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Corn+Soybean Digest

Make data pay: Improve agronomic decisions

Precision ag manager Clint Sires with AgParterns in Albert City Iowa says they optimize decisions by soil type using results from 100000 acres
<p>Precision ag manager Clint Sires with AgParterns in Albert City, Iowa, says they optimize decisions by soil type using results from 100,000+ acres.</p>
Look beyond local trials and seek pooled data

How do you create real value from your GIS field data?

“By converting data to knowledge” that can improve production decisions, says Dan Frieberg, president of Premier Crop Systems, West Des Moines, Iowa, a precision agriculture software company.

That means “going beyond what you can see on a map,” and moving to sophisticated data analysis at the field level and across thousands of acres of pooled data, he says.

Corn+Soybean Digest asked top precision-ag consultants to provide examples of how they use geo-referenced (GIS) data analysis to understand yield variability and make better agronomic decisions. In our series, “Making Data Pay,” you will read about:

  • How to decide if site-specific management makes sense on a particular field;
  • How to create management zones;
  • How to integrate financial and geo-referenced agronomic data and use it to evaluate decisions such as rent contracts and drainage improvements;
  • How to apply “big data” analysis to decisions on your own farm.

Look for these stories in future issues, or online.

Draw on large group data to make decisions

We start with big data.

Big-data analysis — which combines “real-world data from hundreds of thousands of acres” — can be a very powerful decision tool, says Clint Sires, a farmer, and precision ag manager of InSiteCDM, at AgPartners LLC, Albert City, Iowa.

InSiteCDM uses Premier Crop Systems software, which accesses Premier’s group databases, collected from hundreds of thousands of Corn Belt acres. “We try to steer growers away from basing decisions only on strip trials or local test plots,” Sires says. Instead, “We put together group data so growers can use data across many locations to evaluate performance.” That results in more robust recommendations, Sires says.

precision data sources

first-year corn yield response to fungicide in drought years

2012 corn yield response to fungicide by hybrid, soil type

Turn data into knowledge

Sires explains how he used big data analysis to answer a common question in 2012: Does it pay to apply corn fungicide in a drought year?

During the 2012 growing season, many farmers in AgPartners’ InSiteCDM program wanted to know whether applying a fungicide to drought-stressed corn was a smart investment. 

But the InSiteCDM database for northwest Iowa didn’t have much data from a severe drought year. So Sires examined data from tens of thousands of Corn Belt acres that had received fewer than 16 inches of growing-season rainfall from 2000 - 2011. Drawing on this group data, he analyzed first-year corn yield responses to four common fungicides, comparing them to corn yields without fungicide (see table). Using this “Corn Belt drought data,” Sires was able to make preliminary recommendations as to the general benefit of fungicides during drought-stressed years.

After the 2012 season, Sires was able to compare yield response by hybrid and fungicide brand in northwest Iowa, using data collected by the InSiteCDM group. The group represents over 100,000 acres in northwestern Iowa — virtually all hit by severe drought in 2012. This knowledge “will aid us greatly in making these tough decisions the next time northwest Iowa experiences a drought — or even an abnormally dry growing season.”

data map


Make data pay

During the 2012 drought, “we were able to give our growers an idea of whether or not it was smart to use a fungicide in a drought year,” Sires says. “Now, using the data we collected during 2012, we will be able to show which brands of fungicide worked on which varieties of corn, and how large an impact they had.”

Big-data queries can be tailored to match individual field circumstances, Sires adds, such as soil type, rainfall, hybrid, plant population. “We can really drill down through all the data to provide sound recommendations.”

For example, the nearby “As Planted” map shows how InSiteCDM growers track hybrids/varieties planted on their fields. “Using this hybrid data,” Sires says, AgPartners sales agronomists “can analyze data queries from the 100,000-plus-acre group to say, with confidence, ‘On these Clarion/ Nicollet/ Webster type soils, you should be using Brand A fungicide on those two DeKalb hybrids because they are going to increase yield on your farm.’ By contrast, the agronomist might say, ‘Based on the data we’ve collected on those soils for those two hybrids, and considering the amount of rainfall we have received to date, I believe you are better off to save your money on that fungicide treatment for this year.’ ”

As-planted hybrid map with soil types

hybrid map with soil types

Population map

population map

In this 65-acre field with predominantly Clarion/Nicollet/Webster soils, AgPartners precision ag expert Clint Sires worked with an InSiteCDM grower to manage two different Dekalb corn hybrids in 2013. In the green area, the grower planted DKC53-56, a newer hybrid; the red area is DKC53-78, an older hybrid. Both hybrids were planted in 30-inch rows. Plant populations varied from 28,000 seeds per acre in the sandy areas to 38,000 seeds per acre in the best parts of the field. Population in a Learning Block was 41,000 seeds per acre. Both plant population and hybrid have a bearing on fungicide decisions, Sires says. If group data shows that DKC53-78 responded to Headline fungicide, for example, on Clarion/Nicollet/Webster soils over the past several years, then he would definitely consider at least spraying the red area with Headline.

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