United States 

Department of 

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 126

September 2011

Alternatives to a State-Based 

ACRE Program: Expected 

Payments Under a National, 

Crop District, or County Base

Robert Dismukes, Keith H. Coble, David Ubilava, 

Joseph C. Cooper, and Christine Arriola
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Recommended citation format for this publication:

Dismukes, Robert, Keith H. Cole, David Ubilava, Joseph C. Cooper, and 

Christine Arriola. Alternatives to a State-Based ACRE Program: Expected 

Payments Under a National, Crop District, or County Base. ERR-126, U.S. 

Department of Agriculture, Economic Research Service. September 2011.
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

The Average Crop Revenue Election, or ACRE, program is a commodity support 

program that bases coverage on aggregate State-level and individual farm-level revenue 

variability. Changing the level of aggregation from State to one closer to the farm 

level—Crop Reporting District or county—would generally increase payments. This 

report models how expected payments and the level of risk reduction from programs triggered at alternative levels would vary across crops—corn, soybeans, wheat, cotton, grain 

sorghum, and rice. It also considers how benefits from the alternative revenue programs 

would vary relative to benefits from direct payment and price-based commodity 

programs. 

Keywords: Average Crop Revenue Election, ACRE, commodity support, crop revenue 

variability, farm risk management

Acknowledgments

Many thanks to Joy Harwood and Phil Sronce of Farm Service Agency, Joe Prusacki of 

the National Agricultural Statistics Service and anonymous reviewers for their helpful 

comments, to Richard Anderson, Jason Albright and Chris Aulbur of Risk Management 

Agency for data on crop insurance rates, and to Dale Simms, Chris Dicken and Cynthia 

Ray of Economic Research Service for editing, map making, design and layout.

Robert Dismukes, Keith H. Coble, David Ubilava, 

Joseph C. Cooper, Christine Arriola

Alternatives to a State-Based 

ACRE Program: Expected 

Payments Under a National, Crop 

District, or County Base

Economic

Research

Report

Number 126 

September 2011
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Contents

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

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

Determinants of Revenue Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

ACRE and Alternative Revenue Program Triggers . . . . . . . . . . . . . . . . 4

Expected Payments Under Programs Triggering at Different 

Aggregation Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Change in Expected Payments Varies Across Farms . . . . . . . . . . . . . . . 6

Change in Expected Payments When Expected Market and Program

Guarantee Prices Diverge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Risk Reduction Under Different Revenue-Level Triggers . . . . . . . . . . 17

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

Methods and Data Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Appendix Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
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Summary

This report simulates crop revenue variability to examine expected program 

payments and consequences on farm-level risk if the area trigger were 

changed from the State level to a national, Crop Reporting District, or 

county level. The analysis is national in scope and covers seven crops: corn, 

soybeans, wheat, cotton, grain sorghum, long-grain rice, and medium/shortgrain rice, which accounted for 98 percent of the value in 2010 of crops for 

which ACRE was available. We analyze how the change in revenue program 

benefits would vary across crops and regions and how benefits from the 

revenue programs would stack up against benefits from direct payment and 

price-based commodity programs (countercyclical, marketing loan guarantees) available under the 2008 Farm Act.

What Is the Issue?

Enacted in 2008, the Average Crop Revenue Election or ACRE program uses 

a combination of State- and farm-level revenue guarantees or payment triggers that are established from recent prices and yields. At the initial enrollment deadline for ACRE in August 2009, only 8 percent of farms with about 

13 percent of eligible base acres elected to participate, despite the program’s 

unique ability to align guarantee prices with increases in field crop prices. In 

2010, the second year in which ACRE was available, few additional acres 

were enrolled.

The ACRE participation decision is complex. Prospective enrollees must 

weigh the benefits of ACRE relative to those of other programs that must be 

forgone and learn about a markedly different program that uses both farmand State-level revenue triggers. Switching the aggregate (State) revenue 

trigger to a lower level, one closer to the farm level, has been suggested as a 

way to make ACRE more attractive to producers.

What Did the Study Find?

Changing the aggregate level of revenue used to trigger payments in a 

program such as ACRE would change expected benefits and, perhaps, enrollment levels. Moving from ACRE’s State level to one closer to the farm level 

would generally increase payments and reduce risk. 

• If expected market prices equal revenue program guarantee prices, the 

increase in payments and risk reduction would be greatest for crops 

such as wheat, cotton, and grain sorghum—crops with widely varying 

yield across regions and, thus, large differences in revenue variability 

across levels of aggregation. The average expected payments for these 

crops would increase 28-32 percent if the revenue program trigger were 

changed from State to county. Corn and soybean production is more 

concentrated geographically with less varied yields, so the increase in 

expected payments would be less, 16-19 percent. Payments for rice 

would change little if the aggregate revenue trigger were changed 

because most of the crop is irrigated, with price largely determining 

revenue variability.
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• The relationship between expected market price in the covered year and 

the revenue program guarantee price affects the changes in expected 

payments under alternative levels of revenue aggregation. If the expected 

market price increases relative to the guarantee, then difference in 

expected payments across levels of aggregation would increase as yield 

variability becomes a stronger factor in determining revenue variability 

and payments; if the expected market price decreases relative to the 

guarantee, expected payments increase but differences in payments 

across levels of aggregation decrease.

• Because revenue benchmarks or guarantees used in the revenue programs 

are designed to change over time, it is important to consider the different 

crop guarantee price scenarios in evaluating the benefits of a revenue 

program relative to price-based programs. If crop prices continue to 

increase, guarantees and expected payments under ACRE or a similar 

program would increase, while expected payments under programs that 

are based on legislatively fixed targets would decrease. And as the crop’s 

price increases, the size of the direct payment for a crop decreases relative to the expected payment from the revenue program. Changes in 

prices, and thus revenue program guarantees, appear to be much more 

important than level of aggregation/trigger to producers weighing the 

revenue election. 

• Risk reduction from a program that uses an aggregate revenue trigger 

depends greatly on the correlation between the aggregate measure of 

revenue and actual farm revenue. While risk reduction increases as the 

level of aggregation used in the revenue program diminishes, farm-level 

revenue is largely uncorrelated with revenue at even the smallest level of 

aggregation, the county. In addition, expected payments and risk reduction are limited by the cap of revenue program payments at 25 percent of 

the guarantee. 

While a county-level revenue program would generally produce larger 

expected payments and risk reduction for participants than a State-level 

program, it would increase government administrative costs as program 

benchmarks and guarantees would need to be determined for a greater 

number of aggregate units.

How Was the Study Conducted?

To study the effects of changing the aggregation level used for the trigger 

in a revenue program, we constructed three hypothetical revenue program 

alternatives that maintain the structure of the ACRE program but substitute national-, district- and county-level revenue for the State-level revenue 

trigger. The study used data from USDA’s National Agricultural Statistics 

Service and Risk Management Agency to construct a model that simulates random yields, prices, and revenues at farm and national, State, Crop 

Reporting District, and county levels. The model is national in scope and 

represents about 95 percent of the 2010 planted U.S. acres of corn, 89 percent 

of soybean acres, 89 percent of wheat acres, 84 percent of cotton acres, 74 

percent of the grain sorghum acres, and more than 90 percent of rice acres. 
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Introduction

Crop price has served as a basis for commodity support payments for about 

70 years. Tying support payments to low prices had its impetus shortly after 

the end of World War I as commodity prices plunged (Gardner, 2002). Using 

crop revenue, rather than prices, as the basis for agricultural income support 

programs has been studied as a way to reform U.S. farm programs since at 

least the early 1980s. A rationale for using revenue is that it is more closely 

related to farm income than its components, prices and yields. As such, 

revenue programs can more efficiently stabilize income and lessen the annual 

variability in government program expenditures than separate price-based or 

yield-based programs can (Cooper, 2010).

The Food, Conservation and Energy Act of 2008 (the 2008 Farm Act) introduced a revenue-based option to the set of price-based programs available to 

field crop producers. The Average Crop Revenue Election or ACRE program 

uses a combination of State- and farm-level revenue payment triggers that are 

established from recent prices and yields. At the initial enrollment deadline 

for ACRE in August 2009, about 8 percent of farms with about 13 percent 

of eligible base acres elected to participate (USDA, Farm Service Agency, 

2011a). Few additional acres were enrolled the following year (USDA, Farm 

Service Agency, 2011b).

The ACRE participation decision is complex. Crop producers must weigh the 

benefits of ACRE relative to those of other programs that must be forgone 

and learn about a markedly different program that uses both farm-level and 

State-level revenue triggers. Switching the aggregate (State) revenue trigger 

to a lower level, one closer to the farm level, may make the program more 

attractive to producers (Babcock, 2010; Paulson and Babcock, 2008; Zulauf 

et al., 2010). 

This report simulates crop revenue variability to examine expected program 

payments and consequences on farm-level risk if the area trigger were 

changed from the State level to a national, Crop Reporting District1, or 

county level. Our analysis is national in scope and covers seven crops: corn, 

soybeans, wheat, cotton, grain sorghum, long-grain rice, and medium/shortgrain rice.2

1A Crop Reporting District is a 

multiple county unit within a State.

2The crops in our analysis accounted 

for 98 percent of the value in 2010 of 

crops for which ACRE was available. 

ACRE uses different prices and yields 

to measure revenue for long-grain and 

medium/short-grain rice.
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Determinants of Revenue Variability

Revenue variability depends on the variability of prices and production 

(yields multiplied by acres) and the interactions between the two (Cooper, 

2009; Dismukes et al., 2010). Because crop prices depend largely on world 

markets, variability in the price for a crop is similar across much of the 

United States due to potential arbitrage. Yields, in contrast, depend on factors 

such as weather, diseases, and insects that can affect wide areas at once but 

are often localized. Thus, the area over which revenue is measured will affect 

the measured revenue variability (Coble et al., 2007). 

