FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
Not a member yet
471 research outputs found
Sort by
Modelling heat stress on livestock: how can we reach long-term and global coverage?
Full text linkConference presentation PD
TradeM International Workshop 2016 »Assessing climate change adaptation and mitigation options«
Full text linkAims and objectiveIt is well established that Europe will face considerable regional differences with regard to climate change. This requires the regional dimension of climate change for a spatially diverse European agriculture to be better understood. Studies of policies that enhance resilience in the food sector and that formulate policy recommendations have to take into account the spatial nature of agriculture and the regional dimension of climate change. The workshop will focus on applications and methodological advancements.The event has three major goals:(i) to discuss adaptation and mitigation options of agricultural systems under climate change(ii) to study and assess regional approaches implementing adaptation and mitigation options in agriculture(iii) to advance policy implications of climate change for agriculture and food securityKeynote speakersPeter WehrheimHead of Unit “Land Use and Finance for Innovation”, European Commission, DG Climate ActionAlan MathewsProf. em. Trinity College, DublinEric NævdalSenior Research Fellow, Frisch Centre at the University of OsloIgnacio Perez DominguezSenior Researcher, Institute for Prospective Technical Studies (IPTS), JRC SevilleResultsTwenty-five people attended. The workshop started with an introduction to Arctic ecology and regional development. Four keynote speakers from policy, science and JRC gave a great mixture of high quality input into the workshop. It fuelled the discussions and was well appreciated by the participants. Fifteen very interesting and engaging presentations throughout the workshop showed that CC mitigation is a very important research undertaking, that LULUCF in crop and animal production play an important role, and that the role of agriculture in the CC policy debate is high on the agenda
Stakeholder engagement and the perceptions of researchers: How agricultural modellers view challenges to communication
Full text linkConference presentation PD
LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change
Full text linkFood and nutrition security in Europe – a quantification of multi-stakeholder scenarios
Full text linkConference presentation PD
Assessing the impact of agro-climatic factors and farm characteristics on the yield variation of the Norwegian fruit sector
Full text linkMain drivers of ag. yields:–Technology–R&D (new hybrids etc.)–Weather–Etc.•Common sense and anecdotal observations (remember the Tromsø presentation) revealed extreme events tended to impact wide geographic areas•This was called the «systemic» nature of agriculture No semi-aggregation farm-level•Not the boring corn, maize, wheat fruits•No OLS-like Pearson correlation or functional form approach for conditioning spatial correlations on weather SDM•Finally, if we are smart enough to set the explanatory proxies in a meaningful way presumably we can make the distinction between the effects of, say draught and extreme heat.•And much more in policy relevance
Report on results of application of scaling methods for integrated modelling
Full text linkDefining and estimating uncertainty in simulations is essential in order to quantify the reliability of the outcomes or when model improvement is sought. Several general definitions of uncertainty are given for model-based simulations. By defining the uncertainty from different sources, these can be quantified and assessed separately, as well as eventually their absolute or relative contribution to the total uncertainty. Therefore, different types and sources of uncertainty are given. Furthermore, the choice of method when assessing the uncertainty of a given simulation may depend on the purpose and the type of uncertainty to be assessed. Approaches of assessing uncertainty in process-based models are described in general and more specifically for crop models. As a simplistic method, the already established approach of variance decomposition is suggested.The report contains parts of published papers, therefore only the abstract is made available
EU Impact analysis on GHG-emission proposal: Focus on agriculture
Full text linkOutlineMotivation & background work at the JRCEU agricultural emissions in perspectiveMethodology: CAPRI ModelScenario assumptionsMain resultsLimitationsConclusionsConclusions•Without further action, agricultural GHG emissions in the EU-28 are projected to decrease by 2.3% by 2030 compared to 2005.•The setting of GHG emission reduction obligations for the EU agriculture sector without financial support shows important production effects, especially in the EU livestock sector•The decreases in domestic production are partially offset by production increases in other parts of the world (leakage)•Adverse effects on EU agricultural production and emission leakage are significantly reduced if subsidies are paid for the application of technological emission mitigation options… however, with considerable budgetary costs to trigger adoptio
Improved crop modelling for supporting policy design on climate change impacts, adaptation and mitigation — CropM in MACSUR
No full textClimate change conditions the resource base upon which agricultural production is based, and, as such, it as such also severely impacts the ecosystem services delivered by agricultural production systems. Therefore, climate change considerations are relevant for most of the agriculture-related policies, including the Common Agricultural Policy, the Climate Change Policy, and environmental protection policies such as the Water Framework, Groundwater, and Habitat directives. These policies will require relevant changes in the near future because of how climate change will interact with agricultural production systems, and the FACCE-JPI knowledge hub MACSUR provides some of the knowledge base on which to build such policy changes.Assessing climate change impacts and adaptation and mitigation options in European agriculture requires the use of a range of models (crop, livestock, economic) and the integration of their results. By joining the work of many (> 40) European research groups in MACSUR, substantial progress in improving and applying models for assessing climate change impacts and adaptations in crop production has been made that can effectively assist policy design and recommendations. The results from such MACSUR research formed part of the scientific bedrock of the COP21 agreement in Paris in 2015. Without MACSUR, the significant European agricultural contribution to the IPCC and UNFCCC would not have been evident.Areas of progress include the use of crop model ensembles, improved scaling methods, better uncertainty assessment, data generation and model improvements for better capturing effects of extreme and adverse weather, development of context-sensitive adaptive strategies. These efforts have led to robust assessments of climate change impact on crop production and associated effects on ecosystems.The crop modelling community is now ready for conducting a comprehensive assessment of climate change impacts, and to identify adaptation and mitigation options for Europe at multiple scales, in order to play an active role for shaping future European agricultural, environmental and climate policies
Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions
Full text linkCrop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016