United States

Department of

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 120

June 2011 

Roger Claassen, Fernando Carriazo, Joseph C. Cooper, 

Daniel Hellerstein, and Kohei Ueda

Grassland to Cropland 

Conversion in the 

Northern Plains

The Role of Crop Insurance, 

Commodity, and Disaster Programs
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Recommended citation format for this publication:

Classen, Roger, Fernando Carriazo, Joseph C. Cooper, Daniel Hellerstein, and 

Kohei Udea. Grassland to Cropland Conversion in the Northern Plains: The 

Role of Crop Insurance, Commodity, and Disaster Programs, ERR-120, U.S. 

Dept. of Agri., Econ. Res. Serv. June 2011.
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Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Economic

Research 

Report

Number 120

July 2011

Grassland to Cropland Conversion 

in the Northern Plains

The Role of Crop Insurance, Commodity, 

and Disaster Programs

Abstract 

Native grasslands in the U.S. Northern Plains, particularly those located in the Prairie 

Pothole Region, are excellent breeding habitat for migratory birds. The conversion of 

grassland to crop production could damage this habitat and affect bird populations. We 

focus on three questions: How fast are grasslands being converted to cropland in the 

United States and especially in the Northern Plains? Can a temporary (5-year) ban on 

crop insurance purchase for converted grassland slow grassland to cropland conversion? More broadly, what has been the role of crop insurance and other farm programs 

in grassland to cropland conversion? We find that: (1) roughly 770,000 acres (1 percent) 

of 1997 rangeland acreage in the Northern Plains were converted to cultivated crops by 

2007; (2) a 5-year ban on crop insurance purchase for converted grassland could slow but 

is unlikely to stop grassland to cropland conversion; and (3) the benefits of crop insurance, disaster assistance, and marketing loans increased cropland acreage by about 2.9 

percent between 1998 and 2007. 

Keywords: Grassland, cropland, crop insurance, marketing loan benefits, disaster assistance, Northern Plains

Roger Claassen, claassen@ers.usda.gov

Fernando Carriazo 

Joseph C. Cooper, jcooper@ers.usda.gov

Daniel Hellerstein, danielh@ers.usda.gov

Kohei Ueda, dueda@ers.usda.gov
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Acknowledgments

The authors would like to thank Alex Barbarika and Rich Iovanna of the U.S. 

Department of Agriculture’s Farm Service Agency; Nate Higgins and Keith 

Fuglie of the Economic Research Service’s Resource and Rural Economics 

Division; Gary Brown, U.S. Government Accountability Office; Jeff Goebel 

and James Rowe of USDA’s Natural Resources Conservation Service; and 

two anonymous reviewers for their reviews and comments. Thanks also go to 

our editor, Priscilla Smith, and to our designer, Susan DeGeorge.
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

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

Grassland and Grassland-Cropland Conversion, 1997-2007 . . . . . . . . . 4

The Grassland-Cropland Margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Grassland to Cropland Conversion More Likely in 

Northern Plains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Land Use and Land Use Change: Conceptual Issues . . . . . . . . . . . . . . 15

A Model of Land Use and Land Quality . . . . . . . . . . . . . . . . . . . . . . 16

Market Returns, Policy, Technology, and Land Use . . . . . . . . . . . . 18

Many Factors Affect the Cropland-Grassland Margin . . . . . . . . . . 23

Simulation Analysis of Sodsaver: Can It Save Native Grasslands? . . . 25

Policy Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Simulating the Effect of Crop Insurance and Supplemental 

Revenue Assistance Program on Crop Revenue . . . . . . . . . . . . . . . . 26

Simulation Results: The Sodsaver Effect . . . . . . . . . . . . . . . . . . . . . . 28

Sodsaver May Not Be Enough To Stop Grassland Conversion . . . . 35

Econometric Analysis of Land Use Change . . . . . . . . . . . . . . . . . . . . . . 37

Together, Crop Programs Add 8.5 Percent to Average 

Annual Crop Revenue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Farm Programs Have Modest but Measurable Impact on 

Amount of Cropland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
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Summary

What Is the Issue?

Native grasslands in the Northern Plains, particularly those located in the 

Prairie Pothole Region (PPR), are used as breeding habitat by migratory 

birds. The PPR includes parts of Iowa, Minnesota, North Dakota, South 

Dakota, and Montana. Grasslands in the PPR account for about 50 percent 

of North American duck production. There is growing concern that the conversion of grassland to crop production is damaging this habitat. Once lost, 

native grasslands are difficult to re-establish.

Environmental organizations and others have argued that some Federal farm 

programs are encouraging grassland to cropland conversion. While many 

farm commodity programs are now decoupled from farmers’ crop production 

decisions, several USDA programs continue to depend on current production, 

including crop insurance, marketing loans, and disaster assistance. Farmers 

can expand their eligibility to receive benefits from these programs by converting grassland to cropland. 

To address these concerns, the Food, Conservation, and Energy Act of 2008 

included the Sodsaver provision. If implemented, Sodsaver would deny crop 

insurance for the first 5 years of production on native sod (native grassland) 

converted to crop production. Sodsaver is limited to the Prairie Pothole States 

(Iowa, Minnesota, Montana, North Dakota, and South Dakota) and would be 

implemented on a State-by-State basis but only at the request of that State’s 

governor. As of June 2011, none of the governors has requested Sodsaver 

implementation. The manager’s statement accompanying the 2008 Farm Act 

also directed USDA to conduct a study of the role of farm programs in grassland to cropland conversion.

We focus on three questions: (1) How fast are grasslands being converted to 

cropland in the United States, especially in the Northern Plains? (2) Can a 

policy like Sodsaver, if implemented, significantly slow grassland to cropland conversion? (3) More broadly, what has been the role of crop insurance 

and other farm programs in grassland to cropland conversion?

While concern about grassland conversion is often focused on “native” grassland, available data do not identify grasslands as “native” or “non-native.” 

Native grasslands are most likely to be categorized as part of rangeland. This 

study considers a wide range of grassland categories including rangeland, pasture, hay, and Conservation Reserve Program (CRP) lands with grass cover.

What Were the Study Findings?

During 1997-2007, grassland-cropland conversion varied by grassland type and 

region. Compared with other regions, producers in the Northern Plains were 

more likely to convert grassland to cropland or retain land in crops rather than 

returning it to grass. In the Northern Plains, about 1 percent of 1997 rangeland 

had been converted to crop production by 2007 (roughly 770,000 acres), while 

only 100,000 acres were converted from cropland to rangeland. The Northern 

Plains accounted for 57 percent of rangeland to cropland conversion between 

1997 and 2007. In the United States, there was a net shift between 1997 and 
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Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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2007 of roughly 10 million acres from cultivated cropland (about 3 percent 

of 1997 cropland) to hay or pasture. In the Northern Plains, the net shift of 

cropland to hay and pasture was effectively zero. The gross shift of roughly 

3.5 million acres moving from cropland to hay or pasture was exactly offset 

by acreage moving from hay or pasture to cultivated crops. Through the CRP, 

however, producers in the Northern Plains moved some land from cultivated 

crops to grass. Between 1997 and 2007 they enrolled 3.6 million acres of cropland in the CRP, while 1.9 million acres were returned to crop production and 

1.7 million acres previously in the CRP became hay, pasture, or range.

The Sodsaver provision of the 2008 Farm Act, if implemented, is likely to have 

only a modest effect on land use at the grassland-cropland margin. In seven 

North Dakota and South Dakota counties where evidence suggests that grassland to cropland conversion has been relatively high, Sodsaver would reduce 

expected crop revenue by up to 5 percent, reduce expected net return by up to 

14 percent, and increase the variability of crop production (in terms of annual 

standard deviation of crop revenue) by up to 13 percent. Land use change 

depends on how responsive land allocation is to changes in crop revenue, net 

return, and variability. Using elasticities estimated for this study, we find that 

crop insurance could have been responsible for shifting up to 0.9 percent of 

rangeland to cropland in the seven counties we considered. This is an estimate 

of net change in equilibrium acreage and is not directly comparable to gross 

rangeland conversion. These counties are located in an area where an annual 

average rate of rangeland to cropland conversion of 0.6 percent of grassland 

acreage was observed between 1985 and 2003—indicating total conversion 

of about 6 percent over a period of 10 years. In comparison, the 0.9-percent 

change in estimated equilibrium acreage that would result from the withdrawal 

of crop insurance would be modest and indicates that crop insurance is only 

one of a number of factors (e.g., market conditions, technology, and other programs) that are driving land use choices. 

In a study area that includes 77 North Dakota and South Dakota counties, we 

use an econometric model to estimate that crop insurance, marketing loans, 

and disaster payments increased land in cultivated crops by 686,000 acres (the 

average effect between 1998 and 2007)—roughly 2.9 percent of cultivated 

cropland acreage. (This is an estimate of the net change in equilibrium acreage. The estimated effect varies over time with economic and policy conditions. The 2.9-percent change is the average estimated effect between 1998 and 

2007.) The largest overall effect was from disaster assistance (1.2 percent rise 

in cultivated cropland; 292,000 acres), followed by crop insurance (1 percent; 

235,000 acres) and marketing loan benefits (0.7 percent; 161,000 acres). We 

estimate that roughly 60 percent of the increase in crop acreage came from 

hay or pasture (403,000 acres) while the remaining acreage came from range 

(181,000 acres) and CRP (102,000 acres). The estimated rangeland reduction 

of 181,000 acres was 1.1 percent of rangeland acreage in the 77 counties considered in this study. In the absence of these programs, farmers could adjust to 

larger grassland acreages by reducing the rate of grassland to cropland conversion, increasing cropland to grassland conversion, or both. The study period, 

1998-2007, largely predates the recent rise in commodity prices, beginning 

with increased corn prices in 2007. Higher crop prices may be encouraging 

farmers to expand cropland acreage, prompting them to convert grassland 
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to cropland or retain land in crop production that might have otherwise been 

returned to grass. 

How Was the Study Conducted?

The study has three major components. First, we document trends in grassland 

to cropland changes using data from USDA’s Natural Resources Conservation 

Service (NRCS) 2007 National Resources Inventory (NRI). A simulation 

analysis is used to gauge the potential of a Sodsaver-type program to reduce 

crop insurance indemnities and other payments to crops grown on converted 

grassland in seven counties, thereby reducing incentives for grassland conversion. Finally, an econometric (statistical) model is developed to help understand the role of farm program payments in the movement of land among 

cropland, hay and pasture, rangeland, and CRP in 77 counties from 1997 to 

2007, while controlling for changes in market prices, crop yields, and other 

nonpolicy factors that may have also influenced land use decisions. 
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Introduction

Concern about the effect of Federal agricultural programs on grassland to 

cropland conversion has been growing over the past decade. Environmentalists, 

wildlife groups, and some livestock interests are concerned that commodity 

programs, crop insurance subsidies, and disaster payments are supporting 

crop production at the expense of grasslands, particularly native grasslands in 

the Prairie Pothole Region of the Northern Plains (USGAO, Morgan). 

Recent concern has focused on federally subsidized crop insurance. Under 

the Agricultural Risk Protection Act of 2000, subsidy rates for crop insurance 

premiums were increased sharply to broaden participation and encourage 

farmers to purchase higher levels of coverage. By 2002, 80 percent of eligible acreage was enrolled (Dismukes and Vandeveer) and most producers paid 

less than half of the full premium.1 Crop insurance participation has remained 

high. In 2009, more than 80 percent of corn, cotton, soybean, and wheat acreage was insured, while rice producers insured nearly 80 percent of acreage 

and sorghum producers insured nearly 70 percent of acreage (FAPRI). 

To address concerns about crop insurance and grassland conversion, the 2008 

Farm Act included a Sodsaver provision that would, if implemented, deny 

crop insurance coverage for the first 5 years of crop production on land converted from native grass. Congress limited the program to the Prairie Pothole 

States of Iowa, South Dakota, North Dakota, Montana, and Minnesota and 

made program implementation contingent on a request from the governor 

of each State. As of May 2011, there have been no requests for Sodsaver 

implementation 

A second area of concern is disaster assistance. Although premium subsidy 

increases and other changes to the crop insurance program were the latest 

in a series of revisions designed to eliminate the need for ad hoc disaster 

assistance, Congress has continued to provide regular disaster assistance. 

Disaster payments were made in every year between 1985 and 2007, totaling 

$30 billion over those 22 years (Goodwin and Rejesus). Typically, disaster 

payments have compensated farmers who experience losses of 35 percent 

or more in any given season.2 The 2008 Farm Act created the Supplemental 

Revenue Assistance (SURE) program. The SURE program is a permanent 

disaster program that functions essentially as a premium-free addition to 

crop insurance coverage, providing additional assistance to insured farmers 

in counties where disasters have been declared or to individual farmers who 

experience losses of more than 50 percent of expected crop revenue

 in any given year.3

Concerns have also been raised about marketing loan benefits. These payments protect producers of subsidized crops (e.g., corn, wheat, and soybeans) 

from low prices by making up the difference when market prices fall below 

the commodity loan rate. Assuming that farmers expected commodity prices 

to sometimes drop below loan rates, these payments may influence producer 

expectations about the mean and variance of crop revenue. Over time, these 

payments can increase the average return to program crop production and 

reduce the variability of returns to crop production. 

1Farmers who insured at coverage 

levels of up to 75 percent (i.e., insured 

against losses that exceed 25 percent 

of expected revenue or yield) receive 

premium subsidies of more than 50 

percent.

2Loss thresholds of 35 percent have 

been typical. For each year between 

2001 and 2007 (inclusive), producers 

were required to document production losses of 35 percent or more to 

be eligible for disaster assistance. For 

examples, see http://www.fsa.usda.gov/

FSA/newsReleases?area=newsroom

&subject=landing&topic=pfs&newst

ype=prfactsheet&type=detail&item=

pf_20080125_distr_en_cdpqty08.html/.

3We use the term “revenue” to refer to 

gross revenue before production costs 

are considered. We use the term “net 

return” to refer to revenue less production costs.
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Are Federal farm programs contributing to the demise of native grasslands? 

If so, would changes in Federal programs significantly affect the movement of land at the margin between grassland and cultivated cropland? 

Previous studies of the effect of crop insurance on land use (e.g., Goodwin, 

Vandeveer, and Deal; Lubowski et al., 2006) have concluded that the overall effect of crop insurance is small. Depending on the study, subsidized 

crop insurance may have increased land in crop production by 1 million to 3 

million acres nationally (0.2 to 1.1 percent of cultivated cropland acreage). 

More broadly, Lubowski, Plantinga, and Stavins found that farm programs 

increased overall cropland acreage by about 2 percent while Gardner, Hardie, 

and Parks estimated that farm programs caused a 22-percent increase in cropland acreage. 

Despite previous research on this topic, additional research is needed because 

previous studies use data from 1997 or earlier, largely predating some 

major policy changes. The Federal Agriculture Improvement and Reform 

(FAIR) Act of 1996 (the 1996 Farm Act) mandated marketing loan benefits 

for wheat, feed grains, and oilseeds; the Agricultural Risk Protection Act 

(ARPA) of 2000 sharply increased crop insurance premium subsidies; and 

the new, permanent disaster assistance program (SURE) was created in the 

2008 Farm Act. Growth in ethanol production and other demand factors have 

also led to increased commodity prices since 2007. 

For a more up-to-date examination of these programs, we pursued three areas 

of inquiry. First, what are the historical and more recent trends in grassland 

to cropland conversions? How does grassland to cropland conversion compare to other land use changes affecting grassland? In the past, the margin 

between cropland and grassland has been active. Between 1982 and 1997, 

more than 33 million acres of land moved between, into, or out of cultivated 

crops along the cropland-pasture and cropland-rangeland margins while 

another 30 million acres of cropland were enrolled in the CRP (Lubowski 

et al., 2006). The most recent NRI data provide information on land use and 

land use change for 1997-2007, allowing estimation of both gross and net 

movement of land between grassland and other uses, especially cultivated 

crop production (USDA-NRCS, 2009). 

Second, how have farm programs affected crop revenue, and what impact has 

that result had on grassland to cropland conversion? Farmers face constant 

changes in markets, technology, and policy that are likely to affect land use 

and overall agricultural production. Greater demand for corn due to rapid 

expansion of ethanol production, for example, may be encouraging farmers 

to expand crop production into grasslands. Genetically modified varieties 

of corn and soybeans that produce higher yields and are less susceptible to 

drought also may be playing a role in the expansion of cropland acreage in 

the Northern Plains. To sort out these effects, we develop an econometric 

(statistical) model of land use, based on NRI data for 1998-2007, that incorporates the expected mean and variance of market and policy returns and 

underlying land quality including soil productivity, erodibility, propensity for 

flooding or wetness, and the availability of irrigation. Using the model, we 

estimate the number of acres that are likely to have been in crop production 

between 1998 and 2007 largely because of farm program benefits. 
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Finally, we estimate the potential effect of Sodsaver in reducing the role of 

farm programs in grassland to cropland conversion. Because Sodsaver has 

not been implemented, we develop a simulation model using 2008 as a base 

year for the development of a series of representative farms. Because crop 

insurance and other farm programs protect producers against low revenue, 

we develop a model of farm revenue that accounts for variability in prices 

and yields. The joint distribution of crop yields and prices, along with actuarial parameters obtained from USDA’s Risk Management Agency (RMA), are 

used to estimate producer returns from crop insurance purchases, including 

SURE payments on recently converted land. Using elasticities drawn from 

the literature and from our econometric model, we estimate the potential 

effect of Sodsaver on land use. 
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Grassland and Grassland-Cropland 

Conversion, 1997-2007

Grasslands have been defined by land cover and by land use (Sanderson, 

Wedin, and Tracy). Grasses are the dominant vegetation, but grasslands also 

include legumes, forbs, and, depending on the climate in a specific location, 

may be dotted with trees. (A forb is an herb or nonwoody flowering plant that 

is not a grass.) In terms of land use, grasslands are also defined by grazing, 

haying, and other forms of forage harvest. By these definitions, grassland 

encompasses a wide variety of grassland types from minimally managed 

natural or native grasslands to grassland that is extensively managed for forage production to feed livestock. While concern about grassland conversion 

is often focused on native grassland, we consider the full range of grassland 

types: hay, pasture, rangeland, and CRP lands that are in grass cover.

The market value of grassland is derived largely from forage production. In 

terms of forage value, pasture and rangeland are grazing lands and can be 

close substitutes. Hay is also a key source of livestock forage. These grassland types are often distinguished by the mix of grass and other plant species present and by the level of management they receive in agricultural use. 

Rangeland, for example, may include many grasses, forbs, and other plants 

while pasture and hay land are more likely to include a single domestic species or a small number of domestic species. Rangeland is less likely than 

pasture or hay land to be fertilized or periodically re-seeded. Some rangeland 

has never been cultivated, while hay and pasture land are likely to have been 

used for cultivated crops at some time in the past. Although hay (particularly 

Grassland Types

Hayland. Land managed for the production of forage crops that are machine 

harvested. The crop may be grasses, legumes, or a combination of both. 

Pastureland. Land managed primarily for the production of introduced forage 

plants for livestock grazing. Pastureland cover may consist of a single species in a pure stand, a grass mixture, or a grass-legume mixture. Management 

usually consists of cultural treatments: fertilization, weed control, reseeding 

or renovation, and control of grazing. For the National Resources Inventory, 

pastureland includes land that has a vegetative cover of grasses, legumes, and/

or forbs, regardless of whether or not it is being grazed by livestock. 

Rangeland. Land on which the climax or potential plant cover is composed 

principally of native grasses, grasslike plants, forbs, or shrubs suitable for 

grazing and browsing, and introduced forage species that are managed like 

rangeland. This would include areas where introduced hardy and persistent 

grasses, such as crested wheatgrass, are planted and such practices as deferred 

grazing, burning, chaining, and rotational grazing are used, with little or no 

chemicals or fertilizer being applied. Grasslands, savannas, many wetlands, 

some deserts, and tundra are considered to be rangeland. Certain communities of low forbs and shrubs, such as mesquite, chaparral, mountain shrub, and 

pinyon-juniper, are also included as rangeland. 

Source: USDA, Natural Resources Conservation Service, NRI glossary.
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alfalfa) is often grown in rotation with cultivated crops, hay and pasture are 

often grown on land of similar productivity and may involve a mix of grasses 

and legumes. In some cases, the difference between hay and pasture is the 

method of harvest—some grassland may be used for pasture in some years 

and for hay in others.

By virtue of land cover, a large share of land in the CRP can also be considered grassland. More than 20 million of the 31.2 million acres enrolled in 

the CRP (as of May 2010) are in grass cover, not counting grassed waterways, field-edge filter strips, and other grass-based conservation practices 

(USDA-FSA). Although CRP land is sometimes referred to as “retired,” 

CRP grassland can be used for hay or grazing in certain circumstances (e.g., 

severe drought) and can generate recreational and ecological value (Feather, 

Hellerstein, and Hansen). 

Grasslands can provide a range of ecological services under light or moderate 

grazing (Conner et al.). Grasslands in the Northern Plains are a highly productive breeding habitat for ducks. Grasslands habitats produce about 50 percent of U.S. ducks while accounting for only 10 percent of breeding territory 

(USDOC-NOAA and others). Ducks are particularly drawn to small wetlands 

surrounded by grasslands—a key feature of the Prairie Pothole Region of the 

Northern Plains. Fragmentation of grasslands, overgrazing, and the spread of 

invasive species are damaging the quality of habitat for duck and other species of grassland animals (Conner et al.) When compared to cultivated cropland, grasslands store substantial amounts of carbon (Eve et al.) and tend to 

produce runoff that is relatively free of sediment, nutrients, and pesticides. 

