United States

Department of

Agriculture

Economic

Research

Service

Economic

Information

Bulletin

Number 80

August 2011 David Schimmelpfennig and Robert Ebel

On the Doorstep of the 

Information Age

Recent Adoption of Precision Agriculture
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Recommended citation format for this publication:

Schimmelpfennig, David, and Robert Ebel. On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture, EIB-80, U.S. Dept. of 

Agriculture, Economic Research Service. August 2011.
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On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

Economic Research Service / USDA

United States 

Department 

of Agriculture 

www.ers.usda.gov 

A Report from the Economic Research Service

Economic

Information

Bulletin

Number 80

August 2011

On the Doorstep of the 

Information Age

Recent Adoption of Precision Agriculture

Abstract 

The adoption of precision agriculture, which encompasses a suite of farm-level information technologies, can improve the efficiency of input use and reduce environmental 

harm from the overapplication of inputs such as fertilizers and pesticides. Still, the adoption of precision agricultural technologies and practices has been less rapid than envisioned a decade ago. Using Agricultural Resource Management Survey (ARMS) data 

collected over the past 10 years, this report examines trends in the adoption of four key 

information technologies—yield monitors, variable-rate application technologies, guidance systems, and GPS maps—in the production of major field crops. While yield monitoring is now used on over 40 percent of U.S. grain crop acres, very few producers have 

adopted GPS maps or variable-rate input application technologies.

Keywords: Information technology, precision agriculture, variable-rate technology, 

yield monitors, Global Positioning System (GPS) mapping, auto-steering, conservation 

technology 

Acknowledgments

The authors would like to thank David Bullock (University of Illinois) for his help 

through an ERS cooperative agreement and development of an information valuation 

framework; John Horowitz and Utpal Vasavada (ERS) for their guidance and commentary; and Dayton M. Lambert (University of Tennessee), William McBride (ERS), James 

Rowe (USDA Natural Resources Conservation Service), and an anonymous reviewer for 

their helpful suggestions on an earlier draft. We would also like to thank Dale Simms for 

editing the report and Susan DeGeorge for design and layout assistance.

David Schimmelpfennig, des@ers.usda.gov

Robert Ebel, rebel@ers.usda.gov
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Contents

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

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

Increasing Adoption of Yield Monitoring Technology . . . . . . . . . . . . . . . . 3

Variable-Rate Technology (VRT) Adoption Mixed . . . . . . . . . . . . . . . . . . . 5

Producers of Minor Crops Not Neglecting Precision Agriculture . . . . . . . 11

Tillage Systems and Precision Agriculture Adoption . . . . . . . . . . . . . . . . . 12

Precision Agriculture and Input Expenditures . . . . . . . . . . . . . . . . . . . . . . 16 

Precision Agriculture Adopters and Crop Yields . . . . . . . . . . . . . . . . . . . . 18

Factors Likely Affecting the Adoption of Precision Agriculture . . . . . . . . 20

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
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Summary

What Is the Issue?

Efficient input use in agriculture is increasingly a priority of producers, the 

public, and policymakers. One way to increase efficiency in agriculture 

is through the adoption of precision technologies, which use information 

gathered during field operations, from planting to harvest, to calibrate the 

application of inputs and economize on fuel use. While it holds promise for 

improving the efficiency of input use, adoption of precision agriculture—

encompassing a suite of farm-level information technologies to better target 

the application of inputs and practices—has not been as rapid as previously 

envisioned. This report examines the prevalence and effectiveness of these 

technologies based on survey response data collected over the last 10 years.

What Did the Study Find?

Adoption of the main precision information technologies—yield monitors, variable-rate applicators, and GPS maps—has been mixed among U.S. 

farmers. Recent data from the Agricultural Resource Management Survey 

(ARMS) show that use of yield monitors, often a first step in utilization of 

precision technology for grain crop producers, has grown most rapidly, being 

used on 40-45 percent of corn and soybean acres in 2005-06. However, 

farmers have mostly chosen not to complement this yield information with 

the use of detailed Global Positioning System (GPS) maps or variable-rate 

input applicators that capitalize on the detailed yield information. Some 

of the factors that could be contributing to this adoption lag include farm 

operator education, technical sophistication, and farm management acumen. 

The report is not testing the impacts of precision agriculture on other farm 

practices like conservation tillage, but some associations between the various 

factors are noted. Among the report’s findings:

•	Corn and soybean yields were significantly higher for yield monitor 

adopters than for non-adopters nationally. This yield differential for corn 

grew from 2001 to 2005. Yield monitors are being adopted more quickly 

by farmers who practice conservation tillage.

•	Corn and soybean farmers using yield monitors had lower per-acre fuel 

expenses. Average per-acre fertilizer expenses were slightly higher for 

corn farmers that adopted yield monitors, but were lower for soybean 

farmers.

•	In the Corn Belt, GPS maps and variable-rate technologies were used on 

24 and 16 percent respectively of corn in 2005, and 17 and 12 percent of 

soybean acres in 2006, but nationally the adoption rates for variable-rate 

technologies were only 12 percent for corn and 8 percent for soybeans. 

•	Average fuel expenses were lower, per acre, for farmers using variablerate technologies for corn and soybean fertilizer application, as were 

soybean fuel expenses for guidance systems adopters.

•	Adopters of GPS mapping and variable-rate fertilizer equipment had 

higher yields for both corn and soybeans. 
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•	Adoption of guidance systems, which notify farm equipment operators 

as to their exact field position, is showing a strong upward trend, with 35 

percent of wheat producers using it by 2009.

How Was the Study Conducted?

