Screening Yield Monitor Data Improves Grain Yield Maps

Abstract

Yield monitor data contain systematic and random errors, which must be removed for creating accurate yield maps. A general procedure for assessing yield data cleaning methods was applied to a new postprocessing algorithm in which six common types of erroneous yield monitor values were removed: (1) combine header status up; (2) start‐/end‐pass delays; (3) grain flow, distance traveled, and grain moisture outliers; (4) values exceeding minimum and maximum biological yield limits; (5) local neighborhood outliers; and (6) short segments and co‐located points. The algorithm was applied to four yield maps of maize (Zea mays L.) and soybean [Glycine max. (L.) Merr.] grown under irrigated and rainfed conditions. A total of 13 to 20% of the original yield monitor data was removed, with 72 to 85% of the removal occurring in the mandatory, primary screening process (Steps 1 and 2). Only 2.6 to 3.9% of the original yield monitor data were removed during secondary screening (Steps 3 through 6), but this additional screening lead to yield semivariograms with smaller nugget values and sills and a relative increase in map precision of 4.3 to 5.4% compared with conducting primary screening only. The local neighborhood outlier test (Step 5) removed a larger proportion of yield values in soybean (12.8 to 14.9% of all deleted values) than in maize (2.7 to 3.2%). The proposed algorithm is robust enough for implementation in commercial software but requires further testing in other crops and environments and with other brands of yield monitors.