| Home | << 1 2 3 4 5 6 7 8 9 10 >> [11–17] |
|
Reidsma, P., Janssen, S., Jansen, J., & van Ittersum, M. (2017). On the development and use of farm models for policy impact assessment in the European Union – A review. Agric. Syst., 159, 111–125.
Abstract: • Evidence use in EU Impact Assessment reports is limited. • Many scientific studies used farm models for assessment of policies in the EU. • Scientific challenges include understanding farmer decision-making and interactions. • Model codes and data should be published, including evaluation. • Stronger science-policy interaction is required. Farm models are potentially relevant tools for policy impact assessment. Governments and international organizations use impact assessment (IA) as an ex-ante policy process and procedure to evaluate impacts of policy options as part of the introduction of new policies. IA is increasingly used. This paper reviews both the use of farm models in such policy IAs in the European Commission, and the development and use of farm models for policy IA by the scientific community over the past decade. A systematic review was performed, based on 202 studies from the period 2007–2015 and results were discussed in a science-policy workshop. Based on the literature review and the workshop, this paper describes progress in the development of farm models, challenges in their use in policy processes and a research and cooperation agenda. We conclude that main issues for a research agenda include: 1) better understanding of farmer decision-making and effects of the social milieu, with increased focus on the interactions between farmers and other actors, the link to the value chain, and farm structural change; 2) thorough and consistent model evaluation and model comparison, with increased attention for model sensitivity and uncertainty, and 3) the organization of a network of farm modellers. In addition, the agenda for science-policy cooperation emphasizes the need for: 4) synthesizing research evidence into systematic reviews as an institutional element in the existing science-policy-interfaces for agricultural systems, 5) improved and timely data collection, allowing to assess heterogeneity in farm objectives, management and indicators, and 6) stronger science-policy interaction, moving from a research-driven to a user-driven approach.
|
|
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
|
|
Höglind, M., & the partners of LiveM task L1.3. (2017). Bringing together grassland and farm scale modelling. Part 1. Characterizing grasslands in farm scale modelling (Vol. 10).
Abstract: This report provides an overview of how grasslands are represented in six different farmscale models represented in MACSUR. A survey was conducted, followed by a workshop in which modellers discussed the results of the survey, and identified research challenges and knowledge gaps. The workshop was attended by grassland as well as livestock specialists. The investigated models differed largely with respect to how grasslands were represented, e.g. as regards weather and management factors accounted for, spatial and temporal resolution, and output variables. All models had grassland modules that simulate DM yield and herbage N content (or crude protein (CP) content = N content x 6.25). Many models also simulate P content, whereas only one simulate K content. About half of the model simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter digestibility. Critical input data required from grassland models to simulate ruminant productivity and GHG emissions at farm scale was identified by the workshop participants. The different types of input data required were ranked in order of importance as regards their influence on important system outputs. For simulation of ruminant productivity and GHG emissions, herbage DM yield was ranked as the most important input variable from grassland models, followed by CP content together with at least one variable describing herbage fibre characteristics. These findings suggest that work on improving the ability of the current grassland models with respect to simulation of fibre/energy should be prioritized in farm-scale modelling aiming at quantifying livestock production and GHG emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models.
|
|
van Middelkoop, J. C., & Kipling, R. P. (2017). Modelling the impact of climate change on livestock productivity at the farm-scale: An inventory of LiveM outcomes (Vol. 10).
Abstract: The report presented here provides an inventory of reports and conference papers produced by the partners of the livestock and grassland modelling theme (LiveM) of the Modelling European Agriculture with Climate Change for Food Security (MACSUR) knowledge hub. The findings presented illustrate the diverse nature of the multidisciplinary LiveM research community, and provide a reference source for those seeking to identify and pull out farm-level modelling outputs from the work of MACSUR and its partners. The survey of farm-scale outputs from LiveM revealed the interdependent, dual role of a knowledge hub: to increase the capacity of modelling to meet stakeholder and societal needs under climate change, and to apply that increased capacity to provide new understanding and solutions at the policy and (the focus here) farm scale. While capacity building work across disciplines is time-consuming, difficult, and to a large extent invisible to stakeholders, such work is vital to ensuring that subsequent scientific outcomes reflect best practice, and integrated expertise. Long term, sustained funding of network-based capacity building activities is highlighted as essential to ensuring that the farm-scale modelling work highlighted here can continue to build on ongoing improvements in model quality, flexibility and stakeholder relevance.
|
|
Topp, K., Eory, V., Bannink, A., Bartley, D. J., Blanco-Penedo, I., Cortignani, R., et al. (2017). Modelling climate change adaptation in European agriculture: Definitions and Current Modelling (Vol. 10).
Abstract: Confidential content, in preparation for a peer-reviewed publication.
|