The Agricultural Resource Management Survey (ARMS) provides data on 

technology choices, input costs, and yields for a nationally representative 

sample of U.S. farms growing selected commodities. Phase II of the ARMS 

is conducted on a rotating set of commodities, and this study relies primarily 

on the 2001 and 2005 surveys of corn, 2002 and 2006 surveys of soybeans, 

and 2004 and 2009 surveys of winter wheat, with secondary emphasis on 

other crops and years. Descriptive statistics are presented at the national 

level and by production region as defined by USDA’s National Agricultural 

Statistics Service. Simple statistical difference-of-means tests are conducted 

to examine differences in input costs and yields between precision technology adopters and non-adopters.
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Introduction

Farmers and agricultural managers regularly adopt new technologies like 

hybrid seeds, as they become available. Nonetheless, they have been slower 

to adopt new information technologies, like those that contribute to precision 

farming, even though their availability has increased over the last several 

decades (Whipker and Akridge, 2006-09 annual service dealers’ survey). 

Precision agriculture—a suite of information technologies used as management tools in agricultural production—has recently become more accessible 

to farmers, and the use of some of these technologies has become more 

prevalent, but far from universal. In 1997, the National Research Council 

discussed the potential impact of widespread adoption of information technologies on farm structure, rural employment, and environmental quality. 

However, 8 years later, survey data showed that the most widespread information technology, yield monitoring, was being used on less than half of 

corn, soybean, and winter wheat acres nationally, with complementary technologies much less prevalent. 

Fertilizer application rates were once predicated on applying enough nutrients to match the highest requirements of a crop in any part of a specific 

field. This kind of uniform nutrient management, an “optimal risk aversion 

strategy,” has become less common with the advent of precision agricultural technologies (Whelan and McBratney, 2000) and less affordable with 

higher fertilizer prices. The prices of all three main agricultural nutrients—

nitrogen, phosphate, and potash (or potassium)—have risen dramatically in 

the last decade (Huang, 2009). Despite the price increases, fertilizer demand 

increased over this period as farmers aimed to exploit the full potential of 

high-yielding seed varieties during a period of relatively high crop prices. 

In 2006, $119.2 billion was spent on agricultural fertilizer worldwide, with 

62 percent of this on nitrogen, 22 percent on phosphate, and 11 percent on 

potassium. With variable-rate technology (VRT), applicators can apply 

seeds, fertilizer, and pesticides to suit different sections of a field depending 

on soil conditions, nutrient needs, and the severity of pest problems, thereby 

economizing on inputs without sacrificing yield.

In addition to higher costs, there are other pitfalls to overuse of fertilizer. 

Excessive or poorly timed fertilizer application can contribute to nutrient 

runoff from farms into wells, waterways, wetlands, and estuaries (Carpenter 

et al., 1998). And when rainfall increases, typically in the spring, nutrient 

delivery to the Gulf of Mexico can enlarge the size of the hypoxic “dead 

zone” at the mouth of the Mississippi River. In 2009, the delivery of nutrients to the Gulf was 11 percent above the 1979-2009 average and among 

the highest on record (U.S. Department of the Interior, 2009). A large share 

of this pollution may come from agricultural runoff (Goolsby et al., 2001; 

Goolsby et al., 1999). Nitrogen, when overapplied and not incorporated into 

the soil, can oxidize (into N2O) and vaporize into greenhouse gas (GHG). 

The Intergovernmental Panel on Climate Change (2007) included reduced 

N2O emissions through improved agricultural fertilizer application techniques as a key GHG mitigation practice.

This report documents the level of adoption of various precision agriculture 

practices over the last 10 years in the United States. This approach enables 
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us to determine where the adoption of these practices and technologies has 

stalled and offer a few observations on the patchy adoption across different 

growing regions and on various crops. The concluding section discusses 

factors related to pollution and yields that could increase the adoption of 

precision agriculture in the future. 

Data 

The Agricultural Resource Management Survey (ARMS) has been conducted 

every year since 1996 by the Economic Research Service (ERS) and USDA's 

National Agricultural Statistics Service (NASS) (see http://www.ers.

usda.gov/Briefing/ARMS/). The ARMS collects crucial data on resources 

required for agricultural production—including seeds, fertilizer, pesticides, 

machinery, labor, and the use of information technologies, along with yields 

obtained (Phase II questions)—and expenditures on inputs, as well as farm 

household characteristics, such as farm capitalization levels, leveraging, and 

sources of off-farm income (Phase III questions). While the number of crops 

surveyed for specific production practices (Phase II questions) varies each 

year, individual crops are surveyed only every fourth or fifth year, due to 

respondent burden and budget constraints. Corn was most recently surveyed 

in 2001 and 2005 (the 2010 corn survey results were not yet available when 

this report was prepared), soybeans in 2002 and 2006, and winter wheat in 

2004 and 2009. The number of States covered by the surveys depends on the 

crop, though all major producing States and 90 percent of crop acreage are 

represented. In this report, we compare the mean values of different groups 

of farms and technologies and use difference-of-means statistical tests for 

all cost estimates, as well as yields, to determine if the differences observed 

are statistically significant at a 90-percent or higher level of confidence. 

Individual observations with higher-than-normal variability are denoted in 

the tables and charts with a # symbol, indicating that their coefficient of variation is greater than 0.25.
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Increasing Adoption of Yield Monitoring 

Technology

Yield monitoring is the most popular component of precision agriculture, 

with soybean producers reporting its use on 45 percent of acreage in 2006, 

corn producers on 42 percent of acres in 2005, and winter wheat growers on 

35 percent of acreage in 2009 (fig. 1). In the Corn Belt1, yield monitor use 

grew from 28 percent of corn acreage in 2001 to 44 percent in 2005, while 

the Lake States and Northern Plains2 had similarly high corn acreage under 

yield monitoring by 2005 (table 1). Soybean producers in these regions 

increased their use of yield monitors from 30 percent of acreage or less in 

2002 to almost 50 percent by 2006. 

Recently, yield monitors have allowed farmers to use Global Positioning 

System (GPS) maps to pinpoint yield variation within their fields (Gebbers 

and Adamchuck, 2010). Yield monitoring is sometimes used without the 

1Corn Belt States are Illinois, Indiana, 

Iowa, Missouri, and Ohio.

2The Lake States are Michigan, Minnesota, and Wisconsin; the Northern 

Plains include Kansas, Nebraska, North 

Dakota, and South Dakota.

