United States 

Department of 

Agriculture

Economic

Research

Service

Economic

Research

Report

Number 101

September 2010

Robert Dismukes, Christine Arriola, and Keith H. Coble

ACRE Program Payments 

and Risk Reduction

An Analysis Based on Simulations 

of Crop Revenue Variability
www.ers.usda.gov

For additional information on Farm and Commodity Policy, see

www.ers.usda.gov/Briefing/FarmPolicy/

Recommended citation format for this publication:

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

Payments and Risk Reduction: An Analysis Based on Simulations of Crop 

Revenue Variability, ERR-101, U.S. Dept. of Agr., Econ. Res. Serv. 

September 2010.

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United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

Abstract

Crop revenue variability—which differs across crops, regions, and the geographic 

levels at which revenue is measured—is the focus of the Average Crop Revenue 

Election program, which was fi rst available to producers in 2009. Using an empirically based simulation model of an extensive set of representative farm acres of corn, 

soybeans, wheat, and cotton, we analyze how ACRE payments vary under different 

guarantee price and expected market price scenarios, and how potential ACRE 

payments and risk reduction are distributed across crops and regions.

Keywords

Crop revenue variability, Average Crop Revenue Election, ACRE, farm risk 

management

Acknowledgments

The authors wish to express their thanks for the helpful comments from Joseph 

Cooper, Andrea Woolverton, and Ed Young of the Economic Research Service, as 

well as three anonymous reviewers. The authors also thank Dale Simms for editorial 

and production services and Curtia Taylor for the design and layout of the report.

Robert Dismukes, dismukes@ers.usda.gov

Christine Arriola, carriola@ers.usda.gov

Keith H. Coble, Mississippi State University

ACRE Program Payments 

and Risk Reduction

An Analysis Based on Simulations 

of Crop Revenue Variability

Economic

Research

Report

Number 101

September 2010
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Contents

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

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

Crop Revenue Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

 Price Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

 Yield Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

 Price-Yield Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

ACRE: How It Works and What It Covers . . . . . . . . . . . . . . . . . . . . . . . 8

How Prices and Yields Affect ACRE Payments . . . . . . . . . . . . . . . . . . . . 9

 Guarantee Prices and Expected Market Prices. . . . . . . . . . . . . . . . . . . . . 9

 Yield Levels and Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

ACRE Payments and Risk Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

 Revenue Variability at Farm and State Levels . . . . . . . . . . . . . . . . . . . . 18

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
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Summary

Using crop revenue as the basis for agricultural payments has long been 

proposed as a way to reform U.S. farm programs. Proponents recognize that 

revenue is more closely related to farm income than either prices or yields 

individually. Thus, a revenue-based program can more effi ciently stabilize farm income than price-based commodity programs, while lessening 

annual variability in Government program costs. The Average Crop Revenue 

Election program or ACRE, an alternative to price-based commodity 

programs that was fi rst available to producers in 2009, uses a combination 

of State- and farm-level revenue guarantees that are determined from recent 

historic prices and yields. This report examines and simulates crop revenue 

variability for producers of four crops—corn, soybeans, wheat, and cotton—

and estimates potential ACRE payments from a broad range of possible 

revenue outcomes. We analyze how ACRE payments and their effect on 

farm-level revenue variability would be distributed across crops and production regions. The four crops included in our model accounted for about half of 

the value of production of U.S. crops between 2002 and 2007 and more than 

90 percent of the value of crops for which ACRE is available.

What Is the Issue? 

Historic prices and yields that determine ACRE guarantees can change, 

and future prices and yields that determine payments are not known with 

certainty. ACRE revenue guarantees are based on a “moving” 2-year average 

of (national) market prices and 5-year Olympic averages (dropping the 

highest and lowest values) of State- and farm-level yields. ACRE payments 

are triggered when both the farm and State revenues fall below guarantee 

levels at the end of a crop year. The capability of ACRE guarantees to 

refl ect recent prices and yields and to change over time are major differences 

between ACRE and commodity programs based on fi xed target prices and 

loan rates. If direct and countercyclical payments are designed to combat 

unexpectedly low market prices, then the ACRE program can be seen as risk 

protection for producers adjusting to an era of historically high crop prices. 

As such, its potential benefi ts are more pronounced for producers of corn, 

soybeans, and wheat, who can foresee high expected payments based on 

recent high prices. The prospects for cotton producers under ACRE in the 

near term are more mixed. Because revenue variability for a particular crop 

differs across farms and regions, the potential benefi ts of ACRE also vary.

What Did the Study Find?

ERS researchers develop a simulation model of crop revenue variability at 

the State and farm levels for an extensive set of representative farm acres of 

corn, soybeans, wheat, and cotton. This model is used to analyze the distribution, by crop and region, of potential payments and risk reduction from 

ACRE.

Key variables in determining crop revenue and ACRE payments are prices, 

yields, and their interactions, factors that differ across crops and regions. 

Because crop prices depend largely on world markets, variability in the price 

for a crop is similar across much of the United States. Yields, in contrast, 
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depend on factors such as weather, diseases, and insects that can affect wide 

areas at once but are often localized.

An ACRE payment is triggered when annual revenue falls below 90 percent 

of the benchmark measured at the State level and when the farm’s annual 

revenue falls below its benchmark. The magnitude of the ACRE payment 

depends on the size of the State revenue shortfall, relative to its guarantee, as 

well as the farm’s average yield relative to the State average. Because of this 

complexity, ACRE payments are not directly related to variability in farmlevel revenue.

As such, ACRE is ineffective in covering idiosyncratic risks, shortfalls on 

an individual farm that are uncorrelated with more widespread losses, and 

more targeted at systemic or statewide risk. The strength of systemic risk—

and thus, the relative effectiveness of ACRE in reducing risk—depends on 

the level of price variability, the difference between historical prices and 

expected market prices at a particular time, and the degree to which yield 

losses on one farm are matched with yield losses on other farms in the State. 

ACRE is most effective in reducing risk when crop price, which under ACRE 

is the same for all States and farms, causes revenue to change substantially 

and when yield losses are widespread. 

The effectiveness of the ACRE program in reducing risk, or variability of 

farm revenue for a crop, is strongly related to the correlation between the 

farm’s revenue variability and variability in State average revenue. The 

strength of this farm-State correlation differs across crops, States, and across 

farms within States. It is stronger for corn and soybeans, on average (at the 

U.S. level weighted by acres planted for farms and States), than it is for wheat 

and cotton. For example, the top two States in corn and soybean acreage, Iowa 

and Illinois, have average farm-State revenue correlations of 0.60 and above. 

In contrast, the top two States in wheat acreage, Kansas and North Dakota, 

and the top two States in cotton acreage, Texas and Arkansas, have average 

farm-State revenue correlations of about 0.30 to slightly higher than 0.40. 

ACRE payment amounts—based on simulations with expected market 

prices equal to 2009 guarantee prices—vary across regions, but tend to be 

highest in the most productive crop regions, as refl ected in consistently high 

yields. ACRE payments for corn and soybeans, for instance, would be high 

in Midwest areas with high average yields, even though these areas have low 

yield and revenue variability. For cotton, ACRE payments would be high for 

irrigated acreage in California and Arizona, where yield levels are high and 

variability low, and low for Texas, where yield levels are low and variability 

high.

