United States

Department of

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 110

February 2011 

Daniel Hellerstein and Scott Malcolm

The Influence of Rising 

Commodity Prices on 

the Conservation 

Reserve Program
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Recommended citation format for this publication:

Hellerstein, Daniel, and Scott Malcolm. The Influence of Rising 

Commodity Prices on the Conservation Reserve Program. 

ERR-110. U.S. Dept. of Agriculture, Econ. Res. Serv. February 2011.
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The Influence of Rising Commodity Prices on the Conservation Reserve Program / ERR-110

Economic Research Service / USDA

United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Economic

Research 

Report

Number 110

February 2011

The Influence of Rising 

Commodity Prices on the 

Conservation Reserve Program

Abstract

This report considers how increased commodity prices might influence enrollment in 

and benefits from the Conservation Reserve Program (CRP) using two complementary 

models: a likely-to-bid model that uses National Resources Inventory data to simulate 

offers to the general signup portion of the CRP and an opt-out model that simulates 

retention of current CRP contracts. Under several higher crop price scenarios, including 

one that incorporates 15 billion gallons of crop-based biofuels production, maintaining 

the CRP as currently configured will lead to significant expenditure increases. If 

constraints are placed on increasing rental rates, it might be possible to meet enrollment 

goals with moderate increases in CRP rental rates—but this will mean accepting lower 

average Environmental Benefits Index scores as landowners with profitable but environmentally sensitive lands choose not to enroll.

Keywords: CRP, ethanol, commodity prices, likely to bid, REAP.

Daniel Hellerstein, danielh@ers.usda.gov

Scott Malcolm, smalcolm@ers.usda.gov
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Contents

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

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

The Conservation Reserve Program: Background . . . . . . . . . . . . . . . . . . . . 2

Modeling CRP Enrollment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Scenarios for Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Influence of Prices: Likely-To-Bid Model . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Influence of Prices: Opt-Out Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

How Might a CO2 Offset Program Influence CRP Participation? . . . . . . . 22

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Appendix 1: Likely-To-Bid Model: Methodology . . . . . . . . . . . . . . . . . . . 28

Appendix 2: Opt-Out Model: Methodology . . . . . . . . . . . . . . . . . . . . . . . . 31

Appendix 3: The CRP's Soil Rental Rate . . . . . . . . . . . . . . . . . . . . . . . . . . 38
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Summary

What Is the Issue? 

The Conservation Reserve Program (CRP) is a voluntary program that 

enables farmers to retire farmland for conservation in exchange for rental 

payments. After a period of relative stability, the CRP is now facing a 

number of changes. The 2008 Farm Act reduced the CRP’s maximum enrollment to 32 million acres—4.6 million acres less than the program’s peak 

acreage in 2007. Moreover, higher commodity prices since 2006 are likely 

to lead to increases in cropland rental rates and inflate CRP program costs. If 

high prices become the norm, landowner interest in CRP may wane as they 

weigh the expected returns to farming against the CRP payment, particularly 

if CRP rental rates do not keep up with market rental rates. This could lead 

to fewer acres being offered to the program, with a commensurate drop in 

ecosystem services. 

What Did the Study Find?

Using a computer simulation, the authors of this report demonstrate that, in 

an era of elevated crop prices, maintaining the CRP’s acreage, and the environmental benefits it provides, will require higher program payments. 

•	If commodity prices and CRP rental rates prevalent in 2007 were maintained over the long term (commodity prices that are higher than when 

most CRP contracts enrolled) the quantity and quality of land offered to 

the program would decline. An increase in these 2007 rental rates by 60 

percent would largely offset the long-term impact of the higher prices, 

albeit with a corresponding increase in program costs.

•	Given the established interest in the program and its longstanding popularity with landowners, if USDA’s policy of using prevailing rental rates 

were altered, it might be possible to meet acreage goals with moderate 

increases in payment rates. But this would mean accepting offers 

providing fewer environmental benefits, as landowners with environmentally sensitive, but increasingly profitable, lands choose to withhold their 

land from the program.

•	Any additional impacts caused by an increase in crop-based ethanol 

production from 6.5 to 15 billion gallons per year would be relatively 

minor. For example, given a 60-percent increase in CRP rental rates, the 

model predicts 2 million fewer acres (about 5 percent of the current total) 

offered to the program over the long term and a 2-percent reduction in 

environmental benefits (as measured by the CRP’s Environmental Benefit 

Index). When considering currently enrolled CRP acres, this expansion in 

ethanol production leads to about 200,000 acres leaving the program. 

•	Using the unusually high crop prices seen in summer 2008, the model 

shows a large response by CRP participants. Maintaining the CRP 

payments at their current level results in fewer acres offered to the 

program, making it unlikely that the program could reach its goal of 32 

million acres. Over the long term, to enroll acreage that would maintain 
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the environmental benefits currently provided by the program would 

require roughly doubling CRP rental rates. 

•	If a robust carbon market permitted all CRP enrollees to also sell carbon 

offsets from their retired land, the impacts of increased commodity prices 

on the costs of CRP could be substantially reduced. 

How Was the Study Conducted?

We use two modeling strategies. A likely-to-bid model predicts what the 

CRP would look like if the program were to start from scratch and enroll the 

entire acreage in one hypothetical signup— it is a steady-state model that 

starts with postulated prices, which are assumed to stay constant, and predicts 

what the CRP will look like over the long term. An opt-out model treats 

current acreage as a given, and predicts which acres would be withdrawn and 

converted to which crops under different price and rental payment levels. 

Each model has inherent strengths and limitations that are partially compensated for in the other approach. 

•	The Economic Research Service’s likely-to-bid model (LTB) is a simulation model that predicts how representative parcels of land will respond to 

a CRP program under varying circumstances. As part of the methodology, 

the LTB predicts factor scores for the Environmental Benefits Index (EBI).

•	Contract data on all currently enrolled CRP acreage is the basis of the 

opt-out model. Contract data contain for each contract the actual value of 

CRP payments, the maximum payment permitted, and the EBI factors. 

Several scenarios that illustrate possible market and program situations are 

constructed. The scenarios incorporate moderate and high price increases 

over historical trends. For scenarios that consider the impacts of increasing 

ethanol production to 15 billion gallons per year, the Regional Agriculture 

and Environment Programming model (REAP) was used to determine prices 

and crop shares. The mitigating effects of increasing CRP rental payments 

are included as another factor in the scenarios. Offered and accepted acres, 

average Environmental Benefits Index scores, forgone agricultural revenue, 

average rental payments, and regional distribution of CRP acreage are 

computed for each scenario.
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Introduction

The Conservation Reserve Program (CRP) is one of the USDA’s largest 

environmental programs. Now over 25 years old, the CRP is a voluntary 

program that pays landowners to convert environmentally sensitive cropland to grass, tree, and wetland covers. CRP contracts cover a multi-year 

period, with significant penalties for withdrawing before the contract expires. 

Originally established to protect soil quality, the CRP’s primary goals now 

include water and air quality protection and the enhancement of wildlife 

habitat.1

The CRP uses competitive bidding to select which lands to enroll in its 

general signup. Since landowner interest in the CRP is influenced by a 

number of external factors, the makeup of the CRP will change as market 

forces, and program policies, change. For example, a jump in commodity 

prices could reduce acreage offers, both new and renewing. On the other 

hand, if the USDA increases per-acre rental rates, it could counteract the 

effects of increased commodity prices. Or the USDA could change its 

ranking criteria and alter the types of land accepted into the program. In 

either case, the regional distribution of CRP acreage, the types of benefits 

these acres produce, and the cost of the program would likely change.

The goal of this report is to investigate what could happen to the CRP under 

alternative assumptions about commodity price increases and alternative 

assumptions about management of the programs. We focus on several questions, including:

•	How might high prices affect the makeup of the CRP? The past few years 

witnessed a boom in agricultural commodity prices—a boom that corresponded with the growth in the production of biofuels. Even though prices 

have dropped from their summer 2008 peaks, they are still substantially 

higher than when most existing CRP contracts were signed 

•	How might further increases in the production of corn-based ethanol affect 

the CRP?

•	How would higher CRP rental rates affect program expenditures and the 

types of land enrolled? 

•	How will the reduction in the size of the CRP affect the program? The 

2008 Farm Act capped the CRP at 32 million acres, versus nearly 37 

million acres in 2007. In reducing the size of future signups or allowing 

enrolled acres to opt out, which regions or classes of land are likely to be 

less prominent in CRP?

•	How might offset markets for carbon dioxide (CO2) affect CRP enrollment and costs?

1A summary of the CRP’s enrollment, 

with an overview of the program’s 

environmental impact, can be found at 

USDA's Farm Service Agency website: 

http://www.fsa.usda.gov/FSA/webap

p?area=home&subject=copr&topic=r

ns-css
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The Conservation Reserve Program: 

Background

The Conservation Reserve Program (CRP), established by the Food Security 

Act of 1985, enables eligible landowners to remove land from crop production in exchange for a fixed payment over a fixed period. By 1990, 32.8 

million acres were enrolled in 10- or 15-year contracts. Initially, the criteria 

for enrollment were largely based on soil erosion potential, and nearly all 

acres that fulfilled crop history and other eligibility requirements were 

accepted.2 

By 1997, many of the early CRP contracts were expiring, leading to several 

large general signups that enrolled 27 million acres through 1999. Unlike the 

initial signups, CRP offers were ranked on several criteria, with soil erosion, 

water quality, and wildlife habitat of equal importance. A newly created 

Environmental Benefits Index (EBI) used a number of factors—including 

soil erodibility, the land cover planted, and proximity to water bodies—to 

rank each offer. A cost factor was included that rewarded offers less costly to 

2Figure 1 charts the timeline of yearly 

CRP acres, yearly expenditures, and 

yearly continuous CRP acres. For 

further detail on the history of the CRP, 

see http://www.ers.usda.gov/Briefing/

ConservationPolicy/retirement.htm or 

http://www.fsa.usda.gov/FSA/webap

p?area=home&subject=copr&topic=r

ns-css.

0

0.5

1.0

1.5

2.0

2.5

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

0

5

10

15

20

25

30

35

40

1987

Program start

1991

First CPAs 

established1

1997

Continuous 

CRP starts

2006

REX3 2009

32-millionacre cap

1998

Major program 

signups use EBI2

Figure 1

History of CRP: Acreage and expenditures, with important events

Million acres Billion dollars

CRP acres

Source: USDA, Economic Research Service, using CRP contract data maintained by USDA’s Farm Service Agency.

1

CPA = Conservation Priority Areas 

2

EBI = Environmental Benefits Index

3

REX = Re-enrollment and extension program.

Continuous CRP acres Annual program outlays
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USDA.3 During these latter signups, interest in the program (as measured by 

total acres offered) exceeded enrollment limits. In 1997, only 70 percent of 

offers were accepted.4 

The decade following the large general signups of 1997 witnessed CRP 

acreage fluctuating between 30 and 37 million acres, with signups ranging 

from 1 million to 4 million acres per signup. The EBI continued to be used, 

with minor modifications. A more significant change was the advent of 

“continuous CRP.” Starting in 1996 and now comprising about 4 million 

acres, continuous CRP accepts lands that meet more stringent eligibility 

criteria, both in terms of location and cover crop. Acres can be enrolled in a 

continuous signup at any time and are noncompetitive (if an offer is eligible, 

it is automatically accepted). In addition, the program tends to both pay 

higher rental rates and offer targeted incentives for continuous CRP acres.

Program expenditures have been fairly steady throughout the program’s 

history, increasing from $1.5 billion in 1990 to $1.9 billion in 2007 (in 

nominal dollars). The CRP’s per-acre rental payments are closely related to 

non-irrigated cropland rental rates, which were relatively stable in the years 

following 1997.5

However, beginning in 2006 there were several changes that influenced the 

program. First, over 16 million acres of CRP land enrolled in 1997 were set 

to expire in 2007. For several reasons, including administrative efficiency, 

the USDA sought to stagger the timing of these expirations. Hence, in 2006 

USDA offered 2- to 5-year re-enrollments and extensions to owners of 

contracts covering 27 million acres scheduled to leave the program between 

2007 and 2010. Contract holders covering about 23 million acres (83 percent) 

accepted.

