Corn-Based Ethanol Production and Environmental Quality:A Case of Iowa and the Conservation Reserve Program

Silvia Secchi Æ Philip W. Gassman ÆJimmy R. Williams Æ Bruce A. Babcock

Received: 12 December 2007 / Accepted: 28 July 2009 / Published online: 26 August 2009

� Springer Science+Business Media, LLC 2009

Abstract Growing demand for corn due to the expansion

of ethanol has increased concerns that environmentally

sensitive lands retired from agricultural production and

enrolled into the Conservation Reserve Program (CRP) will

be cropped again. Iowa produces more ethanol than any

other state in the United States, and it also produces the

most corn. Thus, an examination of the impacts of higher

crop prices on CRP land in Iowa can give insight into what

we might expect nationally in the years ahead if crop prices

remain high. We construct CRP land supply curves for

various corn prices and then estimate the environmental

impacts of cropping CRP land through the Environmental

Policy Integrated Climate (EPIC) model. EPIC provides

edge-of-field estimates of soil erosion, nutrient loss, and

carbon sequestration. We find that incremental impacts

increase dramatically as higher corn prices bring into

production more and more environmentally fragile land.

Maintaining current levels of environmental quality will

require substantially higher spending levels. Even allowing

for the cost savings that would accrue as CRP land leaves

the program, a change in targeting strategies will likely be

required to ensure that the most sensitive land does not

leave the program.

Keywords Agricultural markets � Ethanol �Conservation Reserve Program � Environmental quality �Environmental Policy Integrated Climate model

Introduction

Growing demand for corn due to the expansion of ethanol

has increased concerns that higher levels of corn produc-

tion might cause environmental damage. Higher crop pri-

ces could negatively affect soil and water resources as

farmers till more land and remove environmentally sensi-

tive land from conservation programs, including the Con-

servation Reserve Program (CRP) and the Wetlands

Reserve Program. Additionally, intensification of produc-

tion could also lead to larger nutrient and soil losses as

farmers attempt to increase their yields. In this article we

focus on the environmental benefits that would be lost if

land is taken out of the CRP.

The CRP program pays landowners an annual rental

payment in exchange for their agreement not to plant their

contracted land. Contract duration is between 10 and

15 years. The CRP was authorized in the 1985 farm bill

during a period when the United States had excess supplies

of wheat and feed grains. Thus, it is not surprising that the

program began with an objective of maximizing the

amount of land taken out of production. In the early 1990s,

enrollment criteria were changed and land began to be

accepted into CRP based on the level of offered environ-

mental benefits and cost. Today the program attempts to

maximize environmental benefits. There is an ample liter-

ature discussing the environmental benefits of the CRP

program, which tends to enroll less productive and more

environmentally sensitive land (Lubowski and others 2006;

Claassen and others 2001; Babcock and others 1996).

S. Secchi (&)

Agribusiness Economics, Southern Illinois University,

Agriculture Building, Mailcode 4410,

1205 Lincoln Drive, Carbondale, IL 62901, USA

e-mail: ssecchi@siu.edu

P. W. Gassman � B. A. Babcock

Center for Agricultural and Rural Development,

Iowa State University, Ames, IA, USA

J. R. Williams

Blackland Research and Extension Center, Temple, TX, USA

123

Environmental Management (2009) 44:732–744

DOI 10.1007/s00267-009-9365-x

Therefore, a return of CRP land to crop production is likely

to lead to lower environmental quality. There is little doubt

that the increased crop prices as a result of expanded bio-

fuel production will have consequences for CRP. A recent

study has analyzed impacts at a national level in an

aggregated manner, with a focus on the impact that crop

production on CRP land would have on crop prices (Tok-

goz and others 2007). Another study has focused on the

logistical aspects of harvesting, storing and transporting

biomass in the southern Great Plains of the United States

(Mapemba and others 2007).

This study extends the current literature by focusing on

the link between crop production on CRP land and envi-

ronmental impacts. It focuses on the state of Iowa because

Iowa has almost three quarters of a million hectares of CRP

land, it produces more ethanol than any other state, and it

produces the most corn. Thus, an examination of the

impacts of higher crop prices on Iowa land moving out of

the CRP and the resulting impacts on soil erosion, nutrient

losses, and carbon sequestration will give insight into what

we might expect nationally in the years ahead if crop prices

remain high because of ethanol production.

