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Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms

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  • McFadden, Jonathan
  • Njuki, Eric
  • Griffin, Terry

Abstract

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.

Suggested Citation

  • McFadden, Jonathan & Njuki, Eric & Griffin, Terry, 2023. "Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms," USDA Miscellaneous 333550, United States Department of Agriculture.
  • Handle: RePEc:ags:usdami:333550
    DOI: 10.22004/ag.econ.333550
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    2. Spykman, Olivia & Gabriel, Andreas, 2023. "Evaluating the impact of government investment support for crop robots: a multi method approach," Land, Farm & Agribusiness Management Department 344216, Harper Adams University, Land, Farm & Agribusiness Management Department.
    3. E. M. B. M. Karunathilake & Anh Tuan Le & Seong Heo & Yong Suk Chung & Sheikh Mansoor, 2023. "The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture," Agriculture, MDPI, vol. 13(8), pages 1-26, August.
    4. Robert Finger, 2023. "Digital innovations for sustainable and resilient agricultural systems," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(4), pages 1277-1309.
    5. Spykman, Olivia & Gabriel, Andreas, 2023. "Evaluating the impact of government investment support for crop robots: a multi method approach," Agri-Tech Economics Papers 344216, Harper Adams University, Land, Farm & Agribusiness Management Department.
    6. Wang, Tong & Jin, Hailong & Sieverding, Heidi & Kumar, Sandeep & Miao, Yuxin & Rao, Xudong & Obembe, Oladipo & Mirzakhani Nafchi, Ali & Redfearn, Daren & Cheye, Stephen, 2023. "Understanding farmer views of precision agriculture profitability in the U.S. Midwest," Ecological Economics, Elsevier, vol. 213(C).
    7. Wenxuan Geng & Liping Liu & Junye Zhao & Xiaoru Kang & Wenliang Wang, 2024. "Digital Technologies Adoption and Economic Benefits in Agriculture: A Mixed-Methods Approach," Sustainability, MDPI, vol. 16(11), pages 1-24, May.

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