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Kipling, R., Topp, K., & Don, A. (2014). Appropriate meta-data for modellers (Vol. 3).
Abstract: Report D-L1.4.1 provided an overview of the data and related resources available online and through EU funded projects, relating to soil organic carbon (SOC), and carbon sequestration in grasslands in particular. Building on D-L1.4.1, the report presented here discusses how meta-data describing these types of data (and experimental data more generally) can best be presented in an online resource useful to grassland modellers requiring data to use in their modelling work. Identifying the useful categories of meta-data is a necessary precursor to providing such a resource, which could facilitate better communication between modelling and experimental research groups, allowing researchers to more efficiently locate relevant data and to link up with other scientists working on similar topics. A survey among grassland modelling teams and an assessment of online meta-data resources was used to produce recommendations about the meta-data categories that should be included in an online resource. The categories are generic, so that the recommendations can be followed in the design of meta-data resources for the more general agricultural modelling community. 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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Kjeldsen, C. (2015). An approach to sustainability management within partnerships between heterogeneous actors – example from a Danish water catchment, dominated by dairy farms (Vol. 5).
Abstract: 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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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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Knox, J. (2015). Meta-analysis of recent scientific evidence on climate impacts and uncertainty on crop yields in Europe (Vol. 5).
Abstract: Projected changes in temperature, rainfall and soil moisture could threaten agricultural land use and crop productivity in Europe, with major consequences for food security (Daccache et al., 2014). We assessed the projected impacts of climate change on the yield of seven crops (viz wheat, barley, maize, potato, sugar beet, rice and rye) in Europe using a systematic review (SR) and meta-analysis of data reported in 67 original publications from an initial screening of 1424 studies. Whilst similar studies exist for Africa and South Asia (Roudier et al., 2011; Knox et al., 2012), surprisingly, no such comparable synthesis has been undertaken for Europe. Our study focussed on the biophysical impacts of climate change on productivity (i.e. yield per unit area) and did not consider ‘food production’ as this is dependent on many ‘non-biophysical’ factors, such as international trade policy and world markets. The data relate to the projected mean yield variations for each crop type, for all crop models, all GCM models and all time slices.For Europe, most studies projected a positive impact on yield; the reported increases largely being due to rising atmospheric CO2 concentrations enhancing both productivity and resource use efficiencies. Overall, a mean yield increase of +14% was identified, but with large differences between individual crops (e.g. wheat +22%; potato +12%) and regions (e.g. northern Europe +17%; southern Europe +7%). It is important to note that projected yield data were not available for all crops in all regions, so lack of a significant response may in part be due to the absence, or limited number of studies for certain crops and/or regions. Furthermore, the results include all reported yield projections, for all time slices, for all GCM combinations (whether single or ensemble) and for all crop modelling approaches (whether based on simple statistical trends or more complex biophysical modelling approaches). This highlights the magnitude of variability that exists when all possible sources of uncertainty are included. Further statistical analyses were conducted to disaggregate the data by time slice, climate and crop model to identify which factors were likely to contribute most to yield variations and uncertainty.The SR showed that evidence of climate change impacts on crop yield in Europe is extensive for wheat, maize, sugar beet and potato but very limited for barley, rice and rye. Interpreting the reported yield observations was compounded by ‘effect modifiers’ or reasons for heterogeneity. These included different emission scenarios and climate ensembles, implicit assumptions regarding crop varieties, the agricultural systems studied, and assumed levels of mechanization and crop husbandry. Despite its limitations, the SR helps identify where further research should be targeted and regions where adaptation will be most needed. It confirms that climate change is likely to increase productivity of Europe’s major agricultural cropping systems, with more favourable impacts in northern and central Europe. No Label
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