% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Morandage:861707,
      author       = {Morandage, Tharaka Shehan and Schnepf, Andrea and Javaux,
                      Mathieu and Vereecken, Harry and Vanderborght, Jan},
      title        = {{P}arameter sensitivity analysis of a root system
                      architecture model based on virtual field sampling},
      journal      = {Plant and soil},
      volume       = {438},
      number       = {1-2},
      issn         = {0032-079X},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2019-02137},
      pages        = {101-126},
      year         = {2019},
      abstract     = {AimsTraits of the plant root system architecture (RSA) play
                      a key role in crop performance. Therefore, architectural
                      root traits are becoming increasingly important in plant
                      phenotyping. In this study, we use a mathematical model to
                      investigate the sensitivity of characteristic root system
                      measures, obtained from different classical field root
                      sampling schemes, to RSA parameters.MethodsRoot systems of
                      wheat and maize were simulated and sampled virtually to
                      mimic real field experiments using the root system
                      architecture (RSA) model CRootBox. By means of a sensitivity
                      analysis, we found RSA parameters that significantly
                      influenced the virtual field sampling results. To identify
                      correlations between sensitivities, we carried out a
                      principal component analysis.ResultsWe found that the
                      parameters of zero order roots are the most sensitive, and
                      parameters of higher order roots are less sensitive.
                      Moreover, different characteristic root system measures
                      showed different sensitivity to RSA parameters. RSA
                      parameters that could be derived independently from
                      different types of field observations were
                      identified.ConclusionsSelection of characteristic root
                      system measures and parameters is essential to reduce the
                      problem of parameter equifinality in inverse modeling with
                      multi-parameter models and is an important step in the
                      characterization of root traits from field observations.},
      cin          = {IBG-3},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000468540800007},
      doi          = {10.1007/s11104-019-03993-3},
      url          = {https://juser.fz-juelich.de/record/861707},
}