The geographic distribution of risk reduction for each crop is similar to its 

ACRE payment distribution: areas where risk reduction is strong tend to be 

areas with above-average ACRE payments. For soybeans, risk reduction 

is highest for representative farms in the Midwestern Corn Belt, areas with 

large shares of planted acres and production, and lowest in Atlantic Coast 

States, such as North and South Carolina. For corn, risk reduction is also 

highest in the Corn Belt, though many farms in other corn-producing areas 

also receive relatively high risk reduction. For wheat, risk reduction is strong 

in Kansas and North Dakota, major wheat-producing States, but also strong 
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in Ohio, Michigan, and Indiana, States with small shares of U.S. acreage and 

production. Expected risk reduction for wheat is below average for almost 

all of the representative farms in Oklahoma and Texas, States with relatively 

low expected ACRE payments. For cotton, risk reduction from ACRE is also 

low in the plains of Texas and highest in the irrigated areas in California and 

Arizona.

How Was the Study Conducted?

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 State levels for corn, soybeans, 

wheat, and cotton. The model accounts for correlations among the random 

variables by use of empirical sampling techniques. The model is national 

in scope and represents more than 90 percent of the average annual planted 

acres for corn, soybeans, and wheat and more than 80 percent of the planted 

acres for cotton.
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Introduction

U.S. agricultural policy has long featured programs intended to support or 

stabilize farm incomes by counteracting the effects of low crop prices and 

yields. Commodity programs like the countercyclical payment and marketing 

loan programs have made payments to producers of several major fi eld crops 

when prices fall short of target levels, while Federal crop insurance and 

disaster assistance programs have addressed yield shortfalls (though in recent 

years, much more revenue insurance than yield insurance has been sold).

Using crop revenue as the basis for agricultural programs has, since at least 

the early 1980s, been proposed and studied as a way to reform U.S. farm 

programs. A 1983 Congressional Budget Offi ce study looked at replacing 

price-based commodity programs with revenue-based programs as a way 

of reducing government spending. Miranda and Glauber (1991) examined 

the use of regional revenue shortfalls as the basis of a disaster assistance 

program. The Iowa Farm Bill Study Team recommended that the 1996 farm 

bill replace commodity programs and crop insurance with a revenue guarantee program; while this proposal was not enacted, it led to the development of federally subsidized crop revenue insurance (Goodwin and Smith, 

1995). In 1999, a proposal for a commodity revenue program, called the 

Supplemental Income Payments for Producers, was introduced in Congress 

but not adopted (Hart and Babcock, 2000). Proponents and analysts of 

revenue-based programs have recognized that revenue rather than its components, prices and yields, is more closely related to farm income. Thus, revenue 

programs can more effi ciently stabilize income and lessen the annual variability in government program expenditures than separate price-based and 

yield-based programs can (Cooper, 2009).

The Food, Conservation and Energy Act of 2008 introduced a commodity 

program based on revenue, which was fi rst available in 2009 to producers of 

covered commodities (Zulauf et al., 2008).1 The Average Crop Revenue 

Election program, or ACRE, uses a combination of State- and farm-level 

revenue guarantees that are determined from recent prices and yields.

In this report, we examine and simulate crop revenue variability and estimate, using simulations, ACRE payments for an extensive set of representative farms.2 Our analysis is national in scope. We analyze how ACRE 

payments and their effect on farm-level revenue variability would be distributed across crops and production regions. Because the components of 

revenue—prices, yields, and their interactions—can change from year to year 

and cause ACRE guarantees to shift, we estimate how expected ACRE 

payments, based on simulations of many possible revenue outcomes, would 

differ under a range of scenarios. Our analysis focuses on four crops—corn, 

soybeans, wheat, and cotton (crops that accounted for about 50 percent of the 

value of production of U.S. crops between 2002 and 2007, and more than 90 

percent of the value of crops for which ACRE is available).

1These crops are wheat, corn, barley, 

grain sorghum, oats, upland cotton, 

long-grain and medium-grain rice, 

peanuts, pulse crops (dry peas, lentils, 

small and large chickpeas), soybeans 

and other oilseeds (sunfl ower seed, 

canola, rapeseed, saffl ower, mustard 

seed, fl axseed, crambe, and sesame 

seed).

2Each farm, one for each crop and 

each county, represents a typical acre 

of corn, soybean, wheat, or cotton 

production. 
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Crop Revenue Variability

Revenue variability depends on the variability of prices and production 

(yields multiplied by acres) and the interactions between the two. Because 

crop prices depend largely on world markets, variability in the price for a 

crop is similar across much of the United States. Yields, in contrast, depend 

on factors such as weather, diseases, and insects that can affect wide areas at 

once but are often localized. 

Price Variability

Crop prices are determined by supply and demand in U.S and world markets. 

Supply factors include stocks-to-use ratios and production in the United 

States and in other countries (Schnepf and Goodwin, 1999). Demand 

factors include domestic and foreign incomes, currency exchange rates, and 

consumer preferences. Price levels can vary across U.S. regions because of 

transportation costs, differences in product attributes, and marketing seasons. 

However, price variability for a particular crop is similar across the United 

States due to potential arbitrage when price differences across regions are not 

consistent with transportation costs.

Measuring price variability requires the specifi cation of a reference price 

from which changes are measured and of a timespan over which changes can 

occur. Price variability measured as changes from year to year in marketingyear average prices provides one perspective. For example, there were sharp 

increases in corn, soybean, and wheat prices between 2005 and 2006 and 

between 2006 and 2007, while annual prices for these crops changed relatively little between 2007 and 2008 (fi g. 1). These price movements were 

Figure 1

Change in marketing-year average price from previous year, 1999 - 2008

Percent

1999

Source: USDA, Economic Research Service analysis of data from USDA, National Agricultural Statistics Service.

2000 2001 2002 2003 2004 2005 2006 2007 2008

60

40

20

0

20

40

60

Corn Soybeans

Wheat Cotton
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driven by several factors, including changes in export markets and rapid 

expansion of domestic biofuel production (Trostle, 2008). Cotton price variability, in contrast, was greatest in the years prior to 2005.

A longer view of prices also suggests that variability for corn, soybeans, and 

wheat has been especially high in recent years. The variability of annual 

prices, measured by the coeffi cient of variation (mean divided by standard 

deviation), for the 10 years 1999-2008 was 0.35 for corn, 0.33 for soybeans, 

and 0.39 for wheat. For the 30 years, 1979-2008, the price coeffi cients were 

much lower: 0.25 for corn, 0.22 for soybeans, and 0.28 for wheat. 

Yield Variability

The localized character of yield variability can be seen by measuring it at 

different geographic levels. Table 1 shows the average variability of yields 

from empirically based simulations for corn, soybeans, wheat, and cotton 

measured at the U.S., State, county, and farm3 levels. Yield variability, 

measured as the coeffi cient of variation, is, by far, the greatest when 

measured at the smallest area: the farm level. As it is measured over larger 

areas, variability declines as high yields in one region offset low yields in 

another. The aggregation effect is especially pronounced between the farm 

and county levels, suggesting that farm-level factors play a large role in yield 

variability.