Second, the Food, Conservation, and Energy Act of 2008 (2008 Farm Act) 

capped CRP enrollment at 32 million acres. This was achieved as of October 

1, 2009, by permitting approximately 2 million acres in expiring contracts to 

leave the program without an offer to renew or extend.

Last, starting in 2007 agricultural prices rose significantly, reaching a peak 

in the summer of 2008.6 For example, corn prices jumped from $3.03 in 

the 2006 market year to $4.20 in the 2007 market year, and then further 

increased to $5.40 in the summer of 2008. Although prices have subsequently moderated, the factors underlying the increase have not disappeared.7

Increasing Commodity Prices and the CRP

Given USDA predictions8 of high commodity prices for the next several 

years (relative to price levels throughout the CRP’s history), the opportunity 

costs of retiring cropland are likely to remain high. This could dampen landowner willingness to enroll in the CRP’s general signup (SWCS, 2008).

Several authors have considered the price-enrollment relationship. Secchi 

et al. (2009) used the EPIC model to estimate the environmental impacts 

of changes in CRP enrollment in Iowa due to increasing commodity prices. 

They conclude that under higher commodity prices, “maintaining current 

levels of environmental quality will require substantially higher spending 

3The EBI is an additive index that includes both environmental components 

and cost components. High-cost offers 

receive fewer points in the cost component. Hence, all else held equal, more 

productive land (that is, likely to have 

high offer prices) will have lower EBI 

scores and is less likely to be accepted 

into the program. Note that cost enters 

“linearly”—the EBI is not used to create a cost-benefit index. This tends to 

reduce the positive (negative) impact on 

EBI scores of very low (high) bids.

4In the large 1997 signups, about 60 

percent of the 22 million acres offered 

to the program was from already enrolled land. However, such lands were 

not given precedence—the EBI scores 

were used to rank all offers.

5USDA's National Agricultural Statistics Service (NASS) reports a rent 

production index (with a 1990 value 

of 100) that reached 136 in 1997, and 

then varied between 113 and 129 for 

the next several years. In 2006 and 

2007 this index rose, reaching 151 in 

2007 (http://www.nass.usda.gov/Publications/Ag_Statistics/2008/CHAP09.

PDF). Average corn prices were $2.43/

bushel in 1997, ranged from $1.94 to 

$2.42 between 1998 and 2005, and then 

rose to $3.04/bushel in 2006 (http://

www.nass.usda.gov/Publications/

Ag_Statistics/2008/Chap01.pdf).

6Abbot et al. (2008) identified three 

broad forces driving food price 

increases: change in production and 

consumption of key commodities, the 

depreciation of the dollar, and growth 

in biofuels production. Trostle (2008) 

identifies a similar set of factors and 

also notes adverse weather and policy 

responses.

7USDA projections show crop prices 

staying high through 2020.

8For example, USDA’s February 

2010 Agriculture Baseline Projection predicts fairly constant commodity prices that are greater than 

prices (between 1998 and 2005) when 

most current CRP contracts were 

enrolled (http://usda.mannlib.cornell.

edu/MannUsda/viewDocumentInfo.

do?documentID=1192).
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levels,” and argue for a targeted approach. To counteract the risk of higher 

crop prices' eroding the CRP’s ability to protect ecologically sensitive 

lands, Baker and Galik (2009) argue for several program modifications to 

increase income flows from CRP lands, such as allowing the sale of carbon 

offset credits.9 Although the commodity prices have dropped from the peaks 

reached in the summer of 2008, the impact of higher prices on the CRP’s 

effectiveness and attractiveness is still a concern. This report investigates the 

implications of probable USDA actions (such as increasing rental rates), and 

potential USDA modifications to the program, such as selective retention 

of current CRP lands, and the consequences of an active market for carbon 

payments.

9While USDA currently allows the sale 

of carbon offset credits on land enrolled 

in the CRP, existing and proposed 

carbon “cap-and-trade” programs generally consider CRP land ineligible to 

participate in carbon offset sales.
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Modeling CRP Enrollment

To examine how enrollment for the CRP general signup might change as 

prices and other factors change, we use two modeling strategies. Our likelyto-bid (LTB) model predicts what the CRP’s general signup acres would look 

like if the program were to start from scratch and enroll the entire acreage 

in one hypothetical signup. Our opt-out model treats current general signup 

acreage as a given and predicts which acres would withdraw given the 

chance of opting out. 

•	The likely-to-bid model (LTB) (see appendix 1, “Likely-To-Bid Model: 

Methodology”) is a simulation model that predicts how representative 

parcels of land will respond to a CRP program under different circumstances. The LTB is a steady-state model: it starts with a postulated set 

of parameters (such as commodity prices, crop yields, and production 

costs), holds them constant, and predicts the long-term distribution of CRP 

acreage. It also predicts the environmental impacts of these acres, using 

predictions of factor scores in the CRP’s Environmental Benefits Index 

(EBI).

•	The opt-out model (see appendix 2) uses contract data on all CRP acreage, 

which contain for each contract the actual value of CRP payments, the 

maximum payment permitted, and the EBI factors. 

In this analysis, we focus on acreage that will enter the program through 

general signups, and abstract from acreage enrolled via continuous signups. 

Since continuous signups offer higher rental payments than general signups, 

continuous signup acreage is less likely to be influenced by changes in 

commodity prices. 

Both models have their strengths and weaknesses. The LTB model is national 

in scope, with disaggregated measures of land quality and other agriculturally important factors. However, its weaknesses include a dependence on 

increasingly dated data (much of it from 1997), the use of approximation 

when assigning EBI factor scores to representative parcels, and the use of 

imprecise rules to determine which representative parcels would actually be 

offered to the program. 

The opt-out model uses actual CRP general signup contracts, which include 

precise measures of the EBI factors and rental rates. Modeling is based on 

actual rather than simulated participants. However, there are very limited 

data on land quality, which complicates estimates of the profitability of alternative uses of the land. And this dataset has no information on non-CRP land 

parcels. 
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Scenarios for Analysis

Our analysis of possible changes to CRP general signup enrollment is based 

on four scenarios (table 1). 

•	A Baseline that captures conditions when most CRP contracts were 

enrolled. This is a “one-shot” simulation that uses 2005 crop prices 

in order to calibrate the LTB model (it is not associated with USDA’s 

published baseline).

•	A MediumPrice scenario that reflects moderate price levels—we use 

prices prevalent in 2007.10

•	A stylized RFS (Renewable Fuels Standard) scenario that captures price 

impacts of increasing ethanol production from 6.5 billion gallons to 15 

billion gallons.11

•	A HighPrice scenario that reflects a high price regime—using prices prevalent in the summer of 2008.

These scenarios were developed based on historical commodity prices and 

may or may not reflect USDA’s current price projections.12 They are meant 

to illustrate the relationships between commodity prices and CRP enrollment 

and should not be considered projections of future trends. The commodity 

prices used in the scenarios increase as one moves from the Baseline to 

the HighPrice scenarios. For example, corn prices are almost 190 percent 

higher in the MediumPrice scenario than in the Baseline, and 140 percent 

higher in the HighPrice than in the MediumPrice scenario (table 2). Some 

commodity prices in the RFC scenario are somewhat higher than those in 

the MediumPrice scenario—for example, corn price is 12 percent higher, but 

soybean prices are basically unchanged.

For each scenario, two variants are analyzed: one that uses 2007 CRP rental 

rates and a second that captures CRP rental rates increases (given changes in 

cropland rental rates) via an across-the-board increase in these 2007 rates. 

Given the 2008 Farm Act’s changes to the CRP, all scenarios use a target 

of 30 million general signup acres. As of early 2009, there were about 33.6 

million acres of CRP land: 29.5 million in general signup and 4 million in 

continuous signup. By fall 2009, the CRP’s enrollment had shrunk to around 

31 million acres (with 27 million in general signup). Thus, although the use 

of a 30-million-acre target may overstate the size of CRP’s general signup 

over the next several years, it is predicated on past levels of enrollment and 

allows for consideration of alternative future targets. 

To compare scenarios, the following set of metrics is used.

•	Offered acres. In general, the more acres that are offered, the better the 

choices available to CRP administrators. 

•	Average environmental score per accepted acre (EEBI). The EEBI is 

the EBI without the cost factor. While not an ideal measure of the real 

environmental impact of CRP land retirement, it captures several environ10Note that 2007 prices are similar to 

prices seen in the spring of 2010.

11The choice of 15 billion gallons of 

crop-based ethanol is predicated on 

targets set in the 2015 Renewable Fuel 

Standard. In this research, we abstract 

from biodiesel, cellulosic, and other 

varieties of biofuels.

12These scenarios focus on the impacts 

of possible price increases. USDA’s 

February 2010 projections (http://usda.

mannlib.cornell.edu/MannUsda/viewStaticPage.do?url=http://usda.mannlib.

cornell.edu/usda/ers/94005/./2010/

index.html) predict a mild, short-term 

decline in commodity prices. However, 

these projected prices stay near the 

price levels used in our MediumPrice

scenario.
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Table 1

Description of scenarios

The following describes the scenarios used in the LTB and opt-out models. 

Table 2 details the commodity prices used in these scenarios.

Scenario Description

 Baseline This scenario serves as a baseline that reflects 

circumstances that prevailed before the recent 

increase in crop prices. Specifically, this scenario 

uses commodity prices, production costs, soil rental 

rates, and EBI factors extant in 2005. The baseline 

captures historical conditions, such as measures of 

environmental benefits, that one can contrast with 

what the CRP would provide as prices and policies change. Furthermore, comparison of baseline 

results to actual CRP offers and contracts indicates 

the accuracy of the LTB model.

MediumPrice This scenario reflects a continuation of current 

conditions. Specifically, its uses commodity prices, 

production costs, CRP rental rates, and EBI factors 

extant in 2007. Note that 2007 prices are similar to 

prices observed in spring 2010 (as are rental rates 

and EBI factor scores). Furthermore, prices in 2007 

incorporate the impacts of 6.5 billion gallons of 

ethanol production.

MediumPrice variant: 

higher rental rates 

(MediumPrice adjusted)

Same as the MediumPrice scenario, but includes 

an across-the-board increase in CRP rental rates of 

60%. This represents a possible response by USDA 

to increased commodity payments, hence increased 

non-irrigated cropland crop rental rates. 

Increase crop-based 

ethanol production to 

15 billion gallons (RFS)

To consider impacts of increased ethanol production as mandated by the Renewable Fuel Standard 

(RFS), we use the REAP (BRDi, 2008) model to 

generate estimates of prices that may prevail if 

crop-based ethanol production increases from 6.5 

billion to 15 billion gallons. These are combined with 

production costs, EBI factor scores, and CRP rental 

rates used in the MediumPrice scenario.

RFS variant:

increased rental rates 

(RFS adjusted)

Same as RFS, but includes an across-the-board 

increase in CRP rental rates of 60%.

HighPrice This scenario reflects a possible high-price environment. It is based on prices observed in summer 2008, along with production costs, EBI factor 

scores, and CRP rental rates used in the MediumPrice scenario.

HighPrice variant: 

much higher rental rates 

(HighPrice large adjusted)

Same as HighPrice, but includes an across-theboard increase in CRP rental rates of 120%. 

Notes: LTB = likely to bid, EBI = Environmental Benefits Index, REAP = Regional 

Environment and Agriculture Programming model, RFS = Renewable Fuel Standard.
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mental attributes and applies weights whose magnitudes reflect the value 

CRP administrators place on different environmental services/attributes. 

•	Forgone net agricultural revenue. This is a measure of the social costs of 

the program in terms of lost agricultural production.

•	Rental payments (total and per acre). This is the cost of the program to 

the Government. 

•	Regional distribution of CRP acres. This is how CRP acres occur across 

the 10 farm production regions (http://www.ers.usda.gov/briefing/arms/

resourceregions/resourceregions.htm).

Table 2

Prices used in scenarios (and their variants)

Scenario Corn Sorghum Wheat Soybeans Cotton

-----------------------------$/bu----------------------------- $/bale

Baseline 2.00 1.86 3.42 5.66 208.00

MediumPrice 3.39 3.21 6.08 9.00 254.00

RFS 3.80 3.32 6.13 9.03 257.00

HighPrice 5.40 4.90 7.25 12.25 364.00

Notes: The same prices are used in the adjusted and large adjusted variants of each scenario. 

