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Van den Pol-van Dasselaar, A., Bellocchi, G., Hutchings, N., Olesen, J., & Saetnan, E. (2014). AnimalChange. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The EU-FP7 project AnimalChange (AN Integration of Mitigation and Adaptation options for sustainable Livestock production under climate CHANGE, http://www.animalchange.eu, 2011-2015) addresses mitigation and adaptation options and provides scientific guidance for their integration in sustainable development pathways for livestock production under climate change in Europe, Northern and Sub-Saharan Africa, and Latin America. The project provides insights, innovations, tools and models for livestock production incorporating socio-economic and environmental (particularly GHG emission) variables. Scenario studies are carried out at scales ranging from animal and pasture, to farm and to region, for given management options. A wide range of livestock production systems is included in the project. The core analytical spine of the project is a series of coupled biophysical and socio-economic models combined with experimentation. This allows exploring future scenarios for the livestock sector under baseline and atmospheric CO2 stabilization scenarios. These scenarios are first constructed and then elaborated and enriched by breakthrough mitigation and adaptation options at field and animal scales, integrated and evaluated at farm scale and finally used to assess policy options and their socio-economic consequences. The modelling results are useful for governments, agricultural and food industry and the agricultural sector (farmers). There are many synergies between the European activities of AnimalChange and those of the LiveM theme of MACSUR, in particular with respect to access to livestock production datasets, dialogue with stakeholders and comparison and integration of grassland and livestock models with crop and socio-economic models in pilot studies at a variety of scales.
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Sinabell, F., & Brouwer, F. (2014). TradeM synergies with AGMIP. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The AgMIP network started activities on intercomparison of global economic modelling at a time when MACSUR was not yet established. The achievements made so far are highly relevant for TradeM and several partners (Wageningen University, IIASA, PIK, University Bonn) are in both networks. The MOU between MACSUR and AGMIP established formal links between the two projects and TradeM is activley working on establishing further collaboration. Preparations are underway to bring together researchers of both networks in a joint workshop to be held in Austria, September 2014. The topic will be on issues related to linking local and regional models with global ones. TradeM will actively contribute to the workshop and will host a one-day side-event.
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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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Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions (Vol. 8).
Abstract: Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016)
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