Data Description and Modeling Approach

Figure 1 shows the location of CRP parcels in Iowa by

county in 2008 according to the US Department of Agri-

culture Farm Service Agency (USDA-FSA), which

administers the program, and Iowa’s ecoregions. In order to

identify the characteristics of the land enrolled in the

program, we use a land use geographic information system

(GIS) layer obtained from the Farm Service Agency (FSA)

combined with the Iowa Soil Properties and Interpretations

Database (ISPAID) GIS soil data layer (Iowa Cooperative

Soil Survey 2003) and the Soil Survey Geographic Data-

base (SSURGO) GIS soil data layer (USDA Natural

Resources Conservation Service 2006). The data we

obtained from FSA does not specify the land cover that the

CRP land is in. However, the great majority of CRP land in

Iowa is planted with grasses (FSA 2007). Therefore, we

assume that the whole area is planted in bromegrass.

Comparing the CRP land to the full soil layer for Iowa,

it is apparent that the CRP land is of a lesser quality for

agricultural purposes. We focus here on a few indicators to

run the comparison: the Corn Suitability Rating, the Highly

Erodible Land Code, and slope. The HEL code refers to

land classified by the Natural Resource Conservation Ser-

vice in Land Capability Class 4, 6, 7, or 8; or land that, if

used to produce an agricultural commodity, would have an

excessive annual rate of erosion as determined by the

Universal Soil Loss Equation and the wind erosion equa-

tion. The three categories are 1 for a highly erodible map

unit, 2 for a potentially highly erodible map unit, and 3 for

a not highly erodible map unit. To measure productivity,

we use the Corn Suitability Rating (CSR), which is an

index from 0 to 100 that measures land’s productivity in

corn production. CSR data was obtained from the ISPAID

soil data layer (Iowa Cooperative Soil Survey 2003).

Table 1 shows the characteristics of CRP land, all Iowa

land, and land planted to either corn or soybeans (the two

dominant crops in Iowa) in 2002 (Iowa Department of

Des Moines Lobe

Northwest Iowa Loess Prairies

Paleozoic Plateau

Iowan Surface

Loess Hills and Rolling Prairies

Southern Iowa Rolling Loess Prairies

Interior River Lowland

Central Irregular Plains

Loess Hills

Missouri Alluvial Plain

Hectares 375 - 1,8 1,868 - 3,0 3,094 - 3,8 3,813 - 4,9 4,903 - 6,3 6,385 - 7,9 7,978 - 10,310,342 - 12,612,648 - 15,315,306 - 21,6

EcoregionsFig. 1 Location of CRP in

2008 (quantiles), and Iowa

ecoregions

Environmental Management (2009) 44:732–744 733

123

Natural Resources 2004). As shown, CRP land is, on

average, less productive, more erodible, and it has a higher

slope than either all of Iowa land or all land planted to corn

and soybeans.

There are some substantial differences in land charac-

teristics within CRP areas as well. Figure 2 illustrates how

land enrolled in CRP in North Central Iowa is of higher

productivity than CRP land in the rest of the state. This

area corresponds to the Des Moines Lobe geological for-

mation, the highly fertile southern extremity of the North

American Prairie Pothole region, and it includes some of

the most fertile soils in the world (Fig. 1).

Land in this region is also less erodible (Fig. 3), as

measured by the proportion of CRP land that is classified

as Highly Erodible Land (HEL). Because of this higher

productivity, enrolling land into CRP in North Central

Iowa is more expensive (Fig. 4). Higher erodibility

and, correspondingly, lower rental rates are the reasons

why more land is enrolled in southern and northeastern

Iowa.

We construct CRP supply curves by estimating whether

each of the land parcels currently enrolled in CRP would

earn more by remaining in the program or by being crop-

ped for various corn and soybean prices. Thus, the net

returns from cropping represent the opportunity cost, or

next best alternative, for the land currently in CRP. The key

factors in this calculation are crop prices, crop yields, and

CRP rental rates. We use FSA Soil Rental Rates (SRR) as

an approximation of CRP payments. Because there are

several categories of CRP in which land can be enrolled,

actual payments do not always match a parcel’s SRR.

However, using SRR gives us a reasonable approximation.

According to the FSA, in 2004, the last year for which

county-level outlays are available, payments for CRP rental

Table 1 Average land characteristics in Iowa

Hectares CSR HEL Slope (%)

CRP lands 729,508 53 1.76 8

All Iowa land 14,576,062 62 2.17 7

Corn and soybean land (2006) area (planted) 9,210,526 71 2.46 5

CSR Corn Suitability Rating. The CSR is an index from 0 to 100 that measures land’s productivity in corn production; HEL code Highly erodible

land code. This refers to land classified in Land Capability Class 4, 6, 7, or 8; or land that, if used to produce an agricultural commodity, would

have an excessive annual rate of erosion as determined by the Universal Soil Loss Equation and the wind erosion equation. (The code has three

possible values: 1 = Yes, this map unit is highly erodible; 2 = This map unit is potentially highly erodible; 3 = No, this map unit is not highly

erodible) Slope is expressed on a percentage scale based on the difference in the number of meters of rise or fall per 30 m of horizontal distance