RFS = Renewable Fuels Standard. 
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Influence of Prices: Likely-To-Bid Model

We begin by comparing the performance of the LTB model with the actual 

data from CRP contracts and offers (table 3). For the Baseline scenario, the 

LTB predicts that 51.9 million acres would be offered (with a hypothetical 

CRP signup from scratch), which is quite close to the 49.2 million acres 

offered between 1997 and 2007.13 Thus, the model’s ability to replicate 

observed results is qualitatively reasonable, though not precise. 

•	The Baseline overpredicts EEBI scores by about 10 percent, but does a 

good job of predicting actual contract rental rates.

•	Comparison of the regional distribution of acres (table 4) indicates that the 

rank order of regions identified by the Baseline is close to the actual order.

•	The Baseline indicates that, on average, over 35 percent ($19/acre) of 

an offer’s estimated rent ($47/acre average) is a premium over the LTB 

model’s predicted net returns ($28/acre on average) from that parcel. 

Kirwan et al. (2008) find that estimated premiums constitute 10-40 percent 

of the program’s rental payouts. Since the LTB assumes no competitive 

bidding (all landowners request their maximum rental payment), it is not 

surprising that the model estimates a premium toward the upper end of this 

range.

When judging the validity of the Baseline model, one must consider that 

both contracts and offers are spread over a 10-year period (1997-2007). Over 

this time span, commodity prices, input costs, and EBI criteria were mostly 

stable, but did change in minor ways. Thus, it would be unlikely for the 

Baseline (a “one-shot” simulation, using 2005 prices) to precisely match any 

summary statistic on actual contracts or offers.14

13The LTB identifies about 212 million 

eligible acres, based on cropping history, soil erodibility, and location.

14 The results from the Baseline suggest that, to the extent the underlying 

simulations are biased, so too will be 

our simulated results. However, since 

these biases are constant across simulations, comparisons of differences across 

simulations (where each simulation 

reflects a different scenario) are likely 

to reduce these biases. Therefore, the 

results from the LTB model are best 

viewed as indicators of broad trends 

that reveal useful insights.

Table 3

CRP general signup contracts and offers: actual and likely-to-bid 

predictions under the Baseline scenario 

Acres

EEBI score and 

normalized4

Forgone net 

agricultural 

revenue 

Rental 

payments 

Million Average/acre ----Average $/acre----

Actual Contracts1

Offers2

32.6

49.2

173 (0.43)

150 (0.37)

n.a.

n.a.

45

45

Baseline 

LTB

Contracts3

Offers3

30.0

51.9

188 (0.48)

165 (0.42)

28

32

47

51

Source: USDA/Farm Service Agency contract and offer files, and likely-to-bid (LTB) simulation 

results. 

n.a.-Not applicable

1 General signup contracts as of March 2008.

2 The Offers row aggregates actual offers to the CRP that were received in general signups 15 

through 33 (conducted between 1997 and 2007).

3 Both LTB rows are predicted values derived from the LTB model under the Baseline scenario.

4 Since the Environmental Benefits Index (EBI) factor weights varied over time (for example, 

the N6 “CPA” factor was dropped after signup 20), the normalized EEBI score is used. This is 

the EEBI score divided by the maximum number of EEBI points available in that signup. For 

the LTB simulations, the signup 26-33 maximum (395) is used as the normalizing value.
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Two variants of the MediumPrice scenario are examined: one that uses the 

2007 CRP rental rates and a second that increases these rental rates across the 

board.15 Far fewer acres (28.8 million) are offered in the first variant (which 

uses 2007 CRP rental rates) than in the Baseline, a result that we attribute 

to the run-up in prices that began in 2007. That is, the gap between a landowner’s projected revenue and CRP payments accounts for the fewer acres 

offered in the MediumPrice scenario. The second variant (the MediumPrice

adjusted scenario) adjusts for this gap by raising CRP rental rates 60 percent, 

which causes offered acres to rebound to 45 million (table 5).16 

Now consider our stylized RFS adjusted scenario, which accounts for 

commodity price impacts as ethanol output grows from 6.5 to 15 billion 

gallons, and which uses the same CRP rental rates as the MediumPrice 

15 CRP rental rates are based on a 

Soil Rental Rate (SRR). The SRR is a 

parcel-specific maximum bid that the 

USDA will accept. These values are 

based on measures of county averages 

of the rental rate for non-irrigated cropland. In general, the actual rent paid by 

the CRP is at, or near, the SRR (Kirwan 

et al., 2005).

16The results of other rental rate increases were in line with the 60-percent 

scenario. For example, with a 120-percent across-the-board increase, results 

(such as offered acres and average 

EEBI score) were close to the Baseline 

but at much higher costs.

Table 4

Geographic distribution of general signup CRP acres: actual and likely-to-bid (LTB) predictions 

under the Baseline scenario 

Farm Production Region

NE AP SE DE CB LS NP SP MT PA

--------------------------------------------------Share of acres------------------------------------------------------------

Actual

Contracts 0.3 1.8 2.8 3.4 12.4 7.3 25.2 15.7 23.8 7.1

Offers 0.4 2.2 3.5 3.3 12.6 7.7 27.7 15.9 21.0 5.9

Baseline

LTB

Contracts 1.0 4.4 4.9 3.2 13.5 12.5 26.9 10.5 18.1 1.5

Offers 1.3 4.4 3.5 2.7 12.7 13.4 32.4 10.6 14.6 4.2

Source: USDA/Farm Service Agency contract and offer files, and LTB simulation results.

Note: Due to rounding, rows may not add to 100 percent.

NE = Northeast: New Hampshire, Pennsylvania, Maine, Maryland, Rhode Island, Massachusetts, Delaware, Connecticut, Vermont, New York, 

New Jersey.

AP = Appalachia: West Virginia, Tennessee, North Carolina, Virginia, and Kentucky.

SE = Southeast: South Carolina, Alabama, Georgia, and Florida.

DE = Delta: Louisiana, Arkansas, and Mississippi.

CB = Corn Belt: Ohio, Iowa, Missouri, Indiana, and Illinois.

LS = Lake States: Minnesota, Michigan, and Wisconsin.

NP = Northern Plains: North Dakota, South Dakota, Kansas, and Nebraska.

SP = Southern Plains: Texas, Oklahoma.

MT = Mountain: Colorado, Utah, Arizona, New Mexico, Wyoming, Nevada, Idaho, and Montana. 

PA = Pacific: Oregon, California, and Washington.

Table 5

LTB predictions of acres offered, and accepted, in the CRP general 

signup MediumPrice, RFS, and HighPrice scenarios 

Scenario

Offered 

Acres

EEBI score1

and normalized 

Forgone net 

agricultural 

revenue1

Rental 

payments1

Million Average/acre ----Average $/acre----

MediumPrice 28.8 161 34 64

MediumPrice adjusted 44.7 179 48 83

RFS adjusted 42.7 176 47 82

HighPrice 22.3 159 27 62

HighPrice large adjusted 43.4 175 56 101

1EEBI score, forgone net agricultural revenue, and rental payments are computed using the 

30 million acres “accepted” by the LTB simulation. 

LTB = likely to bid; EEBI = Environmental Benefits Index, without the cost factor; RFS = 

Renewable Fuel Standard.
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adjusted scenario. Here, crop prices are slightly higher than in the 

MediumPrice adjusted scenario. Since per-acre CRP rental payments are 

the same in both scenarios, we would expect a slight contraction in offers 

under the RFS adjusted scenario, and in fact offered acres do decline about 2 

million (table 5). The average EEBI score decreases by about 2 percent. 

To further illuminate the impacts of increasing ethanol production, we track 

individual data points across the MediumPrice adjusted and the RFS adjusted 

scenarios (table 6):

•	Of the 30 million accepted acres in both scenarios, about 1.8 million acres 

differ. That is, there are 28.2 million acres that are accepted under either 

scenario, and 1.78 million acres that are replaced in the MediumPrice

adjusted scenario with different acres in the RFS adjusted scenario.

Choosing CRP Rental Rate Increases

Changes in commodity prices are likely to lead to changes in cropland rental 

rates. Since the USDA bases its CRP rental rates on county averages of nonirrigated cropland rents, changes in commodity prices will be reflected by 

changes in the CRP’s rental rates.

However, as discussed in Appendix 3, just what these changes will be is uncertain. USDA’s methodology to establish CRP rental rates combines surveyed 

values, such as provided by USDA’s National Agricultural Statistics Service (http://quickstats.nass.usda.gov/?source_desc=SURVEY&commodity_

desc=RENT&agg_level_desc=COUNTY), and feedback from county and 

State shareholders. Thus, it is not straightforward to predict what rental rates 

would be under different commodity price regimes.

Therefore, rather than attempting to construct detailed rental rate increases 

(say, that capture the possibility of higher increases in Iowa than in Texas), 

we use across-the-board proportional increases. While a number of levels were 

simulated, for brevity we present results from simulations that use a 60-percent 

across-the-board increase (i.e., the MediumPrice adjusted) and a 120-percent 

across-the-board increase (i.e., the HighPrice large adjusted). 

The choice of 60 percent (and 120 percent) was predicated on overall plausibility: they yield predicted CRP enrollment that retains most of the program’s 

current environmental characteristics without an implausible increase in program costs. That is: these rental rate increases are meant to represent a possible 

outcome, they are not meant to be an actual prediction of what will occur. 

For comparison purposes, note that (using county averages) actual CRP rental 

rates increased by 33 percent between 2000 and 2008, and by 20 percent between 2008 and 2010. 

Most importantly, although these across-the-board increases may not capture 

the actual increases that come to pass, they allow us to compare changes across 

simulations—comparisons that are the main goal of this research.
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•	Under the RFS adjusted scenario, exiting CRP acreage tends to be more 

productive land that received higher rent and delivered a higher EEBI. 

The new acres entering the program (as crop-based ethanol production 

ramps up) are more expensive (in terms of agricultural revenue and rental 

payments) than the average retained acre. 

•	New acres have lower average EEBI scores than either retained acres or 

exiting acres.17

In other words, the additional acres accepted under the RFS adjusted scenario 

are acres that failed to make the cut in the MediumPrice adjusted scenario, 

but become acceptable when higher quality, but more expensive, acres 

bypass the program due to a higher crop returns. 

As ethanol production increases in the RFS scenario, CRP acreage declines 

by 6.5 percent in the Corn Belt and 3.5 percent in the Lake States, while 

increasing 3.8 percent in the Northern Plains (table 7). Thus, the impacts 

of higher prices in the RFS adjusted scenario are more pronounced in more 

productive regions.

17When considering a parcel of land 

eligible for the CRP, high environmental benefits (high EEBI scores) are not 

necessarily correlated with low soil 

productivity. However, lands accepted 

into the program are likely to display a 

positive correlation between productivity and environmental benefits: since 

productive lands are likely to have a 

large offer price, for the EBI score to 

be sufficiently high the land must also 

have a high EEBI score.

Table 6

Acres entering and leaving the CRP general signup as crop-based ethanol 

production increases to 15 billion gallons

For acres that do 

not change as 

ethanol production 

increases1

For acres that 

enter the CRP as 

ethanol production 

increases

For acres that 

leave the CRP as 

ethanol production 

increases

Forgone net revenue (avg. $/acre) 47 61 137

Rental payments (avg. $/acre) 81 97 113

EEBI score (avg. score/acre) 178 147 192

1 This table is based on a comparison of acres offered, and accepted, into the CRP general signup program under the MediumPrice adjusted scenario and the RFS adjusted scenario, using the LTB model. In each scenario, 

30 million acres are accepted; 28.2 million acres are accepted in both scenarios, with 1.8 million acres differing. 

Note: EEBI = Environmental Benefits Index, without the cost factor.

