INSEA 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 Integrated Sink Enhancement Assessment

INSEA

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? to determine (spatially, trend) emissions and sinks to link GHG emissions and C sequestration to agricultural and forestry activities Ecological approach link

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

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

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

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

WP 3000 structure

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)

WP 3200  Auxiliary Data 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 parameter identification definition of what is “bottom”: management unit access FADN (Farm Accounting Data Network), LUCAS, INVECOS Requirements

WP 3300  Auxiliary Data LULUCF data: C sequestration rates, CO2, CH4 and N2O emission factors (most likely non- representative) Model input data 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 Requirements

WP 3400  Integration 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 Data harmonisation (INSPIRE standards) Results from ongoing data needs from the models connect spatially at the common (smallest) denominator: EU grid (50x50 km) Requirements

WP 3500  Web Portal Example: CarboDat

WP 3000 Work Package Structure

Model Overview (1)

Model Overview (2) mesomicro macro

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

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

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

micro EFEM – Economic Farm Emission Model 4

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

meso2 AreaAnimal Feeding Animal numbers N2O agricultural soils (synthetic fertilizers) X (N use) N2O agricultural soils (crop residues and N-fixing crops) X (N use) N2O agricultural soils (manure applied to soils) X N2O agricultural soils (animal production) X N2O manure management X CH4 manure management X CH4 enteric fermentation X(X) CH4 rice cultivation X Carbon sequestration (X) AROPAj model

meso3 Data (FADN) - Yields - Area - Variable costs - Producing activities - Size of farms - Altitude - … Other sources - Emissions coefficients - Soils characteristics - Fertilizer uses and prices - … Typology 15 countries, 101 regions 734 farm-types Model inputs - Prices - Technical parameters - CAP-related parameters Calibration 734 models Maximize gross margin Subject to : - Technical constraints - Policy constraints Model output - Optimal area - Livestock numbers - Animal feeding - Net emissions Estimation AROPAj model

meso4 Farm Type Country Region Crop Sources European Soil Map (1/10 ) 6 MARS Project JRC DataBase FADN : AROPAj calibrating procedure Manure Irrigation Sources FAO Eurostat Experts  Cultivars  N fertilizer type  Fertilization calendar  Others management crop data for STICS DataBas e soil climat Fertilizer prices AropaStix : Client-Server Architecture SERVER Oracle, MySql, PostGres, ….. Java Client Network in progress Binta Niang AROPAj model

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

soil Data resolution : field-size area - up to 100 ha Soil model: EPIC Possible Non-CO2 GHG abatement in the agricultural sector Major 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. Management components 1

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

Data needs EPIC Approaches SSCRI Approach I

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

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 Map production Input data maps (e.g. N fertilizer input, forest management types) Output data maps (e.g. N2O emissions in Europe) extrapolate into areas with little data coverage compare bottom-up/top-down using area statistics calculate upscaling errors/regional uncertainties