Yield variability, measured as the coefficient of variation in model simulations3, is by far the smallest at the national level, the largest level of aggregation. As yields are measured over smaller areas, variability generally 

increases, though the increase varies by crop and location. For instance, 

average yield variability at the State level for corn is about 40 percent higher 

at the national level (table 1). For wheat, which is grown under a wide variety 

of conditions, average State yield variability is about 140 percent of national 

yield variability. In contrast, yield variability for long-grain rice, which is 

grown only under irrigation and thus has relatively low yield variability, is 

about 16 percent higher at the State than at the national level.4

At the next smallest level (relative to State), the Crop Reporting District 

or district, yield variability increases about 25-45 percent for cotton, grain 

sorghum, and wheat, but increases only 2-5 percent for medium/short-grain 

and long-grain rice. At the county level, yield variability is, on average, 

about 10-20 percent higher than at the district level for corn, soybeans, longgrain rice, cotton, and grain sorghum; for wheat, it is only slightly (less than 

1 percent) higher. Yield variability at the representative farm5 level is much 

higher than at the county level.

3Coefficient of variation is the 

standard deviation divided by the mean. 

For details on how simulations were 

generated, see Methods and Data section.

4In our model medium/short-grain 

rice is only produced in a single State, 

California; there is no difference between the State and national revenues.

5In our model, yield and revenue 

variability at the farm level are for a 

typical, or representative, acre for a 

crop in a county. Farm-level variability, therefore, refers to an individual 

crop, not to the combined variability of 

multiple crops that could be grown on a 

farm.

Table 1

Yield and revenue variability at different levels of aggregation

Item/Level Corn Soybeans Wheat Cotton Grain sorghum

Rice, 

long-grain

Rice, 

Medium/short-grain

Coefficient of Variation

Yield variability:

 National 0.069 0.058 0.056 0.076 0.099 0.037 0.061

 State 0.097 0.099 0.135 0.119 0.123 0.043 0.061

 District 0.110 0.113 0.169 0.152 0.167 0.045 0.062

 County 0.122 0.125 0.195 0.184 0.202 0.052 0.067

 Farm 0.359 0.372 0.520 0.672 0.776 0.335 0.263

Revenue variability:

 National 0.195 0.188 0.185 0.197 0.214 0.272 0.288

 State 0.207 0.205 0.215 0.225 0.230 0.275 0.288

 District 0.214 0.213 0.240 0.250 0.256 0.275 0.288

 County 0.221 0.220 0.261 0.274 0.283 0.276 0.289

 Farm 0.413 0.425 0.558 0.715 0.829 0.440 0.395

Averages weighted by acres harvested in 2010. District = Crop Reporting District. Medium/short-grain rice is for a single State, 

California. Based on simulations.
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Revenue variability, which depends on both yield and price, is greater than 

yield variability at all levels of aggregation. Under our simulations, revenue 

variability at the national level is more than double yield variability for each 

of the seven crops (table 1). The difference between revenue and yield variability decreases as they are measured at the more disaggregated levels. In 

other words, revenue variability is determined more by yield variability at 

smaller levels of aggregation.

Because revenue variability at the farm level is closely linked to yield variability, its geographic pattern is similar to farm-level yield variability, 

though price-yield correlation plays a role.6 Revenue variability for corn 

and soybeans tends to be lowest in counties that stretch across the center 

of the Corn Belt, an area with low yield variability that also has the strongest negative correlation between yield and price. Revenue variability for 

wheat—which, in contrast to corn and soybeans, includes different types 

of wheat that are sold in different markets at different times of the year—is 

low in irrigated areas in Washington and Oregon as well as in non-irrigated 

areas across the middle of Kansas. It is high in the Southern Plains areas of 

Oklahoma and Texas, as well as western Kansas and eastern Colorado and 

parts of the Northern Plains in North Dakota, South Dakota, and Montana. 

The distribution of U.S. cotton production over diverse growing conditions and the weak correlations between U.S. cotton production and price 

result in wide range of revenue variability. For cotton, revenue variability 

is lowest for irrigated production in California and Arizona and highest for 

dryland production in the plains of Texas. Grain sorghum revenue variability 

is generally low in Kansas and high in Oklahoma and Texas. Rice revenue 

variability differs little across its growing regions because yields vary little 

and price variability, which is the same across regions, largely determines 

revenue variability.

6The relationship between prices and 

yields, measured by statistical correlation, is negative when changes in yield 

are associated with offsetting changes 

in price. Negative price-yield correlation moderates or dampens revenue 

variability, and is often referred to as a 

“natural hedge.”
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ACRE and Alternative Revenue Program Triggers

To study the effects of changing the aggregation level used for the trigger 

in a revenue program, we maintain the structure of the ACRE program but 

substitute national-, district-, and county-level revenue for the State-level 

revenue trigger. Payments are based on annual outcomes of the aggregate 

and farm revenues for a crop relative to their guarantee or benchmark. The 

programs use a double trigger. For a farm to receive a payment, revenue at 

the aggregate level (national, State, district, or county) must fall below its 

guarantee—90 percent of its benchmark—and farm revenue must fall below 

its benchmark.7 Both conditions have to be met. The program payment per 

acre is the difference between the revenue guarantee and the actual revenue 

at the aggregate level, up to a maximum of 25 percent of the guarantee, 

multiplied by a farm productivity index, which is the ratio of the farm 

average yield to the average yield at the aggregate level. Under provisions of 

the 2008 Farm Act, the ACRE payment is multiplied by 0.833 in 2009, 2010, 

and 2011; and by 0.85 in 2012. Payments in this report have been multiplied 

by 0.833.

ACRE program benchmarks or expected revenues are based on recent 

historical prices and yields. Because prices and yields can change from year 

to year, the revenue guarantee can change, though the annual change in the 

guarantee is limited to no more than 10 percent.8 To examine the effects 

of changes in prices used to set the revenue guarantees, we construct four 

scenarios, each with a different set of crop guarantee prices: 

(1) prices approximately equal to the “guarantee prices”9 of ACRE 

in 2010; 

(2) prices 20 percent higher than the 2010 prices; 

(3) prices 20 percent lower than the 2010 prices; and 

(4) prices equal to the target prices, set under the 2008 Farm Act, for

the price-based countercyclical program. (The prices for each of the

scenarios are listed in appendix table 1.)

The eventual crop prices for the marketing year, which are not known at 

the revenue program signup, are used to calculate actual revenue and hence 

revenue program payments. As a base case in our analysis, we model revenue 

variability and payments under the condition that the expected marketingyear average price is equal to the revenue guarantee price. In this case, 

payments would be triggered simply by the variability of revenue around 

its expected level. In addition to our base case, we examine cases where 

expected marketing-year average prices are above and below the revenue 

guarantee prices.

7The ACRE State benchmark is 

calculated by multiplying the average 

of the marketing-year average price 

over the previous 2 years by the average 

of the State yield over the previous 5 

years, dropping the highest and lowest 

yields in this period. A farm’s benchmark revenue is calculated by multiplying the same price that is used for the 

State benchmark revenue by the farm’s 

average yield over the previous 5 years, 

dropping the highest and lowest yields. 

The farm revenue guarantee is the farm 

benchmark revenue plus the amount 

of premium paid by the producer for 

Federal crop insurance.

8The results presented in this report 

are from simulations for 1 year, so they 

do not include the potential effect of 

the year-to-year changes in the revenue 

guarantee over a multiple-year time 

path.

9Guarantee price is the average of the 

marketing-year average price over the 

previous 2 years.
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Expected Payments Under Programs Triggering at 

Different Aggregation Levels

Increasing the level of revenue aggregation in a revenue program from State 

to national would, on average, reduce expected revenue program payments 

for each crop; changing from the State to a more local level would, on 

average, increase expected payments. Table 2 shows the percent change, 

relative to the State-triggered ACRE, in the U.S. average expected payment 

under revenue programs using different triggers.10 These proportional 

changes in payments, with expected market price equaling revenue guarantee 

price, are constant as the level of guarantee prices increase or decrease, even 

though the dollar amounts of the payments would increase or decrease with 

the guarantee prices. (Expected payments in dollars per acre under each of 

the four guarantee price scenarios are in appendix table 2.)

Shifting to a national trigger from a State trigger would result in the largest 

proportional decrease in expected payments, about 23 percent, for cotton, 

reflecting the aggregation of its diverse production areas. For soybeans, grain 

sorghum, corn, and wheat, expected payments would drop 8-12 percent. 

For long-grain rice, for which there is little difference in yield or revenue 

variability between the national and State level, a switch from a State to a 

national trigger would reduce expected revenue program payments just 2 

percent.

If a revenue trigger at a substate level were used, then expected payments 

for grain sorghum, cotton, and wheat would see the greatest proportional 

increases: 28-32 percent under a county revenue trigger and 13-17 percent 

at the crop district level of aggregation. Soybean and corn payments would 

increase about 16 and 19 percent for a county trigger and 7-10 percent for a 

district trigger (table 2). The relative increases in expected payments for the 

district- or county-triggered program are consistent with correlation of yields 

across levels of aggregation; for instance, the more correlated the district 

or county yield is with the State yield, the smaller the increase in expected 

payment.

Expected payments for rice would change little. This is because yield variability for rice is smaller than for the other crops and changes little as it is 

10Average expected payments are 

based on representative farm acres 

weighted by acres harvested in 2010. 

The ACRE program bases payments on 

USDA’s Farm Service Agency (FSA) 

definition of “planted and considered 

planted” acres. This excludes, for 

instance, wheat planted for grazing. 

Because there are no county-level 

data on the FSA-defined acres, we use 

harvested acres.

Table 2

Difference in average expected payment, relative to State-triggered 

program, from revenue programs triggered at different levels of 

aggregation

Level Corn Soybeans Wheat Cotton Grain 

sorghum

Rice, 

longgrain

Rice, 

medium/

short-grain

Percent

National -11.3 -8.4 -11.5 -23.3 -10.1 -2.1 0

District 10.4 6.8 15.3 13.1 17.1 Less 

than 1

Less than 1

County 18.8 15.5 28.0 28.5 32.0 2.3 Less than 1

Averages weighted by acres harvested in 2010. District = Crop Reporting District. Medium/

short-grain rice is for a single State, California. Based on simulations of expected market 

price equal to revenue program guarantee price.
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measured at smaller levels of aggregation. Therefore, revenue variability for 

rice is driven largely by price variability, which under the revenue program 

does not change from one level of aggregation to another.

