Ruiz-Ramos, M., Ferrise, R., Rodríguez, A., Lorite, I. J., Bindi, M., Carter, T. R., et al. (2017). Applying adaptation response surfaces for managing wheat under perturbed climate and elevated CO2 in a Mediterranean environment (Vol. 1ß).
Abstract: This study developed Adaptation Response Surfaces and applied them to a study case in North East Spain on winter crops adaptation, using rainfed winter wheat as reference crop. Crop responses to perturbed temperature, precipitation and CO2 were simulated by an ensemble of crop models. A set of combined changes on cultivars (on vernalisation requirements and phenology) and management (on sowing date and irrigation) were considered as adaptation options and simulated by the crop model ensemble. The discussion focused on two main issues: 1) the recommended adaptation options for different soil types and perturbation levels, and 2) the need of applying our current knowledge (AOCK) when building a crop model ensemble. The study has been published Agricultural Systems (Available online 25 January 2017, https://doi.org/10.1016/j.agsy.2017.01.009 ), and the text below consists on extracts from that paper.
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Montesino-San Martín, M., Olesen, J. E., & Porter, J. R. (2015). Can crop-climate models be accurate and precise? A case study for wheat production in Denmark. Agricultural and Forest Meteorology, 202, 51–60.
Abstract: Crop models, used to make projections of climate change impacts, differ greatly in structural detail. Complexity of model structure has generic effects on uncertainty and error propagation in climate change impact assessments. We applied Bayesian calibration to three distinctly different empirical and mechanistic wheat models to assess how differences in the extent of process understanding in models affects uncertainties in projected impact. Predictive power of the models was tested via both accuracy (bias) and precision (or tightness of grouping) of yield projections for extrapolated weather conditions. Yields predicted by the mechanistic model were generally more accurate than the empirical models for extrapolated conditions. This trend does not hold for all extrapolations; mechanistic and empirical models responded differently due to their sensitivities to distinct weather features. However, higher accuracy comes at the cost of precision of the mechanistic model to embrace all observations within given boundaries. The approaches showed complementarity in sensitivity to weather variables and in accuracy for different extrapolation domains. Their differences in model precision and accuracy make them suitable for generic model ensembles for near-term agricultural impact assessments of climate change.
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Olesen, J. E., Porter, J. R., & Christensen, J. H. (2014). Centre for Regional change in the Earth System. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Centre for Regionalchange in the Earth System (CRES, cres-centre.net) is funded by the DanishStrategic Research Council for the period 2009-2014 and is coordinated by theDanish Meteorological Institute. CRES has established a coordinated researcheffort aiming to improve societal preparedness for climate change, inparticular for Denmark. The overall objective of CRES is to extend knowledge ofand reduce the uncertainties surrounding regional climate change and itsimpacts and thereby support future climate change adaptation and mitigationpolicies. Some of the objectives that also have large synergies with theeffects in the CropM theme of MACSUR are a) to reduce uncertainty surroundingregional climate change and its impacts for the period 2020-2050 by improvingmodel formulation and process understanding; b) identify key changes andtipping points in the regional hydrological system, agriculture, freshwater andestuarine ecosystems caused by changes in seasonality, dynamics and extremeevents of precipitation, droughts, heat waves and sea level rise; c) quantifyconfidence and uncertainties in predictions of future regional climate and itsimpacts, by improving the statistical methodology and substance and byintegrating interdisciplinary risk analyses; d) interpret these results inrelation to risk management approaches for climate change adaptation andmitigation. Studies in CRES of particular interest to MACSUR include a)Estimation on generic crop model uncertainties in projection of climate changeimpacts on wheat year, b) Assessment of uncertainties in projected effects onwater balance, crop productivity and nitrate leaching of changes in land use,climate and assessment models.
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Cammarano, D., Rötter, P., Ewert, F., Palosuo, T., Bindi, M., Kersebaum, K. C., et al. (2013). Challenges for Agro-Ecosystem Modelling in Climate Change Risk Assessment for major European Crops and Farming systems. (pp. 555–564).
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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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