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Corn Response to Nitrogen is Influenced by Soil Texture and Weather
Author(s) -
Tremblay Nicolas,
Bouroubi Yacine M.,
Bélec Carl,
Mullen Robert William,
Kitchen Newell R.,
Thomason Wade E.,
Ebelhar Steve,
Mengel David B.,
Raun William R.,
Francis Dennis D.,
Vories Earl D.,
OrtizMonasterio Ivan
Publication year - 2012
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/agronj2012.0184
Subject(s) - soil texture , soil water , agronomy , environmental science , human fertilization , crop , nitrogen , soil science , biology , chemistry , organic chemistry
Soil properties and weather conditions are known to affect soil N availability and plant N uptake; however, studies examining N response as affected by soil and weather sometimes give conflicting results. Meta‐analysis is a statistical method for estimating treatment effects in a series of experiments to explain the sources of heterogeneity. In this study, the technique was used to examine the influence of soil and weather parameters on N response of corn ( Zea mays L.) across 51 studies involving the same N rate treatments that were performed in a diversity of North American locations between 2006 and 2009. Results showed that corn response to added N was significantly greater in fine‐textured soils than in medium‐textured soils. Abundant and well‐distributed rainfall and, to a lesser extent, accumulated corn heat units enhanced N response. Corn yields increased by a factor of 1.6 (over the unfertilized control) in medium‐textured soils and 2.7 in fine‐textured soils at high N rates. Subgroup analyses were performed on the fine‐textured soil class based on weather parameters. Rainfall patterns had an important effect on N response in this soil texture class, with yields being increased 4.5‐fold by in‐season N fertilization under conditions of “abundant and well‐distributed rainfall.” These findings could be useful for developing N fertilization algorithms that would prescribe N application at optimal rates taking into account rainfall pattern and soil texture, which would lead to improved crop profitability and reduced environmental impacts.