Comparing the performance of techniques to improve the quality of yield maps

Yield maps contain a wealth of information and can be an important tool for making informed decisions on paddock management. However, yield datasets obtained from combine harvesters often have many errors arising from a variety of sources. It is therefore important to attempt to rectify as many of these errors as possible so that the yield map represents the true yield as accurately as possible, rather than some systematic or operator error. This research defines the most significant errors associated with raw yield datasets and presents and applies a methodology for dealing with the unknown crop width, the time lag of grain, inappropriate GPS recordings, yield surges, and other outlying values. In total, 16.6% of the original yield dataset acquired over a 96 ha paddock located in the wheat belt of Western Australia was removed because they exhibited one or more of the aforementioned errors. The amount of uncertainty in the filtered dataset was substantially reduced. The accuracy of three spatial interpolation techniques was assessed over the entire paddock using the root mean squared error (RMSE). Finally, it is discussed that the map used to assist paddock management should be chosen depending on how much smoothing and data aggregation is desirable and allowable, not simply on the RMSE statistic.