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@ARTICLE{Brogi:894234,
      author       = {Brogi, Cosimo and Huisman, Johan A. and Weihermüller, Lutz
                      and Herbst, Michael and Vereecken, Harry},
      title        = {{A}dded value of geophysics-based soil mapping in
                      agro-ecosystem simulations},
      journal      = {Soil},
      volume       = {7},
      number       = {1},
      issn         = {2199-398X},
      address      = {Göttingen},
      publisher    = {Copernicus Publ.},
      reportid     = {FZJ-2021-03114},
      pages        = {125 - 143},
      year         = {2021},
      abstract     = {There is an increased demand for quantitative
                      high-resolution soil maps that enable within-field
                      management. Commonly available soil maps are generally not
                      suited for this purpose, but digital soil mapping and
                      geophysical methods in particular allow soil information to
                      be obtained with an unprecedented level of detail. However,
                      it is often difficult to quantify the added value of such
                      high-resolution soil information for agricultural management
                      and agro-ecosystem modelling. In this study, a detailed
                      geophysics-based soil map was compared to two commonly
                      available general-purpose soil maps. In particular, the
                      three maps were used as input for crop growth models to
                      simulate leaf area index (LAI) of five crops for an area of
                      ∼ 1 km2. The simulated development of LAI for the five
                      crops was evaluated using LAI obtained from multispectral
                      satellite images. Overall, it was found that the
                      geophysics-based soil map provided better LAI predictions
                      than the two general-purpose soil maps in terms of
                      correlation coefficient R2, model efficiency (ME), and root
                      mean square error (RMSE). Improved performance was most
                      apparent in the case of prolonged periods of drought and was
                      strongly related to the combination of soil characteristics
                      and crop type.},
      cin          = {IBG-3},
      ddc          = {550},
      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:000653636800001},
      doi          = {10.5194/soil-7-125-2021},
      url          = {https://juser.fz-juelich.de/record/894234},
}