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Updated: Jun 3, 2025

Microplot Design and Plant and Soil Sample Preparation for 15Nitrogen Analysis
Published on: May 10, 2020
A mechanistic model for determining factors that influence inorganic nitrogen fate in corn cultivation
Patrick J Dunn1, Leanne M Gilbertson1,2,3
1Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA. leanne.gilbertson@duke.edu.
Abstract:
Conventional practices for inorganic nitrogen fertilizer are highly inefficient leading to excess nitrogen in the environment. Excess environmental nitrogen induces ecological (e.g., hypoxia, eutrophication) and public health (e.g., nitrate contaminated drinking water) consequences, motivating adoption of management strategies to improve fertilizer use efficiency. Yet, how to limit the environmental impacts from inorganic nitrogen fertilizer while maintaining crop yields is a persistent challenge. The lack of empirical data on the fate and transport of nitrogen in an agriculture soil-crop system and how transport changes under varying conditions limits our ability to address this challenge. To this end, we developed a mechanistic model to assess how various parameters within a soil-crop system affect where nitrogen goes and inform how we can perturb the system to improve crop nitrogen content while reducing nitrogen emissions to the environment. The model evaluates nitrogen transport and distribution in the soil-corn plant system on a conventional Iowa corn farm. Simulations determine the amount of applied nitrogen fertilizer acquired by the crop root system, leached to groundwater, lost to tile drainage, and denitrified. Through scenario modeling, it was found that reducing application rates from 200 kg ha-1 to 160 kg ha-1 had limited impact on plant nitrogen content, while decreasing wasted nitrogen fertilizer by 25%. Delayed application until June significantly increased the f-NUE and denitrification while reducing the amount of fertilizer leached and exported through tile drainage. The value in a model like the one presented herein, is the ability to perturb the system through manipulation of variables representative of a specific scenario of interest to inform how one can improve crop-based nitrogen management.
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