A Vision for Incorporating Environmental Effects into Nitrogen Management Decision Support Tools for U.S. Maize Production.

Kamaljit Banger,Cameron M Pittelkow,Emerson D Nafziger,Junming Wang,Mingwei Yuan

doi:10.3389/fpls.2017.01270

Abstract

Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders.

Highlights

Managing nitrogen (N) on over 130 million ha of cropland is critical for sustainable food production due to the large impact of N fertilizer on farm profits and environmental health (Anderson et al, 2008; Howarth, 2008)
In Corn Belt of the United States, only 37 ± 30% of applied N is utilized by the crop (Cassman et al, 2002), with the remaining portion being susceptible to
Excessive N leaching from corn fields is a leading source for degradation of water resources (Goolsby and Battaglin, 2001; Ewing and Runck, 2015) while N fertilizer inputs are linked to increased nitrous oxide (N2O) emissions (USEPA, 2014)

Summary

INTRODUCTION

Managing nitrogen (N) on over 130 million ha of cropland is critical for sustainable food production due to the large impact of N fertilizer on farm profits and environmental health (Anderson et al, 2008; Howarth, 2008). Remote sensing methods have limitations resulting in low adoption by farmers including the high cost of sensors and the degree of computer and geospatial skills required to process grid based data, and the need for continuous calibration against soil tests To overcome these limitations, a number of site-specific fertilizer recommendation tools based on crop models have recently been developed. Since we are unaware of any publicly available model-based framework linking weather, soils, and farmer management to estimate N losses in real-time in United States maize production systems, a real-time modeling interface would represent an important technological advance in this area, with farmers being able to provide minimal inputs including geographical location and relevant crop management practices, while the user interface would automatically incorporate other necessary input data from public resources (Melkonian et al, 2008).

Ability to predict both crop yield and environmental loss?

Findings

AUTHOR CONTRIBUTIONS