J J

J

J

J

J

B B

B B

B

B

B

H H

H H

H

H

0

5

10

15

20

25

30

35

40

45

50

J Corn

B Soybeans

H Winter wheat

% of planted acres

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

Figure 1

Yield monitor adoption (U.S., by crop), 1996-2009

1996 98 2000 02 04 06 08

Table 1

Yield monitor adoption for corn and soybeans by region, 1996-2006

1996 1997 1998 1999 2000 2001 2002 2005 2006

------------------------------------------Percent of planted acres------------------------------------------

Corn

Corn Belt 21 18 28 34 28 44

Lake States 10 19 18 29 17 39

Northern Plains 15 24 19 28 27 43

Soybeans

Corn Belt 13 15 21 23 28 24 49

Lake States 16# 11 16 15 30 29 48

Northern Plains 9# 17 21 22 17 48

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

# indicates coefficient of variation greater than 0.25. 
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Geographic Information System (GIS)3 capability to store plot-level information. This strategy enables the producer to roughly monitor crop moisture 

(Griffin and Lowenberg-DeBoer, 2005), much as crop-moisture indicators 

on combines have been used for decades to minimize the cost of drying 

harvested grain. Although weather generally determines moisture levels over 

a wide area, precision technology utilizing GIS could allow farmers to see 

if plot-level yields would benefit from a change in management practices 

within a single field. 

3Geographic Information System (GIS) 

refers here to enhanced field mapping 

with GPS that allows the overlaying of 

multiple layers of data for all subsections of a field in a database. In this 

way, multiple characteristics of one 

location can be determined and the data 

used for multiple purposes.
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Variable-Rate Technology (VRT) 

Adoption Mixed

Variable-rate technology allows producers to make use of factors that influence yields by adapting their practices “on the fly.” (The sequential adoption 

of precision agriculture technologies is discussed in the box, “Three-Step 

Approach to...”). VRTs are seeders, sprayers, and other fertilizer and pesticide application equipment that can be continually adjusted during field 

operations to optimize the application of inputs depending on field conditions. VRT use for corn nationwide (12 percent in 2005) has been consistently above VRT use for soybeans (8 percent in 2006), although adoption 

rates are low and fairly flat for both crops. VRT use for winter wheat was at 

14 percent in 2009 and is steadily increasing (fig. 2). 

Guidance systems for field equipment make use of GPS readings to alert 

equipment operators as to their field position coordinates. GPS can improve 

the accuracy of variable-rate applicators and help operators reduce the incidence of overlapping or missed sections in their field operations. Guidance 

systems have recently been adopted on between 15 and 35 percent of nationally planted acres for corn, soybeans, and winter wheat (fig. 3). GPS adoption 

was highest for corn in the Corn Belt in 2005 (table 2), which is also true for 

VRT (table 3).4 GPS use for soybeans is highest in the Corn Belt and Lake 

States, at 17 and 12 percent, respectively, in 2006, but adoption rates for both 

GPS and VRT among soybean growers have been erratic at best. Guidance 

systems are being used more heavily than VRT across regions (table 3). 

Variable-rate technology has been commercially available for more than a 

decade, but commercial crop use—at 8 to 14 percent over 2005-09—is far 

below that projected by farm dealerships (Whipker and Akridge, 2006). 

The uncertain profitability of variable-rate technology has discouraged its 

adoption among some farmers. For example, the GPS mapping of crop 

yield response to managed inputs, field topography/soil characteristics, and 

4Extension agents report that some 

producers have manually applied 

fertilizer using VRT sprayers without 

GPS, relying on personal knowledge of 

field conditions to estimate appropriate 

application rates. Some producers characterized GPS maps as “too troublesome,” but continued to use the VRT 

sprayers that they had already paid for.

J

J J

J

J

B

B

B

B

B

H

H

H

H

0

2

4

6

8

10

12

14

16

J Corn

B Soybeans

H Winter wheat

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

1996 98 2000 02 04 06 08

% of planted acres

Figure 2

Variable rate technology adoption (U.S., by crop), 1998-2009
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weather is regarded by many as too difficult, and thus profitability is difficult 

to gauge under a wide range of circumstances (Bullock et al., 2002). Bullock 

et al. (2009) hypothesize that information and VRT are economic complements—more of either increases the marginal productivity of the other, 

shifting out its demand curve. 

The ARMS also collects data on the adoption of soil mapping for precision 

agriculture applications. The use of these GPS maps increased in the late 

1990s for both corn and soybeans, before declining to about 15 percent in 

2005/6 (fig. 4). Extension agents in the Corn Belt indicated that they were not 

surprised by this dropoff as some farmers had characterized the maps as troublesome (personal communications, 2009, Iowa State University, Keokuk 

J

J

B

B

H

H

0

5

10

15

20

25

30

35

40

J Corn

B Soybeans

H Winter wheat

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

1996 98 2000 02 04 06 08

% of planted acres

Figure 3

Guidance system adoption (U.S., by crop), 1996-2009

J

J

J J

J

B

B

B

B

B

H

H

H

H

0

5

10

15

20

25

J Corn

B Soybeans

H Winter wheat

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

1996 98 2000 02 04 06 08

% of planted acres

Figure 4

Global positioning system mapping adoption (U.S., by crop), 1998-2005
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Table 3

Variable-rate technology (VRT) and guidance system adoption for corn 

and soybeans by region, 1998-2006

1998 1999 2000 2001 2002 2005 2006

--------------------Percent of planted acres--------------------

VRT

Corn

Corn Belt 19 23 32 31 24

Lake States 8 7 15 16 7#

Northern Plains 9# 9# 10 12 5#

Soybeans

Corn Belt 16 21 24 10 17

Lake States 11# 6# 15 11# 12

Northern Plains 9# 9# 6 6# 9

Guidance system

Corn

 Corn Belt 6.9 13.2

 Lake States 3.9# 9.3

 Northern Plains 3.1# 23.3

Soybeans

 Corn Belt 3.6 18.4

 Lake States 4.1# 16.6

 Northern Plains 9.8 30

# indicates coefficient of variation greater than 0.25.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS. 

Table 2

Global Positioning System (GPS) adoption for corn and soybeans 

by region, 1996-2002

1996 1997 1998 1999 2000 2001 2002

--------------------Percent of planted acres--------------------

Corn

Corn Belt 19 23 32 31 24

Lake States 8 7 15 16 7#

Northern Plains 9# 9# 10 12 5#

Soybeans

Corn Belt 16 21 24 10 17

Lake States 11# 6# 15 11# 12

Northern Plains 9# 9# 6 6# 9

# indicates coefficient of variation greater than 0.25 .

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.
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County Extension, Sigourney, Iowa; and University of Illinois Extension, 

Crop Sciences Research and Education Center, Urbana, Illinois).

