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Improving the Prediction of Nitrogen Response in Corn

One of the most difficult yet important decisions U.S. Corn Belt corn producers face each year is the amount of nitrogen fertilizer to apply. While high application rates may seem safer in terms of yield, they increase production costs and raise the potential for loss of nitrogen to the environment. Too little nitrogen means lower yield and lower net returns.

To help farmers improve nitrogen application decisions, graduate student and USDA National Needs fellow Daniel Febrer and Professors Emerson Nafziger and Maria Villamil in the Department of Crop Sciences at the University of Illinois investigated two key components of this decision: the factors that affect nitrogen availability in the soils and nitrogen response in plants; and models of nitrogen recommendation that maximize profit.

“Despite recent progress on formulating guidelines for nitrogen rates, precise prediction of economically optimal nitrogen at a site-specific level remains elusive, mostly because the accuracy of such predictions depend on the weather. This is greatly complicated by how the weather interacts with soil in different fields and different parts of fields.” Nafziger noted.

In a study encompassing 10 years of data at seven locations in Illinois, Febrer identified weather indicators that offered the most precise prediction of nitrogen response. July precipitation and soil temperature during silking were the best predictors of how much nitrogen the crop needed to provide the best return.

The Maximum Return To Nitrogen (MRTN) system (http://extension.agron.iastate.edu/soilfertility/nrate.aspx) developed by Corn Belt researchers including Nafziger, is designed to include data from large numbers of nitrogen response trials. Febrer compared the results from this approach with those from calculating returns for each trial separately, and found that they produced similar answers for corn following corn, but the MRTN system performed better for corn that follows soybean.

“Being able to predict nitrogen response more precisely will allow producers to adjust nitrogen application to be closer to the economic optimum given expected growing conditions, increasing profitability while reducing environmental impact.” Villamil added.

Source: Maria Vilamil, University of Illinois 

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