Precision Agriculture Adoption Statistics for U.S. Farmers

Source: USDA, Economic Research Service illustration.

Introduction

The global AgriTech landscape is a constant source of innovation, as evidenced by 2.1 million patents filed worldwide. To truly understand the market, however, these numbers must translate into real-world usage. The U.S. provides a compelling case study, with 91% of farmers already having adopted some form of AgriTech.

This article will drill down into the specific technologies U.S. farmers are utilizing and the factors driving their adoption, drawing insights primarily from the USDA Economic Research Service (ERS) focused examination of key precision agriculture technologies in U.S. row crop fields (e.g., corn, soybeans, wheat, cotton, sorghum, rice).

Understanding Digital Agriculture (DA) and Precision Agriculture (PA)

• Digital Agriculture (DA) is the ongoing transformation of farming through the digitalization and automation of tasks. It encompasses the use of information technologies, data analytics, automated production processes, and the development of AI applications.

• Precision Agriculture (PA) is a core component within DA. Traditional PA technologies include electronic maps, Variable Rate Technologies (VRT), and guidance systems.

Many major components of DA can be categorized by three key features: (1) data and data collection systems, (2) decision support (DS) tools, and (3) data-driven equipment and input adjustments.

Key Components and Adoption Trends in U.S. Row Crop Farming (2016–2019 Emphasis)

The potential for automation significantly drives the adoption of DA. Technologies primarily fall into three categories: data collection, decision support, and data-driven equipment and input adjustment. Adoption of precision technologies related to DA has increased significantly since the mid-1990s.

• Automated Guidance (Auto-steer) Systems: These systems have seen substantial adoption increases over the past 20 years.

  • Used by 52% of midsize farms and 70% of large-scale crop-producing farms in 2023.

  • A majority of U.S. row crop acreage is managed using these systems.

  • Adoption rates on planted acreage: 58% for corn (2016), 72.9% for sorghum (2019), and 64.5% for cotton (2019). This represents a significant increase from the low single digits or around 10% in the early 2000s.

• Yield Monitors, Yield Maps, and Soil Maps:

  • Yield monitors are now often standard equipment features.

  • Yield maps, available since the early 1990s, quantify within-field variability to inform variable-rate technology (VRT).

  • These technologies were used on 68% of large-scale crop-producing farms in 2023.

  • Across various row crops, adoption rates increased considerably since the mid-1990s. Adoption rates are generally less than 40% of planted acres for most crops, with the exceptions of corn (in 2010) and soybeans (in 2018). For winter wheat, cotton, sorghum, and rice, adoption ranged between 5% and 25% of planted acreage, depending on the year.

• Variable Rate Technologies (VRT):

  • VRT adoption rates were 37.4% on corn-planted acres (2016) and 25.3% on soybean-planted acres (2018).

  • Similar to maps and monitors, adoption rates are generally less than 40% of planted acres for most crops, with corn and soybeans as possible exceptions in specific years. For winter wheat, cotton, sorghum, and rice, VRT adoption ranged from 5% to 25% of planted acreage.

  • In 2023, VRT for fertilizer/lime was used by 40% of large-scale farms, while VRT for seeds was used by 32%, and VRT for pesticides by only 9%.

• Drones, Aircraft, or Satellites (Imagery): Used by 12% of large-scale farms in 2023. Adoption is generally less than 40% of planted acres.

A fundamental technology underlying the widespread use of nearly all these technologies is the Global Positioning System (GPS) and other Global Navigation Satellite Systems (GNSS), which provide geospatial coordinates necessary for mapping, VRT control, and guidance systems.

Adoption Varies by Farm Size

Adoption rates for precision agriculture technologies in the U.S. sharply increase with farm size. Small family farms consistently show the lowest levels of adoption for every technology, relative to midsize and large-scale farms. Large-scale farms, in particular, exhibit significantly higher adoption percentages across the board. For instance, roughly half of the country's large-scale row crop producers use tractor guidance, while small farmers largely do not.

The significant gap between large-scale and small-scale farmers suggests substantial potential for adoption on small farms, which could lead to considerable economic and environmental benefits.

