“We’ve been using today’s form of precision ag for 25-30 years – it started in the ‘90s,” says Bruce Erickson, clinical profession of digital agriculture for Purdue University. “Back then, it was floppy disks and no cloud storage.”
But just because ag precision technology came of age back then and is still in use now, doesn’t mean it has plateaued, says Erickson. In fact, quite the opposite, he maintains, and the answer on adoption is more complex than looking at percentages.
“It’s like when, back in the ‘30s, people asked why everyone wasn’t planting hybrids?” he says. Adoption on a large scale occurs in developed countries like the U.S., Australia, Canada and others, while there’s very little precision ag in places like China, India and other developing countries. Then when the numbers come out, it looks skewed to slow adoption.
Examples of Precision Ag Technologies with High Adoption
Most combines coming off the line have yield monitors, says Erickson. Most sprayers have section control and hydraulic drives. These technologies that operate with little intervention from farmers and retailers have enjoyed widespread adoption.
Autoguidance is another great example, he says, one 50% of corn farmers use. Once autoguidance is set, you don’t have to reset it over and over. The same with planter row clutches and sprayer row shutoffs.
“That autoguidance was quickly adopted, because it was relatively simple for the user,” Erickson explains.
Examples of Precision Ag Technologies with Slow Adoption
If we have struggled with adoption as an industry, it has been more at the minute field level, he says. “Where it seems like we’ve struggled is doing things in just parts of the field.” Variable rate technology (VRT) is odd, he admits. “Thirty years after the fact, most of the world isn’t doing variable rate fertilizer.”
“You can see that a specific area of the field needs a different rate than another area of the field,” Erickson says. “The technology that is data-dependent is a lot harder than we ever predicted to get people to use it.”
Added to that, the ag retail sector looks quite different than the farm sector. “Seventy to eighty percent of ag retailers are using boom section controllers,” he says. The remaining 20 percent or so that isn’t using them could be explained by the fact that not every retailer is doing custom work, and thus may not justify the technology.
Another great example is satellite imagery, he says. “We’ve had it for years, and it’s still used very little on farms, because it’s really hard to figure out.” On top of that, there’s a perception of high cost, meaning farmers may believe they don’t have the budget to utilize it, even if they haven’t explored the options.
“Ag environments are much more complex than the general public ever gives them credit for,” he says. That’s where technology like satellite imagery fits in – where farmers could see down to the foot or inch whether they needed to change rates in the south side of the field, for example.
All of these layers of technology and use patterns and belief systems and industry segments make it difficult to give a finite answer. So when you ask Erickson and other digital ag experts whether precision ag adoption rates are slow or down, their answer: not really, but kind of.
1 comment
Many of us who have worked with N management would point to the lack of consistently profitable variable-rate N technology as the most important reason for lack of adoption. While the amount of fertilizer N needed by the crop varies across most fields, this variation is not consistent over years in the same field; that is, we can’t use a VRN map from one year to vary N rate profitably in that field the next season. We have also found that by the time N deficiency appears in a field, some yield may already have been lost, and that “emergency” N applied to correct deficiencies can take days or weeks to get into the plant, resulting in even more yield loss. Using VRN as it’s currently envisioned adds cost and increases risk, and has not shown much promise of consistently lowering overall N rate or increasing yield. It’s little wonder that producers prefer to use a little more N than the crop is likely to need, and to apply it uniformly or with low- or no-cost variable-rate maps that maintain relatively high minimum rates, without paying extra to see if they can sense N deficiency in time to correct it. Lowering too-high N rates is a much surer way to increase profits than variable-rate N, at least as we know it today.