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Growth curve registration for evaluating salinity tolerance in barley

Overview of attention for article published in Plant Methods, March 2017
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Title
Growth curve registration for evaluating salinity tolerance in barley
Published in
Plant Methods, March 2017
DOI 10.1186/s13007-017-0165-7
Pubmed ID
Authors

Rui Meng, Stephanie Saade, Sebastian Kurtek, Bettina Berger, Chris Brien, Klaus Pillen, Mark Tester, Ying Sun

Abstract

Smarthouses capable of non-destructive, high-throughput plant phenotyping collect large amounts of data that can be used to understand plant growth and productivity in extreme environments. The challenge is to apply the statistical tool that best analyzes the data to study plant traits, such as salinity tolerance, or plant-growth-related traits. We derive family-wise salinity sensitivity (FSS) growth curves and use registration techniques to summarize growth patterns of HEB-25 barley families and the commercial variety, Navigator. We account for the spatial variation in smarthouse microclimates and in temporal variation across phenotyping runs using a functional ANOVA model to derive corrected FSS curves. From FSS, we derive corrected values for family-wise salinity tolerance, which are strongly negatively correlated with Na but not significantly with K, indicating that Na content is an important factor affecting salinity tolerance in these families, at least for plants of this age and grown in these conditions. Our family-wise methodology is suitable for analyzing the growth curves of a large number of plants from multiple families. The corrected curves accurately account for the spatial and temporal variations among plants that are inherent to high-throughput experiments.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Student > Ph. D. Student 6 17%
Student > Master 5 14%
Student > Doctoral Student 3 9%
Student > Bachelor 1 3%
Other 2 6%
Unknown 9 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 46%
Mathematics 2 6%
Engineering 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Unspecified 1 3%
Other 0 0%
Unknown 12 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 April 2017.
All research outputs
#20,412,387
of 22,963,381 outputs
Outputs from Plant Methods
#1,053
of 1,086 outputs
Outputs of similar age
#269,551
of 309,207 outputs
Outputs of similar age from Plant Methods
#23
of 23 outputs
Altmetric has tracked 22,963,381 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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