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Baranowski, P., Krzyszczak, J., & Slawinski, C. (2014). Multifractal analysis of chosen meteorological time series to assess climate impact in field level..
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Acutis, M., & Bellocchi, G. (2013). Briefing on CropM-LiveM model intercomparison protocol..
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Kipling, R., Scollan, N., Bannink, A., & van Middelkoop, J. (2016). From diversity to strategy: Livestock research for effective policy in a climate change world (Vol. 8).
Abstract: European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. ï‚· Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination.
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Ewert, F., Rötter, R. P., Bindi, M., Webber, H., Trnka, M., Kersebaum, K., et al. (2015). Crop modelling for integrated assessment of risk to food production from climate change (Vol. 6).
Abstract: 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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Köchy, M. (2015). FACCE MACSUR Joint Workshops 2015 (Vol. 7).
Abstract: FACCE MACSUR comprises many different groups whose work contribute to improving the European capacity of modelling agriculture with climate change and providing an assessment of these impacts for stakeholders. Some groups work on methodological issues in a single discipline, others work on cross-disciplinary concepts. The meeting provided an opportunity for the members of the groups to meet for intensive discussions and exchange of ideas, which is not as easily done in phone or video conferences. Various groups also met with each other to agree on work plans and common settings for research. Overall, 105 researchers attended the workshops. For coordinating work with the global program AgMIP, AgMIP’s principle investigator John Antle attended the meeting and, meeting in a video call, coordination teams of MACSUR and AgMIP agreed to continue the successful collaboration in the future. Major overarching outcomes of the meetings were agreements on policy and climate scenarios recommended to be used within MACSUR, development of an approach to quantify effects of extreme climatic events on socio-economic indicators, and closer collaboration among several groups at the level of regional case studies.
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