Economic

Research

Service

Economic 

Research 

Report 

Number 152

August 2013

United States Department of Agriculture

James M. MacDonald, Penni Korb, and Robert A. Hoppe

Farm Size and the Organization of 

U.S. Crop Farming
Economic Research Service 

www.ers.usda.gov

Th e U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on 

the basis of race, color, national origin, age, disability, and, where applicable, sex, marital status, familial 

status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because 

all or a part of an individual’s income is derived from any public assistance program. (Not all prohibited 

bases apply to all programs.) Persons with disabilities who require alternative means for communication of 

program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 

720-2600 (voice and TDD). 

To fi le a complaint of discrimination write to USDA, Director, Offi ce of Civil Rights, 1400 Independence 

Avenue, S.W., Washington, D.C. 20250-9410 or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA 

is an equal opportunity provider and employer.

United States Department of Agriculture

Visit our website for more information on this topic: 

www.ers.usda.gov/topics/farm-economy/farm-structure-organization.aspx

Access this report online:

www.ers.usda.gov/publications/err-economic-research-report/err152.aspx 

Download the charts contained in this report:

 • Go to the report’s index page www.ers.usda.gov/publications/

 err-economic-research-report/err152.aspx 

 • Click on the bulleted item “Download err152.zip”

 • Open the chart you want, then save it to your computer

Recommended citation format for this publication:

MacDonald, James M., Penni Korb, and Robert A. Hoppe. Farm Size and the 

Organization of U.S. Crop Farming, ERR-152. U.S. Department of Agriculture, 

Economic Research Service, August 2013.

Cover photo: A fi eld of turnips. 

Photo credit: Shutterstock.
United States Department of Agriculture

Economic 

Research 

Service

Economic 

Research 

Report 

Number 152

August 2013

Abstract

Cropland has been shifting to larger farms. The shifts have been large, centered on a 

doubling of farm size over 20-25 years, and they have been ubiquitous across States and 

commodities. But the shifts have also been complex, with land and production shifting 

primarily from mid-size commercial farming operations to larger farms, while the count 

of very small farms increases. Larger crop farms still realize better fi nancial returns, on 

average, and they are able to make more intensive use of their labor and capital resources, 

indicating that the trends are likely to continue. The report relies on comprehensive farmlevel data to detail changes in farm size and other attributes of farm structure, and to 

evaluate the key driving forces, inclu ding technologies, farm organization and business 

relationships, land attributes, and government policies. 

Keywords: cropland consolidation, crop production, large crop farm, farm size, farm 

size measure, farm structure, farm organization, family farm, industrial agriculture, farm 

fi nances

Acknowledgments

The authors thank the following individuals for their reviews: Nigel Key and Todd 

Kuethe, USDA Economic Research Service; Joy Harwood and Scott Sanford, USDA 

Farm Service Agency; Neil Conklin, Farm Foundation; Mike Duffy, Iowa State 

University; Derek Byerlee, World Bank; and participants at several USDA-ERS workshops and seminars.

James M. MacDonald, Penni Korb, and 

Robert A. Hoppe

Farm Size and the Organization of 

U.S. Crop Farming
ii

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Contents

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

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

Measuring Farm Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Consolidation of Cropland into Larger Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Cropland Consolidation in the States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Consolidation Among Specifi c Commodities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Continuing Advantages to Size in Crop Farming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

 Leasing, Contract Labor, and Custom Services on Crop Farms . . . . . . . . . . . . . . . . . . . . . . . . . 16

Drivers of Consolidation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Drivers of Consolidation: Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Drivers of Consolidation: Farm Organization and Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Drivers of Consolidation: Government Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

The Future: Will Family Farms Still Dominate Crop Production? . . . . . . . . . . . . . . . . . . . . . .46

Family Farms In Today’s Crop Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

Challenges for Family Farms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .49

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

Errata

This report, originally released in August 2013, was reissued with the following correction:

On September 9, 2013, a citation was added to the report’s list of references (U.S. Department 

of Agriculture, National Agricultural Statistics Service (NASS). 2009. Farms, Land in Farms, 

and Livestock Operations 2008 Summary. February), completing a reference to “USDA 

(2009)” in footnote 3 in the box on page 6. 
www.ers.usda.gov

What Is the Issue?

Large farms now dominate crop production in the United States. Although most cropland was 

operated by farms with less than 600 crop acres in the early 1980s, today most cropland is on 

farms with at least 1,100 acres, and many farms are 5 and 10 times that size. This ongoing shift 

in farm structure raises a host of questions. How extensive is this structural change? What forces 

have accompanied and contributed to the shift to larger farms? What implications do these structural shifts have for family farms?

The shift of acreage to larger farms is part of a complex set of structural changes in crop agriculture. The number of mid-size crop farms has declined, while farm numbers at the extremes (large 

and small) are growing. Because of these changes, average farm size has changed little in the last 

three decades, even while cropland and crop production have shifted to much larger farms. The 

report documents the complex nature of modern farm structure and introduces statistics aimed at 

better tracking consolidation of land and production.

What Did the Study Find?

The report introduces a measure of midpoint acreage in which half of all cropland acres are on 

farms with more cropland than the midpoint, and half are on farms with less. Midpoint acreage is 

revealed to be a more informative measure of cropland consolidation than either a simple median 

(in which half of all farms are either larger or smaller) or the simple mean (which is average cropland per farm). Using this measure, ERS researchers found that:

• The midpoint acreage for U.S. cropland nearly doubled between 1982 and 2007, from 589 

acres to 1,105. 

• Midpoint acreages increased in 45 of 50 States and more than doubled in 16. The largest 

increases occurred in a contiguous group of 12 Corn Belt and Northern Plains States. 

• Midpoint acreages more than doubled in each of 5 major fi eld crops (corn, cotton, rice, 

soybeans, and wheat) and increased in 35 of 39 fruit and vegetable crops, where the average 

increase was 107 percent. 

United States Department of Agriculture

A report summary from the Economic Research Service August 2013

ERS is a primary source 

of economic research and 

analysis from the U.S. 

Department of Agriculture, 

providing timely information on economic and policy 

issues related to agriculture, 

food, the environment, and 

rural America.

Find the full report 

at www.ers.usda.

gov/publications/erreconomic-researchreport/err-152.aspx

James M. MacDonald, Penni Korb, and Robert A. Hoppe

Farm Size and the Organization of 

U.S. Crop Farming
• The shifts were persistent, with a general pattern of increase between each Census of Agriculture conducted 

between 1982 and 2007. However, less comprehensive evidence from annual surveys suggests that the pace of 

consolidation slowed between 2007 and 2011, the last year for which data are available. Data from the 2012 

Census will provide more defi nitive evidence of recent trends.

• Larger crop farms continue to realize better fi nancial performance: average rates of return on equity increased 

with farm size in fi ve major commodity categories analyzed in this report (corn, soybeans, wheat, fruits, 

and vegetables). In turn, larger farms utilize labor and capital more intensively, which provide them with the 

primary source of their fi nancial advantage.

The long-term shifts in farm size have been accompanied by greater specialization—beginning with a separation of livestock farming from crop farming in the latter half of the 20th century. As crop and livestock production separated, full-time crop farmers could devote more time to crop production and manage more cropland. At 

the same time, the number of production and marketing contracts to govern the sale of products has increased. 

Contracts covered 32 percent of crop production in 2011, compared with 23 percent in the mid-1990s. Larger 

operations are more likely to use contracts, which can reduce the price and marketing risks faced by farmers.

Technology also plays an important role in driving increases in farm size, by allowing a single farmer to operate 

and manage more acres. Labor-saving innovations—from bigger and faster capital equipment to information 

technology, chemical herbicides, seed genetics, and changing tillage techniques—have substantially reduced the 

total amount of labor used in agriculture and facilitated the shift to larger crop farms. 

Federal policies may affect farm structure through multiple channels, such as taxes, lending programs, environmental or food safety regulation, research and development funding, and commodity programs. Some effects are 

straightforward and fairly direct, while others are subtle and indirect. The impact of broad commodity policy is 

particularly complex. Some have argued that commodity and crop insurance programs, by reducing the fi nancial risks faced by farmers, encouraged the adoption of organizational forms and capital equipment that spurred 

increases in farm size. It is, however, diffi cult to separate the impact of policy from technology itself and from 

other factors that affected farm risks. 

While the above conditions may have facilitated the shifts toward larger farms, family farms continue to dominate 

crop agriculture. In 2011, 96 percent of U.S. crop farms were family farms, and they accounted for 87 percent of 

the value of crop production. 

How Was the Study Conducted?

This study drew upon data from two main sources. The Census of Agriculture, conducted by the USDA’s National 

Agricultural Statistics Service (NASS), provides comprehensive, historical, and publicly available data on consolidation and specialization trends. The study also relied on confi dential farm-level data from the census accessed in 

a secure environment to ensure confi dentiality, to generate measures of consolidation and farm size for the United 

States, and major commodities for 1982-2007.

The second primary source of data is the annual Agricultural Resource Management Survey (ARMS), jointly 

administered by NASS and ERS. The ARMS covers U.S. farming operations and their operators in the 48 contiguous States. The survey was used to supplement historic census data on consolidation with more recent annual 

developments, to provide data on fi nancial performance among crop farms, to assess the role of family farms, and 

to provide evidence on the use of labor, capital, and various production practices among crop farms. 

The study also used several additional datasets and publications from NASS, ERS, and other Government and 

private sources. 

www.ers.usda.gov
1

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Introduction

Large farms now dominate crop production in the United States. While most cropland was on farms 

with less than 600 acres in the early 1980s, today most cropland is on farms with at least 1,100 acres, 

and many crop farms are 5 and 10 times that size. This ongoing shift in control of farm resources 

and production raises a host of questions addressed in this report. How extensive is this structural 

change? Why is cropland moving to larger farms? Is the shift associated with other changes in farm 

structure, and will it continue? Do these structural shifts portend an end to family farms, and the 

emergence of corporate farms with diffused ownership and hired professional managers?

Economic Research Service (ERS) researchers measure and describe the nature of the changes that 

have occurred and assess the driving forces behind those changes, with an emphasis on technology, 

land attributes, farm organization, and public policy.1 The shift of acreage and production to larger 

farms is ongoing and ubiquitous, occurring in most States and for most crops, including major 

fi eld crops as well as fruits and vegetables. Because large farms continue to realize better fi nancial 

returns, on average, than small and mid-size farms, it is reasonable to expect continued shifts of 

production and land to larger farms. 

The complexity of U.S. farm structure makes the shift of acreage to larger farms hard to discern 

with common metrics. In recent years, the number of mid-size crop farms has declined sharply, 

while farm numbers at the extremes (large and small) have grown. As a result the size of the average 

farm (in acres) has changed little, but large farms have gotten considerably larger as they farm more 

of the country’s cropland. The report documents the complex nature of modern farm structure and 

introduces statistics aimed at better tracking shifts of land and production. 

Measuring Farm Size

In this report, farm size is defi ned according to acres of cropland operated by the farm, which is 

a transparent, easily understood measure for which statistics have been kept for many years. It is 

particularly well suited to the primary purpose of the report, assessing the consolidation of cropland 

into larger operations over time.

Farms may own cropland, but they also grow crops on cropland that they rent. The report defi nes 

farm size according to the cropland operated by the farm—that is, the cropland it owns, plus any 

that it rents, minus any rented to others.

1An earlier ERS report (MacDonald and McBride, 2009) analyzed consolidation in livestock agriculture. Hoppe et 

al. (2010) document shifts of production to larger farms, using infl ation-adjusted sales, while O’Donoghue et al. (2011) 

document broad changes in U.S. farm structure.

James M. MacDonald, Penni Korb, and Robert A. Hoppe

Farm Size and the Organization of 

U.S. Crop Farming
2

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

All cropland is not the same. Across regions, important differences exist in soil quality, topography, climatic conditions, and proximity to markets and urban areas. These differences affect the 

types of crops grown, the sales derived from a given amount of acreage, and the size of crop farms. 

To account for some of those differences in cropland attributes, ERS researchers measured and 

analyzed changes in farm size for States and for specifi c crops, in addition to national data.

About one-quarter of cropland is not harvested, and most of that is not planted to any crop.2

Analyses of specifi c commodities rely on harvested acreage of the commodity because the data 

sources report acres harvested, rather than acres planted, on a consistent basis, while analyses 

of farm size include both harvested and non-harvested cropland. (See box: “Data, Current and 

Historic.”)

2The 2007 Census of Agriculture recorded 406 million acres of cropland and 310 million acres of harvested cropland. 

Of the non-harvested cropland, 7 million acres represented planted cropland on which the crops failed, but most fell into 

three other categories: cropland in summer fallow (15 million acres); cropland used only for grazing or pasture (36 million acres); and cropland that was idled or used for cover crops or soil improvement (38 million acres). Land enrolled in 

the Conservation Reserve Program (CRP), Wetlands Reserve Program (WRP), or other conservation programs may be 

reported in harvested cropland, idle cropland, or woodland not pastured, depending on its use.

Data, Current and Historic

ERS researchers supplemented Agricultural Resource Management Survey (ARMS) and Census 

of Agriculture data with information from several other National Agricultural Statistics Service 

(NASS) surveys, from ERS estimates, and from several other public and private sources. These 

sources are described in the report as they are introduced. 

Census of Agriculture data. The report draws on publicly available data from the Census of 

Agriculture, administered by NASS. The census elicits information from all U.S. farms and 

is valuable for following long-term trends, since it dates back to 1840. It provides deep and 

comprehensive coverage of acreage and production for all U.S. States and for a wide range of 

commodities. The census was conducted at irregular 4-, 5-, and 10-year intervals until 1982 and 

at 5-year intervals since then. Information on the census can be found at www.agcensus.usda.

gov/index.php. 

However, besides publicly available census data, ERS researchers also used confi dential farmlevel census records for 1982-2007, accessed under an agreement with NASS that is designed to 

protect data security and confi dentiality. The data were used to develop improved measures of 

consolidation for the Nation, States, and commodities. 

ARMS data. The research also relies heavily on the Agricultural Resource Management 

Survey (ARMS), an annual survey of U.S. farms that is jointly administered by NASS and 

ERS. The ARMS is based on a representative sample of farms and their operators in the 48 

contiguous States. The survey has been conducted annually since 1996, and the most recent data 

available for this report covered farm performance in 2011. 

With a total national sample of just over 20,000 farms, ARMS cannot provide the comprehensive measures at the State and commodity level that the census provides, and it cannot match 

the long temporal span of the census. However, the survey gives more detailed data on farm 

fi nances, operators, resources, and practices than are available elsewhere. As an annual survey, 

it also gives greater and more recent temporal detail than the census. 

—continued
3

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Data, Current and Historic—continued

ARMS comprises three phases, carried out at different times of the year. Phase I is a screening 

module, not used for research. Phase II, conducted in the fall of the reference year, targets up to 

two fi eld crops. It focuses on fi eld-level information, with questions on chemical use, resources 

and input use, production practices, and production outcomes. ERS researchers used Phase II 

data for a few selected topics, including tillage practices, information technology, and seed use. 

Most of the analyses are drawn from Phase III, which is conducted in the winter following the 

reference year and is aimed at all farms. Phase III focuses on production, input use, farm and 

operator attributes, and fi nances for the whole farm. 

During most of the years covered in this report, Phase III includes fi ve questionnaire versions. 

Version 5, shorter than the others, contains a core set of questions. Version 1 adds a detailed 

set of research questions on production and management practices and resource use to the core. 

Versions 2, 3, and 4—known as commodity versions—add commodity-specifi c questions to the 

core and are aimed at representative samples of commercial producers of the Phase II crops and 

one livestock commodity. 

Most ARMS-based statistics in this report are drawn from the core questions that appear on all 

versions of the survey, while some are based on questions that appear only in Version 1.1 As 

necessary, observations from several years are combined to generate larger samples (ARMS is 

not a panel, so different farms appear in different years). In the text discussion and table notes, 

distinctions are drawn between: 

• Data drawn from Phase II and from Phase III;

• Data drawn from all Phase III versions and from version 1 only;

• Farms from a single year and farms pooled across 2008-2011. 

Finally, NASS uses a stratifi ed sampling strategy to improve the reliability of estimates in 

ARMS. That means that some farms have a higher probability of sample selection—larger 

operations are more likely to be selected than smaller ones, and selection probabilities also vary 

across geographic areas and commodity types. Population estimates are generated by weighting 

sample observations to refl ect their varying selection probabilities.

Further information on ARMS, including questionnaires, can be found at www.ers.usda.gov/

data-products/arms-farm-fi nancial-and-crop-production-practices.aspx. 

1The survey is designed so that Version 1 records can be expanded to represent all farms; a separate set of 

weights allows the all-version sample to be expanded to represent all farms. Each commodity version carries additional weights to allow for expansion to represent the population of commercial producers of each commodity.
4

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Consolidation of Cropland into Larger Farms

The average size of a U.S. crop farm has changed little during the past three decades. However, this 

seeming stability masks important structural changes in the complex U.S. farm sector. There are 

growing numbers of very small and very large farms and declining numbers of mid-sized farms. 

Cropland acreage has moved toward much larger farms. 

Start with the complex size pattern of U.S. farms. In fi gure 1, based on 2011 ARMS data, farms 

and cropland acreage are sorted into eight cropland size classes frequently reported in Census of 

Agriculture publications. In that year, 391.6 million acres of cropland were divided among 1.675 

million U.S. farms with cropland, for an average (mean) farm size of 234 acres. However, relatively 

few farms are near the average. Eighty percent of farms with cropland were smaller than the mean 

size, and 70 percent were less than half the mean size. The median farm size (at which half of farms 

were larger and half were smaller) was just 45 acres.

 Similarly, little cropland is on farms near the average. Eighty-three percent of cropland was on 

farms that were larger than the mean size, and 71 percent was on farms that were more than twice 

Figure 1

The size distribution of crop farms, 2011

Note: Farm size is defined according to the cropland the farm operates—that is, the cropland it owns, plus any that it rents, minus any rented 

to others.

Source: USDA Agricultural Resource Management Survey, 2011.

