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Kipling, R. P., Bannink, A., Bellocchi, G., Dalgaard, T., Fox, N. J., Hutchings, N. J., et al. (2016). Modeling European ruminant production systems: Facing the challenges of climate change. Agricultural Systems, 147, 24–37.
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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Seddaiu, G., Ruiu, M. L., & Kipling, R. P. (2015). Report on Stakeholder Engagement Methodologies (Vol. 4).
Abstract: Stakeholder engagement in research projects can take a number of forms according to the scope of the project and the purpose of the interaction. L4.2. has focused on comparing different approaches to stakeholder engagement in collaborative projects. This report presents a synthesis of the experiences and lessons learnt through the stakeholder engagement activities of LiveM researchers within MACSUR, within an Italian (Oristano) case study, and within the SOLID (Sustainable, Organic and Low Input Dairying) project. An overview of these examples, and some of the lessons drawn from them, can also be found in the MACSUR paper on stakeholder engagement methods being developed by researchers from all three MACSUR themes (Koenig et al. under production). The first part of this report describes the stakeholder engagement strategy within the SOLID project. Stakeholder engagement methods are analysed through observations of activities and using semi-structured interviews with researchers and stakeholders. Two aspects of the SOLID approach are described – the stakeholder panel and the Future Dairying workshop. Transcripts of the workshop and the contribution of the stakeholder panel to the SOLID annual meeting in Helsinki are included (Appendices 1 and 2), as a contribution to the analysis of workshop outcomes being undertaken within the SOLID project. As part of a wider suite of stakeholder engagement activities, the SOLID stakeholder panel provided an example of how ongoing oversight of scientific outputs and direction by stakeholders can be effective in identifying weaknesses in approach and communication, and in suggesting relevant and effective directions for research activities. The stakeholder workshop demonstrated a useful structure for the exploration of stakeholder concerns, their view of ideal states and their solutions for reaching them. Low participation levels demonstrated the need to understand the motivations that drive stakeholders to engage in such projects, and highlighted the value of developing long-term relationships between stakeholders and researchers that allow scientific research to become an accepted part of practical problem-solving. The second part of the report describes stakeholder engagement activities carried out in the context of one of the MACSUR regional pilot studies (Oristanese case study in Sardinia, Italy). The Oristanese case study demonstrates the potentialities and constraints of participatory methodologies in relation to the different categories of stakeholder involved. It highlights the importance of creating new spaces for dialogue between farmers, researchers and policy makers in order to promote the generation of “hybrid knowledge” (Nguyen et al. 2013) for the emergence of more sustainable and longer-lasting strategies to adapt to CC. This would require the promotion of open knowledge generation platforms where multiple stakeholders are encouraged to participate and make their views heard. These approaches are designed in order to overcome the misalignment between scientists’ suggestions and policy implementation. In the third part of the report, the outcomes of a “learning event” held in Sassari (MACSUR mid-term meeting) with decision makers from different EU countries, are discussed. Finally, some reflections are presented on the importance of involving local stakeholders and decision makers in research projects, of sharing views and knowledge between scientists and stakeholders, and on the pros and cons of different methodologies at the different scales of stakeholder engagement, drawing on all three examples of practice. The research approach analysed includes two important components, which are represented by “transdisciplinarity” (to be included in the macro area of “scientific knowledge”) and “local knowledge”, as fundamental elements to fill the Science and Policy Gap. No Label
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Virkajärvi, P., Korhonen, P., Bellocchi, G., Curnel, Y., Wu, L., Jégo, G., et al. (2016). Modelling responses of forages to climate change with a focus on nutritive value. Advances in Animal Biosciences, 7(03), 227–228.
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Kipling, R. P., Bannink, A., Özkan Gülzari, Ş., & Van Middelkoop, J. (2016). Editorial. Advances in Animal Biosciences, 7(03)(03), 223.
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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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