Economic Precision Agriculture in the Digital Era:
Research
Service
Recent Adoption on U.S. Farms
Economic
Information
Bulletin Jonathan McFadden, Eric Njuki, and Terry Griffin
Number 248
February 2023
Economic Research Service
www.ers.usda.gov
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Jonathan McFadden, Eric Njuki, and Terry Griffin. February 2023. Precision Agriculture in
the Digital Era: Recent Adoption on U.S. Farms, EIB-248, U.S. Department of Agriculture,
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Precision Agriculture in the Digital Era:
Economic
Research
Service Recent Adoption on U.S. Farms
Economic
Information
Jonathan McFadden, Eric Njuki, and Terry Griffin
Bulletin
Number 248
Abstract
February 2023
Digital agriculture (DA)—the ongoing transformation of farming that includes digitalization and
automation of farming tasks, of which precision agriculture (PA) is a chief element—may be an impor-
tant part of the solution to several challenges facing U.S. agriculture, including rising production
costs, climate change, and labor shortages, among others. Adoption of digital technologies in row-crop
production has generally increased since 1996, though use has varied widely by technology and crop.
Using data from USDA’s Agricultural Resource Management Survey (ARMS), we document trends
in the adoption of digital agriculture technologies between 1996 and 2019, emphasizing changes after
2016. The adoption of yield maps and soil maps (i.e., maps that associate physical characteristics with
geographic coordinates) and variable rate technologies (VRT), in addition to other technologies, has
been substantial on corn and soybean acreage for many years. Though their use has been increasing in
recent years, technologies such as yield maps, soil maps, and VRT have been adopted on only between
5 and 25 percent of total U.S. planted acreage for winter wheat, cotton, sorghum, and rice. However,
adoption of automated guidance has increased sharply in the past 20 years, with application on well
over 50 percent of the acreage planted to corn, cotton, rice, sorghum, soybeans, and winter wheat.
Beyond documentation of trends, this report explores certain drivers of farmers’ uptake—including
pricing, soil variability, USDA programs, labor-saving benefits, expected productivity impacts, and
availability of consultant services.
Keywords: digital agriculture, digital technologies, automated guidance, variable rate technologies,
drones, yield maps, soils maps, precision farming, precision agriculture, sustainability, productivity,
Agricultural Resource Management Survey (ARMS)
Acknowledgments
The authors thank Roger Claassen, USDA, Natural Resources Conservation Service, for valuable assis-
tance with the RCA Data Viewer, and Brent Hueth, Thomas Worth, and Krishna Paudel of USDA,
Economic Research Service, for providing feedback on a draft of this research. We also thank Roberto
Mosheim (USDA, ERS), Madhu Khanna (University of Illinois Urbana-Champaign), Aditya Khanal
(Tennessee State University), and reviewers at OCE, FPAC, and NRCS for technical peer reviews.
Thanks also to ERS staff members Christopher Whitney and Courtney Knauth for editing the report
and to Adele Wilcoxen for design and layout.
About the authors
Jonathan McFadden and Eric Njuki are research economists with USDA, Economic Research Service;
Terry Griffin is an associate professor of agricultural economics at Kansas State University.
Contents
Summary  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii
Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1
Data Sources ..................................................................3
What Are the Components of Digital Agriculture (DA)?  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5
How Has Adoption of DA Component Technologies Changed in Recent Years?  . . . . . . . . . . . . . .9
Yield Maps ...................................................................9
Yield Monitors ...............................................................10
Soil Maps ...................................................................11
Variable Rate Technologies (VRT) and Automated Section Control (ASC) .................12
Drones, Aircraft, or Satellites ....................................................14
Auto-Steer and Guidance Systems ................................................15
Combinations of Technologies ...................................................16
What Drives Farmers’ Adoption of Digital Agriculture?  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18
Technology Prices, Premiums, and Other Costs ......................................19
Soil Variability and Field Topography ..............................................22
USDA Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24
Labor-Saving Benefits ..........................................................28
Farm Size and Expected Productivity Impacts .......................................29
Use of Technical or Consultant Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31
Data Use, Production Recommendations, and Digital Agriculture (DA)  . . . . . . . . . . . . . . . . . .33
Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
References  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39
Appendix: Overview of DA Component Technologies  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43
Yield maps and soil maps .......................................................43
Variable rate technologies (VRT) .................................................44
Guidance systems .............................................................44
Drones, aircraft, or satellite imagery ...............................................45
Automated section control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
Coverage maps and GNSS information .............................................45
Controlled traffic farming, tramlines ..............................................46
Telematics and wireless data transfer ...............................................46
ii
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Summary
A report summary from the Economic Research Service February 2023
Precision Agriculture in the Digital Era:
Recent Adoption on U.S. Farms
Jonathan McFadden, Eric Njuki, and Terry Griffin
What Is the Issue?
The global population is projected to exceed 9.7 billion by 2050 (United
Nations, 2022). This population growth will require substantial increases in
food production, both in the United States and abroad, placing additional pres-
sure on limited agricultural resources. Similarly, there are growing concerns
about rising production costs, labor shortages, environmental changes, and
unsustainability of intensive natural resources use in the U.S. farm sector.
Public awareness of these issues has increased, leading to calls for the agricul-
tural sector to develop innovative solutions. Digital agriculture (DA) provides
an important opportunity to respond to several of these challenges.
DA technologies such as soil maps, yield monitors, yield maps, variable rate
technologies (VRT), auto-steer and guidance systems, unmanned aerial
vehicles, and satellite imagery have been available to farmers for several years.
Adoption rates have varied considerably for wide-ranging reasons, including field topography and soil type, adjust-
ment costs (e.g., subscription fees, training, maintenance, and replacement costs), and farmers’ production scale
and risk preferences. Adoption also varies by crop type and farmer socioeconomic characteristics. The benefits that
accrue from adoption are numerous for most farmers and have been well documented.
As digital technologies and analytics continue to evolve, the digitalization of U.S. agriculture has become a major
focus of the sector. Digitalization entails the use of data analytics, automated production processes, and develop-
ment and commercialization of artificial intelligence applications. The potential to transform the U.S. agricultural
sector by these technologies is considerable. Specifically, we expect that digitalization has the potential to increase
efficiency in the farm sector—while contributing to cost reductions, yield increases, and/or enhancement to the
well-being of farm operators. Although digitalization is a complex transformation with many components, it can be
partly tracked by examining farmers’ use of established and emerging technologies.
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.
www.ers.usda.gov
What Did the Study Find?
The report analyzes farmers’ DA adoption rates for soil maps, yield maps, yield monitors, VRT, auto-steer and guid-
ance systems, and aerial imagery across six major crops: corn, cotton, rice, sorghum, soybeans, and winter wheat.
• A majority of row crop acreage is managed using auto-steer and guidance systems: Auto-steer guid-
ance systems were used on only 5.3 percent of planted corn acres in 2001, growing to 58 percent in 2016.
Estimates for 2019 suggested 72.9-percent and 64.5-percent adoption rates for sorghum and cotton (planted
acreage). In the same year, GPS applications were used on 40 percent of all U.S. farm and ranchland acreage
for on-farm production.
• Adoption rates vary by farm size: At least half of relatively large row crop farms (those at or above the third
quintile of acreage, i.e., with at least 60 percent of fields on farms with lower acreage) rely on yield maps, soil
maps, VRT, and/or guidance systems. Meanwhile (except for cotton), less than 25 percent of smaller farms
(those with acreage in the first quintile) use any of these four technologies. This use could be due to scale
benefits (i.e., the returns to technology adoption could be greater on larger farms than on smaller farms).
• DA technology adopters use data, acquire crop management recommendations, and employ tech-
nical/consultant services at higher rates than DA technology nonadopters: DA technology adopters are
more likely than nonadopters to download public data for use in decision-making, though overall adoption
remains uncommon. By contrast, farmers more frequently obtain crop management recommendations based
on technologies that collect data in their fields. And while technical/consultant services are hired on a small
fraction of surveyed acres, such services tend to be sought somewhat more by DA technology adopters.
• Farmers are likely to use precision agriculture technologies for a variety of reasons: As technological
capabilities continue to evolve, so have farmers’ rationales for their use. For example, corn and winter
wheat farmers tend to rely on yield monitors to track crop moisture content. By contrast, yield monitors are
primarily used to help determine chemical input use in cotton, soybeans, and sorghum production. Many
precision agriculture technologies are used in combination with other precision agriculture technologies.
How Was the Study Conducted?
The study uses data from the Agricultural Resource Management Survey (ARMS), administered jointly by the
USDA, Economic Research Service (ERS) and the USDA, National Agricultural Statistics Service (NASS). The
authors analyze precision agriculture adoption, emphasizing the most recent commodity-specific surveys: rice
(2013), corn (2016), winter wheat (2017), soybeans (2018), cotton (2019), and sorghum (2019). To illustrate the
continuity of historical trends, we also use data from earlier ARMS surveys dating to 1996. Data from the 2013 and
2019 ARMS Cost and Returns Report are also used to track national adoption of global navigation satellite systems
(GNSS). The focus is on the key precision agriculture technologies mentioned above. The study also explores several
drivers of DA adoption using ARMS data and information via the RCA (Soil and Water Conservation Act of 1977)
Data Viewer from USDA’s Natural Resources Conservation Service. Additionally, the study incorporates evidence
from the literature and makes comparisons with other U.S. farm sector trends.
www.ers.usda.gov
Precision Agriculture in the Digital Era:
Recent Adoption on U.S. Farms
Introduction
In recent decades, U.S. row crop production has faced significant pressures from climate change, pesticide
resistance, an aging farmer population, labor shortages, shifts in the geography of international commodity
trade, and dietary transitions, among other challenges (Castillo et al., 2021; Whitt et al., 2021). No single
technology or set of technologies can fully address these challenges. Adjustments will entail, among many
other things: new seed varieties and combinations of more sustainable production practices (e.g., conserva-
tion tillage, cover crops), new pesticide products and methods of pest management, and potential changes
to incentive structures and the mix of labor and capital used on farmers’ fields to partly resolve excess labor
demand. In response, digitalization—among the farthest-reaching technological transformations of the
economy in this era—has become a focal point for agricultural policymakers seeking a holistic, multipronged
solution to these challenges (DeBoe and Jouanjean, 2019; Food and Agriculture Organization of the United
Nations (FAO), 2020; Basso and Antle, 2020; McFadden et al., 2022b).1
Digital agriculture (DA) refers to the diffusion of information technologies (e.g., the internet and Internet
of Things (IoT), mobile devices, and predictive analytics) to enhance the collection, exchange, combination,
analysis, and access of digital content within the agricultural sector. The agricultural digitalization process
entails greater use of precision agriculture technologies; the promotion of data-sharing between farmers, data
aggregators, and input companies; and reliance on data-driven decision making for day-to-day management
of farm operations. This latter form of decision making includes choices that operators make about which
on-farm tasks to automate (e.g., Acemoglu and Restrepo, 2019). Currently, DA encompasses many established
technologies generally referred to as precision agriculture (e.g., electronic maps, VRT, guidance systems)
whose adoption has been tracked nationally by USDA for many years. However, several digital technologies
(e.g., distributed ledger technologies, gene-edited crops and livestock, and cellular agriculture) have emerged
more recently and are not universally considered to be precision agriculture (PA) by conventional standards,
in part because they are aspatial (see figure 1). Because of this relatively recent emergence, farmers’ uptake of
these technologies has yet to be tracked on a consistent and nationally representative basis (Graff et al., 2021).2
1 For context, precision farming involving digitalized tools has been termed in some instances as “Agriculture 4.0” (DeBoe and
Jouanjean, 2019).
2 The same is generally true regarding industry data. Nationally representative surveys of large equipment manufacturers do not
typically inquire about sales of precision equipment. However, the well-known CropLife-Purdue University Precision Agriculture
Dealership Survey is an annual detailed survey of dealers about precision technologies used in their business and products/services
sold to farmers since 1997 (Erickson et al., 2017; Erickson et al., 2020). Moreover, a recent influx of venture capital funding has
resulted in the creation of many new technology start-ups in the digital agriculture sector (Graff et al., 2021).
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Figure 1
Relationship between site-specific management, precision agriculture (PA), and digital agriculture (DA)
Site-Specific Conventional Digital
Analog Precision Argiculture
Technologies Argiculture
Simple soil testing, Yield monitors, Distributed ledger
paper-based electronic maps, technologies
soil maps, variable rate (e.g., blockchain),
paper-based technologies (VRT ) , gene editing, fully
yield maps auto-steer or automated farming,
guidance systems agricultural robots,
other data-analytics
applications
Note: Diagram is meant to be illustrative rather than comprehensive.
Source: USDA, Economic Research Service illustration.
The ongoing digitalization of U.S. agriculture presents considerable opportunity for improvements in farmers’
productivity, environmental footprint, and risk management. For example, to the extent that variable rate
technologies (VRT) lead to lower rates of fertilizer applications or more appropriate timing and location
of applications, nutrient leaching and run-off concerns could be lessened. There are similar implications
associated with variable-rate pesticide applications (e.g., reduced off-target movement and the possibility of
delaying inevitable pesticide resistance) and irrigation applications (e.g., freshwater conservation). The use of
automated guidance or controlled traffic farming systems simplifies farmers’ in-cab time and can potentially
reduce fuel consumption, greenhouse gas emissions, and soil compaction. Mapping technologies are often
used to identify field drainage problems that, when addressed, can prevent overwatering of crops (e.g., Saavoss
et al., 2021).
In this report, we detail recent trends in adoption rates of precision technologies on certain U.S. row crop
fields—first providing an overview and describing the broad types of tools that, collectively, comprise key
features of the digital agriculture architecture. We also analyze differences in certain characteristics between
fields on which DA is adopted and not adopted. In turn, we identify and discuss key drivers of adoption, with
an emphasis on important determinants that have been underexplored to date—including technology prices
(replacement costs, fees, and premiums), USDA programs, and labor-saving benefits. However, we do not
review adoption trends in specialty crops (i.e., fruits, vegetables, tree nuts, and other horticultural crops) or
livestock, as there are no national adoption data for these sectors.
We find that yield map adoption has steadily increased since the late 1990s and early 2000s, in contrast to
Global Navigation Satellite System (GNSS)-based soil maps, which appear to have flattened—with relatively
low adoption rates—for several crops starting between 2009 and 2011. Currently, the majority of acreages
for each of six major row crops (corn, soybeans, winter wheat, cotton, rice, and sorghum) are managed with
guidance systems, up from minimal adoption in the early 2000s. Despite recent upturns in VRT adoption,
its use remains limited, though VRT use is much more common than drones and other aerial-based tech-
nologies. Substantial heterogeneity exists across technologies and crops regarding drivers of adoption, which
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we discuss at length below. Beyond well-documented determinants relating to market structure (farm size,
costs, and expected productivity impacts), we find important roles played by human resources (e.g., expected
labor-saving benefits, skills requirements) and natural resources (e.g., soil variability, field topography, and
conservation program incentives).
This report updates two earlier USDA, Economic Research Service (ERS) studies, Schimmelpfennig and Ebel
(2011) and Schimmelpfennig (2016), which examined precision agriculture adoption trends. We extend their
analyses through the most recent 4 years for which data are available, 2016–19. Unlike Schimmelpfennig
(2016), we do not formally analyze the role of DA technologies for improving farm profitability. Although
digitalization of U.S. row crop farming has deepened in the intervening years, as reflected in this report’s
updated adoption statistics, we do not expect the underlying economics of production to have changed in a
way that would produce estimates of profitability effects that would differ markedly from those reported in
Schimmelpfennig (2016). We also do not formally investigate digitally-facilitated changes in the environ-
mental footprint of U.S. agriculture.