Drivers of Adoption

Farmers' adoption decisions are based on their expectations of how a technology will affect their operation's performance. Often, farmers use combinations of precision technologies. Real-world factors, such as how well a new technology complements existing systems (e.g., VRT, which requires mapping, which in turn requires yield monitoring and soil sampling), and bundled pricing from providers, are compelling economic rationales for adopting technologies together or sequentially.

Key drivers include:

• Expected Productivity Impacts: Yields are often statistically significantly higher for adopters than non-adopters across various technologies and crops (except VRT pesticides, cotton, and sorghum), suggesting productivity expectations are important. (Note: These yield differences are correlations, not necessarily causal impacts, as other factors affecting yields are not controlled for in the simple analysis presented.)

• Labor-Saving Benefits: These are considered necessary. Studies using 2010 ARMS data found that adopters of yield- or georeferenced soil maps had 35% lower total labor hours per bushel of corn compared to non-adopters, and VRT adopters had 28% lower labor hours per bushel.

• Profitability: Although not formally analyzed for recent years, earlier ERS studies found that GNSS mapping increased operating profits by almost 3%, and VRT impact was 1.1%. Farmers' most common reasons for adopting include increasing yields, saving labor time, reducing purchased input costs, reducing operator fatigue, and improving soil quality or minimizing environmental impacts.

• Technology Costs: Technology prices (replacement costs, fees, premiums) are important determinants.

• USDA Programs: Programs administered by the USDA and other Federal agencies directly impact input choices, and several programs have a direct effect on digital agriculture usage. However, this area is noted as underexamined.

• Natural Resources: Field-level variation in soil attributes, land characteristics, field topography, and conservation program incentives play a role. Soil variability is particularly challenging to capture precisely with current ARMS data due to the need for aggregation to ensure privacy and reliability.

Data Use and Recommendations

Data collection is integral to DA and data-driven decision making. While there are discussions about the substantial economic potential of "Big Data" (aggregations of georeferenced production activities from many fields) for generating highly detailed management recommendations ("prescriptions"), few studies have considered how farmers themselves use data from various sources, including publicly provided data.

Between 2016 and 2019, across corn, winter wheat, soybeans, and cotton, a much higher fraction of national planted acreage was managed with data-based management recommendations (ranging from 16.7% for cotton to 32.4% for corn) compared to fields where operators used downloaded public data to create maps (ranging from 0.3% for cotton to 3.4% for corn). This suggests farmers might prefer directly applicable business advice tailored to their fields, possibly provided by input dealers or crop consultants, over performing data analysis themselves. This difference could reflect a digital divide or simply farmers' preferences for ready-made recommendations.

Conclusion: Opportunities for AgriTech Innovators

The USDA ERS report highlights the significant and growing adoption of key precision agriculture technologies in U.S. row crop farming, particularly automated guidance systems. It underscores that adoption is strongly linked to farm size and driven by expectations of economic benefits, labor savings, and productivity gains, alongside factors like technology costs, land characteristics, and USDA programs. The report also highlights the growing importance of data collection and utilization in informing management decisions, noting a preference for data-driven recommendations over farmers directly using raw data.

AgriTech innovators looking to break into the market can focus on large-scale family farms, as they are consistently the most enthusiastic adopters of precision agriculture technologies. Understanding the specific drivers and existing adoption patterns, especially the preference for integrated solutions and clear productivity benefits, will be key to success.

Ready to protect your AgriTech portfolio? Contact Bright-Line IP today to discuss developing a patent strategy tailored to your specific needs.

Sources:

1. Jonathan McFadden, Eric Njuki, & Terry Griffin, Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms, U.S. Dep’t of Agric., Econ. Rsch. Serv. (Feb. 22, 2023), https://www.ers.usda.gov/publications/pub-details?pubid=105893.

2. Jonathan McFadden & Katherine Lim, Precision Agriculture Use Increases with Farm Size and Varies Widely by Technology, U.S. Dep’t of Agric., Econ. Rsch. Serv. (Dec. 10, 2024), https://www.ers.usda.gov/data-products/charts-of-note/chart-detail?chartId=110550.