Table 7

Geographic distribution of acres: LTB model predictions of MediumPrice adjusted, RFS adjusted, and 

HighPrice large adjusted scenarios

Farm Production Region2

NE AP SE DE CB LS NP SP MT PA

-----------------------------------Percent of all CRP general signup acres----------------------------------------

MediumPrice adjusted 1.5 3.8 5.5 3.5 10.5 10.1 29.2 11.8 18.6 5.0

RFS adjusted 1.5 3.7 5.3 3.5 9.8 9.8 30.3 11.9 18.7 5.0

Percent change1 -1.6 -0.7 -3.0 1.0 -6.5 -3.5 3.8 0.1 0.5 0.4

HighPrice large adjusted 2.1 3.2 4.1 2.8 8.0 8.4 32.5 11.0 21.9 5.9

Percent change1 35 -15 -26 -21 -24 -17 11 -7.0 17 18

1Percent change in region, compared to the MediumPrice adjusted scenario. 2See table 4 for States in each region.

Notes: LTB = likely to bid, RFS = Renewable Fuels Standard.
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We model the HighPrice large adjusted scenario using a 120-percent 

increase in CRP rental rates. Results here (in terms of offered acres and 

EEBI scores) are similar to the MediumPrice adjusted scenario (table 5), 

though average per-acre rental payments increase to $101/acre. When 2007 

CRP rental rates are used (that is, rental rates are not adjusted to accommodate high crop prices), less than 30 million acres of land are offered to the 

program.18

In comparing the “transition” of acres from the MediumPrice adjusted to 

the HighPrice large adjusted scenario, substantive changes are evident 

(table 8). Of the 30 million accepted acres, 7 million acres differ between 

the two scenarios. Unlike with transitions between prior scenarios, forgone 

revenues are higher (not surprising, given the high crop prices), as are rental 

payments.19 The land that enters under the HighPrice large adjusted scenario 

is both of lower agricultural productivity and lower environmental quality 

than the land that leaves.

In contrast to the comparison of MediumPrice adjusted and the RFS adjusted 

scenarios, regional shifts in CRP acres under the HighPrice large adjusted

are substantial. The Southeast, Delta, Appalachia, Lake States, and Corn Belt 

regions lose from 15 to 26 percent of their enrolled acres, while acreage in 

the Northern Plains, Northeast, Mountain, and Pacific regions expands by 

10-35 percent (table 7). 

An interesting feature of the CRP is that the program’s total allowable enrollment is capped, but there is no explicit budget cap. Nonetheless, program 

dollar outlays are a concern, as evidenced by inclusion of a cost factor in 

the EBI, so it is interesting to compare the budget/acreage impacts of the 

different scenarios.

18When per-acre CRP rental rates of 

the MediumPrice adjusted scenario 

are used in the HighPrice scenario, 

33.6 million acres are offered, and the 

average EEBI score decreases.

19We also investigated a transition 

from the Baseline to the HighPrice 

large adjusted scenario. Compared to 

a MediumPrice adjusted to HighPrice large adjusted transition, more 

acres leave the program (9.8 million 

acres), and acres that enter the CRP 

are cheaper but have substantially 

lower EEBI scores.

Table 8

Acres entering and leaving the CRP general signup under summer 2008 commodity 

price levels 

For acres that do 

not change as 

prices and rental 

rates increase1

For acres that 

enter the CRP as 

prices and rental 

rates increase

For acres that 

leave the CRP as 

prices and rental 

rates increase

Forgone net revenue (avg. $/acre)2 51 77 153

Rental payments (avg. $/acre)2 104 92 129

EEBI score (avg. score/acre) 178 163 179

1 This table is based on a comparison of acres offered, and accepted, into the CRP general signup program 

under the MediumPrice adjusted scenario and the HighPrice large adjusted scenario, using the LTB model. 

In each scenario, 30 million acres are accepted; 23 million acres are accepted in both scenarios, with 7 million 

acres differing.

2 Forgone agricultural revenue and rental payments are based on values from the HighPrice large adjusted

scenario.

Note: EEBI = Environmental Benefits Index, without the cost factor.
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Figures 2 and 3 display results from five scenarios, along with results from 

actual CRP contracts as of March 2009. Each graph displays the impact of 

increasing the number of accepted acres (where acres with the highest EBI 

scores are accepted first). Each graph examines a single variable. Figure 2 

shows the total costs of enrolling various acre levels. Figure 3 shows the 

average environmental component of the EBI score (the EEBI), per acre, at 

various acre levels. 

Figure 2 illustrates that the contracts as of 2009 (many of them dating from 

1997) are noticeably less costly than all the scenarios (though program costs 

are somewhat close to the Baseline). The HighPrice large adjusted scenario 

costs nearly three times as much as actual contracts when 30 million acres 

are enrolled. The MediumPrice adjusted and RFS adjusted scenarios (whose 

differences are due to projected changes in ethanol production) are quite 

similar.

What happens to general signup acreage if (say, due to budget pressures) 

2009 funding levels were imposed? The 2009 contracts indicate that about 

$1.1 billion is spent on the “best” 30 million acres (in terms of EBI score). 

If this $1.1 billion could not be expanded, under the other scenarios CRP 

acreage would substantially decline. For example, in the MediumPrice

scenario, only 20 million acres could be enrolled. And in the MediumPrice 

adjusted and RFS adjusted scenarios (where CRP rental rates are increased 

across the board), only about 15 million acres could be enrolled, with even 

0 3 6 9 12 15 18 21 24 28 31

0

1,000

2,000

3,000

Baseline

MediumPrice

RFS Adjusted

HighPrice large adjusted

Actual 2009 contracts

Cost of best 30 million

CRP acres (in 2009)

MediumPrice adjusted

Figure 2

Total cost of CRP acquisitions at different acreages, for several scenarios

Total cost (million dollars)

Total acres accepted (million acres)

The vertical axis lists the total acquisition cost for a given acreage.
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less should the commodity prices observed in summer 2008 (the HighPrice 

large adjusted scenario prices) be realized.

Conversely, what is the impact of adjusting CRP rental rates? Does offer 

quality improve? At all acreage levels, the “adjusted” scenarios yield peracre EEBI scores between those of the 2009 contracts and the 2005 baseline 

(fig. 3). In fact, the “adjusted” scenarios are quite similar, which confirms 

that increasing rental payments can help maintain environmental dividends. It 

is also interesting to note what happens in the MediumPrice scenario (where 

no adjustments to CRP rental rates are made): not only is the 30-million-acre 

signup goal unobtainable, but the quality of enrolled acres (as measured by 

average EEBI score) is substantially lower than under current contracts.

0 3 6 8 11 14 17 19 22 25 28 30

150

200

250

300

350

Baseline

MediumPrice

MediumPrice adjusted

RFS adjusted 2

HighPrice adjusted

Actual 2009 CRP Contracts

Average EEBI at 30 

million acres

Total acres accepted (million acres)

Per-acre average EEBI score

Figure 3

Average per-acre EEBI1 score at different CRP acreage levels, 

for several scenarios

The vertical axis lists the average EEBI across all acres, at a given acreage. For example, the 

graph for the MediumPrice scenario has a value of 176 at 21 million acres. This means that when 

the 21 million acres with the best EBI scores (in the MediumPrice scenario) are considered, their 

average EEBI score is 176.

1

The EEBI is the Environmental Benefits Index (EBI) without the cost factor.

2

The RFS (Renewable Fuels Standard) scenario reflects additional price impacts of heightened 

biofuels production. 
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Influence of Prices: Opt-Out Model

Although the LTB model does have appealing features (such as allowing 

new lands to enter), it ignores a key aspect of the CRP—that there are lands 

already in the program. And, these lands may be controlled by rural landowners who embrace the idea of land retirement and may behave differently 

than the average landowner. 

In addition, CRP contracts are for 10 to 15 years, so the notion of “restarting 

the CRP from scratch” is unrealistic. More likely, owners of lands currently 

enrolled in the CRP may choose not to re-enroll when their contracts expire. 

Or, the USDA may enact a policy to let lands more easily opt out of the CRP 

if more production is deemed necessary. 

Therefore, as a complement to the LTB model, we formulate an opt-out

model that focuses on currently enrolled CRP acreage. This analysis uses 

yield and crop price information to assign a “crop” to every CRP contract 

(see appendix 2). This assignation is a prediction of the crop that the land 

would return to without the CRP. Unlike other analyses (Lubowski et al., 

2008), idling land is not an option. The CRP land is assumed to return to 

some kind of crop production (perhaps with a few years’ lag).

The opt-out model can indicate the consequences of releasing CRP acreage. 

For example, to meet increased demands for crops (say, due to a large expansion in crop-based ethanol production), one approach is to release CRP land 

before its scheduled expiration date—to allow contracted acres to opt out. 

If such a policy were enacted, what would be the consequences of releasing 

acreage at different levels? To address this question, we sort contracts that 

our models predict would be likely to opt out and examine the tradeoffs if 

more or fewer acres are allowed to opt out. 

The opt-out model uses contract data as of March 2008.20 It assigns acreage 

in all CRP contracts to a crop type and predicts what would happen to 

program acres if the CRP were to abruptly end. Using this prediction of probable crop choices on current CRP lands, the model then predicts which landowners would opt out of the program if costless exit were permitted. These 

opt-out predictions are informed by parcel-level estimates (derived from the 

LTB model) of the correlation between the profitability of the CRP (relative 

to crop production) and the probability of being enrolled in the CRP.

We first consider scenarios using the prices from the MediumPrice scenario. 

Commodity prices used in these scenarios are higher than those prevalent 

when CRP contracts were signed. Thus, under the assumption that CRP 

rental rates do not change, a significant number of acres—9.4 million or 30 

percent of general signup acres as of March 2008—would leave the program 

if given the chance (table 9). Note that this analysis excludes the 4 million 

acres of continuous signup. By way of comparison, in 2006 CRP offered 

an extension option to CRP participants whose contracts were set to expire 

between 2007 and 2010. Approximately 15 percent of the acreage covered by 

these contracts (about 4 million out of 27 million) opted out.

20While not the most current contract 

data, March 2008 represents a time 

of relative stability in CRP contracts. 

Future analysis that examines re-enrollments and opt-outs of the 39th general 

signup, held in August 2010, may yield 

different insights.
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The opt-out model predicts that about half of acres exiting CRP would go 

into wheat and about a quarter would become either corn or soybeans (table 

9). The probability that a given acre will exit the CRP is a function of what 

crop type the model assigns to it. For example, for acres predicted to “return” 

to corn and soybeans, the opt-out rate is 32-35 percent, while for sorghum the 

rate is below 20 percent.

Next we consider the RFS scenario (table 10), which captures price increases 

due to expanding ethanol production from 6.5 billion to 15 billion gallons, 

without changing CRP rental rates from their 2007 levels. This expansion 

has a minor impact on CRP enrollment, with about 200,000 additional acres 

(from 9.4 to 9.63 million acres) choosing to leave the program. The most 

noticeable change is the 20-percent increase (from 32 to 39 percent) in the 

opt-out rate for acres assigned to corn, even though the share of exiting acres 

assigned to corn increases by a smaller amount.

Table 9

Acreage opting out of the general signup CRP by crop: MediumPrice scenario prices, with and without an 

increase in CRP per-acre rental rates

Barley Corn Cotton Oats Sorghum Soybeans Wheat Hay

Acres 

leaving 

(million)

Crop grown if CRP ended (% 

of all current CRP acres)1 3 13 3 1 3 12 50 15

Share of above acres that 

would opt out of the CRP2 35 32 1 4 17 35 40 4 9.4

As above, with 60% increase 

in soil rental rates (MediumPrice adjusted scenario) 2 17 11 1 2 11 20 26 2 5.9

1 The first row is based on a crop prediction: each CRP contract is “assigned” a crop that would be grown if the CRP ceased to exist (see appendix 2 for a description).

2 The second and third rows use this crop prediction and estimate what share of acres (that are assigned to a given crop) would opt out 

of the CRP. The second row uses CRP rental rates from the MediumPrice scenario, while the third row uses CRP rental rates used in the 

MediumPrice adjusted scenario. 