There is a wide range of yield variability for each crop across its production 

areas. Figures 2a-d illustrate yield variability of representative farms for (a) 

corn, (b) soybeans, (c) wheat, and (d) cotton in each county. Yield variability 

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

center of the Corn Belt. Yield variability for wheat 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 in western Kansas and eastern Colorado and parts of the Northern 

Plains in North Dakota, South Dakota, and Montana. For cotton, yield variability is lowest for irrigated production in California and Arizona and 

highest for dryland production in the plains of Texas.

3Our estimates of farm-level yield 

variability are consistent with Federal 

crop insurance premium rates for “basic 

units,” which are usually subdivisions 

of a farm’s crop acreage.

Table 1

Simulated variability of crop yield at different levels: corn, soybeans, 

wheat, and cotton

Level Corn Soybeans Wheat Cotton

Coeffi cient of variation

National 0.074 0.063 0.074 0.103

State 0.107 0.111 0.166 0.163

County 0.136 0.135 0.228 0.242

Farm 0.378 0.375 0.587 0.685

Averages weighted by planted acres. Coeffi cient of variation = standard deviation divided 

by mean.

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service and USDA, Risk Management Agency.
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Figure 2a

Farm-level yield variability, corn

Source: USDA, Economic Research Service simulations based 

on data from USDA, National Agricultural Statistics Service and 

USDA, Risk Management Agency.

Coefficient of variation

Less than 0.45

0.45 - 0.65

Greater than 0.65

Figure 2b

Farm-level yield variability, soybeans

Coefficient of variation

Less than 0.45

0.45 - 0.65

Greater than 0.65

Source: USDA, Economic Research Service simulations based 

on data from USDA, National Agricultural Statistics Service and 

USDA, Risk Management Agency.
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Figure 2d

Farm-level yield variability, cotton

Coefficient of variation

Less than 0.45

0.45 - 0.65

Greater than 0.65

Source: USDA, Economic Research Service simulations based 

on data from USDA, National Agricultural Statistics Service and 

USDA, Risk Management Agency.

Figure 2c

Farm-level yield variability, wheat

Coefficient of variation

Less than 0.45

0.45 - 0.65

Greater than 0.65

Source: USDA, Economic Research Service simulations based 

on data from USDA, National Agricultural Statistics Service and 

USDA, Risk Management Agency.
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Price-Yield Correlation

The relationship between prices and yields, measured by the statistical 

correlation, is negative when changes in yield are associated with offsetting changes in prices. In other words, when yield and, as a result, aggregate 

production, of a commodity increase, price decreases; a fall in yield is associated with a rise in price. Negative price-yield correlation moderates or 

dampens revenue variability, and is often referred to as a “natural hedge.” 

The strength of the natural hedge depends on the relative variability of price 

and yield, the relationship of U.S. production to world supply, and the degree 

to which U.S. production is concentrated in a geographic area. The negative 

price-yield correlation tends to be strongest in an area where most farm-level 

yields are closely related to areawide production and where the area’s production normally accounts for a signifi cant share of the world market.

Corn and soybeans, for example, show the strongest negative price-yield 

correlation in the Midwest, where Illinois and Iowa together account for about 

a third of the U.S. corn crop. Although the United States typically exports 

a small portion of its corn crop (20 percent), it dominates world corn trade. 

This means that corn prices are largely determined by supply-and-demand 

relationships in the United States, which tends to make prices dependent on 

yields in the major U.S. production area. The pattern of price-yield correlation for soybeans is similar to the pattern for corn; about 35-45 percent of 

the annual U.S. soybean crop is exported, and the United States has been the 

largest supplier in the world soybean market (average annual market share 47 

percent, 1999-2008).

U.S. wheat also accounts for a large share of the world market, typically 

exceeded only by China, the European Union, and India. But in contrast to 

corn and soybeans, U.S. wheat production is widely dispersed, and different 

types of wheat, which are not entirely substitutable, are sold in different 

markets at different times of the year.

Although U.S. cotton plays a large role in world cotton supply, price-yield 

correlations within the United States are weak. U.S. cotton production has 

become a small proportion of U.S. domestic cotton use, weakening the correlation between U.S. production and price. The geographic distribution of 

U.S. cotton production also contributes to the weak price-yield correlations. 

Although about a third of the U.S. cotton crop is produced in a single State, 

Texas, cotton production is spread over a wide geographic and climatic range, 

both within Texas and across the United States. The Delta States of Arkansas, 

Louisiana, Mississippi, and Tennessee account for about 30 percent of the 

U.S. cotton crop, while the major cotton-producing States in the Southeast—

North Carolina, South Carolina, and Georgia—account for about 17 percent. 

Growing conditions in these regions are largely independent of each other.

Because yield and price variability for all crops do not entirely offset each 

other, the variability of revenue exceeds the variability of yield. Under our 

model simulations, revenue variability, measured as the coeffi cient of variation, is more than double yield variability for each of the four crops at the 

national level (table 2). The difference between revenue and yield variability 

decreases as they are measured at the more disaggregated levels. In other 
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words, as one measures revenue variability at smaller geographic levels it is 

determined to a greater extent by yield variability, which increases dramatically. 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 also plays a role. Areas with low revenue variability, especially for corn and soybeans, tend to be those with large amounts 

of production.

Table 2

Simulated variability of crop revenue at different levels: corn, 

soybeans, wheat, and cotton

Level Corn Soybeans Wheat Cotton

Coeffi cient of variation

National 0.221 0.207 0.193 0.245

State 0.236 0.228 0.250 0.278

County 0.253 0.242 0.302 0.339

Farm 0.447 0.438 0.633 0.741

Averages weighted by planted acres.

Coeffi cient of variation = standard deviation divided by mean.

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service and USDA, Risk Management Agency.
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ACRE: How It Works and What It Covers

The ACRE program makes payments when State average revenue and farm 

revenue for a crop fall below recent historical levels (USDA/Farm Service 

Agency, 2009). The State revenue guarantee for ACRE is defi ned as 90 

percent of the State benchmark revenue. The 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, 

disregarding the highest and the lowest yields in this period. Separate State 

yields and benchmarks for irrigated and non-irrigated production are set by 

USDA’s Farm Service Agency when at least 25 percent but less than 75 

percent of the acreage for a crop in a county in a State is irrigated.4 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, disregarding 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.

ACRE payments, if any, are based on annual outcomes of State and farm 

revenues for a crop relative to their guarantees. In order for a farm to receive 

an ACRE payment, State revenue must fall below the State guarantee and 

farm revenue must fall below the farm guarantee. Both shortfalls are 

required. The ACRE payment amount per acre is the difference between the 

State revenue guarantee and the actual State revenue, up to a maximum of 25 

percent of the State guarantee, multiplied by the farm productivity index, 

which is the ratio of the farm average yield to the State average yield.5 The 

magnitude of a farm’s revenue shortfall, once it falls below the farm revenue 

guarantee, does not fi gure in the farm’s ACRE payment calculation; however, 

the farm’s average yield relative to the State-level yield does. Total ACRE 

payment for a farm is the sum of per-acre payments over the number of acres 

“planted or considered planted,” with the restriction that total ACRE payment 

acres for all crops on the farm cannot exceed its base acres.6

Because prices and yields can change from year to year, the State revenue 

guarantee can change, though under the ACRE program the annual change in 

the revenue guarantee is limited to no more than 10 percent. The capability of 

ACRE guarantees to refl ect recent historical prices and yields and to change 

from year to year are major differences between ACRE and commodity 

programs that are based on legislatively set target prices and loan rates. To 

participate in ACRE, producers forgo the entire amount of any price-based 

countercyclical payments as well as portions of their direct payments and 

marketing loan rates. Thus, the relationships between recent market prices 

and legislated target prices and loan rates are a major factor infl uencing 

producers’ ACRE participation decisions.

