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@ARTICLE{Zhao:827626,
      author       = {Zhao, Jiangsan and Bodner, Gernot and Rewald, Boris and
                      Leitner, Daniel and Nagel, Kerstin and Nakhforoosh, Alireza},
      title        = {{R}oot architecture simulation improves the inference from
                      seedling root phenotyping towards mature root systems},
      journal      = {The journal of experimental botany},
      volume       = {68},
      number       = {5},
      issn         = {1460-2431},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2017-01740},
      pages        = {965-982},
      year         = {2017},
      abstract     = {Root phenotyping provides trait information for plant
                      breeding. A shortcoming of high-throughput root phenotyping
                      is the limitation to seedling plants and failure to make
                      inferences on mature root systems. We suggest root system
                      architecture (RSA) models to predict mature root traits and
                      overcome the inference problem. Sixteen pea genotypes were
                      phenotyped in (i) seedling (Petri dishes) and (ii) mature
                      (sand-filled columns) root phenotyping platforms. The RSA
                      model RootBox was parameterized with seedling traits to
                      simulate the fully developed root systems. Measured and
                      modelled root length, first-order lateral number, and root
                      distribution were compared to determine key traits for
                      model-based prediction. No direct relationship in root
                      traits (tap, lateral length, interbranch distance) was
                      evident between phenotyping systems. RootBox significantly
                      improved the inference over phenotyping platforms. Seedling
                      plant tap and lateral root elongation rates and interbranch
                      distance were sufficient model parameters to predict
                      genotype ranking in total root length with an RSpearman of
                      0.83. Parameterization including uneven lateral spacing via
                      a scaling function substantially improved the prediction of
                      architectures underlying the differently sized root systems.
                      We conclude that RSA models can solve the inference problem
                      of seedling root phenotyping. RSA models should be included
                      in the phenotyping pipeline to provide reliable information
                      on mature root systems to breeding research.},
      cin          = {IBG-2},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582) / EPPN - European Plant
                      Phenotyping Network (284443)},
      pid          = {G:(DE-HGF)POF3-582 / G:(EU-Grant)284443},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000397161300008},
      pubmed       = {pmid:28168270},
      doi          = {10.1093/jxb/erw494},
      url          = {https://juser.fz-juelich.de/record/827626},
}