About Yield Monitor Data Cleaning
Related pages: Yield Map Cleaning Examples Raw vs. Clean Raster Yield Maps
Within a raw yield monitor dataset there is important information that can show yield patterns throughout a field. However, it has been well-documented that yield monitor data should be cleaned prior to application or analysis; publications can be accessed in the Yield monitor data cleaning page in the Articles folder. See below for more information about why yield monitor data should be cleaned.
Corn Yield Map Cleaning Example. From left to right, maps are: raw, filtered, clean, polygons, and zones.
Soybean Yield Map Cleaning Example. From left to right, maps are: raw, filtered, clean, polygons, and zones.
Why Yield Maps Should be Cleaned
Sudduth and Drummond (2007) reported that research from others show anywhere from 10 to 50 percent of yield points needed to be removed and that the erroneous data can strongly effect the resulting yield distribution. They state that “if the errors are not addressed, the user of the yield map may reach erroneous conclusions, calling into question the credibility and validity of the results”. Sudduth and Drummond (2007) found the range of yield point removed with their filter was 13 to 27 percent.
It is essential that manual removal of erroneous yield points occur in addition to automated and statistical removal. Sudduth and Drummond (2007) state that: "Almost invariably, yield data will include some data points which are clearly in error, yet are not easily captured by any automated filter. For example, a small but significant number of errors can be introduced when the combine operator harvests a narrow 'cleanup' swath in a field, but does not correctly record the swath width. In another case, the combine operator may forget to lift and lower the head when leaving/entering the crop, precluding the START and END filters from properly removing what may be a significant number of errors."
In general, erroneous yield data occur due to inevitable human error and the mechanical operations of a yield monitor system. Yield data will, overall, be more accurate if the combine operates in a steady, uniform environment but even with excellent attention to driving or auto-steering, the combine will inevitably exit and enter the field or need to be abruptly steered around an object or slow down or stop. These circumstances, and others, can produce erroneous values that need to be cleaned to produce a more coherent and representative yield map.
Shapefile Cleaning Overview
(Click here for Preferred Yield Map Cleaning and Management with Yield Editor 2.0.7 [if correct files are available])
The yield monitor data cleaning method applied here is based on published steps that have been shown to be important to remove erroneous points, plus a series of smoothing techniques. Details of cleaning are included in the (pdf) Yield cleaning steps page. The end result is a visibly and statistically coherent map. Once yield data are cleaned properly, maps can more effectively be applied for variable rate purposes (prescription maps or management zone development), as well as, any analysis. The yield monitor cleaning examples shown in this section are for corn and soybeans, but the cleaning method can be applied to data for most crops.
Another reason to have GIS Ag Maps clean your yield maps is that when multiple seasons of yield monitor data are cleaned for the same field, the result are map with points at precisely the same location (same coordinates); there is a single shapefile with the different seasons of yield represented by different columns in the attribute table. This helps organize data and is advantageous for data analysis because yield changes over time at the same location can be tracked (raw yield maps for different seasons do not result in points being located at precisely the same position.)
Reference
Sudduth, K.A., and S.T. Drummond. 2007. Yield Editor: Software for Removing Errors from Crop Yield Maps. Agronomy Journal99: pp. 1471–1482 (pdf) (document opens in new window)