Percent of farms or acres

15.8

35.7

14.9

11.4 11.1

5.6

3.4

2.2

0.3

3.7 4.4

6.5

14.6

16.8

19.4

34.3

0

5

10

15

20

25

30

35

40

1-9 10-49 50-99 100-199 200-499 500-999 1,000-1,999 >2,000

Cropland acres on the farm

Farms Cropland

Mean farm size (total cropland divided by total farms with cropland) is 234 acres.

Half of all farms have less than 45 acres (the median), and half have more.

Half of all acres are on farms with less than 1,100 acres (the midpoint acreage), 

and half are on farms with more.
5

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

the mean. The midpoint acreage—where half of cropland is on larger farms and half on smaller—

was 1,100 acres.

Figure 2 reports the same data for 2001; together the two fi gures summarize a decade of structural 

change. The mean farm size was little different (235 acres), but the median farm size in 2001 (63 

acres) was substantially larger than that in 2011. There were nearly 100,000 more farms with 1-49 

acres of cropland in 2011 than in 2001, as the count of small farms in USDA statistics increased 

sharply. (See box: “The Increasing Number of Small Crop Farms.”) Cropland moved in the other 

direction: the largest farms (at least 2,000 acres of cropland) accounted for 34.3 percent of cropland 

in 2011, up from 24.1 percent in 2001, and the number of farms with at least 2,000 acres of cropland 

increased during the decade. The midpoint acreage refl ects the shift in cropland: it was 900 acres in 

2001 (fi g. 2), compared to 1,100 in 2011 (fi g. 1).

Because of the complexity of changes in crop farm structure, simple measures of mean farm size are 

not very informative. Simple means and medians focus on the average farm, and the land operated 

by the average farmer. This report is focused on the use of cropland and must focus on the average 

acre of cropland, not the average farmer or average farm. The midpoint acreage effectively tracks 

cropland consolidation and will be used in this report. 

Figure 2

The size distribution of U.S. crop farms, 2001

Note: Farm size is defined according to the cropland the farm operates—that is, the cropland it owns, plus any that it rents, minus any rented 

to others.

Source: USDA Agricultural Resource Management Survey, 2001.

Percent of farms or acres

Cropland acres on the farm

Farms Cropland

Mean farm size (total cropland divided by total farms with cropland) is 235 acres.

Half of all farms have less than 63 acres (the median), and half have more.

Half of all acres are on farms with less than 900 acres (the midpoint acreage), 

and half are on farms with more.

11.2

32.5

16.0 15.1

13.4

6.1

3.9

1.7

0.2

3.5

4.7

8.8

17.8 18.2

22.7

24.1

0

5

10

15

20

25

30

35

40

1-9 10-49 50-99 100-199 200-499 500-999 1,000-1,999 >2,000
6

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

The midpoint acreage is a median, but it differs from the median farm size that is also reported in 

fi gures 1 and 2. Technically, the midpoint acreage is the median of the distribution of acreage by 

farm size, as opposed to the more commonly reported median of the distribution of farms by farm 

size. (See box: “Midpoint measures in industry analyses.”) The median farm size and the midpoint 

acreage differ widely: the 2011 median farm size was 45 acres, while the midpoint acreage was 

1,100 acres, because they measure different concepts and because U.S. farms cover such a wide 

range of sizes. 

The Increasing Number of Small Crop Farms

According to the Census of Agriculture, 872,000 farms harvested 1-49 acres of cropland in 

1974, a decline of 70 percent from the 2.9 million farms in that size class in 1950. If the 1950-

1974 trend had continued, there would have been less than 150,000 farms in that size class by 

2011. However, the trend did not continue; the number of small farms, whether measured by 

sales or by acreage, declined much less rapidly after 1974 and began to increase after 2000. 

In part, the survival and expansion of small crop farms may refl ect farming opportunities as 

well as popular lifestyle choices by people who prefer to combine a rural lifestyle with modest 

crop or livestock production. A farmer can operate a small fi eld crop operation on a part-time 

basis while also maintaining an off-farm job, or while drawing additional income from pensions 

and savings.1

However, how farms are counted also matters when considering trends in farm numbers. USDA, 

under the direction of Congress, has since 1974 defi ned a farm as a place that produces, or 

normally could produce, at least $1,000 worth of agricultural commodities in a year.2 Because 

the defi nition is not adjusted for infl ation, it will defi ne more very small places as farms as 

farm commodity prices rise, and the Producer Price Index for Farm Products (Bureau of Labor 

Statistics) rose by 172 percent between 1974, when the defi nition was set, and 2011. 

USDA/NASS has also made concerted efforts in recent years to better identify and track very 

small farms.3 While these efforts improve survey methodology and provide more comprehensive coverage, their implementation indicates that some part of the observed increase in small 

farm numbers refl ects better counting, not more farms. Because most of these farms are so 

small, with very limited production, their inclusion adds very little cropland or production to 

census aggregates.

1By pooling ARMS Phase III records from 2008-2011, ERS analysts constructed a large sample of smaller, but 

still commercial, crop farms with 50-99 acres of grain or oilseed crops, and no livestock production. On average, 

operators of those farms report working 20 hours per week on the farm, and smaller operations could require 

less labor time. 

2A place with less than $1,000 in sales in a year is classifi ed as a farm if it has cropland or animal assets that 

could generate $1,000 in sales (the “normally could produce” part of the farm defi nition). There were 254,000 such 

farms in 1982, 232,000 in 1992, and 689,000 in 2007, a near-tripling in 15 years. For more on defi ning farms, see 

O’Donoghue, et al. (2009).

3For more on expanded efforts to track small farms, see the “Special Note Regarding the 2007 Estimates” on p. 

31 in U.S. Department of Agriculture (2009). 
7

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

ARMS data were used in fi gures 1 and 2 to summarize modern-day farm structure. ERS analysts 

used Census of Agriculture data to compare longer run trends in midpoint acreages and simple 

means during 1982-2007 (fi g. 3).3 The differences are striking. While mean farm size changed little 

between 1982 and 2007—from 221 acres to 241, or a 9-percent increase—the midpoint for cropland 

grew by 88 percent, from 589 acres to 1,105. The simple mean peaked in 1997, and declined thereafter, but the midpoint acreage rose steadily from 1982 through 2007. The differences refl ect the 

shift of cropland to larger farms, even as increases in the number of very small farms kept the mean 

farm size from increasing substantially. While the average farm did not get much larger, acreage 

moved to much larger farms.4 

 The trend in the midpoint for harvested cropland does not exactly match that for cropland—the 

cropland measure rises steadily from 1982 to 2007, while the harvested cropland measure only rises 

after 1987—but the basic message is similar (fi g. 3). The midpoint acreage for harvested cropland 

rose by 114 percent, from 500 acres in 1982 to 1,071 acres in 2007.

Shifts of cropland to larger farms mirror similar shifts of agricultural production from smaller to 

larger sales classes during 1982-2007. Hoppe et al. (2010) use Census of Agriculture data to show 

that farms with at least $1 million in sales (measured in 2007 dollars, and thus adjusted for changes 

in prices) accounted for 24 percent of the value of agricultural production in 1982 and 59 percent in 

2007. Meanwhile the share held by small commercial farms, with $10,000 to $250,000 in sales, fell 

by two-thirds.

Cropland Consolidation in the States

The pace of cropland consolidation varied widely across States, although most saw substantial 

increases. Figure 4 reports State-by-State changes in the midpoint acreages for harvested cropland 

between 1982 and 2007. Harvested cropland shifted to smaller farms in 5 States, but shifted to larger 

3The mean cropland measures are reported in Census of Agriculture publications, but the weighted median requires 

confi dential record-level data, accessed by ERS under an agreement with NASS. 

4The data can be used in another way to summarize consolidation. In 1982, 184,030 farms were as large as the midpoint 

farm size, and so operated half of U.S. cropland. In 2007, 86,531 farms were as large as the midpoint. Those “top-half” 

farms operated 1,215 acres of cropland, on average, in 1982 and 2,350 acres in 2007. 

Midpoint Measures in Industry Analyses

Midpoints have been widely used in industry analyses, often based on employment, where 

half of industry employees are at larger plants or fi rms, and half are at smaller (Florence, 

1933; Scherer and Ross, 1990; Davis and Haltiwanger, 1991). As distinct from simple medians, 

they have been called Florence medians, referring to the fi rst economics author to use them in 

1933, and weighted medians, since they can be calculated by weighting each observation by 

its size (acreage, or employment in the most common applications). The measure is especially 

useful for size distributions that are highly skewed, with many very small operations while 

employment, acreage, or production is concentrated in a small number of large fi rms. Most 

U.S industries, including agriculture, are characterized by highly skewed size distributions. 

Acreage-based midpoints have been applied to the measurement of farm size by Lund and Price 

(1998), Key and Roberts (2007a), and O’Donoghue et al (2011). Lund and Price (1998) coined 

the midpoint usage.
8

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

farms in the other 45, and midpoint acreages rose by more than 100 percent in 16 States. Declines 

occurred in Hawaii, where plantation agriculture that focused on pineapple and banana production 

was shifting to more diversifi ed crop agriculture. Declines also occurred in Alabama and Southern 

New England. Increases of at least 100 percent occurred across a contiguous set of 12 Corn Belt and 

Northern Great Plains States, as well as Idaho, Maryland, Mississippi, and North Carolina. 

A simple statistical analysis helps to summarize the patterns. Specifi cally, four attributes of a State’s 

agricultural environment account for a substantial share of the variation in growth rates across 

States. (See box: “Accounting for Cross-State Differences in Consolidation.”) Midpoint acreages 

increased more in areas where most land was cropland, with little land in forest, pasture, commercial, or residential uses. In such States, crop fi elds are more likely to be large and contiguous to one 

another, rather than scattered among other land uses. Consolidation also increased faster where 

population was less dense; denser populations make cropland consolidation more costly because of 

greater competition for land. Finally, midpoint acreages increased more in those States with high 

shares of harvested cropland relative to cropland, and where hay accounted for a smaller share of 

harvested cropland. Hay was more likely to be produced in regions with noncontiguous cropland and 

smaller fi elds, and it was also concentrated on hillier land that is often less suited to other fi eld crops. 

Figure 3

Three measures of average farm size

Note: “Midpoint acreage” defined—half of all cropland acres are on farms with more cropland than the midpoint, and half are on farms with 

less. “Mean” defined—total number of cropland acres is divided by the total number of farms with cropland.

Sources: Mean cropland is from the Census of Agriculture, while the midpoint acreages are ERS calculations from unpublished census of 

agriculture data 

Acres

0

200

400

600

800

1,000

1,200

1982 1987 1992 1997 2002 2007

Mean, cropland

Midpoint acreage, cropland

Midpoint acreage, harvested cropland
9

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

The patterns of cropland consolidation do not rule out a role for organization, technology, or policy factors, and do 

not explain why consolidation is occurring, but the data suggest that the pace of cropland consolidation may vary 

with the presence of land and population attributes that constrain farm size. 

Consolidation Among Specifi c Commodities

A closer look at consolidation among specifi c crops provides further insight into the trends observed in total cropland. Table 1 summarizes data on harvested acres for major commodities in 2007 and crop receipts for 2007, 1982, 

and 1950 and reveals three key details. 

First, four crops (corn, hay, soybeans, and wheat) accounted for over 83 percent of harvested crop acres in 2007. 

Developments in these few crops drive national trends in midpoint acreages for all cropland combined. 

Second, harvested acres do not correspond closely to cash receipts for crops. The three high-value categories—vegetables and melons; fruits, nuts, and berries; and greenhouse/nursery crops—accounted for nearly 37 percent of all 

cash receipts from crops in 2007 but less than 4 percent of harvested acreage. Producers of these commodities use 

labor and physical capital intensively to produce high sales per acre. Because acreage and receipts do not correspond 

Figure 4

Changes in midpoint acreage for cropland, by State, 1982-2007

Note: Estimates are “midpoint acreages,” for which half of all cropland acres are on farms with more cropland than the midpoint, and half are 

on farms with less.

AK

AL

AR

AZ

CA CO

CT

DE

FL

GA

HI

KY

LA

MA

MT ME

NM

NV

NY

OK

OR

PA

RI

SC

TN

TX

UT

VA

VT

WA

IA

ID

IL IN

KS MD

MI

MN

MO

MS

NC

ND

NE

OH

SD WI

WV

WY

Decline

Increase 1% to 49%

Increase 50% to 99%

Increase 100% or more

NH

NJ
10

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Accounting for Cross-State Differences in Consolidation

ERS researchers related cross-State differences in cropland consolidation to four attributes of a 

State’s agriculture in 1982, the beginning of the period: the share of a State’s cropland that was 

harvested, the share of harvested cropland that was in hay, cropland density (cropland as a share 

of land area), and population density (population per square mile). The two density measures 

are weighted averages across counties within a State, where the weights are cropland. All of the 

data were from the Census of Agriculture except for population density, which is drawn from 

Census Bureau (U.S. Department of Commerce) data. Density measures are weighted by cropland to measure density in those parts of a State where the cropland is located. For example, 

high population density in Manhattan (New York County) is irrelevant because none of New 

York State’s cropland is in that county.

Consolidation was measured as the growth in a State’s midpoint acreage for harvested cropland (the logarithm of the ratio of the 2007 to the 1982 midpoints), and consolidation was the 

dependent variable in an ordinary least squares regression with the four attributes as explanatory variables:

Variable Mean

Standard 

deviation Coeffi cient t-statistic

Standardized 

coeffi cient

Intercept - - -0.1997 0.87

% harvested 71.5 12.4 0.0111 3.23 0.138

% hay 28.5 22.9 -0.0034 1.99 0.078

Population density 141.7 183.7 -0.0008 4.12 0.147

Cropland density 35.0 22.3 0.0041 2.16 0.091

Notes: The mean of the dependent variable was 0.51. The R2 coeffi cient was 0.64. The standardized coeffi cient is 

the coeffi cient times the standard deviation (impact on the dependent variable of a 1-standard deviation change in 

an explanatory variable).

This simple model accounts for 64 percent of the cross-State variation in the growth in farm 

size. Growth in midpoint acreage is noticeably higher in States with high cropland density, low 

population density, high shares of harvested cropland, and low shares of hay. Each coeffi cient 

is statistically and substantively signifi cant, and each explanatory variable shows substantial 

cross-State variation, to judge from their standard deviations. 

Specifi cally, a one standard deviation reduction in population density is associated with a 0.147 

log point increase in the growth rate of the weighted median—see the last column in the table 

for standardized coeffi cients. A one standard deviation increase in the share of cropland that 

is harvested (12.4 percentage points) is associated with a similar (0.138 log points) increase in 

consolidation. Cropland density and hay have smaller but still meaningful impacts. Relative to 

the sample mean (66.5 percent growth), a one standard deviation change toward slower growth 

in all four variables would reduce predicted growth to 6.6 percent, while a one standard deviation change to greater growth in all four would raise predicted growth to 162.2 percent. That 

range encompasses the growth rates of 42 of the 50 States in fi gure 4.
11

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

closely, changes in farm size for high-value crops must be tracked separately, since their developments will be masked in aggregated acreage measures. 

Third, long-term shifts have occurred in the mix of crops produced. Cotton, tobacco, and oats 

accounted for a third of all crop cash receipts in 1950. Those shares dropped sharply between 1950 

and 1982, and by 2007, the three crops accounted for just 5 percent of cash receipts. The share held 

by wheat also declined, as the combined share of corn and soybeans rose by nearly 25 percentage 

points, and the shares held by the high-value commodity categories rose by 11 percentage points.5

5Acreage also shifted among fi eld crops. Cotton and oats together combined for about 80 million acres (about 40 

million each) in the late 1920s, compared to 12 million in 2007. The reduction in cotton acreage refl ected slowing cotton demand, due to the introduction and spread of manmade fi bers, combined with diminishing yields from traditional 

production areas in the Southeast and sharply rising yields in newer production areas in the Plains and West (Larson and 

Meyer, 1996). The reduction in oats refl ected the 20th century shift from horses and mules, which were fed oats and hay, 

to tractors for farm work.

Table 1

Acreage and revenues, major crops, 1950-2007

Commodity 2007 Harvested acres Percentage of all crop revenues

Acres (millions) Percentage 2007 1982 1950

Field crops 299.7 96.4 63.2 73.8 74.1

 Barley 3.3 1.0 0.5 1.1 1.9

 Corn 86.3 27.7 22.7 17.7 9.2

 Cotton 10.5 3.4 4.3 6.2 23.1

 Hay 58.1 18.7 4.0 2.8 2.9

 Oats 1.5 0.5 0.1 0.5 2.1

 Rice 2.8 0.9 1.4 2.1 1.5

 Sorghum 6.7 2.1 0.9 2.1 1.2

 Soybeans 63.9 20.6 15.4 17.3 5.4

 Tobacco 0.4 0.1 0.9 4.6 8.6

 Wheat 50.9 16.4 7.6 13.6 14.0

 Other fi eld crops 15.5 5.0 5.4 5.8 8.3

High-value crops 11.1 3.6 36.8 26.2 25.9

 Vegetables, melons 5.8 1.9 12.9 11.2 11.6

 Fruits, nuts, berries 5.3 1.7 12.4 9.4 9.6

 Greenhouse/nursery na na 11.5 5.6 4.7

All crops 310.8 100.0 100.0 100.0 100.0

Note: The corn and sorghum estimates exclude crops raised for silage, which are reported in “other fi eld crops”. The 

vegetable acreage estimates include harvested acreage of potatoes and dry beans, and fi eld crops acreage estimates 

exclude them, to make them consistent with the cash receipts estimates. 

Sources: Harvested acres are from USDA, National Agricultural Statistics Service, Census of Agriculture. Cash receipts 

are from Economic Research Service, www.ers.usda.gov/data/FarmIncome/fi nfi dmu.htm.
12

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

ERS researchers measured midpoint acreages for specifi c commodities and found that the shift to 

larger farms occurred in all major fi eld crops and in most fruit and vegetable crops. These measures 

represent enterprises, the part of the farm producing a specifi c commodity. For example, a farm 

producing corn and soybeans has a corn enterprise and a soybean enterprise, and the midpoint 

acreage for corn refl ects harvested corn acreage only, not harvested acreage of all crops on farms 

producing corn.

