages ships and barges that run aground. To avert these delays and 

damages, the navigation industry, through the Army Corps of Engineers, 

dredges harbors and shipping channels. Because the dredging is done to 

avoid future damages, the costs represent averting expenditures. 

The navigation industry model provides HUC-level estimates of the expected 

reduction in averting expenditures resulting from a 1-ton reduction in 

erosion. The model is estimated in two steps. First, an average dollar-per-ton 

cost of erosion is estimated for each site dredged by dividing total site-level 

dredging costs––where sites are harbors and segments of shipping channels–– 

by total upstream erosion. Data on erosion and a hydrologic model are used 

to estimate the total tons of erosion upstream of each site. Second, HUC-level 

per-ton benefi ts are estimated by summing the dollar-per-ton estimates across 

all relevant downstream sites (Hansen et al., 2002). 

The hydrologic data are from the River Reach File of the U.S. 

Environmental Protection Agency (EPA), which interconnects 3.2 million 

miles of streams. Estimates of agricultural erosion by HUC are based on data 

from the 1997 NRI (USDA, NRCS, 2003). Dredging-cost data are from the 

U.S. Army Corps of Engineers (1999a; 1999b). Results show that, across 

HUCs, a 1-ton reduction in soil erosion can reduce dredging costs by from 

$0.0 to $5.00.

Water-Based Recreation 

Suspended sediment in lakes, rivers, and streams harms aquatic wildlife and 

decreases the water’s aesthetic appeal, which lowers the quality of fi shing, 

swimming, and other water-contact activities. To calculate sediment’s impact 

on consumer surplus, a multisite travel-cost demand model for water-based 

 recreation is estimated, where demand is a function of––among other things–– 
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travel costs to each site and its water quality (Feather and Hellerstein, 1997; 

Feather et al., 1999).

The travel cost model is estimated in a two-step process. First, the site selection process is characterized by a random utility model (RUM). The RUM 

is estimated using data on individual and site characteristics. The estimated 

model is then applied to each observation to predict the probability that an 

individual will select a given recreation site. The second step begins by using 

the RUM probability estimates. Based on these estimates, an expected price 

(travel cost) and expected level of environmental quality (where erosion 

levels and the size and type of water body serve as proxies) are calculated for 

each individual. The estimates of expected price and site quality are probability-weighted averages of the prices and qualities of the relevant sites. 

In the second step, the demand for water-based recreation is estimated by 

regressing the number of trips taken against expected price, expected environmental quality, and other demand determinants. The marginal change in 

consumer surplus associated with a change in soil erosion within a HUC is 

calculated for each affected individual. The HUC-level marginal benefi t estimate is the sum of individuals’ consumer surplus changes. 

The model is estimated using behavioral data from the 1994-95 National 

Survey of Recreation and the Environment (2005) and environmental data 

from the 1997 NRI. 

Estimates from the water-based recreation model indicate that a 1-ton reduction in soil erosion can increase societal benefi ts of water-based recreation by 

from $0.0 to $8.81 across the 2,111 U.S. watersheds.

Farm Production Region Benefi t Models

The remaining 11 models deliver benefi t estimates at the FPR level. Six were 

originally derived from State or sub-State models, but the model estimates 

were aggregated and subsequently reported by FPR. The others were derived 

from national-level data on costs that were then apportioned to FPRs.

Marine Recreational Fishing

Soil erosion can harm marine fi sheries by damaging estuaries. Estuaries 

provide year-round habitat and are the principal spawning grounds for shellfi sh and a wide variety of fi n fi sh. Sediment and nutrients can impair estuarine habitats, adversely affect fi sh populations, and decrease the quality of 

marine recreational fi shing. 

Clark et al. (1985) generated a national estimate of erosion’s impact on 

marine sport fi sheries, based upon analyses by Freeman (1982). Ribaudo 

(1986) allocated this national estimate to FPRs based upon which estuaries 

were impaired and the number of saltwater angling days affected. Ribaudo 

used water quality monitoring data from the National Stream Water Quality 

Accounting Network (NASQUAN) of the U.S. Geological Survey (USGS) 

monitoring system to determine which watersheds (USGS’s Aggregated 

Sub-Areas) were impaired by sediment. He assumed that estuaries adjacent 

to sediment-impaired Aggregated Sub-Areas were also impaired. Data on the 
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location of 180 major estuaries were obtained from the National Oceanic and 

Atmospheric Administration. 

The total number of saltwater angling days within each FPR was obtained 

from the U.S. Fish and Wildlife Service’s Hunting and Fishing Survey (U.S. 