Change in Expected Payments Varies Across Farms 

Not all representative farm acres (one per crop per county) would see the 

average change in expected payments; some would see greater changes than 

the average, and some less. The change in expected payment between Stateand national-triggered programs depends on revenue variability for a particular 

State relative to national revenue variability, as well as the correlations between 

farm revenue and State and national revenue. In most, but not all, cases, 

revenue variability at the national level is less than revenue variability at the 

State level, and farm revenue is less closely related to national revenue than to 

State revenue. In these cases, expected payments would decrease if the revenue 

program trigger were changed from the State to the national level. 

Figure 1 shows the proportion of total representative farm acres for each 

crop by the percent to which expected payments would change, according 

to simulations with expected market price equal to revenue guarantee 

price. For corn, soybeans, and wheat, expected revenue program payments 

would decrease on 70-75 percent of the representative farm acres, but 

would increase on 25-30 percent of the acres. Payments would decrease in 

moving from a State to a national trigger for nearly all acres of cotton, grain 

sorghum, and long-grain rice, with more than half of the cotton acres having 

an expected decrease in payments of over 20 percent.

Figure 1 

Proportion of acres by percent decrease in expected payment between State- and 

national-triggered revenue programs, by crop

Percent of acres

Percent decrease in expected payment

Acres = Harvested acres of representative farms. Based on simulations with expected market price equal to guarantee price. Average decrease 

in expected payment for rice is less than 2 percent; expected payments would decrease slightly on about 94 percent of the representative farm 

acres of rice.

Less than -10 -10 - 0 0 - 10 10 - 20 Greater than 20

0

10

20

30

40

50

60

Payments increase

Corn

Soybeans

Wheat

Cotton

Grain sorghum

Payments decrease
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Figures 2a-e show for each crop the regional distribution of the percent 

change in expected payments under a national versus State revenue trigger. 

For corn (fig. 2a) and soybeans (fig. 2b), the greatest decreases, as a percent 

of the U.S. average expected payment, would be in the areas away from the 

heart of the Corn Belt. For wheat (fig. 2c), the greatest decreases would be in 

the Mid-Atlantic States, Montana, eastern Colorado, Michigan, and in counties along the Mississippi River. For cotton (fig. 2d) and grain sorghum (fig. 

2e)—crops for which nearly all acres would have a substantial decrease in 

expected payments—the areas with the largest drops in payments would be 

in the plains of Texas for cotton and in Nebraska for grain sorghum.

While the average expected payment would increase if the level of aggregation used for the revenue program were changed from the State to the county 

level, the degree of change would vary from one farm acre to another. The 

change in expected payment from a State to county trigger for the representative farm acres is driven largely by the differences in revenue variability11

across counties and farms within a State. When revenue variability for a 

particular county is greater than its State’s revenue variability, switching to 

a county revenue trigger would increase expected payments. States with a 

wide range of revenue variability across counties and farms would see a wide 

range of increases in expected payments. 

Figure 3 shows the proportion of total representative farm acres for each 

crop, according to simulations with expected market price equal to revenue 

guarantee price, by the degree to which expected payments would increase 

if the revenue trigger were changed from State to county. For corn and 

soybeans, expected revenue program payments would increase on about 

80 percent of farm acres, whereas about 90 percent of wheat, cotton, grain 

sorghum, and rice acres would receive higher payments. 

Increases in payments under a county revenue trigger would be largest for 

the representative corn acres in eastern and southern Illinois and southern 

Iowa (fig. 4a). Increases for soybeans (fig. 4b) would be largest in northern 

Iowa and eastern Kansas. For wheat (fig. 4c), increases would be large in the 

Northern Plains and in eastern and western Kansas. For cotton (fig. 4d) and 

grain sorghum (fig. 4e), farm acres from counties in Texas with high yield 

and revenue variability would see large increases in payments in moving 

from a State to a county trigger.

11Revenue variability in the ACRE 

program and the alternative revenue 

programs is calculated with a single 

national price for each crop. Yield 

variability and price-yield correlations 

differ at the different levels of aggregation.
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Figure 2a 

Percent decrease in expected payment between State- and national-triggered 

revenue program, corn

Less than -10

-10 - 0 

0 - 10

10 - 20

Percent decrease in

expected payments

Greater than 20

Percent decrease in

expected payments

Less than -10

-10 - 0 

0 - 10

10 - 20

Greater than 20

Figure 2b 

Percent decrease in expected payment between State- and national-triggered 

revenue program, soybeans

Based on simulations with expected market price equal to 

guarantee price. U.S. average percent decrease in expected 

payment between State- and national-triggered revenue program 

= 11.3 percent.

Based on simulations with expected market price equal to 

guarantee price. U.S. average percent decrease in expected 

payment between State- and national-triggered revenue 

program = 8.4 percent.

Increase

Decrease

Increase

Decrease
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Based on simulations with expected market price equal to 

guarantee price. U.S. average percent decrease in expected 

payment between State- and national-triggered revenue program 

= 11.5 percent.

Figure 2c 

Percent decrease in expected payment between State- and national-triggered

revenue program, wheat

Based on simulations with expected market price equal to 

guarantee price. U.S. average percent decrease in expected 

payment between State- and national-triggered revenue 

program = 23.3 percent.

Percent decrease in

expected payments

Less than -10

-10 - 0 

0 - 10

10 - 20

Greater than 20

Percent decrease in

expected payments

Less than -10

-10 - 0 

0 - 10

10 - 20

Greater than 20

Figure 2d

Percent decrease in expected payment between State- and national-triggered

revenue program, cotton

Increase

Decrease

Increase

Decrease
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Based on simulations with expected market price equal to 

guarantee price. U.S. average percent decrease in expected 

payment between State- and national-triggered revenue program 

= 10.1 percent.

Figure 2e 

Percent decrease in expected payment between State- and national-triggered 

revenue program, grain sorghum

Percent decrease in

expected payments

Greater than 20

-10 - 0 

0 - 10

10 - 20

Increase

Decrease

Figure 3 

Proportion of acres by percent change in expected payment between State- and county-triggered 

revenue programs, by crop

Percent of acres

Percent change in payment

Acres = Harvested acres of representative farms. Based on simulations with expected market price equal to guarantee price. Average increase in 

expected payment for rice is 1-2 percent; expected payments would increase slightly on about 57 percent of the representative farm acres of rice.

Less than -15 -15 - 0 0 - 15 15 - 30 30 - 45 Greater than

45

0

5

10

15

20

25

30

35

40

45

Corn

Soybeans

Wheat

Cotton

Grain sorghum

Payments decrease

Payments increase
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Figure 4a 

Percent increase in expected payment between State- and county-triggered 

revenue program, corn

Percent increase in

expected payments

Based on simulations with expected market price 

equal to guarantee price. U.S. average percent 

increase in expected payment between State- and 

national-triggered revenue program = 18.8 percent.

Greater than 45

Percent increase in

expected payments

Figure 4b 

Percent increase in expected payment between State- and county-triggered 

revenue program, soybeans

Less than -15

-15 - 0 

0 - 15

15 - 30

30 - 45

Based on simulations with expected market price 

equal to guarantee price. U.S. average percent 

increase in expected payment between State- and 

national-triggered revenue program = 15.5 percent.

Greater than 45

Less than -15

-15 - 0 

0 - 15

15 - 30

30 - 45

Decrease

Increase

Decrease

Increase
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Based on simulations with expected market price equal to 

guarantee price. U.S. average percent increase in expected 

payment between State- and national-triggered revenue program 

= 28.8 percent.

Figure 4c 

Percent increase in expected payment between State- and county-triggered 

revenue program, wheat

Based on simulations with expected market price equal to 

guarantee price. U.S. average percent increase in expected 

payment between State- and national-triggered revenue 

program = 28.5 percent.

Figure 4d

Percent increase in expected payment between State- and county-triggered 

revenue program, cotton

Percent increase in

expected payments

Greater than 45

Percent increase in

expected payments

Less than -15

-15 - 0 

0 - 15

15 - 30

30 - 45

Greater than 45

Less than -15

-15 - 0 

0 - 15

15 - 30

30 - 45

Decrease

Increase

Decrease

Increase
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Based on simulations with expected market price equal to 

guarantee price. U.S. average percent increase in expected 

payment between State- and national-triggered revenue program 

= 32.0 percent.

Figure 4e 

Percent increase in expected payment between State- and county-triggered 

revenue program, grain sorghum

Percent increase in

expected payments

Greater than 45

Less than -15

-15 - 0 

0 - 15

15 - 30

30 - 45

Decrease

Increase
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Change in Expected Payments When Expected Market 

and Program Guarantee Prices Diverge 

Because guarantee prices in the revenue program are average prices over 

the previous 2 years and expected market prices are anticipated prices for 

the coming year, they will not necessarily be equal. (The base case will 

not hold.) For example, at the 2010 ACRE signup deadline, June 1, one 

measure of expected corn price for the 2010 marketing year—the midpoint 

of USDA’s World Agricultural Supply and Demand Estimates (WASDE) 

price projection for corn made in May—was $3.50, 8 percent below the 

2010 ACRE guarantee price. And the May 2010 WASDE price projection 

indicated that the marketing-year average price for wheat could be about 20 

percent below its ACRE guarantee price.

As market price expectations diverge from revenue program guarantee 

prices, the weight of price relative to yield in triggering a revenue payment 

changes. As expected price increases, it becomes less likely that price will 

be the source of any revenue loss; yield variability becomes a stronger factor 

in determining the revenue payment. In these cases, because yield variability changes with level of aggregation—generally increasing as the level 

of aggregation gets smaller (due to less averaging effect)—differences in 

payments across alternative trigger levels would be greater than they would 

be under the base case of expected price equal to guarantee price. In contrast, 

when expected price decreases relative to the revenue guarantee price, price 

becomes a stronger factor. In these cases, expected payments increase but the 

differences in payments across the levels of aggregation would be less than 

they would under the base case.