Field crop estimates are presented in table 2. The midpoint acreage for corn tripled from 200 acres 

in 1987 to 600 in 2007, a jump that was no doubt affected by the relatively high corn prices of 2007 

(which led farms to increase corn acres at the expense of other fi eld crops in 2007). But other crops 

also showed large long-term increases: midpoint acreages for cotton, rice, soybeans, and wheat all 

more than doubled between 1987 and 2007. 

The exception is hay’s midpoint acreage, which increased from 120 acres in 1987 to 160 acres in 

2007, a much smaller increase than other fi eld crops. Hay accounted for nearly 60 million acres, or 

15 percent of all cropland. As noted above, States with substantial fractions of cropland in hay had 

less consolidation in all cropland. 

Table 3 reports midpoint acreages for 20 vegetable commodities, while table 4 covers 19 fruit, tree 

nut, and berry crops. The commodities listed in the two tables represent 92 percent of all vegetable, fruit, tree nut, and berry acres in the 2007 Census of Agriculture.6 A wider range of shifts 

is reported here—midpoint acreages for plums and cantaloupes declined between 1987 and 2007, 

and a few other commodities showed little change—but most showed large increases. Of the 39 

commodities listed in tables 3 and 4, 30 showed increases of at least 50 percent between 1987 and 

6This list expands on that provided in O’Donoghue et al. (2011), who also summarize several other elements of consolidation.

Table 2

Acreage shifted to larger fi eld crop farms, 1987-2007

Commodity (fi eld crops) 1987 1997 2007

Midpoint acreage, harvested acres

 Corn 200 350 600

 Cotton 450 800 1090

 Rice 295 494 700

 Soybeans 243 380 490

 Wheat 404 693 910

 Hay 120 140 160

Note: Midpoint acreages are the enterprise farm size, in harvested acres, at which half of all 

harvested acres are on larger enterprises, and half are on smaller enterprises.

Source: ERS calculations from unpublished Census of Agriculture records, 1987, 1997, 2007.
13

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

2007. The average increase across commodities was 107 percent.7 All commodities listed in tables 3 

and 4 showed increased midpoint acreages between 1987 and 1997, even if a few are small increases. 

In 1997-2007, 31 of 39 commodities showed increases, and 12 of those increased by more in 1997-

2007 than in 1987-1997.

7The farm size distribution has become noticeably more skewed in fruits and vegetables, and midpoints provide information that is not apparent in simple means. Tomatoes provide an extreme but telling example. The 2007 census counted 

25,809 farms, with 442,225 harvested acres of tomatoes, compared to 14,366 farms and 414,624 acres in 1997. Almost all 

of the increase in farm numbers occurred among farms with less than an acre of tomatoes: with new procedures designed 

to capture more very small farms, the 2007 census counted 17,536 farms with less than an acre of tomatoes, compared to 

7,407 in 1997. The mean farm size—total acreage divided by the number of farms—fell from 29 to 17 acres, even as the 

midpoint acreage rose from 589 to 820 acres (table 3). 

Table 3

Acreage shifted to larger vegetable and melon enterprises, 1987-2007

Crop 1987 1997 2007

Midpoint acreage, harvested acres

Asparagus 160 200 240

Beans, snap 221 225 323

Broccoli 440 780 1,000

Cabbage 113 162 300

Cantaloupe 400 431 388

Carrots 350 900 600

Caulifl ower 240 400 400

Cucumber 115 250 505

Lettuce 949 1,461 1,815

Onions, dry 115 220 320

Peas 100 125 179

Peppers, bell 88 180 300

Potatoes 350 556 990

Pumpkins 20 24 30

Spinach 162 242 423

Squash 35 60 72

Sweet corn 100 173 250

Sweet potatoes 140 250 474

Tomatoes 400 589 820

Watermelons 80 100 150

Note: Midpoint acreages are the enterprise farm size, in harvested acres, at which half of all 

harvested acres are on larger enterprises, and half are on smaller enterprises.

Source: ERS calculations from unpublished Census of Agriculture records, 1987, 1997, 2007.
14

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

To sum up, cropland consolidated onto larger farms in the last three decades. The shifts 

mirror similar shifts in farm size occurring in livestock. (See box: “Consolidation in Livestock 

Production.”) Consolidation has been persistent, in that it has continued throughout the 1982-2007 

period covered by the census data. It has been widespread across States and crop enterprises. Finally, 

the shifts have also been large—centering on a doubling of midpoint farm size over 20-25 years for 

national, State and commodity estimates. 

Table 4

Acreage shifted to larger fruit and nut enterprises, 1987-2007

Crop 1987 1997 2007

Midpoint acreage, harvested acres

Non-citrus fruits

 Apples 83 122 146

 Avocadoes 40 54 40

 Cherries, sweet 32 40 65

 Cherries, tart 65 89 150

 Grapes 205 306 320

 Nectarines 70 120 186

 Peaches 92 100 120

 Plums; prunes 179 250 160

 Pears 50 66 75

Citrus fruits

 Grapefruit 320 478 556

 Lemons 176 177 176

 Oranges 450 769 1,113

Tree nuts

 Almonds 203 292 450

 Pecans 102 125 117

 Pistachios 465 627 627

 Walnuts 85 126 172

Berries

 Blueberries, tame 50 54 75

 Cranberries 90 96 99

 Strawberries 24 60 120

Note: Midpoint acreages are the enterprise farm size, in harvested acres, at which half of all 

harvested acres are on larger enterprises, and half are on smaller enterprises.

Source: ERS calculations from unpublished Census of Agriculture records, 1987, 1997, 2007.
15

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Consolidation in Livestock Production

ERS researchers measured livestock consolidation with midpoint sizes based on herd inventory 

for dairy cows and annual sales or removals for other livestock. In 1987, the midpoint dairy 

herd size was 80 cows; by 2007, it was 570 cows. The change in hogs was even more striking, 

from 1,200 hogs removed in a year to 30,000. But consolidation was widespread: midpoint head 

sold for fed cattle doubled between 1987 and 2007, while those for broilers and cow-calf operations (cattle, less than 500 pounds) more than doubled. 

Technology, in the form of economies of scale, played an important role in shifts to larger 

livestock operations, particularly in hog, dairy, fed cattle, and poultry production (MacDonald 

and McBride, 2009). Hog, poultry, and fed cattle production also became much more tightly 

integrated with processors over time, and changes in processor location, size, and contracting 

practices may have played a further role in livestock consolidation.

Midpoints

Commodity 1987 1997 2007

Annual head removed or sold

Livestock

 Broilers 300,000 480,000 681,600

 Hogs 1,200 11,000 30,000

 Fattened cattle 17,532 38,000 35,000

 Cattle, <500 lbs. 50 65 128

Milk cow inventory

 Dairy 80 140 570

Note: The midpoint is defi ned as the enterprise size, in number of head, at which 

half of animals are on larger enterprises and half are on smaller enterprises.

Source: ERS calculations from unpublished Census of Agriculture records, 

1987, 1997, 2007.
16

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Continuing Advantages to Size in Crop Farming

Larger crop farms perform better fi nancially, on average, than smaller farms. The differences refl ect 

lower costs per unit of production and not higher revenue. In turn, larger farms appear to be able to 

realize more production per unit of labor and capital. These fi nancial advantages have persisted over 

time, which suggests that shifts of production to larger crop farms will likely continue in the future. 

Larger farms realize higher rates of return on equity (table 5).8 For each crop, profi t measures generally increase from one acreage class to the next, and the differences are large in total. The estimates 

are drawn from 2008-2011 data, but the pattern holds for other years as well. These differences in 

profi t performance do not stem from differences in revenue—larger grain and oilseed farms realize 

the same value of production per acre as smaller farms. The smallest vegetable farms (less than 50 

acres) realize a higher value of production per acre than larger farms, but otherwise no apparent 

relation exists between size and revenue per acre at fruit and vegetable farms. These advantages 

must, therefore, arise from differences in farm production costs.

Labor hours per harvested acre decline sharply as harvested acreage increases for corn, wheat, 

and soybean farms (table 6).9 Farms harvesting more than 2,000 acres use less than half as much 

labor per acre as farms harvesting fewer than 500 acres. Table 6 also reports the value of equipment 

and structures (assets) owned by the farm, per harvested acre. Assets per acre decline persistently 

as farm size increases; they are 35 to 50 percent lower for the largest farms than for those in the 

250-499 acre class. 

Table 7 reports the same measures for fruit and vegetable operations. Higher labor and capital 

use per acre in fruits and vegetables than in the fi eld crops refl ects the capital-intensive nature of 

fruit and vegetable production. However, the same general size relationship holds: labor hours per 

harvested acre decline as acreage increases, and capital per acre (the value of equipment and structures) also declines, except for labor for the largest size class (1,000 or more acres) among fruit operations. Moreover, the differences are large enough to be fi nancially meaningful. In each commodity 

class, larger operations appear to be able to apply their labor and capital to more acres than smaller 

farms; with regard to capital, that suggests that larger farms get more hours of use in a year. Of 

course, this pattern may also refl ect a more subtle relationship—that farms that are able to use labor 

and capital more intensively may also be better able to expand.

Leasing, Contract Labor, and Custom Services on Crop Farms

Tables 6 and 7 report on the labor that farming operations directly employ and the capital equipment 

that the operations own. But farms may access labor and capital in other ways: 

8The rate of return on equity measures the return on capital in the farm business (total assets minus total debt, or farm 

net worth). The numerator is net farm income, minus an adjustment for the opportunity cost of the unpaid labor and 

management time provided by farm operators and other unpaid workers. ERS researchers used class aggregates—total 

returns by all farms in a class divided by total net worth—to reduce the effects of extreme outliers, observations with 

near zero values of net worth, in fi nancial data.

9On version 1 of the annual ARMS, respondents are asked to report hours worked on the farm by the farm’s operators, 

the primary operator’s spouse if he or she is not an operator, other unpaid labor (such as children, or spouses of secondary 

operators), and paid workers. The sample is, therefore, a subsample of the farms in table 5, with 2,087 grain and soybean 

farms and 1,471 fruit and vegetable operations. 
17

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

• By leasing capital equipment and structures from dealers or from other businesses that rent out 

equipment and structures. Such assets are not recorded as assets of the farm business.

• By contracting for workers who are hired, employed, and paid through a contract labor 

provider.

• By contracting with custom service providers that bring their own labor, and may bring their 

own equipment, to perform fi eld tasks such as spraying or harvesting.

These transactions afford several advantages, including fl exibility to change the size of operations, 

avoidance of the fi nancial risks of investment in expensive capital equipment, and the freedom to 

focus on specifi c farm tasks, while relying on custom providers’ expertise for other tasks. If larger 

farmers use these services more than small farms, then an exclusive focus on capital assets and labor 

provided by the farm may understate the costs of larger operations and overstate their cost advantages over smaller operations.

Custom services are used extensively by many corn, soybean, and wheat operations, but (except 

in wheat) no apparent relationship exists between farm size and the likelihood that a farm will use 

Table 5

Larger crop farms realize higher average profi t rates

Farm specialization

Harvested acres Corn Soybeans Wheat Fruits/nuts

Vegetables/

melons

Class-average rates of return on equity, 2008-2011

Less than 10 - - - -1.4 -0.9

10-49 - - - -0.2 2.7

50-99 - - - 4.1 -1.9

Less than 100 -0.9 -1.3 -2.6 - -

100-249 1.2 -0.03 -0.6 5.4 8.1

250-499 2.9 1.1 0.6 3.8 17.8

500-999 4.8 1.7 0.4 7.1 8.9

1,000 or more - - - 10.7 17.9

1,000-1,999 5.3 5.4 4.6 - -

2,000 or more 8.0 8.2 5.5 - -

Notes: 

1. The sample consists of farms with no livestock production (crops only), and primary specializations in one of the fi ve 

commodity classifi cations noted above. The primary specialization is the commodity accounting for the largest share 

of farm sales. Farms with livestock were omitted in order to simplify the analysis and focus on fi nancial performance 

for crops.

2. The numerator of the rate of return on equity is net farm income, minus charges for unpaid operator labor and 

management contributions, while the denominator is net worth (assets minus debt). The table presents class 

averages—total returns across all farms in a commodity/acreage class, divided by total net worth in the class.

3. Pooling observations across years provides a larger sample size and more reliable estimates.

Source: Agricultural Resource Management Survey, Phase III, all versions, 2008-2011 pooled (17,351 observations: 6,619 

in corn; 2,810 in soybeans; 2,079 in wheat; 4,492 in fruits; and 1,351 in vegetables).
18

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

custom services (table 8). However, larger farms are much more likely to use contract labor or to 

lease capital equipment and structures. This suggests that table 6 may understate the farm use of 

labor and capital when all sources are accounted for, and that it may therefore overstate the advantages of large farms regarding input use.

However, the bottom panel of table 8 shows that even though larger farms are more likely to incur 

expenses for each practice, dollar expenses fall sharply, per acre, as acreage increases. If there is a 

fi xed set-up cost for these items, as well as a component of expenses that varies with acreage, then 

larger farms could have lower expenses per acre because they are able to apply the contracted inputs 

and custom services that they hire to more acres per hour of hire. Regarding the intensity of resource 

use, table 8 reinforces the message from table 6 that larger crop farms appear to be able to apply a 

given amount of capital and labor to more acres. 

Producers of fruits and vegetables make extensive use of contract labor, leased capital, and custom 

services (table 9). In general larger farms are considerably more likely than smaller farms to use 

each of these practices, but there is no apparent relationship between farm size and the per acre 

expenses that are incurred for these services. Even when contracted and leased inputs are considered, larger fruit and vegetable farms are able to apply a given amount of labor and capital across 

more acres, on average, realizing lower costs per acre.

Table 6

Labor and capital on corn, soybean, and wheat farms

Harvested acres

Less than 

100 100-249 250-499 500-999 1,000-1,999

2,000 or 

more

Mean hours per harvested acre

Labor (all)

 Corn 38.6 12.3 7.8 5.7 3.5 2.7

 Soybeans 45.7 10.4 7.3 5.8 3.8 3.0

 Wheat 40.4 8.7 5.8 5.3 3.2 2.2

Hired labor hours as a percent of total labor hours

Hired labor

 Corn 5.0 2.9 4.6 10.2 16.9 31.2

 Soybeans 2.7 5.2 7.4 14.6 16.4 36.0

 Wheat 4.0 3.2 3.4 16.3 19.5 20.5

Equipment and structures assets ($) per harvested acre

Capital

 Corn 2,532 847 683 568 505 432

 Soybeans 2,880 826 640 535 387 332

 Wheat 3,325 588 396 320 278 242

Note: The labor and hired labor panels are based on version 1 only (2,087 observations), while the capital measures are 

drawn from all versions (11,508 observations). 

Source: Agricultural Resource Management Survey, Phase III, 2008-2011 pooled.
19

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

The fi nancial estimates show continuing advantage to large farms, which should favor further 

consolidation. However, the pace of cropland consolidation also appears to have slowed in recent 

years. The midpoint acreage for cropland, measured with data from the Census of Agriculture, 

nearly doubled between 1982 and 2007, to 1,105 acres (fi g. 3). Data from the 2012 Census have not 

yet been released, but the midpoint acreage estimated from the 2011 ARMS (see fi g. 1) was 1,100 

acres, just 1 percent greater than the ARMS-based estimate of 1,093 acres in 2007 (in fi g. 3), and 

slightly below the 2007 Census estimate.10 More defi nitive evidence will be available in data from 

the forthcoming 2012 Census of Agriculture.

Some recent developments in consumer demand may favor smaller family farms. For example, 

consumer and retailer interest in local food production is growing (Low and Vogel, 2011). Farms 

that produce for local markets are considerably smaller, on average, than other farms. In 2010, the 

midpoint acreage among crop farms that produce for local markets was 310 acres, compared to 1,100 

acres for other crop farms. Among fruit and vegetable producers, the midpoint acreage was 168 

acres, compared to 675 acres among other fruit and vegetable farms. Thus, shifts of consumption 

10The Census of Agriculture data in fi gure 3 are based on all States, while the ARMS estimates are based on much 

smaller samples and exclude Alaska and Hawaii. However, ARMS estimates for the overlap years of 1997, 2002, and 

2007 are quite close to Census measures. In 2007, the the midpoint acreage for Census was 1,105 acres, while that from 

ARMS was 1,093 acres.

Table 7

Labor and capital on fruit, tree nut, and vegetable farms

Harvested acres of fruits, tree nuts, and vegetables

Less 

than 10 10-49 50-99 100-250 250-499 500-999

1,000 or 

more

Mean hours per harvested acre

Labor

Fruit/nuts 564.7 202.9 145.5 145.1 128.4 74.2 159.6

Vegetables/melons 849.3 204.4 150.9 98.5 59.9 46.4 32.8

Hired labor hours as a percent of total labor hours

Hired labor

Fruit/nuts 7.4 23.6 43.0 52.1 51.2 58.2 55.6

Vegetables/ melons 5.1 14.2 37.7 41.7 43.4 47.6 61.4

Equipment and structures assets ($) per harvested acre

Capital

Fruit/nuts 15,862 5,154 3,280 2,173 1,693 1,314 971

Vegetables/ melons 26,439 4,677 2,531 2,240 1,469 1,188 885

Note: The labor and hired labor panels are based on version 1 only (1,471 observations), while the capital measures are 

drawn from all versions (5,843 observations). 

Source: Agricultural Resource Management Survey, Phase III, 2008-2011 pooled.
20

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

to local markets, and hence to the farms that supply them, do appear to favor smaller operations.11

However, farms that sell to local markets account for less than 5 percent of U.S. cropland.

Prices for farm products and for energy inputs were extraordinarily volatile in recent years (Baffes, 

2013). In turn, that volatility increased the fi nancial risks faced by farmers and by lenders. It also 

created uncertainty about the future path of Government policy, as policymakers and commodity 

groups debated the future orientation of crop insurance, commodity programs, and biofuels 

programs. The risk and uncertainty of the last several years may have slowed cropland consolidation, as farm operators and lenders pause to better understand the new fi nancial climate.

