Dan Frieberg

Dan
Frieberg
Premier Crop Systems

 

Dan Frieberg is CEO of Premier Crop Systems, LLC a crop data-analysis company based in West Des Moines, Iowa. For more information on Premier Crop Systems or data-driven decisions, visit www.premiercrop.com. In the year ahead, Premier Crop Systems will push you to dig more deeply into your field data and find just how much power lies in the information layers that hide within your acres.

Articles
Data decisions: Use technology to verify company data  2

As companies line up to offer you solutions based on their proprietary algorithms that you adopt the same position as President Reagan did in negotiating arms treaties with the Former Soviet Union – “trust, but verify”. If a company tells you that their proprietary algorithm says you need 50 lbs. of additional N, use your technology to verify.

Data Decisions: Meaningful data analysis involves agronomic common sense, local expertise 2

“But how do I know if what I am seeing in my data analysis is real?” That’s a question that is not only appropriate but also healthy. For the last 15 years, as I’ve presented agronomic decision data analytics to growers and their advisors, I’ve cautioned that “correlation doesn’t always equal cause and effect,” says Dan Frieberg.

Data Decisions: Find profit in agronomic data 3

Let’s assume you are motivated to get started using your agronomic data to make decisions. What do you do next? Spring planting is hopefully only days or weeks away; what is possible? A great place to start using data to make decisions for the future is to gather all GPS data in an electronic form.

Data Decisions: Share your farm data? 1

Reality is we all share “our” data with other companies, either intentionally in exchange for a benefit or inadvertently because we wanted a cool “app,” and sometimes the tradeoffs are worth it. For me, the difference between consumer data sharing and sharing your geo-referenced agronomic data is profound.

Data Decisions: Compare apples to apples 3

It is easy in all data analysis to have “apples to oranges” comparisons and take data at surface value, but the key to good analysis is to keep digging deeper and deeper to get fair comparisons. Thus creating the most educated and profitable agronomic decisions.

Data Decisions: Group data analysis 1

Through data-sharing agreements, growers can confidentially and anonymously share their field and agricultural data to receive actionable intelligence from fields in their own local area.

Historical farming years offer new ways to learn from data 1

As some growers in the Midwest face yields below expectations, they’re finding new and different ways to learn from their data.

Data Decisions: Start simple with yield data 2

Every grower should have average yield data for each field. By looking at the yields per year for each field, you can easily pick out which fields fare better under specific conditions. And then you can ask and answer more questions and analyze your own corn yield data to help make decisions on your farm.

Build your nutrient data 1

In case you haven’t heard, there’s a target on your back! Our modern crop production system is on a collision course with the non-farming public that’s become more removed from farming with each generation. Many outside of agriculture, including regulators, associate high-yield crop production with being environmentally reckless.

Learning Blocks

Some people remember phone numbers or calendar dates; I remember farm fields. Before the 2005 crop year, the program leaders for Central Advantage from Central Valley Cooperative in southern Minnesota asked me to help generate variable-rate planting prescriptions. The primary question was, “Agronomically, what makes sense?” Field by field, I looked at the data collected for historic yields, soils, fertility levels, CECs, etc., and generated VR prescriptions for each field.

Data Decisions: One rate doesn’t fit all 1

Premier Crop staff likes to joke that when I began my career in crop production, it was in the days of “putting a fish under each corn plant” for nutrient applications. Although funny, that was not quite the case. However, in those days we would routinely pull 20 soil-sample cores, mix them in a bucket, pour 1 pound into a sample bag, send it off to the lab, get the results back and then pretend that what was on that sheet of paper accurately represented the nutrient levels for that entire field. While that may have been the best we could do then, we can do much better now, but many are treating entire fields the same.

Visualize GPS Agronomic Data With Maps to Make Data Easier to Understand 8

While collecting agronomic data is getting easier, using it to make decisions can be a challenge. Visually correlating the relationship between two maps, for example, yield vs. soils, is possible but becomes mind-numbing as you collect more data layers, such as planting data, soil-test values, applied fertility, etc., on dozens of fields.

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