Average CSR for CRP land35 - 4242 - 5050 - 5151 - 5555 - 5858 - 6060 - 6363 - 6565 - 6767 - 71

Fig. 2 Corn suitability rating of

CRP land by county (quantiles)

734 Environmental Management (2009) 44:732–744

123

rates in Iowa amounted to $152,296,812, and overall CRP

payments amounted to $179,349,226 (FSA 2004). Using

the SRRs, our estimate is that it would cost $181,237,348

to enroll the 2004 CRP area in Iowa. Thus, our estimate is

quite close to the actual outlay. Moreover, our estimates of

the county-level payments are very closely correlated to

FSA’s reported county-level payments, with a correlation

coefficient of 0.9915.

We obtain predicted crop yields by constructing a

regression equation of corn yield on CSR and adjusting the

average yield so as to calibrate to realistic current yields.

Both CSR and raw yield information were obtained from the

Average CRP payment /ha160 - 200200 - 235235 - 255255 - 267267 - 279279 - 286286 - 299299 - 316316 - 329329 - 354

Fig. 4 County average per

hectare rental payment for CRP

land (quantiles)

% Highly erodible CRP land3 - 1111 - 1616 - 2525 - 4141 - 5757 - 6363 - 6868 - 7373 - 7979 - 91

Fig. 3 Percentage highly

erodible land in CRP by county

(quantiles); Highly erodible

land is either classified in Land

Capability Class 4, 6, 7, or 8; or

land that, if used to produce an

agricultural commodity, would

have an excessive annual rate of

erosion as determined by the

Universal Soil Loss Equation

and the wind erosion equation.

The slope is expressed on a

percentage scale based on the

difference in the number of

meters of rise or fall per 30 m of

horizontal distance

Environmental Management (2009) 44:732–744 735

123

ISPAID soil data layer. We use a linear equation, without

intercept: Corn yield (tons/ha) = 0.141242938 * CSR. We

use a similar methodology to predict soybean yields. The

equation used for soybeans is Soybean yield (tons/

ha) = 0.045057162 * CSR.

For a given level of crop price we then estimate which

CRP parcels would be returned to production and which

would remain enrolled in CRP. The environmental impacts

of growing crops on the land that leaves CRP are estimated

through the Environmental Policy Integrated Climate

(EPIC) model, originally developed by the US Department

of Agriculture Agricultural Research Service (ARS). The

EPIC model (Williams and others 1984; Williams 1990,

1995; Izaurralde and others 2006) is a field-scale model

that is designed to simulate drainage areas that are char-

acterized by homogeneous weather, soil, landscape, crop

rotation, and management system parameters. It operates

on a continuous basis using a daily time step and can

perform long-term simulations of hundreds of years. A

wide range of crop rotations, tillage systems, and other

management practices can be simulated with EPIC. The

most recent versions of EPIC feature improved soil carbon

cycling routines (Izaurralde and others 2006) that are based

on routines used in the Century model (Kelley and others

1997). EPIC provides crop yield and biomass estimates as

well as estimates of soil erosion, nutrient loss, carbon

sequestration, and other edge-of-field environmental indi-

cators. The model has been applied to a wide range of

environmental problems in many different regions across

the globe as documented in Gassman and others (2005).

This approach of estimating supply curves of environ-

mental damage closely follows the approach of Pautsch

and others (2001) and allows us to make parcel-level

estimates of the environmental impacts of land returning to

crop production for various crop prices. To determine net

returns to crop production, we use 2007 cost-of-production

budgets for Iowa (Duffy and Smith 2007) to obtain the

appropriate variable costs. We restrict the analysis to no till

corn soybean rotations and conventional till continuous

corn. The reason is that cropland in Iowa has traditionally

been in 2-year corn soybean rotations. In more recent

years, there has been an increase in continuous corn due to

high prices for that crop. Thus, these two rotations repre-

sent the two most likely choices for farmers returning their

CRP parcels into production. Since there is evidence that

continuous corn production delivers higher yields and

profits under conventional tillage (Vyn and others 2000;

Wilhelm and Wortmann 2004; Vetsch and others 2007),

continuous corn is simulated as under a conventional till-

age regime. Conventional tillage is simulated as a pass of a

chisel plow in the fall after the harvest and a tandem disk

and field cultivator in the spring before planting.