Data Sources
Multiple years of the Agricultural Resource Management Survey (ARMS) are the main data sources for
this report. ARMS is administered jointly by USDA’s Economic Research Service (ERS) and the USDA,
National Agricultural Statistics Service (NASS) and is an annual survey of farms’ production practices (i.e.,
equipment adoption, field operations, chemical use, crop rotations, tillage, and seed choices), farm financial
characteristics, and farm household demographics (see box, “Overview of the USDA Agricultural Resource
Management Survey”).
We use field-level production data from ARMS Phase II for all survey years between 1996 and 2019, except
for 2008 and 2014 (see table in box, "Overview of the USDA Agricultural Resource Management Survey").
Our sample represents virtually all U.S. acreage planted to the crop in the particular-survey year for which
data are available. We supplement the ARMS data with information about acreages enrolled in USDA’s
Conservation Stewardship Program (CSP), available through the National Planning and Agreements
Database (NPAD) from USDA’s Natural Resources Conservation Service (NRCS).
Overview of the USDA Agricultural Resource Management Survey (ARMS)
The USDA’s Agricultural Resource Management Survey (ARMS) is the main source of data used in
this report. An annual survey of U.S. farms, ARMS is USDA’s primary source of information about the
financial conditions, production practices, and the economic well-being of the agricultural sector. ARMS
is jointly administered by both USDA, Economic Research Service and USDA, National Agricultural
Statistics Service.
ARMS is a multiframe, stratified, and probability-weighted survey conducted in three phases:
• Phase I is undertaken in the summer of the reference year and screens farms for survey eligibility.
• Phase II takes place during the fall of the reference year and collects field-level information on
production practices, natural resource use, pesticide and fertilizer applications, and other input use
for a target crop.
• Phase III is conducted in the spring after the reference year and collects farm-level information
about finances, operator and household characteristics, and demographics. Farms surveyed for a
target crop in Phase II are also asked to complete the Phase III survey, and these respondents form
a subset of the Phase III sample. Phase III samples all types (e.g., row crops, livestock, specialty
crops) and all sizes (e.g., very small to very large) of farms across the contiguous 48 States.
continued on next page ▶
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continued from previous page ▶
Data for this report are primarily from Phase II of ARMS. ARMS surveys the same target crop every 4 to 6
years. Each of the several major field crops (e.g., corn, soybeans, wheat, cotton, sorghum, rice) are surveyed
on a rotating basis every 4 to 6 years, though not all major row crops are surveyed. Each of the surveyed
fields (one per farm in the sample) is randomly selected. Enumerators conduct the survey in person, which
contributes to data accuracy and fewer missing responses.
The trends depicted and discussed in this report rely on responses from surveys conducted in reference
years 1996–2007, 2009–13, and 2015–19. In 2008, Phase II of the survey was not administered to any
farms. Similarly, in 2014, ARMS focused on tenure, ownership, and transition of agricultural land, and
thus no field-level adoption data were collected. Since major crops are surveyed on a rotating basis, adop-
tion data are not available in all years. This report accounts for the statistically complex survey design to
ensure that findings are statistically representative of U.S. corn fields and acreage.
Availability of Precision Agriculture Adoption Data from ARMS Phase II, 1996–2019
Year Corn Soybeans Winter wheat Cotton Sorghum Rice
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Note: ARMS = Agricultural Resource Management Survey. Shaded cells indicate that the particular crop was surveyed in the
indicated year. Since 2001, the ARMS methodology has targeted one or more crops on a rotating basis, which implies that each
of the targeted commodities are not surveyed every year.
Source: USDA, Economic Research Service and National Agricultural Statistics Service (NASS), Agricultural Resource Man-
agement Survey (ARMS), Years 1996–2007, 2009–13, 2015–19.
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What Are the Components of Digital Agriculture (DA)?
Many of the major components of digital agriculture can be decomposed into three features: (1) data and data
collection systems, (2) decision support (DS) tools, and (3) data-driven equipment and input adjustments3
(figure 2). Examples under the first category include data from yield and soil monitoring equipment, various
sensors, and imagery from drones, aircraft or satellites. DS tools include (electronic) maps or other visualiza-
tions of georeferenced data, in addition to smartphone apps and other sources of analysis with management
recommendations. Technologies falling under the third category are largely guidance systems, automated
section control, and variable rate applicators.
USDA adoption data are available for many of the technologies underlying digital agriculture (see items
appearing within red dashed line in figure 2), though not all. For example, adoption of distributed ledger
technologies (e.g., blockchain), which can be used to ensure data quality or assist with input traceability, is
not tracked. Adoption information is also not currently available for telematics (described below), smartphone
applications (apps), or variable rate irrigation among others. A number of industry reports suggest that smart-
phone use is widespread across the U.S. farming sector (e.g., United Soybean Board, 2019), while variable rate
irrigation remains limited (Lamm et al., 2021) and the subject of much ongoing research and development
(e.g., Saavoss et al., 2021).
3 Arrows in figure 2 indicate the flow of hardware, software, or information as inputs. For example, the four arrows pointing away
from the “Yield monitors” box indicate that yield monitor data are used to construct yield maps, weed or other pest maps, profitability
maps, and other non-map decision-support systems. Moreover, the blue dashed box encapsulating the entirety of the features in figure
2 is a conceptual representation of the idea that virtually all features of U.S. row crop farming are, or can be, automatable.
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Fi gure 2
Key component technologies of digital agriculture (DA)
Distributed ledger technologies (DLTs) and/or Distributed ledger technologies
data cleaning algorithms to ensure data quality (DLTs) for input traceability
Data and Data Data-Driven Equipment
Decision Support (DS)
Collection and Input Adjustment
Yield Yield
Monitors Maps Guidance
Systems
Soil
Soil Monitoring: Maps
■ Core samples
■ Proximal sensing
Weed or Other Automatic
Pest Maps Section Control
Other Data Collection Methods:
■ Satellite/aerial/drone imaging Profitability
■ Crop condition sensors Maps Variable Rate
Applications (VRT):
■ Manual/automated scouting
■ Seeds
■ Water meters/sensors Other (non-map) DS Systems:
■ Fertilizer/lime
■ Public- or private sector
■ Pesticides
analysis or crop management
Other Field Data:
recommendations ■ Irrigation
■ Topography
■ Large-scale soil surveys
■ Smartphone apps
■ Temperature, precipitation,
wind, and other weather data
Telematics
Collected in ARMS
Phase II Survey
through 2019
Capable of being
automated
Note: Arrows indicate the flow of hardware, software, or information—as inputs—to decision support (DS) technologies and/or
data-driven equipment or input adjustments. Although gene editing and cellular agriculture are components of digital agriculture,
they are not depicted here as they are beyond the scope of the report. The dashed line indicating “capable of being automated”
is designed to suggest that virtually all components of digitally-enabled production systems can be automated (i.e., autonomous
machinery is not a single-component technology).
Source: USDA, Economic Research Service illustration.
The current status of U.S. agricultural digitalization can only be fully explored and discussed by first
reviewing the underlying technologies and associated data use. In order to do so, basic descriptions of each of
the major technologies or systems are required (see table 1). Additional technical details are provided in the
appendix, “Overview of DA Component Technologies.”
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Table 1
Key component technologies of digital agriculture, 1996–2019
Technology Description Category Year commercialized
In-cab device that displays crop yields (e.g., bushels/ Data
Yield monitor 1993
acre)—and how those yields change over small areas. collection
Soil core samples are taken from grids or other
Precision soil areas, typically less than 10 acres in area. Samples Data
1993
sampling are analyzed in laboratories to determine the soil’s collection
physical and/or chemical properties.
Yield maps are physical maps that visually represent
Yield maps, soil crop yields and variation in yields within and across Decision
Approximately 1993–94
maps fields. Likewise, soil maps display physical and/or support
chemical attributes of soils.
Equipment that provides custom applications of
Variable rate Data-driven
seed, lime, fertilizer, and/or pesticides at rates that 1995
technologies equipment
can change over small distances.
Equipment that reduces field overlaps or skips by
Lightbars: mid-1990s;
Guidance either (1) instructing the operator to steer within Data-driven
full automation: late
systems predefined boundaries (e.g., lightbars) or (2) fully equipment
1990s
automates nearly all steering operations.
These kinds of imagery (with various uses) are
Landsat satellite
Drones, aircraft, remotely-sensed data collected from satellite,
Data, data imagery: 1970s;
or satellite crewed aircraft (typically small propeller planes), or
collection commercial drones:
imagery small unmanned aerial vehicles (e.g., fixed wing or
mid-2000s
rotorcraft drones).
Type of variable rate technology that automatically
shuts off a portion of the equipment (e.g., boom sec-
Automated Data-driven
tion, nozzle on sprayer, or row on planter) when the 2003
section control equipment
portion is in a part of the field that does not require
input application.
A farming practice that limits exposure of the soil’s
surface to farm equipment’s tires or tracks (through
Controlled traffic Data-driven Late 1970s to early
the use of tramlines), so that the equipment returns
farming equipment 1980s
to the same tracks for each trip, for as many field
operations as possible.
The wireless transfer of data between farm equip-
Decision
Telematics ment, connected devices, and/or the cloud via cel- 2002
support
lular systems or local area networks (e.g., Bluetooth).
Note: This table emphasizes the key technologies discussed in this report. Nationally representative data are not generally available
regarding farmers’ use of other technologies frequently considered to be part of digital agriculture (e.g., distributed ledger tech-
nologies, gene-edited crops, highly specialized and mobile agricultural robots). Yield-monitoring technologies date to the 1960s,
though the advent of GPS for civilian purposes has rendered yield monitoring much more available. Similarly, grid soil sampling also
predates the advent of civilian-use GPS.
Source: USDA, Economic Research Service.
Because of their significant use in row crop production, this report mainly focuses on yield monitors, preci-
sion soil sampling, yield and soil maps, VRT, guidance systems, aerial imagery (from drones, aircraft, or sat-
ellites), automated section control, controlled traffic farming, and telematics. As discussed above, these tech-
nologies fall mainly within the realms of data and data collection, decision support, or data-driven equipment
and input adjustment technologies. Underlying the widespread use of nearly all these technologies, however,
was the advent of the U.S. Department of Defense’s Global Positioning System (GPS) in 1978, which was
made “selectively available” to civilians starting in 1983. Geospatial coordinates from GNSS systems (such
as GPS) make it possible for technology providers to create maps, enable the automated controllers mounted
within VRT machinery, and facilitate modern guidance systems. In short, satellite navigation systems like
GPS are a fundamental technology that supports site-specific management.
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In 2013 and 2019, roughly 12 percent of all U.S. farms—representing 37 percent and 40 percent, respec-
tively, of total farm/ranch acreage—made use of GPS for on-farm production activities (figure 3).4 At the
general farm level, adoption of specific GPS applications (e.g., VRT, guidance, mapping) are unknown, but
considerable variability exists across States. In both years, well over half of farm/ranch acres were managed
with GPS in the Corn Belt (Illinois, Indiana, Iowa, Kansas, Minnesota, North Dakota, South Dakota),
while adoption was less than 10 percent of farm/ranch acres for certain Eastern States (Massachusetts, New
Hampshire, and West Virginia). These differences reflect geographic variation in the quality of satellite
signal reception, the types of crops that are grown, and the expected net returns to GNSS applications.
Figure 3
Percent of total farms and total farm/ranch acres using GPS for on-farm production activities, 2013
and 2019
Percent of farms, 2013 Percent of acres, 2013
WA MT ND MN WA MT ND MN
OR ID WY SD WI MI NY ME OR ID WY SD WI MI NY ME
NV UT CO NE KS I MA O IL IN OH WV PA NV UT CO NE KS I MA O IL IN OH WV PA
CA KY VA CA KY VA
AZ NM OK AR TN SCNC AZ NM OK AR TN SCNC
MS AL GA MS AL GA
LA LA
TX TX
FL FL
0–10 10–20 20–30 30–40 0–20 20–40 40–60 60–80
Percent of farms, 2019 Percent of acres, 2019
WA MT ND MN WA MT ND MN
OR ID WY SD WI MI I NY ME OR ID WY SD WI MI NY ME
NV UT CO NE KS I MA O IL IN OH WV PA NV UT CO NE KS I MA O IL IN OH WV PA
CA KY VA CA KY VA
AZ NM OK AR TN SCNC AZ NM OK AR TN SCNC
MS AL GA MS AL GA
LA LA
TX TX
FL FL
0–10 10–20 20–30 30–40 0–20 20–40 40–60 60–80
Note: The top panels depict the percent of each State’s total farms and total farm/ranchland, respectively, in 2013, with operators
who indicated they used GPS for on-farm production activities. Similarly, the bottom panel depicts these percentages for year 2019.
Data are not available for Alaska and Hawaii.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, 2013 and 2019 Agricultural Resource
Management Survey (ARMS).
Beyond the above-mentioned broad types of technologies, however, there are two other related concepts:
emerging digital technologies and digital components used within a system. Emerging technologies avail-
able in other nonagricultural sectors have been implemented within the U.S. farm sector. Automation of
processes is not new to agriculture; as noted above, automated guidance and automated section control have
become quickly adopted technologies within the farmgate. In the last decade, automation of routine farm
4 The disparity between percentages of farms and percentages of acres reflects the well-known facts that: (1) the farm size distribu-
tion is prominently left skewed (i.e., there are many more small farms than large farms), and (2) larger farms adopt advanced technolo-
gies at higher rates than small farms (MacDonald et al., 2013).
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data tasks has been undertaken via telematics for data transfer (Whitacre et al., 2014), algorithms for cleaning
and analyzing yield monitor data, and distributed ledger technology (i.e., blockchain, which allows for data
quality assurance and monitoring chain of custody) (Griffin et al., 2022).
Artificial intelligence (AI), machine learning, and associated computer innovations have been touted to
enable greater productivity across agricultural sectors, although little evidence exists to date because they have
not yet been deployed at scale. Algorithms have replaced human intervention for repeatable tasks involving
certain kinds of decision making (e.g., Acemoglu and Restrepo, 2019).
Moreover, component technologies have been integrated into digital systems for greater use within the farm-
gate. Underlying such integration has been the resurgence of on-farm experimentation managed by farm
operators, where each component technology empowers the farm operator to better implement, harvest,
and analyze the experiment if that component is used in a holistic system (Lacoste et al., 2022). In the past,
ARMS questionnaires asked about how farm operators used GNSS-enabled yield monitors for on-farm
activities, reporting that use in experimentation often was higher than that of use related to other landowner
decisions (e.g., negotiating new crop leases, documenting yields for financial purposes, dividing crop produc-
tion among those receiving a share of the crop). As early as 2002, soybean farmers listed conducting on-farm
experiments as the third-highest use of yield monitor data. The question regarding usage of yield monitor
data included a response category of “conduct in-field experiments (e.g., compare fertilizer applications, seed
varieties, herbicides, pesticides, etc.)” from 2002 to 2011. In 2011, the most recent year for which data are
available, nearly 25 percent of acreage planted to sorghum or barley was harvested with a combine equipped
with a yield monitor to collect data from a deliberate in-field experiment.
How Has Adoption of DA Component Technologies Changed in
Recent Years?