There is a logical sequence, starting with yield monitoring, for the adoption of precision agriculture technologies (see box, “Three-Step Approach 

to Adoption of Precision Agriculture Practices”). To test for this sequential 

adoption, both corn and soybean farmers in ARMS were split according 

Step One—Yield Monitors: Collecting Yield 

Information

Data on crop yields can come from yield monitors, which 

have recently started to become standard equipment on 

some models of farm machinery, especially combines. On 

later models of farm tractors and combines, yield monitors 

can usually be retrofitted at reasonable cost. While farmers 

perform their harvesting routines, the monitor collects data 

on bushels per acre or other yield measures and stores the 

information electronically by location on a digital storage 

disk, often using pre-installed Geographic Information 

System (GIS) computer software. GIS software, like the 

most widely used ArcInfo, allows different kinds of information on yield response to be stored and retrieved easily 

on plots throughout a farmer’s field. GIS software tools like 

ESRI (Environmental Systems Research International) are 

available on the ArcInfo platform through commercial vendors. Another package is MapInfo, which recently became 

part of Pitney Bowes Business Insight Software products.

Step Two—Soil Maps: Collecting Information on 

Field Characteristics

Another step in the use of information for site-specific 

farming is creating a soil map of each farmed field. Some 

soils data are available from the USDA Natural Resources 

Conservation Service’s National Cartography and Geospatial Center, which has archived a Soil Survey Geographic 

database (soildatamart.nrcs.usda.gov/). The soils data 

display the location of features too small to delineate on 

geological survey maps, though still significant enough to 

influence input use and management. The soil map units 

are linked to attributes in the National Soil Information 

System database, which gives the proportionate extent of 

the component soils and their properties. This information is in the form of soil boundaries and could be augmented with pay-for-services onsite sampling, testing, 

and detailed study of specific sites for intensive cropping uses. Additional information may also be obtained 

through field tests on soil organic matter and closely related pH levels, factors that influence nutrient availability. 

Bullock and Bullock (2000) found that measures of topology, in addition to soil characteristics, were significant 

in explaining yields. Specific elevation features they used 

were the compound topographic index (CTI), specific 

catchment area (SCA), and stream power index (SPI). 

These variables capture the fact that on long stretches of 

gently sloping land, the transition from one soil type to another is likely to occur gradually over many feet. Where 

land shifts abruptly from steep to level and vice versa, 

the transition zone might be quite narrow. Both soil and 

topography are likely to influence how moisture and nutrients move through the soil. This in turn is likely to influence optimal nutrient application rates that might vary 

from site-to-site within any individual field. For instance, 

farmers often ponder after a wet spring how much nitrogen has been lost from different sections of their fields.

USDA created a Geospatial Data Gateway (datagateway.

nrcs.usda.gov/, accessed 3/31/2011) to house soils and topography data. The site is especially useful if downloading 

large sets of data from the Web is not feasible for the user. 

A CD or DVD containing only the user’s required data can 

be ordered from the website for a nominal fee. Soil survey 

data, spatial and tabular, are available along with elevation 

data at 10- and 30-meter resolutions. The site also provides 

access to 8- and 12-digit watershed boundary data, land use/

land cover and cropland data layers, general soil maps (from 

STATSGO), and temperature and precipitation averages.

The Agricultural Resource Management Survey (ARMS) 

collects data on the adoption of soil mapping for precision agriculture applications. The development and use 

since 1996 of Global Positioning System (GPS) maps for 

monitoring site-by-site soil properties within a farmer’s 

fields are shown in figure A. The use of these maps increased in the late 1990s for both corn and soybeans, then 

declined to about 15 percent in 2004/5. Extension agents 

in the Corn Belt indicated that they were not surprised by 

this dropoff as some farmers had characterized the maps 

as troublesome (personal communications, 2009, Iowa 

State University, Keokuk County Extension, Sigourney, 

Three-Step Approach to Adoption of Precision Agriculture Practices
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to whether they had used yield monitoring or not. Both corn and soybean 

producers who had not adopted yield monitoring also had much lower rates 

of GPS mapping, particularly since 2002 when their adoption percentages 

were under 5 percent (fig. 5), whereas producers with yield monitors used 

GPS mapping on over 25 percent of acres. The same is true of VRT adoption percentages when survey respondents are split into GPS users and nonusers (fig. 6). VRT and guidance system (GSYS) adoption for both corn and 

Iowa; and University of Illinois Extension, Crop Sciences Research and Education Center, Urbana, Illinois).

There is some evidence that this situation might be 

changing. GPS capability has begun to be offered as standard equipment on some farm machinery. Deere & Co. offers the GreenStarTM Lightbar GPS device, which aids in 

determining row width and location. Wider availability of 

GPS could combine with more detailed mapping to increase 

the value of GPS to producers. Higher resolution topological maps of individual farms are not common at present, 

but some of these maps have been created using Real Time 

Kinematic (RTK) satellite systems. Four-wheeled trailers 

can be mounted with an RTK-GPS device and drawn 

through a field during any other whole-field farming operation. RTK is a processor that makes GPS signal corrections 

that are transmitted to a satellite and subsequently stored 

in real time. The readings that are recorded are based on 

carrier-phase measurements from the GPS where a single 

reference station provides real-time topology corrections, 

providing accuracy up to 1 centimeter of field elevations. 

