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Zimmermann, A. (2014). Exploring yield gaps in the EU, concept and data. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Agreeing that increased future global food demand will have to be met by production intensification rather than land use expansion (Hertel, 2011), scientists have moved to empirically analysing the causes for differences between potentially attainable yields and actually realized yields – the yield gap (Neumann et al., 2010). We aim at disentangling the effects of biophysical, economic and political impacts and farmers’ response to them on crop yields based on reviewing theoretical and empirical literature and some descriptive data analysis.
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Zimmermann, A. (2015). Yield trends and variability in the EU.. Reading (United Kingdom).
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Witzke, P., Frank, S., Zimmermann, A., Havlík, P., & Ciaian, P. (2013). The impact of climate change on food security – results from a European perspective..
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Webber, H., Oomen, R., Gaiser, T., Teixeira, E., Zhao, G., Srivastava, A., et al. (2016). Uncertainty in future European irrigation water demand.. Berlin (Germany).
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Köchy, M., Bishop, J., Lehtonen, H., Scollan, N., Webber, H., Zimmermann, A., et al. (2017). Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security (Vol. 10).
Abstract: Priorities in addressing research gaps and challenges should follow the order of importance, which in itself would be a matter of defining goals and metrics of importance, e.g. the extent, impact and likelihood of occurrence. For improving assessments of climate change impacts on agriculture for achieving food security and other sustainable development goals across the European continent, the most important research gaps and challenges appear to be the agreement on goals with a wide range of stakeholders from policy, science, producers and society, better reflection of political and societal preferences in the modelling process, and the reflection of economic decisions in farm management within models. These and other challenges could be approached in phase 3 of MACSUR.
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