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@ARTICLE{Morandage:902681,
      author       = {Morandage, Shehan and Laloy, Eric and Schnepf, Andrea and
                      Vereecken, Harry and Vanderborght, Jan},
      title        = {{B}ayesian inference of root architectural model parameters
                      from synthetic field data},
      journal      = {Plant and soil},
      volume       = {467},
      number       = {1-2},
      issn         = {0032-079X},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2021-04468},
      pages        = {67 - 89},
      year         = {2021},
      abstract     = {Background and aimsCharacterizing root system architectures
                      of field-grown crops is challenging as root systems are
                      hidden in the soil. We investigate the possibility of
                      estimating root architecture model parameters from soil core
                      data in a Bayesian framework.MethodsIn a synthetic
                      experiment, we simulated wheat root systems in a virtual
                      field plot with the stochastic CRootBox model. We virtually
                      sampled soil cores from this plot to create synthetic
                      measurement data. We used the Markov chain Monte Carlo
                      (MCMC) DREAM(ZS) sampler to estimate the most sensitive root
                      system architecture parameters. To deal with the CRootBox
                      model stochasticity and limited computational resources, we
                      essentially added a stochastic component to the likelihood
                      function, thereby turning the MCMC sampling into a form of
                      approximate Bayesian computation (ABC).ResultsA few
                      zero-order root parameters: maximum length, elongation rate,
                      insertion angles, and numbers of zero-order roots, with
                      narrow posterior distributions centered around true
                      parameter values were identifiable from soil core data. Yet
                      other zero-order and higher-order root parameters were not
                      identifiable showing a sizeable posterior
                      uncertainty.ConclusionsBayesian inference of root
                      architecture parameters from root density profiles is an
                      effective method to extract information about sensitive
                      parameters hidden in these profiles. Equally important, this
                      method also identifies which information about root
                      architecture is lost when root architecture is aggregated in
                      root density profiles.},
      cin          = {IBG-3},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / DFG project 15232683 - TRR 32: Muster und
                      Strukturen in Boden-Pflanzen-Atmosphären-Systemen:
                      Erfassung, Modellierung und Datenassimilation},
      pid          = {G:(DE-HGF)POF4-2173 / G:(GEPRIS)15232683},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000675319900001},
      doi          = {10.1007/s11104-021-05026-4},
      url          = {https://juser.fz-juelich.de/record/902681},
}