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2016 Commodity Classic: Sifting Through Big Data: Where to Start?
In the end, what you choose to do with your farm’s data will determine your destiny. The question still remains: Where do you begin?
The bottom line is, says Doug Hackney, “Start small but start soon. If you want to be a grower, you need to get started on this. If you don’t have a plan for your data within three to five years, it’s going to be extremely hard to compete.”
If big data is valuable, as it has proven to be for virtually every other industry in the world, then the farmer who tries to farm without it will be out of business 10 years from now, cautions Tyler McClendon. “The farmer who decides it matters needs to fully commit and to realize on the front end that there will be change, learning, and pain that require resolve to make it work.”
If you are going to become data-dependent, he believes farmers need to understand some of the basics of data and technology. McClendon suggests spending some time learning about what data has done for other industries.
“Cell networks, GIS systems, and basic data analysis can be learned for free on the Internet,” he says. “Not knowing the basics of those fields will be like not knowing how to change the oil in your tractor. Sure, you can pay someone to do it, but if you don’t understand how to change the oil, how do you know if they are doing a good job?”
One of the most practical things he recommends is finding areas you can measure other than yield.
“Yield is interesting because it includes EVERYTHING that actually happened in that field,” explains McClendon. “Because it is such an information-rich number, sometimes it can be difficult to discern what it means. You wind up asking yourself, ‘OK, what am I supposed to do about that?’ One area we chose to focus on is soil moisture to guide irrigation. Is it perfect? No. Is it better than sticking your toe in the dirt? You bet.
“The really cool thing is that it’s a measurement that stays consistent over time, and we will learn from it and get better every year,” he says.
An Aha! Moment
Having data in a way that the McClendons can use it for the very first time has made them question things they thought they knew.
“This summer, one of our soil-moisture sensors was telling us it was time to irrigate,” says Tyler McClendon. “We had just gotten a good 2-inch rain about five days earlier. We didn’t think we needed to irrigate. When we went to the field, the top 3 inches were really wet – too wet to run equipment.”
Ready to write it off as a sensor problem, they dug a hole 2 feet deep just to be sure.
“We probed with a handheld moisture sensor, and lo and behold, the soil was very dry at 6 inches and bone-dry at 12 inches,” he says. “It was the same in several different places across the field.”
They turned the irrigation on. “It was a real ‘Aha!’ moment,” he says. “It was then that we realized the way we have been making the decision to irrigate was more flawed than we could have imagined, at least in some circumstances. That’s not to say we were doing a bad job before. Until recently, the only way to measure soil moisture was sticking our toe in the dirt. Now that we have a better way to make that decision, why would we not want to use it?”
As a result, the McClendons are starting to differentiate between the things they can measure with technology and the things they cannot. “If we can measure it to make better decisions, then we will,” he notes. “If not, we do it the same way we always have, but maybe with a little less pride about how smart we think we are.”