Precision Agriculture and Multidimensional Attributes of Farmer Wellbeing: Evidence from Iowa

Jonathan McFadden, Alicia Rosburg, Katherine Lim and J. Gordon Arbuckle

No 404611, 2026 Annual Meeting, July 26 - 28, 2026, Kansas City, Missouri from Agricultural and Applied Economics Association

Abstract: Digital agriculture is typically evaluated through productivity and environmental outcomes, yet its heterogeneous effects on farmers’ wellbeing remain poorly understood. Using Iowa farmer survey data, we estimate associations between five digital technologies and eleven wellbeing outcomes and interpret those patterns using thematic coding of farmers’ open-ended comments to assess how digitalization has changed farming and farmer wellbeing. Autosteer is consistently associated with reduced fatigue and higher productivity, whereas yield monitoring and mapping show weaker and narrower benefits. Drones align with coordination- and stressrelated benefits, and variable-rate systems with time-flexibility outcomes. Digital agriculture (DA) is chiefly framed as a transformative pathway to more efficient, resilient, and sustainable global food production1,2. Precision guidance, automated sensing, highly detailed geospatial data, and decision-support tools can help farmers manage within-field variability while reducing input use and improving the timing of operations1,3,4. Yet these technologies also reorganize farm work, reshape how information is interpreted, and redistribute responsibilities between farmers and digital systems2,5. For farmers and their households, such changes may be as meaningful as improvements in yields or input efficiency6. A growing literature examines both the promise and risks of DA. Some studies emphasize its role in sustainability monitoring, precision input use, and climate-smart management1–3. Others highlight a set of tangible risks, including heightened surveillance, data ownership and privacy concerns, technological lock-in, and power imbalances between farmers and technology providers5. Qualitative and critical social-science scholarship has argued that digitalization reorganizes labor, expertise, and control within farming systems5,7,8, raising concerns about farmer autonomy and broader inequities, but quantitative evidence remains limited on how specific technologies have affected multiple dimensions of farmer wellbeing in large samples, and on how these patterns align with farmers’ own accounts of daily work. Policies promoting DA often implicitly assume that technologies generating productivity, environmental, or resilience gains improve farmer welfare9. Yet if technologies redistribute stress, time burdens, managerial complexity, or technological dependency unevenly across farmers and/or tools, then adoption incentives may produce mixed wellbeing outcomes even when agronomic performance improves. Understanding how diverse technologies influence farmer wellbeing differently is therefore important not only for food systems research, but for designing targeted policies to improve rural livelihoods in the face of fundamental changes to the labor structure of farming. We address this gap by evaluating DA through the lens of multidimensional farmer wellbeing, moving beyond conventional evaluations focused mainly on adoption and productivity. We estimate how five common digital technologies—autosteer, yield monitors, soil mapping, variable rate equipment, and uncrewed aerial vehicles (UAV) —associate with eleven wellbeing outcomes—time pressure, physical labor, non-farm time, time flexibility, multiple forms of stress, fatigue, technology-failure pressures, and perceived productivity—and interpret these patterns using farmers’ own accounts. Our approach connects DA research with broader food-systems work emphasizing trade-offs, sustainable transitions, and farmer decision-making with direct evidence on the lived experience of farm work4,10–12. We show that DA impacts vary systematically across technologies and wellbeing dimensions, with a redistribution of physical, cognitive, and managerial burdens that are highly tool specific. Reported adoption rates broadly align with representative adoption estimates from USDA’s Agricultural Resource Management Survey (ARMS) for Iowa and the broader Midwest (Table S1.1), supporting external validity in a major U.S. field crop adoption setting.

Keywords: Consumer/Household Economics; Labor and Human Capital (search for similar items in EconPapers)
Pages: 8
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea26:404611

DOI: 10.22004/ag.econ.404611

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