Farmers Have Trust Issues With Big Data -- For The Same Reasons We All Do Precision agriculture is changing the way people farm. But the familiar pitfalls of data collection threaten to hold the industry back.

Every 10 minutes, the farmers we work with get vital signs from every corner of their fields and orchards. Temperature. Humidity. Barometric pressure. Insect populations. With astounding precision, a system of thousands of networked sensors reports back on conditions with a level of detail unimaginable even a decade ago.

But here's my greatest fear: All this data — the backbone of a burgeoning agtech industry that has the potential to reshape how we grow food — also has the potential to make farmers' lives harder. And for all of our technological sophistication, we could end up doing little more than adding noise to their busy lives, rather than helping them grow more, better, healthier crops.

We're sitting on the precipice of a huge moment for agriculture technology, as it's poised to become a $22 billion industry by 2025. Precision agriculture (using data and AI to optimize farming) has a big role to play in that. But to get there, we need to first understand the fundamental difference between signal and noise for the farmers on the ground using our tools.

In short, as agtech providers, how do we do a better job giving farmers "news they can use" … and filtering out the rest?

The challenge for growers

Data and analytics is now instrumental to nearly every industry. Yet a common theme persists.

In one study, 91% of executives said their data hadn't reached a "transformational level" within their business. Most cited complexity and a lack of training as barriers.

Precision ag providers need to be the same thing — a simple stepping stone to a smart decision, not a mountain to climb

For farmers, these challenges are especially acute. They now have access to more data than ever about their crops. Using it, though, is another story. In fact, a 2020 study showed 70% of farmers said they didn't have the necessary training to use more data in their operations, and 75% said they didn't have time to deal with it.

Put yourself in their shoes. You have multiple systems reporting back on several aspects of your operation. Oftentimes, these tools aren't compatible with each other, so each is delivering a different set of numbers on a different platform. Farmers are left to sift through this flood of information, when they really want actionable insights. Big data needs to be a trusted advisor, not a complicated adversary.

What farmers really want in their data

There's a line from Google's philosophy that a lot of companies can learn from: "We may be the only people in the world who can say our goal is to have people leave our website as quickly as possible." Precision ag providers need to be the same thing — a simple stepping stone to a smart decision, not a mountain to climb. To me, this comes down to a few key considerations:

Data vs. insight

As a scientist working in this field, I know all about the immense amount of data required to make predictions on these complex farms. But what's far more valuable to a grower is the actionable insight. It's like the difference between a diagnosis (knowing what's wrong) and a prognosis (knowing what to do about it). Contemporary sensor technology can give farmers a row-by-row or even plant-by-plant breakdown of things like insect pressure or water absorption. But it's one thing to soak in all that information, and another entirely to know what to do about it.

Timeframe matters

In many cases, "actionable" depends on the timeframe. Farmers in the field need days, if not weeks, to marshal a response to changing conditions. That's why reporting on real-time conditions is less helpful than anticipating what lies ahead. And this is where AI and machine learning are indispensable. A huge pest for growers we work with is navel orangeworm. These moths can decimate an orchard fast. If growers were only being alerted as the attack was underway, they would be left defenseless. Instead, they need tools that can predict an infestation in advance and advise on (or better yet, automatically trigger) an appropriate response.

Zooming out before you zoom in

On any farm, you have different individuals relying on data — from people in the field trying to make quick decisions armed with just a smartphone, to managers poring over charts and tables on desktops. That's why agtech needs to take a cue from consumer technology and put user experience at the forefront. Successful products start with a simple dashboard where anyone can see mission-critical information like weather, pest pressure, water management and plant stress. A grower can dive as deep as they want into the platform for more information, but can take comfort in knowing they don't have to.

Independence is everything

A 2018 study looked at farmers' perspectives on Big Data, and found privacy and ownership were among their biggest concerns. Who is collecting the data gathered from my fields? Is it being sold to others? Is the advice I'm getting on everything from pest control to fertilizer unbiased and objective … or is there a hidden agenda? That's why agtech providers need to offer clarity on what data is being collected, why, and how it will ultimately be used. Farmers have a good nose for conflicts of interest, and nobody wants to see their information sold to third parties, or potentially used against them.

In the face of challenges ranging from shifting weather patterns to supply chain disruptions, precision agriculture represents a powerful way to help nature feed a growing population. The right data means the potential to do more with less: fewer inputs and lower costs for higher yields and greater ROI. But for this potential to truly be realized requires looking beyond data collection, machine learning and cutting-edge technology and seeing eye-to-eye with the one person who makes this all possible: the farmer.