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Complexity
Scenarios and models of land system change
Global Change, Complexity, and Sustainability
Peter Verburg
Petschel-Held: Sahel Syndrome
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 Remote, poor access
 mainly
i l subsistence
b i t
economy
 Climate variation
high
(droughts
– flooding)
Macarringue
 Food shortages, low life expectancy
b hl d
bushland
village
Global Change

Local adaptation
Maputo
3
agriculture
swamp
river
Using the spatial variation of the environment to adapt to climate change
Global Economy

Regional water
management
Adaptation by labour

migration to South Africa
Local impacts

Job availability critical
factor (financial crisis)

Global Economy
Australia
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Macarringue
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Balance between production and consumption
Macarringue
Trans-national nature protection /
International Tourism

Local vulnerability
Blue colours: Production > Consumption
Red coloours: Consumption > Production
Erb et al., 2009 Ecol. Econ.
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8
Complexity
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Description of ‘model’
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Use of models
 Simplified, idealized representation of a part of the real world

 Learning tool
 Experimental tool
 Simulation tool


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Tool to structure information, test hypothesis and validate
narrative models
 tool to provide more insights in the driving factors
and dynamics of LUCC (stakeholder: mainly
scientists)
Decision support
pp system
y
 tool to evaluate trade-offs between alternative land
use strategies
Discussion support system
 tool to trigger discussion among stakeholders and
create awareness of issues in land use and/or
natural resource management
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Modelling global change: the IMAGE model as an
example
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10 N
10 N
13
100
Kilometers
0
100
Kilometers
50 N
50 N
0
0
100
Kilometers
0
0
5E
Tree cover (merged GLC2000 Tree Cover classes)
100
Kilometers
0
5E
Agricultural land
Mosaic Tree cover / Other natural vegetation
Extensive grasslands/pastures
Shrub cover, closed-open (deciduous and evergreen)
Forests
Herbaceous cover, closed-open
Ice
Sparse herbaceous or sparse shrub cover
Regularly flooded shrub and/or herbaceous cover
Cultivated and managed areas
Mosaic Cropland / Tree cover / Other natural vegetation
Grassland/steppes
Desert
Scrubland
Savanna
Mosaic: Cropland / Shrub or Grass cover
Bare areas
Water bodies
Snow and Ice
Artificial surfaces and associated areas
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16
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Model structure
Model rules
 High detail in simulation of
 Land use in each world region represented as one big farm
natural vegetation and crop
growth, biogeochemistry and
atmospheric components and
impacts
 1 ‘farmer’ per region optimizes profit based on: costs of land,
costs of inputs, costs of import
  changes in either land area, land management, or trade
 Very simple representation of
land allocation processes
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20
Spatial allocation within regions: land cover only
Problems
 New agricultural land allocated to ‘most suitable’ pixel based
 Social dimensions of land
allocation not represented
on:
• Distance to existing arable land
• Potential productivity
• Distance to river
 No feedbacks from local to
global e.g. adaptation to
climate change (top-down
pp
only)
y)
approach
 In-balance between modules
and scales (feeding plot-level
models with highly aggregate
information)
 No spatial variation in land use
intensity
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Unraveling land use intensity
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Drivers of agricultural intensity – Global scale
 Data on land use management are not available
 Drivers of land use intensity are context specific (based on
case study evidence)
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Actual yield
Crop specific yields,
5 arc-min
[Monfreda et al., 2008]
Frontier yyield/
yield gap
Stochastic frontier
production function
Reasons for
inefficiency
Inefficiency factors /
Multiple Regressions
Neumann et al., 2010 Agricultural Systems
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Explaining global distributions of yield gab
Explaining global distributions of yield gab
Frontier production function
• Determinants for the frontier yield:
– Temperature, PAR, precipitation, soil fertility constraints
• D
Determinants
t
i
t for
f deviation
d i ti ffrom th
the frontier
f ti yield
i ld
(=inefficiency effects):
vi = noise
ui = inefficiency
xi = actual productivity
¤i = frontier productivity
– Irrigation, market accessibility, market influence,
agricultural population, slope
Neumann et al., 2010 Agricultural Systems
Neumann et al., 2010 Agricultural Systems
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Efficiency is an indicator of the management intensity
Results
Central- USA
Germany,
France,
UK
Efficiency = 1
Efficiency = 1
China
Efficiency = 1
USA
Nile Delta,
Europe,
E-USA
E-China,
E-USA
Afghanistan,
Kazakhstan
Bulgaria,
Argentina
Mexico,
Africa,
India
China,
Japan,
South Korea
Argentina,
NE-China,
SE-Europe
West Africa,
NE-India,
Thailand
Neumann et al., 2010 Agricultural Systems
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Accessibility
Labor
Market influence
Irrigation
Accessibility
Irrigation
Market influence
Accessibility
Irrigation
Market influence
Slope
Irrigation
Accessibility
Market influence
Market influence
Accessibility