By any definition, grassland covers a large part of the United States. In 2007, 

rangeland, pasture, hay, and CRP lands accounted for 606 million acres in 

the contiguous 48 States, 63 percent of the 900 million acres of U.S. agricultural land and more than 25 percent of the 1.9 billion acres that make up 

the conterminous United States (USDA-NRCS, 2009). Roughly 67 percent 

of grassland, 408 million acres, is classified as rangeland. Pasture and hay 

together account for 27 percent of grassland (160 million acres), while 5 percent of grassland is in the CRP. 

Grasslands once covered a larger share of U.S. land area. Between 1850 and 

1950, an estimated 260 million acres of grassland were converted to other 

uses, mostly cropland (Conner et al.). Between 1950 and 1990, grassland 

area declined by another 27.2 million acres. Of the post-1950 loss, 36 percent 

was converted to nonagricultural use. Only about 4 percent of tall-grass prairie, which once covered large portions of Illinois, Iowa, Missouri, Western 

Minnesota, and Eastern Oklahoma and Texas, remains today (USDOI, 

USGS). On the Eastern edge of the prairie (e.g., northern Illinois, Southern 

Iowa, northern Missouri, and parts of Kansas, Oklahoma, and Texas), the 

broad savannas and tall grasses that once existed have been replaced almost 

entirely by cultivated crop production. Of the mixed-grass and short-grass 

prairie that once blanketed the High Plains (roughly the area west of the 

100th meridian and east of the Rocky Mountains), remaining grassland 

ranges from 20-80 percent depending on the State. 
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The Grassland-Cropland Margin

Land that moves between grassland and cultivated cropland is, by definition, at the margin between these uses. Activity along the margin between 

cultivated cropland and grassland varies regionally and by grassland type. 

Initially, we consider the margin between cultivated cropland and four types 

of grassland: range, pasture, hay, and CRP. 

Nationally, the most active grassland-cultivated cropland margin between 

1997 and 2007 was for hay (fig. 1). More than 26 million acres moved 

between cultivated crops and hay, resulting in a net shift of 4.8 million acres 

from cultivated crops to hay. More than 15 million acres moved between 

cultivated cropland and pasture, resulting in an overall shift of 5.5 million 

acres from cultivated crops to pasture. In contrast, the margin between crop-

−30 −20 −10 0 10 20 30 40

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.

Hay

Pasture

Range

CRP

Figure 1a

U.S. grassland-cropland conversion, 1997-2007

Figure 1b

U.S. grassland-cropland conversion, as a percentage of 

1997 land use

−15,000 −10,000 −5,000 0 5,000 10,000 15,000 20,000

Thousand acres

Percent of 1997 acreage

Hay

Pasture

Range

CRP

Net change to grassland From crops To crops

Net change to grassland From crops To crops
7

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land and rangeland involved less than 3.1 million acres. Roughly 1.3 million 

acres of range (0.33 percent of 1997 range) were converted to cultivated 

cropland, while 1.8 million acres went the other way for a net conversion 

from cultivated crops to range of about 500,000 acres (0.11 percent of 1997 

rangeland).5

The CRP appeared to serve as a transitional land use between cultivated crop 

and forage production (see figure 1). Total CRP enrollment was roughly 32 

million acres in both 1997 and 2007. During this period, however, 11.1 million acres of cultivated cropland were enrolled in the CRP for the first time 

while only 6.1 million acres were returned to cultivated crops. Most of the 

other 5 million acres leaving the CRP were used for hay or grazing in 2007.

In sum, U.S. farmers and landowners shifted a net total of 16.5 million acres 

of cultivated cropland to pasture, hay, range, or CRP between 1997 and 2007. 

The net shift included gross conversion of 23.7 million acres of grassland to 

cropland and 39.0 million acres of cropland to grassland. Cultivated cropland 

in the United States declined by more than 21 million acres (6.6 percent) 

while hay, pasture, and rangeland, combined, grew by just under 3 million 

acres (0.5 percent). 

Activity along the grassland-cropland margin is not evenly distributed across 

the United States. In the Northern Plains region (Kansas, Nebraska, North 

Dakota, and South Dakota), the movement of land between grassland and 

cropland favored cultivated crops when compared with other regions. The 

Northern Plains States accounted for 57 percent of U.S. rangeland to cropland conversion between 1997 and 2007, an estimated total of more than 

770,000 acres (fig. 2) and roughly 1.1 percent of 1997 rangeland acreage. 

While some land was converted from cultivated crops to range, net conversion of range to cropland was more than 680,000 acres—roughly 0.8 percent 

5 Not statistically different from zero.

Northeast

Lake States

Corn Belt

Northern Plains

Appalachia

Southeast

Delta

Southern Plains

Mountain

Pacific

−800 −600 −400 −200 0 200 400 600 800 1,000

Figure 2

Rangeland-cropland conversion, 1997-2007, by USDA 

Farm Production Region

Net change to grassland From crops To crops

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.

Thousand acres
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Economic Research Service / USDA

of 1997 range acres. During the same period, other Western regions, including the Mountain and Southern Plains States, were experiencing a net shift of 

land from cropland to range.

Unlike the rest of the country, the land use decisions of Northern Plains’ 

farmers and ranchers produced no net movement of land from cultivated 

crops to hay and pasture (fig. 3). While 3.5 million acres (27 percent) of hay 

and pasture land was converted to cultivated crop production, these changes 

were roughly offset by conversion of cultivated cropland to hay and pasture. 

During the same period, the land use decisions of farmers and landowners 

in every other U.S. region created a net shift of land from cultivated crops to 

hay and pasture. 

Through changes in CRP enrollment, however, farmers and ranchers in the 

Northern Plains shifted a net total of 1.8 million acres from cultivated crops 

to grass between 1997 and 2007 (fig. 4). Here, the Northern Plains largely 

reflects the national trend. A total of 3.6 million acres of cultivated cropland 

was enrolled in CRP for the first time, while 1.9 million acres of CRP land 

were returned to crop production after contracts expired. The balance of land 

leaving the CRP—just under 1.7 million acres—was in hay, pasture, or range 

in 2007. 

For context, we compare grassland-cropland conversion for 1987-1997 and 

1997-2007. During the earlier period, Northern Plains producers converted 

more than 1.1 million acres of rangeland to cropland (fig. 5a) compared with 

770,000 acres during 1997-2007 (fig. 5b). Considering hay and pasture land 

combined, there was a substantial net movement from cropland to grassland 

during 1987-1997 (see figure 5a) but almost no net conversion either way 

during 1997-2007 (see figure 5b). So, considering all three types of forage 

−4,000 −3,000 −2,000 −1,000 0 1,000 2,000 3,000 4,000

Northeast

Lake States

Corn Belt

Northern Plains

Appalachia

Southeast

Delta

Southern Plains

Mountain

Pacific

Figure 3

Hay and pasture-cropland conversion, 1997-2007, by USDA 

Farm Production Region

Net change to grassland From crops To crops

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.

Thousand acres
9

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(range, pasture, and hay), there was essentially no net movement of land 

between grass and crops during 1987-1997, while there was a modest shift 

toward crops (roughly 640,000 acres) during 1997-2007. When CRP land is 

also considered, there was a net shift from cropland to grassland during both 

periods. Because CRP enrollment was large during 1987-1997—more than 

6 million acres—the net shift from crops to grass (including CRP) was much 

larger during 1987-1997 (about 6.2 million acres) than during 1997-2007 

(roughly 1.1 million acres). 

While our study is focused on grassland-cropland conversions, grasslands 

also are being lost to nonagricultural uses. On a national level, while there 

was a net movement from cropland to grass for each of our major grassland 

categories (range, pasture, hay, and CRP), there was also a net conversion 

from grassland to nonagricultural use for every grassland category (fig. 

6a). Nonagricultural uses include forest, roads, and development for housing or commercial purposes. In the Northern Plains, on the other hand, the 

grassland-cropland margin is still primary. For example, net conversion of 

range and hay to cropland is much larger than conversion to nonagricultural 

uses for the grassland categories (fig. 6b). For all three forage categories 

combined, grassland loss to crop production was larger than grassland loss to 

nonagricultural uses. 

While the Northern Plains differs from the overall United States in terms of 

land use and land use change, there is also a great deal of diversity in growing conditions, crop mix, and land use within the Northern Plains. To investigate this diversity, we look more closely at land use and cropping patterns 

for portions of Land Resource Regions (LRR) M, F, and G located within 

the Northern Plains (figs. 7a and 7b). LRRs are areas of relatively uniform 

climate and soil conditions (USDA-NRCS, 2006). The intersection of the 

Northern Plains with LRR F, G, and M covers all of North and South Dakota 

t

−2,000 −1,000 0 1,000 2,000 3,000 4,000

Northeast

Lake States

Corn Belt

Northern Plains

Appalachia

Southeast

Delta

Southern Plains

Mountain

Pacific

Figure 4

Conservation Reserve Program-cropland conversion, 1997-2007, 

by USDA Farm Production Region

Net change to grassland From crops To crops

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.

Thousand acres
10

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and much of Nebraska. These areas are selected for further scrutiny because 

they include a large portion of the Prairie Pothole Region where concern 

about grassland to cropland conversion has been greatest.

We refer to LRR M as the Western Corn Belt because a high proportion of 

land is cropped (70 percent; see figure 7b) and corn and soybeans are the 

predominant crops, accounting for roughly 90 percent of all cultivated acres. 

LRR F, which we refer to as a “transitional” region, covers large portions 

of central and Northwestern South Dakota and all of North Dakota. About 

70 percent of LRR F agricultural land is cropped (see figure 7b). Although 

wheat still covers a majority of cultivated crop acres, corn and soybean 

production doubled between 1997 and 2007 (from 20 percent to 40 percent 

of crop acreage). Finally, land in LRR G, which we refer to as a rangeland 

region, is largely devoted to livestock grazing; rangeland accounts for about 

Hay

Pasture

Range

CRP

Figure 5a

Grassland-cropland conversion in the Northern Plains, 1987-1997

−4,000 −2,000 0 2,000 4,000 6,000 8,000

Thousand acres

Net change to grassland From crops To crops

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.

Figure 5b

Grassland-cropland conversion in the Northern Plains, 1997-2007

−4,000 −2,000 0 2,000 4,000

Thousand acres

Hay

Pasture

Range

CRP

Net change to grassland From crops To crops

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.
11

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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80 percent of agricultural land. Cropland is largely devoted to wheat but irrigated corn is grown where water is available.

Overall, land use change in the portion of the Northern Plains defined by 

these three LRRs is similar to that observed over the entire region. Estimated 

rangeland to cropland conversion during 1997-2007 was roughly 370,000 

acres, roughly 0.9 percent of 1997 rangeland acreage (fig. 8). Net rangeland 

conversion was roughly 0.7 percent. While gross conversion of hay and 

pasture in LRR F, G, and M, was large in both directions, net conversion of 

hay and pasture to cropland was 350,000 acres, about 5 percent of 1997 hay 

and pasture acreage, as compared with zero net conversion observed for the 

Hay

Pasture

Range

CRP

Figure 6a

Net grassland change vs. cropland and nonagricultural 

land uses in the United States, 1997-2007

−8,000 −6,000 −4,000 2,000 0 2,000 4,000 6,000

Thousand acres

Net from cropland Net from non-ag use

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.

Figure 6b

Net grassland change vs. cropland and nonagricultural land uses, 

in the Northern Plains, 1997-2007

-1,500 -1,000 -500 0 500 1,000 1,500 2,000

Thousand acres

Hay

Pasture

Range

CRP

Net from cropland Net from non-ag use

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.
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Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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LRR F=Transitional region (Central SD and ND)

LRR G=Range region (Western SD and Northeastern NE)

LRR M=Western Corn Belt (Eastern SD and Northwestern NE)

Figure 7a Land Resource Regions (LRR) in the Northern Plains 

North Dakota

South Dakota

Nebraska

Note: Boundaries shown are approximate.

Source: USDA, Economic Research Service, based on USDA, Natural Resources 

Conservation Service, 2006. 

LRR M

LRR G

LRR F

Figure 7b

Land use in the Northern Plains, by Land Resource Region 

0 5 10 15 20 25 20 25 40

Thousand acres

Cultivated crops CRP Hay and pasture Range

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.
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Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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entire Northern Plains region. Overall gross conversion of range, hay, and 

pasture to cropland in this area was 2.22 million acres for 1997-2007, about 

4.4 percent of 1997 range, hay, and pasture acreage. Net conversion of these 

grasslands to cropland was about 650,000 acres, or 1.4 percent. While a great 

deal of land moved between cultivated crops and enrollment in the CRP, 

enrollment decisions resulted in a net shift of more than 800,000 acres from 

cultivated crops to CRP. Overall, land use change resulted in a small net shift 

of cultivated cropland to grassland (range, pasture, hay, and CRP). 

Although the NRI sample is not dense enough to make reliable estimates in 

smaller areas (e.g., counties), other studies suggest that grassland to cropland conversion was concentrated in smaller areas. In a study of grassland to 

cropland conversion for 1985-2003, Stephens et al. use satellite imagery with 

ground truth data6 to document annual grassland conversion rates ranging 

from 0 to 1.5 percent in three areas of the Missouri Coteau7 region of North 

and South Dakota. Average annual grassland loss across the three study areas 

was 0.4 percent per year. Grassland loss averaged 0.6 percent in the HydeHand area of South Dakota, a region that includes all of Hyde, Hand, and 

Faulk counties in East-Central South Dakota and parts of surrounding counties, including Sully and Edmunds. 

Grassland to Cropland Conversion More Likely 

in Northern Plains

Available data suggest that grassland to cropland conversion was much more 

likely in the Northern Plains than in other regions. The entire Northern Plains 

region (Kansas, Nebraska, South Dakota, and North Dakota) accounted for 

57 percent of gross rangeland to cropland conversion between 1997 and 

2007. The portion of the Northern Plains located in LRRs F, G, and M, which 

includes a large share of the Prairie Pothole Region, accounted for 27 percent 

of rangeland to cropland conversion. While other regions have, on average, 

shifted land from cultivated crops to hay and pasture, Northern Plains 

6Satellite imagery was from the LandSat V and VII satellites. Field visits 

were made to verify the correctness of 

interpretations.

7The Missouri Coteau is a narrow band 

of land running from South-Central 

South Dakota through central and 

Northwestern North Dakota and into 

Saskatchewan, Canada. The three study 

areas considered are relatively small, 

each encompassing all or part of 5 to 

10 counties

Figure 8

Grassland-cropland conversion for the Northern Plains, LRR F, G, 

and M, 1997-2007

−2,000 −1,000 0 1,000 2,000

Thousand acres

Hay & 

pasture

Range

CRP

Net change to grassland From crops To crops

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory, 2007.
14

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

producers have not. In the sub-region defined by LRRs F, G, and M, there 

was a net movement of land from hay and pasture to cultivated crops.

There is some evidence to suggest that grassland to cropland conversions 

were more likely in the areas that form the transition from the Corn Belt 

to the High Plains. In the Hyde-Hand region studied by Stephens et al., 

observed average annual native grassland conversion of 0.6 percent was 

much higher than the regionwide average of roughly 0.1 percent indicated 

by the NRI. The Hyde-Hand area is also part of LRR F, where farmers have 

been shifting from wheat to corn and soybeans. In the next chapter, “Land 

Use and Land Use Change: Conceptual Issues,” we investigate possible land 

use drivers and contrast conditions and trends found in LRR F (the transitional region) with those of LRR M (the Western Corn Belt) and LRR G (the 

rangeland region). 
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Land Use and Land Use Change: 

Conceptual Issues

Private land is generally allocated to the use that maximizes landowner value. 

Land can be valued for many reasons. The market value of land is derived 

from the direct or onsite benefits of land ownership. These include any benefits that can be captured directly by landowners or through market transactions, including the value of agricultural production, recreational activities 

such as hunting or fishing (through fees, for example), and residential or 

commercial development. Offsite or indirect benefits (or damages) are realized only downwind or downstream. These are often referred to as “nonmarket” benefits (or costs) because their value or cost is generally not captured 

(paid) by the landowner through market transactions.8 For example, water 

quality damage due to nutrient runoff from crop or livestock production does 

not necessarily affect the landowners applying nutrients, who may not even 

be aware of the damage. 

The realized mix of benefits (and costs) depends largely on how land is 

used.9 In the U.S. Northern Plains, a large majority of land is used for agricultural production. Both grassland (used for grazing or hay) and cropland 

provide economic returns that can be captured directly by landowners. Which 

of these broad land use categories is more profitable for the landowner 

depends on the underlying quality of the land, market prices for production 

inputs and outputs, and policy incentives. Grasslands often provide a higher 

level of offsite benefit (or lower level of offsite costs). When compared to 

cultivated land, grasslands typically (1) offer better wildlife habitat, (2) are 

less susceptible to soil erosion and sediment runoff, and (3) receive lower 

levels of fertilizer application, which often translates into less nutrient runoff 

to water. Some landowners may value nonmarket benefits even if they cannot be compensated monetarily for them. Nonetheless, private land allocation 

decisions will not necessarily reflect these benefits and, therefore, may not 

reflect an optimal allocation of land between crops and grass from the perspective of the broader society (Lubowski et al., 2006). 

Federal agricultural policies can affect the mix of benefits (and costs) by 

affecting the mix of grassland and cropland used in agricultural production. 

The CRP compensates farmers for converting cropland to grassland or trees 

and making only limited use of the land for a period of 10 years or more. The 

CRP is designed, in part, to increase wildlife habitat and reduce water quality damage due to nutrient runoff and sediment. Other Federal agricultural 

programs support crop farmers. While the payments associated with many of 

these programs have been “decoupled” from production—that is, current production decisions cannot affect current or future payments—some payments 

continue to depend on current production. Over time, programs designed 

to protect farmers from low prices or low yields can increase the expected 

return and decrease the variability of returns to crop production. If payments 

are available for crops grown on converted grassland, they may encourage 

grassland to cropland conversion and work at cross purposes with agri-environmental programs, including the CRP.

8There are rare exceptions. One example is payment made by New York 

City to Hudson River Valley farmers 

for the adoption of best management 

practices for water quality. Reducing 

upstream pollution was designed to 

reduced water treatment costs.

9The mix of benefits and damages may 

also depend on the spatial arrangement 

of land uses. For example, the value 

of grassland as wildlife habitat may be 

reduced if grasslands become fragmented because of grassland to cropland 

conversion.
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A Model of Land Use and Land Quality

Land quality is a key determinant of land use. Many previous studies have 

used a range of land-quality indicators in explaining land use. Indicators 

have included land capability classification (Lubowski, Plantinga, and 

Stavins; Lubowski et al., 2006; Plantinga), potential crop yields (Claassen 

and Tegene; Wu and Adams), soil water-holding capacity (Lichtenberg), land 

topography including slope and elevation (Nelson and Hellerstein; Turner, 

Wear, and Flamm; Muller and Zeller), and flooding potential (Chomitz and 

Grey). Most land-quality indicators (or sets of indicators) used to represent 

the quality of land for agricultural use attempt to capture the suitability of the 

soil as a medium for plant growth, suitability of the climate for crop production, and suitability of the topography for cultivation. 

In general, high-quality or high-productivity land is more likely to be in cultivated crop production than is medium- or low-productivity land. The top 

half of figure 9 shows a hypothetical, but plausible, relationship between land 

Crops

Y Z

Grass

Area=cropland acreage

Qm

Figure 9 Conceptual model of land use and land quality

Land productivity

Land productivity

Land quality

density

Area= 

grassland 

acreage

Certainty equivalent return, $

Acreage density

Source: USDA, Economic Research Service.
17

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

quality and returns to grass (hay or grazing) and crop production, given that 

prices, technology, and policy are fixed and producers are uniform in their 

degree of risk aversion and their overall assessment of returns to various land 

uses. The concave shape of the curves is based on the assumption that plant 

genetic capacity will increasingly become the limiting factor to production 

(yields) as productivity rises, and is consistent with Lichtenberg’s empirical findings (Lichtenberg). Grass for grazing or hay is best able to utilize 

low-productivity land and reaches its full potential at a relatively low level 

of productivity. Cultivated crops, on the other hand, are better able to take 

advantage of high-productivity land. In the situation depicted in figure 9, land 

with productivity greater than Qm will be used for cultivated crop production, 

while land with productivity less than Qm will be used for grazing or hay. 

The amount of land devoted to cultivated crops and grass depends on the productivity distribution of land. A hypothetical density function for land quality 

is shown in the bottom half of figure 9. The area under the density function 

and to the right of Qm represents the amount of land that would be used in 

crop production while the area under the density curve to the left of Qm represents the area devoted to hay and grazing. When the level of return to either 

land use changes, represented by a shift on one or the other curve in the top 

half of figure 9, it is land with productivity characteristics that place it near 

on the productivity scale that is most likely to change use. We refer to this 

medium-quality land as “economically marginal” land. 

Empirical evidence generally supports this model. While we do not observe 

a sharp, land quality-defined land use margin, available data suggest that 

medium-quality land is more likely to be at the economic margin between 

cultivated crops and grass when compared to high- or low-quality land. 

Figure 10 shows the distribution of land productivity by land use in LRRs F, 

G, and M in the Northern Plains.10 Most high-productivity land is used for 10This is based on the 1997 National 

Resources Inventory (NRI), linked 

to the National Commodity Crop 

Productivity Indicator (NCCPI; Dobos, 

Sinclair, and Hipple, 2008) to describe 

the joint distribution of land use and 

land productivity. NCCPI captures the 

effect of both climate and soil properties on plant growth.

0

20

40

60

80

100

Figure 10

Distribution of land by productivity class and use, LRR F, G, 

and M, Northern Plains

Cultivated

cropland

Conservation

Reserve

Program

Hay Pasture Rangeland

Percent of land by productivity class

Low productivity

Medium productivity

High productivity

See figure 7a for map of LRR F, LRR G, and LRR M. 

