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This article in AJ

  1. Vol. 97 No. 2, p. 452-461
    Received: Jan 27, 2004

    * Corresponding author(s): scharfp@missouri.edu
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Field-Scale Variability in Optimal Nitrogen Fertilizer Rate for Corn

  1. Peter C. Scharf *a,
  2. Newell R. Kitchenb,
  3. Kenneth A. Sudduthb,
  4. J. Glenn Davisc,
  5. Victoria C. Hubbarda and
  6. John A. Lorya
  1. a Agron. Dep., Univ. of Missouri, Columbia, MO 65211
    b USDA-ARS, Cropping Syst. and Water Quality Res. Unit, Columbia, MO 65211
    c USDA-NRCS, Columbia, MO 65203. Contribution from the Missouri Agricultural Experiment Station and the USDA-ARS


Applying only as much N fertilizer as is needed by a crop has economic and environmental benefits. Understanding variability in need for N fertilizer within individual fields is necessary to guide approaches to meeting crop needs while minimizing N inputs and losses. Our objective was to characterize the spatial variability of corn (Zea mays L.) N need in production corn fields. Eight experiments were conducted in three major soil areas (Mississippi Delta alluvial, deep loess, claypan) over 3 yr. Treatments were field-length strips of discrete N rates from 0 to 280 kg N ha−1 Yield data were partitioned into 20-m increments, and a quadratic-plateau function was used to describe yield response to N rate for each 20-m section. Economically optimal N fertilizer rate (EONR) was very different between fields and was also highly variable within fields. Median EONR for individual fields ranged from 63 to 208 kg N ha−1, indicating a need to manage N fertilizer differently for different fields. In seven of the eight fields, a uniform N application at the median EONR would cause more than half of the field to be over- or underfertilized by at least 34 kg N ha−1 Coarse patterns of spatial variability in EONR were observed in some fields, but fine and complex patterns were also observed in most fields. This suggests that the use of a few appropriate management zones per field might produce some benefits but that N management systems using spatially dense information have potential for greater benefits. Our results suggest that further attempts to develop systems for predicting and addressing spatially variable N needs are justified in these production environments.

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