Dept. of the Interior, 1997). The number of impaired fi shing days is estimated by multiplying the percentage of estuaries affected by erosion within 

an FPR by the total angling days. Affected angling days then become weights 

for allocating total damages. Damages in each FPR were divided by total 

erosion in each FPR to arrive at an average damage per ton of erosion. A unit 

reduction in erosion would produce approximately the same level of benefi ts. 

Soil conservation benefi ts, based on the marine recreational fi shing model, 

range from $0.0 in the fi ve inland FPRs to $1.57 per ton. 

Marine Commercial Fisheries

Sediment in estuaries also affects commercial fi sheries. As with modeling of 

impacts on recreational fi shing, Ribaudo (1986) used the damage function 

approach. His analysis begins with a national estimate of total damages to 

marine commercial fi sheries. 

Bell and Canterberry (1975) provide an estimate of total annual damages to 

marine fi sheries from all water pollution. Ribaudo assumes that erosion’s 

share of damages to commercial fi sheries is the same as erosion’s share of 

damages to marine recreational fi shing, as assumed by Clark et al. (1985). 

His erosion damage model allocates soil erosion damages equally across all 

impaired estuaries. Impaired estuaries are assumed to be all those that are 

part of USGS Aggregated Sub-Areas that have been designated as having 

water quality problems due to erosion. The model then links the damaged 

estuaries to the FPRs. Those estuaries that lie along the coast of an FPR are 

linked to that FPR. Total FPR-level damages are estimated by summing 

across estuaries within each FPR. Finally, per-ton damage estimates are 

derived by dividing each FPR-level damage estimate by total erosion in the 

region. 

Soil conservation benefi ts, based on the marine commercial fi sheries model, 

range from $0.0 in the fi ve inland FPRs to $0.94 per ton. 

Freshwater Commercial Fisheries

Water pollutants associated with sediment inhibit fi sh populations and 

decrease revenues of the freshwater fi sheries industry. To derive soil conservation benefi ts to freshwater fi sheries, a model, based on the damage function 

approach, is estimated from data on sediment’s cost to the fi sheries.

The national costs of sediment’s impact on the commercial freshwater 

fi sheries industry reported by Clark et al. (1985) are allocated across FPRs, 

based on the FPR’s share of the total river-miles with concentrations of 

suspended sediment, nitrate-nitrite, and total phosphorus above thresholds 

considered relevant for water-based recreation (Ribaudo, 1986). Estimates of 

national and regional water quality-impaired river miles are based on USGS 

NASQAN data, National Water Discharge Inventories data from Resources 

for the Future, and the EPA River Reach File (Ribaudo, 1986). Recreation-
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based water quality thresholds were obtained from EPA (Zison, Haven, and 

Mills, 1977). Damage estimates are divided by total sheet and rill erosion in 

the FPR from the 1982 NRI to arrive at an average cost per ton of erosion. A 

unit reduction in erosion would produce a like-level of benefi ts.

Soil conservation benefi ts across FPRs range from $0.0 to $0.12 per ton. 

Steam-Electric Powerplants

Sediment and algae caused by soil erosion can affect the operation of steamelectric powerplants––most powerplants are steam-electric––and other facilities that use large amounts of water. Suspended sediment and algae can clog 

condensers, reducing the effi cient operation of cooling systems. Periodic 

removal of algae from condensers restores water infl ow rates. The benefi ts of 

reducing these costs are estimated by using the replacement cost approach.

Clark et al. (1985) generated a national estimate of annual restoration costs, 

based on a study of the cost of removing algae from water cooling systems. 

Ribaudo (1986) allocated these costs across the 10 FPRs, based on the 

amount of sediment withdrawn in water used for thermoelectric power generation. A proxy for sediment withdrawn is the product of gallons withdrawn 

and sediment concentration. Data on gallons of water withdrawn within each 

FPR were obtained from USGS. Average suspended sediment concentrations in each FPR are calculated using NASQUAN monitoring data. The 

FPR restoration cost estimates are then divided by total sheet and rill erosion 

in the FPR, based on the 1982 NRI, to arrive at an average cost per ton of 

erosion. 

Soil conservation benefi ts range from $0.04 to $1.05 per ton. 

Municipal and Industrial Water Use

Treated water can still contain minerals, salts, and other materials that 

damage water-use equipment. The model of erosion’s impact on municipal 

and industrial water use is therefore based on the damage function approach. 