Table 10

Acreage opting out of the general signup CRP by crop: RFS scenario prices, with and without an increase 

in CRP per-acre rental rates

Barley Corn Cotton Oats Sorghum Soybeans Wheat Hay

Acres 

leaving 

(million)

Crop grown if CRP ended 

(% of all current CRP acres)1 3 15 3 1 4 11 48 15

Share of above acres that 

would opt out of the CRP2 35 39 1 4 17 35 40 5 9.6

As above, with with 60% 

increase in soil rental rates 

(RFS adjusted scenario)2 17 25 1 2 11 19 27 3 6.3

1 The first row is based on a crop prediction: each CRP contract is “assigned” a crop that would be grown if the CRP ceased to exist (see appendix 2 for a description).

2 The second and third rows use this crop prediction and estimate what share of acres (that are assigned to a given crop) would opt out of 

the CRP. The second row uses CRP rental rates from the RFS scenario, while the third row uses CRP rental rates used in the RFS adjusted

scenario. 

Note: RFS = Renewable Fuel Standard.
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If CRP rental rates are raised 60 percent (the adjusted scenarios), the number 

of acres that opt out is reduced in both the MediumPrice and RFS scenarios 

by about 35 percent. Except for corn, this level of reduction is consistent 

across crops. For corn, the use of higher rental rates causes far fewer acres to 

opt out in the MediumPrice scenario than in the RFS scenario, indicating the 

allure of biofuels-driven price increases for growing corn.

Under the HighPrice scenario, the model predicts that over 11 million acres 

(about 35 percent of total acres) would opt out. The most noticeable change 

from other scenarios is for soybeans, with more CRP acres predicted to go 

into soybeans, and more of these soybean acres predicted to opt out. Under 

the HighPrice large adjusted scenario (where rental rates are increased by 

120 percent), the opt-out rate for all crops drops by more than 50 percent, 

with especially large changes in corn, soybean, and wheat opt-out rates.21 

Results across scenarios indicate that much of the probable loss of CRP acres 

is likely due to price changes that have already happened, as indicated by the 

results under the MediumPrice scenarios. Additional changes, as reflected in 

the RFS and the HighPrice scenarios, increase opt-out rates somewhat, but 

much of this could be avoided with higher rental rates, albeit at higher overall 

program costs.

These findings reinforce the results from the LTB models. The impacts of 

current prices on CRP enrollment are substantial. Further price increases (and 

CRP effects) due to a large upswing in ethanol output are minor, while CRP 

impacts with crop prices as high as in summer 2008 are larger.

Since a likely goal of releasing CRP lands is to either increase commodity 

production or reduce program costs, our model allows the most productive 

land (having the highest rental rates) to be released first. The CRP’s Rental 

Rate is a reasonable measure of productivity, so we sort acres that want to 

opt out by decreasing rental rate. Note that acres in continuous signup are not 

released; our analysis indicates that about 300,000 acres in continuous signup 

would be candidates to opt out.

21The absolute measure (of acres that 

opt out) is sensitive to a number of 

assumptions, in particular assumptions 

about how one models the relationship 

between the relative profitability of the 

CRP and the decision to offer land to 

the CRP. These results use a smoothed 

relationship that may overpredict the 

responsiveness of enrollment to profitability of agricultural production.

Table 11

Acreage opting out of the general signup CRP by crop: HighPrice scenario, with and without an increase 

in CRP per-acre rental rates

Barley Corn Cotton Oats Sorghum Soybeans Wheat Hay

Acres 

leaving 

(million)

Crop grown if CRP ended (% 

of all current CRP acres)1 3 17 4 1 4 17 48 5

Share of above acres that 

would opt out of the CRP2 51 38 0 9 17 43 42 1 11.1

As above, with with 120% 

increase in soil rental rates 

(HighPrice large adjusted 

scenario)2 25 20 0 1 9 17 15 1 4.6

1The first row is based on a crop prediction: each CRP contract is “assigned” a crop that would be grown if the CRP ceased to exist (see appendix 2 for a description).

2 The second and third rows use this crop prediction and estimate what share of acres (that are assigned to a given crop) would opt out of 

the CRP. The second row uses CRP rental rates from the HighPrice scenario, while the third row uses CRP rental rates used in the HighPrice 

adjusted scenario. 
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Tables 12 to 17 show the cumulative impacts of releasing different levels 

of acres from the CRP. For example, if 3 million acres are released under 

the MediumPrice scenario, the average EEBI of these 3 million acres is an 

estimated 184. If 5 million acres are released, the average EEBI drops to 173, 

implying that these additional 2 million acres provide fewer environmental 

benefits than the prior 3 million acres. In all cases, the first acres to opt out 

tend to go to corn. And, as more acres opt out, the share of wheat acres 

increases.

Table 12

Effects of allowing general signup CRP contacts to opt out: MediumPrice scenario

Cumulative 

acreage 

opting out1 Rental rate2

Annual cost

decline3

EEBI 

per acre4

Corn

share 

per acre5

Corn yield

per acre6

Wheat

share5

Wheat yield 

per acre6

Million acres $/acre $ millions Percent Bushels Percent Bushels

1 69 88 203 44 141 14 61

3 39 194 184 28 132 37 50

5 32 265 173 19 131 53 41

8 23 348 163 13 128 65 35

9.4 7 377 161 12 127 68 33

Note: Average per-acre rental rate (SRR) for all CRP contracts is $47. EEBI = Environmental Benefits Index, without the cost factor.

1 Acres interested in opting out, sorted by descending soil rental rates (SRR). For example, the 2nd row refers to the 3 million “highest SRR” 

acres that would opt out. 

2 The soil rental rate (SRR) of the n millionth acre. Thus, the 8 millionth acre opting out has a SRR of 23.

3 Cumulative expenditure (over n million acres). For example, if 8 million acres were allowed to opt out, total CRP expenditures would decline 

by $348 million/year. 

4 The average EEBI (across n million acres). For example, if 8 million acres were allowed to opt out, their average EEBI score would be 163.

5 The percent (of n million acres) that go into corn [wheat]. For example, if 3 million acres were allowed to opt out, then 28% [37%] of these 

acres would go into corn/wheat.

6 Average corn/wheat yield (across n million acres). For example, if 3 million acres were allowed to opt out, the average corn [wheat] yield (of 

the acres that go into corn/wheat) would be 132 bushels [50 bushels] per acre.

Table 13

Effects of allowing general signup CRP contacts to opt out: RFS scenario

Cumulative 

acreage 

opting out1 Rental rate2

Annual cost

decline3

EEBI 

per acre4

Corn

share 

per acre5

Corn yield

per acre6

Wheat

share5

Wheat yield 

per acre6

Million acres $/acre $ millions Percent Bushels Percent Bushels

1 71 91 204 52 141 11 60 

3 41 202 187 37 131 30 51 

5 32 274 175 25 129 48 41 

8 24 360 165 18 126 60 35 

9.6 7 395 161 15 125 64 32

Notes: Average per-acre rental rate is $47. RFS = Renewable Fuel Standard, EEBI = Environmental Benefits Index, without the cost factor.

1 Acres interested in opting out, sorted by descending soil rental rates (SRR). For example, the 2nd row refers to the 3 million “highest SRR” 

acres that would opt out. 

2 The soil rental rate (SRR) of the n millionth acre. Thus, the 8 millionth acre opting out has a SRR of 24.

3 Cumulative expenditure (over n million acres). For example, if 8 million acres were allowed to opt out, total CRP expenditures would decline 

by $360 million/year. 

4 The average EEBI (across n million acres). For example, if 8 million acres were allowed to opt out, their average EEBI score would be 165.

5 The percent (of n million acres) that go into corn [wheat]. For example, if 3 million acres were allowed to opt out, then 37% [30%] of these 

acres would go into corn [wheat].

6 Average corn/wheat yield (across n million acres). For example, if 3 million acres were allowed to opt out, the average corn [wheat] yield (of 

the acres that go into corn/wheat) would be 131 bushels [51 bushels] per acre.
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Comparing across price scenarios, as prices increase (from MediumPrice 

to RFS to HighPrice), the number of opt-out acres increases (although by a 

relatively small amount in the MediumPrice to RFS case). Other statistics are 

less affected, though corn acres increase slightly (at low opt-out acreage) as 

one moves to higher price scenarios (i.e., from MediumPrice to RFS).

Comparing across different variants of each scenario, an increase in CRP 

rental rates reduces the number of opt-out acres, at higher program cost. In 

terms of proportional change, the HighPrice large adjusted scenario shows 

Table 14

Effects of allowing general signup CRP contacts to opt out: HighPrice scenario

Cumulative 

acreage 

opting out1 Rental rate2

Annual cost

decline3

EEBI 

per acre4

Corn

share 

per acre5

Corn yield

per acre6

Wheat

share5

Wheat yield 

per acre6

Million acres $/acre $ million Percent Bushels Percent Bushels

1 80 100 209 56 151 03 65

3 52 231 195 39 138 15 60

5 36 316 183 28 135 32 47

8 28 412 171 19 133 48 38

11.1 7 486 164 15 131 58 33

Notes: Average per-acre rental rate is $47. EEBI = Environmental Benefits Index, without the cost factor.

1 Acres interested in opting out, sorted by descending soil rental rates (SRR). For example, the 2nd row refers to the 3 million “highest SRR” 

acres that would opt out. 

2 The soil rental rate (SRR) of the n millionth acre. Thus, the 8 millionth acre opting out has a SRR of 28.

3 Cumulative expenditure (over n million acres). For example, if 8 million acres were allowed to opt out, total CRP expenditures would decline by 

$412 million/year. 

4 The average EEBI (across n million acres). For example, if 8 million acres were allowed to opt out, their average EEBI score would be 171.

5 The percent (of n million acres) that go into corn [wheat]. For example, if 3 million acres were allowed to opt out, then 39% [15%] of these 

acres would go into corn/wheat.

6 Average corn/wheat yield (across n million acres). For example, if 3 million acres were allowed to opt out, the average corn [wheat] yield (of 

the acres that go into corn/wheat) would be 138 bushels [60 bushels] per acre.

Table 15

Effects of allowing general signup CRP contacts to opt out: MediumPrice adjusted scenario

Cumulative 

acreage 

opting out1 Rental rate2

Annual cost

decline3

EEBI 

per acre4

Corn

share 

per acre5

Corn yield

per acre6

Wheat

share5

Wheat yield 

per acre6

Million acres $/acre $ millions Percent Bushels Percent Bushels

1 82 115 192 32 129 28 57

3 50 237 171 15 128 59 40

4 44 285 166 13 127 64 36

5.9 11 358 158 09 125 72 32

Notes: Average per-acre rental rate is $78. EEBI = Environmental Benefits Index, without the cost factor.

1 Acres interested in opting out, sorted by descending soil rental rates (SRR). For example, the 2nd row refers to the 3 million “highest SRR” 

acres that would opt out. 

2 The soil rental rate (SRR) of the n millionth acre. Thus, the 4 millionth acre opting out has a SRR of 44.

3 Cumulative expenditure (over n million acres). For example, if 4 million acres were allowed to opt out, total CRP expenditures would decline by 

$285 million/year. 

4 The average EEBI (across n million acres). For example, if 4 million acres were allowed to opt out, their average EEBI score would be 166.

5 The percent (of n million acres) that go into corn [wheat]. For example, if 3 million acres were allowed to opt out, then 15% [59%] of these 

acres would go into corn/wheat.

6 Average corn/wheat yield (across n million acres). For example, if 3 million acres were allowed to opt out, the average corn [wheat] yield (of 

the acres that go into corn/wheat) would be 128 bushels [40 bushels] per acre.
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the most dramatic within-scenario reduction. Interestingly, across all three 

pairs of scenarios the cost savings (of the opted-out acres) are roughly the 

same: larger across-the-board increases in CRP rental rates means fewer 

acres opt out, but each opted-out acre has a higher average SRR. Increasing 

the SRR multiplier also seems to reduce the corn share (at a given level of 

opt-out acres). For example, in the RFS adjusted scenario, at 2 million optedout acres, the corn proportion is 26 percent; in the RFS (without a higher 

SRR) scenario, it is 37 percent. Again, corn growers may be more sensitive 

to changes in relative net returns in a volatile price environment.

