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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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Modelling European ruminant production systems: Facing the challenges of climate change |
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Report |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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L1.1-D1 |
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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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Heinschink, K.; Sinabell, F.; Tribl, C. |
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An index-based production costs system to evaluate costs of adaptation and mitigation in dairy and cattle farming |
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Journal Article |
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2016 |
Publication |
Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
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7 |
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03 |
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242-244 |
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2040-4700 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4869 |
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Sinabell, F. |
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Title |
Adaptation to climate change in the European agriculture: A new tool for explicit cost accounting |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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9 C6 - |
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Sp9-10 |
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farm structure in Austria and level of educationchallenges of more volatile markets / more uncertain yieldsmore uncertainty about revenues and costsspecialisation and liquidity problems – not alleviated by EU direct paymentspolitical measures: late, uncertain, no legal title, wrong incentivestax credits – not relevant in Austria for most farmsprice hedging instruments steep learning curve and intransparent marketsmost frequently used: service of buying co-operatives control of accumulation risksdetails of contract are attractive for farmerse.g. monthly benefits for milk producersbenefits at the time of sale for pig, piglet, grain producerscombination with production risk insurance with discountsgovernment support during introduction period / as a new policy instrumentmarketing and sales: wholesale buyers / dairies / producer organisations offer margin insurance as a service |
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no |
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MA @ admin @ |
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4860 |
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Sinabell, F. |
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Yield potentials and yield gaps in soybean production in Austria – a biophysical and economic assessment |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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9 C6 - |
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Sp9-11 |
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context of analysis:• stakeholders. policy relevance: CC and protein crops• research problem:• how large is the yield gap and what can be done• data• approaches• findings• discussion and outlook yield gap analysis is a daunting task• what can be learned• economics matters: prices of crop and other crops• land expansion: more land becoming more marginal• management matters a lot but – not directly observable in data• significant knowledge gaps still there• way forward:• look at other crops• explore options to improve management |
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MA @ admin @ |
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4829 |
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Banse, M.; Brouwer, F.; Palatnik, R.R.; Sinabell, F. |
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Title |
The Economics of European Agriculture under Conditions of Climate Change (Editorial) |
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2014 |
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German Journal of Agricultural Economics |
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German Journal of Agricultural Economics |
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63 |
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3 |
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131-132 |
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This Special Issue on “The Economics of European Agriculture under Conditions of Climate Change” brings together a selection of papers that contribute to the understanding of recent developments related to agriculture and climate change in four European coun- tries. The focus of the Special Issue is on quantitative modeling and empirical analyses. The papers presented here not only cover the heterogeneity of agriculture in Europe with case studies from the Mediterranean (Italy), central (Austria) and north-western Europe (Ireland and Scotland) but also give insights into the diversity of quantitative modeling approaches in agriculture. |
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English |
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Editorial material |
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TradeM, ft_macsur |
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no |
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MA @ admin @ |
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4763 |
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