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Field‐Scale Variability in Optimal Nitrogen Fertilizer Rate for Corn
Author(s) -
Scharf Peter C.,
Kitchen Newell R.,
Sudduth Kenneth A.,
Davis J. Glenn,
Hubbard Victoria C.,
Lory John A.
Publication year - 2005
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2005.0452
Subject(s) - fertilizer , agronomy , yield (engineering) , mathematics , grain yield , crop , agricultural engineering , environmental science , biology , physics , engineering , thermodynamics
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.