001     856918
005     20210129235425.0
024 7 _ |a 10.1016/j.plantsci.2018.06.018
|2 doi
024 7 _ |a 0168-9452
|2 ISSN
024 7 _ |a 1873-2259
|2 ISSN
024 7 _ |a 2128/22093
|2 Handle
024 7 _ |a pmid:31003609
|2 pmid
024 7 _ |a WOS:000466829900005
|2 WOS
024 7 _ |a altmetric:49313714
|2 altmetric
037 _ _ |a FZJ-2018-06245
041 _ _ |a English
082 _ _ |a 570
100 1 _ |a van Eeuwijk, Fred A.
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding
260 _ _ |a Amsterdam [u.a.]
|c 2019
|b Elsevier Science
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 1556001838_22955
|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 New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.
536 _ _ |a 582 - Plant Science (POF3-582)
|0 G:(DE-HGF)POF3-582
|c POF3-582
|f POF III
|x 0
536 _ _ |a DPPN - Deutsches Pflanzen Phänotypisierungsnetzwerk (BMBF-031A053A)
|0 G:(DE-Juel1)BMBF-031A053A
|c BMBF-031A053A
|f Deutsches Pflanzen Phänotypisierungsnetzwerk
|x 1
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Bustos-Korts, Daniela
|0 0000-0003-3827-6726
|b 1
700 1 _ |a Millet, Emilie J.
|0 0000-0002-2913-4892
|b 2
700 1 _ |a Boer, Martin P.
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Kruijer, Willem
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Thompson, Addie
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Malosetti, Marcos
|0 P:(DE-HGF)0
|b 6
700 1 _ |a Iwata, Hiroyoshi
|0 P:(DE-HGF)0
|b 7
700 1 _ |a Quiroz, Roberto
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Kuppe, Christian
|0 P:(DE-Juel1)161296
|b 9
|u fzj
700 1 _ |a Muller, Onno
|0 P:(DE-Juel1)161185
|b 10
700 1 _ |a Blazakis, Konstantinos N.
|0 0000-0002-2837-0367
|b 11
700 1 _ |a Yu, Kang
|0 P:(DE-HGF)0
|b 12
700 1 _ |a Tardieu, Francois
|0 0000-0002-7287-0094
|b 13
700 1 _ |a Chapman, Scott C.
|0 P:(DE-HGF)0
|b 14
773 _ _ |a 10.1016/j.plantsci.2018.06.018
|g p. S0168945217311548
|0 PERI:(DE-600)1498605-x
|p 23-39
|t Plant science
|v 282
|y 2019
|x 0168-9452
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/856918/files/1-s2.0-S0168945217311548-main.pdf
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/856918/files/1-s2.0-S0168945217311548-main.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:856918
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 9
|6 P:(DE-Juel1)161296
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 10
|6 P:(DE-Juel1)161185
913 1 _ |a DE-HGF
|b Key Technologies
|l Key Technologies for the Bioeconomy
|1 G:(DE-HGF)POF3-580
|0 G:(DE-HGF)POF3-582
|2 G:(DE-HGF)POF3-500
|v Plant Science
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
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 DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b PLANT SCI : 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 IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
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 DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
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 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBG-2-20101118
|k IBG-2
|l Pflanzenwissenschaften
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IBG-2-20101118
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21