11The data are drawn from version 1 of the 2010 ARMS (Phase III), where local producers were farms that had direct 

sales to consumers for human consumption, or that provided crops directly to retail outlets that in turn sold directly to 

consumers. Interest in organic products is also growing, but organic crop production appears to be concentrated on large 

farms; the midpoint acreage for farms with certifi ed organic acreage was 1,968 acres in the 2010 ARMS. 

Table 8

Use of contract labor, custom work, and leased capital in grain production

Harvested acres

Practice and 

commodity

Less than 

100 100-249 250-499 500-999

1,000-

1,999

2,000 or 

more

Percent of farms using practice

Custom work

 Corn 49.6 51.8 48.3 49.0 46.7 48.7

 Soybeans 49.9 46.3 40.3 44.8 41.9 45.8

 Wheat 31.3 40.7 45.7 49.9 52.8 60.0

Contract labor

 Corn 2.9 3.0 6.7 6.8 13.1 11.8

 Soybean 1.9 3.2 4.5 5.7 9.8 17.5

 Wheat 12.8 11.7 9.4 10.3 14.1 14.0

Leased capital

 Corn 7.9 7.2 14.6 17.5 23.4 37.9

 Soybeans 4.6 12.1 12.7 12.8 12.6 23.9

 Wheat 6.9 10.1 12.8 18.8 17.2 28.6

Expense ($) per harvested acre for all three practices

Expense

 Corn 31.13 21.76 15.10 12.51 12.07 10.86

 Soybeans 25.57 18.90 13.58 11.23 7.05 10.56

 Wheat 26.53 17.90 14.63 11.90 11.40 9.98

Notes: A farm is considered to “use a practice” if it has positive expenses for the practice, while expense per acre is the 

sum of expenses for custom work, contract labor, and leased capital.

Source: Agricultural Resource Management Survey, Phase III, all versions, 2008-2011 pooled.
21

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Table 9

Use of contract labor, custom work, and leased capital in fruits and vegetables

Harvested acres

Practice and 

commodity

Less 

than 10 10-49 50-99 100-249 250-499 500-999

1,000 or 

more

Percent of farms using a practice

Custom work

Fruits/nuts 25.8 41.7 56.5 56.7 63.1 70.3 71.6

Vegetables/melons 10.9 16.0 9.2 31.1 42.2 54.5 64.7

Contract labor

Fruits/nuts 31.3 47.7 52.0 59.4 64.8 70.3 79.5

Vegetables/melons 3.1 13.8 17.6 29.0 36.0 33.3 56.3

Leased capital

Fruits/nuts 7.2 9.8 21.3 22.4 30.6 39.0 44.6

Vegetables/melons 3.7 4.4 4.9 25.5 43.2 39.9 59.2

Expense ($) per harvested acre for all three practices

Expense

Fruits/nuts 526 550 625 507 557 631 475

Vegetables/melons 234 122 83 119 203 202 239

Notes: A farm is considered to “use a practice” if it has positive expenses for the practice, while expense per acre is the 

sum of expenses for custom work, contract labor, and leased capital.

Source: Agricultural Resource Management Survey, Phase III all versions, 2008-2011 pooled.
22

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Drivers of Consolidation

ERS researchers focused on three factors: 

• Technology. Labor-saving innovations—in equipment, chemicals, seeds, tillage practices, and 

information technology—allow farmers to operate larger farms. 

• Changes in the organization of farms. Over a long period, crop production has separated 

from livestock production as individual farms have focused on a few commodities and has 

concentrated in regions suited for adopting scale-increasing technology. Specialization and 

investment in costly capital equipment can be risky endeavors, and farmers also adopted 

methods of fi nancing and transacting to reduce risks.

• Government policy. Federal initiatives affect farm structure through many channels—some 

lead to larger farms, while others support smaller farms. Some programs also affect the risks 

perceived by producers, and they can indirectly affect farm size by the way farmers adapt to 

these risks.

Drivers of Consolidation: Technology

Economists studying shifts in farm size often focus on the role of technology, and in particular on 

two concepts: scale economies and labor-saving technological change. Scale economies are said to 

exist if expansions of output lead to reductions in per-unit costs of production holding input prices 

constant.12 Scale economies are technologically based, in that they arise from savings in input use, 

and not because a larger fi rm realizes lower prices for inputs.13 They may be driven by certain 

fundamental physical relationships in production and may arise from the higher specialization in 

tasks that increased production can offer. They often arise because larger production runs may allow 

for the use of capital equipment, and more capital-intensive production, that would not be viable for 

smaller production runs.

Economies of scale matter for fi rm and plant size in non-agricultural industries. They also appear 

to matter, over certain ranges of production, in agriculture and particularly in livestock production (MacDonald and McBride, 2009; Miller et al., 1981). Nonetheless, most economists are skeptical that scale economies usefully explain increased farm sizes. Most farms remain fairly small, 

compared to fi rms in other industries, and crop production still covers a wide range of viable farm 

sizes. Moreover, because many large pieces of capital equipment are mobile and can be rented out to 

small operations for modest production runs, multiple small farms can effectively share equipment.

Instead of focusing on scale economies, analysts more often focus on labor-saving innovations, 

which are especially key for agriculture, where most farms are family-operated, with a certain 

amount of labor available for operation of the farm (Kislev and Peterson, 1982; Gardner, 2002; 

Cochrane, 1993). Innovations that reduce the amount of labor required for fi eld operations allow 

12More specifi cally, the concept relates to the period of time long enough to allow the fi rm to vary all inputs.

13The term “pecuniary economies of scale” describes circumstances in which larger fi rms can obtain inputs at lower 

prices. In agriculture, such circumstances can apply when buying in large bulk quantities leads to lower unit prices, because the unit costs of shipping, storing, or processing a large order are lower. Examples include purchases of fertilizer and 

other farm chemicals, and purchases of transportation for products. While such pecuniary economies of scale can provide 

larger operations with advantages, they do not appear to have become more important, and do not appear to be important 

drivers of recent changes in farm size.
23

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

farming of more acres. Likewise, innovations that give farmers more accurate or timely information 

often increase the amount of land they can effectively manage. Labor-saving innovations do not have 

to explicitly favor larger farms to affect farm structure; instead all they have to do is facilitate the 

expansion of farms.

Equipment

The tractor remains a primary example of a labor-saving innovation. The fi rst general-purpose 

tractor capable of operating among fi eld crops was introduced in 1924, and its use spread until 

around 1960, with tractors steadily replacing horses and mules in fi eld tasks. Olmstead and Rhode 

(2001) estimate that by 1960 the tractor had replaced 23 million draft animals, and the 79 million 

acres of land used to grow feed for them were reallocated to other uses. The tractor reduced the 

amount of labor time required to perform fi eld operations, and required less labor time to maintain 

than draft animals. Olmstead and Rhode estimate that the tractor reduced the labor required to 

produce the agricultural output of 1960 by 1.7 million workers, about 24 percent of farm employment in that year. Finally, they estimate that tractor adoption increased average farm size by 58 

acres, about 37 percent of the growth in average farm size between 1910 and 1960, as it allowed a 

single farmer to operate more acres. 

The tractor wasn’t the only labor-saving mechanical innovation introduced during the period. 

Mechanical harvesters, sprayers, and planters, designed for many specifi c crops, further reduced 

labor requirements (Street, 1957; Rasmussen, 1962, 1968; Reimund et al., 1981; Whatley, 1983; 

Calvin and Martin, 2010). In each case, total labor requirements were reduced, leading to reductions in the total farm workforce, but families that remained in agriculture could expand the farm 

operation with the labor and capital equipment available to them. In some cases, mechanical innovations required complementary biological innovations that created crops that could be more easily 

harvested mechanically.

 Since the time covered by Olmstead and Rhode, tractors and other farm equipment have continued 

to get larger and faster, allowing individual farmers to manage yet more acreage. Bechdol et al. 

(2010) argue that a farmer could plant 40 acres of fi eld crops in a day using the tractors and planters 

available in 1970, but 420 acres in 2005, and 945 in 2010, with larger and faster tractors hauling 

planters that covered far more rows than the 1970 version (table 10). For similar reasons, a farmer 

could harvest more than 12 times as much in a day in 2010 as in 1970.

Moving larger pieces of equipment between fi elds takes more time and expense, and larger pieces 

also require more setup time at fi elds. For these reasons, bigger and faster equipment is most valuable where fi elds are large, fl at, and contiguous. Therefore, it is more valuable, and has greater 

effects on farm size, in the Western Corn Belt, the Plains, and the Delta, than in the Eastern Corn 

Belt, Appalachia, and the Northeast.

To see this effect, consider data on recent purchases of very large tractors—500 horsepower or more 

(which sell for an average price of over $250,000).14 Between 2005 and 2011, 60 percent of those 

tractors were sold in just fi ve Corn Belt and Plains States—Illinois, Indiana, Iowa, Minnesota, and 

North Dakota (table 11). Those are major agricultural States, but collectively account for just 27 

14Because the Agricultural Equipment Manufacturers Association began reporting sales of tractors of 500 horsepower or greater for the fi rst time in 2005, 2005-2011 sales are reported. The fact that the association did not report that 

category before 2005 itself indicates the shift to much larger and faster tractors.
24

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

percent of U.S. cropland, far below their share of large tractor sales. The next fi ve most important 

States for large tractor sales (South Dakota, Texas, California, Michigan, and Ohio) accounted for 17 

percent of sales and 20 percent of cropland, while all other States account for 53 percent of cropland 

and just 23 percent of large tractor sales.

Land attributes in the fi ve leading States are different from other States. Much higher shares of cropland in those States are harvested than in other States, and much lower shares are planted to hay, 

Table 10

Changes in planting and harvesting machinery in fi eld crops, 1970-2010

Year Planting effi ciency Harvesting effi ciency

Technology Outcome Technology Outcome

1970 4 rows @ 2 mph 40 acres/day 4 rows, 12 hrs/day 4,000 bu./day

2005 16 rows @ 6 mph 420 acres/day 12 rows, 12 hrs/day 30,000 bu./day

2010 36 rows @ 6 mph 945 acres/day 16 rows, 12 hrs/day 50,000 bu./day

Notes: mph=miles per hour; hrs=hours; bu=bushels

Source: Bechdol, Gray, and Gloy (2010)

Table 11

Sales of large (500 horsepower or more) four-wheel drive tractors, by State, 2005-2011

State

Large tractor sales, 

2005-2011

Cropland 

acres

Cropland 

density

Share of cropland 

non-harvest hay 

Units Million $ Millions Percent Percent Percent

Top fi ve, sales 3,240 837.7 111.6 71 11 7

 Minnesota 1,119 287.2 21.9 69 12 9

 North Dakota 864 223.0 27.5 69 20 11

 Illinois 633 166.6 23.7 73 5 3

 Iowa 381 97.8 26.3 77 10 5

 Indiana 243 63.2 12.1 64 5 5

2nd fi ve, sales 893 229.1 80.9 43 27 22

 South Dakota 224 57.5 19.1 53 20 21

 Texas 191 49.2 33.7 38 43 27

 California 183 45.4 9.5 27 19 23

 Michigan 160 42.2 7.8 43 12 17

 Ohio 135 34.6 10.8 59 8 12

Other States 1,246 317.0 214.0 40 29 29

United States 5,379 1,383.8 406.6 49 24 21

Note: Cropland contiguity is the weighted average share of land in cropland, where the observations are counties and the 

weights are cropland. 

Sources: Tractor sales are from Agricultural Equipment Manufacturers data provided to ERS, while cropland data are from 

Census of Agriculture. 
25

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

which is frequently grown on hillier and more marginal land. Moreover, cropland density is much 

higher in the top fi ve States (71 percent of the land area is in cropland, on average, compared to 43 

percent in the next fi ve States and 40 percent for the rest of the country).15 High cropland density 

indicates large and contiguous fi elds, where larger and faster tractors will be most effective.

Chemical Pesticides

Pesticides are substances used to control pests, including herbicides for weed control, insecticides 

to control insects, fungicides to control fungi and other disease pathogens, nematocides to control 

parasitic worms, and rodenticides for rodents. Pesticides can be synthetic or natural, and they are not 

the only means by which farmers might control pests. Farmers can control weeds through weeding, 

either by hand or with mechanical tillage, and they can control pests with management practices like 

crop rotations and interplanting.16

The use of chemical herbicides expanded between 1960 and 1980, from 35 million pounds of active 

ingredients to 469 million, as the proportion of acres treated with herbicides rose to over 90 percent 

for corn, cotton, and soybeans, and nearly 50 percent for wheat and potatoes (Fernandez-Cornejo, 

et al., 2013). Fernandez-Cornejo and Pho (2002) show that herbicide prices fell sharply compared 

to labor and machinery prices in this period, leading to two types of adjustments. First, farmers 

substituted existing chemical herbicides for other labor-intensive methods of weed control. Second, 

the relatively high prices for labor and machinery led to more research and development that would 

generate improvements in herbicides and other chemicals, leading to further substitution of chemicals for labor. 

After 1980, the total pounds of herbicides applied stabilized and even declined for some crops, as 

the quality of herbicides improved. Newer substances more effectively controlled weeds with fewer 

applications, less toxicity, and shorter persistence in the soil (Fernandez-Cornejo and Pho, 2002; 

Fernandez-Cornejo et al., 2013). When adjusted for improved herbicide quality, prices of chemical 

methods of weed control continued to fall relative to labor and machinery prices after 1980, and 

farmers shifted to newer labor-saving herbicides, even as total herbicide applications stopped rising.

In their study using annual time series data for the United States covering 1948-1995, FernandezCornejo and Pho (2002) fi nd a strong labor-saving bias to the adoption of herbicides. They estimated 

that a 1-percent increase in the price of labor relative to herbicides would lead to a 0.23 percent 

increase in the ratio of herbicides to labor used in production, during the year of the increase. The 

long-run effect—covering longer term input substitution and the effects of induced innovation—was 

quite large; a 1-percent increase in the relative price of labor would lead to a 13.5-percent increase in 

the ratio of herbicides to labor use.

Substituting herbicides for labor increased the amount of cropland that a farm family could manage 

by reducing the labor and management time required per planted acre of a given crop. The impacts 

were likely sizable between 1960 and 1980, when herbicide use spread widely. Later herbicide 

15Cropland density is the weighted average share of land in cropland, averaged across counties, where each county is 

weighted by its share of the State’s cropland. The measure is designed to capture density in areas where the cropland is 

located.

16Pests often thrive on one crop, but not others. Farmers can, therefore, control pest populations by planting a succession of different crops in a fi eld over time (rotation) or by planting a mix of different crops across a fi eld (interplanting).
26

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

improvements likely allowed for further incremental expansions in farm size, intertwined with 

changes in crop genetics and tillage practices. 

Genetically Engineered Seeds

Developments in seed genetics have also saved labor, either directly or in combination with other 

innovations (Gardner, 2002; Cochrane, 1993). For example, mechanical harvesters for processing 

vegetables, which greatly reduced labor hours, necessitated the complementary development of 

crop products that could withstand mechanical handling in harvest (Rasmussen, 1968; Schmitz and 

Seckler, 1970; Reimund et al., 1981). 

More recently, the spread of genetically engineered (GE) seeds in corn, cotton, and soybeans (fi g. 

5) has affected farm production practices and the allocation of operators’ time.17 Herbicide-tolerant 

(HT) seeds, used in all three crops, allow farmers to apply one herbicide product at a post-emergent 

stage, thereby replacing several herbicides applied at different times (Fernandez-Cornejo, 2007). 

Use of HT seeds reduces machine and machine operators’ time, as well as the time used to evaluate 

the need for and plan of weed management. Other GE seeds (Bt), used in corn and cotton, are pest17GE seeds were also widely used on canola, papaya, and sugar beet acreage, and on some squash and sweet corn 

acreage, in recent years. However, these are minor U.S. crops, and the vast bulk of GE seed acreage is in corn, cotton, 

and soybeans.

Figure 5

Adoption of genetically engineered (GE) seeds in the United States

Source: Fernandez-Cornejo (2005) for 1996-1999; USDA NASS, Acreage, for 2000-2011

0

10

20

30

40

50

60

70

80

90

100

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Percent of acres

GE Cotton

GE Corn

GE Soybeans
27

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

resistant. In principle, they allow farmers to forego spraying pesticides, thereby saving machine and 

machine operator hours, although some farmers facing infestations may have simply accepted lower 

yields and did not use chemical pesticides. To the extent that GE seeds reduce the amount of time 

needed to plan and operate a given amount of acreage, they potentially free time to manage a larger 

farm.

Gardner et al. (2009) analyzed the linkages between labor hours used per acre and GE seed adoption, using ARMS corn, cotton, and soybean data collected in 2001, 2002, and 2003, respectively.18

They fi nd that adoption of HT seeds was associated with statistically signifi cant reductions in labor 

hours in soybean and cotton fi elds, but not in corn fi elds, where HT adoption was still quite low in 

2001. 

ERS researchers used ARMS data for 2006 and 2008 to assess the relationship between GE seed 

adoption and hours of farm labor for corn, cotton, and soybean farms, while controlling for total 

cropland and the mix of crops on the farm, the use of capital equipment, and the use of custom 

services and contract labor, each of which can substitute for on-farm labor. (See box: “GE Seeds and 

Labor Time.”)19

Results suggest that farms with acreage in HT crops realized substantial reductions in labor hours, 

per acre of land. For a farm with 1,500 acres of corn and soybeans, but no land planted to GE seeds, 

labor use on the farms would amount to 4,421 hours. With all of the land planted to HT traits, and 

none to Bt, the estimated labor hours would fall to 3,160 hours, freeing 1,261 hours for off-farm 

work, family time, or expanded farm production. Most farms don’t face such stark choices between 

all or nothing: alternatively, the model estimates indicate that labor hours per acre would fall by 15.5 

percent on farms with 75 percent of acreage in HT seeds compared to farms with 25 percent in HT. 

In contrast, the analysis shows no signifi cant association between Bt adoption and labor hours.20

If GE seeds save management and labor time, farmers could use the freed labor time to work off 

the farm or to devote more time to family activities. Earlier research on the topic indicates that use 

of GE seeds was associated with increased earnings from off-farm employment, for a given size 

of farm (National Academy of Sciences, 2010). That research, driven by data availability, could 

access data on off-farm earnings but lacked direct data on labor hours, so researchers could not track 

whether farmers also used the freed time to expand their farming operation. This analysis described 

above shows a direct link to on-farm labor hours.

