[1686] - Virtual experiments on root sampling methods to infer the specific root traits by inverse modelling

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Morandage, S., Schnepf, A., Vanderborght, J., 2017. Virtual experiments on root sampling methods to infer the specific root traits by inverse modelling. PhD Report, IBG-3, forschungszentrum jülich, Jülich,Germany. Accessed from https://www.tr32db.uni-koeln.de/data.php?dataID=1686 at 2020-06-01.
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Title(s):Main Title: Virtual experiments on root sampling methods to infer the specific root traits by inverse modelling
Alternative Title: PhD REport 3
Description(s):Abstract: Plant phenotyping methods based on architectural root traits to develop new genotypes are becoming increasingly important. These traits play a key role for crop performances under non-optimal environmental and climatic conditions. However, the main challenge in root phenotyping programs is the limited accessibility to the root system because it is hidden in the soil. Although lab based methods are widely used to retrieve root system architecture characteristics, these experiments are not capable of replicating the plant growth in the field, and often the studies are not carried out for the entire lifespan of a plant. On the other hand, classical field root sampling methods provide only the root distribution with depth or arrival times at each depth in the soil profile, which do not provide direct information about the root system architecture of the plant. In order to overcome these challenges, we instigate the possibility of obtaining information about the root system architecture using field based root sampling schemes. The root growth model “CRootBox” was used to generate virtual 3-D root systems of 222 individual wheat plants. Ground truth of the virtual experiment was established for coring, trench profile and rhizotron methods. From these data, root length density, root intersection density profiles and arrival curves for rhizotubes were computed and considered as observation data. Morris OAT sensitivity analyses method was performed to quantify the sensitivity of each parameter of the root growth model to the observation data. The sensitive parameters will be optimized using “DREAMzs” inversion algorithm based on the observation data. The optimized parameters and the input parameters will then be evaluated with the sampling methods to determine the suitability of the sampling schemes to identify specific traits or parameters of the root growth model. Finally, the virtual experimental method will be applied in real field samples to obtain information about the specific traits of different genotypes for plant breeding programs. By combining sensitivity analyses with inverse modelling, parameters of a root system architecture model that can be inferred indirectly from traditional field observation methods were identified. This is an important step in the characterization of root traits from field observations. In a next step, these root architectures can be used in models that simulate water and nutrient uptake so as to evaluate the performance of root systems with certain traits.
Responsible Party
Creator(s):Author: Shehan Morandage
Principal Investigator: Andrea Schnepf
Principal Investigator: Jan Vanderborght
Publisher:CRC/TR32 Database (TR32DB)
TR32 Topic:Soil
Related Sub-project(s):B4
Subject(s):CRC/TR32 Keywords: PhD Report, Root Growth, Root Length Density, Root System
File Details
File Name:3_PhD_Report_Shehan_Morandage.pdf
Data Type:Text
File Size:1913 kB (1.868 MB)
Date(s):Created: 2017-03-01
Mime Type:application/pdf
Status:In Process
Download Permission:OnlyTR32
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:TR32DB Data policy agreement
Measurement Region:Ellebach
Measurement Location:Selhausen
Specific Informations - Report
Report Date:1st of March, 2017
Report Type:PhD Report
Report City:Jülich,Germany
Report Institution:IBG-3, forschungszentrum jülich
Number Of Pages:23
Period of Pages:1 - 23
Metadata Details
Metadata Creator:Shehan Morandage
Metadata Created:2017-07-25
Metadata Last Updated:2017-08-24
Funding Phase:3
Metadata Language:English
Metadata Version:V42
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