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Author Klatt, S.; Haas, E.; Hoffmann, H.; Zhao, G.; Van Bussel, L.G.J.; Enders, A.; Gaiser, T.; Ewert, F.; Teixeira, E.; Kiese, R.; Doro, L.; Specka, X.; Nendel, C.; Kersebaum, K.-C.; Sosa, C.; Lewan, E.; Eckersten, H.; Gebbert, S.; Dechow, R.; Grosz, B.; Bach, M.; Yeluripati, J.; Tao, F.; Constantin, J.; Raynal, H.; Wallach, D.; Kuhnert, M.
Title (up) Responses of soil N2O emissions and nitrate leaching on climate input data aggregation: a biogeochemistry model ensemble study Type Conference Article
Year 2014 Publication Abbreviated Journal
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Series Editor Series Title Abbreviated Series Title CropM International Symposium and Workshop
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Area Expedition Conference CropM International Symposium and Workshop, 2014-02-10 to 2014-02-12, Oslo, Norway
Notes Approved no
Call Number MA @ admin @ Serial 5042
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Author Haas, E.; R. Kiese; Klatt, S.; Hoffmann, H.; Zhao, G.; Ewert, F.; J. Constantin; Raynal, H.; Coucheney, E.; Lewan, E.; Sosa, C.; Dechow, R.; Grosz, B.; Eckersten, H.; Gaiser, T.; Kuhnert, M.; Smith, P.; Kersebaum, K.C.; C. Nendel; Specka, X.; Wang, E.; Zhao, Z.; Weihermüller, L.
Title (up) Responses of soil nitrous oxide emissions and nitrate leaching on climate, soil and management input data aggregation: a biogeochemistry model ensemble study Type Conference Article
Year 2016 Publication Abbreviated Journal
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Publisher Place of Publication Berlin (Germany) Editor
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ISSN ISBN Medium poster
Area Expedition Conference International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany
Notes Approved no
Call Number MA @ admin @ Serial 4907
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Author Hjelkrem, A.-G.R.; Höglind, M.; van Oijen, M.; Schellberg, J.; Gaiser, T.; Ewert, F.
Title (up) Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments Type Journal Article
Year 2017 Publication Ecological Modelling Abbreviated Journal Ecol. Model.
Volume 359 Issue Pages 80-91
Keywords Metropolis-hasting; Morris method; Reducing complexity; Robustness
Abstract Highlights • The parameters to be fixed were consistent across sites. • Model calibration must be performed separately for each specific case. • Possible to reduce model parameters from 66 to 45. • Strong model reductions must be avoided. • The error term for the training data were characterised by timing (phase shift). Abstract Proper parameterisation and quantification of model uncertainty are two essential tasks in improvement and assessment of model performance. Bayesian calibration is a method that combines both tasks by quantifying probability distributions for model parameters and outputs. However, the method is rarely applied to complex models because of its high computational demand when used with high-dimensional parameter spaces. We therefore combined Bayesian calibration with sensitivity analysis, using the screening method by Morris (1991), in order to reduce model complexity by fixing parameters to which model output was only weakly sensitive to a nominal value. Further, the robustness of the model with respect to reduction in the number of free parameters were examined according to model discrepancy and output uncertainty. The process-based grassland model BASGRA was examined in the present study on two sites in Norway and in Germany, for two grass species (Phleum pratense and Arrhenatherum elatius). According to this study, a reduction of free model parameters from 66 to 45 was possible. The sensitivity analysis showed that the parameters to be fixed were consistent across sites (which differed in climate and soil conditions), while model calibration had to be performed separately for each combination of site and species. The output uncertainty decreased slightly, but still covered the field observations of aboveground biomass. Considering the training data, the mean square error for both the 66 and the 45 parameter model was dominated by errors in timing (phase shift), whereas no general pattern was found in errors when using the validation data. Stronger model reduction should be avoided, as the error term increased and output uncertainty was underestimated.
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ISSN 0304-3800 ISBN Medium
Area Expedition Conference
Notes CropM, LiveM, ft_macsur Approved no
Call Number MA @ admin @ Serial 5010
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Author Hoffmann, H.; Gang, Z.; Van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.; Constantin, J.; Casellas, E.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.; Nendel, C.; Kiese, R.; Raynal, H.; Eckersten, H.; Klatt, S.; Edwin, H.; Wang, E.; Kuhnert, M.; Lewan, E.; Bach, M.; Kersebaum, K.-C.; Rötter, R.; Roggero, P.P.; Wallach, D.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F.
Title (up) Sensitivity of crop models to spatial aggregation of soil and climate data Type Conference Article
Year 2014 Publication Abbreviated Journal
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Area Expedition Conference Annual conference of the German/Austrian Agronomical Society & Max-Eyth-Society IS -
Notes Approved no
Call Number MA @ admin @ Serial 5041
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Author Webber, H.; Ewert, F.; Kimball, B.A.; Siebert, S.; White, J.W.; Wall, G.W.; Ottman, M.J.; Trawally, D.N.A.; Gaiser, T.
Title (up) Simulating canopy temperature for modelling heat stress in cereals Type Journal Article
Year 2016 Publication Environmental Modelling & Software Abbreviated Journal Env. Model. Softw.
Volume 77 Issue Pages 143-155
Keywords canopy temperature; heat stress; cereals; crop models; profile relationships; crop production; climate-change; spring wheat; field plots; growth; maize; water; yields; variability
Abstract Crop models must be improved to account for the effects of heat stress events on crop yields. To date, most approaches in crop models use air temperature to define heat stress intensity as the cumulative sum of thermal times (TT) above a high temperature threshold during a sensitive period for yield formation. However, observational evidence indicates that crop canopy temperature better explains yield reductions associated with high temperature events than air temperature does. This study presents a canopy level energy balance using Monin ObukhovSimilarity Theory (MOST) with simplifications about the canopy resistance that render it suitable for application in crop models and other models of the plant environment. The model is evaluated for a uniform irrigated wheat canopy in Arizona and rainfed maize in Burkina Faso. No single variable regression relationships for key explanatory variables were found that were consistent across sowing dates to explain the deviation of canopy temperature from air temperature. Finally, thermal times determined with simulated canopy temperatures were able to reproduce thermal times calculated with observed canopy temperature, whereas those determined with air temperatures were not. (C) 2015 Elsevier Ltd. All rights reserved.
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Language English Summary Language Original Title
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ISSN 1364-8152 ISBN Medium Article
Area Expedition Conference
Notes CropM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4730
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