How Ag Data Issues Have Changed (and Not Changed) in the Past 10 Years
/The Ag Data Transparent organization turns 10 years old this year. After a decade of watching ag data evolve, I thought it would be good to reflect on how the ag data landscape has changed. These are my observations.
Fewer startups. I have worked with many ag tech startups over the past ten years. Ten years ago, the future looked bright for new companies entering into the ag tech space. In recent years, new ventures seemed to have slowed. Ag Tech Funder reported that investment in ag and food tech was around $2 billion in 2013 and rose to over $50 billion at its peak in 2021. (AgFunder Report). Since then, investment has been trending downward. I see this from the seat of my pants as well.
Consolidation. The inevitable result of multiple startups entering the data space is consolidation. One of the fun observations from 10 years ago was that ag tech (and especially data startups) were reversing agriculture’s consolidation trend that dominated the prior 100 years of US agriculture.
Privacy Concerns Remain. When discussing ag data, privacy was the primary concern I heard from farmers and farm organizations 10 years ago. Although not the main headline for any farm show, when farmers filled out surveys, they usually ranked privacy and trust as their chief concerns with ag data platforms. To me, that suggested it was an important issue, just not one farmers wanted to openly discuss.
Benchmarking takes a back seat to other uses. A decade ago, ag data platforms were promising significant advances in benchmarking for farmers who uploaded their data and compared yields and farming practices with other farmers. I think many platforms oversold and underdelivered on this promise. Yes, farmers can find ways to improve their farming practices by examining comparable data, but the results are incremental and not revolutionary. Other uses for ag data have proven their value. Today, farmers use ag data to generate premiums for crop traceability, to prove to buyers that certain farming practices were followed, and to demonstrate that conservation methods were used. These background uses were not the major selling points for platforms collecting ag data ten years ago, but in 2026 they are delivering.
AI brings promise and uncertainty. Ten years ago, we were not talking about using ag data for artificial intelligence (AI) like we are today. There were already examples of ag data uses for machine learning, such as single-decision tools, like “spray” or “don’t spray.” But these tools were simpler than today’s AI, and it was easy to understand the benefit of sharing data to train a machine that performs a task. Today’s generative AI models are much more expansive in their ability to inform, help with decision-making, and streamline paperwork tasks. But these uses also bring in increased anxiety in those usual areas of ag data concern—trust, privacy, uncertainty. Providing your farm’s ag data to a GenAI platform may provide you with new insights, but your data may also be used to inform others or help marketers better advertise to your farm and solicit its business. How exactly these AI platforms will use ag data is unknown to the user, and frequently unknown to the platforms creating the models, based upon reports of AI models going rogue and escaping their sandboxes. All of this does little to instill confidence in an area where farmers have traditionally been very sensitive to sharing ag data.
Overall, it has been enjoyable to see the ag data space evolve over the past ten years. Although the entrants have shrunk and uses have expanded, the central issues of trust and privacy remain. I cannot wait to see what the next ten years bring.
