Growth curve registration for evaluating salinity tolerance in barley.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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.
Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900 Saudi Arabia.
Rui
R
Meng
Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900 Saudi Arabia.
Stephanie
S
Saade
Department of Statistics, The Ohio State University, Columbus, OH USA.
Sebastian
S
Kurtek
Australian Plant Phenomics Facility, The Plant Accelerator, University of Adelaide, Urrbrae, South Australia 5064 Australia.
Bettina
B
Berger
Australian Plant Phenomics Facility, The Plant Accelerator, University of Adelaide, Urrbrae, South Australia 5064 Australia.|Phenomics and Bioinformatics Research Centre, University of South Australia, Adelaide, South Australia 5001 Australia.
Chris
C
Brien
Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Betty-Heimann-Str. 3, 06120 Halle, Germany.
Klaus
K
Pillen
Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900 Saudi Arabia.
Mark
M
Tester
Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900 Saudi Arabia.
Ying
Y
Sun
Functional ANOVA model; High-throughput phenotyping; Nested association mapping; Plant growth; Spatial variation; Temporal variation