Is Your Field Data Telling a Tall Tale?

Read Time: 3 minutes
December 1, 2020
Andrew Knaack
Field Product Specialist, The Climate Corporation

I’ve always wanted a clear picture of what is happening on my acres. Working with my dad on our family farm, I saw firsthand how the numbers on the yield monitor and the grain elevator can be bushels apart. Different field conditions, like down corn, can impact results, and  fine-tuning harvest data to a known scale weight can deliver even more precise metrics. Similar to a bookshelf hung a few degrees off, a slight miscalculation can cause everything to slide away.

If you want field data to help you make decisions, it needs to be accurate. I've worked with fellow farmers, helping them manage their field data and conduct on-farm trials for over ten years. In that time, I've noticed misconceptions about how accurate your data needs to be. Margins are razor-thin. A one percent gain can mean a great deal across hundreds or thousands of acres. But unfortunately, the math cuts both ways. A minor inaccuracy can produce surprisingly different yield results.

Above is an example of how yield data can differ when the wrong crop is selected at harvest. The operator accidentally entered soybeans, when it was actually corn, and created a missing row of yield data in the final yield map.

How Good Data Could Go Bad: Harvesting the Wrong Crop 

If it's corn or soy, a bushel is a bushel, right? Not exactly. If you find yourself running the combine harvesting beans and accidentally selecting corn in the FieldView™ Cab app, immediately stop and adjust the data. The flow sensor in your combine calculates weight using data from the moisture sensor, headwidth and GPS speed to give you bushels per acre. To your combine, comparing 100 bushels of corn to 100 bushels of soy is like comparing a feather to a giant ape.

A 400-Pound Mistake

Comparing 100 bu of corn and soy.

What's the difference between corn and soy? The standard weight for a bushel of corn is 56 pounds. For soy, it's 60 pounds. Over just 100 bushels, that's a 400-pound difference. All that extra weight adds up to inaccurate yield data.

Save the Nicknames for Your Fishing Buddies, Not Your Hybrids 

At the end of harvest, you need yield data to be easy to read. But if you labeled the same hybrid three different ways, it will show up on three different lines, even though it's the same seed! If you find typing the actual seed name cumbersome, scan the seed bag to enter the correct name automatically. This is great winter prep activity and another example of how an ounce of prevention is worth a pound of cure.

Above is a yield analysis breakdown with inconsistent hybrid names. Many lines are referring to the same hybrid, but the yield numbers vary considerably. This further complicates an already challenging task of selecting seed with harvest data.

Calibrate Your Yield Monitor 

Your yield monitor is smart, but it still needs your guidance to do its best work. The sensors inside your combine need to become familiar with the conditions of your fields. Just like fine-tuning an engine, calibration makes sure everything is lined up and ready to go. Even calibrating once at the beginning of harvest and again mid-way will make a noticeable difference. Think of your field data like a house of cards and an uncalibrated yield monitor as a bouncy ball flying into it. This single factor can affect everything else.

Fix Your Field Boundaries

At the end of harvest, you want to evaluate every field in isolation. If field boundaries are inaccurately marked, FieldView™ might combine data from two different fields. This seemingly small detail can have a big impact. It can alter how data is collected throughout planting, spraying, and harvest, it can change results on summary reports, and it can even make it harder to correctly implement a planting prescription for that field.       

A whole book could be written with ideas to improve the quality of field data. But if you only do the three things outlined in this post, you will drastically improve your data quality.

Easy Ways to Improve Data Quality 

 

  1. Keep hybrid names consistent 
  2. Correctly log the crop you’re harvesting
  3. Ensure field boundaries are accurate

Happy number crunching as you wrap up harvest. And if you run into any issues with inputting data either through data inbox or making post-harvest yield adjustments, we have a hotline of experts that can help. Give us a call at (888) 924-7475. We can even have a specialist come out to your operation to assist with data collection.


About the Author

Andrew is a Field Product Specialist in Iowa, working closely with farmers and dealers to help them realize their agricultural goals. After growing up on a family farm, he graduated from Iowa State University with a degree in Agricultural Business.