For highly erodible land, conservation compliance

requirements limit tillage practices, and therefore the most

likely crop choice for farmers returning marginal CRP land

to production is no till corn soybeans. For the no till corn

soybean rotation, the per-hectare costs are $672.1 for corn

and $393.3 for soybeans. The per-hectare costs for continu-

ous corn are $770.6. To calculate the net supply curves if the

land is not highly erodible, the determination of its allocation

is given by Max{pCB, pCC, pCRP}, where pCB, pCC, and pCRP

denotes average returns to a corn soybean rotation, to con-

tinuous corn, and to CRP. In order to be consistent with the

existence of conservation compliance measures, as noted

above, we also assume that, on highly erodible land, the

choice set is limited to Max{pCB, pCRP}. As there is agro-

nomic evidence that there is a yield drag from growing corn

after corn, even after adjusting fertilizer rates and using

conventional tillage (Vyn and others 2000; Wilhelm and

Wortmann 2004), we assume a 3% yield loss for corn grown

after corn. The decision about whether to participate in the

CRP program is therefore based on the 2-year profit margins.

For simplicity, we do not use any discounting and we

abstract from risk to aversion considerations. We use a price

wedge between corn and soybeans which approximates the

recent equilibrium between the two markets and is consistent

with both recent historical trends and some long-term pro-

jections (Elobeid and others 2006). The wedge is shown in

Table 2. The model could easily accommodate any other

type of relationship between the two prices.

To estimate the environmental impact of cropping land

previously set aside from production, we use the EPIC

model to estimate 30-year averages for soil erosion,

nitrogen and phosphorus loss, and carbon sequestration.

The EPIC model was run using daily climate data

generated on the basis of historical monthly statistical

weather parameters from 26 weather stations available in

the EPIC weather generator database for the state of Iowa.

The generated weather parameters from these stations were

allocated to each soil/CRP parcel on the basis of proximity.

We also used the Soil Survey Geographic Database

(SSURGO) to run EPIC, because it has all the required soil

Table 2 Crop prices used in the analysis

Corn price per ton Soybean price per ton Difference

78.74 202.09 141.72

98.42 220.46 140.41

118.11 238.83 139.10

137.79 263.08 137.79

157.48 422.55 136.47

177.16 284.76 135.16

196.84 312.32 133.85

736 Environmental Management (2009) 44:732–744

123

information to run the model (USDA Natural Resources

Conservation Service 2006). Nitrogen and phosphorus

losses in surface pathways are estimated in EPIC via sol-

uble losses in surface runoff and by attachment to sedi-

ment. Nitrate losses can further occur in subsurface

pathways through leaching to groundwater, outflow

through tiles to streams, and by lateral subsurface flow.

Much of the leached nitrate in the soil profile in intensely

tiled and row cropped areas, such as the Des Moines Lobe

region in north central Iowa (Fig. 1), is exported to stream

systems through the tiles instead of leaching to ground-

water. It can be assumed that much of the leached nitrate

would be removed through tile drains in cropped areas

managed with subsurface drainage, as demonstrated by

Chung and others (2001, 2002) in validation studies of

EPIC for tile-drained field sites in southern Minnesota and

northeast Iowa. However, it was assumed in this study that

subsurface tile drains do not exist below the known CRP

fields, due to fact that the vast majority of CRP fields are

located on higher sloped marginal land that does not

require subsurface drainage. Thus, nitrate losses via tile

drainage would not be considered to be a loss pathway for

CRP managed land.

To replicate the management differences between crop

rotations, we assume a fertilizer rate application of

134.4 kg/ha for nitrogen in corn planted after soybeans and

a rate of 201.6 kg/ha for corn planted after corn. Moreover,

we assume a phosphorus rate of 67 kg/ha for corn and

45 kg/ha for soybeans. The phosphorus rates were obtained

from the Agricultural Resource Management Survey

(ARMS), conducted by the USDA (USDA 2000–2006).

The ARMS survey has a very small sample of corn after

corn management practices, therefore the nitrogen rates

were obtained from the Corn Nitrogen Rate Calculator

(Sawyer 2006; Sawyer and others 2006). The Calculator

uses a large database of nitrogen response trials for the State

of Iowa to determine the optimal rates of nitrogen appli-

cation by rotation. The rates correspond to those optimal for

a price ratio of price per pound of nitrogen divided by the

expected per bushel corn price of 0.1. For example, nitrogen

at $0.4/lb ($0.88/kg) and corn at $4/bu ($157.47/Mt) is a

0.10 price ratio (Sawyer 2006). The timing of application

was assumed to be in the Spring because the rates were

derived from spring and sidedress fertilizer applications.

This is likely to result in more conservative nitrogen losses

than if Fall applications are simulated.

Planting is simulated as occurring at the beginning of

May throughout the State, while corn harvesting is in the

middle of October and soybean at the end of September.

The EPIC model was run on all the SSURGO soils

present in the CRP land, and the results we report are

weighted by the area of each soil in the CRP parcels. The

area was calculated by overlaying the SSURGO soils GIS

layer with the CRP GIS layer. Almost 8,400 different

SSURGO soils were simulated in EPIC.

