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INSEA

INSEA. Integrated Sink Enhancement Assessment. Integrated economic and environmental assessment of climate change mitigation options (LULUCF) Integration of farm-level and forest plot-level models with regional and national models. INSEA. Ecological approach.

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INSEA

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  1. INSEA Integrated Sink Enhancement Assessment • Integrated economic and environmental assessment of climate change mitigation options (LULUCF) • Integration of farm-level and forest plot-level models with regional and national models

  2. INSEA

  3. Ecological approach • to determine (spatially, trend) emissions and sinks • to link GHG emissions and C sequestration to agricultural and forestry activities link Economic approach • to estimate abatement costs: • how much does it cost to farmers to reduce emissions? • Total and marginal costs • to assess the potential of mitigation policies • how much emissions can be expected from the use of policy instruments (emission tax, input tax, quotas,…)? • to capture the heterogeneity of abatement costs • where (who) will abatement occur for a given level of incentive?

  4. Strategy of the Commission (1) LAND USE AND SUSTAINABLE DEVELOPMENT S.D. STRATEGIES FOR SENSITIVE REGIONS SCENARIOS SUSTAINABLE IMPACTASSESSMENT COST-EFFECTIVENESSANALYSIS COST BENEFIT ANALYSIS INSEA MULTIFONCTIONAL ASPECTS Landscape Rural development Land use (infrastructures) Environmental protection Agriculture/Forests INSEA INTEGRATED FRAMEWORKSANALYSIS • databases • models/tools for simulation/foresights PARTICIPATORY APPROACHES INSEA EXTERNALITIES & THRESHOLDS of SUSTAINABILITY

  5. Strategy of the Commission (2) AGRICULTURE AND SUSTAINABLE DEVELOPMENT Micro Macro • FARMING SYSTEMS • CHARACTERIST. and BENCHMARKING (SD aspects) • Environment technologies • BEHAVIOURAL CHANGES • EXTERNALITIES • STRATEGIES • LAND USE STRAT. • RURAL DEVELOPM. • PUBLIC GOODS STRAT. • INTERNATIONAL COOPERATION DIM. BOTTOM UP INSEA INSEA TOP DOWN INSEA • MULTIFUNCT. • DEFINITION • MEASURING • TRADE OFF INSEA SUSTAINABILITY IMPACTASSESSMENTandGOVERNANCE

  6. Approach INSEA (1) Monitoring of Negotiations Bio-physical Model Database and Database Strategy Validation and Assessment Cost Model Scenario Model Policy implications

  7. WP 3000 Problems to solve • integrate socioeconomic & biophysical data, spatial & tabular information • create an ecosystem-based GIS • match different scales • maintain thematic and spatial consistency • develop interface with models • build common metadatabase

  8. WP 3000 structure

  9. WP 3100 • GIS coverages • overview: see CarboData (CORINE, SGDB, Topography, Climate, water catchments, etc.) • thematic maps: biomass (see ALTERRA report), soil (see JRC map on soil C) • thematic Maps to be produced in the project (such as litter fall, soil fertility index, N2O emissions – may be the product of 3300 depending on data availability)?or imported from related projects (e.g. CAPRI DynaSpat)

  10. WP 3200 • Auxiliary Data Requirements • parameter identification • definition of what is “bottom”: management unit • access FADN (Farm Accounting Data Network), LUCAS, INVECOS • Farm management/activity data: area statistics/ proportions (farm types, practices, crop production, etc.) • Additional data needed to define farm types with respect to emission factors: animal density, proximity to market, etc

  11. WP 3300 • Auxiliary Data Requirements • List of practices • Access IPCC emission factor data base (public) – check for completeness using national reporting • Results from ongoing research (see ECCP and TWG SOM task 5) • feed EPIC/DNDC • LULUCF data:C sequestration rates, CO2, CH4 and N2O emission factors (most likely non-representative) • Model input data

  12. WP 3400 • Integration Requirements • Data harmonisation (INSPIRE standards) • Results from 3100-3300 • ongoing data needs from the models • connect spatially at the common (smallest) denominator: EU grid (50x50 km) • create the links between data compilation and data utilization • Application of upscaling techniques • model-parameters/farm types need to sooner or later relate to soil+climate • spatially link activity data with auxiliary data (statistics) and LULUCF data

  13. WP 3500 • Web Portal • Example: CarboDat

  14. WP 3000 Work Package Structure

  15. Model Overview (1)

  16. macro meso micro Model Overview (2)