Genetically engineered seeds were commercially introduced in 1995, so they cannot account for 

changes in farm size before that time, and so far they have been used primarily in corn, cotton, 

and soybeans, so they cannot account for the ubiquitous increase in farm size among many crops. 

Their effect on corn, cotton, and soybean farms may be limited by the extent of acreage devoted to 

other crops or by time devoted to livestock production. Nevertheless, GE seeds may partly explain 

increased consolidation among fi eld crop farms since 1995.

18They used ARMS Phase II data, which elicit detailed information on activities carried out with respect to that fi eld, 

including hours of labor expended in each activity. 

19ARMS questionnaires for Phase III, version 1, included questions about GE seed use and about labor hours in those 

years.

20Over time, areawide use of Bt seeds may have reduced insect infestations for all corn, cotton, and soybean farms, 

thereby eliminating any differential labor-saving effect for Bt adopters.
28

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

HT seeds were approved in 2011 for use in alfalfa, and other GE traits may be available in the 

future. Like other innovations, they can lead to increased farm size if they are labor-saving. 

However, other developments may reverse the impact of GE seeds on farm size. In recent years, 

resistance to glyphosate (the herbicide most commonly associated with HT seeds) has emerged 

among weeds. If farmers must return to older and more labor-intensive methods of weed control to 

manage resistance, the effect of GE seeds on farm size may slow and reverse.

Tillage Practices

“No-till” has become an important soil conservation practice in the United States. A no-till system 

leaves crop residue from the previous harvest on the soil, and soil is left undisturbed from prior 

harvest to planting, except for the injection of nutrients. By allowing for fewer machinery passes over 

fi elds and fewer machine operator hours, no-till saves labor, as well as capital and energy. 

GE Seeds and Labor Time

The 2006 and 2008 ARMS Phase III (Version 1) questionnaires included detailed questions 

on the use of genetically engineered (GE) seeds and on labor hours for the whole farm. ERS 

researchers selected a sample of farms from the 2 survey years, consisting of 1,798 operations 

with corn, cotton, or soybean production but no livestock (most raised other crops as well). 

Substantial cross-farm differences exist in the share of acreage planted to GE seeds, because of 

differences in the use of GE seeds for corn, cotton, and soybeans, and because of differences in 

the acreage of other crops planted on the farm. 

ERS researchers sought to determine whether farms with higher shares of acreage planted to 

GE seeds worked fewer hours on the farm. To that end, a simple ordinary least squares regression was estimated, in which the dependent variable was the logarithm of total hours worked 

on the farm by all operators and other labor. Researchers controlled for total farmland acreage 

(in logs), the total dollar value of farm buildings and equipment (also in logs), the share of land 

that was harvested, dummy variables for the use of contract labor or custom services, a dummy 

variable to distinguish 2008 from 2006, and measures of the share of production originating in 

each of 15 fi eld crop categories. The measures of GE acreage were the share of the farm’s land 

planted to herbicide tolerant (HT) and to pest-resistant (Bt) seeds.

Labor hours were higher on farms with more land, more capital equipment, and a higher share 

of land in harvested acres. They were lower where the farm used contract labor or custom 

services and were lower in 2008 than in 2006. Hours varied with the mix of crops.

The coeffi cient on Bt was never statistically signifi cant, but it was consistently positive, in this 

model and in alternatives analyzed for corn and cotton farms separately. Farmers plant seeds 

with Bt traits where there is a risk of insect infestation, and those risks may cause the farmer 

to have to use more hours (per acre) on other control practices. In any case, Bt seeds do not 

appear to reduce labor hours. In contrast, and consistent with the fi ndings of Gardner, Nehring, 

and Nelson (2009), HT seeds reduce labor requirements per acre. The coeffi cient is negative, 

statistically signifi cant, and large. 

—continued
29

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Estimates of the share of planted acreage under no-till are reported in table 12. The estimates cover 

fi ve major fi eld crops for 2000-2010.21 For each crop shown, no-till shares are higher in later years, 

although the increases are modest. Horowitz et al. (2010) estimate that shares of planted acreage 

under no-till expanded at 1.5 percentage points per year between 2000 and 2009, when no-till 

covered 35 percent of planted acreage for eight major U.S. fi eld crops (barley, corn, cotton, oats, 

rice, sorghum, soybeans, and wheat). Gardner et al. (2009) fi nd that the use of no-till is associated 

with large and statistically signifi cant declines in per-acre labor hours in corn, cotton, and soybeans.

HT seeds allow for easier use of no-till practices, and the recent spread of no-till owes something to 

the expansion of acreage planted to HT seeds. However, these phenomena are not entirely interdependent. While the highest share of no-till acreage is in soybeans, which had the earliest wide application of HT seeds, table 12 shows a rapid expansion of no-till in wheat, which had no HT seeds.

21Note that estimates are not reported for all years. USDA has not performed annual comprehensive surveys of tillage 

practices; instead, the ARMS covers fi eld level practices of specifi c crops in different years. 

GE Seeds and Labor Time—continued

Regression results: Labor use on crop farms

Variable Coeffi cient t-statistic

Intercept 3.533 6.62

Log of land in farm 0.340 8.10

Percent of land harvested 0.519 6.41

Log of physical capital 0.187 4.35

Contract labor used (yes=1) -0.379 1.43

Custom work used (yes=1) -0.169 1.97

Bt acreage (percent) 0.167 1.13

HT acreage (percent) -0.336 3.73

R2 0.38

Note: Dependent variable: log of annual paid and unpaid labor hours. Other variables 

include a dummy for 2008, and shares of acreage in each crop (corn, wheat, 

soybeans, cotton, peanuts, potatoes, barley, oats, tobacco, sorghum, hay, sugar cane 

and sugar beets, canola, other oilseeds, other crops). Observations are weighted to 

refl ect sample selection probabilities, and standard errors are estimated using the 

delete-a-group jackknife method to account for complex sample design.

To understand the size of the effect, consider a farm in 2006 with 1,500 acres planted equally to 

corn and soybeans, with no custom work or contract labor, with $300,000 in equipment. With 

no land planted to GE seeds, the farm would require an estimated 4,421 hours. With all of the 

land planted to HT traits, and none to Bt, the estimated labor hours fall to 3,160 hours, freeing 

1,261 hours for off-farm work, family time, or expanded farm production. This is an extreme 

example, given the 100 percent GE assumption, but the impact on hours is striking, as well as 

the implication that farmers with extensive acreage in HT crops could use those hours to noticeably expand farm size.
30

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Information Technology

Modern farm equipment embodies another set of innovations known as precision agriculture: 

management practices and information technologies (IT) that measure and manage intra-fi eld variations in soil attributes, pest presence, product attributes, and production outcomes. Specifi c technologies include yield monitors that measure and map intra-fi eld variations in yields at harvest; pest 

monitors that identify the presence of insects and fungi at spraying; GPS systems that map soil, 

yield, and pest data and transmit the information to vehicles in fi elds; and variable rate spraying and 

injection technologies that match the application of chemicals to the intra-fi eld variations in yields, 

nutrients, and pest presence identifi ed in the other technologies. These technologies, as well as autosteering and guidance systems for tractors, hold the promise of reducing per-acre use of energy and 

chemicals primarily, but also of labor and management. 

In fragile tree fruits like apples and oranges, researchers are experimenting with machines that 

locate and map fruit using a vision technology, combined with robotic machines that take information from mapping machines and use it to selectively harvest fruit as it matures (Calvin and Martin, 

2011). These technologies, which may also apply to other crops, allow farmers to increase the value 

of production per acre, while reducing labor and management requirements. Other precision technologies, such as laser guidance for fi eld leveling and drip irrigation, enhance water management.

Adoption of precision technologies has been growing: some type of precision technology was used 

on 58 percent of wheat acres in 2009, up from 14 percent in 1999; on 49 percent of corn acres in 

2005, up from 35 percent in 1999; and on 45 percent of soybean acres in 2006, up from 31 percent in 

1999 (Schimmelpfennig and Ebel, 2011).

Precision technologies can save on energy, chemicals, and nutrients, and they may also save management time if they allow farmers to gather and retain more accurate information in the course of fi eld 

operations instead of in separate fi eld visits. Because the innovations are embodied in expensive 

capital equipment, they also likely create scale economies: that is, given their expense, many of them 

are likely to only be economical on large operations that can spread their costs over large production 

volumes or through custom provision of services. 

So far, most use of precision technologies has involved yield monitors, and widespread use of those 

has occurred recently, so precision technologies did not play a major role in the past growth of farm 

size (Schimmelpfennig and Ebel, 2011). Still, their role is likely to grow in the future if they are 

Table 12

Use of no-till on planted cropland, 2000-2010

Crop 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Percent of planted acres in no-till system

Corn 17 19 - - - 24 - - - - 25

Cotton - - - 15 - - - 20 - - -

Rice 5 - - - - - 12 - - - -

Soybeans 31 - 35 - - - 45 - - - -

Wheat - - - - 22 - - - - 39 -

Source: Agricultural Resource Management Survey, Phase II, 2000-2010
31

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

labor-saving, and more importantly, if they provide farm managers with localized information that 

they would otherwise only obtain through time-intensive personal inspections.

Drivers of Consolidation: Farm Organization and Location

During the 20th century, crop production largely separated from livestock production, and crop 

farmers focused on producing a few commodities. Greater specialization provided a spur to 

increased size, and crop production also shifted toward regions suited for adopting scale-increasing 

technology. Specialization, and investment in costly capital equipment, can be risky endeavors, and 

farmers also adopted new methods of fi nancing and transacting to reduce risks.

Farm Specialization

Most U.S. farms kept a variety of livestock in the fi rst half of the 20th century, and they raised feed 

crops for their animals as well as cash crops for sale. While they became more specialized during 

the fi rst half of the century, at least half of U.S. farms still had hogs, milk cows, or chickens in 1960 

(fi g. 6). The shift away from widespread and diversifi ed livestock production accelerated after that 

date, and continued through 2000, when less than 10 percent of farms had chickens, milk cows, or 

hogs, and those that did usually specialized in one species.

The presence of livestock affects farm crop choices. In 1900, most farms grew corn, usually to feed 

to their own animals (fi g. 7). As farmers ceased small-scale diversifi ed livestock production, they 

also stopped producing feed for their small herds and fl ocks. By 2010, only one in six farms grew 

corn, and those farms usually specialized in crops, with no livestock.22

Livestock production concentrated onto fewer farms, and those farms focused on livestock, although 

most continued to grow some crops for sale and for manure utilization (MacDonald and McBride 

2009). Today, almost all poultry production, and most pork and fed cattle production, rely exclusively on purchased feeds, while dairy production combines onfarm production with purchased feed. 

As a result, most crop production is concentrated on farms with no livestock, focusing on growing 

a few crops. Commercial markets for feed became deeper, with more transactions and greater 

volumes of feed sales, as less feed was produced for animals on the farm and more was sold by crop 

farms to intermediaries for processing and sale to livestock farms as feed components or complete 

mixes. Deeper markets provided more reliable services, a wider range of desirable feed attributes for 

purchase, and more certain market outlets for farmers.

Crop farmers who no longer pursued small-scale diversifi ed livestock production had more free 

time available, which could be applied to work off the farm or to expanded crop production on 

more acres. As a result, changes in farm organization toward greater specialization also provided an 

impetus for crop farmers who wished to farm full-time to operate more cropland. 

Once they left livestock production, crop farmers needed to decide on the mix of crops to produce. 

If all of a farmer’s cropland has common soil and climatic attributes, then a single specifi c crop 

might be the most profi table for the farm, for a given combination of product and input prices. More 

specialized farms can also realize more intensive use of specialized equipment and structures, and 

22See Gardner (2002) for more on increased specialization of U.S. farms during the 20th century. 
32

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

greater accumulation of crop-specifi c knowledge by producers, leading to reduced production costs 

(Holmes and Lee, 2012). 

However, specialization carries risks. Specialization may create greater production risks from 

pest outbreaks, as well as economic risks from concentrated price shocks. Diversifi cation across 

multiple crops might allow the farmer to reduce the fi nancial risks of relying on one commodity, 

if commodity returns are not highly correlated across crops. Diversifi cation can provide production advantages if farmers can reduce pest presence and improve soil attributes by rotating different 

crops through fi eld. Finally, diversifi cation may also allow farmers to more fully utilize their labor 

and capital over the course of a year, and thereby reduce production costs, if different crops are 

planted and harvested at different times of the year.

U.S. crop farms tend to resolve the trade-off between specialization and diversifi cation by focusing 

on the production of a few crops—few farms are completely specialized, but few are highly diversiFigure 6

Livestock on U.S. farms, 1900-2010

Source: Census of Agriculture, with data interpolated between Census years.

Percent of farms

0

10

20

30

40

50

60

70

80

90

100

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Cattle

Chickens

Milk Cows

Hogs
33

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

fi ed. Table 13 summarizes product diversifi cation on crop farms in 2011. It reports data for all crop 

production, and for production of 18 different crop categories. 23 

Twenty-seven percent of crop production occurred on farms that also had livestock—that is, 73 

percent occurred on farms that specialized only in crops (table 13). The incidence varied across 

commodities—for example, barley, hay, oats, and sorghum producers frequently raise livestock 

(usually cattle), while little fruit and nut, greenhouse, sugar, rice, or vegetable production occurred 

on farms with livestock. 

23The table reports estimates of the value of production for the 18 categories, and to construct this table ERS analysts 

drew on data for harvested acreage elicited in ARMS Phase III questionnaires for 21 crop categories: barley; canola; 

corn (grain and silage combined); cotton; dry edible beans/peas/lentils; fruits, nuts, and berries; hay (alfalfa and all other 

combined); nursery and greenhouse crops; oats; peanuts; potatoes; rice; sorghum (grain and silage combined); soybeans; 

sugarcane; sugarbeets; tobacco; vegetables (processing and all others combined); wheat; other oilseeds; and all other 

crops. 

Figure 7

Selected field crops on U.S. farms, 1900-2010

Source: Census of Agriculture, with data interpolated between Census years.

Percent of farms

0

10

20

30

40

50

60

70

80

90

100

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Corn

Cotton

Wheat

Soybeans
34

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Twenty-two percent of crop production occurred on farms that produced only a single crop 

commodity, while 30 percent occurred on farms with two crops. Only 11 percent occurred on farms 

with fi ve or more crops. Note that the analysis uses just 21 crop categories, and that some categories—such as vegetables or fruits—contain many specifi c crops. Table 13 only captures farm diversifi cation across categories, so production of multiple vegetable crops is not captured in this measure.

Farms that produce major fi eld crops rarely specialize in just one—note that less than 5 percent 

of corn and soybean production occurred on farms with a single crop (that is, only corn or only 

soybeans). Two-fi fths of production was on farms with two crops (typically corn and soybeans, 

in rotations designed to maintain soil quality and limit pest infestations), while another quarter 

occurred on farms with three crops. Moreover, over a third of corn and soybean production was on 

farms that also had livestock production; on many of these farms, livestock manure is “fed” as fertilizer to crops, while the crops are sold and feed is purchased.

Table 13

Diversifi cation in U.S. crop production, 2011

Percent of 2011 value of crop production originating on farms with:

Commodity 1 crop 2 crops 3 crops 4 crops

More than 

4 crops Livestock

All crops 22 30 22 15 11 27

Barley 4 19 33 17 27 43

Canola 0 11 25 20 44 23

Corn 4 42 25 15 14 34

Cotton 12 18 22 25 23 18

Fruits/nuts 84 8 2 1 5 7

Hay 33 23 16 13 15 42

Nursery/greenhouse 87 8 3 1 1 3

Oats 1 3 24 39 33 55

Peanuts 3 16 30 22 29 20

Potatoes 17 13 20 16 34 19

Rice 30 28 23 11 8 6

Sorghum 1 19 28 24 28 51

Soybeans 3 42 23 17 15 33

Sugar beets 0 2 16 40 42 25

Sugar cane 83 7 8 2 0 8

Tobacco 30 10 18 25 17 40

Vegetables/melons 29 15 16 16 24 9

Wheat 15 19 27 20 19 35

Note: Crops are those listed in section B of the ARMS Phase III questionnaire: barley; canola; corn (grain and silage 

combined); cotton; dry edible beans/peas/lentils; fruits, nuts, and berries; hay (alfalfa and all other combined); nursery and 

greenhouse crops; oats; peanuts; potatoes; rice; sorghum (grain and silage combined); soybeans; sugar beets; sugarcane; 

tobacco; vegetables (processing and all others combined); wheat; other oilseeds; and all other crops. 

Source: 2011 Agricultural Resource Management Survey, Phase III, all versions.
35

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Substantial production of hay (33 percent) and tobacco (30 percent) occurs on farms that grow only 

those crops, but there is also a considerable amount of production on farms with four or more crops. 

Sugar cane, fruits and nuts, and nursery/greenhouse operations are highly specialized, but farms can 

and often do grow multiple fruit or nursery crops.

Contracts and Crop Production

Greater specialization can expose farms to greater fi nancial risks. Today, many farm product transactions are organized through contracts between farmers and buyers that are reached prior to harvest 

(or before the completion of a production stage, as in the case of livestock), and that govern the terms 

under which products are transferred from the farm. Contracts can reduce fi nancial risks for farmers 

by providing a more secure outlet for production and by reducing the farmer’s exposure to input and 

output price fl uctuations. Consequently, contracts may also expand the supply of credit available to 

producers by providing lenders with the assurance of outlets for farm products, thus reducing the 

risks of lending (Key, 2004). In each case, the expanded use of agricultural contracts may facilitate 

shifts to larger farm operations.

In ARMS, ERS distinguishes two types of agricultural contracts: production and marketing 

contracts. Production contracts, used primarily in livestock production but also common in vegetables and horticulture, specify services to be provided by a farmer for a contractor who owns the 

commodity while it is being produced. Contracts cover (1) specifi c services to be provided, (2) the 

manner in which the farmer is to be compensated for the services, and (3) specifi c contractor responsibilities for provision of inputs. 