While not simple to create, digital terrain maps do not 

change much over time, and their use has been shown to 

improve crop yields on a few test farms in Illinois (Bullock 

et al., 2009). Three non-satellite-based soil sensing systems are available commercially. These are electrical 

or electromagnetic sensors of soil conductivity, optical 

spectra sensors of visible or near-infrared light (both useful 

for determining nitrogen deficiency), and electrochemical sensors of hydrogen, potassium, and nitrate levels. 

Step Three—Variable-Rate Technology (VRT): 

Putting Yield and Soil Information Together

Investments in field equipment and machinery that enable continuous changes in seeding, fertilizer, and pesticide application rates can be profitable when combined 

with adequate information on soils and yield history. The 

farmer or custom services operator aspires to a profit-maximizing level of inputs on each section of a field, given 

the physical characteristics apparent from a GPS map or 

other management strategy. Custom service operators in 

particular have recently made greater use of GPS guidance 

systems; 56 percent used a GPS guidance system with auto 

control/auto steer for at least some of their work in 2009, 

up from 28 percent in 2008 (Whipker and Akridge, 2009). 

This three-step information technology adoption process 

presumes that yield monitoring is largely adopted by corn 

and soybean producers prior to their embrace of complementary technologies like GPS soil mapping and variablerate input application. Yield monitors (step 1) are available 

as standard equipment on some tractors and combines and 

can be used with little computer programming if the data 

are not stored and combined with other information. Once a 

GPS soil properties map is created (step 2), the infrastructure 

is in place with which to store and retrieve detailed information on specific sites in a farmer’s field. The use of VRT 

(step 3) physically capitalizes on the antecedent technologies. There is, however, no reason why this logical sequence 

has to apply, and it is possible that VRT and GPS mapping could be adopted independently of yield monitoring.1

Bullock et al. (1998) posit a direct link between profitability and how widely precision agriculture’s information 

tools are implemented. If VRT and information on field 

characteristics are complements, VRT might only become 

profitable when enough information is available in an individual producer’s precision agricultural system to make it 

that way. VRT may not be profitable in situations where all 

the farmer gets out of a capital investment in the information technology are recommendations to apply more fertilizer than normal to one area and less to another area in their 

field. The question becomes not so much whether the information technologies are profitable in general, but rather if 

they are profitable when used intensively. In many cases, 

limited adoption of precision information technologies 

is unprofitable because of their substantial startup costs.

1A GPS map could be created based on satellite data for topography and water movement or catchment areas on a parcel of 

land, with that information used to support VRT applications. It 

might also be possible to perform soil tests on subplots in a field 

and create a GPS map with this information, but these tests are 

usually expensive and time consuming as samples must be sent 

to a lab.
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On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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soybean producers nationally were higher for those that used GPS mapping. 

These adoption gaps indicate that precision information technologies are 

adopted sequentially, with yield monitoring the basis of GPS and VRT use. 

Basic soil testing may serve as an entry point for some farmers, who adopt 

GPS maps in order to store information on soil characteristics. The recent 

surge in GSYS adoption might also have been a first step into information 

technology adoption for some corn and soybean producers. 

0

5

10

15

20

25

30

35

40

45

50

% of planted acres

Figure 6

Comparison of VRT and GSYS use among GPS adopters and nonadopters 

*indicates statistically significant difference of ratios, at 10% levels.

GSYS refers to guidance system. VRT= variable-rate technology. GPS = Global Positioning System. 

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

2001 2005 2002 2006 2001 2005 2002 2006

Soybeans*

VRT GSYS

Corn* Corn* Soybeans*

Adopters-GPS

Nonadopters-GPS

corn* soybeans* soybeans*

GPS VRT GSYS

corn* corn* soybeans*

Adopters-YM

Nonadopters-YM

0

5

10

15

20

25

30

35

40

45

50

% of planted acres

Figure 5

Comparison of GPS/VRT/GSYS use among yield monitor adopters 

and nonadopters 

*indicates statistically significant difference of ratios, at 10% levels.

GPS = Global Positioning System. VRT= variable-rate technology. GSYS refers to guidance system.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

2001 2005 2002 2006 2001 2005 2002 2006 2001 2005 2002 2006
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Producers of Minor Crops Not Neglecting 

Precision Agriculture

Despite the more apparent fit of precision agriculture to large-scale crop 

production, information technologies have been applied to a wide range of 

agricultural businesses, including orchards, pasture, turf management, and 

livestock production (Gebbers and Adamchuk, 2010). Among minor crops, 

yield monitoring is more prevalent than GPS or variable-rate technology for 

barley, sorghum, and rice (fig. 7). Earlier data for other minor crops (AREI, 

2006, table 4.7.3) show that sugarbeets (2000) had 29 percent GPS adoption and 15 percent VRT adoption. Potatoes (1999) had 10 percent yield 

monitoring, 19 percent GPS, and 18 percent VRT. Sunflowers (1999) had 17 

percent yield monitoring, 4 percent GPS, and 3 percent VRT. Sugarbeets and 

potatoes are high input-cost and high-value crops (Patterson, 2009), so VRT 

is likely profitable for them under a wider range of circumstances than for 

some field crops and sunflowers. 

0

5

10

15

20

25

30

35

% of planted acres

Figure 7

Precision agriculture use on minor crops (U.S. by crop)

GPS = Global Positioning System. VRT= variable-rate technology.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

GPS device used 

to create soil 

properties map

Yield monitor 

used

Guidance system 

used

VRT for 

any purpose

2003 barley

2003 sorghum

2006 rice
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On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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Tillage Systems and Precision 

Agriculture Adoption

The adoption of information technologies is likely to be influenced by a 

number of regional factors (see box, “Why We Might Expect Regional 

Differences in the Adoption of Precision Technologies”). Factors that influence the adoption of reduced tillage systems are probably similar to those 

influencing the adoption of precision agriculture technologies; both sets of 

technologies have similar positive impacts on production and the environment while reducing costs. This section considers whether specific tillage 

practices and information technologies are being used together in certain 

circumstances. 