LRR F=Transitional region; LRR G=Range region; LRR M=Western Corn Belt. 

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory data, 1997.
18

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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cultivated crop production (80 percent) while most low-productivity land 

is rangeland, used primarily for grazing (73 percent). Medium-productivity 

land is spread across all land uses including cultivated crop production (52 

percent), forage production (hay, pasture, and range; 42.5 percent), and 

CRP (5.5 percent). In aggregate, we observe medium-quality land devoted 

to a wide range of uses because individual landowners differ in their risk 

aversion and overall judgments about the best or most valuable use of land. 

Nonetheless, the amount of medium-quality grassland available for conversion to cropland may be an important determinant of potential conversion.

The amount of medium-quality grazing land varies widely across LRRs 

within the Northern Plains (fig. 11). More than 60 percent of grassland 

(hay, pasture, and range) located in LRR F and M has medium productivity 

while less than 30 percent of grassland in LRR G has medium productivity. 

Potential for conversion in LRR M is limited because grassland accounts for 

only a small portion of overall land (see figure 7b). Conversion potential in 

LRR G may be large because it encompasses a huge area of grassland, even 

though 70 percent of it has low productivity, which is generally not suited 

to crop production. Finally, LRR F has a large area of grassland and a large 

share of that area has medium productivity, representing high potential for 

conversion to crop production.

Market Returns, Policy, Technology, and Land Use

Given the availability of grassland with land quality characteristics that place 

it on the margin with cultivated cropland, changes in the relative profitability 

of cropland and grassland may prompt producers to convert land from one 

use to the other. In terms of figure 9, an increase in crop returns would shift 

the crop curve up and shift the margin between cropland and grassland to the 

left, into lower quality land. The magnitude of the land use change would 

F G M

Figure 11

Distribution of grassland by productivity class, LRR F, G, 

and M, Northern Plains

Percent of land by productivity class

Low productivity

Medium productivity

High productivity

0

20

40

60

80

See figure 7a for map of LRR F, LRR G, and LRR M.

LRR F=Transitional region; LRR G=Range region; LRR M=Western Corn Belt.

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service, National Resources Inventory data, 1997.
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depend on the size of the change in crop returns and the amount of land with 

productivity between the old margin and the new margin. 

Market returns are the most important source of value for agricultural lands. 

Higher demand for corn due to increased ethanol production and other factors led to a large increase in corn prices, beginning in 2007. Other crop 

prices increased as farmers shifted land to corn from other crops. Higher 

prices also may be prompting farmers to expand cropland into grassland or 

other land not previously used for crop production. Farm commodity payments, crop insurance, and disaster assistance payments complement market 

returns by protecting farmers from unexpectedly low yields or an abrupt drop 

in crop prices while allowing them to benefit during periods with high prices 

or high yields. Over time, these payments will increase average returns of 

program crop production and reduce the variability of those returns. New 

technologies that increase crop yields or lower production costs also can 

increase the net return to crop production relative to other land uses.

We focus on marketing loans, crop insurance, and disaster assistance because 

these payments depend on current production.11 Farmers can increase the 

number of acres eligible for these programs by converting land from grass 

to cultivated crops. Marketing loans protect producers against low prices. 

When the market price of a covered commodity (e.g., corn, wheat, soybeans, 

and cotton) drops below a fixed “loan rate,” the Federal Government pays 

producers for the difference between the loan rate and the market price. Crop 

insurance can protect farmers against crop yield or crop revenue loss due to 

unfavorable weather, pests, adverse price movements, etc. Because the premiums are subsidized, producers may realize a net gain to their crop insurance purchase over time. As already noted, disaster assistance payments have 

been made on an ad hoc basis but have been substantial in recent decades. 

Figure 12 shows marketing loan benefits, crop insurance indemnities, and 

disaster payments for the Northern Plains during 1997-2007. Figure 13 

11Beginning in 1996, most farm 

program payments were decoupled 

from current production decisions. For 

decoupled programs, decisions about 

the use of land or other inputs do not 

change program benefits.

Figure 12

Marketing loan benefits, crop insurance indemnities, and 

disaster payments in the Northern Plains, 1997-2007

Percent of crop revenue

0

5

10

15

25

35

20

30

Source: USDA, Agricultural Resource Management Survey, National Agricultural 

Statistics Service and Economic Research Service, 1997-2007.

1997 99 2001 03 05 07

Marketing loan gains

Ad hoc disaster payments

Crop insurance indemnities

Total
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shows how these payments affected crop revenue over time. The spike in 

marketing loan benefits for 1999-2001 was driven largely by payments to 

soybeans. From 1997-2001, the soybean loan rate was $5.26 per bushel while 

season average prices hovered around $4.50 between 1999 and 2001. The 

soybean loan rate was lowered to $5.00 beginning in 2002, while loan rates 

for corn and wheat were adjusted upward. Indemnities increased sharply in 

1999 as a result of higher premium subsidies, which triggered broader participation in the crop insurance program. Subsidies were also designed to 

encourage purchase of high levels of coverage, increasing indemnities. We 

note, however, that producers must pay premiums on crop insurance. Even 

though premiums are highly subsidized, the net value of crop insurance 

indemnities would be considerably less than the full value of the indemnity, 

which is shown in figure 12.

Less attention has been given to the role of technology in land use change, 

although technology adoption can lead to changes in cost structure and new 

cropping patterns (e.g., Lichtenberg)—things that may increase the value of 

cropland relative to grassland and could trigger land use change. Adoption 

of herbicide-tolerant (Ht)12 soybeans and corn has been swift, rising from 

roughly 20 percent in 1998 to nearly 100 percent for soybeans by 2006, 

while adoption of Ht corn was over 50 percent by 2006 (fig. 14). Previous 

research has shown that Ht varieties did little to reduce production costs, but 

did significantly reduce labor requirements in the busy spring planting season 

(Fernandez-Cornejo and McBride). Timely planting, particularly in corn, is 

critical to achieving optimal yields. 

Adoption of no-till methods (fig. 15) may also facilitate land use conversion. 

Where soil and climate conditions are suitable, switching from conventional 

to no-till production can save labor and fuel (fewer field operations are 

needed) and reduce machinery requirements (tillage machines and large 

12Herbicide-tolerant varieties are 

modified to withstand contact with the 

herbicide glyphosate. While glyphosate 

is nonselective (it kills most plants), 

producers planting herbicide-tolerant 

varieties can control weeds by spraying 

fields after the crop has emerged.

Crop value

Marketing loan gains, crop insurance

indemnities, and disaster payment

Total

Figure 13

Marketing loan benefits, crop insurance, disaster assistance and 

the value of crop production in the Northern Plains, 1997-2007

Billion $

0

2

4

6

8

10

Source: USDA, Agricultural Resource Management Survey, National Agricultural 

Statistics Service and Economic Research Service, 1997-2007.

1997 99 2001 03 05 07
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tractors needed to pull them are no longer necessary). No-till also may reduce 

the barrier to land use change posed by the “sodbuster” provisions of U.S. 

agricultural policy. Under these provisions, producers who convert highly 

erodible land to crop production must adopt a soil conservation system that 

prevents a “significant” increase in soil erosion or risk loss of all Federal 

agricultural payments, not just payments on the converted land. In many 

instances in the Northern Plains no-till methods satisfy this requirement 

(USGAO). 

Changes in crop mix do not yield direct information on returns to crop 

production, but may be an indicator of changes in returns. A shift to more 

profitable crops may also encourage farmers to convert land not planted to 

a crop to cultivated crop production. Figure 16 shows how the mix of corn, 

soybeans, wheat, and other crops changed between 1997 and 2007 for LRRs 

Figure 14

Adoption of herbicide-tolerant crop varieties, in the Northern Plains, 

for selected years 

Percent of acres

Corn

Soybeans

1997 2002 2006

0

20

40

60

80

100

Source: USDA, Agricultural Resource Management Survey, National Agricultural Statistics 

Service and Economic Research Service, 1996-2006.

Figure 15

No-till adoption in the Northern Plains, by crop, for selected years 

Percent of acres

Corn Soybeans

1997 2002 2006

Wheat

0

20

10

40

30

50

Source: USDA, Agricultural Resource Management Survey, National Agricultural Statistics 

Service and Economic Research Service, 1996-2006.
22

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

F, G, and M in the Northern Plains. Cropping patterns have been stable in 

LRRs M (Western Corn Belt) and G (Rangeland Region). Cropland in LRR 

M is largely devoted to corn and soybeans while in LRR G a majority of 

cultivated cropland is devoted to wheat production. In LRR F (Transitional 

Region), however, there was a major shift from wheat to corn and soybeans 

between 1997 and 2007. Corn and soybean acreage roughly doubled, going 

from just over 20 percent to almost 40 percent of cultivated cropland. A cropmix shift of the magnitude observed in LRR F would have been less likely 

under U.S. farm commodity policies that existed before 1996, when producers were prevented from expanding plantings of corn, soybeans, wheat and 

other crops, while also continuing to receive farm program payments. Since 

1996, farmers have been (mostly) free to change crop acreages with risking 

the loss of farm programs benefits.

Figure 17 shows the difference in average net returns to crop production and 

grazing (normalized to zero for 1998) by LRR. The value of marketing loan 

benefits and net crop insurance indemnities (indemnities less producer-paid 

premiums) are included. Upward slopes represent an increase in returns to 

cultivated crops, relative to livestock grazing. A downward slope indicates a 

relative increase in returns to grazing. Returns to crop production are an acreweighted average of returns for three major crops: corn, wheat, and soybeans. 

Acreage weights are based on the average acreage of each crop in the 3 previous years, as the shift to higher value crops may have played an important 

role in changing crop returns. These returns also include the effect of farm 

program payments that depend directly on current production: marketing 

loan gains and federally subsidized crop insurance. 

1997

2002

2007

1997

2002

2007

1997

2002

2007

LRR M

LRR G

LRR F

0 20 40 60 80 100

Figure 16

Changes in cropping patterns, Land Resource Regions F, G, and M, 1997-2007

LRR F=Transitional region; LRR G=Range region; LRR M=Western Corn Belt. 

See figure 7 for total cropland acreage by LRR. 

Source: USDA, National Agricultural Statistics Service, Census of Agriculture, 1997, 2002, and 2007.

Percent

Corn Soybeans Wheat Other
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On average, returns to crop production are increasing in LRR M (Western 

Corn Belt) relative to LRR F (Transitional Region) or G (Range Region). For 

most of the period (7 of 10 years), relative returns to crop production in LRR 

M were higher than in 1998. In LRR G, on the other hand, relative returns 

to crop production were higher than 1998 returns in only 1 year (2007). 

Relative returns to crop production in LRR F were higher than relative 

returns in 1998 for only 3 of 10 years, but higher than relative returns in LRR 

G in every year after 1998. So, the evolution of returns over time suggests 

that the probability of converting grassland to cropland (for comparable tracts 

of land) would be larger in LRR M and F than in LRR G. 

Many Factors Affect the Cropland-Grassland Margin

There is evidence to suggest that land quality is an important factor in determining the economic margin between cropland and grassland. There are 

large areas of low-productivity rangeland that seem to have little chance of 

being converted to cropland in the absence of water for irrigation. There are 

also large areas of high-productivity cropland that are unlikely to become 

grassland in foreseeable future. 

Nonetheless, evidence also suggests that economic factors do matter. The 

margin between cropland and grassland is active. A significant share of land, 

particularly medium-quality land, may shift back and forth between cropland 

and grassland depending on the net returns expected from each use. The concentration of grassland to cropland conversions in LRR F (particularly the 

portion that intersects South Dakota), as shown in the chapter, “Grassland 

and Grassland-Cropland Conversion, 1997-2007,” corresponds roughly to 

the broad availability of medium-productivity grassland and changes in crop 

mix that may signal a larger shift in the relative profitability of grassland and 

cropland. These changes may have roots in changing market prices, policy, 

technology, or all three. 

1998 99 2000 01 02 03 04 05 06 07

LRR F 

LRR G 

LRR M 

Figure 17

Change in difference, expected return to cultivated crops and 

grazing, by Land Resource Region 

Dollars per acre

0

20

40

-20

-40

60

80

100

120

See figure 7a for map of LRR F, LRR G, and LRR M. 

LRR F=Transitional region; LRR G=Range region; LRR M=Western Corn Belt. 

Source: USDA, Economic Research Service.
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Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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In the next chapter, “Simulation Analysis of Sodsaver: Can It Save Native 

Grasslands?” we focus on county-level variation in the level of farm program 

payments that could be affected by Sodsaver, if it were implemented. We 

consider the difference in payments and its effect on revenue for counties 

experiencing high levels of grassland conversion and counties that are not. 
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Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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Simulation Analysis of Sodsaver: 

Can It Save Native Grasslands?

If implemented, the Sodsaver provision could deny crop insurance for a 

period of 5 years on native grassland converted for crop production. Because 

the SURE program depends on the purchase of crop insurance (where available), these payments would also be denied if Sodsaver were implemented. 

After 5 years, the land would become eligible for crop insurance coverage 

and SURE payments. 

How much difference would Sodsaver make in grassland to cropland conversion? The answer depends on the size of net crop insurance indemnities and 

SURE payments that would be affected by Sodsaver and the extent to which 

producers respond by altering plans for grassland conversion. To gauge the 

potential effect of Sodsaver, we devise seven representative farms based on 

seven North Dakota and South Dakota counties where there is information 

to suggest that grassland to cropland conversion has been high. Specifically 

we focus largely on counties located in and near the Hyde-Hand area studied by Stephens et al. (Beadle, Edmunds, Faulk, Hand, Hyde, and Sully in 

South Dakota and Stutsman County in North Dakota; all are located in LRR 

F (Transition Region)). For comparison, we develop farms representing two 

Southeastern South Dakota counties (Turner and Union; located in LRR M 

(Western Corn Belt) where most land is already cropped and conditions are 

similar to those in the Western Corn Belt (we refer to these as “comparison” 

counties). 

For each representative farm, we develop a joint distribution of prices and 

yields for three major crops (corn, soybeans, and wheat) and forage harvested 

through grazing. We use these distributions along with data on crop and grazing acreage to estimate the effect of crop insurance and SURE payments on 

the mean and variance of crop revenue, net returns (revenue net of production costs), and the producer’s willingness to pay to avoid greater revenue 

variability under Sodsaver. Elasticities of grassland to cropland conversion 

drawn from the literature are used to estimate the potential change in the rate 

of land use conversion under Sodsaver. The elasticity of grassland to cropland conversion with respect to crop returns is an estimate of the change in 

grassland to cropland conversion given a 1-percent change in net return to 

crop production. 

Policy Scenarios

The effect of Sodsaver on crop revenue depends on crop insurance indemnities, net of premiums, and SURE payments that would be received without 

Sodsaver. We estimate the mean and variance of these payments for two scenarios based on variations in crop insurance eligibility and calculation of the 

actual production history (APH) yield. The APH yield is a key determinant 

of premium rates (USDA-RMA, 2000) and is used along with the producerselected coverage level to determine the level of yield or revenue guaranteed 

by a crop insurance policy. The APH yield is also a factor in the calculation 

of SURE revenue guarantee. In most cases, the APH yield is an average of 

the most recent 4 to 10 yields on a crop insurance unit, depending on the 
26

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

availability of yield history. On land that has been in crop production but has 

not been previously insured (for a specific crop), “transitional” yields, based 

on historic county yields, are used to fill out the yield history until four actual 

yields are available. On land that has not been previously cropped, USDA’s 

RMA uses a special set of “New Land” rules to determine the APH yield. 

Our New Land scenario is patterned after current RMA policy with respect to 

land that has no history of crop production. To be eligible for crop insurance, 

RMA requires at least 1 year of actual production history (i.e., crop insurance 

is not generally available in the first year of production).13 On land that has 

been cropped for fewer than 4 years, the yield history is filled out using a percentage of county transitional yields. If land is first insured in the second year 

of crop production (with 1 year of production history), transitional yields are 

reduced by 20 percent. For land that is first insured in the third year (with 2 

years of actual yield history) transitional yields are reduced by 10 percent. If 

the reduced transitional yields are lower than the producer’s expected yield 

on new land, the producer will pay a higher effective premium rate and will 

be less likely to collect an indemnity than if a yield history were available. 

As a benchmark, we create the No Restriction scenario, which considers the 

effect of dropping the New Land rules: crop insurance can be purchased in 

the first year of production and the APH yield in the first 4 years of crop production is calculated using county transitional yields, as needed, without the 

reductions required by the RMA New Land rules. 

Simulating the Effect of Crop Insurance and 

SURE on Crop Revenue

Depending on the crop insurance product purchased, indemnities are paid 

when either yields or revenue (for a specific crop) fall below a guarantee 

level. In the Northern Plains, revenue insurance is the dominant product. For 

2007, RMA Summary of Business data show that revenue assurance (RA) 

accounted for 74 percent of insured corn acreage (most insured for 70- or 

75-percent coverage), 82 percent of insured soybean acreage (mostly 70- and 

75-percent coverage), and 44 percent of insured wheat acreage, almost all of 

it at 65-, 70-, or 75-percent coverage. For this analysis, we assume that all 

three crops are covered by an RA policy at 70-percent coverage. When revenue for an individual crop falls below 70 percent of revenue expected at the 

beginning of the season (as determined by RMA rules) the insurance indemnity is the difference between 70 percent of expected revenue and actual 

revenue. 

By law, RMA must attempt to set premiums at actuarially fair rates. 

Actuarially fair premiums are equal to expected losses over time so that 

insurance protects farmers against low revenue years, but does not increase 

average net returns to crop production over time. The premiums actually paid 

by farmers are heavily subsidized by the Federal Government. At 70-percent 

coverage, 59 percent of the premium is paid by the Government. So, if the 

full premiums are actuarially fair,14 farmers will realize a gain over time 

equal to the amount of the subsidy, in addition to the revenue-stabilizing 

effect of crop insurance. In our analysis, we assume that crop insurance premiums are actuarially fair so that the full premium is equal to the expected 

13Crops could be insured during the 

first year of production through written 

agreements between RMA and the producer. Written agreement are developed 

(or denied) on a case-by-case basis 

and, therefore, cannot be effectively 

modeled.

14A number of authors have argued that 

premium rates are not actuarially fair 

and that some producers benefit from 

asymmetric information (pay premiums 

that are lower than actuarially premiums while others are charged premiums 

that are higher than actuarially fair 

(e.g., Just, Calvin, and Quiggen; Makki 

and Somwaru)). RMA data show that 

crop insurance losses are persistent in 

the Northern Plains (Glauber), suggesting that our estimates of the crop 

insurance subsidy may be conservative, 

on average.
27

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

indemnity and expected net return to crop insurance purchase is equal to 

expected indemnity less the farmer-paid premium. We refer to this expected 

net return as the expected net indemnity. Our model of crop revenue insurance is detailed in appendix A.

SURE payments provide additional support to farmers who also purchase 

crop insurance (where it is available). Like crop insurance, SURE payments 

are triggered when revenue is low. Unlike crop insurance, however, SURE 

payments depend on whole-farm revenue rather than single crop revenue 

and do not require the payments of an additional premium. Payments can be 

made only to producers who are located in counties where a disaster has been 

declared (for our analysis, we assume that the Secretary of Agriculture determines that there has been a weather-related production loss of 35 percent or 

more in at least one crop15), counties contiguous to disaster counties, or to 

any producer who experiences production 50 percent or more below normal 

levels. SURE payments are based on formulas that account for actual revenue, expected revenue, and the level of crop insurance coverage purchased. 

Our model of SURE payments is also detailed in appendix A.

To estimate crop insurance indemnities and SURE payments, we devise joint 

probability distributions for crop prices and yields for each of our representative farms. Using these distributions, we determine the probability that revenue for specific crops will fall below the crop insurance or SURE revenue 

guarantee (assuming the conditions for a disaster declaration have been met), 

the size of indemnities or payments that would be made when revenue dips 

below the guarantee, and the effect of these indemnities (net of crop insurance premiums) and payments on crop revenue. 

The crop yield distributions are based on county crop yield data collected by 

USDA’s National Agricultural Statistics Service and national and State crop 

price data, with two adjustments. First, grassland is less productive, on average, than cropland (see figure 10). In the seven “high conversion” counties 

we consider, average rangeland productivity is 18 percent lower than average 

cropland productivity.16 Because relatively high-productivity rangeland is 

most likely to be converted to crop production, we assume that crop yields 

will be about 10 percent lower on converted land than on average cropland. 

Second, some farm-level yield variation will be averaged out of county data. 

To reflect farm-level conditions, we inflate crop yield variances using a 

method similar to that of Coble, Dismukes, and Thomas. Means and standard 

deviations, along with transitional yields for corn, soybean, and wheat yields, 

are shown in table 1. A similar procedure is used to estimate the distribution 

of prices and yields for grazing. We assume that grasslands are used as part 

of a cow-calf operation. A joint distribution of cow-calf revenue (the “price”) 

and stocking rates (the “yield”) is developed in conjunction with the crop 

price and yields distributions. Development and use of the price-yield distributions are detailed in appendix A. 

15Thirty-five percent is typical of past 

disaster assistance programs. For each 

year between 2001 and 2007 (inclusive), producers were required to show 

production losses of 35 percent or more 

to be eligible for disaster assistance, 

as noted in footnote 2. See web link, 

footnote 2, for examples.

16Average productivity based on land 

use information from the 2007 National 

Resources Inventory (USDA, Natural 

Resources Conservation Service) and 

the National Commodity Crop Productivity Indicator (Dobos, Sinclair, and 

Hipple).
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Simulation Results: The Sodsaver Effect

Sodsaver would deny crop insurance and could—by extension—SURE 

payments, over a period of 5 years. The 5-year net present value (NPV) of 

expected net crop insurance indemnities (the expected indemnity less the 

producer-paid premium) and SURE payments on converted grassland for 

the New Land scenario (in the absence of Sodsaver) are shown in figure 18. 