Clark et al. (1985) used EPA estimates of the costs of achieving Clean Water 

Act goals to estimate the annual removal and damage costs of dissolved 

materials associated with soil erosion. Ribaudo (1986) allocates these 

damages among the 10 FPRs, based on the amount of water withdrawn 

for municipal and industrial uses. Ribaudo used the same procedure as for 

steam cooling, the only difference being that gallons of water withdrawn by 

industry and households were used to create the weights. FPR-level damage 

estimates were divided by total sheet and rill erosion from the 1982 NRI to 

arrive at an average damage per ton of erosion. A unit reduction in erosion 

would produce a like-level of benefi ts.

Soil conservation benefi ts range from $0.07 to $1.44 per ton. 

Flood Damages

Suspended sediment in stream waters increases the frequency and severity 

of fl ooding. Reservoirs and fl ood plains have helped reduce fl ood damages, 
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yet damages still occur. Erosion plays a dual role in fl ood damages. First, it 

increases suspended sediment in stream fl ow, which then adds to the volume 

of the fl ow. The greater volume increases fl ood frequencies and the height 

of fl ood waters. Second, with greater concentrations of suspended sediment, 

fl oodwaters deposit more sediment, which increases damages to roads, farm 

fi elds, homes, and other fl ooded sites. With available data, the benefi ts of soil 

conservation’s impacts on fl ood damages are estimated using the damage 

function approach.

The total cost of agricultural sediment-related fl ood damages was obtained 

from Clark et al. (1985). The national damage estimate was allocated to FPRs 

based on the distribution of total (sediment and nonsediment) fl ood damages 

reported by the U.S. Water Resources Council (1978). The FPR-level fl ood 

damage estimates were then divided by total agricultural erosion within the 

regions to generate dollar-per-ton benefi t estimates. 

Soil conservation benefi ts of reduced fl ood damages range from $0.10 to 

$0.77 per ton. 

Irrigation Ditches and Canals

Nutrients and sediment originating on fi elds can cause excessive sediment 

buildup and weed growth in irrigation canals, impeding water fl ow in irrigation systems. Removing the sediment and weeds can restore the irrigation 

system to its original condition. With data on sediment and weed removal 

costs, soil conservation benefi ts are calculated using the replacement cost 

approach. 

 Clark and others (1985) estimated that approximately 15 to 35 percent of the 

operation and maintenance costs for irrigation systems is for weed control 

and ditch clearing. Ribaudo (1989) used the midpoint of this range, along 

with data on maintenance costs from the 1978 Census of Agriculture’s Ranch 

and Irrigation Survey (U.S. Bureau of the Census, 1982) to estimate annual 

weed control and ditch-clearing costs associated with erosion for each State, 

which he then aggregated to the FPR level. Dividing this value by total sheet 

and rill erosion provides an estimate of the cost per ton of erosion. 

We have used Ribaudo’s approach and assumptions, erosion estimates from 

NRCS (2007), and weed control and ditch maintenance costs from the 2001 

Census of Agriculture to generate a more up-to-date estimate of soil erosion’s 

impact on irrigation ditches and canals. 

Soil conservation benefi ts range from $0.01 to $1.02 per ton.

Soil Productivity

Erosion carries topsoil off fi elds, which reduces the land’s productivity. 

Some, but not all, yield loss can be offset by increasing nutrient use. Because 

soil loss decreases output and increases costs, the damage function approach 

is appropriate for modeling erosion’s impact on soil productivity. 

The Erosion Productivity Impact Calculator (EPIC) model (Williams et al., 

1985) was used to estimate soil and yield losses and increases in input use 
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across 12,000 combinations of geographic regions, soil groups, crops, tillage, 

and conservation practices (Ribaudo et al., 1990). (The analysis assumes that 

farmers, in order to maximize profi ts, increase nutrient use to offset some 

of the productivity impacts of soil loss). These estimates were aggregated 

to generate FPR-level estimates of soil loss and the value of its productivity 

impact (Ribaudo et al., 1990). Per-ton estimates were derived by dividing 

total productivity impacts by total erosion (water and wind).

Values of soil productivity are based on the assumption that the land is in 

production. Benefi ts will not accrue while the land is fallow. Calculations for 

valuing the productivity effects of retired lands must discount benefi ts from 

the time when the land returns to production.

Soil conservation benefi ts, based on the soil productivity model, range from 

$0.26 to $1.27 per ton. 

Road Drainage Ditches

Sediment carried off farms can fi ll roadside ditches, reducing the capacity 

of ditches to store and move fl oodwaters. Floodwaters can damage roads 

and impede traffi c fl ows. Appropriate maintenance prevents or reduces these 

costs. With data on road maintenance costs, a model, based on the avertingexpenditures approach, was developed to estimate the value of this benefi t of 

soil conservation. 

