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Analysis of root growth from a phenotyping data set using a density-based model

Overview of attention for article published in Journal of Experimental Botany, February 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

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9 tweeters

Citations

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16 Dimensions

Readers on

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36 Mendeley
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Title
Analysis of root growth from a phenotyping data set using a density-based model
Published in
Journal of Experimental Botany, February 2016
DOI 10.1093/jxb/erv573
Pubmed ID
Authors

Dimitris I. Kalogiros, Michael O. Adu, Philip J. White, Martin R. Broadley, Xavier Draye, Mariya Ptashnyk, A. Glyn Bengough, Lionel X. Dupuy

Abstract

Major research efforts are targeting the improved performance of root systems for more efficient use of water and nutrients by crops. However, characterizing root system architecture (RSA) is challenging, because roots are difficult objects to observe and analyse. A model-based analysis of RSA traits from phenotyping image data is presented. The model can successfully back-calculate growth parameters without the need to measure individual roots. The mathematical model uses partial differential equations to describe root system development. Methods based on kernel estimators were used to quantify root density distributions from experimental image data, and different optimization approaches to parameterize the model were tested. The model was tested on root images of a set of 89 Brassica rapa L. individuals of the same genotype grown for 14 d after sowing on blue filter paper. Optimized root growth parameters enabled the final (modelled) length of the main root axes to be matched within 1% of their mean values observed in experiments. Parameterized values for elongation rates were within ±4% of the values measured directly on images. Future work should investigate the time dependency of growth parameters using time-lapse image data. The approach is a potentially powerful quantitative technique for identifying crop genotypes with more efficient root systems, using (even incomplete) data from high-throughput phenotyping systems.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
United Kingdom 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 31%
Student > Ph. D. Student 6 17%
Student > Master 5 14%
Unspecified 4 11%
Professor 3 8%
Other 7 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 56%
Unspecified 6 17%
Environmental Science 4 11%
Mathematics 3 8%
Engineering 2 6%
Other 1 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 30 June 2018.
All research outputs
#2,304,476
of 13,160,482 outputs
Outputs from Journal of Experimental Botany
#734
of 4,391 outputs
Outputs of similar age
#58,664
of 267,086 outputs
Outputs of similar age from Journal of Experimental Botany
#19
of 178 outputs
Altmetric has tracked 13,160,482 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,391 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 83% of its peers.
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 267,086 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 178 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.