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Kuhnert, M., Yeluripati, J., Smith, P., Hoffmann, H., van Oijen, M., Zhao, G., et al. (2016). Impact of climate aggregation over different scales on regional NPP modelling.. Vienna (Austria).
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Kersebaum, K. C. (2012). Analysing climate change impact on agriculture (in German)..
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Hlavinka, P., Kersebaum, K. C., Dubrovský, M., Pohanková, E., Balek, J., Žalud, Z., et al. (2014). Water balance and yield estimates for field crop rotations present versus future conditions based on transient scenarios..
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Boote, K. J., Porter, C., Jones, J. W., Thorburn, P. J., Kersebaum, K. C., Hoogenboom, G., et al. (2015). Sentinel site data for crop model improvement – definition and characterization. In J. L. Hatfield, & D. Fleisher (Eds.), (Vol. Advances in Agricultural Systems Modeling (7)). Madison, WI: ASA, CSSA, and SSSA.
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Ewert, F., Rötter, R. P., Bindi, M., Webber, H., Trnka, M., Kersebaum, K., et al. (2015). Crop modelling for integrated assessment of risk to food production from climate change (Vol. 6).
Abstract: The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. No Label
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