Ribaudo (1989) estimated a model where the annual cost of road maintenance was specifi ed as a function of gross sheet and rill erosion, rural road 

mileage, and the cost of removing a cubic yard of sediment. Data on ditch 

maintenance costs were obtained from 33 State highway departments. 

Results indicate that each ton of gross erosion reduction translates into an 

average reduction in ditch maintenance costs of $0.20. 

Municipal Water Treatment

Sediment in surface waters can increase municipal water treatment costs. 

Sediment, in effect, damages or degrades the quality of water. A model that 

captures municipalities’ willingness to pay to improve water quality can be 

estimated using the damage function approach. 

A model of the effect of sediment on water treatment costs was estimated, 

using a water treatment cost model developed by Holmes (1988). The model 

expresses operation and maintenance costs of a treatment plant per million 

gallons of water withdrawn, as a function of the amount of water treated, 

the water’s turbidity, labor cost, and electricity cost. This water treatment 

cost model was estimated with data from 294 treatment systems around the 

country. 

To apply the treatment cost model to changes in erosion, Ribaudo (1989) 

estimates a water turbidity model, where turbidity is a log-linear function of soil erosion, streamfl ow, and water storage capacity. He uses the 

turbidity model to estimate the change in water turbidity within a USGS 

Aggregated Sub-Area due to a change in erosion. He then uses the treatment 
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cost model to calculate the marginal benefi ts of reductions in turbidity for 

each Aggregated Sub-Area. The per-unit costs were multiplied by estimated 

changes in turbidity given a marginal change in erosion. To develop FPR 

estimates of marginal per-ton benefi ts, estimates within each FPR were 

summed and weighted by the quantity of surface water the municipalities 

withdraw. 

Soil conservation benefi ts, based on the municipal water treatment model, 

range from $0.05 to $1.16 per ton. 

Dust Cleaning

Wind-borne particulates pass through cracks and openings in homes and 

settle on fl oors and furniture. Cleaning is necessary to get rid of the dust. 

Available cleaning-cost data and a replacement cost model are used to estimate the benefi ts of reduced wind erosion. 

Huszar and Piper (1986) estimated a household-cleaning-cost model (which 

was subsequently improved by Huszar, 1989), where costs are a nonlinear 

function of household characteristics and wind erosion within the household’s resident county. Their cost model is estimated with data from a survey 

of households in New Mexico. Ribaudo et al. (1990) used the model, along 

with data from 1980 Household Census and data on wind-erosion from the 

1982 NRI, to estimate household cleaning costs and changes in costs due to 

changes in erosion by State for all States in the Northern and Southern Plains, 

Mountain, and Pacifi c FPRs. Marginal, per-ton benefi t values were derived 

by dividing the changes in cleaning costs by the associated changes in wind 

erosion (Ribaudo et al., 1990).

Soil conservation benefi ts, based on the dust cleaning model, range from $0.0 

in the six eastern FPRs to $1.14 per ton. 

Most of the per-ton benefi t estimates of the 11 FPR models are less than 

$0.50 (table 1). Though per-ton values are not high, each provides insight 

into the benefi ts of soil conservation programs and practices. Furthermore, 

the values provide a rough perspective on where the value of a reduction in 

erosion might be greatest. 
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Soil Conservation Benefi ts: HUCand County-Level Values

The HUC-level water quality benefi t categories (reservoir services, navigation, and water-based recreation) vary across HUCs. For those water quality 

benefi t categories based on FPR-level estimates, values are broken down 

to HUC-level estimates based on the location of the HUC. Most HUCs lie 

in a single FPR. In these cases, the HUC-level values are set equal to the 

FPR-level values. Where HUCs lie in more than one FPR, the HUC-level 

values are erosion-weighted average values of the appropriate FPRs. The 

HUC-level sums of all categories of water-erosion benefi ts range from $1.11 

to $17.55 per ton (fi g. 3). 

Values of wind-erosion impacts are estimated at the FPR level. The same 

approach as that used for HUC-level water quality benefi ts is used to 

generate HUC-level estimates of wind-erosion values. Wind-erosion benefi t 

values, for the most part, follow FPR boundaries (fi g. 4). Values vary along 

the borders of FPRs, where HUC-level estimates are weighted averages of he 
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Soil Conservation Benefi ts: HUCand County-Level Values

The HUC-level water quality benefi t categories (reservoir services, navigation, and water-based recreation) vary across HUCs. For those water quality 

benefi t categories based on FPR-level estimates, values are broken down 

to HUC-level estimates based on the location of the HUC. Most HUCs lie 

in a single FPR. In these cases, the HUC-level values are set equal to the 

FPR-level values. Where HUCs lie in more than one FPR, the HUC-level 

values are erosion-weighted average values of the appropriate FPRs. The 

HUC-level sums of all categories of water-erosion benefi ts range from $1.11 

to $17.55 per ton (fi g. 3). 

