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 IMPACTASSESSMENT
COST-EFFECTIVENESSANALYSIS
COST BENEFIT ANALYSIS
INTEGRATED FRAMEWORKSANALYSIS• databases• models/tools for simulation/foresightsPARTICIPATORY APPROACHES
S.D. STRATEGIES FORSENSITIVE REGIONS
MULTIFONCTIONAL ASPECTSLandscapeRural developmentLand use (infrastructures)Environmental protectionAgriculture/Forests
EXTERNALITIES &THRESHOLDS of SUSTAINABILITY
LAND USE AND SUSTAINABLE DEVELOPMENT
INSEA INSEA
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
Micro Macro
BOTTOM
UP
TOP
DOWN
AGRICULTURE AND SUSTAINABLE DEVELOPMENT
SUSTAINABILITY IMPACTASSESSMENT
andGOVERNANCE
INSEA
INSEA
INSEA
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 structureWP No WP title DB Topic Source and Scale
3000 Data and Database
3100 GIS coverages Biophysical andinfrastructural data
Vegetation (pre-industrial) CORINE/Bohn-Map: Pot. Nat. Vegetation
Land Cover (LC) CORINE
Soils ESGDB 1:1 Mio
Soil degradation ESGDB
Runoff 3-D, GTOPO30 Precipitation
Inner water bodies HYDRO1K Europe
Climate UK CU/MARSTopography (Geomorphology) 3-D, GTOPO30 Infrastructure 3-D, GTOPO30
3200 Agricultural Area Statistics/ socio-economic statistics
Compile metadata and defaults as model input
Forest Ownership/ownership structure
EUROSTAT, other EU projects
farm types EUROSTAT, Capri, MARS3300 GHG Data/
Management levelCompile metadata and defaults as model input
see table xx Network organisation and centralised data delivery: CarboEurope (CarboInvent, CarboAge, FORCAST, Greengrass, CarboData), domestic/regional cropland research, COST E21, COST 629, other domestic C sequestration projects
3400 Spatial Data Processing/Maps
Combination/merging of spatial LULUCFdata,and spatial connection with aggregated GHGand socio-economic information
3500 Public Data Portal web-map server with results, connected to CarboData or independent
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 3100-3300• 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
P r a c t ic e s G ISL a n d u se s ta tis t ic s
R esea rchD a ta
S o il M o d e lD a ta
E co n o m ic M o d e lD a ta
resea rch da ta (partly pub lished)
A c t iv ity D a ta
“ L U L U C FD a ta ”
IP C C -lik e e m is s io n fa c to r s E P IC /D N D C e m is iso n fa c to r s
C t/y e a r
E m is s io n s /re m o v a ls p e r a re a /re g io n
C ro s s-v a lid a tio nf ro m b o tto m -u p /to p -d o w n c o m p a ris o n
E m is s io n s /re m o v a ls p e r fa rm ty p e
+ pheno logy+ com lex m aps like the clim atic w ater ba lance+ ecoreions e tc .)
EU R O STAT N U TS II/N U T SIIIIN V E C O SLU C A SFA D N
N 2 O /y e a r
C O 2 /y e a r
C H 4 /y e a r
c o s t/re v e n u e s /y e a r
IP C C d e fa u lt v a lu e sR e se a rc h P ro je c ts (F o re s t R e se a rc h : C a rb o D a t)
+ la n d c o n v e rs io n (fo c u s A R D )
techn ica l da talabor, em ission fac tors,
c ro pg ra ssfo re s t
defin esc a le a n d fa rmty p e /p ra c tic e s
re la te toc lim a te , s o il ,
to p o g ra p h y, c o n -su m e r/p ro c e s s in g
m a rk e tin ha /reg ion
d a ta e n tr y
- s ca le- d a ta typ e s- u n d err ep re sen te d a re a
Model Overview (1)
Model Overview (2)
meso
micro
macro
microEFEM – Economic Farm Emission Model
Feeding module
Grassland farming
Animal husbandry
N-cycle-N-yield model
Manure module
INP
UT
: M
ean
s o
f p
rod
uct
ion
, em
issi
on
s
OU
TP
UT
: P
rod
uct
s, e
mis
sio
ns
Political background, economical data, farm structures
MechanisierungsverfahrenMechanisation techniques
Arable farming
1
micro
Rhe
in/B
oden
see
Sch
war
zwal
d
Alb/Baar
Allgäu
Oberland/Donau
Albvorland/Schwäbischer Wald
Unterland/Gäue
Bauland/HohenloheMap of homogenous regions in Baden-Würrtemberg
EFEM – Economic Farm Emission Model 2
microEFEM – Economic Farm Emission Model 3
Regional capacities,
factoral capacities and extrapolation
factors of farm types
(VGG2= Rhein/Bodens
ee)
Inte
nsi
ve li
vest
ock
(po
ult
ry)
Inte
nsi
ve li
vest
ock
(pig
s)
Fo
rag
e g
row
ing
(cat
tle)
Fo
rag
e g
row
ing
(sh
eep
)
Per
man
ent
Cro
ps
Reg
ion
al
Cap
acit
ies
VG
G2
Crop land ha 80,0 80,0 21,0 - 3,0 123.539