Adoption of precision technologies related to digital agriculture has increased considerably since the mid-
1990s. Below, we review trends in the adoption of fundamental precision agriculture technologies: yield
maps, yield monitors, soil maps, variable rate technologies (VRT), drones, aircraft or satellites, and auto-steer
and guidance systems, in addition to technology combinations.
Yield Maps
Yield maps have been available since the early 1990s and are primarily used by agricultural producers to
quantify and characterize within-field production variability. Mapping of average spatial and temporal
patterns in yields is valuable to producers because they provide information critical to making informed
and reliable management decisions. The maps are typically used to analyze factors affecting field-yield varia-
tion and to prescribe variable-rate applications for other factor inputs, such as fertilizers and pesticides. Yield
maps are generated using yield monitors, which were once usually mounted on combine harvesters or other
forms of auto-guidance systems but are now standard on new equipment (Schimmelpfennig and Ebel, 2016).
Information from Phase II of the Agricultural Resource Management Survey (ARMS) for several years across
various row crops (including corn, soybeans, winter wheat, cotton, sorghum, and rice) indicates consider-
able (albeit varying) adoption rates by row crop. Adoption rates increased from 5.3 percent in 1996 to 43.8
percent in 2018 across soybean-planted acres; adoption rates across corn-planted acres stood at 43.7 percent in
2016, up from a low of 7.7 percent in 1997 (figure 4).
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Figure 4
U.S. farmers’ increasing yield map adoption, 1996–2019
50
Corn Soybeans Winter wheat
45
Cotton Rice Sorghum
40
35
30
25
20
15
10
5
0
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6991 7991 8991 9991 0002 1002 2002 3002 4002 5002 6002 7002 8002 9002 0102 1102 2102 3102 4102 5102 6102 7102 8102 9102
Percent of crop planted acres
Note: Starting in 2015, the adoption of a yield map is considered to be the use of yield monitor data that were/will be used to create
a map.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey, Years 1996–2007, 2009–13, 2015–19.
Adoption rates across other row crops have ranged from 1.5 percent for sorghum in 2003 to 22.7 percent in
2019; 0.7 percent in 1997 to 21.4 percent in 2017 for winter wheat; and from 0.7 percent in 2000 for cotton
to 15.2 percent in 2019. It is important to note that consistent spatial and temporal patterns in yields may be
confounded by factors ranging from weather variability and changing soil quality to pest pressure and any
other common factors that could prevail during the cropping season (Basso et al., 2012). A long yield history
is essential in order to avoid misinterpreting information generated from yield maps. A solution to this poten-
tial misinterpretation is the use of high-resolution yield monitors.
Yield Monitors
Yield monitors consist of three components: a mass flow sensor that monitors grain flow in order to establish
grain yield measurement; a moisture sensor that captures moisture content in grain in order to aid in storage
and drying of the harvest; and a differential global positioning system (DGPS) receiver that records and
geo-references the location (i.e., associates physical locations with geographic coordinates).5 When properly
calibrated, yield monitors are capable of generating accurate information that can be used to aid current and
future decision making.
Yield monitoring can also be used to identify where chemical inputs are most efficient, enabling the design
of specific crop fertilization programs. Across U.S. planted acreage, farmers have relied on yield monitors for
various reasons. Recent data indicate that these technologies were used to monitor crop moisture content
5 Moisture-sensing is essential to correcting yield data in order to accurately determine the marketable weight of grain.
on 61.6 percent of corn-planted acres in 2016 and 80.5 percent of winter wheat acres in 2017 (figure 5).
Meanwhile, of the farms adopting yield monitors—95.6 percent, 94.4 percent, and 68.6 percent of sorghum
(2019), cotton (2019), and soybean (2018) planted acres, respectively—were managed with yield monitors to help
determine chemical input use. Farmers can also use information generated by yield monitors to add or improve
tile drainage and/or other water-related technologies (e.g., irrigation equipment), among several other uses.
Figure 5
Farmers’ use of yield monitor data: Substantial variation by crop and year
Percent of yield-monitored planted acres
100
7.6
90 16.2
14.5
80
70 16.3
68.6
60
50 94.4 95.6
40 80.5
30 61.6
12.3
20
10 19.2
0 4.0 3.9
Corn Winter wheat Soybeans Cotton Sorghum
(2016) (2017) (2018) (2019) (2019)
Monitor crop moisture content Negotiate new crop leases Help determine chemical input use
Add/improve tile drainage Other uses
Note: In 2016 and 2017, the underlying survey question from which the data were taken had four answer choices, as depicted above.
In 2018 and 2019, wording to the survey question was changed so that respondents could choose from one or more of the three
answer choices depicted above.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey, Years 2016–19.
Soil Maps
Soil mapping is used to identify soils and their properties. There are generally two levels of soil maps for land-
use management. The first level comprises an inventory of soil properties or conditions of the soil at the time
of mapping, and a second level comprises interpretation, which synthesizes soil properties and site context
(Miller, 2017). In agricultural land management, soil mapping is used as a decision tool to determine the suit-
ability of specific soils to particular crops, as well as their drainage capability, among other uses.
A relatively new tool is digital soil mapping, a feature that entails the creation of geographically-referenced
soil databases that facilitate the rapid visualization and quantification of landscape patterns at multiple spatial
scales (McBratney et al., 2003). Digital soil mapping is largely enabled by a highly comprehensive, interactive
database of detailed soil maps maintained by the USDA, Natural Resources Conservation Service (NRCS).
These maps can also track soil characteristics that gradually vary over time. However, such maps do not
reveal certain highly dynamic, localized, and history-dependent soil characteristics like compaction, and
outdated maps will not indicate up-to-date nutrient needs; both characteristics tend to substantially influence
crop performance in any given year.
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Information from ARMS reveals that the adoption of soil maps has lagged behind other DA components,
with adoption rates remaining consistently below 25 percent of planted acreage across surveyed crops. The
proportion of land managed with soil maps includes only 21.5 percent of corn-planted acres in 2016, 14.3
percent of soybean-planted acres in 2018, 6.2 percent of sorghum-planted acres in 2019, and 5.6 percent of
winter wheat-planted acres in 2017 (figure 6). These adoption rates are consistent with a general trend for row
crop farmers in the Central United States to only sample their soils at discrete intervals (usually 3 to 4 years).
Figure 6
Soil map adoption, 1996–2019: Rates increased slightly or flattened
Corn Soybeans Winter wheat Cotton Rice Sorghum
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6991 7991 8991 9991 0002 1002 2002 3002 4002 5002 6002 7002 8002 9002 0102 1102 2102 3102 4102 5102 6102 7102 8102 9102
Percent of crop planted acres
25
20
15
10
5
0
Note: Starting in 2015, the adoption of a soil map is considered to be the use of data from: (1) soil tests on core samples (performed
on-farm or sent out to a laboratory), and/or (2) soil sensor tests that were/will be used to create a map.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey, Years 1998–2007, 2009–13, 2015–19.
Variable Rate Technologies (VRT) and Automated Section Control (ASC)
Variable rate technologies (VRT) are currently used by farmers to perform various on-farm tasks, including
seeding and applications of fertilizer, lime, and pesticides (figure 7). VRT enables greater control of important
variable inputs, possibly leading to more efficient applications without a loss of yields and potentially lowering
total production costs (Schimmelpfennig and Ebel, 2016). VRT can also help to reduce fertilizer leaching or
run-off through adjustment of application rates, thus potentially contributing to more sustainable commodity
production (Basso and Antle, 2020).
Figure 7
Percentages of variable rate technology (VRT) used on U.S. crop-planted acres
Percent of crop planted acres
30
28.2
VR pesticides VR seeds VR fertilizers/lime
25.3
25
20
15.3
15 13.9 14.6 14.3
13.2
11.4
10.4
10 8.6 9.4 9.0
8.0 8.0
6.9
5
0
Corn Soybeans Winter wheat Cotton Sorghum
(2016) (2018) (2017) (2019) (2019)
Note: VRT is commonly used for lime applications. However, due to survey question wording, variable rate (VR) fertilizer applications
cannot be distinguished from VR lime applications. Data are not available for VR irrigation applications.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey, Years 2016–19.
Notwithstanding the prospects of lower costs associated with VRT, its use on U.S. farms is far from universal
(Bullock et al., 2009; Schimmelpfennig and Ebel, 2016; Lowenberg-DeBoer and Erickson, 2019). More
specifically, VRT adoption follows a common pattern: Higher adoption by large farms and low uptake among
smaller farms indicates the potential for scale benefits.6 According to the ARMS data, VRT adoption rates
stood at 37.4 percent on corn-planted acres and 25.3 percent for soybean-planted acres in 2016 and 2018,
respectively (figure 8). VRT adoption rates across other row crops ranged from 18.8 percent of winter wheat-
planted acres in 2017, 13.9 percent for sorghum in 2019, and 22.7 percent for cotton in 2019.
6 Returns to technology adoption may be greater for larger farms, as adjustment costs can be spread over a larger amount of goods
produced.
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Figure 8
Variable rate technology (VRT) use: Substantial expansion from 1996–2019
Corn Soybeans Winter wheat
Cotton Rice Sorghum
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6991 7991 8991 9991 0002 1002 2002 3002 4002 5002 6002 7002 8002 9002 0102 1102 2102 3102 4102 5102 6102 7102 8102 9102
Percent of crop planted acres
40
35
30
25
20
15
10
5
0
Note: Any VRT adoption is considered to be the use of a variable rate applicator for seeding, fertilizer/lime applications, or pesticide
applications. The use of VR applicators for irrigation are not considered, as no data are available.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey, Years 1998–2007, 2009–13, 2015–19.
Beginning in 2019, the ARMS started inquiring about farmers’ use of automated section control (ASC) tech-
nologies such as auto sprayer boom controls and automatic row shut offs for planters. ASC is similar to VRT
in that it can automatically turn “on” parts of the equipment that are directly over areas that need seeds,
fertilizer, or pesticides and “off” when directly over parts of the field that already received inputs or do not
need applications. In 2019, ASC was used on 41 percent of U.S. cotton acres, 45 percent of sorghum acres,
and 48 percent of barley acres.
Drones, Aircraft, or Satellites
While aircraft have been used on many farming operations for decades, unmanned aerial vehicles (UAVs)
or drones are a newer feature of digital agriculture that—when equipped with satellite tracking or GNSS
technology—can help to optimize farmland management. Collectively, these tools are used mostly for: crop
mapping, livestock monitoring, land surveying, crop spraying, and crop dusting. When equipped with GNSS
technology, UAVs can geo-reference vast stretches of farmland, which can aid farmers’ decision making (e.g.,
helping to identify land features or vegetation patterns that are more easily visible from above).
According to the most recent data, adoption of these tools remains limited (figure 9). Across the various row
crops surveyed, adoption rates ranged from 7.0 percent for corn in 2016 to 9.8 percent for soybeans in 2018.
Meanwhile, the adoption rate on winter wheat-planted acreage in 2017 was 3.5 percent, with comparable
adoption in 2019 on cotton acres (2.8 percent) and sorghum (4.6 percent).7
7 The commodity-specific ARMS questionnaires in these years do not elicit information from farmers about barriers to adoption.
Nonetheless, low adoption for aircraft may be driven (in part) by their technological complexity, as well as substantial expense in their use.
These barriers may also underly the low adoption rates for drones, in addition to perceptions of a limited number of uses on the farm.
Figure 9
Drone, aircraft, and satellite imagery use remains low
Percent of crop planted acres
10
9
8
7
6
5 9.8
4
7.0
3
4.6
2
3.5
2.8
1
0
Corn Soybeans Winter wheat Cotton Sorghum
(2016) (2018) (2017) (2019) (2019)
Note: Due to survey question wording, the use of drones, aircraft, and satellites cannot be separately distinguished, and adoption
estimates are only available for these three technologies in aggregate.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey, Years 2016–19.
Auto-Steer and Guidance Systems
Auto-steer and guidance systems—now typically equipped with GNSS receivers—are technologies that
visualize the equipment’s position in the field to reduce skips and overlaps. “Manual” auto-steer systems (e.g.,
light bar systems) are relatively older technologies that have generally been replaced with more sophisticated
automated guidance systems. The newer technologies provide for near-total automated steering of tractors,
greatly freeing up farmers’ time in the cab.
Such benefits, in addition to the potential for lower variable input costs from reduced skips and overlaps (e.g.,
for fuel, seed, nutrients, pesticides), are perhaps the reason why adoption rates have expanded rapidly since
2019, although with variation by row crop (figure 10).8 Adoption rates by row crop ranged from 54.4 and
58.4 percent of planted soybean and corn acres in 2016 and 2018, respectively, to 64.5 and 72.9 percent of
cotton- and sorghum-planted acres, respectively, in 2019.
8 Khanal et al. (2019) found that multiple applications of GNSS guidance systems (post-adoption) would also depend on whether
the technology met farmers’ expectations.
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Figure 10
Cropland area planted under major U.S. row crops, 1996–2019: Over half now managed with guidance
systems
Corn Soybeans Winter wheat
Cotton Rice Sorghum
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6991 7991 8991 9991 0002 1002 2002 3002 4002 5002 6002 7002 8002 9002 0102 1102 2102 3102 4102 5102 6102 7102 8102 9102
Percent of crop planted acres
80
70
60
50
40
30
20
10
0
Note: Prior to 2018, guidance system adoption is considered to be the use of guidance auto-steering (excluding light bar systems) or
use of light bar systems. Starting in 2018, guidance system adoption is considered to be only use of guidance auto-steering as data
on light bar system use are no longer collected, due to its minimal use.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey, Years 2001–07, 2009–13, 2015–19.
Combinations of Technologies
In many instances, farmers use combinations of precision technologies for productivity- or cost-related
reasons (Lambert et al., 2015; Schimmelpfennig, 2016; Schimmelpfennig and Ebel, 2016; Miller et al., 2019).
Technology providers often give discounts on precision equipment purchased jointly rather than individu-
ally, and machinery is increasingly being sold with precision technology already embedded or as “standard”
equipment through marketing practices known as bundling. Regardless of industry practices, farmers may
currently be using their own combinations of technologies based on reconfigurations of various pieces of equip-
ment acquired at different times. Using 2010 ARMS data for U.S. cornfields, Schimmelpfennig (2016) and
Schimmelpfennig and Ebel (2016) found six groupings of PA technologies that were often adopted in tandem:
{yield monitors + yield maps}, {yield monitors + GPS-based soil maps}, {yield monitors + guidance}, {yield
monitors + yield maps + VRT}, {yield monitors + GPS-based soil maps + VRT}, and {yield monitors + guid-
ance + VRT}. The specific reasons underlying farmers’ adoption of these groupings were not surveyed, though
the authors found that total input costs per acre were lower for adopters than nonadopters of each combination.
Although substantial fractions of national acreages for corn (2016), winter wheat (2017), soybeans (2018),
and cotton (2019) are managed with use of only one precision technology, many technology combinations are
commonly used (table 2).9 While the {(yield and/or soil maps) + VRT} combination was used on roughly 2
percent of corn acres and 2 percent of soybean acres, use of {maps + guidance} was more pronounced, ranging
from 7 percent of cotton acres to 15 percent of soybean acres. Similarly, {VRT + guidance} was jointly used
on up to 12 percent of national acreage (2019 cotton), with {maps + VRT + guidance} being more common
on corn acreage (23 percent) and soybean acreage (13 percent) than just {VRT + guidance}. Use of all four
technologies (maps, VRT, guidance, and drones, aircraft, or satellites) occurred on 2 percent to 5 percent of
national acreage across the four crops during 2016–19.