Table 16

Effects of allowing general signup CRP contacts to opt out: RFS adjusted scenario

Cumulative 

acreage 

opting out1 Rental rate2

Annual cost

decline3

EEBI 

per acre4

Corn

share 

per acre5

Corn yield

per acre6

Wheat

share5

Wheat yield 

per acre6

Million acres $/acre $ millions Percent Bushels Percent Bushels

1 93 125 197 50 132 18 58

3 52 257 174 26 128 50 41

5 40 350 164 18 126 62 35

6.3 11 395 160 15 124 67 32

Notes: Average per-acre rental rate is $78. RFS = Renewable Fuel Standard; EEBI = Environmental Benefits Index, without the cost factor.

1 Acres interested in opting out, sorted by descending soil rental rates (SRR). For example, the 2nd row refers to the 3 million “highest SRR” 

acres that would opt out. 

2 The soil rental rate (SRR) of the n millionth acre. Thus, the 5 millionth acre opting out has a SRR of 40.

3 Cumulative expenditure (over n million acres). For example, if 5 million acres were allowed to opt out, total CRP expenditures would decline by 

$350 million/year. 

4 The average EEBI (across n million acres). For example, if 8 million acres were allowed to opt out, their average EEBI score would be 164.

5 The percent (of n million acres) that go into corn [wheat]. For example, if 3 million acres were allowed to opt out, then 26% [50%] of these 

acres would go into corn/wheat.

6 Average corn/wheat yield (across n million acres). For example, if 3 million acres were allowed to opt out, the average corn [wheat] yield (of 

the acres that go into corn/wheat) would be 132 bushels [41 bushels] per acre.

Table 17

Effects of allowing general signup CRP contacts to opt out HighPrice large adjusted scenario

Cumulative 

acreage 

opting out1 Rental rate2

Annual cost

decline3

EEBI 

per acre4

Corn

share 

per acre5

Corn yield

per acre6

Wheat

share5

Wheat yield 

per acre6

Million acres $/acre $ millions Percent Bushels Percent Bushels

1 131 179 198 48 139 9 61

3 65 352 174 25 132 39 39

5 52 411 167 21 131 47 34

4.6 15 441 164 18 130 52 32

Notes: Average per-acre rental rate is $103. EEBI = Environmental Benefits Index, without the cost factor. 

1 Acres interested in opting out, sorted by descending soil rental rates (SRR). For example, the 2nd row refers to the 3 million “highest SRR” 

acres that would opt out. 

2 The soil rental rate (SRR) of the n millionth acre. Thus, the 5 millionth acre opting out has a SRR of 52.

3 Cumulative expenditure (over n million acres). For example, if 5 million acres were allowed to opt out, total CRP expenditures would decline by 

$411 million/year. 

4 The average EEBI (across n million acres). For example, if 5 million acres were allowed to opt out, their average EEBI score would be 167.

5 The percent (of n million acres) that go into corn [wheat]. For example, if 3 million acres were allowed to opt out, then 25% [39%] of these 

acres would go into corn/wheat.

6 Average corn/wheat yield (across n million acres). For example, if 3 million acres were allowed to opt out, the average corn [wheat] yield (of 

the acres that go into corn/wheat) would be 132 bushels [39 bushels] per acre.
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How Might a CO2 Offset Program 

Influence CRP Participation?

Even without commodity prices higher than those used in the MediumPrice

scenario, our results indicate that a substantial increase in program expenditures (for example, from $48 to $86 per acre) would be required to maintain 

the CRP in something close to its present configuration. Other funding mechanisms could reduce costs—we consider payments for carbon sequestration 

as an example.

In particular, land retirement (along with suitable land cover and management) can lead to carbon sequestration (Pineiro et al., 2009). Conceivably, 

lands that enroll in the CRP could generate GHG offsets that could be sold 

(by the landowner). While issues of leakage and additionality may complicate implementation (Baker and Galik, 2009), let us assume that landowners 

are allowed to sell the carbon sequestration of their retired acres, as measured 

by the yearly difference in carbon storage when in a conserving practice 

versus carbon storage (or release) in a continuous crop.22 

The LTB model is used, starting with prices and rental rates from the 

MediumPrice scenario. Sequestration rates by farm production region, which 

measure the additional carbon that conversion from a continuous crop to 

CRP would entail (table 18), are assigned to all data points in the LTB model. 

This sequestration is treated as a marketable carbon offset. Thus, a CRP acre 

will receive both a rental payment from USDA and a carbon offset payment 

from whoever purchases it.23

Three scenarios are examined. The first represents a consensus price for 

carbon offsets, and the latter two are selected to achieve results, in terms of 

EEBI scores and offered acres, that are similar to the current CRP.

22To reiterate, we abstract from issues 

of leakage, additionality, and permanence. Also, the CRP’s EBI does contain a small carbon sequestration factor. 

In these models, this factor is kept constant (so that carbon sequestration does 

not affect EBI scores significantly).

The sequestration rate is specific to a 

multi-State crop production region. It 

does not depend on what crop is being 

grown, nor does it incorporate parcelspecific soil information.

Table 18

CRP acreage characteristics under the MediumPrice scenario, with an active carbon market 

Offered 

acres

Average 

EEBI score 

GHG offset 

revenue 

Forgone 

agricultural 

revenue Rental payments

Million Average/acre ----------------Average $/acre----------------

$25/mt CO2 price, 1.0 

x soil rental rate 37.1 181 16 42 58

$30/mt CO2 price, 1.3 

x soil rental rate 46.9 191 19 53 70

$50/mt CO2 price, 1.0 

x soil rental rate 45.5 190 31.6 51 55

MediumPrice adjusted 44.7 179 .. 48 83

Note: Columns 2-5 are based on the “best” 30 million acres that the model accepts into the CRP.

The increased carbon sequestration rate, compared to continuous agriculture, per acre of CRP are from Eve et al. (2002); 

with values—in metric tons (mt) per acre—that range between 0.29 (for the Mountain farm production region) to 0.74 (for the 

Delta farm production region).

EEBI = Environmental Benefits Index, without the cost factor. GHG = greenhouse gas.
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1. A CO2 price of $25/ton, with no increase in rental rates;

2. A CO2 price of $30/ton, with a 30-percent increase in rental rates; and

3. A CO2 price of $50/ton, with no increase in rental rates.

If CO2 prices were as high as $50/ton, the CRP would be largely unaffected 

by higher commodity prices, even without an increase in rental rates (table 

18). At $30/ton (along with a 30-percent increase in rental rates), the LTB

predicts environmental benefits similar to the $50/ton scenario, but with 

a 27-percent increase in program costs (from $55 to $70 per acre). Total 

revenue per acre (rental rate plus offset revenue) in both these scenarios 

is $87-$89/acre, which is near the $83 rental value of the MediumPrice 

adjusted scenario.
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Conclusions

In this study, we examine how high commodity prices may affect the 

Conservation Reserve Program. As a voluntary program, landowners choose 

whether to offer their land to the program or to engage in agricultural 

production. Higher crop prices, as observed in summer 2008 or subsequent 

to mandated increases in crop-based ethanol production, are likely to sway 

some landowners in favor of agricultural production over conservation. 

The impacts of a change in the economic environment depend on a number 

of factors. Of course, the magnitude of price changes matters. So does the 

reaction of the USDA program administrators, such as by increasing peracre CRP rental rates as per-acre agricultural profits rise. The future goals of 

the program also matter, as CRP size is diminished and as the more closely 

targeted continuous signup portion of the program grows in importance. 

Also, commodity price changes influence not only the amount but the quality 

of lands offered: their agricultural productivity, environmental benefits, and 

geographic location.

We consider these issues using two modeling strategies. The likely-to-bid 

(LTB) model simulates what the CRP would look like if it were to start from 

scratch. The opt-out model takes current CRP contracts as a given, then 

simulates which enrollees would opt out of the program if given the opportunity. Both strategies have their strengths and weaknesses, and together offer 

complementary perspectives on the impacts of price changes.

With a mandated increase in biofuels production, one scenario (RFS) simulates the impact on crop prices due to higher crop-based ethanol output. 

Another scenario (HighPrice) incorporates the high prices observed in 

summer 2008, should crop prices settle at this higher level due to increased 

demand or other factors. 

One key finding is that increased ethanol output would have small additional 

impacts on the CRP. Using the LTB model, offered acres drop by about 2 

million acres (5 percent), and environmental benefits (as measured by the 

environmental component of EBI scores) decrease by about 2 percent. The 

opt-out model tells a similar story, with about 200,000 additional acres 

leaving the program. 

Overall, these results are commensurate with the relatively small change in 

prices predicted by the REAP model (Biomass Research and Development 

Initiative, 2008) for this increase in ethanol production. However, these 

impacts are on top of impacts due to the high commodity prices of the last 

3 years. We find that restarting the CRP, under prices and CRP rental rates 

similar to those currently observed (MediumPrice scenario), would lead to 

significant drops in offered acres and EBI scores and an increase in program 

costs. We also find that, if contracts were allowed to opt out (without any 

penalty), almost one-third might do so under the MediumPrice scenario.

Thus, in a new era of elevated crop prices—even without an increase in 

ethanol production—maintaining the CRP as currently configured will 

require an increase in rental payments. Given the established interest in the 

program, it might be possible to meet enrollment goals (say, 30 million acres 
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of general signup) with moderate increases in CRP rental rates. But this will 

likely mean accepting lower average EBI scores, as landowners with profitable but environmentally sensitive lands choose not to offer their land to the 

program.

With a goal of maintaining the current attributes of retired acres, the LTB

model finds that in the MediumPrice scenario, program costs will almost 

double (from about $45/acre to about $85/acre). In a similar vein, the opt-out 

model indicates that this level of rental rate increase reduces the number of 

acres that choose to opt out by about one-third.

Comparison of enrollment schedules demonstrates that if expenditures are 

capped at current levels and CRP rental rates are held constant, acreage 

enrollment will decline precipitously. Increasing the rental rate can offset 

this decline and lead to enrollment of acreage with environmental benefits 

comparable to current contracts, but at substantially higher program expenditures. In all scenarios, the opt-out model suggests that opt-out acres are most 

likely to end up in corn. However, as the level of acres allowed to opt out 

increases, wheat dominates. 

If carbon offsets could be sold by CRP enrollees, and if additionality 

concerns are ignored, the impacts of increased prices on CRP enrollment 

costs could be substantially reduced. For example, at a price of $50/ton of 

sequestered carbon dioxide, CRP acreage would be mostly retained, with a 

minor (about 16-percent) increase in cost.

In summary, as crop prices rise, the composition and environmental attributes of CRP acreage can be maintained so long as rental rates are updated to 

reflect current crop prices. Such an updating will not be cheap, and within 10 

years (as existing contracts expire) may almost double program costs. To the 

extent such an increase in rental rates does not occur, the program is likely to 

see fewer acres offered, with a commensurate decrease in the EBI scores of 

accepted acres.
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References

Abbott, Philip C., Christopher Hurt, and Wallace Tyner. 2008. “What’s 

Driving Food Prices?” Farm Foundation Issue Report, July.

Associated Press. 2008. “Higher Prices Could Pressure CRP Program,” 

Associated Press Newswires, 5 April. 

Babcock, B.A. 2008a. “When Will the Bubble Burst?” Iowa Ag Review

14(1). http://www.card.iastate.edu/iowa_ag_review/winter_08/article1.aspx

Babcock, B.A. 2008b. “Breaking the Link Between Food and Biofuels,” 

Iowa Ag Review 14(3). http://www.card.iastate.edu/iowa_ag_review/

summer_08/article1.aspx

Babcock, B.A. 2008c. “How Low Will Corn Prices Go?” Iowa Ag Review

14(4). http://www.card.iastate.edu/iowa_ag_review/fall_08/article1.aspx

Babcock, B.A., and C.E. Hart. 2008. “Options for the Conservation Reserve 

Program,” Iowa Ag Review 14(2): 6-7. http://www.card.iastate.edu/iowa_ag_

review/spring_08/article3.aspx

Baker, Justin, and Christopher Galik. 2009. “Policy Options for the 

Conservation Reserve Program in a Low-Carbon Economy.” Climate Change 

Policy Partnership working paper, June. http://nicholas.duke.edu/ccpp/ccpp_

pdfs/low.carbon.policy.pdf

Biomass Research and Development Initiative (BRDi). 2008. Increasing 

Feedstock Production for Biofuels: Economic Drivers, Environmental 

Implications, and the Role of Research. Biomass Research and Development 

Board, Dec.

