January 14, 2026
How Agri-Tech Is Reshaping Labor Demand in Nebraska Agriculture By Shannon Sand
Introduction
Nebraska agriculture has long depended on a mix of family labor, hired workers, and custom operators. In recent years, more producers have added advanced technology to that mix to boost efficiency, manage risk, and cope with labor shortages (USDA, 2022). Automation, precision systems, robotics, and data platforms are not just changing how tasks are done; they are also changing the types of skills farms need on the payroll. Understanding these shifts in labor demand is increasingly important for farm managers, Extension educators, rural communities, and workforce planners (Sand, 2023; USDA, 2022).
Technology Adoption in Nebraska Agriculture
Nebraska farmers are embracing a growing range of digital tools. UNL Extension reports that Nebraska producers are among the national leaders in adopting precision agriculture technologies, including GPS guidance, yield monitoring, variable‑rate input application, and digital irrigation management (Irmak, 2019; Schimmelpfennig, 2016; UNL Extension, 2024a). Recent UNL work on digital agriculture notes rapidly expanding use of sensors, drones, robotics, and cloud‑based systems across both crop and livestock sectors (Balboa, et.al , 2024; Strategic Discussions for Nebraska, 2023). Broadband connectivity, documented in FCC broadband deployment reports, has improved substantially in rural Nebraska, supporting remote monitoring, farm management software, and data sharing (FCC, 2023; USDA NASS, 2023).
In livestock operations, technologies such as electronic ID tags, automated feeding systems, remote water monitoring, and precision livestock tools are spreading quickly. Research by Bewley and others on precision dairy management and automation, as well as work on precision livestock farming adoption, documents how these systems change both labor needs and management tasks (Bewley, 2015; Bewley & Dolecheck, 2018; Brummer et al., 2021; Boyle et al., 2022).
Labor Substitutions and Changing Job Roles
Automation frequently substitutes for repetitive manual labor. Robotic milking systems, automated or semi-autonomous tractors, sensor-driven irrigation systems, and automated feeding equipment can significantly reduce time spent on routine tasks (Bewley, 2015; King et al., 2020; Irmak, 2019). Rather than simply eliminating workers, however, these technologies shift labor demand toward higher‑skill roles focused on oversight, troubleshooting, and decision‑making. Employees are increasingly expected to manage data, interface with software, and coordinate with vendors, instead of performing traditional manual labor. This creates demand for workers with blended skills in agriculture, mechanics, and information technology (USDA, 2022; Nebraska Community College Consortium, 2023).
Precision Agriculture and Data Driven Decision Making
Precision agriculture tools generate large volumes of data about soils, crop performance, input use, and environmental conditions. Workers now need to operate complex machinery, move information across platforms, and use software to translate that information into day-to-day decisions on planting, fertility, irrigation, feeding, and animal health (Schimmelpfennig, 2016; Thompson et al., 2019; Balboa et. al, 2024). This raises demand for skills such as:
- Interpreting yield maps and variable‑rate prescriptions.
- Managing telematics from tractors, combines, and other equipment.
- Using GIS-based planning and mapping systems.
- Calibrating, repairing, and monitoring sensor networks.
- Integrating agronomic and animal‑science recommendations with digital decision aids.
Extension publications from UNL, Iowa State University, and other land‑grant institutions emphasize that successful precision ag use hinges on management capacity and data literacy, not just equipment ownership (Thompson et al., 2019; Schimmelpfennig, 2016; NFBI, 2018). Many Nebraska producers report that hiring employees comfortable with digital tools is now one of their greatest challenges (Strategic Discussions for Nebraska, 2023; Nebraska Community College Consortium, 2023).
Labor Sector Differences in Labor Effects
Row‑Crop Operations
Row‑crop operations, especially irrigated farms, have some of the highest precision ag adoption rates in the United States. Autosteer, telematics, yield monitors, and irrigation scheduling tools can reduce the need for seasonal labor, particularly for steering, in‑field monitoring, and pivot checks (Irmak, 2019; NF96‑305; Schimmelpfennig, 2016). At the same time, they increase demand for operators who can manage complex consoles, interpret data, and coordinate with agronomic advisors and service providers.
Cattle Ranches and Cow‑Calf Operations
Cow‑calf operations lag in technology adoption due to the nature of their operations and the cost of the technology relative to the gain in performance. Remote water monitoring, GPS-based grazing tools, virtual fencing, and RFID systems are reducing repetitive chores such as checking tanks or locating animals across large pastures (Boyle et al., 2022; Strategic Discussions for Nebraska, 2023). Even so, stockmanship, animal health knowledge, and range experience remain central, so technology tends to augment rather than replace labor in these systems (Brummer et al., 2021; USDA, 2022). It is likely, however, with the ever-growing expansion of information technology, management systems, and AI innovations, this will change in unforeseen ways.
Feedlots and Dairies
Feedlots and dairies often realize some of the most direct labor-saving benefits from automation. Automated feeding systems, hydraulic cattle‑handling facilities, and robotic milkers can significantly reduce manual tasks such as feeding, pushing up feed, or milking (Bewley, 2015; Bewley & Dolecheck, 2018). However, these technologies also create strong demand for employees who can operate and update software, recognize and diagnose technical malfunctions, and coordinate system maintenance with vendors and technicians (USDA, 2022; King et al., 2020).
Efficiency vs Labor Scarcity: What Drives Adoption?