No-till and mulch till are conservation tillage practices that leave at least 30 

percent of the soil covered by crop residues after planting, while reduced-till 

leaves 15-30 percent and conventional tillage leaves less than 15 percent of 

the soil covered (Horowitz et al., 2010). Reducing soil erosion and mitigating 

the impact of higher energy prices are both motivations for reducing tillage. 

Switching from conventional or chisel plowing to no-till planting reduces 

soil erosion and requires less fuel (Zinser et al., 1985). Reduced tillage can 

protect soil from erosion because, in contrast to chisel plowing, it leaves crop 

residues relatively undisturbed on the soil surface. Lower machinery-related 

costs related to the number of trips required through a field with reduced 

tillage are offset by higher pesticide costs and potentially higher fertilizer 

costs. No-till also produced slightly lower corn and soybean yields in some 

field tests, and thus slightly lower returns to land and farm management 

(Klemme, 1985, tables 1 and 2). 

Studies on tillage intensity note that, as with information technologies, 

managerial skills, size of operation, years of additional schooling, and use 

of extension services increased the adoption of conservation tillage systems 

(Rahm and Huffman, 1984). Prokopy et al. (2008), in reviewing 25 years of 

studies on the adoption of farm-level best management practices, identify 

the most influential farm characteristics as levels of farm capital and income, 

access to information, and utilization of social networks. 

The use of no-till in U.S. corn production has been increasing since 1998, 

and conventional tillage has declined since 1999 (fig. 8). Herbicide-tolerant 

(HT) crops became more popular over this period, as these crops allow the 

application of glyphosate directly over post-emergent plants to control a 

larger weed problem often associated with no-till. To explore the relationship between tillage systems and the use of information technologies, we 

use the ARMS data on joint adoption of information technologies and tillage 

practices in U.S. corn and soybean production. Yield monitor use is highest 

on no-till soybean acres (50 percent in 2006, table 4). Use of GPS soil maps 

on both conventional and no-till acres for corn and no-till soybeans dropped 

between ARMS surveys (2000-01 to 2002-05) (table 4). The national averages for VRT adoption by corn and soybean producers stalled at 6-9 percent 

of no-till corn and soybean acres between 2002 and 2005 (table 4). We 

suspect that those farmers tending toward early adoption of technology had 

already adopted VRT by 2005. Conventional and no-till corn producers 

changed their VRT acres very little between 2000 and 2005.
13

On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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Table 4

Precision agriculture adoption by tillage system

1996 1997 1998 1999 2000 2001 2002 2005 2006

-----------------------------------------Percent of planted acres-----------------------------------------

Yield monitors

 Conventional corn 11 13 20 22 16 33

 No-till corn 21 23 27 26 27 43

 Conventional soy 10# 5 7# 14 17 18 21

 No-till soy 18 13 22 21 26 26 50

GPS

 Conventional corn 11 13 17 19 14

 No-till corn 11 17 19 24 11

 Conventional soy 8# 16 10 10

 No-till soy 12 12 23 9

VRT

 Conventional corn 8 8 11 9 10

 No-till corn 7# 13# 10 10 9

 Conventional soy 8# 3# 3#

 No-till soy 6 8 9 6

# indicates coefficient of variation greater than 0.25.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS. 

0

5

10

15

20

25

30

35

40

Mulch till

No-till

Reduced till

Conventional

% of planted acres

Figure 8

Tillage systems (U.S. corn), 1996-2005

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

1996 97 98 99 2000 01 05
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On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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Adoption rates for yield monitors and variable-rate technology differ across 

regions. This finding is consistent with earlier research on adoption rates 

for hybrid corn technology, which despite nearly universal use now, varied 

by State in its adoption timetable. Hybrid corn was adopted first in Iowa in 

1933 and more slowly in Alabama starting in about 1943. The curves used 

by Griliches (1957) have become the standard technology diffusion and 

adoption curves for both agricultural and non-agricultural technologies (fig. 

A). The curves are flatter early in the adoption dissemination period and 

have a steep middle section when technology adoption is quickest. 

Skinner and Staiger (2005) extend Griliches’ study to modern technologies including computers in the 1990s and beta-blockers for heart attacks and provide data on the adoption of both of these advances up to 

2000. They find that while profitability influences early adopters, a different set of factors explains why some regions are slow to adopt even 

“highly effective technologies.” The factors that slow adoption in some regions are related to social capital and the strength of information networks. 

For farm information technologies, Fernandez-Cornejo et al. (2001) find that 

farm operators who study beyond high school have a 15-percent greater likelihood of adopting precision technologies. Griffin et al. (2004) consider the 

adoption of precision agriculture to be “human capital intensive” (p. 11). These 

observations could be related to an element of technology adoption that has 

been noted in other agricultural settings: learning-by-doing. This insight offered by Arrow (1962) and applied by Luh and Stefanou (1993) to agricultural productivity, explains technology adoption as a process that takes time 

for new users to master. Farm operators with more education likely learn 

more quickly, and education levels likely vary across the United States. 

Why We Might Expect Regional Differences in the 

Adoption of Precision Technologies

Figure A

Copy of figure from Griliches (1957)
15

On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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Other information technologies are subject to both education and regional 

adoption factors. Stenberg et al. (2009) find that college education and age 

of farm operator are significant in explaining adoption of Internet-based technology on farms. They also find that broadband adoption on farms is most dense 

in the Corn Belt and portions of the other Lake States and Northern Plains, 

where the use of yield monitors for corn and soybean production is highest. 

Paxton et al. (2010) arrived at similar findings for the adoption of precision agricultural practices in cotton production. Younger, better educated 

cotton producers and those that that used computers for management decisions were significantly correlated with the number of precision technologies adopted. Table A combines reported use of yield monitoring, VRT, 

GPS, and auto-guidance from the ARMS survey and shows how regional 

adoption varies for these precision agriculture practices in cotton production. Use of these technologies is higher than the national average in the 

Delta States, but is lower in the Southern Plains, Southeast, and Appalachia. 

