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Kjeldsen, C., Sørensen, A. - M. L., Dalgaard, T., & Graversgard, M. (2015). Report on cross-cutting approaches for the assessment of climate change adaption on selected EU sites or hotspots and potentials for adaption and mitigation in the dairy sector (Vol. 6).
Abstract: Adaption to climate change in the context of agriculture involves collaborative planning and development of practices which is deemed more sustainable than preceeding practices. It is however not given that sustainable development will be the outcome of such efforts. In some cases, even motivated participants experience that despite good intentions, high levels of knowledge, feasible models, appropriate technologies and many other factors present, they still might not succeed bringing about the desired change. The reasons for this can not easily be reduced to just one factor, but is very likely to be the outcome of highly complex interactions between social, technological, institutional, or even personal factors. The report documents attempts to understand the complexities of climate change adaption in a Danish water catchment, Lundgaards Bæk, which is dominated by dairy farming. As part of the EU projects AQUARIUS and MACSUR, a local action group was formed which was composed of local farmers, local agricultural advisors, advisors from the national agricultural advisory service, environmental planners from the local municipality, and environmental planners from the national environmental agency in Denmark. The action group was supposed to develop specific measures, which were supposed to lead to an overall reduction in nitrogen loading of the neighboring fjord, Mariager Fjord. The report addresses three related research themes: (1) how do the stakeholders in question interact during the process of climate change adaption, (2) when do the stakeholders encounter opportunities and barriers during the process, and finally (3) does the adaption process in question lead to the desired outcomes? The empirical background of the report is a detailed process study of dynamics within a group of stakeholders, including farmers and extension officers, who were supposed to develop sustainable management practices in order to reduce nitrogen leaching to the Mariager Fjord. The study is based on the assumption that in order for research and policy to contribute to sustainable practices, deeper understanding of complex dynamics within stakeholder partnerships is needed. Based on a theoretical framework derived from social learning, adaptive co-management and Andrew Pickering’s notion of ‘the mangle’, different in-depth explanations to why sustainable development did not occur, are offered. One explanation concerns social-psychological dynamics of knowledge. Another explanation concerns the mechanisms by which social and material forces affect outcomes of the adaption process. The report concludes by exploring the study’s relevance in relation to policy, research and practice, followed by suggestions for further in-depth case studies and experimentation in practice. No Label
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Kipling, R. P., Bannink, A., Bellocchi, G., Dalgaard, T., Fox, N. J., Hutchings, N. J., et al. (2017). Modelling European ruminant production systems: Facing the challenges of climate change (Vol. 10).
Abstract: Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks
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Christen, B., Kjeldsen, C., Dalgaard, T., & Martin-Ortega, J. (2015). Can fuzzy cognitive mapping help in agricultural policy design and communication? Land Use Policy, 45, 64–75.
Abstract: Highlights •Fuzzy cognitive mapping (FCM)can help to improve agricultural policy design. •We analyse the views on regulation between farmers and non-farmers. •We demonstrate the utility of FCM in disentangling reasons for non-compliance. •Non-compliance is a result of dis-alignment of views rather than unwillingness. •FCM offers a critical, reflexive approach to how a regulatory process is conceived. Agricultural environmental regulation often fails to deliver the desired effects because of farmers adopting the related measures incorrectly or not at all. This is due to several barriers to the uptake of the prescribed environmentally beneficial farm management practices, most of which have been well established by social science research. Yet it is unclear why these barriers remain so difficult to overcome despite numerous and persistent attempts at the design, communication and enforcement of related agricultural policies. This paper examines the potential of fuzzy cognitive mapping (FCM) as a tool to disentangle the underlying reasons of this persistent problem. We present the FCM methodology as adapted to the application in a Scottish case study on how environmental regulation affects farmers and farming practice and what factors are important for compliance or non-compliance with this regulation. The study compares the views of two different stakeholder groups on this matter using FCM network visualizations that were validated by interviews and a workshop session. There was a farmers group representing a typical mix of Scottish farming systems and a non-farmers group, the latter comprising professionals from the fields of design, implementation, administration, consulting on and enforcement of agricultural policies. Between the two groups, the FCM process reveals a very different perception of importance and interaction of factors and strongly suggests that the problem lies in an institutional failure rather than in a simple unwillingness of farmers to obey the rules. FCM allows for a structured process of identifying areas of conflicting perceptions, but also areas where strongly differing groups of stakeholders might be able to gain common ground. In this way, FCM can help to identify anchoring points for targeted policy development and has the potential of becoming a useful tool in agricultural policy design and communication. Our results show the utility of FCM by pointing out how Scottish environmental regulation could be altered to increase compliance with the rules and where the reasons for the identified institutional failure might be sought.
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