Köchy, M. (2015). FACCE MACSUR Joint Workshops 2015 (Vol. 7).
Abstract: FACCE MACSUR comprises many different groups whose work contribute to improving the European capacity of modelling agriculture with climate change and providing an assessment of these impacts for stakeholders. Some groups work on methodological issues in a single discipline, others work on cross-disciplinary concepts. The meeting provided an opportunity for the members of the groups to meet for intensive discussions and exchange of ideas, which is not as easily done in phone or video conferences. Various groups also met with each other to agree on work plans and common settings for research. Overall, 105 researchers attended the workshops. For coordinating work with the global program AgMIP, AgMIP’s principle investigator John Antle attended the meeting and, meeting in a video call, coordination teams of MACSUR and AgMIP agreed to continue the successful collaboration in the future. Major overarching outcomes of the meetings were agreements on policy and climate scenarios recommended to be used within MACSUR, development of an approach to quantify effects of extreme climatic events on socio-economic indicators, and closer collaboration among several groups at the level of regional case studies.
|
Schmid, E. (2017). Integrated land use modelling — a course for doctoral students (Vol. 10).
Abstract: The course on “Integrated land use modelling” took place at BOKU Vienna between 24. – 28. April 2017. It was a five-days course capturing many aspects in quantitative integrated land use modelling using GAMS (see course outline). 10 students have participated the course coming from several countries. Students finishing the course have received 3 ECTS points. The course was offered by BOKU and the Doctoral Certificate Program in Agricultural Economics (https://www.agraroekonomik.de/index.html ).
|
Kipling, R., Scollan, N., Bannink, A., & van Middelkoop, J. (2016). From diversity to strategy: Livestock research for effective policy in a climate change world (Vol. 8).
Abstract: European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. ï‚· Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination.
|
Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions (Vol. 8).
Abstract: Crop 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)
|
Hutchings, N., & Kipling, R. (2014). Inventory of farm-scale models within LiveM (Vol. 3).
Abstract: The aim of WP3 is to improve the assessment of the impact of climate change on livestock and grassland systems at the farm-scale. The first step in this process is to understand the current state of the art in farm-scale modelling, and the resources available within the MACSUR knowledge hub. Here, an inventory of the farm-scale models available within LiveM is presented, along with a summary of the types of model represented. Thirteen farm-scale models were identified, three of which focus on environmental aspects of farm systems (GHG emissions etc.) and ten of which focus on management strategies (productivity, economics etc.).
|