Just et al. (1980), in considering whether farm size matters in technology adoption, find that uncertainty and fixed investment/information costs place a lower limit on farm sizes that may be able to adopt. As 

these fixed costs increase, the size of the farm that will adopt increases 

(Fernandez-Cornejo et al., 2007). And as uncertainty increases, farm size, 

learning, and fixed costs play a larger role in rates of technology adoption (Feder and Mara, 1981); the more uncertain the technology, the 

more likely that only larger farms able to spread the fixed cost will adopt. 

Aversion to risk has been shown to have a large negative impact on the adoption 

of information technologies, contributing more than all other factors combined 

(Fernandez-Cornejo, Daberkow and McBride, 2001, Table 2). El-Osta and 

Mishra (2001) found similar positive empirical results for farm size, which varies 

across the country, and level of education on the adoption of precision technologies, but did not find a profitability benefit of VRT in their data that pre-dates 

the latest ARMS results presented here. Land tenure and borrowing capacity 

also vary by crop and region and have been linked to adoption of other farm technologies through fixed, initial investment costs (El-Osta and Morehart, 1999).

Table A

Precision technology adoption for cotton (by region)

2003 2007

National 19.5 27.6

Southern Plains 17.7 23.7

Delta 28 46.3

Southeast 12.4 19

Appalachia 16.8 20.5

Source: Agricultural Resource Management Survey, USDA, ERS/NASS. 
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Precision Agriculture and Input 

Expenditures 

Fuel and fertilizer expenses might be expected to decline with adoption of 

precision technologies. GPS mapping and guidance systems can reduce the 

need for overspraying by precisely defining the borders of previously sprayed 

areas. Yield monitoring can help farmers identify areas in their fields where 

changes in practices might be beneficial, while variable-rate technology can 

put that knowledge to practical effect. ARMS data show that fuel expenses 

incurred, per acre, by both corn and soybean farmers were lower for those 

who used a yield monitor (fig. 9). Custom service expenses were higher per 

acre for 2001 corn and 2002 and 2006 soybeans, indicating that some additional custom operations were likely used when yield monitors were adopted 

(fig. 9). Average fuel expenses were lower per acre for farmers using VRT 

for corn and soybean fertilizer application (fig. 10). Adopters of guidance 

systems had significantly lower fuel expenses per acre for 2006 soybeans 

(not shown).

To understand the avenues by which VRT and GPS mapping might be 

adopted by producers, we investigate how often they might be obtaining 

these services from outside providers. Guidance system adopters might also 

be influenced by the availability of support services by service providers. It 

is possible that the reason that there are no significant differences between 

fertilizer expenses incurred by corn producers using VRT (fig. 10) and guidance systems (not shown, magnitudes are similar) is that fertilizer application 

itself is reduced, with the cost savings paying for custom application. 

0

10

20

30

40

50

60

70

$/acre

Figure 9

Comparison of expenses among yield monitor adopters 

and nonadopters

*indicates statistically significant difference of ratios, at 10% levels.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

2001* 2005* 2002* 2006* 2001* 2005 2002 2006 2001* 2005 2002* 2006*

Adopters

Nonadopters

corn soybeans soybeans

Fuel Fertilizer Custom operations

corn corn soybeans
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On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

Economic Research Service / USDA

The 2009 Precision Agricultural Services Dealership Survey (Whipker and 

Akridge, 2009) covers 241 cooperatives, local independent seed and chemical suppliers, and regional or national input companies in the Corn Belt. 

Dealerships were asked about the types of precision services that they offer 

and their profitability. Input dealers were also asked in 2009 about foreseeable changes over the next 2 to 3 years in the input retailer-manufacturing 

business. The most common service offered by these input dealerships 

since 1999 has been soil sampling reported in GPS format (52 percent of all 

surveyed). Field mapping with GPS was second most common (44 percent), 

while 39 percent of dealerships offered yield monitor data analysis, a new 

high for the survey and consistent with ARMS results showing a recent jump 

in yield monitor use. Custom VRT application of fertilizer, lime, and pesticides, as well as variable-rate seeding with GPS, was provided by one-third 

of dealerships in 1999 and 2000 (Whipker and Akridge, 2009). By 2009, that 

percentage had increased to 56 percent, with survey respondents indicating 

that they expected that portion of their business to continue to grow to over 

60 percent by 2011. The ARMS results understate the use of VRT because 

some of these services are obtained from custom dealers.

0

10

20

30

40

50

60

70

$/acre

Figure 10

Comparison of expenses among VRT-fertilizer adopters 

and nonadopters

*indicates statistically significant difference of ratios, at 10% levels.

VRT= variable-rate technology.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

2001 2005* 2002 2006* 2001 2005 2002 2006* 2001 2005 2002 2006

Adopters

Nonadopters

corn soybeans soybeans

Fuel Fertilizer Custom operations

corn corn soybeans
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On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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Precision Agriculture Adopters 

and Crop Yields

Many factors influence crop yields. Environmental and management factors 

may favor the adoption of precision agriculture, which may affect crop 

yields. Heisey (2009) points out that research investment has been a major 

factor raising corn yields, and Monsanto is close to having a drought-tolerant 

variety of corn available for field testing (Lybbert and Bell, 2010). Monsanto 

indicates that rather than offsetting the need for precision agriculture, their 

new seed technologies complement it (Monsanto, 2011).

ARMS Phase II and III data show clear relationships between information 

technology adoption and corn/soybean yields. Corn and soybean yields in 

2001/2 and 2005/6 are significantly higher for yield monitor adopters than 

for nonadopters (fig. 11). Many factors could be contributing to this gap, 

including some of the same factors contributing to the very adoption of yield 

monitors themselves, like farm operator education, technical sophistication, 

and farm management acumen. The yield difference increased for corn from 

a little over 20 bushels/acre (10-percent differential) in 2001 to almost 30 

bushels/acre (23 percent) in 2005. Adopters of GPS mapping also had significantly higher yields for both corn and soybeans in 2001/2 and 2005/6, with 

the differential ranging from 14 to 18 percent (fig. 12). The yields obtained 

by adopters of variable-rate fertilizer equipment (fig. 13) are also significantly higher for both corn (2001 and 2005) and soybeans (2002 and 2006). 