Expected payments (the sum of expected net indemnities and SURE payments) are what producers could reasonably expect to receive, on average, 

over a period of years given the likelihood of yields and/or prices low enough 

to trigger these payments. Expected payments are not necessarily what producers receive over any given 5-year period—in a series of particularly high 

(or low) revenue years, producers could realize lower (or higher) payments. 

In calculating net present values, we discount future payments at a rate of 

7 percent.17

In high-conversion counties, the net present value of net crop insurance 

indemnities and SURE payments ranges from $26 per acre (Stutsman 

County) to $58 per acre (Hyde and Sully Counties) (see figure 18). In the 

comparison counties, the payments range from $29 per acre to $36 per acre. 

SURE accounts for 19-26 percent of the estimated payments that would be 

affected by Sodsaver in high-conversion counties and roughly 23 percent of 

Sodsaver-affected payments in each of the comparison counties.

As a percentage of the net present value of expected crop revenue, these payments are modest. In high-conversion counties, they represent between 2 

percent and 5.5 percent of total expected crop revenue and less than 2 percent 

of total revenue in the comparison counties (see figure 18). As a percentage 

of net returns (revenue less crop production cost), net crop-insurance indemnities and SURE payments are somewhat larger, representing 9 percent to 14 

percent of net returns in high-conversion counties and roughly 3.4 percent of 

net return in both of the comparison counties (not shown). 

Stutsman County is somewhat of an outlier among high-conversion counties 

—expected payments in all other high-conversion counties are considerably 

17We tested the sensitivity of the model 

to lower discount rates. The NPV of 

crop insurance indemnities and SURE 

payments increased by 5 percent and 

10 percent when the discount rate was 

reduced to 5 percent and 3 percent, 

respectively. In Hand County, for example, the NPV of expected indemnities would rise from roughly $40 per 

acre to $44 per acre when the discount 

rate was reduced from 7 percent to 3 

percent. Payments as a percentage of 

revenue or net return, however, would 

be largely unchanged and the NPV of 

revenue and net return would also rise.

Table 1

Expected yields, standard deviations, and county transitional yields

Corn Soybeans Wheat

County

Expected 

yield

Yield 

standard 

deviation

County 

transitional 

yield

Expected 

yield

Yield 

standard 

deviation

County transitional yieldExpected 

yield

Yield 

Standard 

Deviation

County 

Transitional yield

Stutsman 94.2 45.2 81 28.8 10.3 22 33.8 14.3 34

Beadle 95.8 46.9 94 30.0 14.7 30 39.8 15.9 38

Edmunds 99.8 44.3 99 27.2 12.7 28 40.7 16.3 39

Faulk 103.5 48.1 100 28.3 12.3 29 41.8 17.7 41

Hand 84.1 44.7 80 26.2 14.9 25 35.0 18.3 34

Hyde 76.6 46.2 68 25.6 15.3 25 32.8 21.3 32

Sully 67.5 38.0 71 31.6 15.9 29 32.4 20.4 31

Turner* 119.7 42.6 121 34.3 11.3 34 na na na

Union* 131.7 49.1 132 38.7 12.7 36 na na na

*Comparison counties.

Source: USDA, Economic Research Service analysis of USDA, National Agricultural Statistics Service and USDA, Risk Management Agency data.
29

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

higher both in absolute dollars and relative to total revenue. A key difference 

between Stutsman and other high-conversion counties is that crop insurance transitional yields are low relative to expected yields (table 1). For 

most counties and crops, our expected yields (which are 10 percent less than 

county average expected yields) are very close to the transitional yields. For 

corn and soybeans in Stutsman County, however, transitional yields are 13 

and 21 percent less than expected yields, respectively. Lower transitional 

yields lead to lower APH yields which, in turn, mean higher premium rates 

and lower guarantees for both crop insurance and SURE, leading to less frequent and smaller net crop insurance indemnities and SURE payments. 

Figure 18

Expected NPV of net crop insurance indemnities and SURE 

payments in the New Land scenario over 5 years

Dollars per acre, NPV

Net crop insurance indemnities

SURE payments

* indicates comparison county. 

NPV=Net present value. SURE = Supplemental Revenue Assistance program.

Source: USDA, Economic Research Service.

Stutsman

Beadle

Edmunds

Faulk

Hand

Hyde

Sully

Turner*

Union*

0

10

20

30

40

50

60

70

0

1

2

3

4

5

6

Percent of total revenue

Net crop insurance indemnities

SURE payments

Stutsman

Beadle

Edmunds

Faulk

Hand

Hyde

Sully

Turner*

Union*
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Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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If the existing RMA New Land rules were eliminated or bypassed (the rules 

requiring reduction in transitional yields in the first few years of crop production), the payments subject to Sodsaver sanction would be significantly 

larger. For the No Restriction scenario in high-conversion counties, expected 

payments range from $40 per acre in Stutsman County (a 50-percent increase 

over the New Land scenario) to $90 per acre in Sully County (55 percent 

higher) (fig. 19). In the comparison counties, per acre payments would be 

$60 in Union County (70 percent higher than under the New Land scenario) 

and $52 in Turner County (82 percent higher). For the No Restriction scenario, payments as a percent of revenue vary from 3 to 8 percent of total revenue in high-conversion counties and are equal to 3 percent of revenue in the 

Figure 19

Expected net return to crop insurance and SURE payments under 

the No Restriction and New Land scenarios

Dollars per acre, NPV

New Land No Restriction

New Land No Restriction

* indicates comparison county. 

NPV=Net present value. SURE = Supplemental Revenue Assistance program. 

Source: USDA, Economic Research Service.

Stutsman

Edmunds

Faulk

Hand

Hyde

Sully

Turner*

Union*

Stutsman

Beadle

Beadle

Edmunds

Faulk

Hand

Hyde

Sully

Turner*

Union*

0

20

100

40

60

80

Percent of total revenue

0

1

2

3

4

5

6

7

8

9
31

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

comparison counties (fig. 19). As a percentage of net return, payments range 

from 7 percent to 20 percent in the high-conversion counties but are under 6 

percent in the comparison counties (not shown). 

Results reported in figures 18 and 19 are for the 5-year period of the 

Sodsaver sanction. For farmers who consider the decision to convert grassland to cropland over a period of more than 5 years, the effect of Sodsaver 

as a percentage of expected revenue would decline. For the New Land scenario, the percentage reduction in the NPV of expected total revenue due to 

Sodsaver would decline from 5.1 percent over 5 years to 2.8 percent over 10 

years, 2.2 percent over 15 years, and 1.8 percent over 20 years. The Sodsaver 

reduction in dollar terms would, of course, remain unchanged.

The crop prices underlying our analysis are based on 2008 conditions, when 

crop prices were high by historical standards. Changing expected prices at 

the beginning of the season will not necessarily change the expected value or 

standard deviation of net crop insurance indemnities and SURE payments. 

Revenue changes that could trigger these payments depend only on intraseason price movements, which are not necessarily related to the expected 

harvest price at the beginning of the season. Lower expected prices could, 

however, increase the expected likelihood and size of marketing loan benefits 

(MLBs). In Hyde County, for example, expected crop insurance and SURE 

payments would remain at or near their initial level (roughly $60 per acre 

for the New Land scenario), even as the expected prices of corn, soybeans, 

and wheat all dropped to 50 percent of initial (2008) levels (fig. 20). When 

expected prices decline to 70 percent of initial levels or lower, expected 

MLBs begin to rise and continue rising to maintain expected revenue at 

about 40 percent of our initial levels (see figure 20). MLBs are not subject 

to Sodsaver sanctions, although they may protect producers from very low 

prices on converted grassland. 

0

50

100

150

200

250

Figure 20

Net crop insurance indemnities, SURE payments, and marketing 

loan benefits with lower crop prices

Dollars per acre

Percent of base price levels

Net crop insurance indemnities

Marketing loan benefits

SURE payments

SURE = Supplemental Revenue Assistance program. 

Source: USDA, Economic Research Service.

100 90 80 70 60 50
32

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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Farmers who convert lower productivity land may face somewhat different 

incentives. Based on research by Skees and Reed, RMA’s rating assumes 

an inverse relationship between expected yields and yield variability. We 

re-estimated key results using yields that are 10 percent lower than yields 

reported in table 1, with higher standard deviations to reflect lower mean 

yields (see appendix A for details). In Hyde County, for example, estimated 

crop insurance and SURE payments under the New Land scenario would be 

about $65 per acre on lower yield land, compared to $58 with our base yields 

(a difference of $7). The NPV of market revenue over 5 years, however, 

drops from $1,085 with base yields, to $978 with lower yields (a difference 

of $107). So, the increase in net crop insurance indemnities and SURE payments offsets less than 10 percent of lower revenue. 

Because crop insurance and SURE protect producers against low revenue, 

they also can reduce the variability of revenue. In high-conversion counties, 

we estimate that the average annual standard deviation of market revenue 

(the variability farmers would face on converted grassland if Sodsaver were 

in force) ranges from 27 percent (Stutsman County) to 48 percent of expected 

market revenue (Sully County) (fig. 21a). Under the New Land scenario, in 

the absence of Sodsaver, average annual standard deviation would decline 

to 24 percent of expected revenue in Stutsman County and 41 percent in 

Sully County. In both of the comparison counties, the standard deviations are 

roughly 23 percent of expected market revenue with Sodsaver in force and 

would be roughly 15 percent under the New Land scenario. 

While annual revenue variations can be large, they tend to average out over 

a period of years. For a farmer considering grassland to cropland conversion, 

the potential variation in the net present value of returns over a period of 5 

years is much smaller than potential year-to-year variation. In Sully County, 

for example, where the average annual standard deviation of revenue is 41 

percent of expected market revenue, the standard deviation of the 5-year 

NPV of expected market revenue is 27 percent (fig. 21b). Farmers may be 

able to adjust for annual variability by shifting large expenses such as the 

purchase of farm machinery or consumer durable goods to high-revenue 

years (Just). Remaining variability is more difficult to avoid. 

A risk premium is the amount a producer would be willing to pay in 

exchange for reduced revenue variability. In our context, the relevant risk 

premium is the value a producer places on risk reduction (reduction in revenue variability) due to crop insurance and SURE during the 5-year Sodsaver 

moratorium. In other words, what would a producer be willing to pay for risk 

reduction that would be lost if the Sodsaver sanction were imposed? Based 

on our risk model (see appendix A), the producers represented by the conversion counties would be willing to relinquish from 0.75 percent of expected 

market revenue (Stutsman County) to nearly 2 percent (Sully County) to 

retain the risk reduction afforded by crop insurance and SURE (table 2). 

Using some alternative values for risk aversion, debt-to-asset ratio, and percentage of grassland converted yields risk premiums as high at 2.5 percent of 

expected market revenue (see table 2). 

Although the effect of Sodsaver on revenue and, particularly, net return to 

crop production, could be substantial during the first 5 years after grassland 

conversion, major land use changes, like grassland to cropland conversion, 
33

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

tend to be relatively unresponsive to changes in revenue or net return. To 

be more specific, we draw land use conversion elasticities from the literature and use them to estimate the change in land use conversion that could 

flow from reducing net return to crop production through implementation of 

Sodsaver. The elasticity of rangeland to cropland conversion with respect to 

crop returns, for example, is the percent change in conversion given a 1-percent change in net returns to crop production. To estimate potential changes 

in conversion, we calculate the change in crop returns as the difference in 

expected net return with and without Sodsaver, plus the producer’s risk premium associated with risk reduction that would be lost under Sodsaver. We 

Figure 21

Standard deviation of crop revenue as a percentage of 

expected market revenue 

Percent of market revenue

a. Average annual standard deviation as a percent of 

average annual expected market revenue

b. Standard deviation of 5-year net present value of revenue

Market revenue

New Land

No Restriction

* indicates comparison county. 

NPV = Net present value. 

Source: USDA, Economic Research Service.

Stutsman

Edmunds

Faulk

Hand

Hyde

Sully

Turner*

Union*

Stutsman

Beadle

Beadle

Edmunds

Faulk

Hand

Hyde

Sully

Turner*

Union*

0

10

50

20

30

40

Percent of market revenue

0

10

50

20

30

40 Market revenue

New Land

No Restriction
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note that this type of analysis is largely illustrative and is intended only to 

provide a general sense of the magnitude of the effect of Sodsaver sanctions 

on grassland to cropland conversion. 

Lubowski, Plantinga, and Stavins report a comprehensive set of land conversion elasticities, generally finding that major land use change is not very 

responsive to changes in revenue. For rangeland conversion with respect to 

cropland returns, they report values of 0.35 or less, although none are significantly different from zero. For pasture conversion, estimated elasticities 

are as high as 0.38 and are all significantly different from zero. Barr et al. 

recently estimated that cropland acreage would increase by 0.029 percent for 

a 1-percent increase in net return to crop production. Although not directly 

comparable to the values reported by Lubowski, Plantinga, and Stavins, they 

do support the finding that major land use is not highly responsive to shortrun economic conditions. Because previously reported values are likely to 

depend on geographic scope and overall economic conditions for the periods 

studied, actual response in high-conversion counties may differ. We consider 

conversion elasticities between 0.1 and 0.5. 

Sodsaver would likely have its greatest effect in Hand, Hyde, and Sully 

counties. If we assume that our best estimate of the grassland to cropland 

conversion elasticity is 0.3, Sodsaver could reduce grassland conversion by 

3.8 percent, 4.9 percent, and 5.4 percent in Hand, Hyde and Sully Counties, 

respectively (fig. 22). The smallest effects would be in Turner and Union 

Counties (the comparison counties), where conversion would be reduced by 

less than 2 percent. A great deal depends on the exact value of the conversion 

elasticity, as the slowing of grass to crop conversions in Sully County, for 

example, could vary from less than 2 percent to more than 9 percent. 

Table 2

Estimated risk premium for risk reduction in selected counties due to crop insurance and SURE

Constant 

relative 

risk aversion

Grassland 

converted 

(percent)

Debtto-asset 

ratio Stutsman Beadle Edmunds Faulk Hand Hyde Sully Turner* Union*

2 15 0.17 $ per acre 10.09 10.96 10.22 10.75 12.22 21.32 20.43 15.81 20.34

Percent of 

revenue 0.84 0.81 0.77 0.80 1.04 1.96 1.98 0.93 1.08

1.5 15 0.17 $ per acre 7.94 8.77 7.71 8.13 9.18 15.82 15.52 12.99 16.39

Percent of 

revenue 0.66 0.65 0.58 0.60 0.78 1.46 1.51 0.76 0.87

2.5 15 0.17 $ per acre 12.22 13.67 12.74 13.47 15.40 26.93 25.45 15.81 23.08

Percent of 

revenue 1.01 1.01 0.96 1.00 1.31 2.48 2.47 0.93 1.22

2 5 0.17 $ per acre 14.05 13.88 10.82 11.09 12.57 19.14 21.51 31.61 32.77

Percent of 

revenue 1.16 1.02 0.82 0.82 1.07 1.76 2.09 1.86 1.73

2 15 0.34 $ per acre 12.22 13.14 12.74 13.22 15.69 27.68 25.34 15.81 21.85

Percent of 

revenue 1.01 0.97 0.96 0.98 1.34 2.55 2.46 0.93 1.16

* comparison county.

SURE = Supplemental Revenue Assistance program.

Source: USDA, Economic Research Service.
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Sodsaver May Not Be Enough To Stop 

Grassland Conversion

Our analysis indicates that the effect of Sodsaver would vary widely across 

counties. Sodsaver would reduce expected crop revenue by 4.2 to 5.4 percent 

and expected net return by 10 to 14 percent in Hand, Hyde, and Sully counties. In other high-conversion counties, Sodsaver would reduce expected revenue by 2.1 to 3.4 percent and net return by 4.9 to 7.7 percent. In comparison 

counties, the expected revenue effect would be 1.7 to 1.9 while the net return 

effect is roughly 3.4 percent in both. Risk effects would be even more varied. 

Hyde and Sully counties stand out with estimated risk premiums approaching 

2 percent of expected revenue. In other high-conversion counties, however, 

risk premiums vary between 0.8 and 1.0 percent while comparison counties 

have risk premiums of 0.9 to 1.1 percent. 

SURE payments, which are intended to replace ad hoc disaster assistance, 

account for 20 to 25 percent of payments subject to Sodsaver. In the Northern 

Plains, where the weather is variable and low yield years are frequent, greater 

certainty about the availability of disaster assistance could encourage farmers 

to include disaster assistance in weighing the costs and benefits of grassland 

conversion. 

Crop insurance and SURE provide producers with protection against relatively low revenue. Regardless of whether prices are historically high or low 

at the beginning of the season, when crop insurance is purchased, an indemnity could be triggered due to an intraseason decline in crop prices and/or 

unexpectedly low yields. The same is true of SURE, although these payments 

would be triggered only when realized farmwide revenue is significantly 

lower than expected revenue at the beginning of the season. If producers 

choose to convert land during periods of historically high prices, crop insurance and SURE could help protect against low revenue due to unexpectedly 

0 0,1 0,2 0,3 0,4 0,5 0,6

Hand

Hyde

Sully

Turner

Counties:

Figure 22

Potential reduction in grassland to cropland conversion 

due to Sodsaver

Percent change in conversion

-6

-5

-4

-9

-8

-7

-10

-3

-2

-1

0

Elasticity of conversion

Source: USDA, Economic Research Service.
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low yields or a large, unexpected intraseason drop in market prices. Crop insurance, however, would not protect against a long-term decline in crop prices. 

In contrast, MLBs are paid only when prices drop to historically low levels. 

In our analysis, which is based on historically high prices, the expected value 

of MLBs starts to rise (from zero) when expected prices are 30 to 40 percent 

lower than our initial prices. So, marketing loan benefits would not necessarily protect producers against intraseason losses in expected revenue, particularly when crop prices are high. In the long term, however, MLBs would 

protect producers against very low prices. Given our price assumptions, it is 

unlikely that MLBs would play a role in grassland to cropland conversion. 

Reductions in the variability of crop revenue due to crop insurance and 

SURE are smaller than the effect of these programs on expected return, as 

measured by risk premiums. So, risk reduction is likely to account for a relatively modest share of producer benefit from these programs. That is particularly true if farmers can reduce the impact of risk on consumption through 

borrowing or shifting large purchases to high-revenue years. 

The results reported here indicate that Sodsaver would do little to slow the 

conversion of grassland to cultivated crop production. The elasticity estimates cited above, however, are for the United States overall rather than 

for the Northern Plains or for portions of LRR F in the Dakotas that are 

represented in our analysis. In the next chapter, “Econometric Analysis of 

Land Use Change,” we use an econometric (statistical) model to estimate the 

response of land use to changes in crop revenue due to changes in market 

conditions or policy. In the last chapter, “Conclusions,” we use these elasticities to estimate the land use response to Sodsaver sanctions.
37

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Econometric Analysis of Land Use Change

The simulation analysis in the previous chapter offers a way to investigate 

the likely effects of specific programs, particularly programs that do not currently exist (e.g., Sodsaver). Econometric (statistical) analysis based on past 

land use can complement simulation analysis by providing fresh insight on 

the overall responsiveness of producer land use decisions to variation in market revenue and cost, program payments, and land quality. While all of these 

factors have been considered in past studies of land use, ongoing changes 

in input and output markets, technology, and Government programs may be 

altering the relationship between farm program payments and land use. 

The core of this chapter is counterfactual, or “what if,” analysis. We estimate an econometric model, then use it to estimate the role of farm programs 

in major land use decisions. What would have happened if crop insurance, 

MLBs, or disaster assistance had been limited or absent altogether in the 

past? We focus on these programs because producers can expand eligible 

acreage by expanding cropland acreage through grassland to cropland conversion. Even small benefits from these programs could encourage some 

producers who were considering grassland conversion to go ahead and make 

the land use change. Other producers who would have returned cropland to 

grass cover could reconsider those plans based on benefits available from 

farm programs.

Previous research on allocation of land to major uses has generally found that 

farm programs affect the balance between cropland and other uses, including 

grassland, although conclusions about the size of this effect vary. Lubowski, 

Plantinga, and Stavins estimated that farm programs increased cropland acreage by about 2 percent from 1982 to 1997. Gardner, Hardie, and Parks (2010) 

argue that in the absence of farm programs, cropland acreage would have 

been 22 percent lower during 1987-1997. A number of studies have focused 

specifically on crop insurance. Lubowski et al. (2006) estimate that the 1994 

crop insurance premium subsidy increase added between 0.5 and 1.1 percent 

to cropland acreage nationwide. Goodwin, Vandeveer and Deal, using data 

from the Corn Belt and Northern Plains, estimate that a 30-percent reduction in crop insurance subsidies would reduce cropland acreage by 0.2 to 1.1 

percent. Young, Vanderveer, and Schnepf use a simulation model to argue 

that acreage in 10 major field crops is about 0.4 percent higher with subsidized crop insurance than without. Wu found that crop insurance encouraged 

producers to grow cultivated crops, particularly soybeans, rather than hay. 

Rashford, Walker, and Bastin (2010) include Government payments in their 

model but find that the effect of these payments on pasture and rangeland 

conversion is very small (the coefficient is not significantly different from 

zero), although the model does suggest that Government payments could 

have a modest role in keeping land in crop production. 