ACRE coverage differs from Federal revenue insurance coverage, which is 

based on changes over the crop growing season in futures market prices and 

determines revenue guarantees and covers losses measured at county or farm 

(insured unit) levels. Producers participating in ACRE have an incentive to 

also participate in the Federal crop insurance program because the ACRE 

farm-level guarantee is increased by the amount paid for the insurance.

4In our model, we use a single 

composite (irrigated and non-irrigated) 

yield for each State and representative 

farm.

5Under provisions of the 2008 farm 

act, the ACRE payment rate 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.

6A producer’s decision to enroll 

in ACRE applies to all “covered 

commodities and peanuts” on the Farm 

Service Agency-defi ned farm. Base 

acres are a farm’s crop-specifi c 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 fl exibility contract 

payments in 2002 and acreage (specifi ed in legislation) planted to other 

covered commodities (oilseed and 

peanut producers). Base acreage refers 

to cropland on a farm, not to specifi c 

parcels of land.
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How Prices and Yields Affect 

ACRE Payments

ACRE payments for a farm depend on revenue levels and variability. The 

components of revenue—prices, yields, and their interactions—can differ 

from year to year and across crops and their growing regions. We analyze the 

effects of each component on ACRE payments by simulating revenue variability, using an empirically based model, for an extensive set of States and 

representative farms for corn, soybeans, wheat, and cotton.7

Guarantee Prices and Expected Market Prices

The prices used to set ACRE revenue benchmarks and guarantees, called 

guarantee prices, are national marketing-year average prices of each crop. 

The national prices are applied to all State and farm revenue calculations. 

The guarantee prices are, as mentioned earlier, averages of annual prices over 

the 2 previous years. These moving averages capture recent market conditions and can change from year to year. In 2009, the fi rst year that ACRE 

coverage was available, moving-average prices for corn, soybeans, and wheat 

were dramatically higher than at any point over the previous 10 years, as the 

sharp price increases in 2007 were included. For cotton in 2009, the movingaverage price, while among the highest over the previous 10 years, was only 

slightly higher than in 2008.

Revenue shortfalls and ACRE payments depend on actual or realized revenue 

calculated from marketing-year average prices for the particular year covered. 

A crop’s marketing year typically begins around harvest time and extends 

for 12 months. The eventual market price is not known with certainty until 

the year is concluded. To estimate potential ACRE payments, we simulate 

revenue variability for a range of expected prices.

Figures 3a-d illustrate how ACRE payments would vary under various 

guarantee price and expected market price scenarios for each crop. Each 

line represents a given guarantee price; the points on the lines indicate 

U.S. average ACRE payments per planted acre from revenue simulations as 

expected market prices vary.

The heavy line on each chart is approximately the guarantee price for 2009: 

for corn, the guarantee price illustrated is $4.10 per bushel, for soybeans $10 

per bushel, for wheat $6.60 per bushel, and for cotton $0.54 per pound. If, for 

instance, the market price for corn is expected to be the same as the guarantee price, the expected ACRE payment, based on revenue simulations, 

would be about $11.40 per acre. Similarly, for soybeans the expected ACRE 

payment would be about $8.60 per acre, for wheat about $5.70 per acre, and 

for cotton about $8.90 per acre. If, however, the level of the marketing-year 

average price is expected to be less than the guarantee price, the expected 

ACRE payment increases (reading to the left on the guarantee-price line).8 

For example, under the $4.10 guarantee price for corn, if the expected market 

price were $3.50, the average ACRE payment would be about $33 per acre. 

As the expected market price declines relative to the guarantee price, the 

ACRE payment increases and gradually fl attens as the 25 percent of the State 

revenue guarantee payment rate limit is reached.

7A description of our model and data 

sources is included in the “Methods and 

Data” box.

8Price forecasts for corn, soybeans, 

wheat, and cotton for the 2009-10 

marketing year that were published in 

USDA’s World Agricultural Supply and 

Demand Estimates on August 12, 2009, 

a few days before the 2009 ACRE 

enrollment deadline, suggested that 

eventual marketing-year average prices 

for corn, soybeans, and wheat would 

be lower than ACRE guarantee prices. 

Midpoints of the forecast price ranges 

indicated that the prices would be about 

10-20 percent lower than guarantees.
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Although the dollar amount of expected ACRE payments varies considerably from crop to crop, U.S. average payments relative to expected revenue, 

when expected market price equals guarantee price, are similar across crops. 

Suppose, for example, that expected revenue for an average producer in 2009 

is based on the U.S. average yield over the previous 5 years with the highest 

Payment (dollars per acre)

Expected price (dollars per bushel)

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service and USDA, Risk Management Agency.

Figure 3a

Average ACRE payment for corn under selected guarantee 

prices and expected prices

0

20

40

60

80

100

120

140

0.50 1.50 2.50 3.50 4.50 5.50 6.50

$2.25

Guarantee price:

$3.80

$4.10 $4.40

0

10

20

30

40

50

60

70

80

1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

Payment (dollars per acre)

Expected price (dollars per bushel)

Figure 3b

Average ACRE payment for soybeans under selected guarantee 

prices and expected prices

$6.50

Guarantee price:

$8.00

$9.60 $10.00

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service and USDA, Risk Management Agency.
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and lowest years omitted, and the market and ACRE guarantee prices are 

equal at 2009 levels (indicated in the previous paragraph). In this case, 

expected ACRE payments for each crop would be equal to about 2 percent of 

expected revenue: corn, soybean, and cotton payments would be 2 percent 

and wheat would be 2.3 percent.

0

5

10

15

20

25

30

35

40

45

50

1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

Payment (dollars per acre)

Expected price (dollars per bushel)

Figure 3c

Average ACRE payment for wheat under selected guarantee 

prices and expected prices

$3.40

Guarantee price:

$4.50

$5.80 $6.60

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service and USDA, Risk Management Agency.

0

10

20

30

40

50

60

70

80

90

0.10 0.18 0.26 0.34 0.42 0.50 0.58 0.66 0.74 0.82

Payment (dollars per acre)

Expected price (dollars per pound)

Figure 3d

Average ACRE payment for cotton under selected guarantee 

prices and expected prices

$0.48

Guarantee price:

$0.54

$0.60 $0.66

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service and USDA, Risk Management Agency.
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The model underlying this research simultaneously simulates 

random yields, prices, and revenues at farm, county, State, and U.S. 

levels for corn, soybeans, wheat, and cotton. The model accounts 

for correlations among the random variables by use of empirical 

sampling techniques.1

The model is based on data for 1,553 counties for corn, 1,164 counties for soybeans, 1,329 counties for wheat, and 233 counties for 

cotton. These counties represent about 97 percent of the 1998-2007 

average annual planted U.S. acres of corn, 92 percent of soybean 

acres, 94 percent of wheat acres, and 84 percent of cotton acres. 

