integrated assessment of agricultural systems (seamless)

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

Food prices

http://www.indybay.org/uploads/2008/04/12/corn.jpg

Trade liberalization

Environmental issues

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

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!

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

Outline of presentation

C. Components

A. Methodology for IA

D. S

cien

ce a

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imp

act

B. Application


C. Components


D. S

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ct

B. Application

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

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)


C. Components


D. S

cie

nce

an

d

imp

act

B. Application

Trade liberalization - WTO proposal

http://test.seamless-ip.org:8080/gromitdemo/wallace/index.html

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

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

Price decline due to WTO proposal: EU vs World

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

Decrease in average farm income by region (%)


Decrease in average farm income by farm type (%)


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 ↓


C. Components


D. S

cie

nce

an

d

imp

act

B. Applications

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




Institutional

GlobeGlobe


Country/Continent

Country/Continent

RegionRegion

LandscapeLandscape

FarmFarm

FieldField

GTAP

CAPRI

EXPAMOD

FSSIM-MPFSSIM-AM

APES

LABOUR

PICA


SLE

Structural change

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

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

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

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



Institutional

GlobeGlobe


Country/Continent

Country/Continent

RegionRegion

LandscapeLandscape

FarmFarm

FieldField

GTAP

CAPRI

EXPAMOD

FSSIM-MPFSSIM-AM

APES

LABOUR

PICA


SLE

Structural change

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

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:

SEAMLESS farm typology

Farm size

Farm specialisation

Land use

The typology combines:

Intensity

An example of mapping farm types to AEnZs

Density of low-intensity farms in agri-environmental zones

Linking models, data and indicators

Methodological linkage: e.g. scaling in time and space

Semantic linkage: ontology Technical linkage: OpenMI

Connecting people!




Institutional

GlobeGlobe


Country/Continent

Country/Continent

RegionRegion

LandscapeLandscape

FarmFarm

FieldField

GTAP

CAPRI

EXPAMOD

FSSIM-MPFSSIM-AM

APES

LABOUR

PICA


SLE

Structural change

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

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

SEAMLESS Landscape Explorer

Baseline Scenario Policy Scenario

Griffon and Auclair


C. Components


D. S

cie

nce

an

d

imp

act

B. Application

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?

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

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

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

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

Thank you for your attention

integrated assessment of agricultural systems (seamless)

Education