Integrated assessment of agricultural systems (SEAMLESS)
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Integrated assessment of agricultural systems;
On integrated science and science integration
Martin van Ittersum
Frank Ewert, Thomas Heckelei, Floor Brouwer, Johanna Alkan Olsson, Erling Andersen, Jan Erik Wien, Jacques Wery
Acknowledgement: all SEAMLESS colleagues
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Trade liberalization
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Environmental issues
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Common challenges for research …
Multi-dimensional analysis Multi-scale analysis
Economic
Social
Natural Institutional
Economic
Social
Environmental Institutional
Global
Continental
National
Regional
Farm
Field
Global
Continental
National
Regional
Farm
Field
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What does research have at hand to analyse? Methods and databases targeted at specific processes or scales:
Market Farming systems Cropping systems ……………
which are ….. developed for a specific purpose often poorly re-used difficult to link for integrated studies not readily used for integrated assessment of indicators
Fragmentation, gaps, lack of integration!
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Aims of SEAMLESS project
Overcoming fragmentation in research models and data in Europe for integrated assessment of agricultural systems
Better informed impact assessment of new agricultural and environmental policies
To advance: Consistent micro-macro analysis Consistent economic, environmental, social and institutional analysis Re-use of research tools for a range of issues
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Outline of presentation
C. Components
A. Methodology for IA
D. S
cien
ce a
nd
imp
act
B. Application
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Outline of presentation
C. Components
A. Methodology for IA
D. S
cie
nce
an
d im
pa
ct
B. Application
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Integrated assessment procedure
Pre - modelling
Modelling
Post - modelling
Problem
definition
Scenario
description
Indicator
development
Definition of
simulation
experiment
Model
selection and
composition
Parameterization
and
simulation
Post - model
analysis
Visualization
of results
Documentation/
communication
Pre - modelling
Modelling
Post - modelling
Problem
definition
Scenario
description
Indicator
selection
Definition of
simulation
experiment
Model
selection and
composition
Parameterization
and
simulation
Post - model
analysis
Visualization
of results
Documentation/
communication
Dat
a an
d kn
owle
dge
base
Use
rs/s
take
hold
ers
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Structural change
Global
Continent/country
Region
Farm
Field MaizeMaizeWheatWheat
Mixed farm type
Mixed farm type
PotatoPotato ……
……Arable farm type
Arable farm type
Midi Pyrenees
Midi Pyrenees ……
Agri-cultural sector
Agri-cultural sector
Global economyGlobal
economy
CAPRICAPRI
FSSIMFSSIM
APESAPES
EXPAMOD
Link to GTAP
Global trade
Global trade
TechnologyTechnology
ClimateClimateEconomyEconomy
FarmsFarms
Natural resourcesNatural
resources
PolicyPolicy
Pre-modelling Modelling Post-modelling
MarketMarket
SocietySociety
-20
0
20
40
60
80
100
Initial (2001)
Baseline & ND
(2013)
Baseline (2013)
Nitrate directive (2013)
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Outline of presentation
C. Components
A. Methodology for IA
D. S
cie
nce
an
d
imp
act
B. Application
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Trade liberalization - WTO proposal
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http://test.seamless-ip.org:8080/gromitdemo/wallace/index.html
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Baseline versus WTO policy scenario Export subsidies EU set to zero Agricultural tariff reductions WTO proposal (according to December
6th 2008 agricultural modalities)
2003 2013
baseline
policy to be assessed
with –withou
t
= impact policy
effect of autonomous developments
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Model chain
CAPRI
EXPAMOD
FSSIM
APES
NUTS-2 and EU indicators
Farm and regional indicators
Data of NUTS-2 and EU
Data of farms in 13 regions (out of 300
regions in EU)
Agricultural sector model - EU
Extrapolate farm to EU
Bio-economic farm model
Agricultural production & externalities
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Price decline due to WTO proposal: EU vs World
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WTO – change in agricultural income (%)
Income declines in all EU27 regions;
Losses vary between 1 and 16%; average decline 5%
Marcel Adenäuer and Marijke Kuiper
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Decrease in average farm income by region (%)
Marcel Adenäuer and Marijke Kuiper
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Decrease in average farm income by farm type (%)
Marcel Adenäuer and Marijke Kuiper
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WTO – change in nitrate leaching (%)
-2.0 -1.0 -0.0
Farm types in Midi Pyrenees
Hatem Belhouchette and Kamel Louhichi
Arable-cereal Arable-other
WTO vs Baseline WTO vs Baseline
Nitrate leaching -2 % +6%
Maize area ↓ ↑
Peas area ↓ ↓
Rape area ↓ ↑
Soya area ↑ ↑
Sunflower area 0 ↓
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Outline of presentation
C. Components
A. Methodology for IA
D. S
cie
nce
an
d
imp
act
B. Applications
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Scales and Dimensions of SD
BiophysicalBiophysical Bio-EconomicBio-EconomicSocial/
InstitutionalSocial/
Institutional
GlobeGlobe
Earth SystemEarth System
Country/Continent
Country/Continent
RegionRegion
LandscapeLandscape
FarmFarm
FieldField
GTAP
CAPRI
EXPAMOD
FSSIM-MPFSSIM-AM
APES
LABOUR
PICA
Landscape Evaluation
SLE
Structural change
Indicator Framework
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Scales and Dimensions of SD
BiophysicalBiophysical Bio-EconomicBio-EconomicSocial/
InstitutionalSocial/
Institutional
GlobeGlobe
Earth SystemEarth System
Country/Continent
Country/Continent
RegionRegion
LandscapeLandscape
FarmFarm
FieldField