Yield variability is 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, State and U.S. levels, we estimated a linear time trend of 

each yield data series, yields from 1975 to 2004, 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 2008. To measure yield variability at the farm level, we 

use county yield variabilities in conjunction with data from the 

Federal crop insurance program, which is administered by USDA’s 

RMA. Specifi cally, we used the 2009 base county premium rate 

for yield coverage (at the 65-percent coverage level) for each crop. 

We subtracted from these premium rates the portions that were to 

cover prevented planting, replanting, and quality adjustments. These 

adjusted premium rates were used to calibrate an additive farm yield 

variability term for each county and crop. 

Our approach to modeling farm yields uses Miranda’s (1991) specifi cation of the relationship between systemic and idiosyncratic 

variability:

(1) ( ) ft f ct c ft y y fc   = + − + ∀∈ μβ μ ε 

where 

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

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

μ f ft = E y( )  , μc ct = E y( )  , and ft is a normally distributed error term 

with ( ) 0 E ft ε = , and ( ) 2 Var ft ε σ= .

 1Typically, a variance-covariance matrix is used for such simulations. Because 

such a matrix could include several thousand yields and prices at various levels of 

aggregation, our model accounts for correlations among random variables by use of 

sampling techniques that maintain their historical relationships.

Methods and Data
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Yield Levels and Variability

In the ACRE program, prices in the revenue calculations are measured only 

at the national level while yields are measured at State and farm levels. 

Hence, differences in yields across States and farms lead to differences in 

revenue and in ACRE payments.9

High yield variability, however, does not simply translate to high revenue 

variability and a high ACRE payment. At both the State and farm levels, 

yield variability (coeffi cient of variation) tends to be inversely related to 

average yield level: generally, the higher the average yield, the lower the variability, and vice versa.10 The inverse relationship between variability and 

average level holds for revenue, but is weaker than it is for yield because of 

the negative correlation between price and yield.

9Offi cial ACRE program guarantees and benchmark State yields for 

2009 are available from USDA’s Farm 

Service Agency at http://www.fsa.usda.

gov/Internet/FSA_File/acrerev_

yieldmaps.pdf. State revenue variability values, from our model simulations, are listed in appendix table 1.

10Correlation coeffi cients in our 

model between benchmark (average) 

yields and yield variability at the farm 

level are -0.70 for corn, -0.84 for 

soybeans, -0.64 for wheat, and -0.76 

for cotton. At the State level, they are 

-0.70 for corn, -0.82 for soybeans, -0.74 

for wheat, and -0.81 for cotton.

The coeffi cient β 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 β =1, which Miranda shows would be the acreageweighted average of all β’s in the county. The error term ft 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 σ 

(the standard deviation of ft ) that in a simulation replicates the crop 

insurance rates. 

Price variability is estimated from NASS national price data. 

National annual marketing-year average (MYA) prices for 1974 

through 2007 for each of the four 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 are placed in a matrix [P] that has 

T rows of annual prices. Yield data for each of the four crops are 

placed in a matrix [Y] that contains county-, 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 are generated from 1,000 random 5-year 

time paths. The revenue variability and ACRE payment estimates in 

this report 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. 

Methods and Data (continued)
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Figures 4a-d show expected ACRE payments, based on simulated crop 

revenue variability, per acre for representative farms (one per crop per 

county) relative to national average ACRE payments. For corn (fi g. 4a), 

ACRE payments would be high in Midwest areas with high average yields, 

even though these areas have low yield and revenue variability and strong 

negative price-yield correlations. ACRE payments also tend to be high along 

the Southeast and Middle Atlantic coast where average yields are low and 

yield and revenue variability are high. The pattern is similar for soybeans (fi g. 

4b). Representative farms in the Midwest with low revenue variability tend 

to receive relatively high ACRE payments because of their high yields, while 

those in Louisiana, Mississippi, and Georgia receive relatively high payments 

because of high revenue variability.

ACRE payments per acre of wheat (fi g. 4c) would be very high in eastern 

Washington, Oregon, and Idaho, where white wheat is produced, refl ecting 

the high average irrigated yields for these representative farms. In major 

spring wheat-producing areas in western Minnesota and eastern North 

Dakota, payments tend to be about equal to or slightly above the U.S. 

average ACRE payment. The representative farm acres in large winter 

wheat-producing areas in central Kansas have ACRE payments that range 

from average to above average, refl ecting high average yields and relatively 

Figure 4a

ACRE payment as percent of U.S. average ACRE payment, corn

Notes: Expected ACRE payment based on simulations with guarantee price and expected marketing-year average price of $4.10 per 

bushel. U.S. average ACRE payment = $11.38 per acre.

Source: USDA, Economic Research Service simulations based on data from USDA, National Agricultural Statistics Service and USDA,

Risk Management Agency.

Percent

Less than 75

75 - 90

90 - 110

Greater than 110
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low revenue variability. In the plains areas of Oklahoma and Texas, ACRE 

payments would be low, refl ecting low yield levels. Payments per acre for 

wheat would be high in Illinois, Indiana, and Michigan, States with small 

shares of the U.S. wheat crop, due to both high yield levels and high revenue 

variability.

For cotton (fi g. 4d), expected ACRE payments would be very high in 

California and Arizona, States with moderate revenue variability but very 

high expected yields. In many areas in Texas, ACRE payments for cotton 

would be low, due to low average yields, even though farm-level revenue variability is high. In the Mississippi River Delta, ACRE payments for cotton 

would, in most areas, be above average, refl ecting moderate State-level 

revenue variability and yield levels that are between those of dryland areas in 

Texas and the irrigated areas in Western States such as Arizona.

Distributions of ACRE payments, it should be noted, depend on the relative 

importance of the price and yield components of revenue variability, which 

can shift from year to year under the ACRE program. The distributions 

shown in fi gures 4a-d, for instance, are based on revenue variability that is 

generated under the situation where the expected market price is equal to the 

ACRE guarantee price. If the expected market price (the level at which we 

Figure 4b

ACRE payment as percent of U.S. average ACRE payment, soybeans

Notes: Expected ACRE payment based on simulations with guarantee price and expected marketing-year average price of $10.00 per 

bushel. U.S. average ACRE payment = $8.56 per acre.

Source: USDA, Economic Research Service simulations based on data from USDA, National Agricultural Statistics Service and USDA,

Risk Management Agency.

Percent

Less than 75

75 - 90

90 - 110

Greater than 110
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center our simulations of variability) were greater than the guarantee price, 

the likelihood that a price decline would be the main cause of a revenue 

shortfall would be reduced; thus, yield variability would be a stronger factor 

in revenue variability. In this case, ACRE payments would shift to farms in 

areas of greater yield variability. Conversely, if the expected market price 

were below the guarantee price, price would be the stronger factor driving 

revenue variability. Because the same price is applied to all State and farm 

revenue calculations, when price is the stronger factor ACRE payments would 

shift to farms in areas with low yield variability. 

Changes in yields can also affect the relative importance of price and yield in 

revenue variability. Yield changes, however, are less likely than price changes 

to shift ACRE payments. The outlook for expected yields at the beginning of 

the crop season is less likely than that of expected prices to be sharply below 

its recent history, and year-to-year changes in guarantee yields under ACRE 

would be more moderate than price changes because of the different lengths 

of historical periods used to calculate guarantees. The guarantee yield is a 

5-year average, less the high and low years, while the guarantee price is a 

2-year average.

