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Rapid phenotyping of crop root systems in undisturbed field soils using X-ray computed tomography

Overview of attention for article published in Plant Methods, August 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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6 X users

Citations

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75 Dimensions

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117 Mendeley
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Title
Rapid phenotyping of crop root systems in undisturbed field soils using X-ray computed tomography
Published in
Plant Methods, August 2015
DOI 10.1186/s13007-015-0084-4
Pubmed ID
Authors

Johannes Pfeifer, Norbert Kirchgessner, Tino Colombi, Achim Walter

Abstract

X-ray computed tomography (CT) has become a powerful tool for root phenotyping. Compared to rather classical, destructive methods, CT encompasses various advantages. In pot experiments the growth and development of the same individual root can be followed over time and in addition the unaltered configuration of the 3D root system architecture (RSA) interacting with a real field soil matrix can be studied. Yet, the throughput, which is essential for a more widespread application of CT for basic research or breeding programs, suffers from the bottleneck of rapid and standardized segmentation methods to extract root structures. Using available methods, root segmentation is done to a large extent manually, as it requires a lot of interactive parameter optimization and interpretation and therefore needs a lot of time. Based on commercially available software, this paper presents a protocol that is faster, more standardized and more versatile compared to existing segmentation methods, particularly if used to analyse field samples collected in situ. To the knowledge of the authors this is the first study approaching to develop a comprehensive segmentation method suitable for comparatively large columns sampled in situ which contain complex, not necessarily connected root systems from multiple plants grown in undisturbed field soil. Root systems from several crops were sampled in situ and CT-volumes determined with the presented method were compared to root dry matter of washed root samples. A highly significant (P < 0.01) and strong correlation (R(2) = 0.84) was found, demonstrating the value of the presented method in the context of field research. Subsequent to segmentation, a method for the measurement of root thickness distribution has been used. Root thickness is a central RSA trait for various physiological research questions such as root growth in compacted soil or under oxygen deficient soil conditions, but hardly assessable in high throughput until today, due to a lack of available protocols. Application of the presented protocol helps to overcome the segmentation bottleneck and can be considered a step forward to high throughput root phenotyping facilitating appropriate sample sizes desired by science and breeding.

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The data shown below were collected from the profiles of 6 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 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 116 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 23%
Researcher 20 17%
Student > Master 19 16%
Student > Doctoral Student 11 9%
Professor 5 4%
Other 18 15%
Unknown 17 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 62 53%
Engineering 11 9%
Environmental Science 6 5%
Biochemistry, Genetics and Molecular Biology 4 3%
Computer Science 3 3%
Other 7 6%
Unknown 24 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 09 May 2016.
All research outputs
#7,751,768
of 25,473,687 outputs
Outputs from Plant Methods
#484
of 1,269 outputs
Outputs of similar age
#84,334
of 279,792 outputs
Outputs of similar age from Plant Methods
#3
of 8 outputs
Altmetric has tracked 25,473,687 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,269 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 61% 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 279,792 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 69% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.