Evidence map›Paper›PMID 27790232›Full record

ReviewFrontiers in plant science2016

Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement.

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

Abstract readReview
In one paragraph

Review in Frontiers in plant science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

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  17. Coupled Gas-Exchange Model for CPlants (Basel, Switzerland) · 2020
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alex WuCentre for Plant Science, Queensland Alliance for Agriculture and Food Innovation, The University of QueenslandBrisbane, QLD, Australia; ARC Centre of Excellence for Translational Photosynthesis, The University of QueenslandBrisbane, QLD, Australia.
Youhong SongCentre for Plant Science, Queensland Alliance for Agriculture and Food Innovation, The University of QueenslandBrisbane, QLD, Australia; ARC Centre of Excellence for Translational Photosynthesis, The University of QueenslandBrisbane, QLD, Australia.
Erik J van OosteromCentre for Plant Science, Queensland Alliance for Agriculture and Food Innovation, The University of QueenslandBrisbane, QLD, Australia; ARC Centre of Excellence for Translational Photosynthesis, The University of QueenslandBrisbane, QLD, Australia.
Graeme L HammerCentre for Plant Science, Queensland Alliance for Agriculture and Food Innovation, The University of QueenslandBrisbane, QLD, Australia; ARC Centre of Excellence for Translational Photosynthesis, The University of QueenslandBrisbane, QLD, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

biochemical photosynthesis modelcanopy photosynthesiscrop improvementcrop modelcross-scale modelingphotosynthesis light responseRUEspecific leaf nitrogen

Identifiers

PMID27790232
PMCPMC5061851

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.