Figure 4c

ACRE payment as percent of U.S. average ACRE payment, wheat

Notes: Expected ACRE payment based on simulations with guarantee price and expected marketing-year average price of $6.60 per 

bushel. U.S. average ACRE payment = $5.69 per acre.

Source: USDA, Economic Research Service simulations based on data from USDA, National Agricultural Statistics Service and USDA,

Risk Management Agency.

Percent

Less than 75

75 - 90

90 - 110

110 - 150

Greater than 150
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Figure 4d

ACRE payment as percent of U.S. average ACRE payment, cotton

Notes: Expected ACRE payment based on simulations with guarantee price and expected marketing-year average price of $0.54 per 

pound. U.S. average ACRE payment = $8.90 per acre.

Source: USDA, Economic Research Service simulations based on data from USDA, National Agricultural Statistics Service and USDA,

Risk Management Agency.

Percent

Less than 75

75 - 90

90 - 110

110 - 150

Greater than 150
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ACRE Payments and Risk Reduction

Whether an ACRE payment is triggered or not depends on the probability 

that annual revenue will be below 90 percent of the benchmark measured at 

the State level and whether the farm’s annual revenue falls below its benchmark; the magnitude of the ACRE payment depends on the size of the State 

revenue shortfall, relative to its guarantee, as well as the farm’s average yield 

relative to the State average. Because of this complexity, ACRE payments are 

not directly related to farm-level revenue variability (fi g. 5). 

Under the simulated scenario of expected price equal to guarantee price, 

more than half of ACRE payments would go to farms with below-average 

revenue variability (fi g. 6).11 For corn and soybeans, for example, many 

farms and acres characterized by low revenue variability are located in States 

with relatively low revenue variability but above-average expected revenue. 

For wheat and cotton, the shares of ACRE payments that go to low-revenuevariability farms are driven by representative farm acres in irrigated areas 

that have expected yields and revenue that are much higher than the U.S. 

average.

Revenue Variability at Farm and State Levels

The effectiveness of the ACRE program in reducing risk, or variability of 

farm revenue for a crop, is strongly related to the correlation between the 

farm’s revenue variability and variability in State farm revenue (fi g. 7). When 

the correlation of variability is high (systemic risk), year-to-year increases or 

decreases in farm revenue tend to be widespread across the State. Revenue 

shortfalls on a farm that are independent of shortfalls on other farms are 

referred to as idiosyncratic risks. Because ACRE payments and risk reduction 

depend largely on the farm-State correlation and use U.S. average prices, they 

tend to address systemic risk. 

The strength of systemic risk—and thus, the relative effectiveness of ACRE 

in reducing risk—depends on the level of price variability, the difference 

between historical prices and expected market prices at a particular time, and 

the degree to which yield losses on one farm are matched with yield losses 

on other farms in the State. ACRE is more effective in reducing risk when 

crop price, which under ACRE is the same for all States and farms, becomes 

a stronger component of revenue variability and when yield losses are widespread. ACRE is ineffective in covering idiosyncratic risks, shortfalls on an 

individual farm that are uncorrelated with more widespread losses. Federal 

crop insurance policies, which in most cases are based on farm-level variability, cover idiosyncratic as well as systemic risk.

While the strength of the correlation between revenue on individual farms 

and aggregate State farm revenue differs across States and across farms 

within States, on average (at the U.S. level weighted by acres planted for 

farms and States), it is stronger for corn and soybeans than it is for wheat and 

cotton (table 3).12 This is because large shares of U.S. corn and soybean 

production and acreage are concentrated in States where farm yield levels and 

variability—and thus, ACRE revenue levels and variability—are closely 

related. For example, the top two States in corn and soybean acreage, Iowa 

11Farm revenue variability quartiles 

were constructed by ranking representative farms (one for each crop in 

each county) by revenue coeffi cient 

of variation and by weighting each 

representative farm by the number of 

acres planted, 2000-09. Total ACRE 

payments are payments per acre multiplied by acres planted.

12Average farm-State revenue 

correlations for each crop and State are 

listed in appendix table 2.
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Percent of average ACRE payment

Percent of average revenue variability

Note: Representative farm observations, all crops.

Source: USDA, Economic Research Service simulations based on data from USDA, 

National Agricultural Statistics Service and USDA, Risk Management Agency.

Figure 5

Revenue variability and ACRE payments

0

50

100

150

200

250

300

0 50 100 150 200 250 300

Percent of payments

Farm revenue variability quartile

Notes: Total ACRE payments = ACRE payment per acre multiplied by acres represented.

Farm revenue variability quartiles = Ranking of representative farms, weighted by acres 

represented, by revenue coefficient of variation.

Acres represented by each crop-county farm are average annual acres planted, 2000-09.

Source: USDA, Economic Research Service simulations based on data from USDA, 

National Agricultural Statistics Service and USDA, Risk Management Agency.

Figure 6

Distribution of total ACRE payments by farm revenue variability

0

5

10

15

20

25

30

35

40

Lowest Lower middle Higher middle Highest

Corn Soybeans Wheat Cotton

and Illinois, have average farm-State revenue correlations of 0.60 and above. 

In contrast, the top two States in wheat acreage, Kansas and North Dakota, 

and the top two States in cotton acreage, Texas and Arkansas, have average 

farm-State revenue correlations of about 0.30 to slightly higher than 0.40.13

13Because our model uses composite 

irrigated and non-irrigated representative farm and State revenues, we 

underestimate the relevant correlation 

between farm and State revenue and 

risk reduction under ACRE in those 

areas where separate irrigated and 

non-irrigated farm acres can be linked 

to separate irrigated and non-irrigated 

guarantees at the State and farm levels.
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Average risk reduction from ACRE payments, or percentage decrease in the 

coeffi cient of variation of revenue, is especially strong, among the four crops, 

for soybeans.14 While corn and soybeans have similar average farm-State 

revenue correlations, average risk reduction may be stronger for soybeans 

because, under the price scenario simulated, the expected ACRE payment is a 

slightly higher proportion of expected revenue for soybeans than it is for corn. 

The relatively weak average risk reduction for corn may also be due, in part, 

to the composite (irrigated and non-irrigated) farm-State revenue correlation 

14Because ACRE payment amounts 

do not depend on the severity of a 

farm’s revenue shortfall (below the 

farm guarantee), it is possible for a 

farm to receive an ACRE payment that 

is less or more than the farm’s shortfall. 

If the payment exceeds the shortfall, 

then the coeffi cient of variation of 

revenue—our measure of risk reduction—could be larger when ACRE 

payments are included.

Table 3

Correlation between farm and State simulated revenues and risk 

reduction from ACRE: corn, soybeans, wheat, and cotton

Item Corn Soybeans Wheat Cotton

Coeffi cient of variation

Correlation of farm revenue 

with State revenue 0.55 0.54 0.40 0.39

Percent

Risk reduction from ACRE 5.4 6.4 5.4 4.8

Based on simulations with expected market price equal to guarantee price.

Averages weighted by planted acres.

Coeffi cient of variation = standard deviation divided by mean.