GTAP
CAPRI
EXPAMOD
FSSIM-MPFSSIM-AM
APES
LABOUR
PICA
Landscape Evaluation
SLE
Structural change
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Simulating cropping systems
Simulation
engine
Weather
Soil water
Pesticides
C-Nitrogen
Agricultural management
Agro-forestry
Crops
Grasses
Vineyard/ orchard
APES
Outputs:
1. Yields
2. Externalities:
- Nitrogen
- Pesticides
- Erosion
- GHGs
Dynamic Cropping System model
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Activities: inputs-outputs
Simulating farm responses - FSSIM
FSSIM-Agricultural Management (AM)
Farm layout
Farm income and costs
Externalities
FSSIM-Mathematical Programming (MP)
Farm objective: profit – risk
Resource constraints
Policy constraints
Bio-economic farm model
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Supply250 Regionaloptimisation
models
Markets Multi-commodityspatial market model
with 18 regionalaggregates
and all EU MS
Prices
Agricultural sector: CAPRI (EU)
Quantities
Combination of
programming model and multi commodity model
University of Bonn
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Micro-macro analysis: Upscaling farm type - marketFSSIM EXPAMOD CAPRI
Supply response toprice and policy
changes on Farmlevel
Extrapolation to regional supplyelasticities and non- sample
regions
Calibration ofregional supply models to this
supply response
Scenario analysisbased on new
supply responseAggregation weights
Structural change
Regional supply
elasticities
Price changes
Price response
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BiophysicalBiophysical Bio-EconomicBio-EconomicSocial/
InstitutionalSocial/
Institutional
GlobeGlobe
Earth SystemEarth System
Country/Continent
Country/Continent
RegionRegion
LandscapeLandscape
FarmFarm
FieldField
GTAP
CAPRI
EXPAMOD
FSSIM-MPFSSIM-AM
APES
LABOUR
PICA
Landscape Evaluation
SLE
Structural change
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Integrated database
Data: Climate and soils Farmtype data (FADN) Agricultural management(!) Policy Trade Regional typologies Indicators (model output)
Two important features:
Common spatial framework Common farm typology
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SEAMLESS Spatial framework
Administrative regions Farm resources Policies Trade
Climatezones Climate
Agri-environmental zones Soil data Farm type allocation Survey data on farm management
The hierarchical framework combines:
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SEAMLESS farm typology
Farm size
Farm specialisation
Land use
The typology combines:
Intensity
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An example of mapping farm types to AEnZs
Density of low-intensity farms in agri-environmental zones
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Linking models, data and indicators
Methodological linkage: e.g. scaling in time and space
Semantic linkage: ontology Technical linkage: OpenMI
Connecting people!
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Scales and Dimensions of SD
BiophysicalBiophysical Bio-EconomicBio-EconomicSocial/
InstitutionalSocial/
Institutional
GlobeGlobe
Earth SystemEarth System
Country/Continent
Country/Continent
RegionRegion
LandscapeLandscape
FarmFarm
FieldField
GTAP
CAPRI
EXPAMOD
FSSIM-MPFSSIM-AM
APES
LABOUR
PICA
Landscape Evaluation
SLE
Structural change
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Structural Change Component
Objective: Forecast regional shares of farm types
Method: Markov chain analysis
Data: FADN 3 size classes 10 specialisations = 30 farm types
Andrea Zimmermann et al., 2009
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Structural Change component
Annual rates of farm number change 2003-2013 [%]
Mobility of farms across farm types [index]
Legend
low
moderate
high
Legend
< -3
-3 - < 0
>= 0
Andrea Zimmermann
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SEAMLESS Landscape Explorer
Baseline Scenario Policy Scenario
Griffon and Auclair
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Outline of presentation
C. Components
A. Methodology for IA
D. S
cie
nce
an
d
imp
act
B. Application
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On integrated science
SEAMLESS: one approach to Integrated Assessment
Benefits: allows to structure the development of IA tools in components using advances of science focusing on parts of the system a degree of flexibility for range of applications
Limitations for specific problems: details of some components not always needed
does ‘generic’ approach allow adequate system representation: • relevant feedback mechanisms and interactions captured?
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On integrated science
High data demand – three routes: statistical sampling (micro-macro upscaling:
Bezlepkina et al.) science-based rules to ‘generate’ crucial but missing
data (agro-management data: Oomen et al.) European data (soils, weather, farm: Andersen et al.)
Questions: trade-off between integration and flexibility? scaling methods to be further tested forecasting farm responses integration of (agro-)ecosystems services
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Science integrationD
isci
pli n
a ry
A
Interdisciplinary
Dis
cipl
i na r
y B
Interdisciplinary
Dis
cipl
i na r
y C
Interdisciplinary
Dis
cipl
i na r
y D
Interdisciplinary
Integrative Scientists
•CRA•JRC
• INRA• CIRAD• IAMM• Cemagref
•UMB •LU•LUEAB
•WU• Alterra• LEI• PRI
• UBER• ZALF• UBONN
•UNEW
• UEDIN
• UNIABDN
•NUI Galway
•
IDSIA
-
SUPSI•AntOptima
• SGGW
• ILE ASCR• VUZE
Mali: IER
USA: UVM
•JRC
•UoC
•UEvora
IT Scientists
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Beyond the project
SEAMLESS Association Overcoming fragmentation Maintenance, extension and
dissemination Continue the network role Open source
New research projects Science Testing and application
• High(er) price scenario
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Matching process:
Contextualisation
Network building
The use of computerized tools in IA
Model types
Role of m
odels
Problem solving stages
Sterk, Van Ittersum and Leeuwis, 2009
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Thank you for your attention