2016-12-23T18:37:48

N to surface water is primarily by subsurface flow of nitrate (Schilling, 2002; Steinheimer et al., 1998), partic-Applying only as much N fertilizer as is needed by a crop has ecoularly when N fertilizer has been applied at rates exnomic and environmental benefits. Understanding variability in need for N fertilizer within individual fields is necessary to guide approaches ceeding crop needs (Burwell et al., 1976).to meeting crop needs while minimizing N inputs and losses. Our ob-Small-plot research has shown that experiments in jective was to characterize the spatial variability of corn (Zea mays L.) different production corn fields can differ substantially N need in production corn fields. Eight experiments were conducted in their need for N fertilizer (Bundy and Andraski, 1995; in three major soil areas (Mississippi Delta alluvial, deep loess, clay- Schmitt and Randall, 1994). Need for N fertilizer may pan) over 3 yr. Treatments were field-length strips of discrete N rates also vary widely over large fields (Malzer et al., 1996; from 0 to 280 kg N ha Ϫ1 . Yield data were partitioned into 20-m Mamo et al., 2003) though very little research has been increments, and a quadratic-plateau function was used to describe published addressing this issue. Attempts to predict the yield response to N rate for each 20-m section. Economically optimal amount of N fertilizer needed have met with limited N fertilizer rate (EONR) was very different between fields and was success in humid regions (Kitchen and Goulding, 2001). also highly variable within fields. Median EONR for individual fields ranged from 63 to 208 kg N ha Ϫ1 , indicating a need to manage NThe dominant practice for agricultural producers is to fertilizer differently for different fields. In seven of the eight fields, apply the same rate of N fertilizer over whole fields and a uniform N application at the median EONR would cause more than even whole farms. In fields with spatially variable N needs, half of the field to be over-or underfertilized by at least 34 kg N this practice leads to frequent mismatches between N ha Ϫ1 . Coarse patterns of spatial variability in EONR were observed fertilizer rate and crop N need. Overapplication is more in some fields, but fine and complex patterns were also observed in frequent since producers have an economic incentive most fields. This suggests that the use of a few appropriate manageto err more frequently in that direction: The cost of ment zones per field might produce some benefits but that N manageunneeded N fertilizer in areas of overapplication is less ment systems using spatially dense information have potential for than the cost of lost yield potential in areas of undergreater benefits. Our results suggest that further attempts to develop application. systems for predicting and addressing spatially variable N needs are justified in these production environments.

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 a field 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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