Steering the EU food system towards a sustainability transformation requires a vast and actionable knowledge base available to a range of public and private actors. Few have captured this complexity by assessing food systems from a multi-dimensional and multi-level perspective, which would include (1) nutrition and diet, environmental and economic outcomes together with social equity dimensions and (2) system interactions across country, EU and global scales. This paper addresses this gap in food systems research and science communication by providing an integrated analytical approach and new ways to communicate this complexity outside science. Based on a transdisciplinary science approach with continuous stakeholder input, the EU Horizon2020 project 'Metrics, Models and Foresight for European SUStainable Food And Nutrition Security' (SUSFANS) developed a five-step process: Creating a participatory space; designing a conceptual framework of the EU food system; developing food system performance metrics; designing a modelling toolbox and developing a visualization tool. The Sustainable Food and Nutrition-Visualizer, designed to communicate complex policy change-impacts and trade-off questions, enables an informed debate about trade-offs associated with options for change among food system actors as well as in the policy making arena. The discussion highlights points for further research related to indicator development, reach of assessment models, participatory processes and obstacles in science communication.
Previous analyses of dairy farm structural change focus on the variation over time in one or a small number of regions. We present an EU-15 cross-regional analysis of the development of dairy farm numbers in different size classes over the period 1995-2005. Our purpose is to measure the explanatory relevance and effect of key factors suggested in the theoretical and empirical literature on structural change. Apart from the unprecedented scope, the underlying Markov chain analysis also contributes by combining observed transitions in micro (farm level) data with macro (sector level) data on farm numbers. Results show widely significant impacts of most considered explanatory variables, but also reflect and illuminate the complexity of the underlying processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.