My most recent blog began focusing on yield monitor calibration. We defined grain flow and talked about its influence on yield monitor accuracy. We probably need to talk about what accuracy really means.
So many times when accuracy is brought up I hear something like, "My monitor is accurate because when I compare total bushels across the scales I am within x.x%." (where x.x is some low number). I always cringe when I hear this for several reasons, not the least of which is my trepidation of having to explain to someone that they really don't understand how their monitor works.
The first problem with that statement is that you really can't accurately compare dry bushels across the scales to dry bushels measured by the yield monitor when the monitor is using default settings. The reasoning behind this is found in shrink factors. Scale measured bushels are shrunk much more in most cases. So when comparing scale data to yield monitor data it is always most accurate to use wet weight from both. If these two are close then you have achieved half of the value the monitor is capable of producing.
You might ask yourself, what is he talking about, "half of the value"? Consider the reasons for your purchase of that yield monitor. When I give calibration clinics I always ask that question and I get two answers. The first is exactly what we talked about above – accuracy to the scales or essentially a mobile weigh wagon. The second is that when hooked to a GPS you can make a map and see the variation in yield across a field.
The ability to assess variations in yield is the entire reason behind precision agriculture. Therefore, without accuracy in these measurements, all other pursuits with this data lose their value.
Would it surprise you if I said you can get accuracy to the scales without properly calibrating your system? It shouldn't, because there are a bunch of systems out there that are running close to the scales with totally incorrect calibrations. I worked for Monsanto on a research project back in the late 1990s and all of our yield monitor data was collected using incorrect calibrations. So while we were able to get great field averages, our whole project was focused on exploiting the variations within each field. Since we did not understand the engineering behind the yield monitor systems, we did not get accurate data and it ultimately killed the project.
Next week we will talk about how ignoring grain flow during calibration can lead to inaccurate maps.