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Ground penetrating radar: a case study for estimating root bulking rate in cassava (Manihot esculenta Crantz)

Overview of attention for article published in Plant Methods, August 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
twitter
7 X users
facebook
2 Facebook pages

Citations

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58 Dimensions

Readers on

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107 Mendeley
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Title
Ground penetrating radar: a case study for estimating root bulking rate in cassava (Manihot esculenta Crantz)
Published in
Plant Methods, August 2017
DOI 10.1186/s13007-017-0216-0
Pubmed ID
Authors

Alfredo Delgado, Dirk B. Hays, Richard K. Bruton, Hernán Ceballos, Alexandre Novo, Enrico Boi, Michael Gomez Selvaraj

Abstract

Understanding root traits is a necessary research front for selection of favorable genotypes or cultivation practices. Root and tuber crops having most of their economic potential stored below ground are favorable candidates for such studies. The ability to image and quantify subsurface root structure would allow breeders to classify root traits for rapid selection and allow agronomist the ability to derive effective cultivation practices. In spite of the huge role of Cassava (Manihot esculenta Crantz), for food security and industrial uses, little progress has been made in understanding the onset and rate of the root-bulking process and the factors that influence it. The objective of this research was to determine the capability of ground penetrating radar (GPR) to predict root-bulking rates through the detection of total root biomass during its growth cycle. Our research provides the first application of GPR for detecting below ground biomass in cassava. Through an empirical study, linear regressions were derived to model cassava bulking rates. The linear equations derived suggest that GPR is a suitable measure of root biomass (r = .79). The regression analysis developed accounts for 63% of the variability in cassava biomass below ground. When modeling is performed at the variety level, it is evident that the variety models for SM 1219-9 and TMS 60444 outperform the HMC-1 variety model (r(2) = .77, .63 and .51 respectively). Using current modeling methods, it is possible to predict below ground biomass and estimate root bulking rates for selection of early root bulking in cassava. Results of this approach suggested that the general model was over predicting at early growth stages but became more precise in later root development.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 107 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 21%
Researcher 20 19%
Student > Master 12 11%
Professor 7 7%
Student > Doctoral Student 6 6%
Other 10 9%
Unknown 29 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 45%
Engineering 9 8%
Environmental Science 5 5%
Earth and Planetary Sciences 5 5%
Biochemistry, Genetics and Molecular Biology 2 2%
Other 6 6%
Unknown 32 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 07 November 2019.
All research outputs
#2,622,371
of 22,996,001 outputs
Outputs from Plant Methods
#126
of 1,087 outputs
Outputs of similar age
#50,929
of 317,751 outputs
Outputs of similar age from Plant Methods
#4
of 21 outputs
Altmetric has tracked 22,996,001 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,087 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 done well, scoring higher than 88% 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 317,751 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 83% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.