Risk reduction from ACRE = percent decrease in coeffi cient of variation of revenue when 

ACRE payments are included.

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service and USDA, Risk Management Agency.

Percent risk reduction

Farm-State revenue correlation coefficient

Notes: Representative farm observations, all crops. Risk reduction from ACRE = percent 

decrease in coefficients of variation of revenue when ACRE payments are included.

Source: USDA, Economic Research Service simulations based on data from USDA, 

National Agricultural Statistics Service and USDA, Risk Management Agency.

Figure 7

Risk reduction from ACRE and farm-State revenue correlation

0

2

4

6

8

10

12

14

0 0.2 0.4 0.6 0.8 1.0
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in Nebraska, which ranks third among corn producing States and accounts 

for about 10 percent of U.S. corn acreage.15

The average risk reduction for wheat, which is similar to corn, is driven by 

strong risk reduction in areas that contain large shares of U.S. wheat acreage 

and production—Kansas, North Dakota, and Montana—and especially 

strong risk reduction in the irrigated areas of Washington. The average risk 

reduction for cotton is the weakest among the four crops. About 45 percent 

of U.S. cotton acreage is in Texas, where farm revenue has an especially low 

correlation with State revenue.

The geographic distribution of risk reduction for each crop is similar to its 

ACRE payment distribution: areas where risk reduction is strong tend to be 

areas with above-average ACRE payments.16 For soybeans, risk reduction is 

highest for representative farms in the Midwestern Corn Belt, areas with large 

shares of planted acres and production, and lowest in Atlantic Coast States, 

such as North and South Carolina. For corn, risk reduction is also highest in 

the Corn Belt, though many representative farms in other corn-producing 

areas also receive relatively high risk reduction. For wheat, risk reduction is 

strong in Kansas and North Dakota, major wheat-producing States, but also 

strong in Ohio, Michigan, and Indiana, States with relatively small shares of 

U.S. acreage and production. Expected risk reduction for wheat is below 

average for almost all of the representative farms in Oklahoma and Texas, 

States with relatively low expected ACRE payments. For cotton, risk reduction from ACRE is also low in the plains of Texas and highest in the irrigated 

areas in California and Arizona.

15The average correlation in our 

model for Nebraska corn, 0.52, is 

below the U.S. average, 0.54, and 

considerably less than the correlations 

in the top two States, Iowa and Illinois, 

0.64. About 60 percent of Nebraska 

corn acreage is irrigated and 40 percent 

non-irrigated. Each of these production 

practices could be covered separately 

under ACRE, which would allow many 

Nebraska corn acres to be more closely 

tied to separately defi ned State averages 

than is the case in our model.

16The correlation between representative farm ACRE payment and risk 

reduction, as percentages of their averages per crop, is 0.790.
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Conclusions

Crop revenue variability—which differs across crops, regions, and the 

geographic levels at which it is measured—is the basis for payments under 

three agricultural programs that provide risk management and farm income 

support to producers. Since the mid-1990s, subsidized revenue insurance has 

been available under the Federal crop insurance program, and revenue plans 

of insurance (both farm- and county-triggered plans) accounted for about 55 

percent of all insured acres in 2008; revenue insurance accounted for about 

three-quarters of the insured acres of corn, soybeans, wheat, and cotton 

(Dismukes and Coble, 2006). Two revenue-based programs were introduced 

under provisions of the 2008 Farm Act: (1) the Supplemental Revenue 

Assistance Payments (SURE)17 program, which provides payments for 

revenue losses due to natural disasters, and (2) the Average Crop Revenue 

Election program, or ACRE, an alternative to the commodity price programs 

available for major fi eld crops.

These programs measure revenue at different levels—farm enterprise (crop), 

whole-farm, and State, for example—and with different prices—marketingyear averages and futures markets—that affect payment levels, program costs 

and farm-level risk reduction. ACRE payments are based on crop revenue 

variability at State and farm levels. Key factors affecting ACRE revenue 

variability and payments include future crop prices and their recent historical levels, measured at the national level, and yield levels and variability, 

measured at State and farm levels.

A producer’s decision to participate in ACRE depends on individual considerations, including an assessment of ACRE benefi ts relative to benefi ts 

from legislatively set direct payment, target price, and loan rate commodity 

programs, the mix of crops on the farm, and the producer’s willingness and 

ability to bear risk. At the initial enrollment deadline for ACRE in August 

2009, about 8 percent of farms with almost 13 percent of eligible base acres 

elected to participate, less than what might have been expected based on 

price and yield analysis alone, suggesting that other factors such as learning 

and negotiation costs were substantial in ACRE’s fi rst year. ACRE enrollment was greatest in regions that typically grow wheat, corn, and soybeans 

(Woolverton and Young, 2009). 

Our simulations indicate that, under price and yield conditions and ACRE 

guarantees similar to those that prevailed in 2009, expected benefi ts to ACRE 

would be higher for corn, soybeans, and wheat than for cotton. Although the 

geographic distribution of simulated ACRE payments is generally similar to 

actual enrollment, there were notable differences. For instance, even though 

our model indicates an ACRE payment for wheat in Oklahoma below the 

U.S. average, the actual enrollment rate in 2009 for wheat in Oklahoma was 

the second highest among wheat producing States, about 33 percent of base 

acres. In 2009, the ACRE enrollment deadline was August 14. At that time 

winter wheat producers had nearly complete knowledge of their farm and 

State yields for the 2009 crop. Oklahoma yields were expected to be below 

the State benchmark. In subsequent years, the ACRE enrollment deadline is 

June 1.

17The SURE program is a supplement to coverage under Federal crop 

insurance or coverage under the 

Noninsured Crop Disaster Assistance 

Program (NAP) for crops for which 

insurance is not available.
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A robust evaluation of the effectiveness of ACRE in providing payments 

and risk reduction to producers needs to consider a range of scenarios and 

possible revenue outcomes. Historical prices and yields that determine 

ACRE guarantees can change, and future prices and yields that determine 

payments are not known with certainty. Our simulations of crop revenue 

variability, which model price and yield uncertainty, lead to several general 

conclusions about ACRE coverage:

• Expected payments increase as revenue guarantee levels, based 

on recent history, increase and as market prices decline relative to 

guarantees.

• ACRE payments and risk reduction per acre tend to be large in areas 

where yield levels and thus expected revenue are high, even though yield 

and revenue variability, measured as a coeffi cient of variation, are relatively low in many of these areas.

• Correlation between farm revenue and State revenue, which differs 

across crops and regions, is important in determining the degree of risk 

reduction provided under ACRE. 
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References

Congressional Budget Offi ce. 1983. Farm Revenue Insurance: An Alternative 

Risk-Management Option for Crop Farmers. Aug.

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

Support. Economic Research Report Number 72. Economic Research 

Service, USDA. April. www.ers.usda.gov/Publications/ERR72/.

Dismukes, Robert, and Keith H. Coble. 2006. “Managing Risk With Revenue 

Insurance.” Amber Waves. Vol. 4, Issue 5. Economic Research Service, 

USDA. Nov. www.ers.usda.gov/AmberWaves/November06/Features/

Managing.htm.

Goodwin, Barry K., and Vincent H. Smith. 1995. The Economics of Crop 

Insurance and Disaster Aid. Washington, DC: AEI Press.