These studies use data for periods ending in 1997 or earlier, predating 

changes in farm commodity policy and an increase in crop insurance subsidies. (We are not aware of any studies using more recent data on major land 

use change.) Farm program changes enacted in the 1996 Federal Agriculture 

Improvement and Reform Act (1996 Farm Act) allow farmers to expand 

cropland acreage without risking loss of farm program payments (except in 
38

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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cases of wetland or highly erodible land). The 1996 Farm Act also mandated 

marketing loans for grains and oilseeds. Increases in crop insurance subsidies 

under the Agricultural Risk Protection Act of 2000 expanded crop insurance 

participation and may be encouraging farmers to expand crop acreage at 

the expense of grassland (USGAO, 2007; Morgan, 2008). These changes in 

policy may have altered—and perhaps increased—the role of farm programs 

in major land use decisions.

Crop insurance, disaster assistance, and MLBs can increase the average revenue to crop production over time and reduce variability in crop revenue, 

particularly downside variability, by making payments in low-price or lowrevenue years. Only a handful of previous econometric studies have considered variability in the context of major land allocation: yield variance was 

included as an explanatory variable by Goodwin, Vandeveer, and Deal and 

by Wu. While Wu and Adams also considered the effect of revenue variance 

and revenue-based crop insurance on the allocation of cropland among crops, 

their work did not address major land use change. To extend previous work, 

our analysis incorporates the effect of expected market revenue, the variance 

of market revenue, and the effect of farm programs on both. Considering 

variability may yield new insight into how farm program payments affect 

decisions about land use. 

For this analysis, we broaden our geographic scope to include 77 North 

Dakota and South Dakota counties (fig. 23). We focus on portions of LRR 

F (Transitional Region) located in South Dakota and the Southern part of 

North Dakota and include nearby counties located in LRR G (Range Region) 

and LRR M (Western Corn Belt; see figure 7a). More specifically, we focus 

on counties in Major Land Resource Area (MLRA) 53B (Southern portion), 

53C, 54, 55B (Southern portion), and 55C. The Hand-Hyde area, where 

Stephens et al. observed an average annual grassland conversion rate of 0.6 

percent (see the chapter, “Grassland and Grassland-Cropland Conversion, 

1997-2007,”), is contained in MLRA 53C and 55C. More broadly, producers 

in LRR F shifted large acreages of cropland from wheat to corn and soybeans 

during 1997-2007, suggesting major changes in overall production patterns, 

which also could be part of broader changes in land use. The resulting data 

cover a wide range of climactic and soil conditions, including Corn Belt conditions along the Eastern edge and rangeland and ranching dominated landscapes in the Far West. For this area, we have data on land use and land use 

drivers for each year from 1998 to 2007, a period that encompasses a wide 

range of economic conditions, including large variations in commodity prices 

and farm program payments. 

We estimate land use response to changes in expected revenue and revenue 

variance (including the effect of farm programs on expected revenue and revenue variance), using a probabilistic choice model. This class of statistical or 

econometric model is designed to estimate the probability that an individual 

decisionmaker will select a specific option from a set of discrete options, 

given the economic return to each option.

We assume that farmers/landowners choose from the four land use categories 

defined in the second chapter: cultivated cropland, hay and pasture, range, 

and the CRP. Land under an ongoing CRP contract is excluded from the 

dataset because continuing CRP enrollment does not represent a land use 
39

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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choice. (The dataset does include CRP-eligible land that has not yet been 

enrolled in CRP and CRP land where contracts are expiring.) Land use is represented using site-specific data from the NRI (USDA-NRCS, 2009). Land 

use choices are observed discretely rather than as shares. From the NRI, we 

define a dataset of 10,655 choice events for 1,171 NRI points over the years 

1998-2007 (see appendix B for details).

To explain land use, our econometric model includes variables on expected 

revenue, the variance of revenue, nonland production costs, and land quality including a productivity index and binary indicators for irrigation, highly 

erodible soil, and hydric (potentially wet) soil. The crop revenue variable 

includes the effect of benefits from crop insurance, marketing loans, and 

disaster assistance programs because these programs were in place when land 

use decisions were made.18 Land quality variables provide site-specific information on the agronomic potential, whereas revenue and cost variables can 

be defined only at a county level. Our dataset does not include information on 

technology adoption and we do not explicitly model crop choice. The effect 

of technology is accounted for, in part, through its effect on revenue (through 

yields) and costs. In estimating revenue and costs, moreover, crop mix (the 

proportion of cropland in corn, soybeans, and wheat) is allowed to evolve 

county by county as a moving average of crop acreages in the most recent 3 

years. Data and methods are detailed in appendix B.

18Given that previous disaster payments 

were based on ad hoc programs that 

varied from year to year, the effect of 

these payments on revenue variability 

could not be systematically represented. 

77 counties in study area

Figure 23

Study area for econometric estimation, North Dakota and South Dakota

Source: USDA, Economic Research Service.

North Dakota

South Dakota
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Finally, for the counterfactual analysis, we estimate the change in the probability of each land use for each observation when revenue (both mean and 

variance) is changed to reflect the loss of farm program benefits. Changes in 

estimated probability are translated to estimated changes in acreage by multiplying the change in probability at each NRI point included in our sample 

by the acreage weight (expansion factor) for the point. Because we are using 

a representative subsample of the data, we adjust the weights so that they add 

up to the full acreage of the study area. 

Together, Crop Programs Add 8.5 Percent 

to Average Annual Crop Revenue

Table 3 shows the effect of crop insurance, marketing loans, and disaster assistance on expected crop revenue, averaged over space and time. 

Marketing loans increased expected return to crop production by 3.3 percent, on average, while crop insurance subsidies and disaster assistance 

added roughly 1.7 and 3.6 percent, respectively, to expected crop revenue. 

Altogether, these programs added 8.5 percent to average annual crop 

revenue. 

In terms of variance, crop insurance has a huge effect, reducing the crop 

revenue variance by 36 percent. MLBs, however, have comparatively little 

effect on revenue variance (and even increase it slightly). MLBs differ from 

crop insurance in the sense that they are triggered by low prices rather than 

by low yields or revenue. In the Northern Plains, crop revenue variance is 

dominated by yield variance so that yield-based and revenue-based crop 

insurance are both strongly countercyclical to market revenue. Low-price 

years, however, are not necessarily low-revenue years. 

We did not attempt to estimate the effect of disaster assistance on crop revenue variance. Before 2008, disaster payments were ad hoc. Congress routinely enacted disaster assistance measures primarily in response to yieldreducing weather events. Actual disaster payments, however, were often 

received by farmers 1-2 years after losses were incurred. Disaster assistance 

payment data (drawn from the Consolidated Federal Funds (CFF) report 

from the U.S. Census Bureau) indicate only when the payments were made 

(not when losses were incurred), making it impossible to accurately estimate 

Table 3

Change in expected value and variability of revenue as a result of farm programs 

Expected revenue Variance of revenue

Dollars per 

acre

Change from 

all revenue 

(percent)

Dollars per 

acre

Change from 

all revenue 

(percent)

All revenue 166.14 0.00 2,282.8 0.0

Exclude: Crop insurance 163.41 -1.65 3,110.0 36.2

Marketing loan 

benefits 160.73 -3.26 2,214.3 -3.0

Disaster assistance 160.23 -3.56 3,000.2 **

All three programs 152.09 -8.48 3,000.2 31.4

**Variance not estimated; see appendix B for details.

Source: USDA, Economic Research Service.
41

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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the countercyclical effect of disaster assistance. Nonetheless, ad hoc disaster payments have been a major feature of U.S. agricultural policy in recent 

decades. Following Goodwin and Rejesus, we use a 5-year moving average of disaster assistance payments to specify the expected value of disaster 

assistance. 

Table 4 shows average estimated elasticities19 for each land use with respect 

to revenue, cost, and land productivity variables. The elasticities measure 

how farmers’ land allocation decisions respond to a 1-percent change in one 

of these variables. For example, a 1-percent increase in expected crop revenue is estimated to increase the probability of using a field as cropland by 

0.32 percent, on average, while reducing the probability of hay and pasture 

by 0.51 percent, range by 0.18 percent, and CRP by 0.15 percent. Given 

that there are roughly 50 million acres of agricultural land in the study area, 

a 0.32-percent increase in the average predicted probability of cropland 

(0.46) could increase cropland acreage roughly 70,000 acres. The effect of a 

change in variance is smaller; a 1-percent increase in crop revenue variance 

would decrease crop acreage by 0.03 percent, about one-tenth the size of the 

expected revenue effect. In reality, however, the variance response is important given the large effect of crop insurance on crop revenue variance.

Table 5 gives the average acreage for each major land use category in our 

study region and the average estimated change in land use probabilities and 

acreages from eliminating crop insurance, MLBs, and disaster assistance 

payments. The model estimates are changes in equilibrium acreage. Farmers 

may adjust to a new equilibrium by slowing grassland to cropland conversion, increasing cropland to grassland conversion, or both. Given the amount 

of land that moves between grassland and cropland, either adjustment is plausible (see figures 1-5). The land use effect of farm programs can vary with 

19Elasticities are estimated for each 

choice event then averaged to arrive at 

the elasticities reported in table 4.

Table 4

Estimated average land use elasticities for revenue, cost, and land 

productivity variables 

Cropland

Hay and 

pasture Range CRP

Expected revenue

Cultivated crops 0.322 -0.508 -0.175 -0.153

Hay and pasture -0.150 0.443 -0.054 -0.015

Rangeland -0.038 -0.052 0.184 -0.136

CRP -0.009 -0.006 -0.040 0.060

Variance of revenue

Cultivated crops -0.026 0.064 0.011 0.020

Hay and pasture 0.006 -0.055 0.010 0.022

Rangeland 0.001 0.007 -0.021 0.075

Production costs

Crop cost -0.192 0.344 0.081 0.128

Hay cost 0.069 -0.273 0.031 0.018

Grazing cost 0.024 0.049 -0.159 0.167

Land productivity 0.225 -0.341 -0.274 -0.037

CRP = Conservation Reserve Program.

Sowurce: USDA, Economic Research Service.
42

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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economic and policy conditions. For example we estimate that marketing 

loan benefits prompted farmers to retain land in crop production during the 

early years of our time series but had very little effect in later years. The figures reported in Table 5 are the average of estimated effects for 1998-2007. 

Without crop insurance, the model indicates that producers would have 

devoted, on average, roughly 235,000 fewer acres to crop production over 

1998-2007, an average increase in cropland acreage of about 1 percent. Our 

estimate is comparable to previous research on the effect of crop insurance, 

which showed cropland effects of 0.4 to 1.1 percent. 

We estimate that roughly 137,000 (58 percent) of crop insurance-induced 

cropland acres would have been in hay and pasture in the absence of these 

programs, while 51,000 acres would have been in range. We note that the 

model underpredicts rangeland and overpredicts CRP (see the Share and 

Predicted Probability lines at the top of table 5) and that this could lead to 

underprediction of rangeland changes and overprediction of CRP change. 

Nonetheless, even if the rangeland acreage effect increased by 10 or 20 percent, the rangeland acreage affect would still be smaller than the hay and 

pasture acreage effect. 

We estimate that marketing loan benefits increased cropland by 161,000 

acres, while disaster assistance resulted in 292,000 additional crop acres. 

Again, rangeland acreage accounts for a relatively small share of the change, 

roughly 29 percent in both cases. The overall increase in cropland acreage 

is estimated to be 686,000 acres, or about 2.9 percent of average cropland 

acreage. The model also predicts that, in the absence of these programs, hay 

and pasture acreage would have been 403,000 acres larger (5 percent of 5 

million acres), range acreage would have 181,000 acres larger (1.1 percent 

Table 5

Estimated land use effect of selected farm programs

Unit Cropland Hay and pasture Range CRP

Acreage Average acres (000) 23,454 8,007 15,804 599

Share 0.490 0.167 0.330 0.013

Predicted probability 0.469 0.162 0.284 0.085

No crop insurance Probability change -0.0050 0.0029 0.0011 0.0010

Acreage change (000) -235 137 51 47

Percent of acres -1.0 1.7 0.3 7.9

No marketing loan benefits Probability change -0.0034 0.0018 0.0010 0.0006

Acreage change (000) -161 86 46 29

Percent of acres -0.7 1.1 0.3 4.8

No disaster payments Probability change -0.0063 0.0039 0.0018 0.0006

Acreage change (000) -292 180 85 27

Percent of acres -1.2 2.2 0.5 4.5

No program payments Probability change -0.0147 0.0087 0.0039 0.0022

Acreage change (000) -686 403 181 102

Percent of acres -2.9 5.0 1.1 17.0

Source: USDA, Economic Research Service.
43

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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of 16 million acres), and CRP enrollment would have been 102,000 acres 

larger (17 percent of 600,000 acres). Our estimate is comparable to that of 

Lubowski, Plantinga, and Stavins, who found that farm programs accounted 

for about 2 percent of cropland. Our estimate is much lower than the estimate 

of Gardner, Hardie and Parks, who found that farm programs accounted for 

more than 20 percent of cropland acreage.

Acreage estimates are net changes in overall land use. As noted in the chapter 

“Grassland and Grassland-Cropland Conversion, 1997-2007,” land moves in 

both directions along the margin between cropland and grassland, particularly between cropland and hay and pasture. So, an adjustment that results in 

increased crop acreage could be achieved by increasing the rate of grassland 

to cropland conversion or retaining land in crop production that might have 

otherwise been converted back to grass. A process that encompasses movements both into and out of grassland is likely along the margin between crops 

and hay and pasture. As shown in the aforementioned chapter, for the entire 

Northern Plains, large acreages moved between crops and hay and pasture 

even though the net movement of hay and pasture to cropland was small. 

Along the cropland-rangeland margin, however, land moved mostly from 

range to crops. So, it may be somewhat more likely that shifts in the allocation of land between crops and range would involve slowing rangeland to 

cropland conversion.

Finally, program effects vary widely across time and space. Temporal variation is largely due to changing market conditions. Between 1998 and 2007, 

MLBs were particularly volatile, varying from zero in high price years, to 

as much as 16 percent of crop revenue in years when crop prices were low, 

particularly soybean prices (fig. 24; see chapter, “Land Use and Land Use 

Change: Conceptual Issues,” for more discussion). In contrast, expected crop 

insurance indemnities (net of farmer-paid premiums) rose steadily over time 

without large year-to-year swings (see figure 24). Unlike MLBs, crop insurExpected disaster 

assistance

Expected marketing 

loan benefits

Expected (net) indemnities

Figure 24

Expected disaster assistance, marketing loan benefits, 

and net insurance indemnities, over time

Percent expected revenue

6

8

2

4

0

10

12

Source: USDA, Economic Research Service.

1998 99 2000 01 02 03 04 05 06 07
44

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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ance protects farmers against unexpectedly low yields or unexpectedly low 

revenue due to either low yields or an unexpectedly large intraseason decline 

in crop prices (see chapter “Simulation Analysis of Sodsaver: Can It Save 

Native Grassland?” for more discussion). Disaster payments provide a similar type of benefit.

The estimated effect of farm programs on land use varies widely across our 

study area (fig. 25). The crop insurance effect is spread over the eastern half 

of the study region. Because crop insurance is tailored to local conditions, 

the fact that land use effects are widespread is not surprising. For MLBs, the 

largest effects are in the East, where corn and soybeans are widely grown. 

The change is lowest in the West, where wheat is the dominant crop on (nonirrigated) cropland and yields are relatively low. As already noted, soybean 

producers enjoyed very large MLBs in the early years of the time series and 

Crop insurance

Marketing loan benefits

Disaster assistance

= 5 percent

Figure 25

Estimated acreage effect of marketing loan benefits, crop insurance, 

and disaster assistance as a percentage of cultivated cropland 

acreage by major land resource area in 77-county study area

Source: USDA, Economic Research Service analysis of USDA, Natural Resources 

Conservation Service data.

North Dakota

South Dakota
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a large share of the MLB effect can be attributed to those soybean payments. 

In percentage terms, disaster payments have a relatively large effect in western areas. Disaster payments require countywide disaster declarations, which 

are more common in areas where rainfall is marginal for crop production, 

as is the case in the western portions of our study region. While percentage 

changes in cropland acreage are high is the West, overall cropland acreage is 

small so the total number of acres affected is also comparatively modest. 

Farm Programs Have Modest but Measureable Impact 

on Amount of Cropland

Farm programs have had a measureable impact on the amount of cropland 

in the portion of the Northern Plains we have studied. We estimate that, on 

average, for the years 1998-2007, cropland in our study region was 2.9 percent larger than it would have been without MLBs, crop insurance, and disaster payments. The largest single effect was due to disaster assistance, which 

increased crop production by roughly 292,000 acres (1.2 percent). MLBs 

are estimated to have increased cropland by 161,000 acres (0.7 percent), and 

crop insurance increased crop acres by 235,000 (1.0 percent). In the absence 

of these programs, we estimate that 181,000 acres would have remained in 

or been returned to rangeland, roughly 1.1 percent of rangeland acreage. 

Benefits from marketing loans were more likely to boost crop production in 

the eastern part of the study area, while disaster payments were more likely 

to affect land use in the western counties in the study area. 

Going forward, one could expect that the effect of marketing loan benefits 

will likely be zero. Given increased worldwide demand for agricultural 

commodities due to the boom in ethanol-related demand and other factors, 

crop prices are not expected to drop below commodity loan rates at any point 

in the foreseeable future. So long as that is true, marketing loan gains will 

have little impact on agricultural land use (barring an increase in commodity loan rates). Crop insurance and disaster assistance (the SURE program) 

will continue to protect farmers from unexpectedly low yields or low revenue 

relative to price and yield expectations at planting time. Even when plantingtime prices are at historically high levels, producers can benefit from crop 

insurance and disaster payments. 
46

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

Conclusions

Grassland to cropland conversion was largely focused in the Northern 

Plains between 1997 and 2007. Compared to other regions, producers in 

the Northern Plains were far more likely to convert rangeland to cropland 

between 1997 and 2007 and more likely to retain land in crop production 

rather than converting it to hay or pasture. Over the same period, however, 

producers in the Northern Plains were more likely than others to enroll new 

land in the CRP, nearly all of it converted to some type of grass cover. 

Different types of grassland have different interactions with cropland. The 

margin between cropland and hay and pasture is characterized by constant 

movement of land into and out of crop production as producers rotate some 

of their land into and out of cultivated crops. Net adjustments in the allocation of land between these uses could be accomplished by changing the rate 

of conversion into or out of cultivated crops. The margin between rangeland 

and cropland is different in the sense that land moved mostly from range to 

crops between 1997 and 2007, although some land did move from crops to 

range. Northern Plains’ producers enrolled more than 3.5 million acres of 

cultivated cropland in the CRP, while only half of the land exiting the CRP 

was returned to crop production. CRP land that remained in grass was classified, in roughly equal shares, as hay, pasture, or range.

Our econometric model predicts the net movement of land between uses, 

which is the sum of gross movements into and out of specific land uses. 

Based on our econometric model, we estimate that the combined effect of 

crop insurance, MLBs, and disaster assistance increased cropland acreage by 

an average of 2.9 percent (686,000 acres in our 77-county study area) during the study period (1998-2007). We also estimate that 26 percent of these 

acres were drawn from rangeland (181,000 acres; 1.1 percent of rangeland) 

where native grasslands are most likely to found. Most of the difference is 

drawn from hay or pasture (403,000 acres; 5 percent of hay and pasture) with 

the balance coming from land that might have otherwise been enrolled in the 

CRP. Because land routinely moves in both directions at the margin between 

cropland and grassland uses, a movement of cropland to grassland could 

be accomplished by reducing the rate of grassland to cropland conversion, 

increasing the rate of cropland to grassland conversion, or both. 

Actual gross rangeland to cropland conversion between 1997 and 2007 

was roughly 1.1 percent, while net conversion was roughly 0.8 percent. In 

comparison, the model prediction of a 1.1-percent net shift from cropland 

to rangeland due to the withdrawal of all three farm programs is relatively 

large.20 Crop insurance accounts for about one-third of the effect, increasing cropland acreage by 1 percent and reducing rangeland acreage by 0.3 

percent, while disaster payments and MLBs reduce rangeland acreage by 0.5 

percent and 0.3 percent, respectively. The extent to which native grasslands 

would have been preserved depends on the extent to which adjustment would 

have been accomplished by slowing rangeland to cropland conversion and 

the proportion of these rangelands that are native rangeland. The size of the 

adjustment and mix of changes in grass-to-crop and crop-to-grass movements 

may vary spatially and over time. 

20These numbers are not exactly 

comparable, as our estimated acreage 

change is an equilibrium effect, not an 

estimated change in the rate of grassland to cropland conversion. However, 

reducing grassland to cropland conversion is one way producers could adjust 

to a new equilibrium.
47

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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Since 2007, higher commodity prices may have reduced the role of farm 

programs while increasing incentives for grassland to cropland conversion. 

Given sharply increased corn demand (for ethanol production and other 

uses) and the secondary or indirect effect on the price of other commodity crops, prevailing prices for corn, wheat, and soybeans in 2008-2010 are 

significantly higher than pre-2007 levels. (It is unlikely that prices will be 

low enough to trigger MLBs at any time in the near future.) Crop insurance 

and SURE will continue to protect producers against intraseason drops in 

crop revenue, regardless of whether prices are historically high or low at the 

beginning of the season. So, even when crop prices are quite high, crop insurance and SURE could increase expected revenue, reduce revenue risk, and 

increase net movement of grassland to cropland at the margin, although the 

role of these programs could be diminished in the face of very strong commodity markets.

Finally, a key question is whether Sodsaver would significantly slow grassland to cropland conversion. Based on the simulation model developed in the 

chapter “Simulation Analysis of Sodsaver: Can It Save Native Grasslands?” 

and rangeland to cropland conversion elasticities drawn from the literature, 

we estimated that Sodsaver could slow the grassland to cropland conversion 

by up to 9 percent (in Sully County, SD). In the Hyde-Hand area studied by 

Stephens et al. (which includes part of Sully County), average annual grassland conversion was observed to be 0.6 percent per year between 1985 and 

2003. A 9-percent reduction would reduce the average annual rate of conversion from 0.60 percent to roughly 0.55 percent. 

