You've likely heard this assumption: When AI-enabled and autonomous machines arrive on the farm, jobs will disappear. But researchers at the University of California, Davis, who studied 16 fruit tree operations in California and Washington for three years, found that automation still takes human knowledge. What changes is how the work gets done. The study, drawing on 165 in-depth interviews with workers and growers, was published in the journal New Technology, Work and Employment.
The study examined what researchers call the "last mile" of agricultural automation: what happens after robotic and AI- enabled tools arrive on the farm and have to work in real conditions that can change by the hour.
"Automation isn't something that just happens to a farm or happens to a labor force," said lead author M. Anne Visser, professor of community and regional development in the UC Davis Department of Human Ecology. "People have to actively make it happen."
Every orchard is different, so technology must be adapted to each one. That often requires human knowledge about the land, the fruit or even the expected weather. No matter how good these machines get, Visser said, they may not be able to match that embedded place-based knowledge.
Where orchard technology falls short
For example, a tractor or ATV can automatically travel down orchard rows and scan trees with a digital camera that counts buds and fruit to help forecast yield. The technology mimics a farmworker physically counting buds on every sixth or seventh tree to estimate yield. But researchers found the technology tended to overestimate yield and often required interpretation through historical knowledge of the field. One farm manager with over a decade on the farm adjusted the camera's counts down by 14% because of historical understanding of block performance and recent weather events. His estimates ended up more accurate than the cameras'.
Even with tools in place, people are still needed to interpret what the technology is seeing, adapt it to field conditions, troubleshoot problems and bridge the gap between how a tool was designed to work and how farming actually works.
Autonomous harvesters are another example. Many prototypes can't seem to match the knowledge needed to pick fruit, Visser said. Even as the technology improves, many growers and workers believe it will still take a human to integrate it into a farm's production system and work alongside it.
"Replacing a task on a farm is not the same as replacing a job," said Visser. "These tools do not simply remove jobs on farms. They often change who does what and reshape the work around technology."
Fresh-market specialty crops are harder to automate than row crops such as corn or soybeans, Visser said, because they require quality as well as yield. Orchards are multiyear investments, and growers cannot switch out these crops in a single season. Still, as workers move into other farm roles, the technology can improve efficiency, occupational health and planning for labor shortages.
What it means for farm jobs
Visser said focusing only on displacement of workers can obscure opportunities for policy, workforce development, worker mobility and rural job creation.
"Our hope is that our research helps advance the conversation surrounding agricultural automation in agricultural and its impact on the labor force in a way that's more useful versus what it can often do, which is divide people pretty fast," she said.
Alex Bladimir Hernandez of UC Davis co-authored the paper. It was funded by the National Science Foundation and the University of California Office of the President.
