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König, H.J.; Helming, K.; Seddaiu, G.; Kipling, R.; Köchy, M.; Graversgaard, M.; van den Pol-van Dasselaar, A.; Nguyen, T.P.L.; Quaranta, G.; Salvia, R.; Sieber, S.; Ithes, S.; Kjeldsen, C.; Turner, K.G.; Dalgaard, T.; Roggero, P.P. |
| Title |
Stakeholder participation in agricultural research: Who should be involved, why, and how? |
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Research in sustainable agricultural management requires appropriate participatory processes and tools enabling efficient dialogue and cooperation to allow researchers and stakeholders to co-produce knowledge. Research approaches that encourage stakeholder participation are in high demand because they allow a better understanding of human-nature interactions and interdependencies between actors. Participatory approaches also support multiple goals of agricultural management: improved productivity, food security, climate change adaptation, environmental conservation, rural development and policy decision making. Approaches to stakeholder engagement in the field of agricultural management research are manifold. Therefore, selecting the “right” approach depends on the specific purpose and contextualized issues at stake. We analyzed ten stakeholder approaches and propose a new framework with which to identify and select appropriate approaches for stakeholder engagement. The framework consists of three components: whom to engage (i.e., stakeholder type and mandate), why to engage (i.e., research purpose: consult, inform, collaborate), and how to engage (i.e., different methodological approaches). We identified different stakeholder groups (who?): farmers, agricultural actors, land users, and policymakers; different purposes (why?): facilitate engagement process, inform stakeholders, and obtain stakeholder perceptions; and different types of engagement methods (how?): participatory field experiments, desk simulations, interviews, panel discussions and different types of workshops. The framework was applied to arrange these approaches, organize them to improve understanding of their main strengths, weaknesses and supports for identifying and selecting an appropriate approach. We conclude that understanding the different facets of available approaches is crucial for selecting an appropriate stakeholder engagement approach. ; |
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MA @ admin @ |
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2564 |
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Dalgaard, T.; Kjeldsen, C.; Meyer-Aurich, A.; Özkan, S.; Rolinski, S.; Köchy, M.; Olesen, J.E.; Brouwer, F.; van den Pol-van Dasselaar, A.; Kipling, R. |
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Farming systems models for regional scale impact assessment in Europe – case studies of N-losses and greenhouse gas emissions |
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2014 |
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LiveM |
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Scaling in global, regional and farm models, 2014-09-24 to 2014-09-24 |
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MA @ admin @ |
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2380 |
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Kipling, R.P.; Bannink, A.; Bellocchi, G.; Dalgaard, T.; Fox, N.J.; Hutchings, N.J.; Kjeldsen, C.; Lacetera, N.; Sinabell, F.; Topp, C.F.E.; van Oijen, M.; Virkajärvi, P.; Scollan, N.D. |
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Modeling European ruminant production systems: Facing the challenges of climate change |
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Journal Article |
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2016 |
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Agricultural Systems |
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Agricultural Systems |
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147 |
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24-37 |
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Food security; Livestock systems; Modeling; Pastoral systems; Policy support; Ruminants |
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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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0308521x |
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LiveM, ft_macsur |
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MA @ admin @ |
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4734 |
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Kjeldsen, C. |
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An approach to sustainability management within partnerships between heterogeneous actors – example from a Danish water catchment, dominated by dairy farms |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-29 |
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In this paper we present an approach to sustainability management within partnerships between heterogenous actors. This multi-disciplinary approach is also relevant for the assessment of climate change adaptation and mitigation in the context of www.macsur.eu; and especially in areas with dairy farming hot-spots. Established approaches within this field such as Adaptive Co-Management and Social Learning focus on social-material interactions, feedback mechanisms, knowledge integration and institutional change as drivers in sustainable development. However, the role of micro-scale power dynamics as part of these processes have received less attention. In a case study of land-water management in a Danish water catchment, dominated by dairy farms, we analyze how dynamics of power within knowledge integration processes interacts with institutions at different scales. Thereby, we show ways in which power-knowledge dynamics shape development outcomes. Finally, we propose how increasing reflexivity of power-knowledge dynamics might contribute to institutional change and sustainable development. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2144 |
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Dalgaard, T.; Kjeldsen, C.; Graversgard, M. |
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Review of regional scale models in the EU and methods commonly used when modelling outcomes of the implementation of the climate change mitigation policies |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-L4.1 |
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Management of Nitrogen (N) losses and the related greenhouse gas emissions is one of the most important environmental issues related to agriculture. This report shows examples of an integrated model tool, developed to quantify the N-dynamics at the complex interface between agriculture and the environment, and quantify effects of different management practices. Based on results from the EU funded research projects NitroEurope (www.NitroEurope.eu) and MEAscope (www.MEA-scope.org), examples from the quantification of farm N-losses in European agricultural landscapes are demonstrated. Applications of the dynamic whole farm model FASSET (www.FASSET.dk), and the Farm-N tool (www.farm-N.dk/FarmNTool) to calculate farm N balances, and distribute the surplus N between different types of N-losses (volatilisation, denitrification, leaching), and the related greenhouse gas emissions, show significant variation between landscapes and management practices. Moreover, significant effects of the nonlinearities, appearing when integrating over time, and scaling up from farm to landscape, are demonstrated. Finally, perspectives for stakeholder involvement is included and general recommendations for landscape level management of farm related nitrogen and greenhouse gas fluxes are made, and discussed in relation to ongoing research in the European research projects. No Label |
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2110 |
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