JRC-AL: EC4MACS kickoff, IIASA 6.3.2007 - 1 GHG-AFOLU and EC4MACS Adrian Leip Joint Research Centre Institute for Environment and Sustainability Climate.

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Presentation transcript:

JRC-AL: EC4MACS kickoff, IIASA GHG-AFOLU and EC4MACS Adrian Leip Joint Research Centre Institute for Environment and Sustainability Climate Change Unit

JRC-AL: EC4MACS kickoff, IIASA Approach in CAPRI-DynaSpat CAPRI – economic model with regional environmental indicators → Ammonia volatilization, → IPCC approach for GHG emissions, … CAPRI – economic model with sub-regional environmental indicators → Full GHG balance in livestock/crop production systems → Nutrient balances, … 1.Downscaling to 1 km pixels 2.Linkage to process-based models

JRC-AL: EC4MACS kickoff, IIASA Agricultural land use map area of interest: EU27 30 Crops: Common wheat, Durum Wheat, Barley, Rye, Oats, Maize, Rice, Other cereals, Potatoes, Sugar beet, Other root crops, Sunflower, Rape and turnip rape, Soya, Fibre and oleaginous crops, Tobacco, Other non permanent industrial crops, Dry pulses, Tomatoes, Other fresh vegetables, Floriculture, Fodder other on arable land, Fallow land, Fruit tree and berry plantations, Citrus fruits, Olive groves, Vineyards, Nurseries, Other crops, Permanent grass and grazing temporal reference: year ~2000, linked to CAPRI scenarios resolution: 1km by 1km STEP1: Delineate Homogenous Spatial Mapping Units -HSMU’s- i.e. areas with similar environmental conditions (Soil Mapping Unit, Corine, FSS-NUTS, slope) → ~ HSMU’s in EU15) STEP2: Construct models (binomial logit) to estimate “crop shares” at observation points (LUCAS, ESTAT) in different land cover classes (Corine2000) and regions (explanatory variables soil, climate, topography) STEP3: Estimating the shares of the individual crops in the HSMU’s consistently with ESTAT statistics at NUTS 2 level (highest posterior density)

JRC-AL: EC4MACS kickoff, IIASA Agricultural Land Use Map – Results I BarleyGrassland

JRC-AL: EC4MACS kickoff, IIASA Agricultural Land Use Map – Results II SoftwheatMaize

JRC-AL: EC4MACS kickoff, IIASA Applications Biodiversity index → Biodiversity indicator (CAPRI-DynaSpat) → Energy use in Agriculture (CAPRI-DynaSpat) → Link to Land-Use Change model (CLUE) (NitroEurope) → Carbon sequestration modeling (CarboEurope) → Nitrogen and Carbon interactions (NitroEurope) → Cross Compliance Impact Assessment (CCAT) → Pest Risk Assessment (PRATIQUE) → Rural Development indicator (RWER) → Extension to forestry → Extension to livestock production →…→…

JRC-AL: EC4MACS kickoff, IIASA Changsheng Li, 1992, 2000, …

JRC-AL: EC4MACS kickoff, IIASA CAPRI-DNDC Data Flow EUROSTAT national/regional statistics Disaggregation of land use into HSMU Production statistics estimation (HSMU level) MARS potential yield estimates A priori estimates (environmental parameters, LUCAS observation points) FAO/IFA national fertiliser use by crop, MAFF soil nutrient status Nitrogen losses - partitioning DNDC maximum yield HSMU aggregation DNDC-EUROPE: simulation of environmental indicators Input parameter estimation for a closed nitrogen balance (soil surface, HSMU level) DNDC input files CAPRI DNDC Complete and consistent set of agricultural production statistics at the NUTS 2 level

JRC-AL: EC4MACS kickoff, IIASA DNDC Emission Scenarios – Common Wheat N2O/N2 RatioLeached NN-N2O No nitrogen application Mineral fertilizer+ manure nitrogen application max > 10 kg N/ha/yearmax > 50 kg N/ha/yearmax >3

JRC-AL: EC4MACS kickoff, IIASA DNDC Emission Scenarios – Barley N2O/N2 RatioLeached NN-N2O No nitrogen application Mineral fertilizer+ manure nitrogen application max > 10 kg N/ha/yearmax > 50 kg N/ha/yearmax >3

JRC-AL: EC4MACS kickoff, IIASA Pathway of nitrogen losses weighted average of all crops simulated [kg N/ha/year] LeachingNH3NON2N2O Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Portugal Spain Sweden UK EU

JRC-AL: EC4MACS kickoff, IIASA Possible interactions → Improve environmental indicators in CAPRI → Link to GAINS Stratification of emission factors, fractions or functions? Stratification of abatement technology efficiency Evidencing interactions / side effects of activities or measures Can acceptance be improved if based on validated model? → Improved input data for modeling (e. g. soil carbon) → Zooming into area of focus (e.g. mountain areas, NVZ, NATURA2000) → Potential to include local policies? → Link to nitrate leaching → More attention to crop growth module  impact assessment → Comparison of land use map with SEI land cover map