The objective of this research was to determine if unsupervised classification of topographic attributes and soil electrical conductivity could identify management zones for use in precision agriculture. Data collected in two fields located in central Missouri were used to test the proposed methodology. Principal component analysis was used to determine which layers of data were most important for representing within-field variability. Unsupervised clustering algorithms implemented in geographic information system (GIS) software were then used to divide the fields into potential management zones. Grain yield data obtained using a full-size combine equipped with a commercial yield sensing system and global positioning system (GPS) receiver were used to analyze the "goodness" of the potential management zones defined for each field. Principal component analysis of input variables for Field 1 indicated that elevation and bulk soil electrical conductivity (EC) were more important attributes than slope and Compound Topographic Index (CTI) for defining claypan soil management zones. The optimum number of zones to use when dividing afield may vary from year to year and was mainly a function of weather and the crop planted. The number of zones decreased if adequate moisture conditions were present throughout the cropping season (unpredictable) or if crops tolerant to water stress were planted (predictable). This classification procedure is fast, can be easily automated in commercially available GIS software, and has considerable advantages when compared to other methods for delineating within-field management zones.
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