Abstract: The spatial distribution of future European landuse will be influenced by yield changes arising from climate change and changes in profitability as a consequence of socio-economic change (arising from changing food demand; prices; technology etc).  To understand how these factors affect future land use allocation, a modelling system has been set up to predict agricultural land use across the EU under any scenario set of climate and socio- and techno-economic data. Metamodels of crop and forest yields, and optimal cropping and profit are derived from the outputs of the IMPEL, GOTILWA+, SFARMODand WaterGAP models. Profitability of each possible land use is modelled across the EU, assuming that use will change to the most profitable in the timescale being considered (2050). Land use in a grid is then allocated based on profit, with minimum profit thresholds set for intensive agriculture (arable or grassland), extensive agriculture, managed forest and finally unmanaged forest or unmanaged land.  The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available.  The model iterates prices until demand is satisfied (or cannot be met) and basin water usage for irrigation is not more than is available.This presentation describes the application of the modelling system across future climate change uncertainty space (as given by 60 combinations of downscaled 10’x10’ gridded climate outputs from 5 Global Climate Models, 3 climate sensitivities and 4 emissions scenario) under both baseline and four future socio-economic scenarios to identify those areas of Europe in which the spatial allocation of agricultural landcovers are robust to this uncertainty. No Label

Abstract: 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

Abstract: The dairy sector may expand by over 2% per annum with expiration of the milk quota system in countries with a major and intensive dairy sector. Such expansion will increase pressure to further reduce on-farm nitrogenous emission per unit of milk produced even more. A straightforward N-reducing measure is the manipulation of the cow diet resulting in a lower excretion of ammoniacal N excreted with urine in particular. However, dietary N-reducing measures also affect enteric methane emissions and P excretion. For an integral evaluation of the consequences of N-reducing dietary measures on on-farm emissions, the trade-offs between N emissions and P and methane emissions at the cow level need to be taken into account. Therefore, a simulation study was performed to simulate the consequence of various N-reducing and/or P-reducing dietary measures (altered grassland management, grass silage replaced by low-N feeds, increased concentrate allowance) on enteric methane emission and on N and P excretion. Results indicate a large scattering, but there was a trend of higher methane emissions with lower N excretion was significant. Specific measures had a synergistic effect on emissions such as the exchange of maize for grass silage. The present detailed model evaluations may aid in quantifying the extent of trade-offs between various types of emissions at the cow level, but also prove to be relevant when evaluating consequences of management options taken at the farm scale. No Label