While the difference between expected prices and guarantee prices affect 

expected payments as alternative units—national, Crop Reporting District, 

and county—are substituted for State-level revenue, the level of expected 

payment, in dollars per acre, for the State-based ACRE program also depends 

on the difference between expected prices and revenue guarantee prices. As 

expected prices increase relative to guarantee prices, the expected payment 

decreases; when expected prices decrease, the expected payment increases 

(Dismukes et al., 2010).

Because yield variability and how it changes from one level of aggregation 

to another varies across crops, the effect on expected payments relative to 

the State-based ACRE of the movement of expected market prices relative to 

guarantee prices differs across crops. Crops with relatively large changes in 

yield variability across levels of aggregation—cotton and grain sorghum—

would see particularly strong changes in expected payments when prices 

are above revenue guarantee prices and yield variability is a larger factor in 

revenue variability. Under a national-triggered program (fig. 5a), if expected 

price were 10 percent above guarantee price, expected payments would 

decrease relative to the State-triggered program by about 25 percent for corn 

and wheat and more than 40 percent for cotton, much larger than the 11- to 

23-percent decrease under the base case (expected price equal to guarantee 

price). If expected price were 10 percent below the guarantee price, expected 

payments for corn, wheat, and cotton under the national-triggered program 

would decrease relative to the State-triggered program by 7-12 percent. For 
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long-grain rice, there is little difference between a national-triggered and a 

State-triggered program because there is little difference in yield variability 

at the different levels.

Under revenue programs triggered at substate levels—crop district (fig. 5b) 

and the county (fig. 5c)—the proportional difference in payments as expected 

market price increases relative to guarantee price also reflects the difference in yield variability and how it varies across levels of aggregation and 

crops. If, for instance, expected price were 10 percent above guarantee price, 

expected payments for corn would be 13 percent greater under a districttriggered program than under a State-triggered program (versus 10 percent 

higher when expected price equals guarantee price). For cotton, however, 

expected payments would be 40 percent greater under the district-triggered 

program, much greater than the State-district difference (13 percent) under 

the base case when yields are less a factor in revenue. Under a countytriggered program, if expected price were 10 percent greater than guarantee 

price, payments for corn would be 27 percent greater than under the Statetriggered program; for cotton, expected payments would be 64 percent 

greater.
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Figure 5a 

Percent change in expected payment between national- and State-triggered revenue programs 

as expected market price varies relative to guarantee price

Percent change in expected payment

-20 -10 0 10 20

-70

-60

-50

-40

-30

-20

-10

0

Wheat

Cotton

Long-grain rice

-20 -10 0 10 20

0

10

20

30

40

50

-20 -10 0 10 20

0

20

40

60

80

100

Percent difference between expected market price and guarantee price

Average payment weighted by planted acres.

Figure 5b 

Percent change in expected payment between district- and State-triggered revenue programs 

as expected market price varies relative to guarantee price

Percent change in expected payment

Long-grain rice

Long-grain rice

Cotton

Cotton

Wheat

Wheat

Corn

Corn

Corn

Figure 5c 

Percent change in expected payment between county- and State-triggered revenue programs 

as expected market price varies relative to guarantee price 

Percent change in expected payment

District = Crop Reporting District. Average payment weighted by planted acres.

Percent difference between expected market price and guarantee price

Percent difference between expected market price and guarantee price

Average payment weighted by planted acres.

Base case

Base case

Base case
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Risk Reduction Under Different Revenue-Level 

Triggers 

The effectiveness in reducing risk, or variation of farm revenue for a crop, 

of a revenue program triggered at an aggregate level depends greatly on the 

relationship between crop revenue at the farm level and crop revenue at the 

aggregate level. The higher the correlation between an individual farm and 

aggregate trigger level, the more likely that the program payment will coincide with the farm-level loss. 

The strength of correlation between individual and aggregate revenue differs 

across crops and levels of aggregation. On average, when market price equals 

guarantee price (base case), the correlation is stronger for corn, soybeans, 

and rice than it is for wheat, cotton, and grain sorghum (table 3). For corn, 

a large share of U.S. production is concentrated in the Corn Belt and the 

United States dominates the world corn trade; thus, Corn Belt yields have 

a large effect on aggregate production and price. For rice (and other crops 

grown under irrigation), much of revenue variability is determined by price 

variability, which, in the simulations, is identical across farms and aggregate 

regions.

Correlation between farm- and aggregate-level revenue generally increases as 

the aggregation level decreases. The difference in correlation across levels of 

aggregation is especially large for wheat and cotton, reflecting the geographic 

spread and differences in yield variability for these crops across regions. At 

the other extreme is rice, which has little yield variability. Farm-level variability of rice revenue correlates strongly with the aggregate level whether it 

be national, State, district, or county.

Although the correlation between farm and aggregate revenue is highest at 

the county level, farm-level revenue diverges strongly even at this smallest 

level of aggregation; for grain sorghum, cotton, and wheat, the correlation 

coefficient ranges from 0.33 to 0.47 (table 3). 

Another way of looking at how well a revenue program covers farm risk is 

to consider how often a farm-level revenue shortfall would coincide with a 

Table 3

Correlation between farm revenue and revenue at different levels of 

aggregation

Level Corn Soybeans Wheat Cotton Grain 

sorghum

Rice, 

longgrain

Rice, 

medium/

short-grain

Correlation coefficient

National 0.50 0.47 0.32 0.31 0.29 0.62 0.74

State 0.52 0.51 0.40 0.35 0.30 0.62 0.74

District 0.54 0.53 0.44 0.36 0.32 0.62 0.74

County 0.55 0.55 0.47 0.37 0.33 0.63 0.74

Averages weighted by acres harvested in 2010. District = Crop Reporting District. 

Medium/short-grain rice is for a single State, California. 
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shortfall at the aggregate level. When market price equals guarantee price 

(base case), farm revenue would fall below the revenue program benchmark 

most frequently for cotton, grain sorghum, and long-grain rice, though there 

is little difference, with the exception of medium/short-grain rice, across the 

crops (table 4).

The ACRE program and the alternative programs simulated here require that 

both the aggregate-level trigger and the farm-level trigger be met in order for 

a payment to occur. Under these programs, the magnitude of the payment 

is determined by the size of the shortfall at the aggregate level and the farm 

productivity index: the ratio of farm average yield to aggregate yield. The 

level of risk reduction—percentage decrease in the coefficient of variation of 

farm revenue—afforded by revenue programs triggered at different levels of 

aggregation generally follows the pattern of payments, increasing as the level 

of aggregation decreases. The increase in risk reduction between a Statelevel and a county-level program is largest for cotton and wheat (fig. 6). The 

change in risk reduction is smallest for rice, even though the levels of risk 

reduction for rice are highest.

Risk reduction under the revenue programs is limited. The gap in correlation between farm and aggregate revenue, along with the limit on payments 

as specified in ACRE (25 percent of the revenue guarantee), suggests that a 

considerable amount of revenue risk would not be covered by the revenue 

program, even from a program triggered at the smallest level of aggregation. 

Consider the case of a farm with revenue identical to (average) aggregate 

revenue. In this case, the revenue risk coverage from the aggregate trigger 

would be at its maximum.12 With a revenue guarantee level of 90 percent 

of the benchmark and a maximum payment of 25 percent of the guarantee, 

losses between 90 percent and 67.5 percent (90 minus 25 percent of 90) 

12We assume in this example that the 

farm productivity index is 1, farm average yield equals State average yield.

Table 4

Frequency of farm revenue loss and coincidence with revenue trigger 

at different levels of aggregation

Item Corn Soybeans Wheat Cotton Grain 

sorghum

Rice, 

longgrain

Rice, 

medium/

short-grain

Frequency

Farm 

loss

0.480 0.484 0.489 0.509 0.495 0.502 0.472

Farm loss and triggering at level:

National 0.167 0.186 0.170 0.195 0.189 0.241 0.215

State 0.177 0.178 0.199 0.218 0.195 0.247 0.215

District 0.187 0.192 0.223 0.222 0.206 0.254 0.217

County 0.193 0.201 0.232 0.239 0.219 0.255 0.222

Farm loss and no triggering at level:

National 0.313 0.298 0.319 0.314 0.306 0.261 0.257

State 0.303 0.306 0.290 0.291 0.300 0.255 0.257

District 0.293 0.292 0.266 0.287 0.289 0.248 0.255

County 0.287 0.283 0.257 0.270 0.276 0.247 0.250

Loss = revenue loss as defined in ACRE. Based on simulations with expected market price 

equal to ACRE guarantee price. Averages weighted by acres harvested in 2010. District = 

Crop Reporting District. Medium/short-grain rice is for a single State, California
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of the program-defined revenue guarantee would be covered. Risk-averse 

producers who want to cover more risk might need to use other instruments, including the Federal crop insurance and the Supplemental Revenue 

Assistance programs. 

The effect of 25-percent payment cap on the revenue guarantee varies from 

crop to crop and increases as the level of aggregation used in the program 

gets smaller. Under our simulations, switching from the State-triggered 

ACRE program to a county trigger increases the frequency at which the limit 

would be binding for corn from about 7 percent of the time that payments are 

triggered to about 14 percent. For wheat, the binding frequency grows from 

about 14 percent of the time to about 26 percent; for cotton, the frequency 

grows from 18 percent of the time to 24 percent.

Payments Under a Revenue Versus Price-Based Program 

Under the ACRE program, producers elect to forgo payments and other benefits of the price-based commodity support programs that are available under 

the 2008 Farm Act. Specifically, ACRE participants must give up any countercyclical program payments, 20 percent of direct payments, and 30 percent 

of marketing loan rates (USDA/Farm Service Agency, 2009).

The decision to participate in ACRE is complex (Woolverton and Young, 

2009). Among the factors to consider: 

• A farm13 must have farm program base acres,14

• ACRE applies to all of a farm’s crops for which ACRE is available,15

• All landowners for the farm must agree in writing to enroll in ACRE, 

13For program purposes a farm is an 

entity with a FSA Farm Serial Number.