National USDA analyses and producer surveys highlight labor scarcity as a major driver of technology adoption, especially for physically demanding tasks with irregular hours (USDA, 2022). UNL Extension outreach and producer panels similarly report that many Nebraska operations struggle to find and retain enough workers, particularly during peak seasons, and use technology to reduce dependence on hard-to-fill positions (Strategic Discussions for Nebraska, 2023; Balboa et al, 2024). For younger producers and multi‑generation farms, motivation also includes improving work-life balance, increasing efficiency, enhancing records for lenders and regulators, and reducing risk through improved decision accuracy (Sand, 2023; USDA NASS, 2023).
For smaller and medium-sized operations, access to cost-share programs, custom service providers, and strong dealer support often determines whether adoption is practical. Extension resources from UNL, Kansas State, and Iowa State document how cost‑sharing, shared ownership, and custom hiring can lower financial and managerial barriers to adopting advanced technologies (Thompson et al., 2019; Schimmelpfennig, 2016).
Economic Considerations for Producers
While agricultural technology can save labor and boost efficiency, it typically involves substantial upfront investment and ongoing costs. Producers must weigh equipment purchases and installation, subscription and software fees, training or hiring needs, and potential downtime from connectivity or mechanical issues (Dolecheck & Bewley, 2018; USDA, 2022). Research on precision agriculture by USDA ERS and land‑grant economists shows that returns depend heavily on management quality, farm or ranch scale, and the degree to which data are integrated into agronomic and livestock decisions (Schimmelpfennig, 2016; Thompson et al., 2019). This means investments that make sense for a large, well-staffed operation may not be profitable for a smaller farm without adequate technical support.
Long-Term Workforce Implications
As technology adoption expands, Nebraska’s agricultural workforce is undergoing structural change. Demand is rising for workers who can manage software, analyze production and financial data, and maintain complex mechanical electronic systems, and those technical skills often command higher wages in rural labor markets (USDA, 2022; Nebraska Community College Consortium, 2023). At the same time, demand for some types of low‑skill manual labor is declining in sectors where automation can perform routine or physically intensive tasks (Bewley, 2015; King et al., 2020).
In response, Extension programs, community colleges, and high school agriculture departments are expanding ag‑technology training and creating clearer pathways into roles such as precision agriculture technicians, livestock data managers, and integrated farm systems operators (Nebraska Community College Consortium, 2023; Strategic Discussions for Nebraska, 2023). UNL’s partnerships with USDA on precision agriculture infrastructure and digital tools are also likely to create new career paths in data management, remote sensing, and cyber-secure farm operations (UNL Research, 2024; USDA‑ARS & UNL, 2024).
Conclusion
Ag‑tech adoption is reshaping the labor landscape in Nebraska agriculture. Automation and digital tools are reducing the need for some types of manual labor while increasing demand for workers with technical aptitude, problem-solving skills, and digital literacy. Although adoption patterns differ across row‑crop farms, cow‑calf ranches, feedlots, and dairies, the overall trajectory points toward a more knowledge-intensive agricultural workforce that blends traditional production expertise with advanced technological competence.
References
Bewley, J. M. (2015). New technologies in precision dairy management. In Proceedings of the Western Canadian Dairy Seminar.
Bewley, J. M., & Dolecheck, K. (2018). Economics of robotic milking systems. University of Kentucky Cooperative Extension Service.
Boyle, K., et al. (2022). Technology use in rangeland livestock operations. Rangelands.
Brummer, E., et al. (2021). Precision livestock farming adoption in the United States. Applied Animal Science.
Balboa, G., Luck, J., Paccioretti, P., Cesario Pinto, J., & Puntel, L. (2024). How digital is agriculture in Nebraska? University of Nebraska–Lincoln Extension.
Dolecheck, K., & Bewley, J. (2018). Economic considerations of robotic milking systems. University of Kentucky Cooperative Extension Service.
Federal Communications Commission. (2023). 2023 broadband deployment report.
Irmak, S. (2019). Irrigation management technologies in Nebraska. University of Nebraska–Lincoln Extension.
King, A., et al. (2020). Autonomous farm equipment: Trends and labor implications. Choices.
Nebraska Community College Consortium. (2023). Agricultural technology workforce programs.
Nebraska Farm Business, Inc. (2018). Farm financial analysis and technology use.
Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture (Economic Research Report No. 217). U.S. Department of Agriculture, Economic Research Service.
Sand, S. (2023). Agri‑technology transforming farms and ranches across Nebraska. Cornhusker Economics. University of Nebraska–Lincoln.
Strategic Discussions for Nebraska. (2023). Data drives Nebraska. University of Nebraska–Lincoln.
Thompson, N. M., et al. (2019). Returns to precision agriculture technologies. Journal of Agricultural and Resource Economics, 44(2), 356–372.
University of Nebraska–Lincoln Research. (2024). Precision ag facility a hub for sustainable, resilient practices.
U.S. Department of Agriculture. (2022). Labor and technology use in U.S. agriculture.
U.S. Department of Agriculture, National Agricultural Statistics Service. (2023). 2022 Census of Agriculture: Nebraska state profile.
U.S. Department of Agriculture, Agricultural Research Service, & University of Nebraska–Lincoln. (2024). National Center for Resilient and Regenerative Precision Agriculture project materials.
Shannon Sand
Associate Extension Educator
West Central Research & Extension Center
Department of Agricultural Economics
University of Nebraska-Lincoln
ssand2@unl.edu