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Author Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. url  doi
openurl 
  Title Estimating model prediction error: Should you treat predictions as fixed or random Type Journal Article
  Year 2016 Publication Environmental Modelling & Software Abbreviated Journal Env. Model. Softw.  
  Volume 84 Issue Pages 529-539  
  Keywords Crop model; Uncertainty; Prediction error; Parameter uncertainty; Input uncertainty; Model structure uncertainty  
  Abstract Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEPfixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEPuncertain(X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEPuncertain(X) can be estimated using a random effects ANOVA. It is argued that MSEPuncertain(X) is the more informative uncertainty criterion, because it is specific to each prediction situation.  
  Address (up)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1364-8152 ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4773  
Permanent link to this record
 

 
Author Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; Constantin, J.; Coucheney, E.; Dechow, R.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Kiese, R.; Klatt, S.; Lewan, E.; Nendel, C.; Raynal, H.; Sosa, C.; Specka, X.; Teixeira, E.; Wang, E.; Weihermüller, L.; Zhao, G.; Zhao, Z.; Ewert, F. openurl 
  Title Impacts of soil and weather data aggregation in spatial modelling of net primary production of croplands Type Conference Article
  Year 2016 Publication Abbreviated Journal  
  Volume Issue Pages  
  Keywords CropM;  
  Abstract  
  Address (up)  
  Corporate Author Thesis  
  Publisher Place of Publication Belrin (Germany) Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference International Crop Modelling Symposium iCropM 2016, 2016-03-15 to 2016-03-17, Belrin  
  Notes Approved no  
  Call Number MA @ admin @ Serial 2579  
Permanent link to this record
 

 
Author Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; van Oijen, M.; Zhao, G.; Constantin, J.; Raynal, H.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Sosa, C.; Kersebaum, K.-C.; Nendel, C.; Grosz, B.; Dechow, R.; Kiese, R.; Haas, E.; Klatt, S.; Teixeira, E.; Zhao, Z.; Wang, E.; Weihermüller, L.; Gaiser, T.; Ewert, F. openurl 
  Title Impact of climate aggregation over different scales on regional NPP modelling Type Conference Article
  Year 2016 Publication Abbreviated Journal  
  Volume Issue Pages  
  Keywords CropM;  
  Abstract  
  Address (up)  
  Corporate Author Thesis  
  Publisher Place of Publication Vienna (Austria) Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference European Geosciences Union General Assembly 2016, 2016-04-17 to 2016-04-22, Vienna  
  Notes Approved no  
  Call Number MA @ admin @ Serial 2578  
Permanent link to this record
 

 
Author Kahiluoto, H.; Rötter, R.; Webber, H.; Ewert, F. openurl 
  Title The Role of Modelling in Adapting and Building the Climate Resilience of Cropping Systems Type Book Chapter
  Year 2014 Publication Abbreviated Journal  
  Volume Issue Pages 204-215  
  Keywords CropM  
  Abstract  
  Address (up)  
  Corporate Author Thesis  
  Publisher CAB International Place of Publication Wallingford Editor Fuhrer, J.; Gregory, P.J.  
  Language Summary Language Original Title  
  Series Editor Series Title Climate Change Impact and Adaptation in Agricultural Systems Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number MA @ admin @ Serial 2513  
Permanent link to this record
 

 
Author Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. url  doi
openurl 
  Title Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables Type Journal Article
  Year 2015 Publication Climate Research Abbreviated Journal Clim. Res.  
  Volume 65 Issue Pages 141-157  
  Keywords crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i  
  Abstract We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.  
  Address (up)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0936-577x ISBN Medium Article  
  Area Expedition Conference  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4754  
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