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Bennetzen, E.; Smith, P.; Porter, J.R. |
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Identity-based analysis of GHG emissions from agriculture |
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2016 |
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Berlin (Germany) |
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International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany |
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MA @ admin @ |
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4914 |
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Ramirez-Villegas, J.; Watson, J.; Challinor, A.J. |
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Identifying traits for genotypic adaptation using crop models |
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Journal Article |
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2015 |
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Journal of Experimental Botany |
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J. Experim. Bot. |
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66 |
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12 |
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3451-3462 |
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Adaptation, Physiological/*genetics; Crops, Agricultural/*genetics; Environment; Genotype; *Models, Theoretical; *Quantitative Trait, Heritable; Climate change; crop models; genotypic adaptation; ideotypes; impacts |
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Abstract |
Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation. |
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English |
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0022-0957 1460-2431 |
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Review |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4645 |
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Bellocchi, G.; Ma, S.; Köchy, M.; Braunmiller, K. |
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Identified grassland-livestock production systems and related models |
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Report |
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2013 |
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FACCE MACSUR Reports |
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2 |
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D-L2.1.1 |
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This report describes grassland-livestock production systems, as selected for model-basedstudies. A list of grassland models was identified for evaluation against such datasets(WP2) and application at reference farm (WP3) and regions (WP4) across Europe and peri-European countries. No Label |
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MA @ admin @ |
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2244 |
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Palosuo, T.; Rötter, P.; Lehtonen, H.; Virkajärvi, P.; Salo, T. |
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How to assess climate change impacts on farmers’ crop yields? |
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2013 |
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327-334 |
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Impacts World 2013, International Conference on Climate Change Effects, 2013-05-27 to 2013-05-30, Potsdam |
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MA @ admin @ |
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2706 |
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Author |
Challinor, A.E.A. |
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How have uncertainties in projected yields changed between AR4 and AR5 |
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2014 |
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CropM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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no |
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MA @ admin @ |
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2359 |
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