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@PHDTHESIS{Bauer:1046644,
      author       = {Bauer, Felix},
      title        = {{E}xploring {P}lant {R}esponses to {C}hanging
                      {E}nvironments: {I}ntegrating {P}henotyping and {M}odeling
                      {A}cross {S}cales},
      volume       = {672},
      school       = {Bonn},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2025-03876},
      isbn         = {978-3-95806-845-2},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {xxix, 188},
      year         = {2025},
      note         = {Dissertation, Bonn, 2025},
      abstract     = {Climate change and the depletion of essential resources
                      like phosphorus are challenging agriculture by reducing
                      water and fertilizer availability and ultimately threatening
                      the security of the human food supply. Knowledge of how
                      plants respond to changing environmental conditions is
                      required to cope with these challenges. Plant growth
                      information and corresponding environmental data are key to
                      unraveling stress responses and revealing the underlying
                      mechanisms. Understanding architectural and functional plant
                      adaptations to stresses, such as water and nutrient
                      limitation, is crucial to exploring new pathways to
                      sustainable agriculture. It is vital to consider all organs,
                      including the often-overlooked root system and surrounding
                      soil, that are essential for water and nutrient uptake.
                      Plant phenotyping and functional-structural plant modeling
                      are key technologies for understanding plant responses to
                      changing environments, making their continued development
                      and application imperative. This doctoral project is
                      dedicated to advancing the field of plant research by 1.
                      developing a novel in situ phenotyping method for roots, 2.
                      applying this method to assemble a comprehensive collection
                      of in-field root and soil data, 3. investigating the
                      architectural responses of Zea mays to phosphorus
                      deficiency, 4. gaining a deeper understanding of the
                      responses to stress by investigating the effects of
                      phosphorus deficiency on the root system’s conductance,
                      and 5. placing the findings into an overall context. First,
                      a new method combining deep neural networks and automated
                      feature extraction was developed and validated to analyze
                      root images, reducing processing time by $98\%$ while
                      achieving high precision compared to manual annotation
                      (r=0.9). Second, besides other technologies, this method was
                      applied to assemble a comprehensive collection of in-field
                      root and soil data over time in two minirhizotron facilities
                      in distinct soil domains. The resulting open-access,
                      timeseries dataset includes dynamic crosshole
                      ground-penetrating radar, minirhizotron camera measurements,
                      and static soil sensor observations at a high temporal and
                      spatial resolution over five years of Zea mays and Triticum
                      aestivum experiments, including drought stress treatments
                      and crop mixtures trials. Third, a combined approach of the
                      developed phenotyping workflow and functional-structural
                      plant modeling was used to investigate the responses of Zea
                      mays to varying phosphorus availability. Combining measured
                      architectural plant parameters with root hydraulic
                      properties enabled time-dependent simulations of plant
                      growth and root system conductance under different
                      phosphorus regimes, revealing that only plants with optimal
                      phosphorus availability sustained a high root system
                      conductance. In contrast, all other phosphorus levels led to
                      significantly lower root system conductance under light and
                      severe phosphorus deficiency. It was also shown that root
                      system organization is critical for its function rather than
                      mere total size. Finally, this thesis contributes to
                      collaborative studies aiming to enhance phenotyping methods
                      and further investigate Zea mays responses to environmental
                      changes. We found that ground-penetrating radar could be
                      employed as a root-sensing tool in the future. By linking
                      aboveground crop data to the belowground dataset, we
                      revealed that maize responses to water stress vary
                      significantly with soil conditions. We combined the
                      automated analysis method with functional-structural
                      modeling to show that Zea mays domestication was driven by
                      water availability, with seminal root number emerging as a
                      critical adaptation trait, possibly providing key
                      information for breeding drought-tolerant varieties. Lastly,
                      we applied an in silico approach using a game engine that
                      visualizes plant models in high-performance computing
                      environments to generate virtual data for neural networks,
                      enhancing their precision and informative power. This work
                      explores different methods, data, and models to understand
                      plant responses to a changing environment across scales and
                      provides new insights into the combined stress responses and
                      development of Zea mays.},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      url          = {https://juser.fz-juelich.de/record/1046644},
}