14Base acres are a farm’s cropspecific acreage of wheat, feed grains, 

upland cotton, rice, oilseeds, pulse 

crops, or peanuts eligible to participate 

in commodity programs. Base acreage 

includes land that would have been 

eligible to receive production flexibility 

contract payments in 2002 and acreage 

(specified in legislation) planted to 

other covered commodities (oilseed and 

peanut producers). Base acreage refers 

to cropland on a farm, not to specific 

parcels of land.

15Crops for which ACRE is available 

are wheat, corn, barley, grain sorghum, 

oats, upland cotton, long-grain and 

medium/short-grain rice, peanuts, 

pulse crops (dry peas, lentils, small and 

large chickpeas), soybeans, and other 

oilseeds (sunflower seed, canola, rapeseed, safflower, mustard seed, flaxseed, 

crambe, and sesame seed).

Figure 6 

Risk reduction from revenue programs triggered at different levels of aggregation 

Revenue program trigger level

Risk reduction = percent decrease in coefficient of variation of revenue due to revenue program. Based on simulations of expected market price 

equal to guarantee price. Guarantee price scenario I. Data for all guarantee price scenarios listed in appendix table 3.

National State District County

0

2

4

6

8

10

12

14

16

Percent risk reduction

Corn

Soybeans

Wheat

Cotton

Grain sorghum

Long-grain rice

Medium/short-grain rice
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• A farm can enter ACRE at any year covered by the 2008 Farm Act, but 

must remain enrolled for the duration of the act (through 2012), and

• ACRE participants trade direct payments, which are certain, for revenue 

program payments, which depend on the variability of revenue. 

The decision rests largely on the relative advantages of ACRE versus those 

of the countercyclical payment, marketing loan, and direct payment program. 

The ACRE program is more nimble, adjusting benchmarks and guarantees to 

reflect recent historical prices and yields, whereas other commodity programs 

use legislatively fixed target prices, payments rates, and loan rates. As 

market prices for crops increase (dramatically so in the last 5 years), revenue 

program guarantees increase, whereas the expected benefits of fixed target 

price and loan rate programs decrease. 

We consider the expected payments from a revenue program triggered at 

different levels of aggregation relative to the forgone payments—as specified under ACRE—under the four guarantee price scenarios (table 5).16

Under guarantee price scenario I—where simulated guarantee prices and 

market prices are approximately equal to the 2010 ACRE guarantee prices—

expected revenue program payments, on average, exceed forgone payments 

for each of the crops except cotton and long-grain rice, crops with large 

direct payments.17 Under this guarantee price scenario, expected revenue 

program payments for cotton—revenue program price about 20 percent 

below the target price and slightly above the marketing loan rate—would be 

less than half of the forgone payments at all levels of aggregation. In contrast, 

for soybeans—where revenue program price is about 60 percent higher than 

the non-ACRE target price—expected payments from the revenue program 

would greatly exceed forgone payments, regardless of aggregation level.

Under guarantee price scenario II—with expected market prices and revenue 

guarantee prices 20 percent higher than scenario I—expected payments from 

a revenue program would exceed forgone payments at all revenue program 

trigger levels for all crops except cotton. The revenue payments for longgrain rice are only slightly above forgone payments, across all levels of 

aggregation.

Under guarantee price scenario III—with expected market prices and 

guarantee prices 20 percent lower than the 2010 ACRE guarantee prices—

revenue program payments would be reduced such that they would exceed or 

would roughly equal forgone payments for only soybeans, corn, and medium/

short-grain rice, across all aggregation trigger levels. Wheat’s revenue guarantee price would be about 10 percent above the non-ACRE target price, and 

moving from a State-triggered to a county-triggered revenue program would 

increase the average revenue program payment so that it would nearly equal 

the forgone payments. Cotton’s revenue guarantee price would be about 

40 percent below its target price, so the average expected revenue program 

payment would not exceed 30 percent of forgone payments, even at the 

county aggregation level.

Under guarantee price scenario IV—with market prices and revenue guarantee prices equal to the non-ACRE target prices under the 2008 Farm 

Act—revenue program payments for all crops at all levels of aggregation 

16We adjusted, at the representative 

county-crop farm level, commodity 

program payments that are made per 

base acre to harvested acres, the unit 

of the revenue program payments. The 

average ratios of planted acres to base 

acres for each crop and selected States 

are listed in appendix table 3. State-byState breakouts of the relationship of 

revenue program payments to forgone 

payments are in appendix tables 4a-d.

17In our model, the amounts of direct 

payments per acre forgone are $5.37 

for corn, $1.69 for soybeans, $4.44 

for wheat, $14.75 for cotton, $5.91 for 

grain sorghum, $26.84 for long-grain 

rice, and $28.95 for medium/shortgrain rice.
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would be less than forgone payments, though payments from a county-level 

revenue program would be 90 percent or more of forgone payments for corn, 

soybeans, grain sorghum, and medium/short-grain rice. The ratio of revenueto-price-based payments would be lowest for cotton.

Figures 7a-d show the proportion of representative farm acres for each of the 

seven crops where the revenue program payments would exceed the forgone 

payments under each guarantee price scenario.18 Under guarantee price 

scenario I (fig. 7a) for corn, soybeans, cotton, long-grain rice, and medium/

short-grain rice, the level of aggregation used to trigger revenue payments 

makes little difference in the proportion of acres where revenue program 

payments exceed forgone payments. For soybeans and medium/short-grain 

rice, all representative farm acres would have revenue payments greater than 

forgone payments at all levels of aggregation. For these crops, expected 

market prices and revenue guarantee prices greatly exceed the fixed 

18If expected payments from the revenue programs exceed the forgone payments then we count all of the acres for 

that county and crop as having revenue 

payments exceeding forgone payments. 

Table 5

Expected payments from revenue programs triggered at different 

levels relative to payments forgone under ACRE, by guarantee price 

scenario and crop

Level Corn Soybeans Wheat Cotton Grain 

sorghum

Rice, 

longgrain

Rice, 

medium/

short-grain

Ratio

Scenario I:

National 1.14 1.69 1.03 0.44 1.02 0.98 1.21

State 1.19 1.82 1.06 0.45 1.05 0.98 1.21

District 1.24 1.90 1.10 0.46 1.10 0.98 1.21

County 1.28 2.00 1.13 0.47 1.16 0.98 1.21

Scenario II:

National 1.20 1.86 1.07 0.64 1.14 1.02 1.29

State 1.27 2.02 1.11 0.66 1.17 1.03 1.29

District 1.33 2.12 1.16 0.68 1.23 1.03 1.29

County 1.37 2.24 1.20 0.71 1.30 1.03 1.29

Scenario III:

National 0.98 1.42 0.88 0.27 0.82 0.90 1.13

State 1.03 1.54 0.91 0.28 0.84 0.90 1.13

District 1.07 1.61 0.94 0.28 0.89 0.90 1.13

County 1.10 1.68 0.97 0.29 0.94 0.91 1.13

Scenario IV:

National 0.83 0.77 0.80 0.74 0.86 0.87 0.90

State 0.87 0.85 0.82 0.77 0.89 0.87 0.90

District 0.89 0.90 0.85 0.80 0.93 0.87 0.90

County 0.91 0.96 0.87 0.83 0.98 0.87 0.90

Averages weighted by acres harvested in 2010. Guarantee price scenario I is expected 

market prices and revenue program guarantee prices approximately equal to ACRE program 

guarantee prices for 2010. Guarantee price scenario II is prices 20 percent higher than guarantee price scenario I; guarantee price scenario III is prices 20 percent lower than guarantee 

price scenario I. Guarantee price scenario IV is prices equal to target prices. Prices for each 

scenario are listed in appendix table 1. District = Crop Reporting District.
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Corn Soybeans Wheat Cotton Grain Sorghum

0

20

40

60

80

100

National State District County

Corn Soybeans Wheat Cotton Grain sorghum Long-grain rice Medium/

short-grain

0

20

40

60

80

100

Corn Soybeans Wheat Cotton Grain Sorghum

0

20

40

60

80

100

Corn Soybeans Wheat Cotton Grain Sorghum

0

20

40

60

80

100

Figure 7a 

Percent of acres with revenue program payments, triggered at different levels, 

greater than other commodity payments forgone, guarantee price scenario I 

Representative farm = average farm for crop in a county. Acres = acres harvested in 2010. District = Crop Reporting District.

Percent of representative farm acres

Figure 7b 

Percent of acres with revenue program payments, triggered at different levels, 

greater than other commodity payments forgone, guarantee price scenario II

Percent of representative farm acres

Figure 7c 

Percent of acres with revenue program payments, triggered at different levels, 

greater than other commodity payments forgone, guarantee price scenario III

Percent of representative farm acres

Figure 7d 

Percent of acres with revenue program payments, triggered at different levels, 

greater than other commodity payments forgone, guarantee price scenario IV

Percent of representative farm acres

Long-grain rice Medium/

short-grain

Long-grain rice Medium/

short-grain

Long-grain rice Medium/

short-grain
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target prices. For corn—which under guarantee price scenario I also would 

have prices well above non-ACRE targets—the proportion of representative farm acres where revenue payments would exceed forgone payments 

ranges from just less than 80 percent for the national-triggered program to 

about 85 percent for the county-triggered program. For long-grain rice, there 

is little difference across program trigger levels because revenue variability 

differs little across levels of aggregation. For cotton, forgone payments 

exceed expected revenue payments for all representative farm acres under all 

revenue program levels. By contrast, reducing the trigger level for wheat and 

grain sorghum leads to steeply increasing proportions of acres where revenue 

payments exceed forgone payments.

Under guarantee price scenario II (fig. 7b), the proportions of farm acres 

where revenue program payments exceed forgone payments at different 

aggregation levels are similar to those of scenario I for all crops except longgrain rice. Under guarantee price scenario III (fig. 7c), the proportions of 

such acres are lesser than under scenarios I and II across all levels of aggregation for all crops but soybeans and cotton. Under guarantee price scenario 

IV (fig. 7d), few acres would have revenue payments greater than forgone 

payments, regardless of level of aggregation.
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Conclusions

Changing the level of revenue aggregation used to trigger payments in a 

program like ACRE would alter expected payments and the reduction in farm 

revenue risk from the program. Generally, moving to a smaller aggregation, 

one closer to the farm level, would increase payments and risk reduction. 

