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Ruiz-Ramos, M., Ferrise, R., Rodríguez, A., Lorite, I. J., Tao, F., Pirttioja, N., et al. (2016). An ensemble of projections of wheat adaptation to climate change in europe analyzed with impact response surfaces.. Berlin (Germany).
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Brilli, L., Ferrise, R., Dibari, C., Bindi, M., & Bellocchi, G. (2017). Needs on model improvement (Vol. 10).
Abstract: The need to answer new scientific questions can be satisfied by an increased knowledge of physiological mechanisms which, in turn, can be used for improving the accuracy of simulations of process-based models. In this context, this report highlights areas that need to be further improved to facilitate the operational use of simulation models. It describes missing approaches within simulation models which, if implemented, would likely improve the representation of the dynamics of processes underlying different compartments of crop and grassland systems (e.g. plant growth and development, yield production, GHG emissions), as well as of the livestock production systems. The following rationale has been used in the organization of this report. We first briefly introduced the need to improve the reliability of existing models. Then, we indicated climate change and its influence on the global carbon balance as the main issue to be addressed by existing crop and grassland (section 2), and livestock (section 3) models. In section 2, among the major aspects that if implemented may reduce the uncertainty inherent to model outputs, we suggested: i) quantifying the effects of climate extremes on biological systems; ii) modelling of multi-species sward; iii) coupling of pest and disease sub-models; iv) improvement of the carry-over effect. In section 3, as the most important aspects to consider in livestock models we indicated: i) impacts and dynamics of pathogens and disease; ii) heat stress effects on livestock; iii) effects on grassland productivity and nutritional values; iv) improvement of GHG emissions dynamics. In Section 4, remarks are made concerning the need to implement the suggested aspects into the existing models.
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Köchy, M., Bishop, J., Lehtonen, H., Scollan, N., Webber, H., Zimmermann, A., et al. (2017). Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security (Vol. 10).
Abstract: Priorities in addressing research gaps and challenges should follow the order of importance, which in itself would be a matter of defining goals and metrics of importance, e.g. the extent, impact and likelihood of occurrence. For improving assessments of climate change impacts on agriculture for achieving food security and other sustainable development goals across the European continent, the most important research gaps and challenges appear to be the agreement on goals with a wide range of stakeholders from policy, science, producers and society, better reflection of political and societal preferences in the modelling process, and the reflection of economic decisions in farm management within models. These and other challenges could be approached in phase 3 of MACSUR.
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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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Fronzek, S., Pirttioja, N., Carter, T. R., Bindi, M., Hoffmann, H., Palosuo, T., et al. (2017). Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change (Vol. 10).
Abstract: Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes, Figure 1) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities. The full manuscript of this study is currently under revision (Fronzek et al. 2017).
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