Field-scale variability in optimal nitrogen fertilizer rate for corn

• Published in: Agronomy journal Vol. 97; no. 2; pp. 452 - 461
• Main Authors: Scharf, P.C, Kitchen, N.R, Sudduth, K.A, Davis, J.G, Hubbard, V.C, Lory, J.A
• Format: Journal Article
• Language: English
• Published: Madison American Society of Agronomy 01.03.2005
• Subjects: corn; fertilizer rates; field experimentation; grain crops; grain yield; nitrogen fertilizers; precision agriculture; response to fertilizers; spatial variation; Zea mays; Missouri
• ISSN: 0002-1962; 1435-0645
• DOI: 10.2134/agronj2005.0452

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.