Values of wind-erosion impacts are estimated at the FPR level. The same 

approach as that used for HUC-level water quality benefi ts is used to 

generate HUC-level estimates of wind-erosion values. Wind-erosion benefi t 

values, for the most part, follow FPR boundaries (fi g. 4). Values vary along 

the borders of FPRs, where HUC-level estimates are weighted averages of he 

$/ton/year

< 3

3 - 5

5 - 9

> 9

Figure 3 

Range and distribution of all water-erosion benefit values, by HUC

HUC-level1 water-erosion benefit categories are: reservoir services, navigation, water-based recreation, marine 

fisheries, freshwater fisheries, municipal industrial, steam electric, irrigation ditches, flood damages, soil productivity, 

road ditches, and municipal water treatment. Only reservoir services, navigation, and water-based recreation are 

estimated at the HUC level. Other values are based on FPR-level2

 estimates.

1

HUCs are watersheds defined by the U.S. Geological Survey’s 8-digit hydrologic unit codes.

2

FPRs are USDA’s multi-state Farm Production Regions.
21

Economic Measures of Soil Conservation Benefi ts: Regional Values for Policy Assessment / TB-1922

Economic Research Service / USDA

value of two or more FPRs. When the HUC-level wind-erosion benefi t values 

are summed, values range from $0.41 to $1.54 per ton. 

The county-level estimates of the benefi t values that are derived from 

HUC-level models (reservoir services, navigation, and water-based recreation) are erosion-weighted average values. Note that for counties that lie 

in a single HUC, the county-level values equal the HUC-level values. The 

county-level benefi t values are, in effect, taken directly from the FPR-level 

estimates because counties do not cross FPR borders. The county-level sums 

of the water-erosion benefi t estimates range from $1.70 to $18.24 per ton 

(fi g. 5). The county-level sums of the wind erosion benefi t values range from 

$0.41 to $1.54 per ton. Because both wind-erosion benefi t values have been 

estimated at the FPR level, values follow FPR boundaries (fi g. 6). 

Although many of the benefi t models were formulated some time ago, the 

values they generate are the most complete summary of soil erosion reduction benefi ts available. More accurate assessment of soil conservation benefi ts will be possible in the future if additional research improves the accuracy 

and geo-resolution of available estimates or expands upon the benefi ts that 

have been assessed to date.

$/ton/year

< .75

.75 - 1.00

1.00 - 1.25

> 1.25

HUC-level wind erosion benefit categories are dust cleaning and soil productivity. Values are based on FPR-level2

 estimates.

1

HUCs are watersheds defined by the U.S. Geological Survey’s 8-digit hydrologic unit codes.

2

FPRs are USDA’s multi-state Farm Production Regions.

Figure 4

Range and distribution of all wind-erosion benefit values, by HUC1
22

Economic Measures of Soil Conservation Benefi ts: Regional Values for Policy Assessment / TB-1922

Economic Research Service / USDA

$/ton/year

< .57

57 - .90

.90 - 1.27

> 1.27

Figure 6

Range and distribution of all wind-erosion benefit values, by county

County-level wind erosion benefit categories are dust cleaning and soil productivity. Values are based on FPR-level1 estimates.

1

FPRs are USDA’s multi-state Farm Production Regions.

$/ton/year

< 3

3 - 5

5 - 9

> 9

Figure 5

Range and distribution of all water-erosion benefit values, by county

County-level water-erosion benefit categories are: reservoir services, navigation, water-based recreation, marine fisheries, 

freshwater fisheries, municipal industrial, steam electric, irrigation ditches, flood damages, soil productivity, road ditches, and 

municipal water treatment. None are estimated at the county level. Reservoir services, navigation, and water-based recreation

are estimated at the HUC level.1

 Other values are based on FPR-level estimates.2

1

HUCs are watersheds defined by the U.S. Geological Survey’s 8-digit hydrologic unit codes.

2

FPRs are USDA’s multi-state Farm Production Regions.
23

Economic Measures of Soil Conservation Benefi ts: Regional Values for Policy Assessment / TB-1922

Economic Research Service / USDA

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