Neumann et al., 2010 Agricultural Systems
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Neumann et al., 2010 Agricultural Systems
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Irrigation
Labor
Irrigation
Market strength
Accessibility
Labor
Neumann et al., 2010 Agricultural Systems
Portmann et al., 2010
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Variables at grid cell level
Variable name
Description [unit]
Irrigation
1 if irrigation,
0 if rainfed
Slope
Slope [%]
Discharge
River discharge [mm/yr]
Humidity
Humidity, calculated as precipitation [mm] /
potential evapotranspiration (PET)
(
) [mm/yr]
/
[index]
Evap
Evaporation [mm/yr]
ET
Evapotranspiration
[mm/yr]
Access
Travel time to markets [hours]
Population
Population density
[persons/km2]
Variables at country level
Variable name
Description [unit]
Water
Natural total renewable water resources [m3/yr/ha]
Political stability
Likelihood that the government will be destabilized [index]
Control of corruption
Control of corruption (the extent to which public power is
exercised for private gain) [index]
Government
effectiveness
Quality of public and civil service and the degree of its
independence from
political pressures [index]
GDP
Gross Domestic Product per capita
[US$]
Democracy
Level of institutionalized democracy
[index]
Autocracy
Level of autocracy [index]
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Variable name
Model 1
Unstand.
coeff.
Model 2
T-ratio
Unstand.
coeff.
Binary logistic
regression
T-ratio
Unstand.
coeff.
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Local scale
Wald
test
Grid cell level (level one)
Fixed effects
Intercept
-0.566**
Ln(slope)
-0.018
-3.2
-0.570**
-3.2
0.542***
119.3
-0.3
0.009
0.2
0.136***
248.7
Ln(discharge)
0.150***
5.3
0.133**
5.3
0.078***
Humidity
-1.211***
-5.4
-1.039**
-2.6
-0.347***
88.6
0.002
1.7
0.001
0.6
0.003***
221.0
-0.0011
-1.7
-0.002***
470.8
-0.319
-0
319***
Evap
ET
<-0.001
-0.1
94.6
-4 3
-4.3
-0.382
-0
382***
467 9
467.9
0.278**
3.4
0.241***
1467.8
Ln(water)
-0.006
<-0.1
Government_performance
0.409*
2.2
-0.434**
-2.7
Ln(access)
Ln(population)
Country level (level two)
Government_type
Variance
0.558
0.557
Model fit (ROC)
0.806
0.812
0.724
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to structure dis
scussions on
future landscap
pes
Multi-agent mo
odels as a tool
10/20/2011
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Bottom-up methodology:
multi-agent models
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Bottom-up methodology:
multi-agent models
Landscape change
Internal factors
Collective behaviour
Agent-interactions
External factors
Ability
Options
Policies & subsidies
Willingness
Decisions
Demand
Feedback
Actions
Social networks
& institutions
Advice
Farm scale
Individual behaviour
Feedback
Land-use pattern
Regional scale
Valbuena et al., Landsc. Ecol., 2010
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Modelling….a multi-agent approach
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Agent-based modelling
Agent typology
Farm
5Km
Agent
Hobby
Non-expansionist
Expansionist
Valbuena, Verburg, Bregt, Agriculture, Ecosystems and Environment 2008
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Processes modelled
Sample of Multi-agent simulation results
 Farm expansion
2005
 Land abandonment
 Management of treelines/hedgerows
2000
2030
Agriculture w/o landscape elements
Parcels with landscape elements
Nature
Targeted sustainability (2025)
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Liberalisation of agriculture (2025)
Agriculture w/o landscape elements
Parcels with landscape elements
Nature
Goal setting: landscape services perceived important
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Backcasting results: local interventions in land use
system
Activation
of
positive
process
Rezoning
of
farm
management
types
to
appropriate
environmental
locations
Local measure interventions
-Land reallotments schemes
-Restriction and zoning based on landscape profiles (attractiveness, environmental robustness)
-Nature farming in environmentally sensitive areas
-Economic valuation and remuneration of nature services
-Regulate synergies between functions
-Targeted subsidies for different environmentally appropriate uses
-Communication between different stakeholders
Attract tourist
-Increase cooperation between entrepreneurs and policymakers
-Maintenance of the landscape (promotion of diversified farms)
-Organic and local products
Attract entrepreneurs
-Invest in local social cohesion
-Promote
Promote the region to outsiders (Advertising campaign)
-Prevent degradation of landscape aesthetics while allowing for some restructuring to help develop
new functions
-Continual adaption of zoning plans to stay in step with new innovations (e.g. Solar-panels)
Increase economic output/
-Promote new economic sectors through correct economic incentives (e.g., niche markets in organic
diversification
products)
-Develop appropriate infrastructure for entrepreneurs (e.g. fibre optics)
-Targeted subsidies for business types that fit the local character
-Macro-credit for large projects
-Landscape restructuring (e.g. empty barn/building schemes)
-Innovation assistance – smart non-partisan solutions
-Consider other incentives than subsidies
-A decentralised communal funds for community lead initiatives
Develop
an
energy -Create a synergistic cycle where small scale farms produce material from hedgerows, which supply
landscape
on farms bio-digester giving incentive to maintain the landscape for fuel that in turn attracts tourism
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Conclusions on the use of models
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Use of models
 Models help to visualize processes in spatial context