Permanent pasture ha - - 45,4 78,0 - 48.554
Orchards ha - - 0,0 - 5 15.985
Vineyards ha - - 0,0 - 3,6 11.596
Sugar beet ha 1,3 1,3 - - 1.512
Potatoes ha - - - - 0,5 1.658
Male cattle Cap. - - 19,0 - 20.289
Dairy cows Cap. - - 30,0 - 32.109
Suckler cows Cap. - - 6,2 - 6.594
Sheep Cap. - - - 700,0 36.100
Pigs for fattening Cap. - 150,0 - - 53.884
Breeding pigs Cap. - 35,2 - - 12.660
Laying hens Cap. 396,3 - - - 310.731
Broiler Cap. 13,0 - - - 10.230
Extrapolation factor Value 784,2 359,2 1070,3 51,6 3197,0
Database : FADN
Database : agricultural census data
microEFEM – Economic Farm Emission Model 4
EFEMEFEM DNDCDNDC
soil mapsoil map land use mapland use map climate dataN-depositionclimate dataN-deposition
GISdatabase
GISdatabase
crop areafertilizer intensityC + N of manure
crop areafertilizer intensityC + N of manure
C-balanceSOC, C-pools
emissions (soil)N2O, CH4, CO2
leachingNO3, DOC
C-balanceSOC, C-pools
C-balanceSOC, C-pools
emissions (soil)N2O, CH4, CO2
emissions (soil)N2O, CH4, CO2
leachingNO3, DOCleaching
NO3, DOC
farm emissionsN2O, CH4, CO2, NH3
return ratesshadow prices
mitigation costseconomic indicators
farm emissionsN2O, CH4, CO2, NH3
farm emissionsN2O, CH4, CO2, NH3
return ratesshadow prices
mitigation costseconomic indicators
return ratesshadow prices
mitigation costseconomic indicators
EFEM-DNDC managementphenology
managementphenology
farmstructures
farmstructures
politicalenvironment
politicalenvironment
economicindicatorseconomicindicators
emissionfactors
emissionfactors
meso
Data resolution :FADN regionAdministrativeregions
AROPAj model
Estimation of GHG abatement andcarbon 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)
Yieldsfunctions
Climatechange
adaptation
Modular structure
1
meso 2
Area Animal 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
meso 3
Data(FADN)
- Yields- Area- Variable costs- Producing activities- Size of farms- Altitude- …
Other sources
- Emissions coefficients- Soils characteristics- Fertilizer uses and prices- …
Typology15 countries, 101
regions734 farm-types
Model inputs- Prices- Technical parameters- CAP-related parameters
Calibration
734 modelsMaximize gross margin
Subject to :- Technical constraints
- Policy constraints
Model output- Optimal area- Livestock numbers- Animal feeding- Net emissions
Estimation
AROPAj model
meso 4
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
DataBase
soil
climat
Fertilizer prices
AropaStix : Client-Server Architecture
SERVER
Oracle, MySql, PostGres, …..
Java ClientJava Client
Java Client
Network
in progressBinta Niang
AROPAj model
macroIMAGE POLESCanada CanadaUSA USA
Mexicorest of Central AmericaBrazilrest of South AmericaEgyptAlgeria & Lybiarest of North Africa
West Africa West AfricaEast Africa East AfricaSouthern Africa Southern Africa
FranceUnited KingdomItalyGermanyAustriaBelgium & LuxemburgDenmarkFinlandIrelandThe NetherlandsSwedenSpainGreecePortugalrest of West EuropeHungary, Poland, Czech and Slovak Republicsrest of Eastern Europe
Former Soviet Union exUSSR TurkeyGulf countriesRest of Middle EastIndiaRest of South AsiaChinaRest of East Asia
Southeast Asia South East AsiaOceania OceaniaJapan Japan
Eastern europe
Middle East
South Asia
East Asia
Central America
South America
North Africa
OECD Europe
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 GHGabatement 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 UnitHRU
E P IC
R ain , S no w , C hem ica ls
S ubsurface F low
S urface F lo w
B elow R oot Zo ne
E vap oration and
Tran sp ira tion = homogenous combination of soil/topography/climate/management
Soil model: EPIC 2
Data needs EPIC Approaches SSCRI
S oil a lbedo
F ie ld water capacity
S oil pH
etc.Ra
ster
s/ve
cto
rs w
ith
ad
de
d
qu
an
tita
tiv
e va
lue
s fo
r s
oil
inp
ut
pa
ram
eter
s in
to S
GB
1:M
IL
CLUSTER classification (ISO DATA , k-M EANS etc.)VE CTOR ISATIO N
HR
U v
ecto
r u
nit
s fo
r so
il im
pu
ts
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 eachfarm type/practiceif not available: derive (regional) productivity indexfrom land use/EUROSTATS statistics andrelate 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