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2016 Commodity Classic: Collecting Data: Measure It to Manage It

The Bright Penny Tip-Off

Sometimes, leveraging farm data is akin to spotting a bright penny at the bottom of a murky swimming pool. 

Back in 2013, Widmar Family Farms in Franklin, Kansas, harvested corn after a bleak weather year. That’s the year wet spring weather nixed corn planting until after May 20. 

“Then in July and August, it never rained again,”says David Widmar, a Purdue University economics research associate and partner in Widmar Family Farms.  

Not surprisingly, poor-yielding corn resulted at harvest. “Yields were about one-half of our expectations,” says Widmar. 

What their harvest monitor revealed, though, was a bright-penny eyeopener.

“We saw areas of fields yield 200 bushels (per acre), while other areas barely made 20 bushels,” says Widmar. We thought, ‘how this could happen?’ We had never seen variations like that occur.”

Subsequent yield map analysis revealed a counterintuitive discovery. 

“All of our high spots in the field actually yielded best. We would have thought the opposite – that drought would hit the high spots worst,” says Widmar. “We had a hypothesis that the data did not support. We thought drought hurt yields, but the data did not support that. Instead, the data  pointed to the wet spring.”

They Built Their Own Black Box

In vehicles and airplanes, a black box tracks everything an implement has done. Widmar Family Farms wanted to create its own black box for farm data. 

“If you don’t measure it, you can’t manage it,” says Widmar. 

Making data-driven decisions has potential, but it’s been challenging. In one case, confusion about who was responsible for data collection between implements lost two years of data. 

Occurrences like this and 2013 yield variances led Widmar to revamp their data strategy to learn about: 

• Management zones

• Optimal populations

• Optimal fertility

   To do this, they invested in:

• SMS software

• Software planter updates

• Minimal hardware for the farm’s fertilizer applicator 

What They’ve Learned

Widmar Family Farms is still sifting the data findings before making wholesale agronomic changes across the operation. Widmar says he has some insights, though, on creating their farm’s data management system. Action steps include:

Set up a budget.

“If you go into this without a budget, you can buy a little of everything,” says Widmar. “If you start with a budget, you can say this is how much money I have to spend. Otherwise, it can get out of hand.”

Designate a data leader. 

“You really need a leader responsible for data collection,” says Widmar. “We didn’t have as much data as we thought, because we had two years that just disappeared. Since then, we have stopped the combine to make sure it’s collecting data. If we would have always done this, it would have saved us two years worth of data.” 

Rethink your order of operations. 

“We planted some acres on March 15, 2014, just to make sure all the bugs were worked out,” he says. “Every time we start something new, we want to make sure it works.” 

Analyze data soon after field operations are conducted. 

“If we wait too long, all we see are just little dots,” he says. 

Write it down. 

“We do an annual look back,” says Widmar. “We write down what we did and what we would do differently.”

Be patient. 

“It takes us two years to do what we thought we could do in one year,” says Widmar.

This story is a segment of "Playing Small Data = Agronomic Success." Click the link to see full story and other related content.

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