  17. Political background, economical data, farm structures Mechanisierungsverfahren Mechanisation techniques Grassland farming Arable farming Feeding module INPUT: Means of production, emissions OUTPUT: Products, emissions Animal husbandry Manure module N-cycle-N-yield model micro 1 EFEM – Economic Farm Emission Model

  18. Bauland/Hohenlohe Map of homogenous regions in Baden-Würrtemberg Albvorland/Schwäbischer Wald Unterland/Gäue Rhein/Bodensee Alb/Baar Schwarzwald Oberland/Donau Allgäu micro 2 EFEM – Economic Farm Emission Model

  19. micro 3 EFEM – Economic Farm Emission Model Database : agricultural census data Database : FADN Regional capacities, factoral capacities and extrapolation factors of farm types (VGG2= Rhein/Bodensee)

  20. micro 4 EFEM – Economic Farm Emission Model

  21. meso 1 AROPAj model Estimation of GHG abatement and carbon sequestration costs from agriculture Modular structure Yieldsfunctions • Animal « block » • cattle demographic balance • capital adjustment • feeding • Crop « block » • yields • fertilizers (N org. & min.) • use (market / on-farm) • Manure • CH4 • organic N Climatechangeadaptation • C sequestration • soils (change in practice, land use) • upper biomass (trees) • GHG • CH4 • N2O • NO, O3 ? • + C • Data resolution :FADN regionAdministrativeregions

  22. meso 2 AROPAj model

  23. meso 3 AROPAj model Model inputs - Prices - Technical parameters - CAP-related parameters • Data • (FADN) • - Yields • Area • Variable costs • - Producing activities • - Size of farms • Altitude • … • Other sources • Emissions coefficients • Soils characteristics • Fertilizer uses and prices • … Estimation Typology 15 countries, 101 regions 734 farm-types • 734 models • Maximize gross margin • Subject to : • Technical constraints • Policy constraints Calibration Model output - Optimal area - Livestock numbers - Animal feeding - Net emissions

  24. Binta Niang AropaStix : Client-Server Architecture in progress Sources Sources 6 DataBase • European • Soil Map (1/10 ) Oracle, MySql, PostGres, ….. • FAO • Eurostat soil Fertilizer prices Country • MARS Project JRC DataBase climat Region • Cultivars • N fertilizer type • Fertilization calendar • Others management crop data for STICS • Experts Farm Type • FADN : • AROPAj • calibrating • procedure • Manure Crop • Irrigation SERVER Network Java Client Java Client Java Client meso 4 AROPAj model

  25. macro Agripol model 8 agricultural activities • dairy livestock • non-dairy livestock • rice • cereals • pulses and oil seeds • roots and tubers • artificial pastures • biofuel. Data resolution :IMAGE (17 regions).FAO statistical data (38+2 Poles regions)

  26. soil 1 Soil model: EPIC Possible Non-CO2 GHG abatement in the agricultural sector Major components Management components • weather simulation • hydrology • erosion-sedimentation • nutrient and carbon cycling • pesticide fate • plant growth and competition • soil temperature • tillage • economics • plant environment control • crop rotations • tillage operations • irrigation scheduling • drainage • furrow digging • liming • grazing • burning operations • tree pruning • thinning and harvest • manure handling • fertilizer and pesticide application rates and timing. Data resolution :field-size area - up to 100 ha

  27. soil 2 Soil model: EPIC Hydrological Response Unit HRU = homogenous combination of soil/topography/climate/management

  28. Approaches SSCRI Data needs EPIC Approach I

  29. WP 3400 Work Approach (1) • Definitions • scale • farm type/practices • compile frame conditions of each model • Data availability • activity data (feasability: see AGRIPOL work plan) • LULUCF data (“external” research, EPIC) • Data base • compile model input data • compile model error budgets

  30. WP 3400 Work Approach (2) • Method development • expert matrix to connect data types identify site factors [soil/climate(topography)] for each farm type/practice if not available: derive (regional) productivity index from land use/EUROSTATS statistics and relate to mapped site factors • extrapolate into areas with little data coverage • compare bottom-up/top-down using area statistics • calculate upscaling errors/regional uncertainties • Map production • Input data maps (e.g. N fertilizer input, forestmanagement types) • Output data maps (e.g. N2O emissions in Europe)

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