 Structure discussion towards future challenges
 Provide a common frame for discussions

 Limitations: not all measures can be easily quantified
 Multi-agent modelling requires large investment

Tool to structure information, test hypothesis and validate
narrative models
 tool to provide more insights in the driving factors
and dynamics of LUCC (stakeholder: mainly
scientists)
Decision support
pp system
y
 tool to evaluate trade-offs between alternative land
use strategies
Discussion support system
 tool to trigger discussion among stakeholders and
create awareness of issues in land use and/or
natural resource management
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Top-down impact assessment: modelling
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Spatial trade-offs
350
 Scenario
300
 Scenario
 European
E
scale
l
 European
scale
CLUE-Scanner
CLUE
S
models
models
 Impact assessment
 Trade-off analysis
Agricu
ultural area
250
 Global models
 Global models
200
150
100
50
0
-50
Africa
Asia C&SAmer EU27
Reference
Biofuel, w/o EU
NAFTA
World
Biofuel, with EU
Verburg et al., 2008 Annals of Regional Science
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Banse et al., 2010 Biomass and Bioenergy
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 Scenario
 Global models
 Scenario
 European
E
scale
l
models
 Global models
 Impact assessment
 European scale
models
 Impact assessment
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(2000-2030)
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Global and Euro
opean directives
(2000-2030)
Global dire
ectives only
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(2000-2030))
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Reference s
scenario (B1)
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Spatial trade-offs
Global scale
Landscape scale
Orchids Vs.
Bears
Increased competitiveness of agriculture
Marginal areas:
Prime agricultural areas:
Abandonment
Intensification/scale enlargement
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Bottom-up methodology:
scenario development
Projections without models
Van Berkel et al., 2011
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Cultural-aesth
hetic function
Baseline
New Communalism Scenario
Territorial Sustainability Scenario
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Assets
‐ Rich traditional know-how (communal oven, bee keeping, handicrafts,
cultural traditions)
‐ Increasing demand in Portugal for vacation homes, which can be a
migratory pull factor for the region
‐ Local tradition of unique culinary dishes well-marketed and preserved in
small but strong restaurant sector
‐ Realisation and revitalization of key cultural symbols and cultural
traditions (Castro Laboreiro Dog, Museum)
Constraints
‐ No young people carry on with traditional agricultural-management
activities
ti iti ((associated
i t d with
ith urban
b pull)
ll)
‐ Limited education level and training for farm diversification
‐ Strong attachment valley housing and little financial incentive for selling,
limiting opportunity for new functionality
‐ No local networks and linkages with urban areas for marketing of local
traditional products
‐ Poor cooperation between key stakeholders in the region
‐ A lack of associative spirit between newcomers and locals, which has
stunted cooperation
‐ Power struggle regarding communal lands subsidy funding coming from
the ITI initiative, which has resulted in decentralisation of decision making
and less cohesion of regional development planning
‐ Competition of neighbouring parish for tourist draw
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Thank you!
Conclusions
 Land systems are the result of complex, multi-scale
interactions
 Land systems are at the interface of socio-economic and
biophysical system components
 Models are simplified representations of reality
• >> comparison with reality is needed to learn about reality
• >> as simple as possible, as complex as needed
Land change models play different roles:
-to learn
-to explore/predict
-to discuss the future and emergent properties of system
interactions
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[email protected]
Institute for Environmental Studies
VU University Amsterdam
http://www.ivm.vu.nl
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