| |
Records |
Links |
|
Author |
Köchy, M.; Aberton, M.; Bannink, A.; Banse, M.; Brouwer, F.; Brüser, K.; Ewert, F.; Foyer, C.; Jorgenson, J.S.; Kipling, R.; Meijs, J.; Rötter, R.; Scollan, N.; Sinabell, F.; Tiffin, R.; van den Pol-van Dasselaar, A. |
|
| |
Title |
MACSUR — Summary of research results, phase 1: 2012-2015 |
Type |
Report |
| |
Year |
2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
|
|
| |
Volume |
6 |
Issue |
|
Pages  |
D-H3.3 |
|
| |
Keywords |
Hub |
|
| |
Abstract |
MACSUR — Modelling European Agriculture with Climate Change for Food Security — is a knowledge hub that was formally created in June 2012 as a European scientific network. The strategic aim of the knowledge hub is to create a coordinated and globally visible network of European researchers and research groups, with intra- and interdisciplinary interaction and shared expertise creating synergies for the development of scientific resources (data, models, methods) to model the impacts of climate change on agriculture and related issues. This objective encompasses a wide range of political and sociological aspects, as well as the technical development of modelling capacity through impact assessments at different scales and assessing uncertainties in model outcomes. We achieve this through model intercomparisons and model improvements, harmonization and exchange of data sets, training in the selection and use of models, assessment of benefits of ensemble modelling, and cross-disciplinary linkages of models and tools. The project engages with a diverse range of stakeholder groups and to support the development of resources for capacity building of individuals and countries. Commensurate with this broad challenge, a network of currently 300 scientists (measured by the number of individuals on the central e-mail list) from 18 countries evolved from the original set of research groups selected by FACCE. In the spirit of creating and maintaining a network for intra- and interdisciplinary knowledge exchange, network activities focused on meetings of researchers for sharing expertise and, depending on group resources (both financial and personnel), development of collaborative research activities. The outcome of these activities is the enhanced knowledge of the individual researchers within the network, contributions to conference presentations and scholarly papers, input to stakeholders and the general public, organised courses for students, junior and senior scientists. The most visible outcome are the scientific results of the network activities, represented in the contributions of MACSUR members to the impressive number of more than 200 collaborative papers in peer-reviewed publications. Here, we present a selection of overview and cross-disciplinary papers which include contributions from MACSUR members. It highlights the major scientific challenges addressed, and the methodological solutions and insights obtained. Over and above these highlights, major achievements have been reached regarding data collection, data processing, evaluation, model testing, modelling assessments of the effects of agriculture on ecosystem services, policy, and development of scenarios. Details on these achievements in the context of MACSUR can be found in our online publication FACCE MACSUR Reports at http://ojs.macsur.eu. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
MA @ admin @ |
Serial |
2086 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Köchy, M.; Bannink, A.; Banse, M.; Brouwer, F.; Brüser, K.; Ewert, F.; Foyer, C.; Kipling, R.; Rötter, R.; Scollan, N.; Sinabell, F. |
|
| |
Title |
MACSUR Phase 1 Final Administrative Report: Public release |
Type |
Report |
| |
Year |
2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
|
|
| |
Volume |
6 |
Issue |
|
Pages |
D-H3.5.3 |
|
| |
Keywords |
Hub; |
|
| |
Abstract |
MACSUR’s foremost charge is improving the methodology for integrative inter-disciplinary modelling of European agriculture. In addition to technical changes, improvements include the involvement of stakeholders for setting research priorities, scenarios (if-then evaluations), and model parameters to more realistic or region-specific values. The Knowledge Hub currently brings together 300 members from 18 countries and has generated 300 scientific papers, over 500 presentations and 20 workshops and conferences within the first three years. Scientific results are communicated in conferences and workshops, where policymakers take part by invitation or because of professional interest. These events also provide opportunities for direct dialogues between policymakers and scientists. The primary form of output of the research network is scientific publications that are cited in policy documents by relevant administrative departments, ministries, intergovernmental agencies, and directorate-generals, and non-governmental interest groups. MACSUR members also contribute directly to policy documents as authors, e.g. the EEA’s indicator report on CC impacts or the IPCC’s 5th assessment report’s chapter on food security. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
MA @ admin @ |
Serial |
2080 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
|
| |
Title |
Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions |
Type |
Report |
| |
Year |
2016 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
|
|
| |
Volume |
8 |
Issue |
|
Pages |
C4.1-D |
|
| |
Keywords |
MACSUR_ACK; CropM |
|
| |
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) |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
MA @ office @ |
Serial |
2954 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ruiz-Ramos, M.; Ferrise, R.; Rodríguez, A.; Lorite, I.J.; Bindi, M.; Carter, T.R.; Fronzek, S.; Palosuo, T.; Pirttioja, N.; Baranowski, P.; Buis, S.; Cammarano, D.; Chen, Y.; Dumont, B.; Ewert, F.; Gaiser, T.; Hlavinka, P.; Hoffmann, H.; Höhn, J.G.; Jurecka, F.; Kersebaum, H.-C.; Krzyszczak, J.; Lana, M.; Mechiche-Alami, A.; Minet, J.; Montesino, M.; Nendel, C.; Porter, J.R.; Ruget, F.; Semenov, M.A.; Steinmetz, Z.; Stratonovitch, P.; Supit, I.; Tao, F.; Trnka, M.; de Wit, A.; Rötter, R.P. |
|
| |
Title |
Applying adaptation response surfaces for managing wheat under perturbed climate and elevated CO2 in a Mediterranean environment |
Type |
Report |
| |
Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
|
|
| |
Volume |
1ß |
Issue |
|
Pages |
C4.4-D |
|
| |
Keywords |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
CropM |
Approved |
no |
|
| |
Call Number |
MA @ admin @ |
Serial |
4955 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Fronzek, S.; Pirttioja, N.; Carter, T.R.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, M.; Acutis, M.; Asseng, S.; Baranowski, P.; Basso, B.; Bodin, P.; Buis, S.; Cammarano, D.; Deligios, P.; Destain, M.-F.; Dumont, B.; Ewert, F.; Ferrise, R.; François, L.; Gaiser, T.; Hlavinka, P.; Jacquemin, I.; Kersebaum, K.-C.; Kollas, C.; Krzyszczak, J.; Lorite, I.J.; Minet, J.; Minguez, M.I.; Montesino, M.; Moriondo, M.; Müller, C.; Nendel, C.; Öztürk, I.; Perego, A.; Rodríguez, A.; Ruane, A.C.; Ruget, F.; Sanna, M.; Semenov, M.A.; Slawinsky, C.; Stratonovitch, P.; Supit, I.; Waha, K.; Wang, E.; Wu, L.; Zhao, Z.; Rötter, R.P. |
|
| |
Title |
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change |
Type |
Report |
| |
Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
|
|
| |
Volume |
10 |
Issue |
|
Pages |
C4.3-D1 |
|
| |
Keywords |
|
|
| |
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). |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
CropM |
Approved |
no |
|
| |
Call Number |
MA @ admin @ |
Serial |
4956 |
|
| Permanent link to this record |
