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Ewert, F.; al, E. |
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Title |
Uncertainties in Scaling-Up Crop Models for Large-Area Climate Change Impact Assessments |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C3.3 |
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Problems related to food security and sustainable development are complex (Ericksenet al., 2009) and require consideration of biophysical, economic, political, and social factors, as well as their interactions, at the level of farms, regions, nations, and globally. While the solution to such societal problems may be largely political, there is a growing recognition of the need for science to provide sound information to decision-makers (Meinke et al., 2009). Achieving this, particularly in light of largely uncertain future climate and socio-economic changes, will necessitate integrated assessment approaches and appropriate integrated assessment modeling (IAM) tools to perform them. Recent (Ewertet al., 2009; van Ittersumet al., 2008) and ongoing (Rosenzweiget al., 2013) studies have tried to advance the integrated use of biophysical and economic models to represent better the complex interactions in agricultural systems that largely determine food supply and sustainable resource use. Nonetheless, the challenges for model integration across disciplines are substantial and range from methodological and technical details to an often still-weak conceptual basis on which to ground model integration (Ewertet al., 2009; Janssenet al., 2011). New generations of integrated assessment models based on well-understood, general relationships that are applicable to different agricultural systems across the world are still to be developed. Initial efforts are underway towards this advancement (Nelsonet al., 2014; Rosenzweiget al., 2013). Together with economic and climate models, crop models constitute an essential model group in IAM for large-area cropping systems climate change impact assessments. However, in addition to challenges associated with model integration, inadequate representation of many crops and crop management systems, as well as a lack of data for model initialization and calibration, limit the integration of crop models with climate and economic models (Ewertet al., 2014). A particular obstacle is the mismatch between the temporal and spatial scale of input/output variables required and delivered by the various models in the IAM model chain. Crop models are typically developed, tested, and calibrated for field-scale application (Booteet al., 2013; see also Part 1, Chapter 4 in this volume) and short time-series limited to one or few seasons. Although crop models are increasingly used for larger areas and longer time-periods (Bondeauet al., 2007; Deryng et al., 2011; Elliottet al., 2014) rigorous evaluation of such applications is pending. Among the different sources of uncertainty related to climate and soil data, model parameters, and structure, the uncertainty from methods used to scale-up crop models has received little attention, though recent evaluations indicate that upscaling of crop models for climate change impact assessment and the resulting errors and uncertainties deserve attention in order to advance crop modeling for climate change assessment (Ewertet al., 2014; R¨ otteret al., 2011). This reality is now reflected in the scientific agendas of new international research projects and programs such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweiget al., 2013) and MACSUR (MACSUR, 2014). In this chapter, progress in evaluation of scaling methods with their related uncertainties is reviewed. Specific emphasis is on examining the results of systematic studies recently established in AgMIP and MACSUR. Main features of the respective simulation studies are presented together with preliminary results. Insights from these studies are summarized and conclusions for further work are drawn. No Label |
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2096 |
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Hoffmann, H.; Ewert, F. |
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Title |
Review on scaling methods for crop models |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C3.1 |
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Agricultural systems cover a range of organisational levels and spatial and temporal scales. To capture multi-scale problems of sustainable management in agricultural systems, Integrated assessment modelling (IAM) including crop models is often applied which require methods of scale changes (scaling methods). Scaling methods, however, are often not well understood and are therefore sources of uncertainty in models. The present report summarizes scaling methods as developed and applied in recent years (e.g. in SEAMLESS-IF and MACSUR) in a classification scheme based on Ewert et al. (2011, 2006). Scale changes refer to different spatial, temporal and functional scales with changes in extent, resolution, and coverage rate. Accordingly, there are a number of different scaling methods that can include data extrapolation, aggregation and disaggregation, sampling and nested simulation. Comparative quantitative analysis of alternative scaling methods are currently under way and covered by other reports in MACSUR and several publications (e.g. Ewert et al., 2014; Hoffmann et al., 2015; Zhao et al., 2015). The following classification of scaling methods assists to structure such analysis. Improved integration of scaling methods in IAM may help to overcome modelling limitations that are related to high data demand, complexity of models and scaling methods considered. No Label |
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2094 |
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Ewert, F.; Rötter, R.P.; Bindi, M.; Webber, H.; Trnka, M.; Kersebaum, K.; Christian,; Olesen, J.E.; Van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.A.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Gaiser, T.; Palosuo, T.; Tao, F.; Nendel, C.; Roggero, P.P.; Bartošová, L.; Asseng, S. |
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Crop modelling for integrated assessment of risk to food production from climate change |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C0.3 |
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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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MA @ admin @ |
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2089 |
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Köchy, M.; Banse, M.; Tiffin, R.; Ewert, F.; Rötter, R.; Van den Pol-van Dasselaar, A.; Brouwer, F.; Sinabell, F.; Jorgenson, J.; Sætnan, E.; Kipling, R. |
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General outline of plans for an extension phase of MACSUR |
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2014 |
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FACCE MACSUR has indicated a possible extension of funding by two years until May 2017 (phase 2).For phase 2, hub and theme coordinators suggest the following main activities, that will be discussed during the remainder of the meeting and in the coming months.Evolution, upscaling, and transfer of knowledge gained in regional case studies.Assessment of additional scenarios of socio-economic and climate trends.Further development of an interdisciplinary scientific community.Extending scaling methods for crop models to the European and global scale.Intensification of feed quality and animal health modelling with climate change.Economic models from farm to global level capable of reflecting climate change. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5084 |
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Ewert, F.; Rötter, R. |
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Title |
MACSUR CropM – progress overview |
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2014 |
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Activities in the first 1 ½ years of CropM were related to key issues identified as critical at the beginning of the FACCE MACSUR the knowledge Hub. These include: Model intercomparisonGeneration of new data for model improvementMethods for scaling and model linkingUncertainty analysisBuilding research capacity Climate scenario data for crop models The key ambition of CropM has been to develop scientific excellence on methods for a comprehensive assessment of climate change impact, adaptation and policy on European crop production, agriculture and food security. Much progress has been made in developing a first shared continental assessment and tool for: A range of important cropsImportant crop rotationsAdvanced scaling methodsAdvanced link to farm and sector modelsNovel impact uncertainty assessment and reportingState-of-the-art scenario construction A number of concrete studies towards this aim have been launched in CropM workpackages (WPs): WP1-2: Two multi-facetted studies on crop rotation, launched in summer 2013 WP3: comprehensive scaling exercises, launched in March 2013WP4: Studies on (a) Climate scenario development, (b) impact response surface method and (c) Extremes, launched in summer 2013WP5: Analysis of transect across Europe with temperature effect (Space for Time) In addition, extended activities related to capacity building including several PhD courses (WP5) workshops (in WPs1-4) and an International Symposium (10-12 Feb, Oslo, Norway) have been organized. Present and future work is and will be focused on framing and advancing crop modelling as integrated part of comprehensive climate risk assessment and modelling of agricultural systems for food security from farm to supra-national level. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5099 |
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