Marketing contracts are far more important in crop production. They focus on the commodity as it 

is delivered to the contractor, rather than specify the services to be provided by the farmer. They set 

a commodity’s price or a mechanism for determining the price, a delivery outlet, and a quantity to 

be delivered. Forward contracts, with a specifi c price set at the time of agreement, are one type of 

marketing contract. But other types, often of longer duration, specify a method or formula for determining prices, rather than a specifi c price, at the time of agreement. The pricing mechanisms may 

limit a farmer’s exposure to the market price risks. They usually specify minimum acceptable levels 

of various product attributes, and they often specify price premiums to be paid for desired levels of 

attributes (such as oil content in corn), thereby providing incentives to produce higher cost but desirable product varieties. 

Contracts covered 40 percent of all agricultural production in 2011 and 32 percent of crop production, where their use is growing; contracts covered 28 percent of crop production in 2001-02 and 23 

percent in the mid-1990s (MacDonald and Korb, 2011). Earlier data on contract use in agriculture 

is sparse, but the Census of Agriculture reports that contracts covered 11 percent of all agricultural 

production in 1969, which suggests that there was an important shift toward greater reliance on 

contracts since then.

Does contract use affect farm size and structure? Larger crop farms are more likely to use marketing 

contracts (see also Key, 2004). Table 14 compares attributes of farms that use marketing contracts 

to those that do not, in each of eight major crop categories. Contracting farms tend to be larger: 

they operate substantially more farmland than farms that do not contract; they have higher values 

of production; and they harvest more acres of each crop. Corn, soybean, and wheat farmers who 

contract tend to place a little over half of their crop production under contract, often in multiple 
36

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

contracts with different buyers. They also combine contracting with cash sales, sales from storage, 

on-farm feeding, and fi nancial hedging in a broad risk-management strategy. Other contract 

producers commit almost all of their crop production to the contract, usually with a single buyer. 

In each commodity, farmers who use contracts for one crop also contract extensively for their other 

Table 14

Contract and noncontract U.S. fi eld crop producers, by commodity, 2011

Whole farm attributes Share under contract

Crop and contract 

status

Farmland 

acres

Value of 

production ($)

Reference 

crop acres

Reference 

crop

Other 

crops

Mean values

Corn

 Contract 911 690,148 409 53.6 40.7

 Noncontract 545 338,795 208 0.0 4.7

Cotton

 Contract 1,593 1,271,321 669 81.6 56.9

 Noncontract 1,276 812,944 413 0.0 16.3

Fruits/nuts

 Contract 170 619,268 109 96.2 31.3

 Noncontract 91 217,220 41 0.0 2.8

Peanuts

 Contract 1,509 1,037,972 240 99.0 55.8

 Noncontract 665 544,094 127 0.0 15.1

Rice

 Contract 1,587 1,234,746 593 85.7 52.6

 Noncontract 1,197 767,206 413 0.0 29.9

Soybeans

 Contract 920 652,975 380 56.3 43.1

 Noncontract 513 320,672 196 0.0 5.3

Vegetables/melons

 Contract 1,386 1,631,856 136 96.2 34.0

 Noncontract 126 209,557 24 0.0 0.0

Wheat

 Contract 468 872,068 468 59.7 45.9

 Noncontract 271 484,596 271 0.0 11.0

Note: The estimates are drawn from farms with any harvested acreage in the listed crops, with 5,109 observations for 

corn, 693 for cotton, 1,445 for fruits and nuts, 204 for peanuts, 284 for rice, 5,107 for soybeans, and 3,590 for wheat. 

The estimate for the share of other crops under contract is based on farms that produced other crops in addition to the 

reference crop.

Source: 2011 Agricultural Resource Management Survey, Phase III, all versions.
37

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

crops, while noncontract producers rely on cash markets. For large-scale producers, contracts form 

part of a portfolio of marketing strategies to manage risk.

The Location of Crop Production

As livestock and feed production separated, they became concentrated in different regions, and 

changing regional specialization played a role in changes in farm size. States in the Northeast, 

Appalachian, and Southeast regions held 18.5 percent of all cropland used for crops in 1950, and 

11.2 percent in 2007, a 7.3 percentage point decline that in part refl ected declines in cotton and 

tobacco acreage (table 15). Corn Belt and Northern Plains States increased their share of U.S. cropland from 43 percent to 50 percent. 

These long-term acreage adjustments refl ected the organizational shifts noted earlier, and also refl ect 

important regional shifts in commodity orientation: as farms became more specialized, Corn Belt 

States concentrated more heavily on the production and sale of feed crops, and livestock production moved from the Corn Belt to Mountain, Southeastern, and Southern Plains (Hart, 2003). Two 

regional belts of States illustrate the shifts (table 16). 

In 1950, crops accounted for less than a third of cash receipts from farming in the four Corn Belt 

States of Illinois, Indiana, Iowa, and Ohio. Most cash receipts came from sales of livestock fed on 

the crops grown on those farms. Corn and soybeans accounted for 63 percent of receipts from crop 

sales. Sixty years later, livestock sales were no longer dominant: crops accounted for 68 percent 

of total cash receipts, and corn and soybeans for 93 percent of crop receipts. Those four States 

Table 15

Regional shifts in cropland, 1950-2007

Item 1950 1982 2007

Total acres (millions) 383.2 382.8 334.9

Percent of U.S. cropland used for crops

Region:

Northeast 5.5 3.6 3.3

Appalachian 6.8 5.0 5.1

Southeast 6.2 3.8 2.8

Delta 4.8 5.0 4.5

Lake States 10.6 10.4 10.8

Corn Belt 21.0 22.6 24.7

Northern Plains 22.2 24.5 25.2

Southern Plains 11.4 9.6 9.2

Mountain 7.0 9.8 9.2

Pacifi c 4.5 5.7 5.2

All 100.0 100.0 100.0

Note: “Total acres” refers to cropland used for crops (and harvested, failed, or left 

fallow), and excludes cropland used only for pasture or left idle. 

Source: ERS Major Lands Uses, http://www.ers.usda.gov/Data/MajorLandUses/
38

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

accounted for a quarter of all U.S. cash receipts from U.S. livestock production in 1950, but that 

share fell by half over the next 60 years as beef and dairy production moved out of the region. Those 

changes refl ected a movement of acreage into corn and soybeans, but also an expansion of hog 

and cattle production in other regions and the rapid growth of U.S. poultry production, carried out 

largely in the South.

A near mirror shift occurred in a belt of six Southern States of Arkansas, Mississippi, Alabama, 

Georgia, South Carolina, and North Carolina (table 16). Crops accounted for 72 percent of cash 

receipts in 1950, and most of that refl ected just two crops—cotton and tobacco. By 2010, crops 

fell to 40 percent of cash receipts as cotton and tobacco declined and poultry and hog production 

expanded. Together, those States accounted for 17 percent of all U.S. livestock receipts in 2010, 

compared to 5 percent in 1950. While feed crops are grown in those States, they import much of 

their feed from the Corn Belt.

The interregional shifts in cropland affected crop farm size. Cropland declined in regions with hilly 

topography and mixed land use—cropland interspersed with forests, residences, and commercial 

uses—and shifted toward regions with fl atter land and more of the land base devoted to crops. 

The latter were more suited to the types of equipment that could allow a farmer to manage a much 

larger farm. As a result, interregional shifts in cropland supported the impacts of new technology in 

driving production to larger farms.

Drivers of Consolidation: Government Policies

Federal policies may affect farm structure through multiple channels. Some programs may increase 

consolidation, while others may reduce consolidation. Some effects are straightforward and fairly 

direct, while others are subtle and indirect.

Table 16

Shifts in agricultural specialization in selected States, 1950-2010

Item 1950 1980 2010

Percent

Crop share of region cash receipts

Corn Belt 4 29 61 68

Corn and soybean share of crop receipts 63 89 93

Southern 6 72 51 40

Cotton and tobacco share of crop receipts 71 32 18

Share of U.S. livestock cash receipts

Corn Belt 4 26 16 13

Southern 6 5 10 17

Share of U.S. crop cash receipts

Corn Belt 4 13 23 22

Southern 6 18 10 9

Note: “Corn Belt 4” is Illinois, Indiana, Iowa, and Ohio, while “Southern 6” is Alabama, Arkansas, Georgia, Mississippi, 

North Carolina, and South Carolina.

Source: ERS Farm Income database (www.ers.usda.gov/Data/FarmIncome/fi nfi dmu.htm)
39

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Tax policies can affect prices for capital goods, thereby affecting purchases of farm machinery 

and structures. Durst (2009) notes that the treatment of agriculture in the tax code, through capital 

cost recovery provisions, has effectively reduced the costs of capital to agriculture. LeBlanc and 

Hrubovcak (1986) found that changes in tax policy had important impacts on the cost of capital and 

equipment investment in agriculture. If lower tax rates lead to lower costs of capital, and if farmers 

respond by purchasing more capital equipment, then tax policy can enhance technology’s effect on 

farm size.

USDA lending programs extend credit to farmers who may not be able to obtain loans through 

commercial channels, and some programs also provide subsidized interest rates. To the extent that 

these programs either help new small farms become established or prevent existing small farms 

from closing, they can limit cropland consolidation. 

USDA research programs may also affect the availability and prices of technologies, and hence, 

farm sizes. Schmitz and Seckler (1970) analyze one well-known example, the development of the 

mechanical tomato harvester. Government provided about 40 percent of the total research and 

development expenses for the harvester, which once introduced led to the replacement of farm labor 

with capital and thereby induced a shift to larger operations. Similarly, Fernandez-Cornejo and Pho 

(2002) argue that public and private research programs to produce improved herbicides generated a 

labor-saving innovation that allowed farmers to operate larger farms with the hours saved from weed 

control.

Regulatory policies aimed at environmental or food safety targets may alter the relative costs of 

large and small farms, thereby inducing changes in farm structure. For example, Sneeringer and Key 

(2011) showed that, after the Environmental Protection Agency imposed size-based thresholds for 

regulation of concentrated animal feeding operations (CAFOs) under the Clean Water Act, newly 

constructed operations tended to enter the hog industry at sizes just under the regulatory threshold. 

During this period, scale economies in hog production were leading to much larger operations (Key 

and McBride, 2007), so the CAFO rules induced some farmers to constrain farm size, compared to 

what they would have developed absent the regulation. 

Issues related to farm structure played a role in the design of the Food Safety Modernization Act 

(FSMA) of 2011, which gives the Food and Drug Administration new enforcement authorities 

to improve compliance with risk-based food safety standards. The rules apply new standards for 

growers of fruits, vegetables, nuts, mushrooms, herbs, and sprouts. When the rules are fully implemented, they will impose higher costs on farms not already in compliance. The bill includes new 

requirements for testing and reporting that would likely have imposed higher costs, per unit of 

production, on smaller farms. The Act exempts certain farms from compliance—those that sell the 

majority of their products directly to consumers or retail outlets within 275 miles or within the same 

State, and that have annual sales of less than $500,000. 

Federal commodity programs have been a key focus of discussion among analysts of farm consolidation. The Government operates broad-based commodity programs that affect a range of fi eld 

crops, as well as several programs aimed at individual commodities. The programs’ designs create 

varied impacts on farm structure.
40

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Specifi c Commodity Programs: Marketing Quotas

In the case of peanuts and tobacco, marketing quotas regulated prices and production until the early 

2000s and appear to have limited farm size (Dohlman et al., 2009; Kirwan et., 2012). Marketing 

quotas set limits on aggregate supply by giving quota owners the exclusive right to sell a set amount 

of the commodity at or above a support price within a geographic area. Tobacco producers without 

quota could not sell the commodity, so tobacco marketing quotas also fi xed annual supply, while 

peanut producers without quota faced marketing restrictions that left them selling at lower prices to 

the export and crush markets (Dohlman et al., 2009). Because support prices were set well above 

cash expenses, quotas became valuable assets that could be exchanged.

The transfer of quota rights among quota owners was restricted. In most States, tobacco quotas could 

not be exchanged across county lines. Peanut restrictions were more complex, but transfers of quotas 

across State lines were generally forbidden, and transfers across county lines were limited. The 

programs had been in place with some adjustment since 1930 for tobacco and the 1940s for peanuts. 

Since then, production of other crops has shifted to much larger farms while also undergoing a series 

of locational adjustments. Because quotas made such adjustments more diffi cult and costly, they 

limited geographic adjustment, growth in farm size, and productivity growth. 

The peanut quota system was eliminated in 2002, and the tobacco system was eliminated in 2004. 

Each policy change featured buyouts of quota owners. Peanut production came under broad-based 

commodity program support, while all Federal support for tobacco producers was ended.

Each crop saw rapid shifts of production to larger farms and different regions after marketing 

quotas were eliminated; the shifts to larger farms were much stronger than any previous trends in 

the sectors, and much stronger than shifts in other commodities. In 2001, just before the buyout, the 

average peanut farm operated 860 acres in total, with 120 in peanuts; just 6 years later, mean farm 

size had increased to 1,525 acres, with 227 in peanuts (Dohlman et al., 2009). Average peanut yields 

per acre rose by 21 percent.24

In fl ue-cured tobacco, mean tobacco acres per farm increased from 33, at the time of the buyout in 

2004, to 84 in 2007, while mean farmland acres rose from 566 to 906. Burley tobacco farms are 

much smaller, but average burley tobacco acreage rose from 5 to 10.5 acres, and mean farm size 

rose from 191 to 247 acres (Dohlman et al., 2009). In a detailed study of Kentucky tobacco operations, Kirwan et al. (2012), using Census of Agriculture data, found that tobacco farm productivity 

increased by 44 percent between 2002 and 2007 (a period encompassing the policy change) after 

declining by 7 percent in 1997-2002. They found that average tobacco farm size doubled during the 

buyout period, and that structural change played a major role in driving the increase in productivity. 

Other factors also contributed to the changes. Many older tobacco producers anticipated the buyout 

and remained in tobacco farming to be eligible for transition payments made to active growers. As a 

result, farm closures were lower just before the policy change, and higher just after, than they would 

have been without transition payments (Dohlman et al., 2009). In peanuts, market prices and planted 

peanut acreage fell after the buyout, and those shifts had independent effects on peanut acreage per 

farm. However, in each case, the design of the quota systems suggested that they would have limited 

farm size, and the rapid shifts of production to much larger farms in the aftermath of quota elimina24The increase in mean farm size does not mean that individual tobacco farms all increased acreage; rather, many 

very small farms shut down, and tobacco acreage and production shifted toward larger farms in different locations.
41

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

tion provide strong evidence that the programs effectively did limit farm size, aside from other more 

transitory factors.

Broad-Based Commodity Programs

Broad-based USDA commodity programs direct their support primarily to producers of certain fi eld 

crops: wheat, feed grains, cotton, rice, and oilseeds, and some programs also cover pulse crops, 

honey, and wool. A redesign of the programs in the mid-1990s made direct payments (DPs) to 

farmers based upon the acreage they historically enrolled in commodity programs and the historic 

crop yields from that acreage. The programs also provide countercyclical payments (CCPs), on 

the same production basis, when market prices fall below target levels and provide for marketing 

assistance loans, based on current production, that offer support when prices are below target levels. 

Marketing assistance loans, which effectively set minimum prices that farmers can expect to receive 

for their crops, have been part of commodity programs in one form or another since the 1930s, while 

DPs and CCPs were more recent developments.25

Broadly speaking, payments tend to follow production: farms with greater production of a program 

commodity—either because they have more acreage of the commodity or higher yields on their 

acreage—receive greater total payments. There are some limits on the aggregate payments that farm 

operators can receive, but the limits have not affected many existing operations (White and Hoppe, 

2012). Despite receiving higher overall payments, larger farms do not receive systematically higher 

payments on a per-acre or per-bushel basis than small farms receive, nor do commodity payments 

represent a higher share of their gross income. Moreover, payments tend to be capitalized into land 

values, and to raise cropland rents, thus benefi ting landlords and raising renters’ costs. They do not 

provide an obvious direct impetus to increase farm size.

However, several authors have argued that commodity programs contributed indirectly to the explosion of agricultural productivity growth, and the concurrent and ongoing shifts of production to 

larger farms that followed the New Deal introduction of the programs, because of their effects on 

farm fi nancial risks, farmer behavior, and lenders. Cochrane and Ryan (1976, p. 373) argued that 

commodity programs “provided the stable prices, hence price insurance, to induce the alert and 

aggressive farmers to invest in new and improved technologies and capital items, and the reasonably 

acceptable farm incomes and asset positions to induce lenders to assume the risk of making farm 

production loans.”

Clarke (1994) offered a specifi c application of the Cochrane-Ryan hypothesis focused on the diffusion of the tractor in the 1920s and 1930s. She developed a model of the value of adopting tractors 

on farms, and argued that farms with more than 100 acres of land should have adopted the tractor 

during the 1920s. Yet many did not, and Clarke argues that the riskiness inherent in farming at the 

time prevented adoption; before the introduction of New Deal commodity programs, farm prices 

and incomes varied widely from year to year. In addition, credit markets were focused on short-term 

loans, and many farmers did not have access to credit—in more modern language, they were “creditconstrained.” Farmers, therefore, hesitated to make the long-term fi nancial commitment necessary to 

purchase durable goods like tractors.

25S. 954, the Agriculture Reform, Food, and Jobs Act of 2013 (being considered in Congress as of this writing), would 

eliminate DPs and CCPs, while retaining marketing assistance loans and expanding crop insurance.
42

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

The introduction of New Deal commodity programs, according to Clarke, reduced farmers’ fi nancial 

risks by setting fl oors below which commodity prices would not fall. New Deal lending programs 

made credit more available and also gave competition to commercial lenders, who reduced rates 

and broadened terms in response to the competition. Improved credit and reduced fi nancial risks 

led farmers to purchase capital goods and also to make cash outlays for intermediate goods like 

commercial fertilizers, prepared feeds, and hybrid seeds. With increased and more assured demand 

for capital equipment, manufacturers invested in improvements to equipment.

