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Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement

Overview of attention for article published in Frontiers in Plant Science, October 2016
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Title
Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement
Published in
Frontiers in Plant Science, October 2016
DOI 10.3389/fpls.2016.01518
Pubmed ID
Authors

Alex Wu, Youhong Song, Erik J. van Oosterom, Graeme L. Hammer

Abstract

The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 151 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 22%
Researcher 25 17%
Student > Master 20 13%
Student > Doctoral Student 10 7%
Student > Bachelor 9 6%
Other 22 15%
Unknown 32 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 62 41%
Biochemistry, Genetics and Molecular Biology 13 9%
Environmental Science 9 6%
Computer Science 7 5%
Engineering 4 3%
Other 9 6%
Unknown 47 31%
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 13 October 2016.
All research outputs
#20,336,031
of 22,881,154 outputs
Outputs from Frontiers in Plant Science
#16,164
of 20,270 outputs
Outputs of similar age
#276,501
of 319,503 outputs
Outputs of similar age from Frontiers in Plant Science
#282
of 397 outputs
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So far Altmetric has tracked 20,270 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 397 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.