Table 2
Percent of planted acreage managed with technology combinations, 2016–19
Survey and year
Corn Winter wheat Soybeans Cotton
Technology combination (2016) (2017) (2018) (2019)
Percent
Maps only 2.32 1.81 4.97 0.80
Drones, aircraft, or satellites (DAS)1 only 0.49 0.61 0.02 0.04
Variable rate technologies (VRT) only 2.00 1.55 2.53 1.98
Guidance only 11.12 38.93 14.92 36.47
{Maps + VRT} 1.97 0.61 1.94 0.11
{Maps + guidance} 12.40 12.47 15.36 7.47
{VRT + guidance} 5.56 7.78 2.50 12.13
{Maps + DAS + guidance} 1.36 0.21 3.66 0.87
{Maps + VRT + guidance} 22.85 7.33 13.07 5.94
{Maps + DAS + VRT + guidance} 4.52 2.05 4.26 1.52
Note: DAS denote drones, aircraft, or satellites (imagery). The maps variable indicates whether the field was managed with a yield
map or soil map (or both). Cells indicate the fraction of national planted acreage operated with the particular technology or combi-
nation of technologies indicated in the left-most column. USDA, National Agricultural Statistics Service (NASS)-provided expansion
factors have been used to ensure the percentages are representative of national acreages. Other combinations of technologies
include: {maps + DAS}, {DAS + VRT}, {DAS + guidance}, {maps + DAS + VRT}, and {DAS + VRT + guidance}. Acreage percentages
for these five combinations are below 1 percent (individually) for each of the four crops, and thus, the percentages are not listed in
the table.
Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey
(ARMS), years 2016–19.
As in earlier ERS analyses, we do not pinpoint the exact reasons for the popularity of specific technology
groupings. Technological complementarity (e.g., VRT reliance on mapping; mapping reliance on yield moni-
toring and/or soil sampling) and bundled pricing are among the most compelling economic rationales for
farmers’ sequential adoption behavior. At the same time, greater farm consolidation and increasing farm size
may boost farmers’ demand for suites of increasingly sophisticated technologies that simplify management of
larger operations (MacDonald et al., 2013; Griffin et al., 2017). Determinants of individual technology adop-
tion are explored in more depth below.
9 Given progressively greater use of yield monitors and their installation on new combine harvesters as standard equipment, we opt
to focus on other technologies that are generally more advanced and less widespread.
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What Drives Farmers’ Adoption of Digital Agriculture?
Economists have identified various field, farm, and operator characteristics associated with farmers’ adoption
of precision agriculture technologies and other digital tools (e.g., Khanna, 2021). Decades of studies have
generated important insights on drivers of adoption, with emphasis centering on: field level variation in soil
attributes and land characteristics, the owner-operator’s level of human capital (e.g., education and years of
experience) and risk perceptions, productivity or profit potential of the tool, availability of complementary
(or substitutable) inputs, farm size, and behavioral preferences or other operator-specific factors (e.g., taste or
distaste for technologies associated with early or late adoption). In short, many farmers’ adoption decisions
are based on their expectations of how the tool(s) will affect their operation’s performance (see box, “How
Do Precision Technologies Impact Farm Performance?”). We focus and report on several main drivers of
adoption between 2016 and 2019, though with more consideration given to important factors that have been
underexamined to date (e.g., technology costs, USDA programs, labor-saving benefits, and hiring of technical
or consultant services).
How do Precision Technologies Impact Farm Performance?
Economists have been studying the impacts of precision agriculture on measures of U.S. farm perfor-
mance for well over two decades. In general, it is conceptually difficult to generate compelling evidence
on the impacts because: (1) use of real-world data from commercial farms may result in biased estimates
of impacts if other confounding factors (e.g., managerial expertise, farmers’ “tastes” for technologies) are
not corrected, (2) use of field-trial data (typically from small samples) may result in overstated impact esti-
mates because the carefully controlled—oftentimes ideal—conditions of experimental trials rarely occur
on commercial farms. The most compelling estimates, therefore, may result from a synthesis of the two
approaches, though we give preference to estimates here using large samples of commercial farms.
Among the most-studied economic dimensions of precision agriculture is profitability. Based on 2010
Agricultural Resource Management Survey (ARMS) data from U.S. corn farms, Schimmelpfennig (2016)
found that global navigation satellite system (GNSS) mapping increased operating profits by almost 3
percent, while variable rate technologies’ (VRT) impact was 1.1 percent. It is challenging to separate the
effects of these technologies on total revenues from total costs, though revenue effects are likely the result
of increased yields (Schimmelpfennig and Ebel, 2011). Such impacts are consistent with studies demon-
strating that precision technologies raise the productivity of certain inputs, like nitrogen fertilizer (Isik and
Khanna, 2002) and irrigation applications. These impacts are also consistent with stated motivations for
the use of the technologies (Thompson et al., 2019).
Equally, if not more important, are the technological contributions to improvements in U.S. agricul-
tural sustainability. As explained above, these technologies hold great promise for improving the environ-
mental impacts of farming (e.g., Basso and Antle, 2020), though the evidence to date has been mixed.
Adopters generally have lower input costs for fertilizers, pesticides, and fuels (which is consistent with
lower use)—though reductions have been modest and vary by technology type (Schimmelpfennig and
Ebel, 2016; Schimmelpfennig, 2016). Joint adopters of soil nitrate testing and crop rotations on U.S. corn
fields have substantially lower fertilizer applications rates than those only adopting crop rotations (USDA,
Economic Research Service, 2021). Moreover, analysis of operations on U.S. soybean and rice fields has
suggested farmers who invest in precision agriculture technologies are also likely to use conservation prac-
tices, though this research does not establish a causal link between their use and a reduced environmental
footprint (Schimmelpfennig, 2018; Schimmelpfennig, 2019).
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Many of these same technologies help farmers manage risk from various sources, including prices, weather,
pests, and other major drivers of uncertainty throughout the growing season (Khanna, 2021). For example,
late-spring soil nitrate testing is a tool to reduce uncertainty about crop nutrient needs; its use in corn
production can significantly reduce fertilizer applications (Babcock and Blackmer, 1992). Similarly,
farmers’ use of yield and/or soil maps reduces variability in the efficiency of Midwest corn production
(McFadden et al., 2022c). Risk reductions may also come about through the use of automated guidance
or other input adjustments directly facilitated by guidance (Shockley et al., 2011). More broadly, it is also
likely that more accurate and spatially-detailed data on input use, harvests, and growing conditions (i.e.,
the kind of data that underlie precision machinery and detailed management recommendations) will, over
time, be useful for designing new—or improving currently existing—crop insurance products.
Technology Prices, Premiums, and Other Costs
Information on equipment replacement costs, annual fees, and premiums paid for equipment are available
from ARMS surveys of soybean and cotton fields in 2018 and 2019, respectively, for yield monitors, VRT,
guidance systems, and drones (table 3). Data on replacement costs are specific to yield monitors, guidance
systems, and drones. Premiums paid for VRT are reported and interpreted for the entire farm operation.
Annual fees are typically subscription fees (i.e., “unlock codes”) that enable equipment capabilities.
Since each equipment manufacturer has company-specific pricing schedules and fee structures, costs are not
homogeneous for any technology; some manufacturers are considered to be the high-cost provider for specific
equipment, while other manufacturers and third-party technology providers typically have lower costs. Many
farm operators do not have homogeneous equipment sets from a single manufacturer but rather an assort-
ment from two or more manufacturers.
Since cost data in ARMS are reported for 2018 and 2019, the change in the target commodities implies vastly
different acreage distributions and production practices across the 2 years. For example, half of all soybean
acres in 2018 were farmed on operations with 1,105 cropland acres or fewer. By contrast, half of all cotton
acres in 2019 were located on farms with 2,500 cropland acres or fewer. The costs reflect the likely number
of machines for the respective crops, given that median cropland acreage on cotton farms was twice that of
soybean farms.10
10 However, ratios of costs to acreage differentials are similar between the soybeans and cotton data.
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Table 3
Replacement costs, annual fees, or premiums associated with precision technologies, 2018–19
Technology Cost, fee, or premium Soybeans (2018) Cotton (2019)
Yield monitor Equipment replacement $8,051 $13,775
($350) ($1,293)
Annual fee $1,041 $1,772
($76) ($482)
Variable rate technologies Premium paid for equipment $5,630 $10,319
($1,320) ($3,546)
Guidance Equipment replacement $20,165 $25,168
($5,019) ($2,836)
Annual fee $1,154 NA
($76) NA
Drones, aircraft, or satellites Equipment replacement $2,610 #
($630) #
Note: Entries are total dollars for equipment replacement costs, annual feels, or premium paid—depending on precision technol-
ogy. Delete-a-group jackknife standard errors are reported in paratheses. The “NA” indicates data are not available; the 2019 USDA,
Agricultural Resource Management Survey (ARMS) Phase II survey did not survey farmers about their annual fees for guidance
systems. The “#” indicates that the sample size for equipment replacement costs associated with drones, aircraft, or satellites in the
2019 ARMS Phase II cotton survey was too small to provide a meaningful estimate.
Source: USDA, Economic Research Service and National Agricultural Statistics Service, Agricultural Resource Management Survey,
years 2016–19.
Yield monitors
Yield monitor sensors for soybeans and cotton are as different as the harvesters on which they are installed.
The cost of yield monitor replacement was just over $8,000 for soybeans in 2018 and $13,775 for cotton in
2019, likely a partial reflection of the difference in harvesting costs (see table 3). Moreover, acreage differ-
ences between soybean farms (especially those in the Midwest) and cotton farms in the southern United
States could impact the total costs of the equipment. The annual fee for soybean ($1,041) was about half (59
percent) that of cotton ($1,772), most likely an artifact of the number of harvesters on specific crop farms.
Variable rate technologies (VRT)
VRT equipment is the same for soybeans and cotton. Therefore, no machinery differences are expected that
would contribute to crop-specific VRT premiums.11 However, the average premium paid for VRT applicators
used in cotton fields in 2019 ($10,319) is double that reported by operators of soybean fields in 2018 ($5,630).
As with yield monitor replacement costs, this cost differential likely reflects, at least partially, differences in
the total number of VR machines implied by the difference in the acreage distribution.
An examination of per acre fees for custom variable-rate applications provides valuable insights into this
segment of the equipment market. Recent custom rate surveys in the Great Plains region of the United States
indicated that premium upcharges for variable rate applications (either through hire of the service itself or
rental of VRT equipment) were negligible (McLure and Jansen, 2020). The survey evidence suggested that
Nebraska farmers paid an additional $1.52 and $1.91 per acre (on average) for variable rate application of dry
11 Two partial exceptions are expected. First, nitrogen fertilizers are not commonly recommended on soybean crops; thus, acre for
acre, other crops (i.e., cotton, corn, and wheat) would have an additional field operation for fertilizer application relative to soybeans.
Second, the price of VR planters for cotton may exceed those for soybeans, although on a “row-wise” basis, costs are not expected to be
nearly double those of cotton.
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and liquid fertilizer, respectively, over solid uniform rate applications. The average rental rate of VRT equip-
ment for liquid applications was $1.17 over the average rental rate for uniform rate technology. Although the
upcharge for variable rate is 20 percent above the solid uniform rate, this differential is relatively low when
compared with gross production revenues on similar operations (Griffin and Traywick, 2020).
Guidance systems
Average guidance system replacement costs are higher among operators of cotton fields in 2019 compared
to operators of soybean fields in 2018, likely indicative of cotton farmers’ use of systems that require more
expensive guidance sensors. Annual fees for guidance systems vary considerably based on guidance quality.12
Publicly available GNSS signals are provided at zero cost for basic accuracy; however, subscription-based
correction services are commonly employed by row crop farm operations. Local ground-based stations
provide the highest accuracy, real-time kinematic (RTK) correction via equipment purchase or as subscrip-
tion from a service provider. Satellite-based correction services are available at various accuracy levels from
equipment manufacturers for combinations of activation fees and annual subscription maintenance fees.
Drones, aircraft, or satellites
Since farm operators are not expected to fund equipment replacement for U.S.-owned satellites and are
unlikely to fund replacement for crewed aircraft, the $2,610 estimate in table 3 should be interpreted as
pertaining to drones. Even though drones are much less commonly used than the other DA component tech-
nologies discussed in this report, their replacement costs are better understood, in large part because farm
operators are likely to experience catastrophic damage (i.e., irreparable damage that renders the drone inoper-
able) much more often with drones than with VRT equipment or yield monitors. In addition to the drone
platform, which itself can cost several thousand dollars if purchased new, the onboard camera(s) and commu-
nication devices have additional costs. Color-imaging devices are less expensive than near infrared devices
or thermal sensors. Minor drone crashes may require one or more propellers to be replaced, but catastrophic
damage may result in old equipment being salvaged and may require purchasing new equipment. The 2018
estimate of $2,610 is lower than the list price of new equipment in that period, suggesting that surveyed
farmers may have purchased pre-owned drones (or intend to purchase pre-owned drones if their current
equipment needed to be replaced).
Caveats regarding premiums, replacement costs, and annual fees
In addition to the technologies presented in the table, farmers are increasingly subscribing to cloud-based
farm management information systems (FMIS), sometimes in conjunction with their service providers or
input suppliers. As of 2021, one popular tool offered by the John Deere company is being marketed with a
zero-subscription fee, though annual maintenance fees are still being charged for GNSS correction signals.
Many of these FMIS tools are bundled with inputs and other consulting services in such a way that their
incremental costs (i.e., costs solely attributable to the FMIS) are difficult—or impossible—to observe by the
farm operator.
In the 1990s, technologies currently considered to be precision agriculture equipment were seldom available
from the original equipment manufacturer; over the past decade, most digital technology has become stan-
dard on new equipment. For example, when a new fertilizer applicator is manufactured, VRT hardware is
also installed; activation fees are then charged by the manufacturer to unlock various access levels. Similarly,
12 Annual fees for guidance systems, as well as yield monitors, are in line with fees publicly available in catalogs of major U.S.
manufacturers.
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farm operators with a GNSS-enabled yield monitor in 1995 (for example) were early-stage adopters who made
a deliberate effort to acquire and install the hardware. By 2015, many farm operators with yield monitors were
using harvesters on which the technology had been bundled (Lambert et al., 2015; Miller et al., 2019).
Purchase prices of DA technologies are therefore more difficult to estimate today than in the 1990s when
catalog prices more clearly indicated the incremental cost of the technology.13 In many ways, several digital
technologies have become inseparable from basic equipment, so adoption, replacement, and upgrade costs
may be imprecisely estimated; however, one-time activation fees and annual subscription fees remain simpler
to understand and can be more accurately measured.
Soil Variability and Field Topography
One of the most widely recognized determinants of farmers’ precision equipment uptake is in-field soil vari-
ability (Cassman, 1999; Bongiovani and Lowenberg-DeBoer, 2004). Farmers are expected to benefit from
greater awareness of spatial trends—largely through yield and soil mapping—when they have fields that
exhibit high degrees of variability in soil texture, slope, organic matter, drainage, and other factors partly
indicative of cropland productivity. With this knowledge, farmers can better capitalize on spatial variability by
adjusting seeding, fertilizer, pesticide, and irrigation rates within the field—mainly through the use of VRT.
