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@ARTICLE{vanEeuwijk:856918,
      author       = {van Eeuwijk, Fred A. and Bustos-Korts, Daniela and Millet,
                      Emilie J. and Boer, Martin P. and Kruijer, Willem and
                      Thompson, Addie and Malosetti, Marcos and Iwata, Hiroyoshi
                      and Quiroz, Roberto and Kuppe, Christian and Muller, Onno
                      and Blazakis, Konstantinos N. and Yu, Kang and Tardieu,
                      Francois and Chapman, Scott C.},
      title        = {{M}odelling strategies for assessing and increasing the
                      effectiveness of new phenotyping techniques in plant
                      breeding},
      journal      = {Plant science},
      volume       = {282},
      issn         = {0168-9452},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2018-06245},
      pages        = {23-39},
      year         = {2019},
      abstract     = {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.},
      cin          = {IBG-2},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582) / DPPN - Deutsches Pflanzen
                      Phänotypisierungsnetzwerk (BMBF-031A053A)},
      pid          = {G:(DE-HGF)POF3-582 / G:(DE-Juel1)BMBF-031A053A},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31003609},
      UT           = {WOS:000466829900005},
      doi          = {10.1016/j.plantsci.2018.06.018},
      url          = {https://juser.fz-juelich.de/record/856918},
}