This is because revenue variability, particularly yield variability, and correlation between aggregate and farm-level revenue increase as the aggregation 

level decreases. 

If expected market prices equal revenue program guarantee prices (the base 

case), then the change in revenue program payments would be greatest for 

crops—such as wheat, cotton, and grain sorghum—with large differences 

in revenue variability across levels of aggregation. The average expected 

payments for these crops would increase 28-32 percent if the revenue 

program trigger were changed from State to county. Corn and soybean 

production is more concentrated geographically with less varied yields, so 

the increase in expected payments would be less, 16-19 percent. Payments 

for rice would change little if the aggregate revenue trigger were changed 

because most of the crop is irrigated, with price largely determining revenue 

variability.

Not all farms would see the average changes in expected payments; some 

would see changes greater than average, and some less. The change in 

expected payment between a State-triggered and a county-triggered program, 

for instance, depends on the revenue variability for a particular county 

relative to the State, as well as the ratio of the farm’s yield to the State 

and county yield (farm productivity index) and correlations between farm 

revenue and State/county revenue—all factors that differ by crop, county, 

State, and farm.

The relationship between expected market prices and revenue program guarantee price affects the changes in payments from alternative levels of revenue 

aggregation. If expected market price increases relative to the guarantee, 

then the difference in payments across levels of aggregation would increase 

as yield variability becomes a stronger factor in determining revenue variability and payments; if expected market prices decrease relative to guarantees, expected payments increase but differences across levels of aggregation 

decrease.

Risk reduction from a program that uses an aggregate revenue trigger 

depends greatly on the correlation between the aggregate measure of revenue 

and actual farm revenue. While risk reduction increases as the level of aggregation used in the revenue program diminishes, for all crops that we examined except rice, farm-level revenue is largely uncorrelated with revenue 

at even the smallest level of aggregation, the county. In addition, expected 

payments and risk reduction are limited by the cap of revenue program 

payments at 25 percent of the guarantee. This suggests that while the revenue 

coverage under an aggregate revenue program and revenue coverage under 

Federal crop revenue insurance may overlap, the amount of overlap—

payment from both programs for the same revenue loss—could be small 

(Zulauf et al., 2010).19

19Both ACRE and subsidized Federal 

crop insurance offer revenue coverage. 

The two programs, however, define 

revenue differently, using different 

yields and prices; crop insurance offers 

revenue coverage that triggers at the 

county or farm level (insured unit), 

while ACRE uses a combined State and 

farm trigger. Producers participating 

in crop insurance pay a portion of the 

actuarially based insurance premium; 

there is no premium charge for coverage under ACRE.
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Because revenue benchmarks or guarantees used in the revenue programs are 

designed to change over time, it is important to consider the different crop 

guarantee price scenarios in evaluating the benefits of a revenue program 

relative to the price-based programs available under the 2008 Farm Act. If 

crop prices continue to increase, guarantees and expected payments under 

ACRE or a similar program would increase, while expected payments under 

programs that are based on legislatively fixed targets would decrease. And as 

the crop’s price increases, the size of the direct payment for a crop decreases 

relative to the expected payment from the revenue program. Changes in 

prices, and thus revenue program guarantees, appear to be much more important than level of aggregation/trigger to producers weighing the revenue 

election. 

While a county-level program would generally produce larger expected 

payments and risk reduction than a State-level program, it would increase 

government administrative costs as program benchmarks and guarantees 

would need to be determined for more aggregate units and would not, 

without additional resources, be feasible. For the current State-level ACRE 

program, which has 22 covered commodities, USDA’s Farm Service Agency 

(FSA) uses USDA National Agricultural Statistics Service (NASS) data, 

where available, to determine yields. However, these data are not produced 

for many State-crop combinations. Of the 670 State-crop combinations in the 

2009 ACRE program, NASS data were not available for 407 (Tice, 2011). In 

a few cases, NASS data can be supplemented with data from USDA’s Risk 

Management Agency (RMA). For the remaining State-crop combinations, 

FSA must use historical data and relationships with similar crops and States 

to derive benchmark yields.20 Moving to a county-based program would 

require additional investments in data to ensure credible program parameters and program integrity. And for some county-crop combinations, the 

number of farms would be so few that NASS would have to sample the same 

producers year after year, unduly burdening survey respondents.

Experience with county-level yield and revenue insurance under the Federal 

crop insurance program illustrates the data challenges with a county-crop 

program. In 2009 and 2010, RMA deleted about a third of the county revenue 

and yield insurance programs, Group Risk Plan (GRP) and Group Risk 

Income Protection (GRIP), for corn, soybeans, grain sorghum, cotton, and 

peanuts (USDA, Risk Management Agency, 2009). While most of these 

counties had little or no participation in GRP or GRIP, NASS’ ability to 

produce estimates for counties outside the major growing areas for these 

crops was uncertain. 

20When at least 25 percent and less 

than 75 percent of the acreage for a 

crop in a State is irrigated, the ACRE 

program requires that FSA establish 

separate State-level yields for irrigated 

and non-irrigated production.
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Methods and Data

The model underlying this research simultaneously simulates random yields, 

prices, and revenues at farm, county, Crop Reporting District, State, and U.S. 

levels for corn, soybeans, wheat, cotton, grain sorghum, and long-grain and 

medium/short-grain rice. The model accounts for correlations among the 

random variables by use of empirical sampling techniques.

The model is based on data for 1,199 counties for corn, 1,014 counties for 

soybeans, 985 counties for wheat, 177 counties for cotton, 139 counties for 

grain sorghum, 54 counties for long-grain rice, and 8 counties for medium/

short-grain rice. These counties represent about 95 percent of the 2010 planted 

U.S. acres of corn, 89 percent of soybean acres, 89 percent of wheat acres, 84 

percent of cotton acres, 74 percent of grain sorghum acres, 94 percent of longgrain rice acres, and 100 percent of medium/short-grain acres.1

Yield variability was estimated with data from USDA’s National Agricultural 

Statistics Service (NASS) and data from USDA’s Risk Management Agency 

(RMA). To measure yield variability at the county, Crop Reporting District, 

State, and U.S. levels, we estimated a linear time trend of each yield data 

series, yields from 1975 to 2009, and calculated the residuals (differences 

between actual yield in individual years and the trend yield). The trend estimate is used to predict the expected yield for 2010. 

To measure yield variability at the farm level, we used county yield variabilities in conjunction with data from the Federal crop insurance program, which 

is administered by USDA’s RMA. Specifically, we used the 2009 base county 

premium rate for yield coverage (at the 65-percent coverage level) for each 

crop.2 These rates are for “basic units,” as defined in crop insurance policy 

provisions. Basic units are intermediate between “optional units,” the level at 

which many crop insurance policies are insured, and “enterprise units,” the 

level closest to farm-level risk. We subtracted from the basic premium rates 

the portions that were to cover prevented planting, replanting, and quality 

adjustments. The adjusted premium rates were used to calibrate an additive 

farm yield variability term for each county and crop.3

Our approach to modeling farm yields uses Miranda’s (1991) specification of 

the relationship between systemic and idiosyncratic variability:

(1)

where 

ft y is the realization of the random yield on farm f in year t, 

ct y is the realization of the random yield in county c in year t, 

( ) ( ), μ f E y E y ft c ct μ ft =  =  and ε is a normally distributed error term with 

( ) 0 E ft ε = and ( ) 2 Var ft ε s= .

1We assume medium/short-grain rice 

is grown only in California and that all 

California rice is medium/short-grain.

2Although there is evidence to 

suggest that RMA rates, particularly 

in low-risk regions, are too high, 

quantifying yield variability in lowrisk regions is quite difficult when 

one must deal with yield and variance 

trends. 

3Another approach would be to use 

Actual Production History (APH) 

yield data from RMA. While actual 

losses fully reflect the underlying 

yield variability, APH is used by RMA 

only to estimate mean yield, the first 

moment of the distribution and not the 

higher moments. We did not use this 

approach largely because estimates 

of higher moments from a very small 

sample (10 or fewer observations) 

would be subject to large error.

( ) ft f ct c ft y y   = + − + ∀ ∈ μ b μ ε f c
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The coefficient, b measures the responsiveness of deviations in farm yield 

relative to the expected value to deviations in county yield relative to the 

expected value. In our analysis, the representative farm is assumed to have 

a b =1, which Miranda shows would be the acreage-weighted average of all 

b’s in the county. The error term ft ε represents idiosyncratic effects on farm 

yield deviations relative to the expected value that are orthogonal to county 

yield deviations relative to the expected value. A grid search is conducted for 

the value of s (the standard deviation of ft ε ) that in a simulation replicates the 

base crop insurance rates.

Price variability was estimated from NASS national price data. National 

annual marketing-year average (MYA) prices for 1974 through 2009 for each 

of the seven crops are used to calculate a percentage price change from the 

previous year’s price level. The percentage changes in prices were adjusted 

to account for increased price volatility, as measured from options on futures 

contracts, in recent years. These price data were placed in a matrix [P] that has 

T rows of annual prices. Yield data for each of the seven crops were placed in a 

matrix [Y] that contains county-, CRD-, State-, and U.S.-level yield deviations 

relative to their expected values. The yield matrix has T rows representing T 

years of historical yields.

The revenue simulations can be generated from 1,000 random 5-year time 

paths. In this report, however, the revenue variability and ACRE payment estimates are based on year-one outcomes. For every location, a row is simultaneously drawn randomly from yield matrix [Y] and price matrix [P] (i.e., 

all yield deviations from trend and price changes are drawn from the same 

historical year) to maintain the empirical correlations between prices and 

yields, between yields at different levels of aggregation, and between yields 

in different counties. The idiosyncratic portion of farm yield is independently 

drawn (5 draws) for each representative farm for each of the 1,000 draws. This 

approach is similar to that used by RMA to generate random yields to simulate 

insurance rates for its COMBO product.