It is challenging to capture the full importance of soil variability and land characteristics with ARMS (Phase
II) data for two reasons. First, the survey generally elicits information relevant at the whole-field level. It
would be impractical to ask detailed questions on sub-field variation in soils because any responses would be
necessarily general and imprecise with respect to locations within the field. Second, only management prac-
tices pertaining to one field—not multiple fields—are surveyed. This is potentially problematic because some
operators condition their technology choices on soil and land variability across multiple fields. Researchers
have attempted to overcome these challenges through the use of geolocated field data, but: (1) the geoloca-
tions (i.e., exact location of the field) are inaccurate for many fields, and (2) for privacy and reliability, the
data must be aggregated to higher spatial levels (e.g., 3 kilometers or above), which tend to smooth out the
kinds of spatial variability most important for farmers’ decisions (McFadden et al., 2022c; Schimmelpfennig
and Lowenberg-DeBoer, 2021).
However, the ARMS questionnaires do elicit information about whether the operator’s field was nearly flat
(0–2-percent grade), moderately sloped (3–9-percent grade), or steeply sloped (more than 10-percent grade).
Other things being equal, greater slopes tend to coincide with lower yields, and since lower yields imply lower
revenues, the operator will generally choose to adjust inputs accordingly. This input adjustment includes the
possibility of taking parts of a field—or the entire field itself—temporarily out of production, in line with
what is sometimes termed “zone management.” Despite these considerations, adoption rates do not differ
considerably by field slope (table 4). One exception is guidance systems for soybeans in 2018; adoption was 45
percent on nearly flat fields, in contrast to 26 percent on steeply-sloped fields where automated guidance may
be relatively costly or impractical.14
13 Regardless of year, however, catalog list prices do not include the cost of human capital investments that farmers may need to
undertake to fully understand and make appropriate use of the technologies. Such prices also do not adequately capture search costs or
switching costs associated with farmers’ purchases.
14 In related work, Schimmelpfennig (2018) found significant correlations between adoption of certain precision technologies and
environmentally related best management practices: soil care, nutrient control, field condition monitoring, interseasonal field opera-
tions planning, and written long-term planning. These correlations were found for soybean fields from the 2012 ARMS wave, though
similar findings hold for rice fields from the 2013 ARMS (Schimmelpfennig, 2013).
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Table 4
Percent of surveyed fields managed with precision technologies by slope of field, 2016–19
Yield Soil Variable rate Drones, aircraft,
Guidance
maps maps technologies or satellites
Percent
Corn (2016)
Nearly flat 29.9 14.7 25.6 41.9 4.7
(1.9) (1.5) (1.8) (2.5) (0.8)
Moderate slope, 3–9-percent grade 27.8 13.1 23.7 33.9 3.7
(1.7) (1.3) (1.5) (1.6) (0.9)
Steep slope, over 10-percent grade 35.6 10.0 22.8 30.0 0.6
(9.6) (4.5) (6.2) (7.4) (0.6)
Winter wheat (2017)
Nearly flat 16.7 5.2 13.0 46.6 2.0
(2.5) (1.7) (2.0) (3.3) (0.8)
Moderate slope, 3–9-percent grade 14.3 2.6 13.4 42.9 3.0
(2.0) (0.6) (1.6) (3.4) (0.1)
Steep slope, over 10-percent grade 14.4 6.1 14.8 36.5 1.9
(6.5) (2.7) (6.6) (14.1) (1.3)
Soybeans (2018)
Nearly flat 33.8 13.4 19.5 44.6 7.3
(1.8) (1.4) (1.6) (1.8) (0.9)
Moderate slope, 3–9-percent grade 30.7 10.0 22.0 35.9 5.4
(2.2) (1.3) (2.1) (2.1) (0.9)
Steep slope, over 10-percent grade 26.2 7.0 14.8 25.6 5.6
(6.5) (4.1) (5.0) (5.8) (3.7)
Cotton (2019)
Nearly flat 15.3 7.0 24.4 64.4 3.9
(5.3) (1.5) (6.0) (3.5) (1.1)
Moderate slope, 3–9-percent grade 9.8 8.3 24.4 53.8 2.0
(2.0) (1.6) (3.2) (4.0) (0.9)
Steep slope, over 10-percent grade 29.5 16.3 56.7 65.7 4.2
(23.1) (14.4) (24.4) (20.4) (4.7)
Note: Cell entries indicate mean adoption rates (i.e., percent of surveyed fields) by slope of field. Means have been expanded to be
representative using the USDA, National Agricultural Statistics Service (NASS)-provided expansion factor (base weight). The mod-
erately sloped category aggregates fields that have even grades and fields that have variable grades. The steeply sloped category
also aggregates fields that have even and variable grades. Delete-a-group jackknife standard errors are in parentheses.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey (ARMS), years 2016–19.
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USDA Programs
Programs administered by the USDA and other Federal agencies have a direct bearing on farmers’ input
choices. For example, the Conservation Reserve Program provides an annual rental payment to contracted
farmers who remove environmentally sensitive land from production (McFadden and Hoppe, 2017). More
generally, many economic analyses have sought to measure the extent to which decoupled farm payments
and/or federally subsidized crop insurance premiums induce changes in farmers’ management practices
(Weber and Key, 2012; Weber et al., 2016). However, to our knowledge, no major studies have documented
the various Federal programs that directly impact digital agriculture usage, in spite of the fact that several
such programs have been developed and administered in recent years.
The Conservation Stewardship Program (CSP), established in the 2008 farm bill, is a USDA working-lands
conservation program that provides annual payments to participants for improving their environmental
performance to address resource concerns. Some of the benefits from CSP include: enhanced resilience
to weather and market volatility, decreased input usage, and improved wildlife habitats, among others. In
general, a participant signs a 5-year CSP contract, which includes an opportunity to compete for contract
renewal if the participant has met the initial contract requirements and agrees to achieve additional conser-
vation goals. Contract payments, including those for enhancements involving digital agriculture, are based
on (1) existing land use and stewardship at enrollment time; (2) implementation of additional conservation
activities; and (3) supplementation with the adoption of one additional resource-conserving system15 (USDA,
Natural Resources Conservation Service, 2021).
Enrolled acreages for digital agriculture-related CSP enhancements that aim to improve cropland soil quality
display considerable variation by practice code (table 5).16 For example, 342,000 acres of cropland in 2017
were managed by operators who contracted to enhance their baseline stewardship by using precision pesticide
applications to reduce the risk of pesticides in surface water. In 2020, this particular practice was again the
most common CSP enhancement, with more than 2.24 million contracted acres, followed by 1.51 million
contracted acres on which participants use precision agriculture technologies to reduce the risk of nutrient
losses to surface water. Although these DA-related enhancements have become more frequently used over
time, table 5 tends to overstate year-on-year growth for any particular practice because only practices on
contracts signed in fiscal year 2017 or later are reported by NRCS through the RCA Data Viewer. For this
reason, it is more accurate to compare contracted acres across practices within a given year.
15 A supplemental payment is provided if the participant adopts or improves a resource-conserving crop rotation or adopts advanced
grazing management (USDA, Natural Resource Conservation Service, 2021).
16 NRCS also reports national summaries of contracted acreage for many of these same enhancements, though for other goals (e.g.,
improving water quality, irrigation efficiency, grazing land conservation, forest land conservation). Since NRCS performance measure
rules stipulate counting any given practice in every performance measure for which it is listed (regardless of why the practice was
installed), for any given DA-related CSP enhancement, acreage totals for cropland soil quality are very similar to—if not exactly equal
to—acreage totals for water quality and/or irrigation efficiency (where applicable). For example, 2020 acreage totals for enhancement
E449114Z1 (soil moisture is monitored, recorded, and used in decision making) are 33.66 thousand for cropland soil quality, 33.77
thousand for irrigation efficiency, and 33.77 thousand for water quality. Acreage totals for the DA-related enhancements for grazing
land conservation and forest land conservation are much lower; those are not reported as they are beyond the scope of this report.
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Table 5
Conservation Stewardship Program (CSP) acres for Cropland Soil Quality with enhancement practices
involving digital agriculture technologies, 2017–20
Year
2017 2018 2019 2020
Cropland Soil Quality per 1,000 acres
Practice
Percent
Controlled traffic farming to reduce compaction 110.60 198.00
Percent of total annual CSP Cropland Soil Quality acres [3.05] [3.37]
Soil moisture is monitored, recorded, and used in decision making 1.64 15.48 28.22 33.66
Percent of total annual CSP Cropland Soil Quality acres [0.27] [0.49] [0.78] [0.57]
Use precision pesticide applications to reduce risk of pesticides in
341.81 1,336.27 1,055.13 2,247.15
surface water
Percent of total annual CSP Cropland Soil Quality acres [56.61] [42.45] [29.13] [38.21]
Use precision agriculture technologies to reduce risk of nutrient
11.66 514.45 693.04 1,514.99
losses to surface water
Percent of total annual CSP Cropland Soil Quality acres [1.93] [16.34] [19.13] [25.76]
Use precision agriculture technologies to reduce risk of nutrient
0.2 10.72 51.57
losses to ground water
Percent of total annual CSP Cropland Soil Quality acres [0.01] [0.30] [0.88]
Note: Only acres for which the practice is counted toward the Cropland Soil Quality performance measure are listed. These acreage
trends are similar to those for practices counted toward irrigation efficiency or water quality performance measures. Since a practice
can be counted toward more than one performance measure, we do not sum acreages across measures in order to avoid double- or
triple-counting. By row, the enhancement codes correspond to these five practices: E334107Z, E449114Z1, E595116X, E590118X, and
E590119X, respectively. The USDA, Natural Resources Conservation Service (NRCS) is currently only reporting data based on con-
tracts signed in fiscal year 2017 or later. Because these contracts are 5 years long and not every enhancement is implemented in the
first year, there is a strong upward trend in the data over the 4 years 2017–2020—an artifact of excluding contracts signed before fis-
cal year 2017. Units are in 1,000s of acres. Blank cells indicate that program acreage was either zero or not reported by NRCS. Brack-
eted numbers are the percentages of that year’s annual CSP acres for cropland soil quality represented by the particular practice.
Source: USDA, Natural Resources Conservation Service, RCA Data Viewer.
Basic economic principles suggest that farmers’ decisions to enroll their acreage in one or more of these CSP
practices are strongly influenced by payment rates (table 6). National payment rates in 2021 (the most recent
year for which data are available) averaged $8.18/acre for controlled traffic farming, $20.85/acre for soil mois-
ture monitoring in years 2–5 of the farmer’s CSP contract, $11.45/acre for precision pesticide applications,
and $15.44/acre for use of PA technologies to reduce the risk of nutrient losses to surface water.17 As expected,
this compensation represents a small fraction of annual total crop production costs, though it represents the
full estimated cost to implement the enhancement. USDA estimated that national average total produc-
tion costs in 2021 (excluding Government payments) were $698.73/acre for corn, $513.96/acre for soybeans,
$332.26/acre for wheat, and $728.69/acre for cotton (USDA, Economic Research Service, 2022).
17 These payment rates do not include defrayment of equipment costs. Moreover, data were not available for payments related to
reducing the risk of nutrient losses to ground water, a practice reported in table 5.
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Table 6
Conservation Stewardship Program (CSP) per acre payment rates for enhancement practices
involving digital agriculture technologies, 2021
Precision pesticide Precision agriculture
Advanced soil
Controlled applications to reduce to reduce risk of
State moisture monitoring,
traffic farming risk of pesticides nutrient loss to
years 2–5
in surface water surface water
Alabama $18.89 $11.43 $15.50
Alaska $9.52 $25.87 $12.89 $16.81
Arizona $8.17 $19.81 $10.77 $14.69
Arkansas $6.97 $17.64 $10.49 $14.61
California $9.74 $23.37 $12.15 $15.90
Colorado $8.34 $20.52 $11.09 $14.96
Connecticut $8.50 $21.91 $10.78 $14.40
Delaware $21.14 $12.91 $17.03
Florida $7.64 $17.91 $11.23 $15.29
Georgia $18.89 $11.43 $15.50
Hawaii $27.42 $13.35 $17.43
Idaho $8.98 $19.04 $11.46 $15.29
Illinois $24.52 $11.22 $14.90
Indiana $7.46 $20.30 $10.94 $14.99
Iowa $19.33 $11.22 $15.23
Kansas $7.71 $19.56 $11.75 $15.80
Kentucky $19.06 $11.13 $15.23
Louisiana $8.34 $19.93 $10.90 $14.77
Maine $18.73 $11.16 $15.15
Massachusetts $25.57 $11.71 $15.57
Michigan $8.36 $21.46 $11.88 $15.80
Minnesota $24.46 $12.03 $15.87
Mississippi $17.63 $11.17 $15.30
Missouri $21.64 $11.44 $15.63
Montana $20.99 $11.08 $15.15
Nebraska $7.91 $19.71 $11.41 $15.42
Nevada $7.97 $22.04 $11.10 $15.08
New Hampshire $8.06 $19.99 $11.34 $15.28
New Jersey $8.68 $25.56 $11.22 $15.13
New Mexico $8.16 $17.73 $12.17 $16.15
New York $9.02 $24.91 $12.47 $16.45
North Carolina $18.46 $11.86 $15.85
North Dakota $22.15 $11.14 $15.21
Ohio $8.10 $20.92 $10.71 $14.67
Oklahoma $18.99 $10.20 $14.05
Oregon $23.36 $11.86 $15.65
Pennsylvania $22.77 $12.45 $16.47
Rhode Island $8.29 $22.70 $11.58 $15.59
South Carolina $7.06 $18.47 $11.35 $15.55
South Dakota $7.38 $17.76 $11.23 $15.28
Tennessee $7.25 $17.20 $11.38 $15.47
continued on next page ▶
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Texas $8.30 $18.39 $11.46 $15.34
Utah $7.15 $18.28 $10.09 $13.85
Vermont $8.07 $18.31 $11.28 $15.25
Virginia $8.25 $16.21
Washington $9.63 $25.93 $11.84 $15.62
West Virginia $18.38 $11.46 $15.63
Wisconsin $8.16 $21.98 $11.40 $15.38
Wyoming $21.14 $11.12 $15.09
Note: CSP practice codes for the four columns are E334, E449C, E595A, and E590B. None of the payment rates include partial
equipment costs (e.g., “equipment and soil moisture or water level monitoring”). Multiple irrigation-related practices differentiate
payment rates between year 1 and years 2–5. In four States in 2021, USDA, Natural Resources Conservation Service (NRCS)
provided payments for variable rate irrigation water management associated with practice code E449 (not listed). Moreover, in 38
States in 2021, NRCS provided payments for “reducing nutrient loss by increasing setback awareness via precision technology for
water quality” associated with practice code E590D (not listed). Blank cells indicate that payment rates were not reported by NRCS.
No data were available for Maryland.
Source: USDA, Economic Research Service using data from USDA, Natural Resources Conservation Service, National State Pay-
ment Schedule website.
These national averages mask considerable variation in payment rates across the practices by State. For
example, States with among the highest per acre payments for advanced soil moisture monitoring included
Hawaii, Washington, Alaska, Massachusetts, and New Jersey (all paying well over $25/acre). By contrast,
South Dakota, New Mexico, Arkansas, Mississippi, and Tennessee had among the lowest rates for this prac-
tice (offering under $18/acre). Generally, high-cost States tended to provide larger average payments, while
lower cost States tended to provide smaller payments. This finding comports well with regional evidence from
the annual USDA, ERS Commodity Costs and Returns data. States with high payment rates tended to be in
the Heartland, Northern Crescent, and Fruitful Rim—whereas States with low rates were broadly located in
the Southern Seaboard, Prairie Gateway, and Eastern Uplands (U.S. Department of Agriculture, 2022).
