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Comparing Corn Variety Yields in Center Pivot Irrigated Fields


Koehn Farm – SW Kansas,


2008 John Deere 9570

Crop Type:

Corn, Milo, Wheat

Comparing Crop Varieties in Circular Fields with FarmTRX

The Setting:

Milton Koehn farms is in an area of southwest Kansas where many farmers use center pivots to irrigate their corn fields while planting drought resistant crops in the corners. “This is common practice in these parts but was new to the FarmTRX guys” says Koehn. The FarmTRX system was originally devised for western Canada where this type of irrigation is not common. “They were willing to spend the time and work with me to figure out how to create yield maps for circular fields with different crops at the corners”, said Koehn. He bought the FarmTRX yield monitor for his 2008 John Deere 9570 because it was the right price and it seemed like it would be simple and effective for his 800 acres.

The Issue:

Koehn, like many other corn farmers with center pivot irrigation, plants different varieties in his fields along with different crops in the corners, like milo. “We were pretty confident that the circular harvesting pattern wouldn’t make a difference to the way our system automatically generates yield maps”, said Mark Hammer – FarmTRX VP Sales and Business Development. An unforeseen bonus was the ability to map the corners as a separate field and export them quickly and easily for crop insurance submission. It turns out that farmers can spend a lot of time and effort doing this manually.

Corn Variety Mapping Solution:

Koehn wanted to learn what could be done to assess different variety types in one of his center pivot corn fields. SW20 was planted with three different varieties in the center, middle and outer sections of the field. “When Milton was deciding on buying our yield monitor we discussed post-harvest calibration. He knew which rows were which variety, so he had independent weigh tickets for each variety tabulated”, recalls Hammer. Koehn knew where to draw the three sub-field boundaries for each variety and provided the total for each sub-field in bushels to calibrate each variety trial independently.

Defining sub-fields and calibrating after harvest, with a known total yield per variety, takes the pressure off while harvesting and lets a grower focus on what is critical at that time – getting the crops in.

Processing the sub-fields allowed for comparison and assessment of independent varieties and how the field behaved overall. Assessment of yield patterns with total yield by variety revealed the effect of irrigation irregularities. Center field variety A produced 221.25 bu/ac. Middle field variety B produced 242.41 bu/ac. Outer field variety C produced 255.93 bu/ac. The middle field variety had a broad section of good yield but much of the area appears impacted by irrigation system variance. This is another example of how yield patterns versus yield totals may reveal other influences and are an essential part of the many components that go into the decision-making process.

Koehn was happy with the ease of use the system provided during harvest. It was quick to install, easy to use and produced corrected yield maps automatically, but there were a couple of issues. “I was reviewing one of my brother’s corn fields and there was this half circle of low yield (red) in the raw yield map, but it was gone in the corrected yield map”, recalls Koehn. He connected with Hammer on-line to review this difference. “I really enjoy these reviews with farmers. I can see the patterns and anomalies but cannot explain them since I am not a farmer by trade and do not know their fields. It’s really interesting but sometimes a bit stressful when you can’t explain a result” says Hammer. He could not explain the crescent moon shaped low-yield strip; it was clearly man-made as it followed the path of the combine, but he did not know why.

Automated Solution:

The good news was the advanced FarmTRX Web App cleaning and filtering process detected these anomalous low points as false and the corrected yield map addressed the issue automatically. Though the question remained, what was the cause? “Milton was curious and that was great. He identified an issue and we tackled it together”, recalls Hammer. The FarmTRX Mobile App had disconnected and reconnected while harvesting, resetting the crop type in the process. “I remember harvesting this field and it was showing Canola as the crop type. I reset it properly and then everything was fine”, recalls Koehn.

Upon inspecting the yield data, it was determined that the FarmTRX Mobile App reset to Canola as the default crop type when it disconnected/reconnected, an issue that has since been addressed. “Sometimes things go wrong with technology during harvest but the FarmTRX system was built to catch inconsistencies like this during processing” said a relieved Hammer. “We’re glad the system worked automatically, just like it should”.

The raw data associated with the false low-yielding canola points was re-processed as corn, along with the FarmTRX Web App’s automated processing for headland turns, partial rows, static in-field offload points and other data anomalies. The result was a nicely corrected yield map, absent the crescent moon false lows.


The FarmTRX Internet of Things (IOT) approach makes cost-effective yield mapping a reality with older equipment. Post-processing of raw yield data after harvest allows users to define and calibrate sub-fields for trials and/or varieties when they have more time. It has a demonstrated ability to catch errors and correct mistakes easily and automatically. And, thanks to Milton, we now know that it works well in both circular fields and the corners too!