Hart, Chad E., and Bruce A. Babcock. 2000. “Counter-Cyclical Agricultural 

Program Payments: Is It Time to Look at Revenue?” Center for 

Agricultural and Rural Development Briefi ng Paper 99-BP 28. Dec. 

http://www.card.iastate.edu/publications/DBS/PDFfi les/99bp28.pdf.

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

Journal of Agricultural Economics. Vol. 73, No. 2. May.

Miranda, Mario J., and Joseph W. Glauber. 1991. “Providing Crop Disaster 

Assistance through a Modifi ed Defi ciency Payment Program,” American 

Journal of Agricultural Economics. Vol. 73 No. 4, Nov.

Schnepf, Randall D., and Barry K. Goodwin. 1999. “Long-Run Price Risk 

in U.S. Agricultural Markets.” Selected paper presented at American 

Agricultural Economics Association Annual Meeting, Nashville, TN. 

http://ageconsearch.umn.edu/bitstream/21687/1/sp99sc02.pdf.

Trostle, Ronald. 2008. Global Agricultural Supply and Demand: Factors 

Contributing to the Recent Increase in Food Commodity Prices. Outlook 

Report No. (WRS-0801). Economic Research Service, USDA. July. 

www.ers.usda.gov/publications/wrs0801/.

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.

Woolverton, Andrea, and Edwin Young. 2009. Factors Infl uencing ACRE 

Program Enrollment. Economic Research Report 84. Economic Research 

Service, USDA. Dec. www.ers.usda.gov/Publications/ERR84/.

Zulauf, Carl R., Michael R. Dicks, and Jeffrey D. Vitale. 2008. “ACRE 

(Average Crop Revenue Election) Farm Program, and Farm Decision 

Analysis,” Choices. Agricultural and Applied Economics Association. Vol. 

23, No. 3. http://www.choicesmagazine.org/magazine/pdf/article_33.pdf.
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Appendix

Appendix table 1

Simulated variability of crop revenue at State level: corn, soybeans, 

wheat, and cotton

State Corn Soybeans Wheat Cotton

Coeffi cient of variation

Alabama 0.234 0.295 0.294 0.285

Arizona 0.251 0.243 0.209 0.250

Arkansas 0.248 n/a 0.260 0.243

California 0.242 n/a 0.267 0.265

Colorado 0.257 n/a 0.295 n/a

Delaware 0.247 0.237 0.257 n/a

Florida 0.277 0.285 n/a 0.266

Georgia 0.257 0.339 0.278 0.278

Idaho 0.247 n/a 0.220 n/a

Illinois 0.227 0.199 0.266 n/a

Indiana 0.235 0.220 0.248 n/a

Iowa 0.237 0.230 0.285 n/a

Kansas 0.196 0.214 0.217 n/a

Kentucky 0.236 0.231 0.276 n/a

Louisiana 0.246 0.317 0.265 0.272

Maryland 0.245 0.216 0.259 n/a

Michigan 0.263 0.243 0.241 n/a

Minnesota 0.248 0.245 0.236 n/a

Mississippi 0.233 0.280 0.264 0.248

Missouri 0.236 0.201 0.265 0.271

Montana 0.257 n/a 0.296 n/a

Nebraska 0.227 0.213 0.238 n/a

New Jersey 0.270 0.225 0.264 n/a

New Mexico 0.245 n/a 0.360 0.257

New York 0.266 n/a 0.241 n/a

North Carolina 0.294 0.209 0.283 0.240

North Dakota 0.261 0.261 0.240 n/a

Ohio 0.247 0.223 0.227 n/a

Oklahoma 0.232 0.329 0.257 0.360

Oregon 0.254 n/a 0.263 n/a

Pennsylvania 0.235 n/a 0.252 n/a

South Carolina 0.347 0.271 0.268 0.295

South Dakota 0.203 0.224 0.233 n/a

Tennessee 0.233 0.240 0.285 0.263

Texas 0.247 0.304 0.259 0.292

Utah 0.233 n/a 0.246 n/a
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Appendix table 1

Simulated variability of crop revenue at State level: corn, soybeans, 

wheat, and cotton (continued)

State Corn Soybeans Wheat Cotton

Coeffi cient of variation

Virginia 0.265 0.224 0.265 0.245

Washington 0.257 n/a 0.237 n/a

West Virginia 0.263 n/a 0.261 n/a

Wisconsin 0.242 0.240 0.262 n/a

Wyoming 0.234 n/a 0.251 n/a

n/a = not available.

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service.

Appendix table 2

Correlation between farm simulated revenue and State simulated 

revenue, by State: corn, soybeans, wheat, and cotton

State Corn Soybeans Wheat Cotton

Coeffi cient of variation

Alabama 0.380 0.409 0.494 0.498

Arizona 0.639 0.368 0.418 0.609

Arkansas 0.427 n/a 0.379 0.439

California 0.517 n/a 0.415 0.666

Colorado 0.460 n/a 0.304 n/a

Delaware 0.464 0.497 0.646 n/a

Florida 0.326 0.321 n/a 0.435

Georgia 0.347 0.344 0.541 0.387

Idaho 0.555 n/a 0.544 n/a

Illinois 0.637 0.590 0.497 n/a

Indiana 0.618 0.604 0.534 n/a

Iowa 0.640 0.670 0.460 n/a

Kansas 0.337 0.416 0.384 n/a

Kentucky 0.548 0.478 0.547 n/a

Louisiana 0.386 0.359 0.274 0.449

Maryland 0.488 0.433 0.604 n/a

Michigan 0.548 0.523 0.581 n/a

Minnesota 0.607 0.597 0.417 n/a

Mississippi 0.425 0.398 0.447 0.497

Missouri 0.454 0.439 0.452 0.552

Montana 0.429 n/a 0.433 n/a

Nebraska 0.524 0.561 0.350 n/a

New Jersey 0.489 0.413 0.632 n/a

New Mexico 0.573 n/a 0.309 0.454

New York 0.526 n/a 0.545 n/a

North Carolina 0.481 0.324 0.504 0.488
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Appendix table 2

Correlation between farm simulated revenue and State simulated 

revenue, by State: corn, soybeans, wheat, and cotton (continued)

State Corn Soybeans Wheat Cotton

Coeffi cient of variation

North Dakota 0.433 0.560 0.412 n/a

Ohio 0.609 0.582 0.589 n/a

Oklahoma 0.441 0.358 0.394 0.431

Oregon 0.510 n/a 0.683 n/a

Pennsylvania 0.403 n/a 0.616 n/a

South Carolina 0.392 0.299 0.472 0.458

South Dakota 0.405 0.549 0.309 n/a

Tennessee 0.497 0.418 0.505 0.516

Texas 0.448 0.230 0.264 0.303

Utah 0.450 n/a 0.357 n/a

Virginia 0.476 0.401 0.569 0.651

Washington 0.616 n/a 0.680 n/a

West Virginia 0.398 n/a 0.495 n/a

Wisconsin 0.494 0.537 0.546 n/a

Wyoming 0.440 n/a 0.386 n/a

Based on simulations with expected market price equal to guarantee price. Averages 

weighted by planted acres. 

n/a = not available.

Source: USDA, Economic Research Service simulations based on data from USDA, National 

Agricultural Statistics Service and USDA, Risk Management Agency.