001     865012
005     20210130002821.0
024 7 _ |a 10.1093/jxb/erz060
|2 doi
024 7 _ |a 0022-0957
|2 ISSN
024 7 _ |a 1460-2431
|2 ISSN
024 7 _ |a 2128/22704
|2 Handle
024 7 _ |a altmetric:55772978
|2 altmetric
024 7 _ |a pmid:30799498
|2 pmid
024 7 _ |a WOS:000483174300016
|2 WOS
037 _ _ |a FZJ-2019-04577
082 _ _ |a 580
100 1 _ |a Koch, Axelle
|0 0000-0002-9828-9679
|b 0
245 _ _ |a Functional–structural root-system model validation using a soil MRI experiment
260 _ _ |a Oxford
|c 2019
|b Oxford Univ. Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1568028368_22205
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Functional–structural root-system models simulate the relations between root-system architectural and hydraulic properties, and the spatio-temporal distributions of water and solutes in the root zone. Such models may help identify optimal plant properties for breeding and contribute to increased water-use efficiency. However, it must first be demonstrated that they accurately reproduce the processes they intend to describe. This is challenging because the flow and transport processes towards individual roots are hard to observe. In this study, we demonstrate how this problem can be addressed by combining co-registered root and tracer distributions obtained from magnetic resonance imaging with a root-system model in an inverse modeling scheme. The main features in the tracer distributions were well reproduced by the model using realistic root hydraulic parameters. By combining the functional–structural root-system model with 4D tracer observations, we were able to quantify the water uptake distribution of a growing root system. We determined that 76% of the transpiration was extracted through 3rd-order roots. The simulations also demonstrated that accurate water uptake distribution cannot be directly derived either from observations of tracer accumulation or from water depletion. However, detailed tracer experiments combined with process-based models help decipher mechanisms underlying root water uptake.
536 _ _ |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255)
|0 G:(DE-HGF)POF3-255
|c POF3-255
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Meunier, Félicien
|0 0000-0003-2486-309X
|b 1
700 1 _ |a Vanderborght, Jan
|0 P:(DE-Juel1)129548
|b 2
700 1 _ |a Garre, Sarah
|0 P:(DE-Juel1)129457
|b 3
700 1 _ |a Pohlmeier, Andreas
|0 P:(DE-Juel1)129521
|b 4
700 1 _ |a Javaux, Mathieu
|0 P:(DE-Juel1)129477
|b 5
|e Corresponding author
773 _ _ |a 10.1093/jxb/erz060
|g Vol. 70, no. 10, p. 2797 - 2809
|0 PERI:(DE-600)1466717-4
|n 10
|p 2797 - 2809
|t The journal of experimental botany
|v 70
|y 2019
|x 1460-2431
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/865012/files/erz060-1.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/865012/files/erz060-1.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:865012
|p openaire
|p open_access
|p driver
|p VDB:Earth_Environment
|p VDB
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)129548
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)129457
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)129521
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)129477
913 1 _ |a DE-HGF
|l Terrestrische Umwelt
|1 G:(DE-HGF)POF3-250
|0 G:(DE-HGF)POF3-255
|2 G:(DE-HGF)POF3-200
|v Terrestrial Systems: From Observation to Prediction
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Erde und Umwelt
914 1 _ |y 2019
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b J EXP BOT : 2017
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b J EXP BOT : 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1060
|2 StatID
|b Current Contents - Agriculture, Biology and Environmental Sciences
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
915 _ _ |a National-Konsortium
|0 StatID:(DE-HGF)0430
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
920 1 _ |0 I:(DE-Juel1)IBG-3-20101118
|k IBG-3
|l Agrosphäre
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21