The major nitrogen cycling components were also

incorporated in the simulations based primarily on standard

approaches that have been used in the model as described

by Williams (1990, 1995). Two different approaches have

been used in the EPIC model over its history to simulate

nitrogen mineralization: straight mineralization from

organic nitrogen to nitrate (NO3) versus transformation

from organic N to ammonia (NH3) first, and then further

transformation to NO3. The latter approach was used for

this study, which was believed to more realistically capture

the higher mineralization rates of the high carbon content

soils simulated in this study and also allows for simulation

of nitrification and volatilization. A new alternative deni-

trification routine option developed by Kemanian (2008,

Personal communication. Blacklands Research and

Extension Center, Texas AgriLIFE, Texas A&M System,

Temple, Texas) was also used in this study which produces

improved results over a wide range of conditions.

Results

We construct the CRP land supply curves for corn prices

ranging from $78.75 to $196.84 per ton. This range spans

from historical corn prices to the high corn prices seen in

the last 2 years. Figure 5 illustrates the statewide curves.

At $118.10 corn, we estimate that almost 400,000 ha,

almost half of the land in the program, would go back into

production. Note that not all the area would go back into

continuous corn because highly erodible land is restricted

to corn soybean no till. We estimate that over 420,000 ha

of CRP in Iowa are highly erodible land and would

therefore be planted in a corn soybean rotation to follow

conservation compliance measures. Our assessment pre-

sumes that profit is the main driver of CRP enrollment

Fig. 5 Area out of CRP as a function of corn prices

Environmental Management (2009) 44:732–744 737

123

decisions. However, there are many reasons why land-

owners decide to enroll in the CRP program; in practice,

profit is not always the driving force behind their choice to

enroll. Therefore, our estimates should be considered an

upper-bound estimate of the area that would go back into

production. It is also important to note that this is a long-

term equilibrium analysis of the alternative land uses for

CRP land. We are abstracting from penalties for early

termination and re-enrollment provisions such as the re-

enrollment and extension (REX) offer being implemented

by FSA.

Figure 6 illustrates the geographical location of the

areas that would go back into production with corn prices

at $118.10/ton. The majority of the land in CRP is in the

eastern and southern parts of Iowa, and a large part of the

land that would go back into production is located in those

areas. However, note that since CRP land in the Des

Moines Lobe (Fig. 1) is quite productive, high corn prices

would drastically reduce the amount of land retired from

production in that area.

To estimate how the CRP program may respond to

higher corn prices to maintain enrollment, we increase the

SRRs by 25, 50, 75, and 100% to estimate the CRP land

supply curves with higher CRP payments. We then calcu-

late program costs for all these scenarios. Figure 7 illus-

trates how higher CRP payments would limit the return of

land into production. For example, at a corn price of

$118.10/ton, we estimate that it would cost over $277

million to limit the return of CRP land into production to

less than 70,000 ha. For higher corn prices, even doubling

the payments becomes a relatively ineffective policy. For

example, we estimate that for corn prices of $157.47/ton,

doubling the rental rate paid to farmers would result in

program costs of slightly more than $182 million, a sum

comparable to recent historical costs for Iowa, but would

only result in the enrollment of a little more than

420,000 ha. The tables in the Appendix detail the program

outlays and corresponding areas enrolled in the program in

this simulation exercise.

It is important to note that a driving force behind the

current and projected spatial distribution of CRP land is the

relationship between SRRs and the productivity of the land.

CRP payments are quite inelastic with respect to the CSR

index. This implies that highly productive land tends to be

under-enrolled because of higher returns from production.

As corn prices rise, there is a pivoting of the returns to

Hectares 201 - 1,7751,775 - 2,6032,603 - 3,3603,360 - 4,8174,817 - 10,312

Fig. 6 Distribution of area out

of CRP at $118.10/ton corn

Fig. 7 Land out of CRP as a function of corn prices and CRP

payments. SRR soil rental rate, or current estimated payment

738 Environmental Management (2009) 44:732–744

123

production. At corn prices of $118/ton, CRP payments are

higher than returns from production for land with CSR of

less than 50 or so. At corn prices of $157/ton, land with a

CSR index as low as 39 can gain higher returns if put back

into production.

The soil information necessary to run EPIC was not

available for around 18,600 ha. Therefore, our results have

to be considered somewhat of an underestimate, though

almost 97% of the CRP area is included in the analysis.