Alternatively, farmers can instead receive USDA assistance for one or more DA-facilitated conservation prac-
tices through the Environmental Quality Incentives Program (EQIP), which provides financial assistance
(payments) to producers implementing conservation practices on working agricultural lands.18 For each prac-
tice, NRCS defines a group of scenarios that are a set of activities that can be used to implement a specific
practice. Substantial variation exists in how individual practices are applied (e.g., nutrient management
plans), so there are multiple scenarios with differing costs and payments. However, NRCS no longer collects
cost data for these practices directly from farmers. Although scenario-based acreage data are not available for
recent years, EQIP-assisted conservation practices related to DA have lately become more popular across the
United States (USDA, Natural Resources Conservation Service, 2012).
Other Government programs or initiatives have indirect “knock-on” effects that can stimulate farmers’ DA
adoption. For instance, broadband access to the internet is required for many digital technologies to function
(or function well), and a lack of such access has been identified as a major barrier to their adoption (Whitacre
et al., 2014; USDA, 2019). USDA’s Rural Utilities Service has administered several programs that finance
broadband and related infrastructure, including Community Connected/Reconnect Grants, the Farm Bill
18 Three additional points should be mentioned. First, a participant is not eligible for payments for conservation practices or activi-
ties when the participant receives payments or other benefits under any other USDA conservation program for the same practice or
activity on the same land at the same time. Second, EQIP payments provide financial assistance that offsets up to 90 percent of the
practice implementation costs, while CSP payments typically provide 100 percent or more of the enhancement costs (and up to 10
percent of practice costs). Third, EQIP and CSP also provide technical assistance to farmers for implementing conservation practices.
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Broadband Program, and the Telecommunications Infrastructure Loans and Guarantees Program. While
these programs do not directly pay farmers to adopt or to improve their adoption of digital technologies, it
is clear that the programs have had an accelerating effect. Astill et al. (2020) reported that USDA’s Rural
Development agency provided $3.4 billion of funding across 280 digital infrastructure projects between fiscal
years 2010 and 2018.
Labor-Saving Benefits
Many component technologies of digital agriculture (especially autonomous machinery) are widely consid-
ered to save farmers’ time and effort, a term denoted by economists as “labor-saving” (Gallardo and Sauer,
2018; Lowenberg-DeBoer et al., 2021). Automated guidance systems, for instance, can directly reduce the
number of hours that operators must spend on field operations by eliminating redundant passes of equipment
across cropland. Moreover, fewer workers may be needed during peak field-operating periods (e.g., during
planting and harvesting). Adopters can then reallocate their “saved” hours to farm work other than field
operations, off-farm work, and/or leisure.19 The conceptual relationship between labor hours and adoption of
other digital technologies is less clear. However, we would still expect an inverse relationship between labor
hours and DA adoption because these technologies should contribute to enhanced decision making, likely
resulting in fewer (and/or higher quality) work hours for the farmer.
Using a sample of corn fields from the 2010 ARMS, Schimmelpfennig (2016) found that total labor hours
per bushel of corn for adopters of yield and georeferenced soil maps were 35 percent lower relative to those of
nonadopters. Although the difference in labor hours per bushel between adopters and nonadopters of guid-
ance systems was insignificant, labor hours per bushel were 28 percent lower for VRT adopters, as compared
to nonadopters. These findings are corroborated in a sample of ARMS cornfields from 2010 and 2016 across
10 Midwestern States, as analyzed by McFadden et al. (2022c).
In that sample, labor hours per acre (totaled across the farm’s entire corn enterprise) were 0.15 for adopters
of yield and soil maps—half as large as the labor input for nonadopters of 0.30 hours/per acre. Moreover,
adopters of guidance systems worked 49-percent fewer hours per acre than nonadopters; similarly, VRT
adopters worked 41 percent less per acre than VRT nonadopters. Reductions in per bushel or per acre labor
hours are unlikely to be mainly attributable to VRT adoption, however. Larger operations adopt VRT at
greater rates than smaller farms, but these operations also tend to adopt other labor-saving practices (e.g.,
conservation tillage) and labor-saving equipment that directly embeds DA technologies (e.g., modern, ever-
wider implements).
There is mixed evidence regarding per acre labor costs (Schimmelpfennig, 2016). Hired labor expenses are
generally greater for adopters, while unpaid labor expenses—as measured by the operators’ opportunity
costs—are significantly lower for adopters than nonadopters.20 To some extent, labor expenses have a degree
of inaccuracy because implicit wage rates that underly these expenses are not quality-adjusted. Much anec-
dotal evidence suggests automated guidance substantially reduces operator fatigue; this improvement in the
quality of fieldwork and potential improvement in quality of life for farm households—although very impor-
tant—is not currently accounted for in labor-expense or labor-quantity data.
19 Because VRT also better enables farmers to perform field operations outside of typical daylight hours, it could improve the
quality of other work during daylight hours or perhaps reduce total labor hours due to a more optimized arrangement of tasks
throughout the entire work cycle.
20 As Schimmelpfennig (2016) notes, the cost of unpaid labor is estimated using whole-farm data for all types of farms. Statistical
analyses of farmers’ attributes (e.g., age, education, marital status, location, and other variables) are used to predict their off-farm earn-
ings. These earnings, together with data on the total number of unpaid hours worked across the entire corn enterprise, are used to
calculate the opportunity costs of farmers’ unpaid labor hours.
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Farm Size and Expected Productivity Impacts
Some operators adopt digital agriculture technologies for purely mechanical reasons related to farm size:
Larger operations can be managed more easily and/or efficiently with high-resolution field data, automated
guidance, or equipment capable of adjusting input applications in real time continuously across fields. Yet
there are more nuanced reasons for the high degree of correlation between adoption and farm size.
Operations with greater cropland acreages tend to have: (1) reduced risk aversion; (2) lower per unit costs
of inputs (made possible by spreading out high fixed-equipment costs over large areas, in conjunction with
potential quantity discounts for input purchases that can directly reduce variable costs); (3) greater soil vari-
ability (because soil quality is generally most similar over smaller areas); (4) access to more favorable credit
terms, often needed to finance the purchase of sophisticated, expensive equipment; and (5) larger numbers
of managers, permitting the kind of specialization of managerial labor that could lead to greater awareness
of—and expertise in using— digital technologies (Daberkow and McBride, 2003; MacDonald et al., 2013;
Khanna, 2021).
Regardless of specific motivation, it is evident from the most recent data that operators of large farms adopt
these tools at higher rates than operators of smaller farms (table 7). For example, only 7 percent of farms with
total cropland less than 200 acres (the first quintile of cropland associated with surveyed corn fields in 2016)
had an operator who adopted yield maps.21 In contrast, 50 percent of farms with total cropland greater than
1,725 acres (the fourth quintile of cropland on farms with a surveyed corn field in 2016) had an operator who
adopted yield maps. More broadly, adoption rates tend to increase with farm size, regardless of technology
(maps, VRT, and guidance) or crop (corn, soybeans, wheat, and cotton).
Table 7
Percent of farms adopting precision technologies by farm size, 2016–19
Variable rate
Yield maps Soil maps Guidance
technologies
Percent
Corn (2016)
Below 1st quintile 7 5 8 10
Between 1st and 2nd quintiles 14 6 12 18
Between 2nd and 3rd quintiles 23 11 19 35
Between 3rd and 4th quintiles 35 15 35 53
Greater than 4th quintile 50 26 43 73
Winter wheat (2017)
Below 1st quintile 2 2 3 7
Between 1st and 2nd quintiles 8 3 9 21
Between 2nd and 3rd quintiles 22 7 21 52
Between 3rd and 4th quintiles 24 8 25 63
Greater than 4th quintile 37 10 35 82
continued on next page ▶
21 In statistics, quintiles are cut-off points that determine five groups into which a population can be divided according to the
distribution of values of a specific variable. In this report, the first quintile is the acreage threshold, such that 20 percent of the
surveyed fields on farms have total acreage less than this amount. The second quintile is the acreage threshold of which 40 percent of
the surveyed fields are on farms with total acreage less than this amount. The quintiles differ across crops because of (1) differences in
the underlying economics of each crop, (2) time-specific factors, and (3) structure of the ARMS survey.
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Variable rate
Yield maps Soil maps Guidance
technologies
Percent
Soybeans (2018)
Below 1st quintile 11 5 9 11
Between 1st and 2nd quintiles 13 7 13 24
Between 2nd and 3rd quintiles 30 11 16 46
Between 3rd and 4th quintiles 39 15 32 65
Greater than 4th quintile 50 13 28 68
Cotton (2019)
Below 1st quintile 7 4 17 50
Between 1st and 2nd quintiles 7 6 21 53
Between 2nd and 3rd quintiles 21 11 27 57
Between 3rd and 4th quintiles 29 7 33 66
Greater than 4th quintile 34 18 40 67
Note: Entries are a percent of farms adopting the particular precision technology (column) within each cropland acreage quintile.
Due to substantial heterogeneity in farm sizes by crop, quintiles vary by crop. For corn (2016), the cropland acreage quintiles are
200, 460, 890, and 1,725. For winter wheat (2017), the cropland acreage quintiles are 300, 730, 1,600, and 3,140. For soybeans (2018),
the cropland acreage quintiles are 310, 824, 1,610, and 3,430. For cotton (2019), the cropland acreage quintiles are 1,150, 1,940, 3,200,
and 4,500. In statistics, quintiles are cut-off points that determine five groups into which a population can be divided according to
the distribution of values of a specific variable. In this report, the first quintile is the acreage threshold for which 20 percent of the
surveyed fields are on farms with total acreages less than this amount. The second quintile is the acreage threshold for which 40
percent of the surveyed fields are on farms with total acreages less than this amount. The quintiles differ across crops because of (1)
differences in the underlying economics of each crop, (2) time-specific factors, and (3) structure of the ARMS survey.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service (NASS), Agricultural Resource Man-
agement Survey (ARMS), Phase II and Phase III, years 2016–19.
As discussed earlier, adoption rates are the highest for guidance systems, with notable rates of increase across
the farm size distributions. On the largest farms (i.e., operation sizes at or above the fourth quintile), the
adoption rate for guidance was 73 percent for corn in 2016, 82 percent for winter wheat in 2017, 68 percent
for soybeans in 2018, and 67 percent for cotton in 2019. Even among the smallest cotton farms with 1,150 or
fewer total acres in 2019, the adoption rate was 50 percent.
Farmers’ perceptions of productivity impacts are closely related to the dimensions of farm size and its effects
on adoption. Using field-level data for corn production across 10 Midwestern States, McFadden et al. (2022c)
demonstrated that: (1) farmers using yield and/or soil maps are more efficient than nonadopters, and (2) these
technologies increase the entire range of production possibilities. Although the effects were estimated to be
small, because crop farmers’ revenues are proportional to yields, we would expect these productivity effects to
influence farmers’ profitability. Using similar data and methods, Schimmelpfennig (2016) found small posi-
tive effects on farmers’ net returns: 1.8 percent for soil and yield mapping, 1.5 percent for guidance systems,
and 1.1 percent for VRT.
A simple analysis of farmers’ production outcomes reveals that, in many instances, yields are statistically
significantly higher for adopters than nonadopters (table 8). This trend holds across all technologies except
variable rate pesticide applicators, which remain less commonly used across the United States. Differences in
yields between adopters and nonadopters are generally significant for all crops except cotton and sorghum,
perhaps a consequence of the relatively smaller sample sizes for both crops. Although productivity expecta-
tions are important adoption determinants, the results shown in table 8 are mere correlations between yields
and technology use. They are not causal impacts because, unlike estimates from carefully implemented causal
studies (Khanna, 2001; Bullock et al., 2009; Schimmelpfennig, 2016; McFadden et al., 2022c), these yield
differences are not controlling for other important factors that affect yields (e.g., fertilizer quantities, growing-
season weather conditions, irrigation rates, pest management, soil productivity).
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Table 8
Average crop yields for adopters and nonadopters of precision technologies, 2016–19
Variable Drones,
Yield Soil Variable Variable rate
rate Guidance aircraft, or
maps maps rate seeds fertilizer/lime
pesticides satellites
Corn yield (2016)
Adopters 183*** 182*** 184*** 187*** 176** 175*** 179**
(2.87) (4.87) (3.39) (3.41) (10.5) (2.10) (10.79)
Nonadopters 139 147 146 143 150 137 150
(2.13) (2.05) (2.19) (2.16) (1.81) (2.71) (2.19)
Winter wheat
(2017)
Adopters 62*** 70*** 53 58*** 57 55*** 51
(2.23) (4.34) (3.28) (2.95) (4.21) (1.41) (12.12)
Nonadopters 46 47 58 47 48 43 48
(1.76) (1.54) (1.56) (1.62) (1.54) (2.07) (1.54)
Soybeans (2018)
Adopters 59*** 59*** 58*** 60*** 57 56*** 61***
(0.54) (1.22) (1.24) (1.03) (2.02) (0.59) (1.69)
Nonadopters 52 54 54 53 54 53 54
(0.59) (0.42) (0.44) (0.42) 0.46 (0.61) (0.39)
Cotton (2019)
Adopters 1,095** 1,067*** 953 1,022 962 951 1,009
(77.43) (35.68) (58.06) (58.03) (60.75) (28.46) (98.36)
Nonadopters 903 922 931 913 929 907 931
(22.66) (20.82) (18.68) (22.38) (19.51) (30.04) (19.61)
Sorghum (2019)
Adopters 82 80 78 70 78 74 99***
(5.82) (13.10) (9.39) (7.36) (12.23) (4.02) (6.70)
Nonadopters 72 73 73 74 74 73 73
(4.65) (3.93) (4.12) (4.12) (4.01) (7.43) (3.90)
Note: Means have been expanded to be representative of the particular crop acreage using the USDA, National Agricultural Statis-
tics Service (NASS)-provided expansion factor (base weight). Yields for corn, winter wheat, soybeans, and sorghum are expressed
in bushels/acre. Cotton yields are expressed in pounds/acre. Delete-a-group jackknife standard errors are in parentheses. Asterisks
indicate if the difference in means from a two-sample t-test was significant at the 10-percent level (*), 5-percent level (**), or 1-per-
cent level (***).
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey (ARMS), years 2016–19.
Use of Technical or Consultant Services
As precision technologies have become more complex over the previous two decades, the cost to farmers of
learning about advanced machinery options has risen. As already noted, on some operations (typically larger
farms), multiple owners and/or hired managers have the flexibility and human capital necessary for learning
how to properly install, use, and maintain various digital agriculture technologies. Moreover, if equipment
ownership provides an adoption gain, then supply chains could emerge in which larger farms purchase the
equipment and rent excess capacity to smaller farms (Lu et al., 2016).
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Though ARMS does not generally track whether PA technologies are owned or rented, ARMS does contain
information about farmers’ hiring of technical or consultant services to advise on various site-specific
management practices (table 9).22 For some farms, hiring consultant services could be a potential pathway for
greater adoption if hiring the service increases operators’ experience and trust with the technologies and/or
reduces uncertainty about their gains from adoption (Fausti et al., 2021; McFadden et al., 2022a). Whatever
the mechanism, there is a positive correlation between hiring consultant services and use of digital agricul-
ture. Across all crops and for each type of site-specific advice service, greater shares of planted acreage were
managed by adopters of any precision technology (maps, guidance, VRT, drones, aircraft, or satellites) than
nonadopters. For example, 7 percent of 2016 corn acres were managed with at least one precision technology
and employment of a consultant to develop/interpret a yield map or remote sensing map.23 Similar trends,
though with somewhat different percentages, prevail for the other five site-specific advisory services.
