Screening Yield Monitor Data Improves Grain Yield Maps

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.

[1]  R. M. Lark,et al.  A Method to Investigate Within‐Field Variation of the Response of Combinable Crops to an Input , 2003 .

[2]  J. V. Stafford,et al.  Mapping and interpreting the yield variation in cereal crops , 1996 .

[3]  L. T. Evans.  Crop evolution, adaptation, and yield , 1993 .

[4]  Thomas A. Doerge,et al.  Yield Map Interpretation , 1999 .

[5]  J. Stafford,et al.  Processing of point data from combine harvesters for precision farming. , 1999 .

[6]  P. C. Robert,et al.  An expert filter removing erroneous yield data. , 2000 .

[7]  Alex. B. McBratney,et al.  A Parametric Transfer Function for Grain-Flow Within a Conventional Combine Harvester , 2002, Precision Agriculture.

[8]  J. Specht,et al.  Soybean yield potential: A genetic and physiological perspective , 1999 .

[9]  Simon Blackmore,et al.  Remedial Correction of Yield Map Data , 2004, Precision Agriculture.

[10]  T. Arkebauer,et al.  Understanding Corn Yield Potential in Different Environments , 2003 .

[11]  Lei Tian,et al.  Time Shift Evaluation to Improve Yield Map Quality , 2001 .

[12]  Alex. B. McBratney,et al.  An Approach to Deconvoluting Grain-Flow within a Conventional Combine Harvester using a Parametric Transfer Function , 2004, Precision Agriculture.

[13]  Viacheslav I. Adamchuk,et al.  Classification of Crop Yield Variability in Irrigated Production Fields , 2003 .

[14]  J. Stafford,et al.  An algorithm for automatic detection and elimination of defective yield data. , 2003 .

[15]  D. Duvick,et al.  Post–Green Revolution Trends in Yield Potential of Temperate Maize in the North‐Central United States , 1999 .

[16]  Thomas S. Colvin,et al.  An Evaluation of the Response of Yield Monitors and Combines to Varying Yields , 2002, Precision Agriculture.

[17]  Sun-Ok Chung,et al.  Determining yield monitoring system delay time with geostatistical and data segmentation approaches , 2002 .

[18]  Thomas S. Colvin,et al.  Grain Yield Mapping: Yield Sensing, Yield Reconstruction, and Errors , 2002, Precision Agriculture.

[19]  Achim Dobermann,et al.  Creating Spatially Contiguous Yield Classes for Site‐Specific Management , 2003 .