The economic analysis was conducted on the basis of

expected yields calibrated to current realized Iowa yields,

as discussed in the methods section. The realized EPIC

yields compare quite well with expected yields for both

soybeans and corn. The average EPIC soybean yield for

CRP land converted to a corn soybean rotation was

2.6 tons/ha, while the economic model’s average yield was

2.39 tons/ha. The average EPIC corn yield for CRP land

converted to a corn soybean rotation was 8.17 tons/ha, and

the yield for continuous corn was 7.22 tons/ha, while the

corresponding economic model’s average yields were 7.49

and 7.27 tons/ha, respectively. Therefore, the EPIC simu-

lations well captured the yield drag effects of continuous

corn.

Tables 3, 4 and 5 illustrate the environmental impacts

by slope for the three crop rotations included in the

analysis. On average, the main pathway of nutrient loss

for CRP land is nitrate leaching. However, there are large

variations in the amounts of nitrate leaching across CRP

soils. In particular, the flat land currently in CRP, which

also tends to be the most productive, has very high

carbon levels (see Table 3). This is the main reason

behind the high leaching numbers. The nitrate losses are

comparatively small when the CRP land is converted

back into cropland due to the relatively high slope of the

fields and the lack of tile drainage, so on average

nitrogen lost through sediment and surface runoff become

much more important pathways (Tables 4, 5). This is

consistent with recent preliminary results from the Con-

servation Effectiveness Assessment Project (CEAP) on

the conservation benefits of CRP in the Upper Missis-

sippi River Basin, which estimate that almost 70% of

nitrogen losses in CRP occur via groundwater losses

(Atwood 2008).

The levels of soil erosion for no till corn soybeans and

for conventional till continuous corn on flat land are

comparable to those reported by the CEAP project

(Potter and others 2006). The CEAP report estimated an

average soil loss of 4.5 tons per ha per year in the Upper

Midwest for cropland. Our EPIC simulations produce

much higher sediment losses in highly sloping land for

conventional till continuous corn, which is why this land

is under conservation compliance and would not be

cropped under intensive tillage. This indirectly illustrates Ta

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Environmental Management (2009) 44:732–744 739

123

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740 Environmental Management (2009) 44:732–744

123

the importance of conservation compliance measures on

working lands.

The results of Tables 4, 5 represent lower and upper

bounds for any other tillage/rotation choices likely to

occur in Iowa, since no till corn soybeans is the less

intensive form of row crop production currently prac-

ticed, and continuous corn using conventional tillage is

one of the most intensive ones. Thus, because of the

impact of conventional tillage on more fragile land and

the higher levels of fertilizer required by continuous corn,

the conversion of land to conventional tillage continuous

corn would have much more dramatic effects than no till

corn soybeans cultivation for soil erosion and nitrogen

losses.

Our results show that some of the environmental

impacts increase drastically as higher corn prices bring into

production more and more environmentally fragile land.

Table 6 shows the marginal impact of additional hectares

put into production. In particular, per hectare sediment

losses and nitrogen and phosphorus lost through sediment

increase quite substantially as crop prices increase. For

example, per hectare sediment losses increase from 0.1

tons/ha for the almost 700,000 ha of CRP included in the

EPIC analysis to almost 1.9 tons/ha at corn prices of

$196.84/ton, for the over half a million hectares that would

go back into production.

Per hectare nitrogen and phosphorus losses follow a

similar pattern. Losses through sediment have the largest

increase because more erodible land goes into production

as crop prices increase. The nitrate losses through leaching

stay more or less constant because, as we noted before,

they are driven by the high carbon levels of the most

productive CRP land. To put our estimates in context, the

national 2006 CEAP report estimated nitrogen losses of

32.3 kg/ha/year for corn in cropland of the Upper Midwest,

and 20.2 kg/ha/year for soybeans, with over 50% of the

losses occurring though sediment. Note that the CEAP

report did not explicitly account for tile drainage because

of the lack of information on this in their sample (Potter

and others 2006).

The same report estimated phosphorus losses of 4.8 and

1.8 kg/ha/year for corn and soybeans, respectively, in the

Upper Midwest, with 70–80% of the losses occurring

through sediment (Potter and others 2006).

The carbon sequestration numbers illustrate that it is the

most productive, high carbon land that goes back into

production first. That is the reason why the per hectare final

carbon pool levels are higher for land back into row crop

production than they are for the average CRP parcel

(Table 3). However, the carbon sequestration levels would

have been higher for the parcels going back to production if

they had remained into CRP. For example, at corn prices of

$78.74/ton, we estimate that the final per hectare carbon Ta

ble

6A

ver

age

env

iro

nm

enta

lim

pac

tso

fla

nd

bac

kin

pro

du

ctio

nat

var

iou

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rnp

rice

s(s

tan

dar

dd

evia

tio

ns

inp

aren

thes

is)