Eve, M.D., M. Sperow, K. Howerton, K. Paustian, and R.F. Follett. 2002. 

“Predicted impact of management changes on soil carbon storage for each 

cropland region of the conterminous United States,” Journal of Soil and 

Water Conservation 57(4): 68a-73a.

Johansson, R., M. Peters, and R. House. 2007. Regional Environment and 

Agriculture Programming Model. U.S. Department of Agriculture, Economic 

Research Service, TB-1916, March.

Kirwan, Barrett, Ruben N. Lubowski, and Michael J. Roberts. 2005e. “How 

Cost-Effective Are Land Retirement Auctions? Estimating the Difference 

between Payments and Willingness to Accept in the Conservation Reserve 

Program,” American Journal of Agricultural Economics 87(5): 1239-1247.

Lubowski, Ruben N., Andrew Plantinga, and Robert Stavins. 2008. “What 

Drives Land-Use Change in the United States? A National Analysis of 

Landowner Decisions,” Land Economics 84(4): 529-550.

Parks, P.J., and I.W. Hardie, eds. 2003. The Economics of Land Use.

International Library of Environmental Economics and Policy. Aldershot, 

UK, and Burlington, VT: Ashgate.
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Pineiro, Gervasio, G. Esteban, Justin Baker, Brian C. Murray, and Robert B. 

Jackson. 2009. "Set-Asides Can Be Better Climate Investment Than Corn 

Ethanol," Ecological Applications, 19(2): 277-82.

Reuters. 2008. “Lower enrollment cap likely in US farmland reserve,” 

Reuters News, 3 April.

Secchi, Silvia, John Tyncal, Lisa A Schulte, and Heidi Asbjornsen. 2008. 

“Raising the Stakes,” Journal of Soil and Water Conservation 63(3): 

68a-73a.

Secchi, Silvia, Phillip W. Gassman, Jimmy R. Williams, and Bruce Babcock. 

2009. “Corn-Based Ethanol Production and Environmental Quality: A 

Case of Iowa and the Conservation Reserve Program,” Environmental 

Management 44: 732-744. 

Soil and Water Conservation Society (SWCS) and Environmental Defense 

Fund. 2008. Conservation Reserve Program (CRP): Program Assessment. 

April. http://www.swcs.org/documents/filelibrary/CRPassessmentreport_3BE

FE868DA166.pdf

Trostle, Ronald. 2008. “Fluctuating Food Commodity Prices: A Complex 

Issue With No Easy Answers,” Amber Waves, Nov. http://www.ers.usda.gov/

amberwaves/november08/PDF/FoodPrices.pdf 
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Appendix 1

Likely-To-Bid Model: Methodology

The likely-to-bid (LTB) model, developed jointly by USDA’s Economic 

Research Service (ERS) and Farm Service Agency (FSA), uses data from a 

number of sources. Field-specific data on land use and land quality are from 

the 1997 National Resources Inventory (NRI). NRI data are collected and 

maintained by USDA’s Natural Resources Conservation Service (NRCS) for 

more than 800,000 points of land throughout the contiguous United States. 

For each NRI data point, the LTB model determines CRP eligibility using 

point-specific information on cropping history, soil erodibility, location 

(within a conservation priority area), and other factors.

For these eligible points, the LTB assigns or calculates:

(1) Farm Service Agency (FSA) per-acre soil rental rate (SRR), 

(2) an estimate of net returns to agricultural production, and 

(3) an estimate of the Environmental Benefits Index (EBI) score. 

The SRR is computed using average non-irrigated cropland rental rates for 

the county (obtained from USDA’s National Agricultural Statistics Service 

(NASS)), adjusted for soil quality using the Soil Rating for Plant Growth 

(SRPG) obtained from NRCS. Using NRI cropping history data, agricultural net returns (NRET) are estimated using State-level information on crop 

prices (NASS), production costs (ERS), and other information (such as hay 

prices).

The ratio of soil rental rate over agricultural net returns (SRR/NRET) is 

used to determine whether the landowner would be interested in CRP 

enrollment. If the ratio exceeds a region-specific threshold value, the landowner is assumed to be “interested” in the program—he or she is “likely 

to bid.” For most of the country, a threshold value of 0.8 is used to reflect 

the fact that CRP returns are risk free. That is, the model assumes that riskaverse producers would be willing to accept a lower return in exchange for 

the reduction in risk realized through CRP enrollment. In some regions, 

however, land has significant “option” value—such as the value of 

converting lands to housing or commercial development. Land cannot be 

sold or converted to another use while a CRP contract is in force. To account 

for lost option value, threshold values are set at 1.3 in the Northeast and 1.2 

in the Appalachian States.

To determine the EBI score, a prediction of the value for each of the EBI 

factors is required. For most factors, NRI variables can be used to compute 

factor scores. For example, the EBI’s soil erodibility factor can be assigned 

using the NRI’s water (and wind) erodibility variables. Similarly, the NRI 

sample point’s location can be used to assign a value to the water quality 

zone factor. 

Since it depends on specific practices applied, the EBI wildlife factor cannot 

be exogenously assigned. Therefore, the LTB model assigns conservation 
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practices to each NRI point using information on eligible practices and geography, such as whether the point is located in a State conservation priority 

area. Practices are chosen to replicate the observed distribution of practices 

in the CRP as of 2005. Once practices are assigned, the EBI wildlife factor 

scores can be calculated.

Last, the cost factor component of the EBI is based on the SRR assigned to 

the point of land sampled by the NRI. We assume that “landowners” of these 

“representative parcels” would ask for the maximum allowed rent (the SRR). 

While landowners can ask for less than this maximum (and thereby increase 

the size of the cost factor), in actual practice most landowners do not reduce 

their offer. Over the last several signups, the average bid in CRP offers (as a 

share of the parcel’s SRR) has been over 95 percent. 

Threshold Values in the LTB Model

The LTB model treats NRI points as parcels of land whose landowners may 

be interested in the CRP. The LTB assumes that its prediction of net agricultural returns more accurately measures landowner profits than the soil rental 

rate (SRR) assigned to a parcel. Given this assumption, the LTB finds parcels 

where the net agricultural returns are low, relative to what the CRP would offer 

in rental payments, and assumes that owners of these parcels will take advantage of this discrepancy and offer the parcel to the program. If the SRR were a 

perfect predictor of net agricultural returns, then the LTB would predict that all 

(or none) of the eligible acres would be offered to the program.

Several factors can yield low net agricultural returns. For example, using 2005 

data, the model finds a weak correlation (0.21) between low soil quality (as 

measured by the SRPG) and willingness to enroll. This implies that the SRR 

does not fully capture the effects of soil quality when it is lower than the county 

average.

The use of the 0.8 (and 1.2 and 1.3) thresholds is based on historical use of 

the LTB (to help determine an EBI cutoff in several general signups). These 

values, while not entirely arbitrary, are not derived by a formal modeling process. That is, the LTB model does not reference actual CRP contracts. Calibrating the LTB model with actual contract and offer data, with a goal of 

deriving point-specific thresholds, would be a real improvement. However, a 

number of calibration methods were attempted, none of which yielded clearly 

superior results.1 

Thus, the ad hoc (but honored by prior use) thresholds (0.8 for most of the 

country) are used. While this is not an ideal approach,2 it should not seriously 

bias the overall results obtained from comparing simulations. (Unless landowner behavior changes dramatically as commodity prices change, differences 

across simulations should be relatively robust to the exact choice of thresholds.)

1A serious difficulty is presented by the existence of county caps (by law, no more 

than 25 percent of a county’s cropland may be enrolled in the CRP). These caps are 

constraining; for example, LTB discards almost a third of “interested” parcels due to 

county cap reasons.

2 Solutions to these and other modeling issues should become more precise as more 

recent data (such as 2007 NRI data) become available.
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Once the environmental factors (or the EEBI) and the cost factor have been 

assigned, a full EBI score is computed for each NRI point. The NRI points 

are then sorted by decreasing EBI score so that the “first accepted acres” are 

from sample points with the highest EBI scores. A high score could be due 

to a low SRR (a large cost component score) or from high environmental 

values (high EEBI score). Moreover, a point of land with high agricultural 

productivity (a high SRR) will have a high EBI score only if it has high environmental attributes (a high EEBI). This implies that “accepted” points with 

high SRRs will also have high EEBIs.

The LTB model can be used for scenario analysis by varying what EBI score 

to use as a cutoff level, and by changing variables that affect allocation of 

NRI points to the simulated CRP. These variations include estimates of 

SRR values, crop prices, crop yields, crop variable costs, and the EBI factor 

weights. LTB simulations can be compared statistically, say by examining 

average EBI scores of accepted acres, at a national or regional level. By 

combining simulations, one can also simulate land-use changes, such as the 

distribution of land (NRI points) moving into or out of the CRP.

The LTB model is run once per scenario. Additional robustness could be 

achieved by running the model several times per scenario and averaging the 

results, with each run distinguished solely by different draws of the random 

numbers used at several steps (i.e., to assign a practice to a NRI data point). 

However, given the intrinsic size of the NRI (800,000 points), a single run 

should capture the “law of large numbers” benefits that would be provided by 

multiple runs on a smaller dataset. Hence, the LTB is run only once per simulation. Note that to focus on changes of interest (say, increasing commodity 

prices), where random values are used (i.e., assigning wildlife factor points), 

the values used (for a given point) are the same across simulations.
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Appendix 2: Opt-Out Model: Methodology

The CRP is in place. Evidence suggests that many enrollees have been happy 

with the program. For example, in 2007 when enrollees were offered the 

chance to extend their contracts (from 2 to 5 years), with some offered immediate re-enrollment (for 10 years), about 85 percent of all enrollees accepted. 

While these offers occurred prior to the run-up in crop prices, the rate of 

acceptance does suggest that the attitudes of current CRP contract holders 

might be different than those of non-contract holders. 

Therefore, using general models to measure changes in the CRP (as prices 

change) may miss important details on probable participants, details that may 

be captured by working with actual CRP contracts.

However, CRP contract data lack crucial information. In particular, no variable indicates what contract holders would do with their land if it were not in 

the CRP. 

To analyze the impacts of changes in commodity prices, in the opt-out model 

we compare future agricultural profits to current CRP rents. As agricultural 

profits increase, a greater share of land currently in the CRP may revert back 

to crops. So, computing agricultural profits for the non-CRP alternatives (for 

every contract) is necessary, requiring a guess as to which crop would be 

grown on the land in the absence of the program. 

To formulate this guess, where possible, we use the REAP model to guide us. 

In particular, we posit some scenario (say, changed prices), modify the REAP 

model accordingly, and use the resulting REAP predictions to help assign 

CRP contracts to crop choices.

REAP is a partial-equilibrium model that determines U.S. regional crop and 

livestock production levels and national prices for 10 major crops and several 

livestock and agricultural product categories (Johansson et al., 2007). Import, 

export, and storage flows are included.

The following describes this “REAP-based” assignment of contracts to crops:

 1. For a given scenario (say, the RFS-mandated increase in biofuels production), REAP is used to predict the distribution of crop shares (for all 

cropland acres). 

a. For each of several crop types, a predicted “cropland share” is 

computed. This is the share of cropland acres (in a REAP region) 

that are in this crop type.

b. A separate schedule of cropland shares is computed for each of the 

46 different REAP regions.

c. The REAP scenario also provides estimates of the prices and variable costs for each crop type (on a national basis).

d. The crop types are barley, corn, cotton, oats, sorghum, soybeans, 

wheat, and hay. 
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2. County-level yield information is derived from NASS statistics (averaged between 2000 and 2005).

3. Relative productivity (RP) information is derived for each contract. This 

measures how much more (or less) productive this contract is than its 

county average (for the year of the contract).

RP is defined as: ContractSRR/AverageSRR_inCountyYear 

Example:

Contract JXT13 in county 19037 has an SRR of 135

The average SRR in county 19037 is 144

Then: RP = 135/144 =0.87

Thus, if the average soybean yield in 19037 is 50 bu/acre

then contract JXT13’s predicted soybean yield is 0.87 * 50 = 43.5.

