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).

Abstract: The report presented here provides an inventory of reports and conference papers  produced by the partners of the livestock and grassland modelling theme (LiveM) of the  Modelling European Agriculture with Climate Change for Food Security (MACSUR)  knowledge hub. The findings presented illustrate the diverse nature of the multidisciplinary  LiveM research community, and provide a reference source for those seeking  to identify and pull out farm-level modelling outputs from the work of MACSUR and its  partners. The survey of farm-scale outputs from LiveM revealed the interdependent, dual  role of a knowledge hub: to increase the capacity of modelling to meet stakeholder and  societal needs under climate change, and to apply that increased capacity to provide new  understanding and solutions at the policy and (the focus here) farm scale. While capacity  building work across disciplines is time-consuming, difficult, and to a large extent invisible  to stakeholders, such work is vital to ensuring that subsequent scientific outcomes reflect  best practice, and integrated expertise. Long term, sustained funding of network-based  capacity building activities is highlighted as essential to ensuring that the farm-scale  modelling work highlighted here can continue to build on ongoing improvements in model  quality, flexibility and stakeholder relevance.