Co

rnp

rice

/to

n$

78

.74

$9

1.7

3$

10

4.7

2$

11

8.1

0$

13

1.1

0$

14

4.0

9$

15

7.4

7$

17

0.4

6$

18

3.4

6$

19

6.8

4

Hec

tare

s2

8,9

70

12

3,4

14

25

4,4

93

33

8,0

36

41

5,9

81

45

3,3

71

48

3,9

73

50

3,0

86

51

7,6

93

53

1,7

20

Wat

erer

osi

on

MU

SL

E(t

on

s/h

a/y

ear)

0.1

2(0

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)0

.16

(0.2

0)

0.2

4(0

.31

)0

.34

(0.4

9)

0.8

8(1

.34

)1

.39

(2.0

8)

1.7

1(2

.47

)1

.83

(2.6

4)

1.8

5(2

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)1

.90

(2.7

4)

Nlo

ssse

dim

ent

(kg

/ha/

yea

r)5

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10

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7(1

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25

(28

)2

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2)

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31

(34

)

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3–

Nlo

ssru

no

ff(k

g/h

a/y

ear)

4(3

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(3)

5(3

)5

(3)

7(6

)9

(9)

10

(10

)1

0(1

0)

10

(10

)1

0(1

0)

NO

3–

Nlo

sssu

bsu

rfac

e(k

g/h

a/y

ear)

1(1

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NO

3–

Nle

ach

ing

(kg

/ha/

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r)2

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23

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4)

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29

(46

)

Plo

ssru

no

ff(k

g/h

a/y

ear)

0(0

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(0)

0(0

)0

(0)

0(0

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(0)

0(0

)0

(0)

0(0

)0

(0)

Plo

ssse

dim

ent

(kg

/ha/

yea

r)0

(0)

0(0

)0

(0)

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)1

(1)

2(3

)2

(3)

2(3

)2

(3)

2(3

)

To

tal

po

ol

fin

alC

(kg

/ha/

yea

r)2

25

,95

5

(13

5,1

49

)

25

8,1

94

(15

1,1

79

)

25

1,8

72

(14

9,6

02

)

22

9,0

49

(14

3,9

57

)

21

7,1

22

(15

8,5

95

)

20

9,9

18

(16

1,0

74

)

20

3,7

99

(16

2,9

96

)

20

0,1

71

(16

4,9

48

)

19

7,3

66

(16

4,6

63

)

19

4,4

34

(16

4,1

29

)

Environmental Management (2009) 44:732–744 741

123

pool for land going back into production would be

225,955 kg/ha. If those same parcels had remained in CRP,

their final carbon pool would have averaged 241,839 kg/ha.

Similarly, for corn prices of 157.47/ton, the final carbon

pool of the almost half a million hectares back into pro-

duction is estimated at 203,799 kg/ha. If those same par-

cels had remained enrolled into CRP, they would have had

a final carbon pool of 219,030 kg/ha.

Note that, even with very high crop prices, there are

over 158,000 ha included in the EPIC simulations that do

not go back into production. This suggests that some of the

land in CRP is too marginal to be considered for crop

production.

Conclusions

The results of our work carry implications for large parts of

the United States, particularly in the Corn Belt. The results

indicate that the land returning to production will be spa-

tially distributed according to the quality of the land in

CRP in the area, which is itself likely a function of overall

land productivity. These results suggest that conservation

policy could be substantially impacted by the increase in

commodity prices. Maintaining current levels of environ-

mental quality will require either substantial budget

increases or a focused targeting approach to increase the

cost/benefit ratios of land retired from production. In par-

ticular, high corn prices may accelerate the trend that

started with the 2002 farm bill in which CRP has shifted

from the idling of whole fields for conservation purposes

toward implementing ‘‘high-priority ‘buffer’ practices

(e.g., filter strips, grassed waterways) that support working

lands by reducing the environmental implications of

ongoing agricultural production (USDA 2006, p. 24).’’ To

preserve whole fields in CRP, higher payments will have to

be considered. Since these will keep only part of the land

out of production, it is not certain that more money will

have to be devoted to conservation programs. For example,

at corn prices of $170.46/ton, doubling soil rental rates

would keep over 349,000 ha in the program, as opposed to

less than 206,000 ha with current payment levels, and the

program costs would be lower than they are now by over

$36 million.

In any case, some areas could see considerable reduc-

tions in CRP enrollment, thereby affecting wildlife popu-

lations. In particular, several recent studies indicate that

CRP land has positive impacts on game birds and that their

populations could be severely affected (Nielson and others

2006; Schroeder and Vander Haegen 2006; Riffell and

Burger 2006).

Acknowledgments We thank Todd Campbell for excellent com-

putational assistance. This research was made possible in part by

USDA-CSREES grant 2005-51130-02366, USDA-Rural Develop-

ment grant 68-3A75-6-511 and EPA Collaborative agreement

CR83371701-1. The views expressed here are those of the authors

and do not necessarily represent the view of USDA or EPA.

