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Bojar, W. (2013). Factsheets of the models (Vol. 1).
Abstract: The exploration of adaptation and mitigation measures in the context of global challenges like climate change, food security and expected demographic boom is an field of research of growing importance. Over the last decades many research groups have been developing economic-trade models to analyse consequences on farm welfare, market supply and trade, some of them also address food security and other global concerns. There are many different ways to tackle these issues and the specific advantages and limitations of alternative modelling strategies are not yet well understood. The objective of the WP1 T1.1 task within TradeM theme of MACSUR is to use the results of a survey on trade and economic models of MACSUR Consortium partners to show which topics are currently addressed in the different models, which methods are used and how well these tools are prepared for an integration with other models like climate, crop and livestock models. This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 – P100 PARTNER No Label
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Bojar, W. (2013). MACSUR TradeM Workshop Exploring new ideas for trade and agriculture model integration for assessing the impacts of climate change on food security (Vol. 1).
Abstract: The first TradeM workshop was held at Haifa University (Israel), 3-5 March 2013. It was a state-of-the-art Workshop ‘Economic Modelling on Agriculture with Climate Change for Food Security’. Sixteen papers are presented, following a call for abstracts submitted in December 2012. Presented, reviewed and discussed models, their inputs, outputs and main results of case-study analyses let indicate of how the model can be used to analyze the impacts of climate change on food security, how the model can contribute to, and benefit from other economic and/or crop and livestock models and what input is needed from CropM and LiveM. There were explored ideas for closer integration and linkage between agriculture and economic models and between economic models at different levels, addressing issues of model structure, scale and data processing. Focus was on model comparison, gap analysis, scientific advancements and improvements. We also addressed the key challenges of the economic models (macro- versus micro-economics; uncertainty versus risks; variability and distribution), and identified ways to cope with scaling, uncertainty, risks. The workshop let identify the requirements from CropM and LiveM, find policy questions that MACSUR is going to address, start with the content of the case studies and plan for publication of scientific papers. The sessions were broadcast live via the internet. Twenty-four registered participants and about 65 local visitors attended the workshop.This work was co-financed by NCBiR, Contract no. FACCE JPI/04/2012 – P100 PARTNER No Label
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Rolinski, S., & Sætnan, E. (2013). Uncertainties in climate change prediction and modelling (Vol. 1).
Abstract: As models become increasingly complex and integrated, uncertainty among model parameters, variables and processes become critical for evaluating model outcomes and predictions. A framework for understanding uncertainty in climate modelling has been developed by the IPCC and EEA which provides a framework for discussion of uncertainty in models in general. Here we report on a review of this framework along with the results of a survey of sources of uncertainty in livestock and grassland models. Along with the identification of key sources of uncertainty in livestock and grassland modelling, the survey highlighted the need for a development of a common typology for uncertainty. When collaborating across traditionally separate research fields, or when communicating with stakeholders, differences in understanding, interpretation or emphasis can cause confusion. Further work in MACSUR should focus on improving model intercomparison methods to better understand model uncertainties, and improve availability of high quality datasets which can reduce model uncertainties. No Label
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Braunmiller, K., & Köchy, M. (2013). Grassland datasets (Vol. 1).
Abstract: In the MACSUR project, there are several grassland models in use that were designed for and adjusted with data from different climatic regions. To be able to run these modelsfor a wide geographical range, there is a need to validate and calibrate them on the same basis.Therefore, a high-quality dataset is needed, which includes a wide range of climatic conditions, management systems and other variables.Through this search 23 grassland related institutes from eleven countries were found and contacted, where 12 of them responded to the request. Nine institutes from cooler (e.g. Finland) and warmer regions (e.g. Israel) are now willing to provide their experimental data. One contributor is even planning to join the project bringing its own grassland model.These new grassland datasets cover in addition to already available ones (Fig. 1) a wide range of climatic regions for a substantiated calibration and validation of the models. Data supplied by the institutes have been checked for internal consistency and cast into a common format. The data have been passed on to WP L2 (Model intercomparison on climate change in relation to livestock and grassland). No Label
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Köchy, M. (2013). Maps of grasslands in Europe (Vol. 1).
Abstract: Modelling of climate effects on agriculture and food security at the European scale requires a harmonized spatially, explicit database of European land use. It can be used for scaling results of point models to an area. A recent review of land cover maps focused on the global scale (Köchy, 2010). European land use as a subset of global land use is contained in the product GlobCover representing the year 2009 with a resolution of 0.3 km. A European product is the CORINE data set with a resolution of 100 m and a minimum mapping unit of 25 ha representing the year 2006 (version 16, European Environmental Agency, 2012). For scaling the results obtained for individual points to larger regions one needs fine-grained maps using the same categories as represented by the sample points. The CORINE map of pasture cover (Fig. 1) has the advantage of being very fine-grained and the classification being supervised. The visual differences to coarser maps of cover matched to census (Fig. 4), however, indicate, that none of the existing maps is reflecting reality perfectly. Since MACSUR will likely work with official national statistics it may be preferable to use one of the census-calibrated maps. For a better match, official EU spatial reporting schemes may be used at a grain that ensures data privacy of the land owners. No Label
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