Table 9
Percent of planted acreage with hired technical or consultant services by adoption status, 2016–19
Adopters Nonadopters
Percent
Corn (2016)
Yield map or remote sensing map development/interpretation 7.07 0.12
Soil or tissue sample collection 13.37 1.93
Nutrient recommendations/management service 13.67 2.59
Pest scouting 9.39 1.89
Pest control recommendations/management service 10.00 2.15
Irrigation management service 2.11 0.39
Winter wheat (2017)
Yield map or remote-sensing map development/interpretation 1.50 0.09
Soil or tissue sample collection 4.99 1.99
Nutrient recommendations/management service 5.31 2.89
Pest scouting 5.11 3.03
Pest control recommendations/management service 4.15 3.34
Irrigation management service 0.59 0.15
Soybeans (2018)
Yield map or remote sensing map development/interpretation 6.70 0.18
Soil or tissue sample collection 10.28 0.94
Nutrient recommendations/management service 11.74 1.44
Pest scouting 10.14 1.63
Pest control recommendations/management service 9.87 1.34
Irrigation management service 1.75 0.03
continued on next page ▶
22 Starting in 2018, ARMS began collecting information on whether guidance auto-steering equipment and VRT applicators were
owned or leased. Of those reporting use of guidance for management of their 2018 soybean crop, 97 percent indicated they owned the
technology. Similarly, of those reporting use of VRT for seeding, fertilizer/lime applications, or pesticide applications, 97 percent, 80
percent, and 90 percent (respectively) indicated they owned the applicator. For 2019 cotton, ownership rates were 98 percent for guid-
ance auto-steering, 95 percent for VRT-seeding, 91 percent for VRT-fertilizer/lime, and 96 percent for VRT-pesticide applicators.
23 The national acreage of PA technology nonadopters with consultant services for yield and/or remote sensing map development/
interpretation must not necessarily equal zero. Shares of national acreage for PA nonadopters who hired map-related services could be
nonzero if these services were hired for maps other than yield or soil maps, or for development/interpretation of yield maps for a field
other than the one surveyed in ARMS. These reasons (or others) could explain the 0.12 percent of 2016 corn acreage in the first row of
table 9.
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Adopters Nonadopters
Percent
Cotton (2019)
Yield map or remote sensing map development/interpretation 3.00 0.08
Soil or tissue sample collection 11.42 1.77
Nutrient recommendations/management service 15.03 2.00
Pest scouting 21.56 4.26
Pest control recommendations/management service 19.07 2.67
Irrigation management service 4.33 0.17
Note: Cells indicate the fraction of national planted acreage managed by operators who hired technical or consultant services for
the field. Adoption is defined to be whether the field was managed with maps (yield or soil); drones, aircraft, or satellites (imagery);
or (VRT) variable rate technologies; or guidance. USDA, National Agricultural Statistics Service (NASS)-provided expansion factors
have been used to ensure the percentages are representative of national acreages.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey (ARMS), years 2016–19.
Average total per acre costs of hiring technical or consultant services were broadly higher on DA adopters’
fields than DA nonadopters’ fields. For adopters and nonadopters, these costs were $6.71/acre and $5.31/
acre, respectively; for 2016 corn, $5.97/acre and $2.16/acre for 2017 winter wheat; $6.76/acre and $5.80/acre
for 2018 soybeans; and $15.01/acre and $12.24/acre for 2019 cotton. While these average cost differentials
could be partially explained by differences in the quality of services, the total number of services hired likely
plays an important role: DA adopters generally hired more types of consultant services than DA nonadopters.
Across the four crops, for fields on which these services were hired, the average number of services for DA
adopters was 2.8–3.1, while the average for DA nonadopters was 2.1–2.6.
Data Use, Production Recommendations, and Digital
Agriculture (DA)
Data from farmers’ operations underlie virtually all aspects of digitalization. Because data collection is at
the heart of data-driven decision making (i.e., for the use of decision-support tools), it is an integral part of
digital agriculture (figure 2). To date, researchers have examined the economics of data ownership and secu-
rity, anonymity and privacy, governance, and productivity effects (e.g., Coble et al., 2016; Jouanjean et al.,
2020; McFadden et al., 2022b). Most analyses focus on the substantial economic potential stemming from
farmers’ data being collected—with or without their knowledge—for use in highly-detailed, field manage-
ment recommendations (i.e., “prescriptions”) for their farms and then aggregated for re-use in recommenda-
tions for operations with similar characteristics (see box, “Big Data and Digital Agriculture”). Few studies
have considered, however, how farmers themselves use data from various sources, including publicly provided
data and data downloads.
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Big Data and Digital Agriculture
Discussions about big data in the U.S. agricultural sector have been frequently occurring among policy-
makers and throughout academia and the private sector since at least 2014.1 This is mainly, though not
wholly, due to the fact that most evidence-based farm management—including the use of virtually all
digital agriculture tools—is based heavily on the collection, compilation, and analysis of large volumes
of data. Historically, U.S. agricultural data have been “small” by conventional standards among data
scientists and engineers primarily because such data have been generated mainly from surveys, experi-
mental plots, or “one-field-at-a-time” methods. The move toward the use of much greater volumes of data
delivered at much finer temporal (e.g., hourly or by the minute) and spatial (e.g., meters, feet) resolution
represents a large paradigm shift in operational management and agricultural production.
For this report, big data refer to aggregations of farmers’ general production activities, usually georefer-
enced, from millions of fields over the most detailed time unit possible (e.g., annually, monthly, daily,
hourly). Such data include, but are not limited to, highly detailed information on machinery operations
(e.g., type and quantity); seeding depth and rates; cultivar (type of seed); machinery diagnostics; tillage and
planting dates; conservation practices; estimated pest populations, pest scouting, and pesticide spraying
activities; fertilizer and irrigation applications (if any); and use of other inputs. In addition, these data are
typically supplemented with information on precipitation events (e.g., rainfall, snowfall), temperature,
evapotranspiration (water loss from the soil surface, crop surface, and through crop leaves), windspeeds,
and other weather conditions. Moreover, these data may be supplemented with information at less finely
detailed aggregations, such as operators’ demographics; financial details about the farm operation and
farm household; and administrative data related to participation in USDA commodity, conservation, and
Federal crop insurance programs.
The many ways in which big data are transforming agriculture are, in large part, indistinguishable from the
agricultural digitalization process itself. Many millions of agriculture-related data points (when analyzed
and acted upon in agricultural decision making) have substantial promise for increasing farm productivity,
enhancing environmental quality, and limiting downside production risk. This is because data pooled across
millions of fields tend to reveal useful patterns undetectable with data from any one field. Thus, in a broad
sense, the inherent value of big data is impossible to separate from the value of systemwide digitalization.
Nonetheless, a central question in analyses of the digital farm economy concerns the value of data.2 Raw
data in their original, unprocessed form often have no value—at least not until converted to information
suitable for decision making. Moreover, given that data are electronic (not physical objects), identical copies
of data are indistinguishable from the original; therefore, once one copy has been made available to another
party, the data are no longer excludable (i.e., capable of being limited to certain market participants). In
attempting to determine the value of data, it is often useful to consider the extent to which data are private
goods or public goods. The use of public goods is “nonrivalrous” between consumers and “nonexcludable”
from one group of consumers to another.
1 There are numerous definitions, both narrow and broad, of big data. One popular definition has focused on volume, velocity
(i.e., frequency of observations), and variety (i.e., available in many formats, including text) as the defining features of big data
(Coble et al., 2016; Coble et al., 2018). We adopt this definition for our purposes but acknowledge that other characterizations
may be equally suitable, depending on context, including those with relatively narrower or broader scope.
2 This is partly motivated by ongoing debates centering on farmers’ concerns with ownership of data originating from their
fields and potential compensation for the data, in addition to concerns about data privacy and security.
continued on next page ▶
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Data are considered to be a “nonrival” good because the usage of data by one consumer does not alter
another consumer’s ability to use the same data. Examples of nonrival data include weather forecasts from
the National Weather Service or USDA, National Agricultural Statistics Service (NASS) crop produc-
tion reports. Importantly, the value of data to an individual farmer is not impacted by another farmer’s
accessing or using the information. Farm operations and many other agribusiness entities, when given
access, can consume farm data without reducing its value to the originator.
Data may also be considered “excludable” or “nonexcludable,” depending upon relative access rights.
Ownership of excludable goods carries a right to exclude others from obtaining access; thus, most privately
held goods are excludable. For example, some weather forecasts may be privately owned and accessible
only to subscribers. However, if the weather forecasts are generated with public funding and reported by
a Government entity like the National Weather Service, then those data (the forecasts themselves) are
nonexcludable. Agricultural data can be excludable only while it is controlled by the originating party;
however, once data have been shared with any other parties or aggregated into a community, the data
are no longer excludable. Excluding others from accessing and benefiting from data typically implies the
forfeit of any potential network benefits that could result when significant volumes of data are pooled
together for analysis.
In recent years, there have been large differences in the amount of acreage with operators who download
public data relative to those who receive crop management recommendations based on data collected on their
own fields (table 10).24 Although adopters of DA components are more likely than nonadopters to download
public data for use in decision making, adoption rates are low and do not vary substantially by crop. Public
data downloads have tended to be highest among VRT adopters; of the 2018 soybean acres managed with
VRT, 8.9 percent were operated by those who downloaded public data. Interest in public data tended to be
lowest for nonadopters of guidance; of the 2017 winter wheat acres managed without guidance (just under
half of all winter wheat acres that year), only 0.2 percent had operators who actively downloaded public data.
Rather, the data that farmers use from their fields tend to appear indirectly in the form of crop management
recommendations,25 which play a major role in the management practices of DA adopters.26 For example, 63
percent of the corn acreage managed with VRT had operators who used crop management recommendations
based on data from their fields. Adoption rates were relatively lower for soybeans in 2018 and cotton in 2019,
though the basic trend remains: DA adopters are much more likely to make use of data-based management
recommendations (and thus, implicitly, crop-productivity-related public data) than nonadopters.
24 Due to the wording of the ARMS Phase II questionnaires in 2016–19, we do not know which types of public data farmers are
downloading. However, we know that a small number of farmers are using the data to create maps. The fractions of national crop
acreage managed by operators who used the downloaded data to produce maps were 3.4 percent (2016 corn), 1.4 percent (2017 winter
wheat), 1.0 percent (2018 soybeans), and 0.3 percent (2019 cotton).
25 Many privately sold management “solutions” rely extensively on detailed, publicly collected data (e.g., information about soils,
yields, input and output prices, and weather).
26 The crop management recommendations reported in this section specifically refer to recommendations based on data from the
farmer’s field obtained through use of a yield monitor, soil core test or soil sensor test, crop condition sensor, data from custom field
work, downloaded public data, or drones, aircraft, or satellite images. These (tool-based) recommendations are conceptually distinct
from (person-based) recommendations obtained by a farmer who hired technical or consultant services to advise on site-specific prac-
tices, referred to above in the subsection “Use of Technical or Consultant Services.”
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Table 10
Average adoption rates (percent of crop acreage) of public data downloads and crop management
recommendations by adoption status, 2016–19
Crop management
Public data download
recommendations
Percent
Corn (2016)
Total sample (percent of total corn acres) 2.8 29.3
Yield map and/or soil map adopters 7.8 59.4
Yield map and/or soil map nonadopters 0.7 16.1
VRT adopters 8.0 63.3
VRT nonadopters 1.2 18.3
Guidance adopters 6.6 51.2
Guidance nonadopters 0.6 16.3
Winter wheat (2017)
Total sample (percent of total wheat acres) 1.5 22.5
Yield map and/or soil map adopters 3.8 39.8
Yield map and/or soil map nonadopters 1.0 19.1
VRT adopters 4.3 46.2
VRT nonadopters 1.1 18.9
Guidance adopters 3.2 29.0
Guidance nonadopters 0.2 17.2
Soybeans (2018)
Total sample (percent of total soybean acres) 2.7 16.1
Yield map and/or soil map adopters 7.3 33.2
Yield map and/or soil map nonadopters 0.4 7.5
VRT adopters 8.9 35.2
VRT nonadopters 1.1 11.2
Guidance adopters 5.1 29.8
Guidance nonadopters 1.2 7.3
Cotton (2019)
Total sample (percent of total cotton acres) 1.4 8.3
Yield map and/or soil map adopters 4.8 23.9
Yield map and/or soil map nonadopters 0.5 5.0
VRT adopters 3.2 14.9
VRT nonadopters 0.7 6.1
Guidance adopters 2.1 10.9
Guidance nonadopters 0.3 4.5
Note: VRT = Variable rate technologies. Means have been expanded to be representative of the particular crop acreage using the
USDA, National Agricultural Statistics Service (NASS)-provided expansion factor (base weight). Entries are average adoption rates
for public data downloads and crop management recommendations by adopters and nonadopters of precision technologies. For
example, in 2016, 2.8 percent of national corn acres were managed by operators who downloaded public data, while 29.3 percent
of national corn acres were managed by operators who obtained crop management recommendations. However, of the 2016 corn
acres managed with either yield or soil maps (or both), 7.8 percent of such acres were operated by those who downloaded public
data. Of the 2016 corn acres not managed with yield or soil maps, only 0.7 percent of such acres were operated by those who down-
loaded public data.
Source: USDA, Economic Research Service and USDA, National Agricultural Statistics Service, Agricultural Resource Management
Survey (ARMS), years 2016–19.
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The fact that data-based management recommendation use is much higher than direct public data use is
intuitive: Many farmers are more interested in—and better equipped for—actively making management
decisions than conducting data analyses. Thus, the substantial differences could reflect farmers’ relative pref-
erences for straightforward, directly applicable business advice tailored to them based on data from their own
fields similar to that provided by the Cooperative Extension Service or private input dealers. However, to a
lesser extent, the differences could signal a kind of digital divide between DA adopters who are very data-
savvy and those who are less so. The differences also point to the successes, at least partially, of input dealers
and crop consultants. The sources of these distinctions matter as there are direct productivity implications
arising from data use in crop production (McFadden et al., 2022c).
However, the value of field-level data beyond the farmgate for businesses and individuals that are not the
farmer’s technology providers remains uncertain. As farmers become more aware of their operational risks
from data privacy and security issues, they may become more reluctant to provide their data. In recent years,
ARMS has asked farmers whether they opted out of allowing their agricultural technology provider’s website
to share their field’s data with any third party. The proportions of national acreage managed by operators who
opted out of data-sharing with third parties were 5.1 percent (2016 corn), 3.4 percent (2017 winter wheat), 7.1
percent (2018 soybeans), and 9.6 percent (2019 cotton).
Conclusion
The adoption of precision agriculture technologies—basic to the ongoing digitalization of U.S. agriculture—
has been rising steadily over the past two decades. The use of auto-steer and guidance systems now occurs on
well over 50 percent of U.S. acreages planted to corn, soybeans, winter wheat, cotton, rice, and sorghum—up
from roughly 10 percent of planted acres (or fewer) in the early 2000s. Adoption rates of other technologies
(such as GNSS-based yield and soil maps, VRT, and drones) have been far less than 40 percent of planted
acres, with the exception of their use in corn (in 2010) and soybeans (in 2018), the most recent years for
which data are available. For winter wheat, cotton, sorghum, and rice, the adoption of these other technolo-
gies has been lower (between 5 percent and 25 percent of total U.S. planted acreage, depending on the year),
though their use with these crops has still generally increased over time.
