↓ Skip to main content

Identifying traits for genotypic adaptation using crop models

Overview of attention for article published in Journal of Experimental Botany, March 2015
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
5 X users
facebook
1 Facebook page

Citations

dimensions_citation
55 Dimensions

Readers on

mendeley
162 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Identifying traits for genotypic adaptation using crop models
Published in
Journal of Experimental Botany, March 2015
DOI 10.1093/jxb/erv014
Pubmed ID
Authors

Julian Ramirez-Villegas, James Watson, Andrew J. Challinor

Abstract

Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Argentina 2 1%
Brazil 1 <1%
Mexico 1 <1%
Czechia 1 <1%
Belgium 1 <1%
United States 1 <1%
Unknown 155 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 39 24%
Student > Ph. D. Student 26 16%
Student > Master 21 13%
Other 20 12%
Student > Bachelor 11 7%
Other 23 14%
Unknown 22 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 84 52%
Environmental Science 11 7%
Earth and Planetary Sciences 6 4%
Biochemistry, Genetics and Molecular Biology 4 2%
Engineering 3 2%
Other 14 9%
Unknown 40 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 July 2015.
All research outputs
#13,229,066
of 23,314,015 outputs
Outputs from Journal of Experimental Botany
#4,086
of 6,734 outputs
Outputs of similar age
#118,899
of 259,813 outputs
Outputs of similar age from Journal of Experimental Botany
#52
of 158 outputs
Altmetric has tracked 23,314,015 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,734 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one is in the 37th percentile – i.e., 37% 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 259,813 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 158 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.