Guidance system (GSYS) adopters had significantly higher yields for corn 

and soybeans in 2001/02.

0

20

40

60

80

100

120

140

160

180

Bushels/acre of grain

Figure 11

Comparison of yields among yield monitor adopters and nonadopters

Corn (2001)* Corn (2005)* Soybeans (2002)* Soybeans (2006)*

Adopters

Nonadopters

*indicates statistically significant difference of ratios, at 10% levels.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.
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On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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A recent survey of 234 studies of precision agriculture found that 210 

addressed profitability (Griffin and Lowenberg-DeBoer, 2005). In the 26 

corn or soybean studies that investigate VRT use and profitability, all find 

that growers increased profit.5 These results seem to support Bullock et al.’s 

(2009) contention (restated slightly) that low adoption rates for VRT are 

not necessarily caused by VRT’s inherent nonprofitability, but by a lack of 

information concerning the profitable incorporation of VRT data into farming 

practices. 

5Of the 234 surveyed studies, 37 percent discuss corn in combination with 

any precision agriculture practice, and 

73 percent of these report increased 

profit (Griffin and Lowenberg-DeBoer, 

2005, table 6). Two percent of the studies discuss soybeans only, and all report 

higher profits. Corn and soybeans are 

discussed together in 9 percent of the 

studies, with 76 percent of these finding 

higher profits with VRT. 

0

20

40

60

80

100

120

140

160

180

Bushels/acre of grain

Figure 12

Comparison of yields among Global Positioning System mapping 

adopters and nonadopters

Corn (2001)* Corn (2005)* Soybeans (2002)* Soybeans (2006)*

Adopters

Nonadopters

*indicates statistically significant difference of ratios, at 10% levels.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.

0

20

40

60

80

100

120

140

160

180

Bushels/acre of grain

Figure 13

Comparison of yields among VRT-fertilizer adopters and nonadopters

Corn (2001)* Corn (2005)* Soybeans (2002)* Soybeans (2006)*

Adopters

Nonadopters

*indicates statistically significant difference of ratios, at 10% levels.

VRT= variable-rate technology.

Source: Agricultural Resource Management Survey, USDA, ERS/NASS.
20

On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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Factors Likely Affecting the Adoption 

of Precision Agriculture 

Precision agriculture remains in the early stages of adoption, and the suite 

of information technologies are not expected to be adopted at the same time 

across the country (see box “Why We Might Expect Regional Differences in 

the Adoption of Precision Technologies,” p. 14). For all the crops considered, 

yield monitors have had the most widespread adoption, probably because 

they are available as standard equipment on some new farm machinery or 

can be installed with the fewest technical difficulties. The creation of GPS 

maps and use of VRT equipment both require an extra level of expertise with 

data and information management, entail greater additional cost, and pose 

the most potential problems in successful implementation. Both technologies need more information and data than yield monitors, requiring many 

times the storage capacity and programming skill to use. And VRT requires a 

substantial investment in the equipment to apply fertilizer and pesticides or to 

seed at variable rates. 

Changes in the popularity and cost of other farming practices may have 

affected the rate at which information technologies are adopted. Since 1998, 

no-till has gained in popularity for corn production while conventional tillage 

has declined (fig. 8); when mulch-till, reduced tillage, and no-till are considered altogether, total acres planted to alternative tillage far exceeded the acres 

planted conventionally in 2005. Yield monitors have been adopted more 

quickly by corn and soybean farmers using conservation tillage practices like 

no-till (table 4). 

The potential for these information technologies has also been linked to a 

host of emerging environmental and farm management questions (Hatfield, 

2000; Frisvold, 2000; NRC, 1997). Precision agriculture may have a positive 

impact on environmental quality through more efficient use of inputs. Farm 

management might also be enhanced by more complete information on field 

conditions.
21

On the Doorstep of the Information Age: Recent Adoption of Precision Agriculture / EIB-80

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Conclusions

The recent rapid adoption of yield monitoring technology is likely due to its 

increased retail availability, reduced cost, and increasing ease of use. Yield 

monitoring appears to be an entry to precision agriculture technology, with 

very few nonadopters of yield monitors creating global positioning system 

(GPS) maps or using variable-rate input application technologies (VRT) that 

rely on GPS maps. Coincident with yield monitor adoption is the growing 

popularity of conservation tillage systems. This report does not attempt to 

determine whether these practices are linked, but yield monitors are most 

popular among corn and soybean farmers practicing conservation tillage. 

Average fuel expenses, per acre, for both corn and soybean farmers are lower 

for farmers who use yield monitors. VRT for fertilizer application is associated with lower fuel expenses for both corn (2005 ARMS) and soybeans 

(2006). Average fertilizer expense—which includes expenses for custom 

application—is higher for soybeans if applied by variable-rate technology. 

ARMS data indicate that adopters of yield monitors had higher corn and 

soybean yields than non-adopters in 2001/2 and again in 2005/6. Even 

though the adoption of GPS mapping is less prevalent than yield monitors, 

both corn and soybean farmers achieved higher yields nationwide when GPS 

was used. Likewise, when variable-rate technology was used to apply fertilizer, higher yields were obtained for both crops. 

The future viability of precision agriculture will likely depend on (1) whether 

the technologies become less expensive and/or easier to install/maintain; (2) 

whether conservation tillage becomes even more widespread; and (3) the 

relative prices of fuel, fertilizer, and custom applications. The reduced use of 

fuel and more economical application of fertilizer under precision agriculture 

indicate its potential utility in reducing air and water pollution. The value of 

the data obtained from these information technologies increases as farm costs 

increase, as maps grow more detailed, and as yield responses become more 

certain. 
22

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