| Records |
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Savary, S.; Nelson, A.D.; Djurle, A.; Esker, P.; Sparks, A.; Amorim, L.; Bergamin Filho, A.; Caffi, T.; Castilla, N.; Garrett, K.; McRoberts, N.; Rossi, V.; Yuen, J.; Willocquet, L. |
| Title |
Concepts, approaches, and avenues for modelling crop health and crop losses |
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| Year |
2017 |
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FACCE MACSUR Reports |
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10 |
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C1.5-D |
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Main text in preparation for publication in a peer-reviewed journal) |
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CropM |
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MA @ admin @ |
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4952 |
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| Author |
Bishop, J.; Lotze-Campen, H. |
| Title |
XC8 Extreme events – Final report |
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2017 |
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FACCE MACSUR Reports |
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10 |
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XC8-D |
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Following a MACSUR Workshop a joint working paper preliminary titled “More than a change in crop production: metrics and approaches to understand the impacts of extreme events on food security” is now in an advanced stage. A conference paper based on an M.Sc. thesis by Christoph Buschmann, titled “A model-based economic assessment of future climate variability impacts on global agricultural markets” has been presented and the International Conference of Agricultural Economists, 2015. We are working on a journal publication at the moment. Based on a B.Sc. thesis by Patrick Jeetze, we have submitted an abstract and held a presentation at the GlobalFood Symposium 2017, 28-29 April 2017 at Georg-August-University of Goettingen, Germany. Title: “Implications of future climate variability on food security: A model-based assessment of climate-induced crop price volatility impacts” We are currently working on a journal publication on this. Finally, we contributed one section to MACSUR's Research Gap Report (H0.1-D). |
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XC |
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MA @ admin @ |
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4953 |
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Trnka, M.; Hlavinka, P.; Wimmerová, M.; Pohanková, E.; Rötter, R.; Olesen, J.E.; Kersebaum, K.-C.; Semenov, M. |
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Paper on model responses to selected adverse weather conditions |
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Report |
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2017 |
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FACCE MACSUR Reports |
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10 |
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C1.2-D |
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Based on the Trnka et al. (2015) study that indicated that heat and drought will be the most important stress factors for most of the European what area the further effort focused on these two extremes. The crop model HERMES has been tested for its ability to replicate correctly drought stress, heat stress and combination of both stresses. While data on the drought stress were available for both field and growth chambers, heat stress and its combination with heat stress was available only for the growth chambers. The modified version of the HERMES crop model was developed by Dr. Kersebaum and is being currently prepared for the journal paper publication. |
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4954 |
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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 |
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Report |
| Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
Issue |
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Pages |
C4.3-D1 |
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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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CropM |
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4956 |
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Höglind, M.; the partners of LiveM task L1.3 |
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Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling |
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Report |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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L1.3-D |
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This report provides an overview of how grasslands are represented in six different farmscale models represented in MACSUR. A survey was conducted, followed by a workshop in which modellers discussed the results of the survey, and identified research challenges and knowledge gaps. The workshop was attended by grassland as well as livestock specialists. The investigated models differed largely with respect to how grasslands were represented, e.g. as regards weather and management factors accounted for, spatial and temporal resolution, and output variables. All models had grassland modules that simulate DM yield and herbage N content (or crude protein (CP) content = N content x 6.25). Many models also simulate P content, whereas only one simulate K content. About half of the model simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter digestibility. Critical input data required from grassland models to simulate ruminant productivity and GHG emissions at farm scale was identified by the workshop participants. The different types of input data required were ranked in order of importance as regards their influence on important system outputs. For simulation of ruminant productivity and GHG emissions, herbage DM yield was ranked as the most important input variable from grassland models, followed by CP content together with at least one variable describing herbage fibre characteristics. These findings suggest that work on improving the ability of the current grassland models with respect to simulation of fibre/energy should be prioritized in farm-scale modelling aiming at quantifying livestock production and GHG emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models. |
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LiveM |
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MA @ admin @ |
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4957 |
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