Using elasticities from table 4, we re-estimated the effect of crop insurance on rangeland acreage in five counties in or near the Hyde-Hand area 

(Edmunds, Faulk, Hand, Hyde, and Sully). In the absence of crop insurance, 

rangeland acreages would have been 0.7 percent to 0.9 percent higher in 

these counties. As already noted, these are estimates of net change and are 

not directly comparable to estimates based on the conversion elasticities 

drawn from the literature. Nonetheless, the effects based on elasticities from 

our econometric model appear to be considerably larger. A critical difference 

is the scope of the studies from which elasticities are drawn. Our elasticity 

estimates are based on data from the Northern Plains and reflect the fact that 

the Northern Plains are a “hot spot” of grassland to cropland conversion, 

while the elasticities from Lubowski, Plantinga, and Stavins are average elasticities based on a national estimation. 

If the average annual rate of gross rangeland to cropland conversion in the 

Hyde-Hand area continues to be 0.6 percent—6 percent over 10 years—our 

results suggest that Sodsaver, which would deny crop insurance coverage on converted native grassland for 5 years, may result in only a modest 

course correction. Over a period of years, ongoing conversion of grassland 

to cropland indicates an ongoing increase in the relative profitability of crop 

production when compared to grazing. The rate of conversion is unlikely to 

be constant over time as farmers respond to new technologies (e.g., corn seed 

with greater drought tolerance or improved resistance to natural pests) as 

they become available and changes in market conditions as they are realized. 

Given our estimate of the crop insurance effect on rangeland (rangeland was 

reduced by 0.7-0.9 percent), the loss of crop insurance may cause producers to return some cropland to grass, discourage producers from converting 
48

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

native grassland to crop production, or both. In the context of strong markets 

and technical innovation, however, the temporary loss of crop insurance 

could slow native grassland conversion but is unlikely to stop it. 

On a broader scale, the rate of rangeland to cropland conversion is much 

lower, averaging less than 0.1 percent per year in the portion of the Northern 

Plains that intersects with LRRs F, G, and M. Our analysis suggests that 

much of the rangeland in LRR G (Range Region) has very low agricultural 

productivity and very little potential as cropland. Roughly 80 percent of agricultural land in LRR G is range while only 11 percent is cultivated for crop 

production. Nonetheless, our analysis indicates that disaster payments are 

encouraging producers to retain land in crop production that might otherwise 

be returned to grazing use. In the four MLRAs located with LRR G, disaster 

assistance is estimated to have increased cropland acreage by as much as 10 

percent. Of course, a comparatively small amount of rangeland remains in 

LRR M, where crop production has long been the dominant land use.

Our analysis also shows that “new land” provisions governing crop insurance purchase on land that has not previously been in crop production would 

affect crop revenue on converted grassland. Using our model for Sully 

County, South Dakota, for example, we estimate that crop insurance indemnities and SURE payments would increase expected crop revenue by 5.4 percent with the new land rules in place and by 8 percent in the absence of these 

rules—an effect equal to 2.6 percent of expected revenue. The effect in the 

other counties considered in our analysis range from 1 percent to 2.5 percent 

of expected revenue. As already noted, relatively low T yields in Stutsman 

County appear to reduce the effect of crop insurance on crop revenue, at least 

in the first 5 years of crop production. 

Ultimately, this analysis suggests that preventing native grasslands from 

being converted to crop production may require a broader approach than 

is implicit in the Sodsaver provision of the 2008 Farm Act. Other environmental compliance provisions, such as the Swampbuster provision of the 

1985 Farm Act, include stronger sanctions to discourage the conversion of 

environmentally sensitive land to crop production. The Swampbuster provision can be used to deny all farm program payments on an entire farm for 

producers who drain wetlands on any part of their farm for crop production. 

Wetlands are similar to native grassland in the sense that they are ecologically important and, once they have been destroyed, are difficult to recreate. 

These stronger provisions would likely have greater effects on grassland conversion and might prove to be more effective at deterring the loss of native 

grasslands than we estimate for the current Sodsaver provision.
49

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

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Appendix A

Policy Models and Representative Farms 

for “Simulation Analysis of Sodsaver: 

Can It Save Native Grasslands?” 

Crop Insurance Model: Net return to crop insurance participation is equal to 

the crop insurance indemnity less the producer-paid premium:

it it I − ρ

where it I is the per acre indemnity for crop i at time t, and ρit is the peracre producer-paid premium. As noted in the text, we assume that producers 

purchase revenue assurance (RA) at 70 percent coverage. Under the base 

price option, the per-acre indemnity is:

max(( ), 0) b

it it it it it I p y p y = θ −

where θ is the coverage level, b

it p is the RA base (expected) price, it y is the 

producer’s actual production history (APH) yield, it p is the realized price, 

and it y is the actual yield. By law, USDA must attempt to devise actuarially 

fair premiums.21 Actuarially fair premiums are equal to the expected indemnity, but farmer-paid premiums are subsidized by the Federal Government:

(1 ( ) ) ρ γ it = − E Iit

where γ is the premium subsidy (59 percent for 70 percent coverage). 

The APH yield is a key parameter in the calculation of premiums and indemnities. Typically, the APH is based on an average of 4-10 previous crop 

yields. For land that has not been previously cropped, including converted 

grassland, special rules apply. Assuming that crop insurance is purchased in 

the second year of crop production, the APH yield for crop i would evolve 

as: 

, 1 , 1 , 2 , 3

, 2 , 1 , 2 , 1

, 3 , 1 , 1 , 2

1

1

,

0

((1 0.20)( ) ) / 4

((1 0.20)( ) ) / 4

((1 0.20) ) / 4

4, 5.

NL

it

NL

i t i t i t i t it

NL

i t i t i t it i t

NL

i t i t it i t i t

t

NL

i t is

s t

y

y y y y y

y y y y y

y y y y y

y y

τ

τ τ τ

+ − − −

+ − − +

+ − + +

+ −

−

+

=

=

= − + + +

= − + + +

= − + + +

= ∑ =

% % %

% %

%

where NL

it y is the APH yield for crop i at time t, i t, 1 y − % is the transitional yield 

for time t-1, and so on. In the No Restriction scenario, where the new land 

rules are relaxed, the APH would evolve as: 

21Some researchers have argued that 

premiums are not actuarially fair (Just, 

Calvin, and Quiggen; Makki and 

Somwaru). Data from USDA’s Risk 

Management Agency show that crop 

insurance losses are persistent in the 

Northern Plains (Glauber), suggesting 

that premiums are low and that our 

estimates of crop insurance premiums 

may be, on average, higher than actual 

premiums.
55

Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

Economic Research Service / USDA

, 1 , 2 , 3 , 4

, 1 , 1 , 2 , 3

, 2 , 1 , 2 , 1

, 3 , 1 , 1 , 2

1

1

,

( ) / 4

( ) / 4

( ) / 4

( ) / 4

4, 5.

NR

it i t i t i t i t

NR

i t i t i t i t it

NR

i t i t i t it i t

NR

i t i t it i t i t

t

NR

i t is

s t

y y y y y

y y y y y

y y y y y

y y y y y

y y

τ

τ τ τ

− − − −

+ − − −

+ − − +

+ − + +

+ −

−

+

=

= + + +

= + + +

= + + +

= + + +

= ∑ =

% % % %

% % %

% %

%

Supplemental Revenue Assistance (SURE) Payments Model: Payments 

can be made only to producers who are located in counties where a disaster 

has been declared (for our analysis, we assume that the U.S. Secretary of 

Agriculture determines that there has been a weather-related production loss 

of 35 percent or more in at least one crop), counties contiguous to disaster 

counties, or to any producer who experiences production 50 percent or more 

below normal levels.22

Once a disaster is declared, the SURE payment is made when whole-farm 

revenue drops below a revenue guarantee:

max(0.60( ), 0) Dt = G R t t −

where Gt is the SURE guarantee and Rt is total farm revenue. The SURE 

guarantee depends on the level of crop insurance coverage selected by the 

producer, expected prices, and the producer’s APH yield, but is limited to no 

more than 90 percent of expected revenue:

min 1.2 ( ), 0.90 max( , ) b b ccp

t it it it ti it it i

i i

G a p y a p y y θ

  =     ∑ ∑

where ait is planted acreage of crop i at time t (or acreage where planting 

was prevented) and ccp

i y is the producer’s counter-cyclical payment program 

yield. Total farm revenue (for crops) includes market revenue, commodity 

program payments, and crop insurance indemnities:

( ) 0.15 t it it it it it t t

i

R a p y I L DP CCP = + + + + ∑

where Lit is the per-acre marketing loan benefit, DPt is the producer’s total 

(farm-level) direct payment, and CCPt is the total countercyclical payment.23

When the market price of a covered commodity (e.g., corn, wheat, soybeans) 

drops below a fixed “loan rate,” the marketing loan benefit is the difference 

between the loan rate and the market price: 

max(( ) , 0) L p p y it = −i it it

where it p is the loan rate. The change in the SURE payment triggered by 

bringing new land into crop production is: 

max(0.60( ), 0) ∆Dt = ∆ ∆ G R t t − .

22Smith and Watts (2010) note that 

offering payments to individuals who 

experience low revenue may result in 

considerable moral hazard for individual farmers who anticipate losses 

large enough to trigger crop insurance 

indemnities but not large enough to 

trigger SURE payments. Once losses 

are large enough to trigger crop insurance indemnities, additional losses are 

fully offset by indemnities (assuming 

the market price is at or below the insurance price, which varies depending 

on the insurance product purchased). 

Producers who destroy enough of their 

crop (through lax practices or outright 

fraud) to qualify for SURE payments 

would see an increase in overall revenue due to the addition of the SURE 

payment. While we do not attempt to 

model this behavior, we recognize that 

producers may engage in this type of 

behavior.

23Farm revenue, as specified in the text, 

assumes the farmer will stay with traditional commodity programs. Farmers 

who choose the Average Crop Revenue 

Election program (ACRE) will lose 

their Counter-Cyclical Program (CCP) 

payment while Direct Payments (DPs) 

will be reduced by 20 percent and the 

loan rate by 30 percent.
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If cropping patterns on new land reflect those of the overall farm, the per-acre 

change in the guarantee will be:

1.2 ( ) b

t it t it it

i

∆G a A p y = ∑ θ or 0.90 ( ) max( , ) b ccp

t it t it it it

i

∆G a A p y y = ∑

where t it

i

A a =∑ is total crop acreage at time t. Under the new land rules, 

the per-acre change in farm revenue will be:

( )( ) NL

t it t it it it it

i

∆R a A p y I L =∑ + +

where NL

it I is the crop insurance indemnity under the new land rules (the 

change in revenue for the No Restriction scenario is obtained by replacing NL

it I with NRit I ). We do not include direct and countercyclical payments 

because they do not apply to new land. For the sake of brevity, we assume 

that planting decisions on existing cropland will not be affected by land use 

conversion. 

Risk Model. When facing the new land rules, the producer’s risk premium—

his willingness to pay for the risk reduction due to crop insurance and 

SURE—is defined as: 

(A1) ( ( ) ( ) ( )) NL NL NL NL E u wt t t + − ≡ + + π ψ E u wt t t π φ , 

where u is utility, wt is wealth at the beginning of period t, NL ψt is the risk 

premium, NL πt is the producer’s (stochastic) net return during period t, and 

NL φt is a term that eliminates the risk-reducing effect of crop insurance and 

SURE on converted grassland while holding expected wealth constant. Under 

the new land rules, farm profit is represented by:

( ( )( ))

( ( )( ))

( )( ),

NL

t i i i i it i it t

i

NL NL

conv i i i i it i it t

i

g conv g g

A a A p y L I C D CCP DP

A a A p y L I C D

A A R C

π ρ

ρ

= + + − − + + + +

+ + − − +∆ +

− −

∑

∑

where A is crop acreage before conversion; i p is market price for crop i; 

Yi is the yield; Li is the marketing loan benefit; Iit is the crop insurance indemnity, Ci is per-acre cost; ρit and is the per-acre insurance premium; Di is the 

SURE payment; CCP is the countercyclical payment; DP is the direct payment; Aconv is acreage converted from grass to crops; NL

it I is the indemnity on 

converted land; NL ∆Dt is the change in SURE payment due to conversion; 

Ag is grazing acreage before conversion; Rg is annual per-acre grazing land 

revenue; and Cg is annual per-acre grazing land (beef cow-calf) cost. Finally, 

NL φt is a term that eliminates the countercyclical effect of crop insurance and 

SURE payments on acres converted from grassland to cropland: 

( )( ( ) ) ( ) . NL NL NL NL NL

t conv i it it t t

i

φ A a A E I I E D D ∆ ∆

  =  − + −    ∑
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This term effectively replaces the crop insurance and SURE terms in NL πt

with their expected values, so that NL NL π φ t t + is a mean preserving spread of 

NL πt .

The left hand side (LHS) of (A1) is expected utility with variance reduction due to crop insurance and SURE, less the risk premium. The right 

hand side (RHS) of (A1) is expected utility without this variance reduction. To simulate risk premiums, we specify utility as a power function: 

1 ( ) ( ) (1 ) t t u v v η η − = − , where η is the coefficient of relative risk aversion 

and vt is end of period wealth. This function has been used previously in 

similar work (see Vedenov and Powers; Gray et al.). 

Representative Farms: Price-Yield Distributions 

and Simulation Methods

We develop a series of representative farms based on county data. 

Underlying each representative farm is a joint distribution of prices and 

yields for the three predominant crops (corn, soybeans, and wheat) and grazing land. Our work builds on, but is distinct from, previous efforts to develop 

joint price-yield distributions such as Vedenov and Powers, Featherstone 

and Kastens, and Grey et al. In this section, we (1) develop the price and 

yield distributions, (2) show how the mean and variance effects of program 

benefits are calculated using these distributions, and (3) specify the utility 

function, farm-level profit, and risk parameters needed (along with the joint 

distributions) to estimate risk premiums. 

The joint distribution of yields and prices for corn, soybeans, and wheat is 

modeled by generating correlated within-season price and yield deviates 

(Cooper 2009a, 2009b, 2010). First, national average yields (obtained from 

USDA’s National Agricultural Statistics Service) are re-expressed as withinseason yield deviations for crop i in year s as ∆Yis = ( ( )) ( ) is is

is

Y E Y

E Y

− , 

where expected yields, E(Yis) are estimated by regressing national average 

yields on a linear trend using data for 1975-2008. We use capital letters to 

denote past yields and prices, distinguishing them from the prospective yields 

and prices, and s to denote past years. County yields, obtained from NASS, 

are also transformed to deviation form (denoted as k ∆Yis ) where k indexes 

the county. 

Realized harvest prices are also transformed into deviation form:

∆Pis = ( ( )) ( )

, is is

is

P E P

E P

−

where E(Pis) is the planting time expected price. We follow RMA definitions for expected (RA base) and realized prices. The expected price of corn 

is the average of daily closing prices in February for the December Chicago 

Board of Trade (CBOT) corn contract. The realized price is the average of 

daily closing prices during October for the CBOT December corn contract. 

Expected and realized soybean prices are based on the February and October 

prices, respectively, for the December CBOT soybean contract. For hard red 

spring wheat, expected and realized prices are based on March and August 
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prices, respectively, for the Minneapolis Grain Exchange (MGE) September 

contract. 

The relationship between price and yield vectors is estimated by regressing 

onand other explanatory variables (zi):

(A2) ∆Pis = g( ∆Yi , zi ) + εi

where εi is the error term. We expect that i

i

d P

d Y

∆

∆ < 0, i.e., the greater 

the realization of national average yield over the expected level, the more 

likely harvest time price will be lower than the expected price. See Cooper 

(2009a, 2009b, 2010) for details. 

We jointly estimate the distributions of price and yield deviations by repeated 

estimation of equation (A2) using a pairs-bootstrap approach in a joint resampling methodology that involves drawing i.i.d. observations with replacement from the original data set (e.g., Yatchew). For each draw of a yield 

deviation, we estimate a price-yield coefficient vector using (5). The procedure creates M (=1,000) coefficient vectors representing uncertainty in the 

yield-price relationship. 

Next, deviation vectors for national and county yields, ˆ ∆Yi and ˆ k ∆Yi , 

respectively, i = 1,..,3, (i.e., corn, soybeans, and wheat) are generated using 

a block-bootstrap approach (e.g., Lahiri) in which the pair-wise relationship 

between yield values is maintained across each crop and yield aggregation. 

We draw N (=1,000) times with replacement from ∆Yi and ∆Yi

k

 , i = 1,..,3, 

k = 1,...,9, always drawing from the same row (same s) from all vectors. 

The simulated yield data maintains the underlying historical Pearson and 

rank correlation—as well as any other relationship between the variables—

between county and national yield data, both within crops and across crops. 

For each element of the simulated national yield deviation vector (e.g., ˆ , ∆Yin

where n indexes the elements of ˆ ∆Yi ) we generate M simulated price deviations for each crop based on the M price-yield coefficient vectors, resulting 

in an MxN (1000x1000) price deviation matrix, ˆ ∆Pi , with typical element ˆ ∆Pmni .

We do not extrapolate yields into the future using estimated trends. If yields 

are trending upward, APH yields will lag behind actual yields, decreasing 

the probability of a crop insurance indemnity. Modeling the trend, however, 

would require modeling the effect of higher yields on crop prices without 

other variables used in estimating price deviations. Forecasting those variables would add considerably to the uncertainty of our results.

We make two adjustments to the crop yield distributions. First, grassland is 

less productive, on average, than cropland. In the seven “high conversion” 

counties we consider, average rangeland productivity is 18 percent lower 

than average cropland productivity. Because relatively high productivity 

rangeland is most likely to be converted to crop production, we assume that 

crop yields on converted grassland will be about 10 percent lower than on 

average cropland. 
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Second, farm-level crop yields are typically more variable than county average 

yields. To represent farm-level yields, county-level yield standard deviations 

are inflated using a method similar to that of Coble, Dismukes, and Thomas. 

We select the inflation factor, αki , such that the APH indemnity calculated 

from our yield distribution is equal to the APH premium:

{ ( ) }

2

1 max ( ) , 0 ,2008 ki

k APH k k

i i i ni

n

MIN N p E Y y

α

ω θ −    − −    ∑ ’

where (( ) ( ) )

0.5 2 2 ˆ ˆ ˆ ( ) ( ) k k k k

ni in in ki i i y Y h Y Y = + ⋅ − α σ σ , ,2008 ˆ ˆ ( )(1 ), k k Y E Y Y in i = +∆ in 

hin is a N(0,1) random variable, ˆ ( ) k σ Yi is the standard deviation for ˆ k Yi , k ωi

is the RMA premium rate (excluding the fixed rate load), 

APH

i p is the APH 

price, and the coverage rate, θ , is .65. The expected value and standard deviation of resulting yields are reported in table 1 in the text, “Expected yields, 

standard deviations, and county transitional yields.” In the balance of the discussion, we drop the county superscript (k) to reduce clutter. 

As no better estimate of prices and yields (excluding the yield trend) exists for 

periods t = 1,..., T than the estimates for period 0, we assume that the density 

of price and yield is the same for each period, the allocation of acreage across 

crops is fixed, and total crop acres are fixed except for the conversion of grassland from within the farm. We drop time subscripts for acreages, expected (RA 

base) prices, realized prices, and actual yields but retain the time subscript for 

the APH yields because they evolve through time during the first few years of 

crop production.

Using the joint distribution, the expected value of crop insurance indemnities 

that would be denied by Sodsaver, assuming the new land rules are in force, 

would be: 

1 1 ( ) ( ) ( ) ( , | , ) NL NL NL

t i c it mni ni it

m n i t

E I MN T a A I p y y δ θ − − = ∑∑∑ ∑ ,

where T is the time horizon (=5) and δt is a discount factor based on a 7 percent 

discount rate. Similar expressions are used to calculate the expected value of 

market revenue, SURE payments, marketing loan benefits, and total revenue 

under USDA-RMA new land rules and the No Restriction scenario. The variance of crop revenue on converted acreage, given the new land rules, would be:

( )

2 1 1 ( ) ( ) ( , | , ) ( ( , | , )) NL NL NL

c t ct mni ni it ct mni ni it

m n t

V R MN T R p y y E R p y y ∆ δ ∆ θ ∆ θ − − = ∑∑ ∑ −

Other variance expressions are obtained by changing the APH calculation or 

excluding crop insurance and SURE from the revenue calculation.

Yield and price deviation vectors are also created for grazing land. We 

assume that grasslands are used for cow-calf operations. Cow-calf revenue 

per animal unit (AU) is based on ERS farm cost and returns estimates for 

the Northern Plains for 1975-2008. Expected revenue is the trend revenue 

obtained by regressing revenue on lagged revenue, futures prices (average of 

July closing for fed cattle for the following year August contract (i.e., July 

2002 closing prices for August 2003 contract)), and a time trend. Revenue 

variability is based on the error term. Forage yield variability is based on Soil 
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Survey estimates from USDA’s Natural Resources Conservation Service 

for normal years, favorable years, and unfavorable years. Following the 

U.S. Government Accountability Office’s 2007 report (GAO), we assume 

that favorable conditions are realized with 20 percent higher than average 

rainfall and unfavorable conditions occur when rainfall is 20 percent lower 

than average. Forage yields are converted to stocking rates (animal units 

(AU) per acre) using rules derived from NRCS technical documents (see 

Metz). Deviation vectors for cow-calf revenue (the “price” in dollars per 

AU) and stocking rate (the “yield” in AU per acre) are generated as part of 

the block-bootstrap procedure already described. We draw N (=1,000) times 

with replacement from the cow-calf revenue and forage yield vectors always 

drawing from the same row (same year) as crop yield vectors to maintain historical correlations between the grazing “price” and “yield” as well as with 

crop prices and yields. 