Data on direct payments are from county summaries of Farm Service Agency 

records. Countercyclical payments and marketing loan payments are generated by model simulations. Producer-paid crop insurance premiums, which are 

included in farm benchmark revenue, are estimated from model simulations of 

a 70-percent revenue insurance policy.
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References

Babcock, Bruce A. 2010. “Costs and Benefits of Moving to a County ACRE 

Program.” CARD Policy Brief 10-PB 2. Center for Agricultural and 

Rural Development, Iowa State University. May.

Coble, Keith H., Robert Dismukes, and Sarah Thomas. 2007. “Policy 

Implications of Crop Yield and Revenue Variability at Differing Levels 

of Aggregation.” Selected paper, American Agricultural Economics 

Association, annual meeting, Portland, OR.

Cooper, Joseph. 2009. Economic Aspects of Revenue-Based Commodity 

Support. Economic Research Report 72, U.S. Department of Agriculture, 

Economic Research Service. April.

Cooper, Joseph C. 2010. “Average Crop Revenue Election: A RevenueBased Alternative to Price-Based Commodity Payment Programs.” 

American Journal of Agricultural Economics. 92(4) July.

Dismukes, Robert, Christine Arriola and Keith H. Coble. 2010. ACRE 

Program Payments and Risk Reduction: An Analysis Based on 

Simulations of Crop Revenue Variability. Economic Research Report 101. 

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

Gardner, Bruce L. 2002. American Agriculture in the Twentieth Century: 

How It Flourished and What It Cost. Cambridge: Harvard University 

Press.

Miranda, Mario J. 1991. “Area-Yield Insurance Reconsidered,” American 

Journal of Agricultural Economics, Vol. 73, No. 2. pp. 233-242. May.

Paulson, Nicholas, and Bruce A. Babcock. 2008. “Get a GRIP: Should 

Area Revenue Coverage Be Offered Through the Farm Bill or as a Crop 

Insurance Program?” Journal of Agricultural and Resource Economics

33(2): 137-153.

Tice, Tom. 2011. Director, Food Grains Analysis, U.S. Department of 

Agriculture, Farm Service Agency. Personal communication. June 21.

U.S. Department of Agriculture, Farm Service Agency. 2009. 2009 Average 

Crop Revenue Election (ACRE) Program, Fact Sheet, March 18. http://

www.fsa.usda.gov/Internet/FSA_File/acre.pdf.

U.S. Department of Agriculture, Farm Service Agency. 2011a. “Final DCP/

ACRE Enrollment Summaries.” http://www.fsa.usda.gov/FSA/webapp?ar

ea=home&subject=dccp&topic=09cy 

U.S. Department of Agriculture, Farm Service Agency. 2011b. “Results of 

2010 DCP and ACRE Sign-up.” http://www.fsa.usda.gov/FSA/webapp?ar

ea=home&subject=dccp&topic=09cy 
29

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U.S. Department of Agriculture, Risk Management Agency. 2009. “RMA 

Releases GRP/GRIP County Program Deletions for 2010 Corn, Soybeans, 

Grain Sorghum, Cotton, Peanuts.” Program Announcement. Dec. 

Woolverton, Andrea and Edwin Young. 2009. Factors Influencing ACRE 

Program Enrollment. Economic Research Report 84. U.S. Department of 

Agriculture, Economic Research Service. Dec. 

Zulauf, Carl. 2006. “U.S. Agricultural Policy at a Crossroads.” Testimony of 

Carl Zulauf, Agricultural Economist, before the House of Representatives 

Committee on Agriculture, Subcommittee for General Farm Commodities 

and Risk Management, Washington, DC, Sept. 21.

Zulauf, Carl, Gary Schnitkey, and Michael Langemeier. 2010. “Average 

Crop Revenue Election, Crop Insurance, and Supplemental Revenue 

Assistance: Interactions and Overlap for Illinois and Kansas Farm 

Program Crops,” Journal of Agricultural and Applied Economics. Vol. 

42, No. 3. pp. 501-515. Aug.
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Appendix table 1

Prices and program parameters in the analysis

Corn Soybeans Wheat Cotton

Grain 

sorghum Rice, long-grain

Rice, medium/

short-grain

Dollars per bushel Dollars per 

pound

Dollars 

per bushel Dollars per hundredweight

Scenario I 3.80 9.75 5.80 0.55 3.20 13.90 21.30

Scenario II 4.56 11.70 6.96 0.66 3.84 16.68 25.56

Scenario III 3.04 7.80 4.64 0.44 2.56 11.12 17.04

Scenario IV/

target price

2.63 6.00 4.17 0.71 2.63 10.50 10.50

Direct payment rate 0.28 0.44 0.52 0.0667 0.35 2.35 2.35

Marketing loan rate 1.95 5.00 2.94 0.52 1.95 6.50 6.50

Marketing loan rate 

with ACRE

1.37 3.50 2.06 0.364 1.37 4.35 4.35

Scenario I prices are approximate guarantee prices for ACRE in 2010. Scenario II prices are a 20-percent increase in prices. 

Scenario III prices are a 20-percent decrease in prices. Scenario IV guarantee prices are target prices. Simulations are based on expected 

market price equal to guarantee price, unless otherwise indicated.

Appendix table 2

Expected payments from revenue program triggered at different levels of aggregation

Scenario/level Corn Soybeans Wheat Cotton

Grain 

sorghum Rice, long-grain

Rice, medium/

short-grain

Dollars per acre

Scenario I

National 7.89 6.21 4.29 5.66 3.48 21.87 58.84

State 8.90 6.78 4.85 7.38 3.87 22.33 58.84

District 9.83 7.24 5.59 8.35 4.53 22.43 58.74

County 10.57 7.83 6.21 9.48 5.11 22.84 58.76

Scenario II

National 9.47 7.45 5.15 6.80 4.17 26.25 70.61

State 10.68 8.14 5.83 8.86 4.64 26.80 70.61

District 11.80 8.68 6.71 10.02 5.43 26.92 70.49

County 12.69 9.39 7.45 11.38 6.13 27.41 70.51

Scenario III

National 6.31 4.97 3.43 4.53 2.78 17.50 47.07

State 7.12 5.43 3.88 5.91 3.10 17.87 47.07

District 7.87 5.79 4.47 6.68 3.62 17.94 46.99

County 8.46 6.26 4.97 7.58 4.09 18.27 47.01

Scenario IV

National 5.46 3.82 3.08 7.31 2.86 16.52 29.01

State 6.16 4.17 3.49 9.53 3.18 16.87 29.01

District 6.81 4.45 4.02 10.78 3.72 16.94 28.96

County 7.32 4.82 4.47 12.24 4.20 17.25 28.97

Averages weighted by acres harvested in 2010. Based on simulations of market price equal to guarantee price.
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Appendix table 3

Risk reduction from revenue programs triggered at the different levels of aggregation

Level Corn Soybeans Wheat Cotton

Grain 

sorghum Rice, long-grain

Rice, medium/

short-grain

Percent

Scenario I

National 7.1 5.7 10.4 12.8 9.8 14.7 14.1

State 7.6 6.2 11.0 13.5 10.2 14.8 14.1

District 8.1 6.6 11.8 14.0 10.7 14.9 14.1

County 8.5 7.0 12.5 14.4 11.1 15.0 14.1

Scenario II

National 6.6 5.5 9.4 11.3 8.8 13.4 13.3

State 7.1 6.0 10.0 12.1 9.2 13.5 13.3

District 7.6 6.3 10.9 12.6 9.7 13.5 13.3

County 8.0 6.8 11.5 13.0 10.1 13.6 13.3

Scenario III

National 7.8 6.0 11.9 15.6 11.3 16.6 15.2

State 8.4 6.5 12.4 16.3 11.6 16.7 15.2

District 8.9 6.9 13.2 16.8 12.1 16.8 15.2

County 9.3 7.4 14.0 17.2 12.6 16.9 15.2

Scenario IV

National 8.4 6.6 12.7 10.8 11.1 17.2 18.6

State 8.9 7.0 13.2 11.5 11.5 17.3 18.6

District 9.4 7.4 14.0 12.0 11.9 17.3 18.6

County 9.8 7.9 14.7 12.5 12.4 17.4 18.6

Risk reduction = Percent decrease in coefficient of variation of representative farm acre revenue due to revenue program. Averages weighted 

by acres harvested in 2010. District = Crop Reporting District. Based on simulations of revenue where expected market price equals revenue 

program guarantee price. 
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Appendix table 4a

Expected payments from revenue program triggered at different levels relative to payments forgone under 

ACRE, by State, guarantee price scenario I

State

Revenue program level

National State District County

Ratio

Alabama 0.00 0.00 0.00 0.00

Arkansas 0.50 0.55 0.55 0.55

California 1.38 1.38 1.39 1.38

Colorado 0.76 1.06 1.16 1.29

Delaware 1.93 2.51 2.72 2.72

Florida 0.08 0.14 0.16 0.16

Georgia 0.40 0.53 0.59 0.68

Idaho 1.22 1.45 1.51 1.65

Illinois 1.82 1.71 1.95 2.12

Indiana 1.71 1.65 1.70 1.82

Iowa 1.89 2.13 2.37 2.50

Kansas 1.08 1.18 1.57 1.80

Kentucky 1.57 1.79 1.84 2.06

Louisiana 3.17 4.12 4.13 4.00

Maryland 1.52 1.99 2.09 2.18

Michigan 1.47 1.71 1.99 2.15

Minnesota 2.02 2.64 2.87 3.12

Mississippi 1.27 1.55 1.72 1.87

Missouri 1.15 1.43 1.62 1.71

Montana 0.72 0.88 1.22 1.42

Nebraska 1.36 1.47 1.53 1.66

New Jersey 1.65 2.09 2.18 2.34

New Mexico 0.06 0.08 0.09 0.11

New York 0.53 0.48 0.51 0.54

North Carolina 2.92 4.03 4.13 4.58

North Dakota 0.76 0.78 0.92 1.05

Ohio 1.57 1.55 1.69 1.84

Oklahoma 0.79 1.04 1.09 1.21

Oregon 1.15 1.82 2.19 2.25

Pennsylvania 2.63 3.20 3.30 3.90

South Carolina 0.97 1.44 1.43 1.63

South Dakota 1.75 2.10 2.25 2.51

Tennessee 1.53 1.76 1.85 2.06

Texas 0.21 0.24 0.26 0.32

Utah 0.38 0.40 0.46 0.46

Virginia 2.45 3.55 3.67 4.22

Washington 0.99 1.07 1.09 1.17

West Virginia 1.02 1.08 1.06 1.16

Wisconsin 1.12 1.38 1.44 1.53

Wyoming 0.39 0.37 0.38 0.44

U.S. 1.39 1.57 1.74 1.90
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Appendix table 4b

