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

Overview of attention for article published in Frontiers in Plant Science, July 2017
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Title
A Vision for Incorporating Environmental Effects into Nitrogen Management Decision Support Tools for U.S. Maize Production
Published in
Frontiers in Plant Science, July 2017
DOI 10.3389/fpls.2017.01270
Pubmed ID
Authors

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

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 23%
Student > Master 7 15%
Other 4 9%
Professor 2 4%
Unspecified 2 4%
Other 8 17%
Unknown 13 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 28%
Medicine and Dentistry 5 11%
Environmental Science 4 9%
Unspecified 2 4%
Economics, Econometrics and Finance 2 4%
Other 5 11%
Unknown 16 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 September 2017.
All research outputs
#17,913,495
of 22,999,744 outputs
Outputs from Frontiers in Plant Science
#12,195
of 20,492 outputs
Outputs of similar age
#227,152
of 316,675 outputs
Outputs of similar age from Frontiers in Plant Science
#356
of 505 outputs
Altmetric has tracked 22,999,744 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 20,492 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 316,675 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 505 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.