Cropland and grazing acreages are county averages from the 2007 Census 

of Agriculture (appendix table A-1). The proportion of cropland in corn, 

soybeans, and wheat is based on three year averages (2005-07) of NASS 

county estimates. Crop-specific non-land production costs are based on ERS 

estimates for the Northern Great Plains (NGP) for 2007 (we assume that net 

return is the residual return to land). Crop revenues and production costs are 

aggregated using these proportions as weights. Non-land cost per animal unit 

in the NGP is obtained from the ERS and converted to cost per acre using the 

estimated stocking rate. 

Initial wealth (at t=0) is based on the county average value of land and buildings, adjusted for the average proportion of land rented (ranges from 43 to 

61 percent, based on the 2007 Agriculture Census) and debt-to-asset ratio 

(0.17, based on the 2007 ERS farm balance sheet for the Northern Plains) 

Appendix table A-1

Acreages, initial wealth, and production costs in representative farms

County

Initial cropland†Initial grassland†Converted 

grassland**

Proportion of land 

rented†

Initial

wealth Corn†† Soybeans†† Wheat††

Crop 

cost†††

Grazing 

cost†††

--------------Acres-------------- Dollars ------Proportion of cropland------ Dollars per acre

Stutsman 932 155 23 0.48 403,326 0.16 0.55 0.28 207.63 128.08

Beadle 756 231 35 0.47 579,196 0.39 0.41 0.20 234.07 155.41

Edmunds 972 560 84 0.39 798,872 0.33 0.39 0.28 225.71 118.38

Faulk 1,334 684 103 0.42 982,699 0.23 0.43 0.34 214.13 124.69

Hand 1,026 777 116 0.40 856,701 0.29 0.22 0.49 217.35 143.67

Hyde 1,016 1,594 239 0.46 764,702 0.20 0.05 0.76 200.56 123.20

Sully 2,556 471 71 0.44 1,308,987 0.18 0.05 0.77 198.24 127.69

Turner* 451 43 6 0.54 317,515 0.53 0.47 0.00 254.14 198.19

Union* 471 41 6 0.57 256,874 0.52 0.48 0.00 253.25 126.55

* comparison county.

**15 percent of initial grassland.

Source: † USDA, National Agricultural Statistics Service, Census of Agriculture 2007; †† USDA, NASS county crop data 2005-07; 

††† USDA, Economic Research Service, Farm Costs and Returns Estimates, available at: http://www.ers.usda.gov/Data/CostsAndReturns/.
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(see appendix table A-1). Wealth may accumulate (decline) during the 5-year 

Sodsaver moratorium, possibly reducing (increasing) the risk premium in 

years 2-5. Producers may also see greater uncertainty as the possibility of 

a series of particularly bad years creates uncertainty about initial wealth in 

these years, possibly increasing the risk premium in years 2-5. Given these 

uncertainties, we elect to calculate a risk premium for each year assuming 

wt = w0, t=1,...,4 and report the 5-year NPV of annual risk premiums. 

Finally, we assume that the coefficient of constant relative risk aversion 

(CRRA) is constant and equal to 2 (see Harwood et al.) but test the sensitivity of the model using values of 1.5 and 2.5. A range of values has been 

estimated for U.S. agriculture. Many studies report values in the range of 1-3, 

as reported in table 2 of Saha, Shumway, and Talpaz, although some studies 

report higher values, at least on the upper end of a range (e.g., Chavas and 

Holt report a range 1.42-6.76 for CRRA). More recently, Lence reports an 

estimated CRRA of 1.136. Just and Peterson suggest that many risk aversion coefficient values reported in the literature are implausibly high. So, we 

consider only a relatively narrow range of values. We also consider changes 

in acreage and debt-to-asset ratio (see table 2 in the text “Estimated risk premium for risk reduction due to crop insurance and SURE”). 
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Appendix B

Econometric Model of Agricultural Land Use

Our main behavioral assumption is that producers and landowners face a 

choice among four alternative land uses (cultivated crops, hay or pasture, 

range, and CRP; see chapter, “Grassland and Grassland-Cropland 

Conversion, 1997-2007,” for details) and in each choice event t, choose 

the alternative with the highest utility. The underlying theoretical model is 

the random utility model (RUM), which assumes that each alternative has 

a utility function and an additive random error (see Hensher, Rose and 

Greene for details). 

Econometric Model Selection and Specification

Many previous econometric models of major land use implement multinomial (MNL) or nested logit (NL) models because they are analytically tractable, have closed-form probability expressions, and tend to perform well in 

terms of model fit (Gardner, Hardie, and Parks, 2010; Lubowski, Plantinga, 

and Stavins; and others). In an MNL model, errors are assumed to be independently and identically distributed following a type I extreme value distribution, which implies a property known as the independence of irrelevant 

alternatives (IIA). Under IIA, choice patterns must be proportional to estimated probabilities. Suppose, for example, that an increase in crop revenue 

increases the probability of cropland. Under IIA the estimated probabilities 

that the land would come from hay and pasture, range, or CRP must be proportional to the overall probability of each land use. As shown in the chapter 

“Grassland and Grassland-Cropland Conversion, 1997-2007,” however, the 

margin between cultivated cropland and hay/pasture is much more active 

than the margin between cultivated cropland and range, even though the 

study region includes a lot more rangeland than hay and pasture. Nested logit 

models have been used to avoid IIA but are also quite restrictive; correlation among alternatives within each nest must be constant and IIA must hold 

across nests. If the pattern of correlations across land uses is complex, for 

example, patterns of correlation that could lead to overlapping nests which 

cannot be accommodated in a nested logit model.

To avoid IIA, we estimate a mixed logit model with random parameters and 

error components to account for correlation among the utilities for different alternatives (Train; Hensher, Rose, and Greene). We specify an error 

component for each pair-wise combination of land use alternatives, a total 

of six. Each error component is assumed to follow a normal distribution 

with zero mean and finite variance. These components capture unobservable 

similarities and differences across alternatives that a standard multinomial 

logit model cannot capture and that a nested logit model may not be able to 

capture, depending on the complexity of the pattern of these similarities and 

differences. 

Random parameters are specified for the three economic variables: expected 

revenue, revenue variance, and production costs. Random parameters capture 

differences in producer response to changes in revenue or cost that may flow 

factors that cannot otherwise be modeled. For example, full-time farmers 

may be more responsive to market and policy incentives than are part-time 
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farmers who work primarily off the farm. Producers’ response to the variability of returns may reflect differences in risk aversion or the ability (or inability) to diversify risk. Differences in the difficulty of converting land from one 

use to another and differences in location relative to markets may also lead to 

differences in response. 

The random parameters are assumed to follow a lognormal distribution. 

Because the lognormal distribution admits only non-negative values but we 

expect negative coefficients on the revenue variance and cost variables, we 

multiplied the revenue variance and production cost variables by -1 so that 

the associated parameter values would be positive. We estimated the model 

without random parameters to confirm that the expected signs of the economic variables would be realized without the restrictions imposed by the 

lognormal distribution. Because the economic variables vary across alternatives, a single parameter is estimated for each. Implicitly, we assume that the 

effect of a dollar of expected revenue, revenue variance, or production cost 

effects each alternative-specific utility function in the same way. 

We assume that error components and random parameters vary across 

National Resource Inventory (NRI) points (in cross-section) but are fixed 

across observations on a single NRI point (in time series). By incorporating 

the panel structure of the data into our simulation-based model, we assume 

that idiosyncratic factors are constant across time, owing to the likelihood 

that location and land-specific factors will not change over time and that the 

landowner and/or land manager will also be constant in many cases. 

Land quality parameters are assumed to be fixed. Land quality factors include 

an indicator of the availability of irrigation, an index of soil productivity, and 

indicators of highly erodible land and hydric soil (a proxy for soil wetness). 

Because these variables do not vary across the alternatives, separate parameters are estimated for each land use-specific utility function except for the 

normalized alternative—Conservation Reserve Program (CRP) land. In other 

utility functions, these parameter estimates represent the difference between 

the parameters for utility of land use j and CRP utility. A positive and significant parameter on the land productivity variable in the cropland equation, for 

example, implies that higher productivity land is more likely to be devoted to 

crop production than to be enrolled in the CRP. 

For cropland, hay and pasture, and rangeland, the general form of the random 

utility function is: 

(A3) Ujnt=αj + βn' Xjnt+γj'Xn+θjn+εjnt,

where 

•	U is utility, j indexes land use alternatives, n indexes individual points of 

land, and t indexes time;

•	 αj is the alternative-specific constant term;

•	 is the (random) effect of economic attributes that vary across alternatives, points of land and choice 

events (expected revenue, variance of revenue, and production cost), 
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where m indexes the attribute, ωm and σmare parameters to be estimated, 

and Zm~N(0,1);

•	 is the (deterministic) effect of land quality attributes that vary across points of land but not across alternatives or choice 

events (irrigation indicator (crop equation only), productivity index, highly 

erodible land (HEL) indicator, and hydric soil indicator), where Xnm is the 

attribute and γjm is the corresponding parameter;

•	 , where θjkn represents the error 

component for alternatives j and μjk, represents the correlation between 

error terms for alternatives j and k and Zjkn~N(0,1);

•	and εjnt is the usual vector of individual specific random errors, with type I 

extreme value as in the multinomial logit model. 

Because the model is normalized on the CRP alternative, land quality variables are excluded but some CRP-specific variables are included:

4 4 4 4 4 ' U = + + + + α β ξ θ ε n nt n n nt X B ’

where j=4 indexes the CRP alternative, Xjnt are the economic attributes 

(revenue variance and cost are equal to zero and expected revenue is 

equal to zero for points that are not eligible for CRP), Bn is the exogenous 

environmental benefits index (EBI), ξ is the associated parameter, and 

4 4 4 4

4 4

n kn k kn

k k

θ θ μ Z

≠ ≠

= = ∑ ∑ and Zn4k ~N(0,1).

Probabilities, Elasticities, and Counterfactual Analysis

Once model parameters were estimated, we used them to estimate the probability of each land use at each NRI point in the sample. The probability of 

land use j for the error components version of the mixed logit model is given 

by (subscripts for the NRI point and choice event have been dropped to 

reduce clutter):

( ) ( ) P L f d j j =∫ φ φ φ

where φ is the overall parameter vector, 

exp( ( )) ( ) exp( ( ))

j j

j

k k

k

U

L

U

φ φ φ = ∑ , and f ( ) φ is 

a density function. In practice, the integrals are approximated numerically at 

each NRI point using 1,000 draws on the random variables in the specified 

model (Zm, m=1,2,3 and Zjk, j≠k, j,k=1,2,3,4)—a total of 9 random variables. 

Elasticities can be derived by taking derivatives of the probability functions. 

For any variable, x, that varies across alternatives (expected revenue and production cost) “own” elasticies are:

( )(1 ( )) ( ) j j j j

j j

j j j j

P x x U

L L f d x P P x

φ φ φ φ

∂ ∂ = − ∂ ∂ ∫

And the “cross” elasticities are:

( ) ( ) ( ) j j j j

j j

j j j j

P x x U

L L f d x P P x

φ φ φ φ

∂ ∂ = ∂ ∂ ∫
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For variables that do not vary across alternatives, we consider “relative” 

elasticities derived from the ratio of the probability of land use j to the probability of CRP (r). Conceptually, the elasticity is equal to the difference in 

expressions that are analogous to the “own” elasticity expression:

( ) j r j r

j r j r

P P x x x P P

x P P P x P x

∂ ∂ ∂ = − ∂ ∂ ∂ .

Because the CRP equation does not actually contain land quality variables, 

however, relative elasticies can be calculated as:

( / ) ( )(1 ( )) ( ) /

j r j j j j

j j

j r j j j

P P x x P x U

L L f d x P P P x P x

φ φ φ φ

∂ ∂ ∂ = = − ∂ ∂ ∂ ∫

Finally, we use the probability model to develop counterfactual analysis on 

acreage change that would result from the elimination of crop insurance, 

marketing loan benefits, and disaster payments. First, we calculate the change 

in land use probabilities due to a change in expected crop revenue and crop 

revenue variance that reflects the withdrawal of payments from individual 

programs and all three combined. The change in probability at each point, 

multiplied by the NRI expansion factor, gives the estimated acreage change 

for the NRI point. Because only a subset of NRI points are used in our 

econometric estimation, we increase the expansion factors, proportionally, 

so that the total amount of farmland represented by NRI points used in our 

study is equal to the number of farmland acres estimated using all NRI points 

in the study region (farmland points are NRI points that remain in cultivated 

crops, hay and pasture, range, or CRP throughout the study period.) Finally, 

we sum over point-specific changes to obtain an overall estimate of land use 

change for the study region.

Model Results

Appendix table B-1 reports parameter estimates. Based on a likelihood ratio 

test, we reject the null hypothesis that all model parameters (other than the 

constant terms) are zero, at a level of significance of well under 1 percent 

(the LR statistic is 24,668, well above the critical value of 45.6 for the χ2 distribution for α = 0.01 with 26 degrees of freedom). The McFadden pseudoR2 equals 0.83 and represents a good fit of the data. 

The mean and standard deviation coefficients for expected revenue, revenue variance, and cost variables are significantly different from zero at the 

1-percent level. Because the underlying parameters are assumed to follow a 

lognormal distribution, their sign is fixed as already noted. The productivity parameter in the cropland equation is positive, not significant in hay and 

pasture, and negative in rangeland, indicating that high-productivity land is 

more likely to be cropland and less likely to be rangeland when compared to 

CRP. Highly erodible and hydric soils are less likely to be cropland and more 

likely to be rangeland relative to CRP. Irrigated land is more likely than other 

land to be in cultivated crops. 

The parameters imply inelastic response to changes in market conditions and 

policy. Elasticities reported in table 4 in the main text are a weighted average of elasticities calculated for each observation in our data set (as shown 

above). On average, a 1-percent increase in expected revenue for cultivated 

crops increased the probability of crop production by only 0.32 percent. 
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Appendix table B-1

Estimated parameters for mixed logit model of land use

Cultivated cropland equation Estimate Standard error t-statistic p-value

Constant 16.9822 *** 2.13684 7.947 <.0001

Economic variables Expected revenue–meana -3.71026 *** 0.10853 -34.187 <.0001

Expected revenue–SDa 0.87806 *** 0.05588 15.713 <.0001

Variance revenue–meanb -14.2645 *** 1.18924 -11.995 <.0001

Variance revenue–SDb 5.27541 *** 0.70082 7.527 <.0001

Production cost–meanb -4.55713 *** 0.24411 -18.668 <.0001

Production cost–SDb 0.80778 *** 0.10897 7.413 <.0001

Land quality variables Productivity index 15.0706 *** 5.66399 2.661 0.0078

Highly erodible soil -9.02793 *** 1.62314 -5.562 <.0001

Hydric soil -5.04334 *** 1.69155 -2.981 0.0029

Irrigation 4.94041 *** 1.51155 3.268 0.0011

Error components Cultivated and hay/pasture 20.7692 *** 1.26807 16.379 <.0001

Cultivated and range 1.44906 *** 0.33947 4.269 <.0001

Cultivated and CRP 7.66778 *** 0.41269 18.58 <.0001

Hay and pasture equation (see crop equation for revenue and cost parameters)

Constant 10.3645 *** 2.07533 4.994 <.0001

Land quality variables Productivity index 1.83095 5.6167 0.326 0.7444

Highly erodible soil 0.90594 1.64906 0.549 0.5828

Hydric soil 3.82606 ** 1.63373 2.342 0.0192

Error components Cultivated and hay/pasture 20.7692 *** 1.26807 16.379 <.0001

Hay/pasture and range 4.36186 *** 0.45152 9.66 <.0001

Hay/pasture and CRP 4.51635 *** 0.35791 12.619 <.0001

Rangeland equation (see crop equation for revenue and cost parameters)

Constant 8.40158 *** 2.0947 4.011 0.0001

Land quality variables Productivity index -5.76774 5.92495 -0.973 0.3303

Highly erodible soil 10.9581 *** 1.88448 5.815 <.0001

Hydric soil 3.18464 * 1.66202 1.916 0.0553

Error components Cultivated and range 1.44906 *** 0.33947 4.269 <.0001

Hay/pasture and range 4.36186 *** 0.45152 9.66 <.0001

Range and CRP 5.31803 *** 0.63999 8.309 <.0001

Conservation Reserve Program (CRP) equation (see crop equation for revenue parameter)

Economic variable Exogenous EBI 0.20499 *** 0.03229 6.348 <.0001

Error components Cultivated and CRP 7.66778 *** 0.41269 18.58 <.0001

Hay/pasture and CRP 4.51635 *** 0.35791 12.619 <.0001

Range and CRP 5.31803 *** 0.63999 8.309 <.0001

EBI = Environmental Benefits Index

CRP = Conservation Reserve Program.

Mean = mean of normal distribution underlying the lognormal parameter.

SD = standard deviation of normal distribution underlying the lognormal parameter.

a Parameter is common to all equations.

b Parameter is common to cultivated crop, hay and pasture, and range equations.

***, **, and * = significance at 1-, 5-, and 10-percent levels, respectively.

Source: USDA, Economic Research Service.
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The 1-percent increase in crop revenue would reduce the probability of hay 

and pasture by 0.50 percent, the probability of range by 0.18 percent, and 

the probability of CRP by 0.09 percent. Using a standard multinomial logit 

model (assuming IIA) these “cross” elasticities would have been constant 

across all three alternative land uses. Similar sets of elasticities are reported 

for changes in expected revenue to hay and pasture, rangeland, and CRP.

The average effect of a 1-percent change in crop revenue variance on cropland acreage is less than one-tenth the size of the change that would flow 

from a 1-percent change in expected crop revenue. A 1-percent increase in 

crop revenue variance would decrease cultivated crop acreage by 0.026 percent while hay and pasture, rangeland, and CRP acreage would rise by 0.064 

percent, 0.011 percent, and 0.010 percent, respectively. The average effect 

of production costs are also smaller than that of expected revenue, but much 

larger than the effect of revenue variance. A 1-percent increase in crop cost 

would decrease cropland acreage by 0.192 percent while increasing hay and 

pasture, range, and CRP acreage by 0.34, 0.081, and 0.063 percent, respectively. Again, similar sets of elasticities are reported for changes in expected 

revenue to hay and pasture, rangeland, and CRP.

Data—Land Use

In estimating land use response, we assume that farmers/landowners choose 

from the four land use categories defined in the chapter “Grassland and 

Grassland-Cropland Conversion, 1997-2007”: cultivated cropland, hay and 

pasture, range, and the Conservation Reserve Program. Land use is represented using site-specific data from the National Resources Inventory collected and maintained by the USDA Natural Resources Conservation Service 

(USDA-NRCS, 2009). For each year from 1998 to 2007, NRI includes 

annual land use observations for a nationally representative set of 110,771 

“core” points. We use 1,171 core points that (1) were located in our study 

region (defined in the text; see fig. 23), (2) remained in cultivated crops, 

range, hay, pasture, or CRP throughout 1998-2007, and (3) were designated 

as Point No. 1 within each primary sampling unit. Primary sampling units 

are plots of 40-160 acres that typically include three NRI points. Selecting 

only the first point reduces spatial correlation within the data and reduces 

the size of the data set to make advanced, simulation-based econometric 

techniques possible. 

A land use choice event is defined as a decision to use a specific plot of land 

as cultivated cropland, pasture or hay, range, or to enroll land in the CRP in a 

specific year. We observe up to 10 land use choice events for each NRI point 

included in our data set, although fewer are observed for points where land is 

under CRP contract. Land under an ongoing CRP contract is excluded from 

the data set because continuing CRP enrollment does not represent a land use 

choice. The data include CRP-eligible points that are not yet enrolled in CRP, 

and points under an existing CRP contract that will expire before the end of 

the year. Expiring CRP land is automatically eligible for re-enrollment—producers can offer bids during the last year of an existing contract. The resulting data set contains a total of 10,655 land use choice events.
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Data—Economic Variables 

In this section, we describe the development of per acre expected revenue, 

revenue variance, and production costs for cropland, hay and pasture, and 

grazing land. For cropland, the revenue variables include the effect of crop 

insurance, marketing loan benefits, and disaster payments. For crop revenue, we begin by describing the development of joint distributions for crop 

prices and yields. We then develop models of marketing loan benefits and 

crop insurance indemnities and describe disaster assistance data. Finally, we 

show how the joint distribution and policy models are combined to estimate 

expected revenue and revenue variance. The discussion of crop revenue is 

followed by discussions of hay and pasture revenue, rangeland revenue, and 

production costs for all three land uses. 