Expected payments from revenue program triggered at different levels relative to payments forgone under 

ACRE, by State, guarantee price scenario II

State

Revenue program level

National State District County

Ratio

Alabama 0.00 0.00 0.00 0.00

Arkansas 0.70 0.76 0.77 0.78

California 1.65 1.66 1.66 1.66

Colorado 0.92 1.27 1.40 1.55

Delaware 2.31 3.01 3.27 3.27

Florida 0.14 0.25 0.27 0.28

Georgia 0.54 0.74 0.82 0.95

Idaho 1.47 1.74 1.81 1.98

Illinois 2.19 2.05 2.34 2.55

Indiana 2.05 1.99 2.04 2.18

Iowa 2.27 2.56 2.85 3.00

Kansas 1.32 1.44 1.91 2.19

Kentucky 1.88 2.14 2.20 2.47

Louisiana 3.83 4.99 5.00 4.86

Maryland 1.82 2.38 2.51 2.61

Michigan 1.76 2.05 2.39 2.58

Minnesota 2.42 3.17 3.44 3.74

Mississippi 1.55 1.89 2.09 2.27

Missouri 1.41 1.74 1.97 2.08

Montana 0.87 1.05 1.46 1.71

Nebraska 1.64 1.78 1.85 2.00

New Jersey 1.98 2.51 2.61 2.81

New Mexico 0.09 0.12 0.13 0.16

New York 0.64 0.58 0.61 0.65

North Carolina 3.53 4.86 4.99 5.54

North Dakota 0.91 0.94 1.10 1.26

Ohio 1.89 1.86 2.03 2.21

Oklahoma 13.80 15.65 16.66 19.93

Oregon 1.38 2.19 2.63 2.70

Pennsylvania 3.15 3.84 3.96 4.68

South Carolina 1.19 1.79 1.77 2.01

South Dakota 2.10 2.53 2.70 3.02

Tennessee 1.89 2.18 2.29 2.54

Texas 0.29 0.34 0.38 0.46

Utah 0.45 0.48 0.56 0.55

Virginia 2.94 4.26 4.40 5.06

Washington 1.19 1.28 1.31 1.40

West Virginia 1.22 1.29 1.27 1.40

Wisconsin 1.35 1.65 1.73 1.84

Wyoming 0.46 0.44 0.46 0.52

U.S. 1.92 2.18 2.40 2.66
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Appendix table 4c

Expected payments from revenue program triggered at different levels relative to payments forgone under 

ACRE, by State, guarantee price scenario III

State

Revenue program level

National State District County

Ratio

Alabama 0.00 0.00 0.00 0.00

Arkansas 0.32 0.35 0.35 0.35

California 1.07 1.07 1.07 1.07

Colorado 0.42 0.59 0.64 0.71

Delaware 1.23 1.60 1.74 1.74

Florida 0.05 0.08 0.09 0.09

Georgia 0.25 0.32 0.36 0.42

Idaho 0.67 0.79 0.82 0.90

Illinois 1.18 1.11 1.27 1.38

Indiana 1.11 1.08 1.11 1.18

Iowa 1.24 1.40 1.55 1.64

Kansas 0.62 0.68 0.91 1.04

Kentucky 1.00 1.13 1.16 1.31

Louisiana 0.77 1.01 1.06 1.08

Maryland 0.96 1.25 1.31 1.37

Michigan 0.94 1.10 1.28 1.38

Minnesota 1.32 1.73 1.89 2.06

Mississippi 0.90 1.10 1.21 1.32

Missouri 0.71 0.88 1.00 1.06

Montana 0.39 0.48 0.66 0.77

Nebraska 0.85 0.93 0.96 1.04

New Jersey 1.06 1.35 1.40 1.51

New Mexico 0.03 0.04 0.04 0.05

New York 0.32 0.29 0.31 0.33

North Carolina 1.52 2.09 2.15 2.38

North Dakota 0.42 0.43 0.50 0.58

Ohio 1.01 1.00 1.09 1.19

Oklahoma 0.32 0.46 0.47 0.50

Oregon 0.63 0.99 1.19 1.22

Pennsylvania 1.61 1.96 2.02 2.39

South Carolina 0.65 0.95 0.94 1.08

South Dakota 1.08 1.30 1.39 1.55

Tennessee 1.04 1.20 1.26 1.40

Texas 0.11 0.12 0.14 0.17

Utah 0.21 0.22 0.26 0.25

Virginia 1.53 2.22 2.30 2.64

Washington 0.60 0.64 0.65 0.69

West Virginia 0.55 0.59 0.58 0.63

Wisconsin 0.73 0.89 0.93 0.99

Wyoming 0.22 0.21 0.21 0.24

U.S. 0.86 0.97 1.07 1.17
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Appendix table 4d

Expected payments from revenue program triggered at different levels relative to payments forgone under 

ACRE, by State, guarantee price scenario IV

State

Revenue program level

National State District County

Ratio

Alabama 0.00 0.00 0.00 0.00

Arkansas 0.28 0.30 0.30 0.31

California 0.47 0.47 0.47 0.47

Colorado 0.26 0.36 0.39 0.43

Delaware 0.52 0.68 0.74 0.74

Florida 0.15 0.29 0.30 0.28

Georgia 0.25 0.40 0.42 0.48

Idaho 0.41 0.48 0.50 0.55

Illinois 0.45 0.43 0.49 0.53

Indiana 0.42 0.40 0.42 0.44

Iowa 0.48 0.54 0.60 0.64

Kansas 0.30 0.31 0.42 0.48

Kentucky 0.39 0.45 0.46 0.52

Louisiana 0.24 0.30 0.32 0.33

Maryland 0.44 0.58 0.61 0.63

Michigan 0.37 0.43 0.49 0.54

Minnesota 0.43 0.55 0.58 0.62

Mississippi 0.19 0.22 0.24 0.25

Missouri 0.30 0.37 0.42 0.44

Montana 0.25 0.30 0.41 0.48

Nebraska 0.37 0.40 0.42 0.45

New Jersey 0.40 0.50 0.52 0.56

New Mexico 0.07 0.09 0.09 0.11

New York 0.18 0.16 0.17 0.18

North Carolina 0.39 0.54 0.56 0.61

North Dakota 0.24 0.25 0.29 0.34

Ohio 0.40 0.39 0.42 0.46

Oklahoma 0.17 0.23 0.24 0.27

Oregon 0.38 0.59 0.72 0.74

Pennsylvania 0.73 0.88 0.92 1.07

South Carolina 0.18 0.31 0.31 0.34

South Dakota 0.48 0.58 0.62 0.69

Tennessee 0.26 0.30 0.31 0.34

Texas 0.16 0.19 0.21 0.26

Utah 0.13 0.14 0.16 0.16

Virginia 0.48 0.70 0.73 0.84

Washington 0.32 0.35 0.35 0.38

West Virginia 0.35 0.37 0.36 0.39

Wisconsin 0.32 0.40 0.41 0.44

Wyoming 0.13 0.13 0.13 0.15

U.S. 0.36 0.40 0.44 0.49
36

Alternatives to a State-Based ACRE Program: Expected Payments Under a National, Crop District, or County Base / ERR-126

Economic Research Service/USDA

Appendix table 5

Ratio of planted acres to base acres, selected States and U.S. average

Scenario/level Corn Soybeans Wheat Cotton

Grain 

sorghum Rice, long-grain

Rice, medium/

short-grain

Arkansas 2.73 1.40 0.34 0.47 n/a 0.82 n/a

California 0.69 n/a 0.68 0.65 n/a n/a 0.94

Colorado n/a n/a 0.82 n/a n/a n/a n/a

Georgia 0.74 2.97 0.60 0.71 n/a n/a n/a

Illinois 0.98 1.12 0.69 n/a 0.42 n/a n/a

Indiana 0.85 1.31 0.73 n/a n/a n/a n/a

Iowa 0.93 1.26 n/a n/a n/a n/a n/a

Kansas 1.47 2.15 0.76 n/a 0.70 n/a n/a

Kentucky 0.74 1.51 0.91 n/a n/a n/a n/a

Louisiana 1.94 1.51 n/a 0.25 n/a 0.60 n/a

Minnesota 0.93 1.25 0.58 n/a n/a n/a n/a

Mississippi 1.96 2.01 0.48 0.19 n/a 0.61 n/a

Montana 0.32 n/a 0.79 n/a n/a n/a n/a

Nebraska 0.96 1.79 0.68 n/a 0.16 n/a n/a

North Carolina 0.91 1.79 1.15 0.54 n/a n/a n/a

North Dakota 1.49 1.60 0.62 n/a n/a n/a n/a

Ohio 0.82 1.30 0.91 n/a n/a n/a n/a

Oklahoma n/a 2.93 0.81 0.42 0.91 n/a n/a

Oregon 0.71 n/a 0.80 n/a n/a n/a n/a

Tennessee 0.95 n/a 0.89 0.39 n/a n/a n/a

Texas 1.14 2.66 1.11 0.69 0.98 0.30 n/a

Virginia 0.99 1.57 n/a 0.47 n/a n/a n/a

Washington 1.27 n/a 0.52 n/a n/a n/a n/a

Wisconsin 0.74 1.64 2.00 n/a n/a n/a n/a

U.S. 0.94 1.37 0.76 0.52 0.69 0.67 0.94

Planted acres in 2010.