Clarke (1994) and Cochrane and Ryan (1976) argued that commodity programs complemented and 

accelerated the spread of labor-saving technology, by hastening the adoption of capital equipment, 

and may also have affected the types of farmers who adopted new equipment. That position is not 

undisputed; others see a far greater role for technological advances, often fueled by Government 

investment in research, in driving productivity growth and farm structure during the New Deal and 

thereafter.26 Moreover, farm commodity programs have changed considerably since the period that 

they analyze. In particular, commodity programs since the 1980s have been built around DPs to 

farmers, and income stabilization, instead of supply controls aimed at supporting commodity prices 

(Dimitri et al., 2005).

However, the historical analyses are valuable because they focus on the effects policy has on risks; 

on the responses of farmers, input providers, and lenders to programs that reduce or manage farmlevel risks; and on the consequent effects on farm structure. Modern commodity programs still 

clearly target the fi nancial risks of farming (Westcott and Price, 2001). With lower risks, farmers 

may specialize more and may make more commodity-specifi c equipment investments, which 

may in turn lead to farm expansion. DPs may also give assurance of lower risks to lenders, who 

may be more willing to lend to producers who already have a guaranteed revenue source through 

commodity payments. Changes in perceived risks may infl uence farm structure, by infl uencing the 

adoption of technology as well as farmers’ production, investment, and work decisions.

Recently, Key and Roberts (2007a; see also Key and Roberts, 2007b, and Roberts and Key, 2008), 

hereafter KR, developed an empirical analysis of modern links between farm program payments and 

consolidation. They used Census of Agriculture data to calculate midpoint acreages for cropland at 

a highly granular level—postal zip codes—for 1987, 1992, 1997, and 2002.27 They also used census 

data to calculate total Government payments per cropland acre, from commodity and conservation 

programs, for each zip code. Payments per acre vary widely across zip codes, depending on the 

composition of crops grown in the area as well as historic yields from those crops.

KR found that cropland consolidated more rapidly in those zip codes with higher levels of 

Government payments per acre at the beginning of the period. For example, during 1987-2002, 

the midpoint acreage farm size increased by 46.3 percent in zip codes with the highest levels of 

payments, compared to 23.6 percent in zip codes with the lowest levels of payments and 11.2 percent 

in zip codes with no payments.28 The association between payments per acre and the growth in 

midpoint acreages was statistically signifi cant and large. It was also robust: growth rates increased 

26See Gardner (2002), pp. 254-271 for a discussion.

27Half of cropland in the zip code is on farms that are at least as large as the midpoint, and half is on farms that are no 

larger. 

28These growth rates are measured as the natural logarithm of the ratio of beginning and end of period value, whereas 

the growth rates in fi gure 4 and the associated text are expressed as a percentage of the beginning value. The specifi c comparisons are for zip codes sorted into six classes, based on payments per acre.
43

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

consistently from one payment class to the next, and the relationship held in each time period (1987-

1992, 1992-1997, and 1997-2002, as well as 1987-2002).

As part of the same research project, KR (2007b), analyzed the linkage between payments and farm 

business survival. They focused on wheat, corn, soybean, and cash grain (diversifi ed) crop farms and 

found that farms with higher 1987 levels of Government payments per acre were more likely to still 

be in business in subsequent censuses. Surviving farms were also likely to grow faster the higher the 

1987 levels of per-acre Government payments they received. Each effect was stronger for farms that 

started out larger, with size and growth measured in acres of farmland.29

KR identifi ed a robust association between Government payments and later consolidation, but did 

higher Government payments cause faster consolidation? Three factors weigh against a causal 

interpretation: 

Confl icting evidence at the commodity level. Midpoint acreage for fruit, vegetable, and livestock commodities that are not covered by commodity programs increased during 1987-2007 at 

rates comparable to those for fi eld crops (tables 2-4). That pattern suggests that factors other than 

payments must play an important role.

Marginal association between payments and consolidation. Much of the cropland in zip codes 

with low levels of payments per acre is idle, devoted to pasture and grazing, or in land retirement 

programs. These zip codes show very little consolidation. However, in the zip codes where most 

planted cropland is (and most consolidation), once payments per acre reach a minimal level, differences in payment rates have a much weaker association with consolidation. 

Omission of alternative drivers. The highest rates of consolidation occurred in the Plains, Corn 

Belt, and Delta. They have the highest values of Government payments per acre because they have 

heavy concentrations of program crops and high yields on crops. They also have large, fl at, and 

contiguous fi elds--land best suited for the labor-saving innovations described earlier. Although KR 

included several control measures (sales per cropland acre, the fraction of land devoted to cropland, 

beginning-of-period median size, and locational controls), there is still reason to believe that the 

association between payments and growth may refl ect the greater adoption and use of labor-saving 

innovations in those regions.

KR focused on the association between Government payments and later cropland consolidation 

between 1987 and 2002. Signifi cant changes in policy and in commodity markets have occurred since 

then. In particular, as a result of higher commodity prices and Federal spending limits on commodity 

programs, commodity programs payments now account for a much smaller share of the value of crop 

production (fi g. 8). Between 1999 and 2001, Federal commodity program payments amounted to 

over 20 percent of the value of U.S. crop production. If the value of fruit and vegetable production is 

subtracted from crop production (since those commodities do not receive support), then commodity 

payments came to over 30 percent of the value of production. After 2005, commodity payments fell 

sharply, as a share of production, and by 2011, they amounted to just over 3 percent of the value of 

crop production (or 4 percent of crop production, excluding fruits and vegetables). Over the same 

period, crop insurance subsidies rose sharply and by 2011 exceeded commodity program payments.

29The analysis controlled for initial farm size as well as attributes of the farm’s organization. It also used county fi xed 

effects, so that higher levels of payments per acre, relative to the county’s average, resulted in higher growth and survival 

rates, again relative to a mean for the county.
44

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Crop Insurance

The Federal crop insurance program has become a major component of agriculture support via 

premium subsidies and support for administrative costs provided by the Federal Government. 

Federal subsidies for premiums totaled $7.15 billion in 2012, and Federal support for insurance 

company expenses were $1.38 billion, by which time the program covered 282 million acres 

(Shields, 2012).

Federal crop insurance was created in 1938 but was limited in scope until Congress expanded the 

program in 1980 to cover many more crops and regions; in 1994 and 2000, Congress expanded 

premium support through subsidies and a wider range of commodity coverage. By 2012, over 80 

percent of planted acreage of corn, cotton, soybeans, and wheat were covered by crop insurance 

(Shields, 2012; Glauber, 2013). 

The Federal Crop Insurance Reform Act of 1994 (FCIRA) led crop insurance coverage to increase 

in the mid-1990s, from about 100 million acres of cropland in 1994 to 220 million the next year. 

Enrollment fell to 180 million acres in 1997 and 1998 as some initial mandatory enrollment provisions of the 1994 legislation were dropped, but the legislation nonetheless had a major impact on 

enrollment, which expanded steadily from 1998 to 2008. 

Figure 8

Commodity program payments and crop insurance subsidies, relative to the value of crop production

Sources: USDA Economic Research Service for direct payments and value of crop production; Glauber (2013) for Federal crop 

insurance subsidies. 

Percent of value of production

0

5

10

15

20

25

30

35

1990 1993 1996 1999 2002 2005 2008 2011

Commodity payments: percent of crop 

value of production, net of fruits, tree 

nuts, and vegetables

Commodity payments: 

percent of crop value 

of production

Crop insurance subsidies: 

percent of crop value of 

production
45

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

The FCIRA provided a natural experiment for analysts interested in farmer responses to risk: 

expanded premium subsidies led to suddenly reduced production risks for farmers, which varied 

for different crops and different locations. In two papers, O’Donoghue, Key, and Roberts evaluated 

how the change affected farmer behavior (O’Donoghue et al., 2009; Key et al. 2006). Farmers can 

manage the income risks associated with farming by diversifying their sources of income. Working 

off the farm leaves a farmer’s household income less exposed to fl uctuations in farm performance. 

Likewise, producing a range of commodities leaves household income less exposed to fl uctuations in 

the performance of a single crop. Each strategy carries costs of its own. Expanded crop insurance, 

by reducing the fi nancial risks of farming, may lead farmers to reduce their other risk management 

activities.

In O’Donoghue et al. (2009), farms with expanded insurance coverage reduced their levels of 

commodity diversifi cation slightly—that is, they specialized more—as greater insurance subsidies 

became available. In Key et al. (2006), large farms reduced their hours of off-farm work after the 

1994 legislation, while small farms increased off-farm work. Because off-farm work is a substitute 

for onfarm work, one can infer that larger farm operators increased their hours devoted to farm 

work, while smaller operators reduced their farm hours.30 With more hours to apply to farming, a 

farmer will likely expand the farm. The net effect would have been an increase in farm consolidation. While the effect on commodity diversifi cation was small, the effect on labor was more substantial—up to 22 days per year for larger farms. These studies likely captured a short-run response, 

since they covered changes between 1992 and 1997, with the new legislation being passed in 1994. 

Major changes in farm size—which requires assembling land, capital equipment, and labor—may 

take longer. 

A third study (Claassen et al., 2011) modeled the combined impact of Federal disaster programs, 

crop insurance, and commodity program support on land use changes, through farmer decisions to 

convert grasslands to cultivated croplands in North and South Dakota. They found that the programs 

raised the returns to cultivation, thereby increasing cultivated cropland by 2.9 percent between 1998 

and 2007. The largest effect was from disaster assistance (1.2 percent) followed by crop insurance 

(1.0 percent) and marketing loan benefi ts from commodity programs (0.7 percent). By shifting more 

land from grassland to cropland, the programs would also increase midpoint cropland acreage by a 

similar modest amount.

Agricultural production is subject to substantial production and price risks. To manage the risks 

associated with greater specialization and substantial investments in capital equipment, many 

farmers look to institutions for support. Some strategies to manage farm risks, like contracting, 

have been developed in the private sector. Government commodity and crop insurance programs 

give additional tools for managing farm fi nancial risks. To the extent that the programs reduce 

risks facing farmers, they may encourage farmers to invest more in labor-saving innovations and to 

specialize more in agriculture and in specifi c commodities, which in turn could affect farm structure 

and help shift production and acreage to larger farms.

30The analysis relied on Census of Agriculture data, which records days worked off the farm, but does not record onfarm labor hours. Hence, the analysis focused on off-farm employment. 
46

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

The Future: Will Family Farms Still Dominate 

Crop Production?

The largest U.S. farms are now very large indeed. For example, a large cattle feedlot fi rm operates 

10 feedlots, with a combined capacity of 520,000 head; an Oregon dairy farm has a milking herd 

of 16,000 head, and also farms over 30,000 acres of cropland for forage, wheat, and vegetables; a 

Florida fi rm farms over 180,000 acres of sugar cane; and a California fi rm farms 150,000 acres, 

primarily in cotton and tomatoes, in that State and another 30,000 acres in Australia.31 Each has 

hundreds of employees and teams of professional managers; none are owned and operated by a 

single family.

These are exceptional operations, easily identifi ed through Internet searches. But there are also many 

less well-known but very large farming operations: 1,140 U.S. farms had at least 10,000 acres of 

cropland in 2011, up from 409 in 2001 (each estimate is based on ARMS data). Do the largest farms 

simply represent the size of a farm that a family can now manage, and that many more families will 

manage in the future, or do they signal a more fundamental shift toward more corporate, complex, 

and bureaucratically organized farm fi rms?

This is not a new question. Fitzgerald (2003) summarizes the enthusiasm for large, bureaucratically organized farming corporations expressed by some economists, engineers, businessmen, and 

political leaders during the 1920s. Those fi rms often operated multiple farms with hired farm operators and skilled professionals on staff; invested signifi cant amounts in machinery, structures, and 

transportation links; and developed detailed operating protocols and production plans to be applied 

across their farms. 

They were organized much like the “bonanza” wheat farms in the Red River Valley in Minnesota 

and North Dakota in the late 19th century (Allen and Lueck, 2002). Bonanza farms were integrated 

into all stages of production, from sod-busting to processing, and were highly mechanized, with the 

latest large-scale farm machinery. Owned by absentee investors, most were managed in multiple 

units, with divisions of approximately 5,000 acres as well as subdivisions of 1,200 to 1,800 acres. 

Managers received salaries and profi t-sharing commissions, manual laborers were paid a daily wage 

and worked under rigid rules, and technical specialists at the fi rms’ headquarters received salaries. 

Between 1875 and 1890, bonanza farms were generally profi table and were hailed as the future 

of agriculture. Eventually, however, the farms became unprofi table, and most had disappeared 

by the 1920s. Similarly, few of the farms touted by the subjects of Fitzgerald’s book survived the 

Great Depression. Family farms accounted for most agricultural production in the period, and they 

continue to do so in the United States today despite the major changes in farm structure and despite 

the differing organizational structures used by the very largest farm fi rms.

31The cattle feeder is Cactus Feeders (www.cactusfeeders.com); the dairy operation is Three Mile Canyon Farms 

(www.threemilecanyonfarms.com); and the sugar business is U.S. Sugar (www.ussugar.com); data on each are drawn 

from their websites, and not from USDA data. The California fi rm is the JG Boswell Company, and the information is 

taken from the 2009 Western Farm Press obituary for founder JG Boswell.
47

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Family Farms in Today’s Crop Agriculture

ERS defi nes a family farm to be one in which the principal operator, and people related to the principal operator by blood or marriage, own more than half of the farm business.32 Family farms are 

defi ned by ERS only by ownership and operation, and not by size or by labor commitments. Large 

family farms rely heavily on hired labor, rented land, and contracted services to operate their businesses. However, the defi nition makes an important point: businesses owned and operated by family 

groups—a specifi c and distinctive type of organization—continue to dominate agricultural production in the United States, even as production has shifted to much larger family businesses.

Today, 96 percent of U.S. farms with crop production are family farms, and they originate 87 

percent of the value of crop production (table 17). Non-family farms account for less than 6 percent 

of corn and soybean production; they are far more important to fruit, vegetable, and nursery production, where the 3-7 percent of farms that are non-family operations account for 30-35 percent of the 

value of production (implying that some are very large farms). But even there, family farms handle 

65-70 percent of the value of production. 

32A farm operator makes day-to-day management decisions for the farm business. A farm may have multiple operators. ARMS questionnaires ask for a principal operator, and ask whether that operator and persons related by blood or 

marriage own more than 50 percent of the farm business, where the business consists of assets owned by the farm—

including land; equipment and structures; inventories of livestock, production inputs, and harvested crops; and liquid 

assets such as cash.

Table 17

Family farms in crop production, 2011

Family farm share

Commodity U.S. farms Value of production

Percent

All crop production 96.4 86.6

Corn 97.2 94.2

Cotton 96.0 91.5

Peanuts 93.8 84.0

Rice 89.4 88.2

Soybeans 97.2 94.8

Wheat 96.7 93.5

Vegetables/melons 96.0 69.3

Fruits/nuts 92.8 65.7

Nursery 97.0 70.3

Note: Family farms are defi ned as those in which the principal operator, and people 

related to the principal operator by blood or marriage, own more than 50 percent of 

the farm business.

Source: 2011 Agricultural Resource Management Survey, Phase III, all versions.
48

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Family farms can be sole proprietorships, but they are also often organized as partnerships or corporations (table 18). Non-family farms include those operated by cooperatives, by hired managers on 

behalf of non-operator owners, by large corporations with diverse ownership, and by small groups of 

unrelated people (often in partnerships or corporations).

There is no evidence of any systematic decline in family operations: the shares of farms and value 

of crop production held by family farms show no trends since the initial ARMS in 1996. Although 

none of the four large farm fi rms noted at the beginning of this section above could be classifi ed as a 

family farm, 86 percent of farms with at least 10,000 acres of cropland are family operations. 

While there are large and complex nonfamily operations in U.S. crop agriculture, and some have 

been organized recently, there are also examples of large nonfamily corporations that have left farm 

production in recent years and moved to a coordinating role. For example, Dole Foods once operated 

extensive farming operations in California and Arizona. Today, it leases 14,000 acres in those States 

from landowners who purchased the land from Dole. Most of that land is now farmed by independent growers, most of which are family operations, under contract arrangements with Dole. Fresh 

Del Monte, a major competitor of Dole in fresh fruits and vegetables, has taken a similar approach 

on U.S. land that it once farmed and now sources primarily from contract growers.33 These more 

complex arrangements are shifting agricultural production to family operations, while leaving large 

distributors as coordinators of production and providers of inputs.

The success of family farms in agriculture arises from several factors. First, although technological 

scale economies exist in many farm production processes, they are rarely large enough or extensive enough to be beyond the scope of a family business (Deininger and Byerlee, 2012). Even in 

industrialized livestock sectors, where large corporations coordinate most hog and broiler production through their role as integrators, most production is still carried out on family operations under 

contract with the integrators. Measured scale economies in farm production, while important, 

appear to be exhausted or largely realized at still-modest sizes.34

33See the 2006-2011 annual reports for each company, in sections referring to “business” and “properties.”

34See MacDonald and McBride (2009), Key and McBride (2007) for hogs, MacDonald and Wang (2011) for broilers, 

and Mosheim and Lovell (2009) for dairy. 

Table 18

Family and nonfamily crop farms, by legal status, 2011

Family farms Nonfamily farms

Legal status Farms Production Farms Production

Percent of all family or nonfamily farms

Sole proprietorship 92.1 66.5 46.5 11.1

Partnership 3.8 16.7 24.8 31.3

Corporation 3.1 15.4 17.7 49.6

Other 1.0 1.4 11.0 8.0

100.0 100.0 100.0 100.0

Note: Production refers to the value of crop production. The legal status “other” includes estates, trusts, cooperatives, 

grazing associations, and public agencies.

Source: 2011 Agricultural Resource Management Survey, Phase III, all versions.
49

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Second, analysts widely believe that managerial diseconomies of scale set in as farms get large 

enough to have to rely extensively on hired managers and workers: the fi rm’s owners must then 

closely monitor the decisions made by hired staff (Allen and Lueck, 2002; Deininger and Byerlee, 

2012; Gardner, 2002). These diseconomies are thought to be particularly acute in agriculture, 

because of the degree of local knowledge of soils, nutrients, pests, and weather patterns required 

to effectively manage cropping and animal husbandry decisions, and because sudden changes in 

weather or in animal performance demand quick and informed reactions. 