4. To account for the possibility that acres in the CRP tend to be systematically different from observationally equivalent acres not in the CRP, 

State-specific adjustment factors are computed for each crop type (see 

box, “Computing an Adjustment Factor”). 

5. Compute an adjusted crop share, using the REAP share (from step 1) 

and the State-specific adjustment factor (from step 4) for each crop 

type. These are normalized to sum to 1.0. Note that all points in a REAP 

region will have the same adjusted crop share values.

6. Each NRI point is located within a REAP region and randomly assigned 

a crop, such that the assigned distribution of crops equals the predicted 

REAP-region crop shares.

7. Using price and variable cost information associated with the REAP 

scenario, yield information (from NASS statistics) for this crop (chosen 

in step 6) and a contract’s RP (from step 3), compute net agricultural 

returns for this randomly assigned “crop.”

8. Compare the net agricultural returns from step 7 to the contract’s 

scenario-specific SRR. If the net agricultural returns are sufficiently 

high, this contract is assumed to want to “opt out” of the CRP (or not 

re-enroll) and will go into the randomly assigned crop (assigned in step 

6). The determination of “sufficiently high” is based on a State-specific 

probability of enrollment schedule (see box “Computing a Probability of 

Enrollment Schedule”).
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Computing an Adjustment Factor

It is possible that acres actually in the CRP are systematically different from identical (in terms of land characteristics) acres that are not in the CRP. To account for 

this possibility, NRI data are used to compute differences in cropland shares between 

CRP and non-CRP lands. These differences are then used to compute adjustment 

factors.

For each State, an adjustment factor for each crop type is computed using the following algorithm:

1. Divide the 1997 NRI into CRP points and cropland points (discard points not 

in one of these categories).

2. For points that are in the CRP in 1997, look up the land use in 1982 (pre-CRP), 

and compute (State-specific) crop shares for each type t (t=corn, wheat, etc.):

 inCRPSharet = 1997 CRP acres in crop t in 1982 / total 1997 CRP acres

3. For the 1997 non-CRP acres, do the same (compute crop shares)

 nonCRPSharet = 1997 nonCRP acres in crop t in 1982 / total 1997 nonCRP acres

4. For each crop type (t), compute an adjustment factor:

crpAdjustt = inCRPsharet / nonCRPsharet

For example, if a State’s CRP acres had a high share of corn in 1982 (if inCRPsharecorn > nonCRPsharecorn), then one might expect that CRP acres in this State are 

more likely to go into corn than the State’s typical cropland acre. 

The crpAdjustt adjustment reflects systematic differences between CRP and nonCRP lands. Values greater than 1.0 indicate crops that the CRP land was more likely 

to have been from (based on State-level trends), and hence more likely to return to. 

The construction of this adjustment factor assumes that 1982 land uses are reflective 

of what might happen 25 years later. Since agronomic and economic factors have 

changed (i.e., average corn yield has increased from about 110 to 150 bu/acre), one 

may question the validity of this measure.

However, this measure considers the relative relationship between CRP and nonCRP land, in terms of overall agricultural productivity. If this has not changed significantly, this adjustment factor is likely to be valid. Such constancy, between CRP 

vs. non-CRP, is much more plausible.
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Computing a Probability-of-Enrollment Schedule

How can one determine whether a contract will stay in the CRP should its agricultural profits, or soil 

rental rate, change? While a generic rule of thumb could be used, such as the “0.8” factor used in the 

LTB model, a more data-driven approach is available. This approach uses farm production regionspecific probability-of-enrollment schedules that are calculated as follows:

Step 1. For each CRP contract, 2005 information is used to generate baseline information.

 a) Cropland share information (averaged across farm production regions (FPR) using NASS data 

from 2000 to 2005) is used to assign each CRP contract to a crop. This is similar to the REAP 

method described in the Opt-out methodology, with actual crop shares used rather than REAP 

predictions.

b) 2005 commodity price and variable-cost information are then used to compute a baseline 

netAgReturnContract (net agricultural profit) for this contract. 

Basically, this is a guess as to what the profitability of this contracted land would have been in 2005, 

if the contract had not been in the CRP.

Step 2. Compute probability-of-enrollment schedules. A separate schedule is computed for each 

FPR.

a) Using the predictions from an LTB model run with 2005 price information, retain all NRI 

points deemed eligible for the CRP. For these “eligible-for-CRP points,” extract the point’s 

Soil Rental Rate (SRRNRI) and net agricultural return (netAgReturnNRI). Note that both of 

these are computed by the LTB model.

b) Compute an SRR profitability ratio for each eligible NRI point: 

 SRRprofit= SRRNRI / netAgReturnNRI.

c) Recode SRRprofit to a class (from 1 to 50):

 SRRprofitClass= int(SRRprofit/0.1).

Note that an SRRprofitClass of “1” means “SRR is much less than agricultural profit,” while a value 

of 50 means “SRR is much higher than agricultural profit.”

d) For all NRI points in each of the n=1..10 farm production regions, and for c=1..50 (c is a 

SrrProfitClass computed in step 2c), compute:

 probEnrolln (c) = CRP acres in class c in FPR n / all cropland acres in class in FPR n

To compute the numerator, we use an NRI variable that indicates whether a point is enrolled in the 

CRP.

Each probEnrolln is smoothed (across c=1..50), and forced to be non-decreasing.1

This yields 10 FPR specific schedules that relate the relative profitability of the CRP (the SRRprofitClass) to a probability of being in the CRP (the probEnroll).

1That is, probEnrolln is adjusted so that the predicted probability of enrolling never decreases as the SRRprofitClass increases. In some regions, this is not always the case. Thus, this adjustment represents a fairly strong 

assumption: that (ceteris paribus) increasing the relative profitability of CRP enrollment will never lead to a 

reduction in enrollment probability.
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Step 3. For each contract, compute baseProbEnroll. 

a) Using the contract’s actual SRR, and the contract’s 2005 netAgReturnContract (derived in 

step 1b), compute cc: the 2005 SRRprofitClass for this contract.

b) Using the appropriate (FPR-specific) schedule computed in step 2e, compute 

baseProbEnroll=probEnrolln (cc).

This baseProbEnroll is used in the next step to help construct a retain probability for a contract, 

given new conditions (say, new prices).

Step 4. Given a new scenario, for every contract:

a) Predict a new SRR and a new netAgReturnContract (using the crop assigned to this contract 

in step 1a).

b) Using these, calculate a new SRRprofitClass.

c) Using this new SRRprofitClass and the appropriate (FPR-specific) probEnrolln schedule, 

look up a scenarioProbEnroll.

d) Create a normalized probability that a contract stays in the CRP:

 ProbRetain= scenarioProbEnroll/ baseProbEnroll.

e) If probRetain>1, set it equal to 1.0; otherwise, use probRetain to randomly decide if this 

contract stays in the CRP.

Basically, probRetain is the probability that this contract will stay in the CRP under this new 

scenario. 

The underlying notion is that contracts currently in the program may be unusual and not necessarily typical of all lands. For example,

•	 Contract XX, which is in FPR 1, has an srrProfitClass of 18.

•	 In FPR 1, probEnroll1 (18) = 20%.

•	 Since this landowner is enrolled, and since only 20% of “similar” landowners in FPR 1 are

enrolled: then this landowner is more interested in enrolling than (at least) 80% of other

similar landowners.

•	 Assume that under a new scenario the contract’s srrProfitClass equals 15, and

probEnroll1(15)= 15%; then probRetain=15/20 = 0.75.

This implies that 75 percent of the time this landowner will stay in the program. In other words, 

the 15 percent to 20 percent most “interested” landowners will now want to drop out of the program (which is a quarter of contracts that ceteris paribus were in profit class 18). Given our lack 

of further information on this contract, that means a quarter of the time the contract will want out 

(or, three-quarters of the time the contract will not want out).
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Opt-Out Model: Modifications under the 

HighPrice Scenario

The REAP-based model provides a fairly rigorous means of predicting what 

crops CRP contracts would go into if the program were to end. Because 

REAP is an equilibrium model that employs average yields and other market 

factors, it is unable to predict the potential distribution of crops when prices 

fall well outside the range of baseline prices driven by factors that are not 

included in the model. In particular, we could not tweak the REAP model to 

generate the summer 2008 prices (that were used in the HighPrice scenarios). 

Thus, for the HighPrice scenario, the above methodology was modified. 

The modification to the above (REAP-based) methodology occurs in the 

computation of crop shares for each contract (step 1 above). A “weighted” 

combination of observed county-specific crop shares, and crop profitability, 

is used to predict these crop shares. 

(A) Compute current land shares (LS). 

a. NASS data from 2000 to 2006 are used to predict State-specific 

crop acreage shares for each of the major crops. Specifically, land 

shares are computed by dividing the statewide acreage in each crop 

by total cropland (and averaging over the several years of data).

b. As described in step 4 above, a “CRP adjustment factor” is applied 

to these crop acreage shares.

c. The shares are normalized to sum to 1.0

(B) Generate a normalized profitability metric (PT)

a. As described in steps 2 and 3 above, county-specific yield information and contract-specific RP are used to compute contract-specific 

yields for each of several crops.

b. Price and cost information (for the scenario of interest) is 

combined with these yields to predict crop-specific profitability 

(for each contract).

c. The three crops with the highest predicted profit are retained, and 

a normalized ratio of “profit rate” is computed for each of these 

crops.

Example:

Assume that contract UK512 has the following net returns (per acre):

Corn: 50

Soybeans: 30

Sorghum: 10

Hay: 15

Wheat: 100

The top three are corn, soybeans, and wheat. The sum of profits is used 

as a normalizer: 50+20+100=180.
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The “normalized profit ratio” would be:

	 Corn: 	 50/180 = 28%

	 Soybeans: 	 30/180 = 17%

	 Wheat:	 100/180= 55%

This normalized profit ratio is an ad hoc metric, a proxy for the probability 

that a given crop would be the most profitable choice. An order statistic 

would generate rankings based on the variability of net returns for each 

crop on each point. Unfortunately, data of the requisite scope and detail to 

compute such theoretically superior models are not readily available.

(C) Combine land shares and normalized profitability

To allow both profitability and current land share to influence probabilities, a “weighted” combination is used:

Where:

Probi = probability that contract goes into crop type i

LSi = land share in crop type i, this county (from step A);

PTi = “normalized profit” share in crop type i, this county 

(from step B);

m = the sum is over all eight crops.

This crop assignation probability is then used in the same way as the REAP 

assignation—a crop type is assigned to each contract by randomly drawing 

a number and comparing it to the crop assignation probabilities, profits are 

estimated under the new price scenario, and this profit is used to generate a 

probability of enrollment. 

*

*

i i i

m m

m

LS PT prob LS PT = ∑
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Appendix 3: The CRP’s Soil Rental Rate

CRP rental rates are based on a Soil Rental Rate (SRR) that is assigned to 

every parcel offered to the program. The SRR is the maximum amount the 

landowner can request. In practice, most landowners request a payment equal 

to, or close to, the assigned SRR.

 The SRR is constructed using two components:

1. The average rental rate for non-irrigated cropland for the county

the offered land is located in. For most of the history of the CRP, the 

average rental rate was derived from the USDA’s Land Value Survey. 

Starting in 2008, a special NASS survey is used to compute county 

average rental rates for use in the CRP. Since rental rates can fluctuate, 

a moving average (over several prior years) is used to compute the 

average cropland rental rate for use in SRR computations. 

2. A soil-specific adjustment factor. This is used to increase (or decrease) 

the county average rental rate by as much as 50 percent. These adjustments are based on measures of crop productivity that are specific to 

soil types, and are normalized by the average soil productivity across 

the county. Thus, acres on soils whose crop productivity is greater than 

the county average will have an SRR greater than the county average 

cropland rental rate.

Commodity prices do not have a direct impact on the SRR. Rather, changes in 

commodity prices (or other factors, such as input prices) will affect the profitability of farming. If land markets are competitive, a portion of these changes 

should be transmitted to rental markets for agricultural lands. The size and 

speed of this transmission depends on the transience of price changes and the 

market power of renters versus landowners (Parks et al., 2003).