Appendix

Area and Program Costs Results From the Economic

Simulations

See Tables 7 and 8.

Table 7 Area remaining in

CRP for various crop prices and

rental payments

SRR Soil Rental Rate, used to

approximate CRP payments

Corn price/ton Hectares

SRR SRR*1.25 SRR*1.5 SRR*1.75 SRR*2

$78.74 680,177 704,806 708,077 708,628 708,628

$91.73 585,715 667,530 699,197 706,834 708,616

$104.72 454,636 574,403 651,504 692,419 703,507

$118.10 371,094 447,763 560,600 641,669 683,380

$131.10 293,148 370,434 445,481 535,876 624,513

$144.09 255,759 295,506 367,112 437,557 514,656

$157.47 225,157 258,036 296,150 355,770 422,185

$170.46 206,043 225,412 256,849 290,843 349,519

$183.46 191,437 207,950 226,808 257,386 288,261

$196.84 177,410 191,707 210,064 227,542 254,374

Baseline 708,704

742 Environmental Management (2009) 44:732–744

123

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economics of a public fund for environmental amenities: a study

of CRP contracts. American Journal of Agricultural Economics

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Chung SW, Gassman PW, Huggins DR, Randall GW (2001)

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three Minnesota cropping systems. Journal of Environment

Quality 30(3):822–830

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EPIC for assessing tile flow and nitrogen losses for alternative

agricultural management systems. Transactions of the ASAE

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Duffy M, Smith D (2007) Estimated costs of crop production in

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State University, Ames, Iowa. http://www.card.iastate.edu/publi

cations/DBS/PDFFiles/06bp49.pdf. Accessed 17 Oct 2008

Farm Service Agency (FSA) (2004) Cumulative crp enrollment by

county, FY—2004. http://www.fsa.usda.gov/Internet/FSA_File/

fy2004public.xls. Accessed 10 Dec 2007

Farm Service Agency (FSA) (2007) The conservation reserve pro-

gram: summary and enrollment statistics 2007. http://www.fsa.

usda.gov/Internet/FSA_File/annual_consv_2007.pdf. Accessed

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Gassman PW, Williams JR, Benson VW, Izaurralde RC, Hauck L,

Jones CA, Atwood JD, Kiniry J, Flowers JD (2005) Historical

development and applications of the EPIC and APEX models.

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May 2007

Iowa Cooperative Soil Survey (2003) Iowa soil properties and

interpretation database. http://icss.agron.iastate.edu/. Accessed

12 Feb 2007

Iowa Department of Natural Resources (IDNR) (2004) Land cover of

the state of Iowa in the year 2002, Grid dataset. ftp://ftp.igsb.

uiowa.edu/gis_library/IA_State/Land_Description/Land_Cover/

Land_Cover_2002/lc_2002.htm. Accessed 12 Feb 2007

Izaurralde RC, Williams JR, McGill WB, Rosenberg NJ (2006)

Simulating soil C dynamics with EPIC: model description and

testing against long-term data. Ecological Modelling 192(3–4):

362–384

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Poulton PR, Richter DD (1997) Simulating trends in soil organic

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Mapemba LD, Epplin FM, Taliaferro CM, Huhnke RL (2007)

Biorefinery feedstock production on conservation reserve pro-

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Johnson DS (2006) Estimating response of ring-necked pheasant

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Accessed 26 Oct 2008

Table 8 Program costs corresponding to the CRP area above

Corn price Program costs

SRR SRR*1.25 SRR*1.5 SRR*1.75 SRR*2

$78.74 $170,332,771 $222,879,682 $268,760,041 $313,792,968 $358,620,535

$91.73 $139,269,773 $208,083,090 $265,086,096 $313,000,936 $358,614,879

$104.72 $100,030,010 $170,165,733 $242,308,499 $305,889,528 $356,064,685

$118.10 $76,376,423 $122,884,447 $197,951,626 $277,302,467 $344,441,922

$131.10 $55,811,884 $95,624,770 $146,646,096 $220,164,741 $305,850,819

$144.09 $46,588,722 $70,746,640 $114,229,342 $167,376,134 $238,202,924

$157.47 $39,467,784 $59,056,194 $85,242,145 $127,988,797 $182,471,374

$170.46 $35,002,623 $49,480,352 $70,553,212 $97,136,575 $143,154,509

$183.46 $31,825,882 $44,280,332 $59,904,254 $82,566,684 $110,091,175

$196.84 $29,094,866 $40,007,865 $53,956,431 $70,210,235 $93,289,500

Baseline $179,325,900

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