Challenges for Family Farms

Despite the historical predominance of family farms as an organizational mode, several factors 

could undermine this tendency. First, even though families can manage much larger operations than 

in the past, the land and capital equipment requirements for these businesses may create unacceptable fi nancial risks for many families. With Corn Belt cropland selling for $7,000 an acre in 2012, 

a farmer aiming to produce 600 acres of corn and 500 of soybeans, close to the midpoint acreages 

of table 2, would need to assemble over $8 million of land, equipment, and structures. Irrigated 

California cropland was valued at $12,000 per acre in 2012; a farmer with 300 acres of irrigated 

fruits and vegetables would need to assemble over $4 million in land, equipment, and structures.35

Much of this capital may be leased or rented, but the equity and debt required for the owned assets 

may be daunting; even if feasible, most or all of a family’s wealth could be tied up in one risky 

enterprise.

The risks have become more salient in recent years, with sharp fl uctuations in global commodity 

prices for crops and for energy inputs (Baffes, 2013). While public policies give some insulation 

in the form of commodity and crop insurance programs, and private institutions in the form of 

contracting for inputs and products, these movements represent unprecedented volatility for many 

producers. As a result, operators of large family farms may show more interest in different ways 

of organizing farm production and accessing capital. They are likely to explore more complicated 

ownership and management structures for large family farms, with non-operator equity and resource 

providers, the sharing of assets and services among farms, and more complex contractual relationships with input suppliers and with processors. 

A second deterrent to would-be family farms may be created by new precision technologies that 

erode the managerial diseconomies associated with using hired managers and labor, in turn reducing 

the advantages of close-knit family operations. In livestock production, specialized confi ned feeding 

operations rely on housing with automated climate controls and sanitation equipment, strict biosecurity procedures, specifi cally formulated purchased feeds, and tightly controlled animal genetics. 

Those features may have reduced the value of localized farm and herd-specifi c knowledge and 

improved the capability for monitoring the performance of hired managers, allowing for greater reliance on hired managers, workers, and nonfamily organization (Allen and Lueck, 2002: Deininger 

and Byerlee, 2012). 

Some see such developments emerging in crop agriculture (Deininger and Byerlee, 2012; Byerlee 

et al., 2012; Boehlje and Gray, 2009). Large fi rms now routinely manage businesses with well over 

100,000 acres in South America, Ukraine, and Russia. These fi rms tend to use genetically engi35Cropland values are from Land Values: 2012 Summary (USDA National Agricultural Statistics Service), while the 

equipment and structures estimates are averages for like farms drawn from the 2011 ARMS, Phase III.
50

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

neered seeds and no-till cultivation techniques to limit the amount of management time expended 

on fi eld-level weed, pest, and nutrient management decisions. They also use the communications, 

measurement, and monitoring capabilities now incorporated in precision agriculture technologies to 

provide the detailed and localized fi eld and farm level information that was previously available only 

through persistent personal experience in fi elds. They have introduced routines, process controls, and 

standardized operating procedures into fi eld-level farm management, and they employ highly trained 

scientifi c and business staff to handle technical tasks. Their size and geographically dispersed operations provide them with opportunities to reduce costs through more intensive use of capital equipment, and they have been able to tap independent investors for fi nancing.

Such organizations remain rare in the United States, where family farms still dominate U.S. crop 

agriculture even as it has shifted to much larger farms. They will continue to do so as long as they 

are able to limit and manage the fi nancial risks associated with managing large and capital-intensive 

businesses, and as long as the strengths of family organizations—localized knowledge, quick and 

fl exible adjustments to changed circumstances, and the incentives to act on each of those—remain 

necessary to crop production. 
51

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

References

Allen, Douglas W., and Dean Lueck. 2002. The Nature of the Farm: Contracts, Risk, and 

Organization in Agriculture. Cambridge, MA: The MIT Press.

Baffes, John. 2013. Commodity Market Outlook. Development Prospects Group. The World Bank. 

January.

Bechdol, Elizabeth, Allen Gray, and Brent Gloy. 2010. “Forces Affecting Change in Crop Production 

Agriculture.” Choices: The Magazine of Food, Farm, and Resource Issues (25): 11-16.

Boehlje, Michael T., and Allen Gray. 2009. “Biological Manufacturing and Industrialization of 

Production Agriculture.” Purdue University. Department of Agricultural Economics Working Paper 

No. 0912.

Byerlee, Derek, Alex Lissitsa, and Paula Savanti. 2012. “Corporate Models of Broadacre Crop 

Farming: International Experience from Argentina and Ukraine.” Farm Policy Journal (9): 13-25.

Calvin, Linda, and Philip Martin. 2010. The U.S. Produce Industry and Labor: Facing the Future 

in a Global Economy. U.S. Department of Agriculture, Economic Research Service, ERR -106. 

November.

Clarke, Sally H. 1994. Regulation and the Revolution in United States Farm Productivity. 

Cambridge, UK: Cambridge University Press.

Claassen, Roger, Fernando Carriazo, Joseph C. Cooper, Daniel Hellerstein, and Kohei Ueda. 

2011. Grassland to Cropland Conversion in the Northern Plains: The Role of Crop Insurance, 

Commodity, and Disaster Programs. U.S. Department of Agriculture, Economic Research Service, 

ERR-120. June.

Cochrane, Willard W. 1993. The Development of American Agriculture: A Historical Analysis. 2nd 

edition. Minneapolis: University of Minnesota Press.

Cochrane, Willard W., and Mary E. Ryan. 1976. American Farm Policy, 1948-1973. Minneapolis: 

University of Minnesota Press.

Davis, Steven, and John Haltiwanger. 1991. “Gross Job Creation and Destruction: Microeconomic 

Evidence and Macroeconomic Implications.” Microeconomic Brookings Papers on Economic 

Activity: 115-200.

Deininger, K., and D. Byerlee. 2012. “The Rise of Large Farms in Land-Abundant Countries: Do 

They Have a Future?” World Development (40): 701-714.

Dimitri, Carolyn, Anne Effl and, and Neilson Conklin. 2005. The Twentieth Century Transformation 

of U.S. Agriculture and Farm Policy. U.S. Department of Agriculture, Economic Research Service, 

EIB-3. June.

Dohlman, Erik, Linda Foreman, and Michelle Da Pra. 2009. The Post-Buyout Experience: Peanut 

and Tobacco Sectors Adapt to Policy Reform. U.S. Department of Agriculture, Economic Research 

Service, EIB-60. November.
52

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Durst, Ron. 2009. Federal Farm Policies and Farm Households. U.S. Department of Agriculture. 

Economic Research Service. EIB-54. May.

Fernandez-Cornejo, Jorge, and Yvan Pho. 2002. “Induced Innovation and the Economics of 

Herbicide Use.” In Darwin C. Hall and L. Joe Moffi tt, eds., Economics of Pesticides, Sustainable 

Food Production, and Organic Food Markets. Oxford, UK: Elsevier Science.

Fernandez-Cornejo, Jorge. 2007. Off-farm Income, Technology Adoption, and Farm Economic 

Performance. U.S. Department of Agriculture, Economic Research Service, ERR-36. February.

Fernandez-Cornejo, Jorge, Richard Nehring, Craig Osteen, Seth Wechsler, Andrew Martin, Alex 

Vialou, and Arthur Grube. 2013. Pesticide Use in U.S. Agriculture, 1960-2008. U.S. Department of 

Agriculture, Economic Research Service, Forthcoming Economic Information Bulletin.

Fitzgerald, Deborah. 2003. Every Farm a Factory: the Industrial Ideal in American Agriculture. 

New Haven: Yale University Press.

Florence, P. Sargent. 1933. The Logic of Industrial Organization. London: Kegan, Paul, Trench & 

Trubner.

Gardner, Bruce L. 2002. U.S. Agriculture in the 20th Century: How It Flourished and What It Cost.

Cambridge, MA: Harvard University Press.

Gardner, Justin G., Richard F. Nehring, and Carl H. Nelson. 2009. “Genetically Modifi ed Crops and 

Household Labor Savings in US Crop Production.” Agbioforum 12 (3&4): 303-312.

Glauber, Joseph W. 2013. “The Growth of the Federal Crop Insurance Program, 1990-2011.” 

American Journal of Agricultural Economics (95): 482-488.

Hart, John Fraser. 2003. The Changing Scale of American Agriculture. Charlottesville: University of 

Virginia Press.

Holmes, Thomas J., and Songhoon Lee. 2012. “Economies of Density vs. Natural Advantage: Crop 

Choice on the Back Forty.” Review of Economics and Statistics (94): 1-19.

Hoppe, Robert, James M. MacDonald, and Penni Korb. 2010. Small Farms in the United States: 

Persistence Under Pressure. U.S. Department of Agriculture, Economic Research Service, EIB-63. 

February.

Horowitz, John, Robert Ebel, and Kohei Ueda. 2010. “No-Till” Farming Is a Growing Practice. U.S. 

Department of Agriculture, Economic Research Service, EIB-70. November.

Key, Nigel. 2004. “Agricultural Contracting and the Scale of Production.” Agricultural and Resource 

Economics Review (33): 255-271.

Key, Nigel, and William McBride. 2007. The Changing Economics of U.S. Hog Production. U.S. 

Department of Agriculture, Economic Research Service, ERR-52. December.

Key, Nigel, and Michael J. Roberts. 2007a. Commodity Payments, Farm Business Survival, 

and Farm Size Growth. U.S. Department of Agriculture, Economic Research Service, ERR-51. 

November.
53

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Key, Nigel, and Michael J. Roberts. 2007b. “Do Government Payments Infl uence Farm Size and 

Survival.” Journal of Agricultural and Resource Economics (32): 330-349.

Key, Nigel, Michael J. Roberts, and Erik O’Donoghue. 2006. “Risk and Farm Operator Labor 

Supply.” Applied Economics 38: 573-586.

Kirwan, Barret E., Shinsuke Uchida, and T. Kirk White. 2012. “Aggregate and Farm-Level 

Productivity Growth in Tobacco: Before and After the Quota Buyout.” American Journal of 

Agricultural Economics (94): 838-853.

Kislev, Yoav, and Willis Peterson. 1982. “Prices, Technology, and Farm Size.” Journal of Political 

Economy (90): 578-95. 

Larson, James A., and Leslie A. Meyer. 1996. “Supply, Demand, and Prices,” in The Cotton Industry 

in the United States. U.S. Department of Agriculture, Economic Research Service, AER-739. July.

LeBlanc, Michael, and James Hrubovcak. 1986. “The Effects of Tax Policy on Aggregate 

Agricultural Investment.” American Journal of Agricultural Economics (68): 767-777.

Low, Sarah, and Steven Vogel. 2011. Direct and Intermediated Marketing of Local Foods in the 

United States. U.S. Department of Agriculture, Economic Research Service, ERR-128. November.

Lund, Philip, and Roger Price. 1998. “The Measurement of Average Farm Size.” Journal of 

Agricultural Economics (49): 100-110.

MacDonald, James M., and Penni Korb, 2011. Agricultural Contracting Update: Contracts in 2008. 

U.S. Department of Agriculture, Economic Research Service, EIB-72. February.

MacDonald, James M., and William D. McBride. 2009. The Transformation of U.S. Livestock 

Agriculture: Scale, Effi ciency and Risks. U.S. Department of Agriculture, Economic Research 

Service, EIB-43. January.

MacDonald, James M., and Sun Ling Wang. 2011 “Foregoing Sub-therapeutic Antibiotics: the 

Impact on Broiler Grow-out Operations.” Applied Economic Perspectives and Policy (33): 79-99.

Miller, Thomas A., Gordon E. Rodenwald, and Robert G. McElroy. 1981. Economies of Size in Crop 

Farming. U.S. Department of Agriculture, Economics and Statistics Service, AER- 472. July.

Mosheim, Roberto, and Lovell, C. A. Knox. 2009. “Scale Economies and Ineffi ciency of U.S. Dairy 

Farms.” American Journal of Agricultural Economics (91): 777-794. 

National Academy of Sciences. 2010. Impact of Genetically Engineered Crops on Farm 

Sustainability in the United States. National Academies Press. Washington, DC.

Nickerson, Cynthia, Robert Ebel, Allison Borchers, and Fernando Carriazo. 2011. Major Uses of 

Land in the United States. U.S. Department of Agriculture, Economic Research Service, EIB-89. 

December.

O’Donoghue, Erik J., Robert A. Hoppe, David E. Banker, Robert Ebel, Keith Fuglie, Penni Korb, 

Michael Livingston, Cynthia Nickerson, and Carmen Sandretto. 2011. The Changing Organization 

of U.S. Farming. U.S. Department of Agriculture, Economic Research Service, EIB-88. December.
54

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

O’Donoghue, Erik, Michael J. Roberts, and Nigel Key. 2009. “Did the Federal Crop Insurance 

Reform Act Alter Farm Enterprise Diversifi cation?” Journal of Agricultural Economics (60): 

80-104.

Olmstead, Alan L., and Paul W. Rhode. 2001. “Reshaping the Landscape: The Impact and Diffusion 

of the Tractor in American Agriculture, 1910-1960”. Journal of Economic History (61): 663-698.

Rasmussen, Wayne D. 1968. “Advances in American Agriculture: The Mechanical Tomato 

Harvester as a Case Study.” Technology and Culture (9): 531-543.

Rasmussen, Wayne D. 1962. “The Impact of Technological Change on American Agriculture, 1862-

1962.” Journal of Economic History (22): pp. 578-591.

Reimund, Donn A., J. Rod Martin, and Charles V. Moore. 1981. Structural Change in Agriculture: 

The Experience for Broilers, Fed Cattle, and Processing Vegetables. U.S. Department of 

Agriculture, Economics and Statistics Service, TB-1648. April.

Roberts, Michael J., and Nigel Key. 2008. “Agricultural Payments and Land Concentration: A 

Semiparametric Spatial Regression Analysis.” American Journal of Agricultural Economics (90): 

627-643.

Scherer, F.M., and David Ross. 1990. Industrial Market Structure and Economic Performance.

Boston: Houghton Miffl in.

Schimmelpfennig, David, and Robert Ebel. 2011. On the Doorstep of the Information Age: Recent 

Adoption of Precision Agriculture. U.S. Department of Agriculture, Economic Research Service, 

EIB-80. August.

Schmitz, Andrew, and David Seckler. 1970. “Mechanized Agriculture and Social Welfare: The Case 

of the Tomato Harvester.” American Journal of Agricultural Economics (52): 569-577.

Shields, Dennis. 2012. Federal Crop Insurance: Background. Congressional Research Service. CRS 

Report to Congress. R40532. December.

Sneeringer, Stacy, and Nigel Key. 2011. “Effects of Size-Based Environmental Regulations: 

Evidence of Regulatory Avoidance.” American Journal of Agricultural Economics (93): 1189-1211.

Street, James H. 1957. “Mechanizing the Cotton Harvest.” Agricultural History (31): 12-22.

U.S. Department of Agriculture, National Agricultural Statistics Service (NASS). 2009. 

Farms, Land in Farms, and Livestock Operations 2008 Summary. February 

Westcott, Paul C., and Michael J. Price. 2001. Analysis of the U.S. Commodity Loan Program 

with Marketing Loan Provisions. U.S. Department of Agriculture. Economic Research Service. 

Agricultural Economics Report No. 801. 

Whatley, Warren C. 1983. “Labor for the Picking: The New Deal in the South.” Journal of 

Economic History (43): 905-929.

White, T. Kirk, and Robert A. Hoppe. 2012. Changing Farm Structure and the Distribution of Farm 

Payments and Crop Insurance. Economic Research Service, EIB-91. February.
55

Farm Size and the Organization of U.S. Crop Farming, ERR-152

Economic Research Service/USDA

Glossary

Acre: An area equal to 43,560 square feet or 4,840 square yards.

Cropland: Land that is suitable for or used for crops. In USDA data, cropland includes cropland 

harvested, cropland used only for pasture or grazing, cropland on which all crops failed or were 

abandoned, cropland in cultivated summer fallow, and cropland idled or used for cover crops or used 

for soil improvement but not pastured or grazed.

Family farm: In ERS analyses, any farm for which the principal operator, and persons related to the 

principal operator by blood or marriage, own more than 50 percent of the farm business. 

Farm: In USDA data, any place from which at least $1,000 worth of agricultural commodities were 

produced or sold, or normally have been sold, in a year.

Farm operator: A person who operates a farm, either doing the work or making day-to-day operating 

decisions for the farm.

Genetically engineered (GE) crop varieties: GE techniques allow a more precise alteration of a 

plant’s genetic makeup than is possible under conventional plant breeding, and permit targeting 

single genes in a plant’s DNA. Genetic engineering has led to the introduction of traits for herbicide 

tolerance (HT) in alfalfa, corn, cotton, sugar beets, and soybeans, and for resistance to certain insect 

pests (Bt) in corn, cotton, and papaya.

Harvested cropland: In USDA data, cropland from which crops were harvested and hay was cut, 

land used to grow short-rotation woody crops, orchards, citrus groves, Christmas trees, vineyards, 

nurseries, and greenhouses.

Labor-saving innovation: An innovation that reduces the amount of labor needed for any given 

amount of output. Innovations may be mechanical, material, biological or organizational. Strictly 

speaking, it is distinct from input substitution, in which other inputs are substituted for an input 

whose price rises, in an amount suffi cient to maintain output.

Mean: For a dataset, the mean is the sum of the values divided by the number of values. The mean 

size of a farm, measured by cropland, is the sum of all cropland acres divided by the number of 

farms with cropland.

Median: The numerical value separating the top half of a distribution from the bottom half—the 

50th percentile of values. For cropland, the median farm size is that value which splits the distribution of farms: half of all farms have at least that value of acres, and half have no more.

Midpoint: In this report, the midpoint acreage for cropland is that value for which half of all cropland is on farms that are at least that large, and half of all cropland is on farms that are no larger 

than that size. Technically, it is the median of the distribution of cropland by farm size.

Precision agriculture: A set of management practices and information technologies (IT), often incorporated in farm equipment, that measure and help to manage intra-fi eld variations in soil attributes, 

pest presence, product attributes, and production outcomes.

Principal farm operator: The person primarily responsible for day-to-day management decisions on 

the farm.