The Secrets of Agricultural Data
The difference between a cook at Waffle House and a cook at Disfrutar in Barcelona might not be the chosen recipe ingredients, but how the ingredients are used.
The same is true for data in agriculture. Most of the highest quality databases in agriculture originate their cornerstone data from the same source – Farm Service Agency programs. Farmers apply and receive support from FSA and the information collected becomes a part of public record.
The data from FSA is not collected for data purposes, however, so that’s the point when the cooks separate in skill. The question many need to ask is are they dining at Waffle House or Disfrutar?
“Most people grab a list and FOIA (Freedom of Information Act) data that they think is ready-made,” said Matt Yaske, RooData partner and longtime agricultural data expert. “It is absolutely not ready-made. It has no acres. It offers no roles. It’s often just a list of names.”
That means it takes a lot of effort to make the data actionable for sales and marketing purposes. Building data relationships, learning who is making the decisions, creating groups of contacts within a farm, determining roles within the farm groups are all important tasks that require both data artistry and data science. It also takes a lot of effort to keep the data actionable. Circumstances often change on the farm. Retirements, deaths, and sales happen and each event can take a database further away from accurate.
Agricultural expertise is pivotal throughout the process. For instance, Yaske points out that no one cash rents land to receive CRP payments, so the likelihood is anyone applying for that program is an owner. Cross-referencing them in other programs can tell you that the individual is an owner and grows a specific acre size of given crops.
“It’s just a continuous build of continuous quality and accuracy checks,” Yaske said. “It takes a lot of meticulous work looking at data. We’ve been doing nothing but that process for years.”
Effort, Yaske says, separates the data available to agricultural brands. But if there is no comparative measurement on the level of effort, so it is impossible to know if you’re seated at Waffle House or Disfrutar.
“There are two types of data – bad data and good data,” Yaske says. “If your clients keep coming back and you help them build success, you are going to keep a client. If you want to make forecasting errors, use bad data.”
That’s why quality data sources don’t just provide lists — they provide context. Their process is not about volume, but about verification. It's long hours of phone calls to confirm who’s still farming, which sibling now runs the operation, or whether that LLC merged with another. It’s connecting the dots between publicly available data and what’s actually happening on the ground, in the fields, and in the farm office.
It also means knowing the rhythms of agriculture itself. Yaske and the team understand the seasonal patterns of a farm business—when key decisions get made, when acreage might shift, when crop rotations suggest future product needs. That insight allows clients not just to react to what’s already happened, but to anticipate what’s next.
“The best data isn’t a list of names—it’s a map of relationships,” Yaske says. “Who influences what, who buys what, who listens to who. That’s where the real value is.”
It’s not glamorous work, but like in the kitchen at Disfrutar, every small action adds up to something remarkable. The result is data that’s living, flexible, and deeply tuned to the complexities of agriculture. For marketers and sales teams, that means fewer wasted calls, smarter conversations, and strategies built on insight—not guesswork.