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@ARTICLE{Hao:897475,
      author       = {Hao, Shirui and Ryu, Dongryeol and Western, Andrew and
                      Perry, Eileen and Bogena, Heye and Hendricks-Franssen,
                      Harrie-Jan},
      title        = {{P}erformance of a wheat yield prediction model and factors
                      influencing the performance: {A} review and meta-analysis},
      journal      = {Agricultural systems},
      volume       = {194},
      issn         = {0308-521X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-03808},
      pages        = {103278 -},
      year         = {2021},
      abstract     = {CONTEXTProcess-based crop models provide ways to predict
                      crop growth, evaluate environmental impacts on crops, test
                      various crop management options, and guide crop breeding.
                      They can be used to explore options for mitigating climate
                      change impacts when combined with climate projections and
                      explore mitigation of environmental impacts of production.
                      The Agricultural Production Systems SIMulator (APSIM) is a
                      widely adopted crop model that offers modules for simulation
                      of various crops, soil processes, climate, and grazing
                      within a modelling system that enables robust addition of
                      new components.OBJECTIVEThis study uses APSIM Classic-Wheat
                      as an example to examine yield prediction accuracy of
                      biophysically based crop yield modelling and to analyse the
                      factors influencing the model performance.METHODSWe analysed
                      yield prediction results of APSIM Classic-Wheat from 76
                      published studies across thirteen countries on four
                      continents. In addition, a meta-database of modelled and
                      observed yields from 30 studies was established and used to
                      identify factors that influence yield prediction
                      uncertainty.RESULTS AND CONCLUSIONSOur analysis indicates
                      that, with site-specific calibration, APSIM predicts yield
                      with a root mean squared error (RMSE) smaller than 1 t/ha
                      and a normalised RMSE (NRMSE) of about $28\%,$ across a wide
                      range of environmental conditions for independent evaluation
                      periods. The results show increasing errors in yield with
                      limited modelling information and adverse environmental
                      conditions. Using soil hydraulic parameters derived from
                      site-specific measurements and/or tuning cultivar parameters
                      improves yield prediction accuracy: RMSE decreases from 1.25
                      t/ha to 0.64 t/ha and NRMSE from $32\%$ to $14\%.$ Lower
                      model accuracy was found where APSIM overestimates yield
                      under high water deficit condition and when it
                      underestimates yield under nitrogen limitation. APSIM
                      severely over-predicts yield when some abiotic stresses such
                      as heatwaves and frost affect the crop growth.},
      cin          = {IBG-3},
      ddc          = {640},
      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)16},
      UT           = {WOS:000701028300010},
      doi          = {10.1016/j.agsy.2021.103278},
      url          = {https://juser.fz-juelich.de/record/897475},
}