Several economic features of farmers’ technology choices help to explain differences in adoption across
technology by crop over time. Many of these digital tools have considerable up-front costs, repair or replace-
ment costs, and annual user fees. These costs could be an impediment to adoption if farmers cannot “spread
them out” over a large enough cropland base while earning sufficiently high revenues. USDA conservation
programs providing technical or financial assistance to farmers for adopting digital agriculture technologies
on working lands play a role. Certain intangible benefits like improved quality of life due to lower fatigue and
stress are deemed to be major drivers of certain automation technologies, though it is difficult to quantify this
relationship due to data limitations. Ultimately, key determinants of DA use are potential improvements to
farmers’ productivity, better environmental quality, and/or decreased exposure to downside production risks.
The availability and use of technical or consultant services for providing site-specific management advice are
also positively associated with farmers’ DA adoption. A number of implications stem from our analysis:
First, the adoption of DA technologies on U.S. row-crop farms is far from universal. To the extent that
increased productivity, improved sustainability, and enhanced risk management—individually or collec-
tively—create valuable societal benefits above and beyond the technologies’ value reflected in private markets
(i.e., positive externalities), economic theory indicates there is some role for public action. Careful analysis
would be needed to estimate the benefits of these technologies that are external to the farmer (if any). For
example, more work is needed to quantify potential linkages between farmers’ (upstream) use of variable-rate
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fertilizer applications and improved water quality downstream; similar research could investigate farmers’
adoption of variable-rate pesticide applications and reductions in the off-target movement of agricultural
chemicals. Moreover, the literature has not determined if the pricing of automated guidance systems incorpo-
rates the broader societal benefits of lower operator fatigue and reduced greenhouse gas emissions (as a result
of fewer overlapping passes during field operations).
Second, it is clear from the analysis that adoption data are not available for major row crops in all years, and
even among the crops with adoption data, information is not available for all technologies (including for
controlled traffic farming, variable-rate irrigation, and distributed ledger technologies). Moreover, nationally
representative data do not exist for DA adoption in livestock and specialty crop agriculture. Filling this gap in
our understanding of DA uptake in those sectors would thus appear to be a significant priority. Large input
firms in DA marketplaces (e.g., Bayer, John Deere) are collecting increasingly large amounts of data directly
from farmers’ fields.
While this direct data collection holds some potential, many caveats currently exist. Data from input
firms via farm equipment or smart applications may not be: (1) accurate (equipment may not be calibrated
correctly); (2) representative (e.g., farmers differ with respect to brand loyalty); (3) accessible (e.g., firms are
not likely to share microdata with policymakers and researchers); or (4) comprehensive (e.g., equipment does
not capture operator demographics, farm household income and assets, and off-farm labor).
A third implication from the analysis is that the role of skills and human capital formation as precursors to
DA adoption should not be overlooked. Much has already been written on digital divides between operators
who are technologically savvy and those who are not. The latter group may increasingly miss opportuni-
ties for DA-facilitated improvements to farm performance as digitalization deepens in the coming decade.
However, such divides may somewhat lessen over time as younger generations, increasingly digital natives
who are technologically savvy, displace older generations in the agricultural workforce. Nonetheless, the
nature of day-to-day farming operations is likely to change—potentially drastically—as automation becomes
more commonplace; thus, more analysis is needed to determine whether and to what extent skill-building
programs are needed for the agricultural labor force.
Fourth, DA nonadoption tends to be correlated with smaller farm sizes, lower crop yields, less use of crop
management recommendations, and limited employment of technical or consultant services for various
reasons that cannot be solely attributed to the lack of DA uptake. This places renewed focus on recurring
policy interest in the linkages between technical change and structural change. Past research has provided
compelling arguments for the case that conventional precision agriculture—alongside larger and faster equip-
ment, no-till practices, genetically engineered seeds, and other innovations—has contributed to larger farm
sizes (MacDonald et al., 2013). Yet, if autonomous robots prove to be scale-neutral, their commercialization
and widespread use could push against the trend of greater crop and livestock consolidation (MacDonald
et al., 2018; Lowenberg-DeBoer et al., 2020). Such a scenario, however, would not necessarily reverse the
consolidation trend—especially if new technologies are developed and released that continue to favor larger
farms and operators with perhaps greater technical skill sets.
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Appendix: Overview of DA Component Technologies
Yield maps and soil maps
Sensor and other data associated with geospatial coordinates from global navigation satellite systems (GNSS)
provide the information needed to create maps. Farm operators then use computerized yield and soil maps to
customize farm management decisions (e.g., McFadden et al., 2022 c).
For yield maps, instantaneous mass flow sensors detect grain flow in combine harvesters or seed cotton on
cotton pickers. Harvester-mounted sensors on grain combines usually measure force per unit of time (e.g.,
pounds per second) in the clean grain elevator, and modern sensors on cotton harvesters use microwave tech-
nology to detect cotton lint moving through the vacuum shoot. Sensor data (including mass flow, moisture,
GNSS coordinates, and harvester machine data such as swath width, logging duration, and elevation) are
recorded on local logging devices and pushed to cloud-based systems in near real-time via telematics over
wireless communication systems (e.g., cellular devices). Grain-yield monitor sensors predate the advent of
GNSS, but modern cotton-yield monitors were first commercialized for the 2000 growing season (Vellidis et
al., 2003). Other than cotton, combines harvest the crops reported in this study.
Post-processing for quality assurance of yield data is often necessary for on-farm research, input application
prescriptions, meeting conservation compliance requirements, and other farming activities, especially for flag-
ging erroneously measured observations for omission and geographically adjusting data points to compensate
for flow delays (Griffin et al., 2007).
For soil maps, physical and chemical characteristics of within-field variability may be mapped from a
combination of publicly available and personally held sources, such as chemical analyses from precision soil
sampling. Soil chemical characteristics from laboratory analysis of soil samples include nutrient levels, pH,
and percent organic matter and are typically personally held by the farm operator, crop consultant, and/or
landowner. Precision soil sampling implies multiple soil samples taken across a field, usually at less than 10
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acres per sample or on predefined sub‐field management zones. Management zones may be based on a combi-
nation of soil mapping units, previous yield history, prior soil test results, topography, or other yield-affecting
factors. By 2017, two-thirds of precision soil samples were 2.5-acre grids, followed by 5-acre and 1-acre
samples (Erickson et al., 2017). Soil physical characteristics may include texture, water holding capacity,
apparent electrical conductivity, soil surface color, and combinations of the factors from publicly available
USDA NRCS soil surveys.
Soil maps are associated with the adoption of map-based variable rate technologies that require knowledge of
site-specific soil nutrient requirements. Soil nutrient and physical characteristic maps have been used to deter-
mine optimal input rates of fertilizer and seeds—and to justify alternative land uses, such as soil conservation
or rotation into noncropping systems.
Variable rate technologies (VRT)
Variable rate technologies (VRT) are equipment that can provide customized applications of seeds, lime,
fertilizers, or crop protection chemicals such as herbicides, insecticides, and other pesticides at specific sites.
Their purpose is in contrast to status quo uniform rate technologies (URT) that only allow for uniform input
applications within or across the field. Some farm operators consider VRT to underlie the application of site-
specific, prescription-based rates of inputs within individual fields—while others consider VRT to underlie
applications that are uniform within each field but vary across fields. The latter concept (inter-field VRT)
was possible before civilian access to GNSS; however, the former (intra-field VRT) became feasible with the
advent of GNSS in the early 1990s.
Intra-field VRT is usually accomplished with GNSS-enabled automated controllers mounted within the
machinery and/or tractor for map-based or on-the-go applications. Map-based VRT applications rely upon
preprocessed prescriptions based on geo-referenced yield, soil, imagery, topography, and in-field scouting
data. On-the-go VRT (such as Trimble’s GreenSeeker) relies, at least in part, on real-time sensor data
processed via algorithms rather than human intervention. One form of within-field VRT allows distinct
input types to be applied on-the-go or via map-based prescriptions; specifically, these forms are multiple-
cultivar planters that place crop varieties at precise locations within the field during an individual pass
through the field.
As-applied data collected from sensors provide information on how much product was applied at each site-
specific location. These as-applied data are useful for farm management recordkeeping, on-farm experimenta-
tion, regulatory compliance, and crop share lease negotiations.
Guidance systems
Guidance systems have included manual control (i.e., lightbars that help the operator understand the equip-
ment’s position in the field) and automated guidance for parallel or contour passes. The main distinction
between manual and automated control is that the latter obviates the need for the equipment operator to
make manual adjustments with the steering wheel through the field. Even with fully automated guidance,
however, the equipment operator must turn the equipment around near field boundaries.
GNSS-enabled harvesters, sprayers, and tractors replaces manual-operator labor with autonomous control
that can reduce operator errors and fatigue while potentially improving quality-of-life of rural households.
Guidance systems with manual control (i.e., lightbars) were commercialized in the mid-1990s but subse-
quently replaced by automated guidance in the late 1990s for most field operations. GNSS-enabled guidance
systems have become standard features of equipment used for row crops, and by 2021, the guidance receivers
were fully integrated into the cabs of equipment for at least one major manufacturer.
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Guidance systems have incidental benefits for farm data. High accuracy GNSS systems log elevation data
that may be useful for developing topographical maps for modeling watershed, variable rate prescriptions, and
land-forming operations. These data are being collected on passes through the field from harvesters, tractors,
sprayers, and other equipment.
Drones, aircraft, or satellite imagery
Imagery is a form of remotely-sensed data collected from satellite, crewed aircraft, and small unmanned aerial
vehicles (UAVs), i.e., fixed-wing and rotorcraft drones. Imagery from remotely sensed sources has been avail-
able for farm management purposes for many decades. The U.S. Landsat satellites provided publicly available
imagery during the 1970s, though imagery from film cameras via hot-air balloons predated satellite imagery
by several decades. Renewed interest in commercializing imagery data has arisen from the availability of
drones due to the reductions in lithium battery prices.
Imagery has been available in black and white, true color (i.e., from RGB, which mixes the primary colors
red, green, and blue to produce a range of other colors), and in multiple variations of normalized difference
vegetative indices (NDVI, which measures and assesses changes in vegetation greenness and density using
near infrared and thermal infrared). Pixel resolution tends to be inversely proportional to the age and eleva-
tion of the sensor. The temporal resolution of imaging data has traditionally been a limitation to practitioners
(i.e., image capturing may not occur on desired dates or during a desired series of dates).
Automated section control
Automated section control is associated with liquid sprayers and planters. An automated boom section or
nozzle on sprayers and row shutoffs on planters are examples of automated section control. Automated section
control can be considered an on-the-go form of VRT, where the rates are “zero” or “full.” Control sections
automatically shut off while associated portions of equipment are in specific subfield areas that do not need
any input applications (e.g., areas that have already received applications), and it keeps remaining control
sections operating on areas where the application is intended. Control sections may be a section of the appli-
cation boom or planter bar or a single nozzle or planter row-unit.
Coverage maps and GNSS information
The benefits of automated section control tend to be associated with field geometries nearly opposite those
favoring automated guidance. The kinds of irregular field geometries that benefit from section control are those
that require portions of equipment booms or planter rows to be on large areas with diverse needs (e.g., some
subareas require input applications, while other subareas do not) as the machine moves along the transect.
Multiple machines can implement automated section control by coverage map-sharing technology. Coverage
map sharing allows two or more machines to share data regarding where applications have already been
made, usually in real time via local area networks (LANs) but more typically via wireless cellular providers.
For in-field coverage maps, sharing has been implemented between planters, sprayers, and harvesting systems
since 2016.
Turn compensation is a special case of automated section control, typically on sprayers (Porter et al., 2013)
and planters, that adjusts the targeted application rate for each nozzle or row-unit independently to either
more closely match a variable-rate prescription or account for differences in actual travel speed due to
matching turning at some radial degree. Turn compensation is the specific technology that changes the rates
for individual nozzles along the boom as the sprayer or row units on the planter move along a contour pass;
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this technology can be considered a form of VRT along the boom or planter bar. Turn compensation became
feasible on planters with the advent of electric drive row units and on sprayers with the introduction of pulse
width modulation (PWM—a system in which each nozzle has a pulsing device that adjusts its flow rate).
Controlled traffic farming, tramlines
Controlled traffic farming (CTF) is a practice that limits the soil surface of the field from experiencing wheel
tire or track traffic, usually establishing tramlines so that individual, repeatable passes of farm equipment
return to the same tire tracks each trip across the field for as many field operations as possible. To be consid-
ered controlled traffic, the proportion of the field with tire traffic is limited to less than 25 percent of the soil
surface. Tramlines usually do not receive seed during planting under the assumption that those areas will
not be productive, given that the routine traffic would destroy any plant growth. Reducing soil compaction
across the field from equipment passes has been a leading incentive for farm operators to adopt controlled
traffic farming, especially in regions without freeze and thaw cycles (Gasso et al., 2013; Griffin et al., 2004;
McHugh et al., 2009).
Farmers in the United States have adopted controlled traffic farming at lower rates than those in Australia,
Canada, Europe, and other parts of the world; however, the technique has been used as a conservation prac-
tice for several decades in parts of the United States and is somewhat gaining in popularity nationwide.
Automated guidance has empowered farm operators to adopt CTF by reducing the complexity of establishing
tramlines. One ancillary benefit of tramlines is that many farm operations that rely upon GNSS automated
guidance can continue operating in the event of a GNSS outage, given quasi-permanent visual tramlines.
Telematics and wireless data transfer
Telematics is the wireless transfer of data between farm equipment, connected devices, and/or the cloud
either via local area networks, e.g., Bluetooth, or cellular systems. Data transferred may be prescription maps
sent to applicators or as-applied maps of seeds, chemicals, or fertilizers, sensor data (yield monitor data),
or coverage maps. Remote monitoring of field equipment is possible via telematics. Farm data are typically
collected from technology while the machinery is performing intended operations in the field and pushed to
the cloud; however, an important exception is downloading prescription maps to VRT applicators.
Due to residential user behavior, internet service providers typically calibrate speeds to favor data downloads
rather than uploads; however, field operations typically require uploads of various large files and downloads
of only relatively small map-based prescription files to VRT applicators (Whitacre et al., 2014). Less than
40 percent of agricultural service providers implemented telematics in their business operations in 2020
(Erickson et al., 2020). The first telematics devices were available for agricultural equipment as early as 2002
and, for at least one major equipment manufacturer, became standard in 2011.
Calibration of wireless connectivity for agricultural purposes (i.e., data uploads) requires adequate connec-
tivity. Yet in crop producing regions, wireless connectivity may be weak or nonexistent, especially if fields are
not within proximity of interstate highways or municipalities (Whitacre et al., 2014). There is debate about
whether next-generation connectivity will be useful for agricultural purposes, which hinges on (1) the extent
to which 5G networks will have geographical coverage similar to that of earlier 3G and 4G networks, and (2)
whether satellite internet services will provide sufficient latency.
Telematics improve farm data systems—both within and beyond the farmgate—by automating the other-
wise-tedious, manual process of physically moving flash media from farm equipment to office computers.
Although the physical transfer of flash media is an uncomplicated task, barriers to optimal data use have
resulted from loss of small flash-media cards.
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