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2016 Commodity Classic: Unlocking precision ag data

Updated: 01/12/2016 @ 4:17pm

Mike Zeedyk doesn’t have to be a detective to realize there are valuable facts and statistics cloaked within the data he’s been gathering on his fields since 2003.

“Right from the get-go, I knew there was useful information there, because I was seeing differences on my yield monitor,” recalls the Ohio farmer. “For example, I always thought the bigger beans that were leaning a little bit were my best-yielding beans. However, I realized when I was harvesting them that first year, it was the other way around. Those were my lower-yielding beans.”

As he looked through the clues, he wrote the variance off as a fluke. That is, until the same thing happened in year two.

“The second year, it repeated itself,” he continues. “It was then that I realized there was information in the data that I needed to be utilizing to make changes in my operation. Yet, even though I knew it in my head, it was just so easy to look at the data and not do anything with it.”

Rather than try something new based on what the clues were telling him, Zeedyk continued along the same path.

“I had to figure out how I could learn from the data,” he says. “I needed someone to do the kind of work that I didn’t like doing – computer work.”

Decoding the message

Learning from the data meant partnering with someone who could decode the multitude of evidence he’d been gathering for years. He’s not alone. Converting the clues to unlock the mysteries that lie within their fields has escaped many farmers.

“I think, in general, data has taken a backseat to hardware,” says John McGuire, Simplified Technology Services, LLC. “In other words, if you look at precision ag, what it boils down to is that you can put your hands on hardware. You can go out and calculate the return very quickly when you think about shutoffs and section control. It’s one of those things that’s an easy payback. Yet with data – although every time you go to the field with the technology, you are collecting data – it really has not ever been something that’s been focused on.”

McGuire says the quality of the data has contributed to the mystery surrounding its decoding.

“Even today, I don’t think there are many people out there who understand what real quality is when it comes to yield monitor information,” he says. “A red flag goes up for me when you tell me you’re within 1% or 2% of the scales.”

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