Crop Revenue—Joint Distribution of Prices and Yields. We use three major 

crops (corn, wheat, and soybeans) to represent cropland revenue. Crop yields 

are represented by empirical distributions derived from NASS county data 

using a methodology developed by Cooper (2009a, 2009b, 2010). Expected 

yields are calculated as an Olympic average of the most recent five yields 

(the average with the high and low values removed). To estimate yield deviations, we start with deviations from a linear trend, fitted using the most recent 

S (=23) yields. Because these deviations are based on county-average yields, 

however, they reflect the effects of averaging and most likely underestimate 

farm-level deviations. To approximate farm-level conditions, we inflate the 

county yield deviations until expected losses, based on a yield guarantee 

of 65 percent, equal yield-based (MCPI) crop insurance premium rates for 

65 percent coverage—a procedure similar to that of Coble, Dismukes, and 

Thomas. The yield distribution vector for crop i at time t, denoted, contains S

elements defined as: 

, ,

,

( ) ˆ ( ) ( ) 1

T

i i t s i t s

its it its it T

i t s

y y y E y y E y

y

α

∆ − −

−

 − 

= =  +     

 ,

where E(yit) is an Olympic average of the most recent five yields, ∆yits is the 

yield deviation factor, yi,t–s is the realized yield, ,

T

i t s y − is the trend yield at 

time (year) t-s, and αiis the inflation factor. County- and crop-specific inflation factors are chosen, using 2007 data, so that the expected loss per dollar 

of liability, based on a 65-percent yield guarantee, is equal to the MCPI premium based on 65 percent coverage:

(A4) 

2

1 ,2007 ,2007,

,2007

,2007

(0.65 ( ) ( )) min ( ) max , 0 i 0.65

i i i s i

i

s i

y E y y y S α y∆ α

ω −

    −    −          

∑

where ,2007 ( ) i ω y is the APH (yield insurance) premium rate (dollars per 

dollar of liability) for 65 percent coverage (excluding the fixed rate load) 

calculated from RMA county actuarial data, and i,2007 y is a simple average 

of 1997-2007 yields for the county, which approximates the APH yield. The 

right hand side of (A4) is the expected loss per dollar of liability given 65 

percent coverage. The inflation factors are used for all years, even though 

they are calculated using 2007 data.

Crop prices are also represented by empirical distributions based on futures 

price data. Expected prices are represented by planting time prices for the 
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harvest month future contract. For example, the expected price of corn is 

the average of daily closing prices in February for the December Chicago 

Board of Trade (CBOT) corn contract. The realized price is the average of 

daily closing prices during October for the CBOT December corn contract. 

Expected and realized soybean prices are based on the February and October 

prices, respectively, for the December CBOT soybean contract. For hard red 

spring wheat, expected and realized prices are based on March and August 

prices, respectively, for the Minneapolis Grain Exchange (MGE) September 

contract. The price distribution vector for crop i at time t, denoted ˆit p , contains S elements defined as:

, ,

,

( ) ˆ ( ) 1

( )

i t s i t s

its it

i t s

p E p p E p E p

− −

−

 − 

=  +     

, 

where

E(pit) is the planting time (expected) price, harvest month futures contract, 

time t,

Pi,t–s is the harvest time (realized) price, harvest month futures contract, 

time t-s, 

E(Pi,t–s) is the planting time (expected) price, harvest month futures contract, time t-s.

Crop Revenue—Marketing Loan Benefits. Marketing loans protect crop 

producers from very low prices. When the market price of a covered commodity (e.g., corn, wheat, soybeans) drops below a fixed “loan rate” (see 

appendix table B-2), the Federal Government pays producers for the difference. On a per-acre basis, the marketing loan benefit is:

max(( ) , 0), L p p y it = −i it it

where Lit is the marketing loan benefit for crop i at time t; it p is 

the loan rate; Pit is the realized price; and yit is the producer’s actual 

yield. An S-element distribution vector is denoted ˆ

Lit with typical element 

ˆ max(( ) , 0). ˆ ˆ L p p y its = −i its its

Crop Revenue—Net Crop Insurance Indemnity. Revenue- and yield-based 

crop insurance are widely purchased in the Northern Plains. Revenue insurance products were introduced only in late 1990s. RMA data show that 

producers switched to revenue insurance gradually between 1997 and 2007. 

For 2007, the RMA Summary of Business data show that revenue assurance 

(RA) accounted for 74 percent of insured corn acreage (most insured for 70 

and 75 percent coverage), 82 percent of insured soybean acreage (mostly 70 

and 75 percent coverage), and 44 percent of insured wheat acreage, almost all 

of it at 65, 70, or 75 percent coverage. 

Coverage levels also increased over that period because of changes in the 

premium subsidy schedule. Before 1999, most insurance was sold at 65 percent coverage and carried a subsidy rate of 42 percent. Producers could also 

buy 70 percent coverage, which carried a premium subsidy of 32 percent. In 

1999 and 2000, Congress enacted temporary premium subsidy increases that 
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were made permanent under the Agricultural Risk Protection Act of 2000 

(see appendix table B-3). The new subsidy schedule encouraged producers 

toward 70 percent coverage by applying a subsidy rate of 59 percent to both 

65 and 70 percent coverage. 

Based on RMA Summary of Business data, we devised acreage shares and 

coverage levels for yield and revenue insurance by crop, county, and year. 

Revenue assurance with the base price option is used to represent revenue 

insurance. Because grassland to cropland conversion is most likely to happen 

on nonirrigated land, we considered insurance for nonirrigated production 

only. 

The per-acre indemnity for the RA base price option is paid when crop revenue drops below a guarantee level:

max(( ), 0) ra b

it it it it it I p y p y = θ − ,

where ra

it I is the RA indemnity for crop i at time t, θ is the coverage level, 

b

it p is the RA base (expected) price, it y is the producer’s APH yield, pit is 

the realized price, and yit is the actual yield. As S-element distribution vector, 

ˆra

it I , has typical element

ˆ max(( ˆ ˆ ), 0) ra b

its it it its its I p y p y = θ − . 

Appendix table B-2 

National average commodity loan rates

Soybeans Corn Wheat

-------Dollars per bushel-------

1998 5.26 1.89 2.58

1999 5.26 1.89 2.58

2000 5.26 1.89 2.58

2001 5.26 1.89 2.58

2002 5.00 1.89 2.80

2003 5.00 1.89 2.80

2004 5.00 1.95 2.75

2005 5.00 1.95 2.75

2006 5.00 1.95 2.75

2007 5.00 1.95 2.75

Source: USDA, Farm Service Agency. 

Appendix table B-3 

Crop insurance subsidy rates 

Crop insurance

coverage level

Pre-1999 

subsidy rates

Subsidy rates

1999-present

Percent

55 46 64

60 38 64

65 42 59

70 32 59

75 24 55

Source: USDA, Farm Service Agency. 
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The per-acre indemnity for APH (yield) insurance is paid when realized yield 

falls below the yield guarantee:

max(( ( )), 0) aph aph

it it it it I p y y = θ − ,

where aph

it I is the APH indemnity and aph

it p is the (predetermined) APH 

price for crop i at time t. As S-element distribution vector, ˆaph

it I , has typical 

element

ˆ max(( ( )), 0) ˆ aph aph

its it it its I p y y = θ − . 

By law, USDA must attempt to devise actuarially fair premiums. Actuarially 

fair premiums would be equal to the expected indemnity, but farmer-paid 

premiums are subsidized by the Federal Government:

( ) 1 ˆ 1 ( ) ( ) (1 ( )) ra ra ra

it it its

s

ρ θ E I θ S I − = −φ = −φ ∑

( ) 1 ˆ 1 ( ) ( ) (1 ( )) aph aph aph

it it its

s

ρ θ E I θ S I − = −φ = −φ ∑ ,

where ra ρit and aph ρit are the farmer-paid premiums for RA and APH insurance, respectively, and φ is the premium subsidy rate for coverage level θ. 

Crop Revenue—Crop Disaster Assistance. Northern Plains producers 

received disaster assistance a number of times during the study period based 

on ad hoc legislation passed by Congress in response to drought or other 

adverse conditions. Producer expectations about ad hoc programs are difficult to model because program rules about eligibility, payments, and other 

provisions can change from year to year and are not known by producers 

before the beginning of the season when land use and cropping decisions 

are finalized. Moreover, ad hoc disaster payments are typically received 1-2 

years after the disaster. Nonetheless, the frequency and size of disaster assistance payments implies that producers are likely to have some expectation 

of disaster assistance. As a proxy for expected disaster assistance payments, 

Goodwin and Rejesus use a 4-year rolling average of county-level disaster assistance payments reported in the Consolidated Federal Fund Report 

(CFFR), divided by the number of cropland acres in the county. We adopt a 

very similar method, using a 5-year moving average of county-level disaster 

assistance, based CFFR data, divided by cropland acreage. We do not attempt 

to assess the effect of disaster payments on the variability of crop returns. 

Because the CFFR data reflect payments based on disasters in previous years, 

the countercyclical effect of ad hoc disaster assistance cannot be determined. 

Crop Revenue—Expected Revenue and Revenue Variance. Finally the 

expected value and variance of crop revenue, including the effect of farm 

programs, can be represented as:

1 ˆ ˆ ( ) ( ) ( ˆ ˆ ) ( ) t t it its its its its it t

s i

E R A S a p y L I E D ρ − = ∑∑ + + − +

2

1 ˆ ˆ ( ) ( ) ( ˆ ˆ ) ( ) t t it its its its its it t

s i

V R A S a p y L I E R ρ −   =  + + − −    ∑ ∑

where:
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Rt is crop revenue in year t; 

ait is acreage of crop i in year t; 

t it

i

A a =∑ is overall crop acreage in year t;

ˆ ˆ ˆ ( ) ( ) ( ) ( ) ra ra ra aph aph aph

its it it its t it it it t I a a I a a I = θ + θ

aph

it a is acreage of crop i in year t with APH (yield) coverage;

ra

it a is acreage of crop i in year t with RA (revenue) coverage;

aph

t θ is coverage level for crop i in year t with APH (yield) coverage;

ra

t θ is coverage level for crop i in year t with RA (revenue) coverage;

( ) ( )) ( ) ( )) ra ra ra aph aph aph

it it it it t it it it t ρ = a a ρ θ + a a ρ θ

Et (D) is the expected disaster payment in year t.

(All variables are county-specific but are not indexed by county to reduce 

clutter.) 

Hay and Pasture Revenue—Expected Revenue and Revenue Variance.

We use hay prices and yields to represent hay and pasture revenue. Yields 

are based on county data obtained from NASS. The yield distribution vector is developed using the same method applied to cultivated crops. Prices 

are based on State-level season-average prices, also obtained from NASS. 

The price distribution vector is developed using the same methods applied to 

crops, except that the expected hay price is a moving average of the previous 

3 years’ prices. Using these data, expected revenue and revenue variance are 

derived using the same applied to cultivated crop revenue:

1 ( ) ˆ ˆ ht hts hts

s

E R S p y − = ∑

( )

2 1 ( ) ˆ ˆ ( ) ht hts hts ht

s

V R S p y E R − = ∑ −

where:

Rht is hay and pasture revenue in year t; 

ˆ hts p is element s from the hay price distribution vector for year t; 

ˆhts y is element s from the hay yield distribution vector for year t; 

Rangeland Revenue—Expected Revenue and Revenue Variance. Revenue 

per animal unit (the “price” in this case) is based on ERS cost and returns 

estimates for cow-calf operations in the Northern Plains. Expected revenue 

at time t is the predicted revenue obtained from a regression of revenue on 

lagged revenue, futures price (average of July closing for following year’s 

August fed cattle contract, i.e., July 2002 closing prices for August 2003 contract), and a time trend using the most recent S (=23) years. Deviations are 

the difference between realized revenue (as reported in the cost and returns 

data) and expected revenue. Using this data, the price distribution vector is 

developed using the methods applied to cultivated crop revenue. 
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Rangeland forage yields (pounds of dry matter per acre) for normal years, 

favorable years, and unfavorable years are obtained from NRCS soil survey 

data and converted to carrying capacity in animal units per acre (the “yield” 

in this case) using rules derived from cooperative extension literature and 

NRCS technical documents (Metz). Each animal unit requires roughly 915 

pounds (lbs) of forage per month. In a 6-month grazing season, total forage 

needs for one animal unit would be 5,490 lbs. Suppose the available forage 

is estimated at 2,000 lbs per acre per year. Good grazing practice involves 

leaving some forage in the range/pasture—a widely used rule is to “take” 

only half of the available forage—up to 1,000 lbs per acre in this example. 

Grazing efficiency is only about 50 percent, as some of the available forage 

is trampled, located far from water, or otherwise goes unutilized. Using the 

“take half” rule and 50-percent grazing efficiency, about 25 percent of available forage is actually consumed by cattle. In the example, cattle consume 

500 lbs per acre over the course of the season (0.25*2,000 lbs per acre). The 

number of animal units (AU) supported by 1 acre is the forage available per 

acre divided by the forage need for one AU: (500 lbs per acre)/(5490 lbs 

per AU)=0.091 AU/acre (or roughly 11 acres per AU). A coefficient that 

converts forage yield (lbs per acre) into a stocking rate (AU per acre) can be 

defined as: j = 0.25/5490 = 0.00004554.

Yield variability is estimated using annual rainfall data to adjust forage yields 

to year-specific conditions following a method used by the U.S. Government 

Accountability Office (GAO). We assume that producers are able to adjust to 

variation in forage production on grazing land by varying stocking rates and/

or buying or selling hay. The GAO cites local expert opinion in assuming 

that favorable conditions are realized with a 20-percent increase in rainfall 

over average levels and unfavorable conditions occur when rainfall is 20 percent below average. We use a similar rule, approximating the effect of variations in rainfall on forage yields as:

( ) 1.2 0.8

low high

normal g g

gt g t

y y

y y z z

z z

φ

 − 

= +  −   −   

, 

where

gt y is estimated forage yield for grazing in year t;

normal

g y is expected forage yield in a normal year;

low

g y is expected forage yield in an unfavorable year;

high

g y is expected forage yield in a favorable year;

t z is rainfall in year t;

z is average annual rainfall.

The yield distribution vector for rangeland, ˆgt y , would have typical element: 

, ˆ 1 .

normal

normal g t k g

gts g normal

g

y y

y y

y

−  − 

=  +     

Using the price and yield distribution vectors described above, expected revenue and revenue variance are calculated as:
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Grassland to Cropland Conversion in the Northern Plains:The Role of Crop Insurance, Commodity, and Disaster Programs / ERR-120

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1 ( ) ˆ ˆ gt gts gts

s

E R S p y − = ∑

( )

2 1 ( ) ˆ ˆ ( ) gt gts gts gt

s

V R S p y E R − = ∑ − ,

where:

 Rgt is rangeland revenue in year t; 

ˆ gts p is element s from the rangeland price distribution vector for year t; 

ˆgts y is element s from the rangeland yield distribution vector for year t; 

Per-Acre Production Cost—Crop Costs. Cost data are derived from annual 

production cost estimates published by ERS (USDA-ERS, 2010). Cost estimates for corn, wheat, and soybeans are based on estimates for the Northern 

Great Plains. Because grassland-cropland conversion involves a major 

change in non-land capital input needs, we include both allocated overhead 

and operating costs in our estimate of crop costs, except that land costs are 

excluded because net return is assumed to be the residual return to land. To 

derive a single measure of crop production cost, we use an acre-weighted 

average of corn, wheat, and soybean costs. Weights are the average acreage 

over the 3 most recent years based on NASS county crop data: 

1

t t it it

i

C A a C − = ∑ ,

where:

Ct is overall cropland production cost at time t; and

Cit is production cost for crop i at time t.

Per-Acre Production Cost—Hay and Pasture Cost. We use hay production 

costs to represent hay and pasture costs. We assume that producers plant a 

mix of alfalfa and smooth brome grass. Costs include tillage, planting, and 

fertilizer for establishment, annual fertilizer for maintenance, and harvest 

(bailing costs). 

Establishment is assumed to involve tandem disking (twice), fertilizer application, harrowing, and drilling (Barnhart, Duffy, and Smith). The cost of 

machine operations is taken from custom farming rates obtained from Statespecific rates (Aakre; Anderson and Noyes; Jose and Bek). For each State, 

custom rates for the most recent available year are used with adjustments to 

account for inflation. For establishment, we assume that producers will apply 

60 lbs per acres of nitrogen, 15 lbs of phosphate, and 45 lbs of potash. We 

assume that establishment costs are amortized over a period of 5 years. 

Annual costs. Annual fertilizer includes 15 lbs of phosphate and 45 lbs of 

potash. Nitrogen is not applied annually because the pasture is seeded to a 

legume-grass mixture. Fertilizer prices are obtained from NASS (USDANASS, 2010). To calculate annual costs of mowing-conditioning, we assume 

that farmers harvest three cuttings each year. Bailing and hauling costs are 

estimated from per-ton custom bailing and hauling rates (Aakre; Anderson 

and Noyes, Jose and Bek). These figures are converted to a per-acre basis 

using expected hay yields. 

Putting all of the pieces together, the annual cost of hay production is: 
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3* ( ) C C C C E y C ht e at m ht B = + + + δ ,

where:

Cht is annual production cost for hay;

δCe is the annual share of establishment cost, amortized over 4 year;

Cat is annual maintenance (fertilizer) cost;

Cm is the cost (per acre) of hay mowing and conditioning;

E(yht) is the expected yield at time t; and

CB is bailing and hauling cost.

Per-Acre Production Cost—Rangeland Cost. We use cow-calf production 

cost estimates for the Northern Great Plains, published by ERS (USDA-ERS, 

2010). ERS cost estimates are expressed on an animal-unit basis. To convert 

cost to a per-acre basis, we multiply by the number of animal units that can 

be supported by a single acre, given the expected (normal) forage yield: 

,

normal C C y gt gt AU g = φ .

We include both fixed and variable costs in our estimate of rangeland costs.

Data—Land Quality Variables

Productivity is represented by the National Commodity Crop Productivity 

Indicator (NCCPI) developed by USDA’s Natural Resources Conservation 

Service for use in implementing USDA programs, including CRP (Dobos, 

Sinclair, and Hipple). Productivity is measured on 0-1 scale where 0 is least 

productive and 1 is most productive. In our study region, NCCPI ranges from 

0 to .62 with a mean of 0.27. The NCCPI assumes nonirrigated production. 

Soils can have low ratings due to dry climate, even though irrigation (if available) could make them very productive. To capture the effect of irrigation, 

we use a site-specific (NRI-based) indicator of irrigation. 

The suitability of land to crop production is described by two variables. 

Potential for high erosion rates make highly erodible land (HEL) unsuited 

to crop production without the use of practices that help slow erosion. 

Conservation tillage (particularly no-till), terraces, contour farming, and 

other practices can help reduce soil erosion. Producers who crop HEL are 

required to implement a soil conservation system to retain eligibility for 

farm program payments (all payments, not just on land where conservation 

requirements have not been satisfied, with the exception that crop insurance 

indemnities are not subject to the HEL conservation requirement). Farmers 

who convert highly erodible grassland to crop production must apply stricter 

and more costly plans or risk loss of farm program payments. A site-specific, 

binary indicator of HEL, drawn from NRI, is used to capture the effect of 

HEL status on grassland-to-cropland conversion. 

Wet soils may also impede crop production, as producers may be forced 

to delay planting or farm around wet spots. The Prairie Pothole Region is 

named for small, isolated wetlands that dot the region. Under the Wetland 
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Conservation (Swampbuster) provision currently in force, farmers who drain 

these wetlands become ineligible for farm program payments. Nonetheless, 

many are dry enough in some years to farm and are often referred to as 

farmable wetlands. Soils that develop under wet conditions are classified as 

“hydric soils.” To capture the effect of soil wetness on land use, we use a 

binary indicator of hydric soil. 

Data—Conservation Reserve Program Variables

CRP enrollment decisions are made jointly by the producer (or landowner), 

who decides to offer a CRP contract, and by USDA, which decides whether 

or not to accept the offer. Because CRP is a longstanding program and 

producers are given a great deal of information about their environmental 

benefits index or EBI score before they finalize bids, most applicants have 

prior beliefs about the EBI cutoff that determines which contract offers will 

be accepted. CRP applicants are informed of their EBI environmental score 

before bids are finalized. While the weight given to the contract cost component of EBI and the overall cutoff score (above which contracts are accepted) 

are not known by the producer when bids must be finalized, the cost weight 

and the cutoff score have changed only modestly over time, providing producers with reasonably good prior information (see Barbarika, et al., page 

A-29). Given this level of prior information, the producer’s decision to offer 

a contract will depend on roughly the same factors as the Government’s decision to accept or reject the offer: (1) whether the land is eligible for CRP, (2) 

the potential revenue and costs, and (3) the EBI environmental score.

CRP eligibility is based on program criteria for 1997-2007. In general signups, that includes land (1) with an erodibility index greater than or equal to 

8 or (2) located in any one of seven national and State priority areas, including the Prairie Pothole Region, which includes a majority of our study area, 

is eligible for CRP. Land can also be eligible for continuous signup for 

high-priority practices or the Conservation Reserve Enhancement Program 

(CREP)—a Federal-State partnership program designed to encourage 

concentrated effort to clean up a specific resource such as a river or lake. 

Because a large majority of CRP acres were enrolled in general signup during 1997-2007, we use general signup criteria in determining eligibility.

CRP revenue is based on county soil rental rates (SRRs) obtained from 

USDA’s Farm Service Agency (USDA-FSA). The SRR is then adjusted to 

field-specific conditions using productivity indicators and other information. 

USDA generally adds $4 to $10 per acre to cover the costs of annual maintenance, depending on the type of cover or practice installed. We represent 

CRP revenue using the county-average SRR but also include an indicator 

of soil productivity in the model (NCCPI). The SRR is set to zero for NRI 

points that are ineligible for CRP.

The “exogenous” portion of the EBI is an indicator of the likelihood of CRP 

enrollment, if the producer or landowner decides to make an offer. The 

exogenous EBI is made up of factors that cannot be controlled or changed 

by the producer. That includes the water quality, soil erosion, air quality, and 

conservation priority area criteria. Wildlife benefits are not included because 

they depend largely on the type of cover applied. Producers and landowners 

who score high on the exogenous EBI may be more likely to submit CRP 
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bids when compared to producers with land of similar value, but lower 

exogenous EBI scores. Those with high EBI scores are also less likely to 

need to “sweeten” bids by bidding down financial assistance or offering 

cover that is better for wildlife but more expensive to install and maintain. 

The EBI for CRP general signups 26-33 are used to develop our estimates 

of the exogenous EBI. The exogenous EBI is set to zero for land that is not 

eligible for CRP.