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Rivington, M.; Wallach, D. |
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Information to support input data quality and model improvement |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C4.2.4 |
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Data quality is a key factor in determining the quality of model estimates and hence a models’ overall utility. Good models run with poor quality explanatory variables and parameters will produce meaningless estimates. Many models are now well developed and have been shown to perform well where and when good quality data is available. Hence a major limitation now to further use of models in new locations and applications is likely to be the availability of good quality data. Improvements in the quality of data may be seen as the starting point of further model improvement, in that better data itself will lead to more accurate model estimates (i.e. through better calibration), and it will facilitate reduction of model residual error by enabling refinements to model equations. This report sets out why data quality is important as well as the basis for additional investment in improving data quality. No Label |
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MA @ admin @ |
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2103 |
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Heinschink et al. |
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Index-based costs of livestock production (INCAP.l) in Austria – the suckler cow and beef calf production activity |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-1 |
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Conference presentation PDF |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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no |
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MA @ admin @ |
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4846 |
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Liu, X. |
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Incentivising for climage change mitigation in the context of adaptation to climate and market changes at the farm level in North Savo region |
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2015 |
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FACCE MACSUR Reports |
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4 |
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SP4-10 |
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Authors: Lehtonen, H., Liu, X. & Purola, T. No Label |
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TradeM International Workshop 2014 »Economics of integrated assessment approaches for agriculture and the food sector«, 25–27 November 2014, Hurdalsjø, Norway |
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MA @ admin @ |
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2200 |
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Britz, W. |
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Importance of considering crop management adaptation in CC impact studies: A Pan-European integrated assessment |
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2015 |
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FACCE MACSUR Reports |
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4 |
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SP4-4 |
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No abstract. No Label |
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TradeM International Workshop 2014 »Economics of integrated assessment approaches for agriculture and the food sector«, 25–27 November 2014, Hurdalsjø, Norway |
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no |
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MA @ admin @ |
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2194 |
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Helming, J. |
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Implementation of the GTAP emission database in MAGNET; applications at European and global scales |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-21 |
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World agriculture accounts for approximately 14% of all anthropogenic greenhouse gas. The share of agriculture in total greenhouse gas emissions in the EU 28 increased from 8.7% in 2007 to about 10.3% in 2012. This includes methane and nitrous oxide emissions (European Environment Agency; Gugele et al., 2005; Beach et al., 2008). This increase is mainly explained by emission reductions in the rest of the economy. Reductions in greenhouse gas emissions from agriculture remained limited in the recent past.Options to reduce emissions in agriculture depends on macro-economic trends, including international trade, agricultural policies, economic growth and consumption patterns. Global trade patterns will affect the regional distribution of agricultural production and the corresponding greenhouse gas emissions. The ability to introduce cost-effective measures to reduce greenhouse gas emissions are difficult to assess on a global scale. To tackle this problem there is a need for an interdisciplinary model instrument, in which both knowledge from macro and trade economy and natural sciences are included.The global equilibrium model MAGNET (Modular Applied GeNeral Equilibrium Tool) is developed by LEI and is an adaptation to the GTAP model (Woltjer & Kuiper, 2014). The main purpose of MAGNET is to provide a globally applied general equilibrium modelling framework, having the standard GTAP model as the core. MAGNET is complemented with the greenhouse gas emission dataset for the year 2007 that is made available by the GTAP consortium. The database includes emissions of carbon dioxide (CO2), nitrous dioxide (N2O) and methane (CH4). N2O and CH4 emissions are especially relevant for the agricultural sector. The incorporation of these emissions in MAGNET enables us to analyse current and future greenhouse gas emissions under different policies and mitigation measures on a global scale, simultaneously taking into account interactions between the rest of the economy (by sectors) and across regions in the world.The GTAP emissions dataset estimates the share of European agriculture in total greenhouse gas emissions in the EU 28 to be about 11.5% in 2007. This deviates from total emission figures on Europe as presented by the European Environment Agency (EEA). The presentation will focus on some possible explanations for this difference. We will compare gaps in the dataset in agriculture and the rest of the economy. Next we will report the emission per EU member state in a 2020 baseline scenario. Here we will present percentage differences in changes in greenhouse gas emissions in 2020 vis-a-vis a baseyear in 2012. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2136 |
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