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Got yield data. Now what?
I may have 16 years of yield data on most of my Oakland, Iowa, fields, but the pretty yield maps were not always particularly useful. Knowledge gained from the yield monitor came mainly from the driver's seat of the combine.
In the early days, the data gathered had two purposes. First, it was used to compare seed varieties. I've done hundreds of side-by-side tests over the years for seed selection, and sometimes the yield maps would reveal placement information for certain varieties. But, generally, the best seed worked well wherever it was placed.
The second purpose of data was to define the wet areas of a field for tiling, which was quite useful.
Yet none of the information was ever used to make decisions on a broader scale. Today, however, yield data is used to define zones for soil testing as well as planting rates.
In the beginning, I used grid soil sampling to understand my fields better. I was frustrated, though. I didn't think the maps modeled fields very well. Then I considered a zone map, because it would provide a better model. But zones are hard to define. In the end, yield maps did give me the high-resolution data I needed to develop the high-resolution zones I wanted.
My zones average 4 acres; some are as small as 1.5 acres. I experimented with multitemporal yield analysis years ago, but it was difficult with the software at the time. The analysis is much easier now, and it's built into the SMS software I use.
Based mostly on yield maps, zones were drawn by hand. RTK elevation data was used to create a three-dimensional map of the yield data to help visualize zones, which are exported to an ATV for sampling. An automatic probe on the ATV makes it easy to collect cores from 10 sample points spread across each zone. Each zone is tested, and results are associated with each sample point. This takes a little extra work, but I've found that soil tests do model the field better.
Overall, I'm happy with the new sampling method.
Zones or yield data also make sense in variable-rate planting because all factors are accounted for – measured or not. I selected a high-yield year for basing my variable rate. A year that shows the yield potential of the field gives better information for a planting-rate prescription.
Yet a prescription requires quality data. One difficulty with yield maps is the noise in the data. Noise (or bad data) comes from sources including poor calibration, variety differences, and wind. Most noise happens at the end of the rows.
An easy way to filter data is to select the low and high yields, and delete them from the map. Other bad data can be selected and deleted from the map. Noise isn't much of an issue, however, when hand-drawing zones. A contour map fills in blank areas for the prescription.
Doug Applegate Applegate Acres
Doug Applegate farms 1,500 acres near Oakland, Iowa, with his wife, Kathy. Their primary crops are corn and soybeans. They began using precision ag technology in their operation more than a decade ago. Learn more at applegateacres.com.
By Doug Applegate