This document discusses using agent-based modeling (ABM) to study resilience in agrifood systems. It notes that ABM is well-suited to represent the dynamics of complex social systems by modeling individual actors and their interactions. ABM can simulate how macro patterns emerge from micro behaviors and feedback between individual and system levels. The document argues ABM is an attractive tool for resilience research because it focuses on understanding systems through interactions with practitioners rather than optimization. It can integrate diverse data and theories to study interactions and interdependencies in agrifood systems and identify early warnings of instability.

1. Resilience as a topic to apply ABM
Krijn Poppe (Wageningen Economic Research)

2. Krijn J. Poppe
 Business economist, EUR 1981
 Programme manager Wageningen Economic Research
● Many European projects, FADN, chair SCAR-AKIS
 Rli: member of the council (1 day / wk)
 Former Secretary-General of the EAAE
 Secretary-Treasurer EAAEP Foundation (ERAE)
 Chair SG of EuroChoices
 Member PAL Zuid-Holland
 Member of the Governing Board of SKAL

3. Wageningen University & Research
3
Wageningen University & Research
Agrotechnology
& Food
Sciences Group
Animal
Sciences Group
Environmental
Sciences Group
Plant Sciences
Group
Social
Sciences
Group
Wageningen University Wageningen
International
Agrotechnology
& Food
Sciences
Animal
Sciences
Environmental
Sciences
Plant Sciences Social sciences Wageningen
Academy
Wageningen Research (DLO) Wageningen
Marine
ResearchWageningen
Food &
Biobased
Research
Wageningen
Livestock
Research
Wageningen
Environmental
Research
Wageningen
Plant
Research
Wageningen
Economic
Research
Centre for
Development
Innovation
RIKILT
Wageningen
Bioveterinary
Research

4. 4 grand challenges: tomorrow’s business
Food & nutrition
security
Climate
change
Healthy diet
for a healthy
life
Environmental
issues
New GRIN technologies come to the rescue (many based on ICT):
• Genetics (including do-it-yourself biology: CRISPR etc.)
• Robotics
• Informatics
• Nano-technology

5. Food chain: 2 weak spots
Input industriesFarmerFood processorConsumer Retail
• Public health issues –
obesity, Diabetes-2 etc.
• Climate change asks for
changes in diet
• Environmental costs
need to be internalised
• Climate change (GHG)
strengthens this
Is it coincidence that these 2 are the weakest groups?
Who influences farmer most: the chain or CAP greening?
> Food system approach needed

6. Chain organisation changes (©Gereffi et al., 2005)
inputsEndproduct
PRICE
Shops
Complete
Integration
Lead
company
Lead
company
Turnkey
supplier
Relational
supplier
Market Modular Relational Captive Hierarchy
Low Degree of explicit coordination and power asymmetry High
Lead
company
Farmers

7. Development of farm systems
Net value
/ ha
Time
Agricul-
tural
Family
Firms
(sme)Family
farming
Lati-
fundia
socialist
state
farms
Subsis-
tence
farming
Ag. policy
AKIS.gov
Food supply networks
3rd gen. uni
Urban
farming
Residen
-tial
farming
Metropolitanagriculture

8. Europe towards
2030
3 scenarios to
explore the future
of EU agriculture +
implications for
Agricultural
Knowledge and
Innovation
Systems (2014/5)

9.  HighTech: strong influence new technology owned by
multinationals. Driverless tractors, contract farming and a
rural exodus. US of Europe. Rich society with inequality.
Sustainability issues solved. Bio-boom scenario.
 Self-organisation: Europe of regions where new ICT
technologies with disruptive business models lead to self-
organisation, bottom-up democracy, short-supply chains,
multi-functional agriculture. European institutions are weak,
regions and cities rule. Inequalities between regions,
depending on endowments.
 Collapse: Big climate change effects, mass-migration and
political turbulence leads to a collapse of institutions and
European integration. Regional and local communities look
for self-sufficiency. Bio-scarcity and labour intensive
agriculture. Technology development becomes dependent on
science in China, India, Brazil.

10. Scenario 1: High Tech
multinationals, clean technology, strong EU
(c) Toshiba

11. Scenario 2: Self organisation
new business models, regions and cities rule, diversify
Tempelhof, Berlin (c) Kasper Jensen

12. Scenario 3: Collapse
climate change, migration, breaking up of the EU

13. Why resilience thinking at WUR (1)
 Increased vulnerability to natural, technological and
social changes is being observed or anticipated at
various of agrifood systems and related ecosystems.
 Reduced resilience in agrofood systems
● Reductions in diversity (system and genetic)
● Ecosystem-production interaction
● Intensification (antibiotics, GMOs, non-renewables)
 Shift needed from optimal control and maximum
efficiency to focus resilience, transformability
adaptability.
 Climate, agrifood, ecological, social and political are
increasingly interrelated at many levels. Focus on
systems integration and communication.
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14. Why resilience thinking at WUR (2)
 Major global player in research on agro-food, nature,
health and related fields
 Long-tradition in multi-disciplinary research: further
novel ways of this research
 Leader in systems analysis
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15. Resilience concepts
15
.....things change – and to ignore or resist this change is to increase our vulnerability and
forego emerging opportunities.” (Walter and Salt 2012)
Resilience: Ability of complex systems to adapt to (sudden/severe)
changes and continue functioning and developing.
Focusses on adaptability and
transformability

16. Resilience concepts
 Perturbations & change
● Predictable: aging, price fluctuations, normal
periodic droughts etc
● Unpredictable (black swan type): e.g. discovery of
America, green revolution, banking crisis
  Fundamentally different strategies and concepts in
resilience i.e., robustness vs adaptability
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17. Resilience concepts
 Latitude: the maximum amount a system can be changed before
losing its ability to recover (L)
 Resistance: the ease or difficulty of changing the system; how
resistant it is to being changed (R).
 Precariousness: how close the current state of the system is to a
limit or “threshold” (Pr).
 Panarchy: How the above three attributes are influenced by the
states and dynamics of the (sub) systems at scales above and below
the scale of interest.
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L
R

18. Alternative stable states
18
Grassland – water dynamic
Forest – savanna transition
Selective effects of agriculture
Fresh water algal blooms
Rich and poor farmers in Africa
Tittonell 2010

19. Adaptive cycles:
19
Potential: Number of future options
available.
Connectedness: Extent to which
systems act independently are in
control of their own fate
Exploitation phase (r): Pioneer species tolerant to
variation
Conservation phase (K): Species that modify
conditions
Release (Omega): Forest fire releasing nutrients
from biomass.
Reorganization (a) : Processes that maintain
nutrients in the systems

20. Panarchy: System dynamics is influenced by the states and dynamics
of the (sub) systems at scales above and below the scale of interest.
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Revolt: When small fast elements
overwhelm fast slow ones.
Forest fires, smaller-scale forest
fragmentation  forest collapse
Bankruptcies  chain or sector change
Remember: When the potential
accumulated and stored in the larger, slow
levels influences the reorganization
Species diversity accumulated in forest
landscapes increases local potential
recovery
Advances medical/biological science
increase options for control new diseases

21. Panarchy example: The slow and fast lanes of malaria
21
Fast: Malarial problem solved with
use pesticides.
Slower:
More resistance of mosquitos to
pesticides.
Reduced resistance in humans
Aging populations
Ecological effects pesticides on
natural enemies.
But also Increases in medical and
ecological of insects
Problem: Major global disease
claiming 1 – 1.5 M lives annually.
Even slower:
Climate change: increased global
spread.
Other examples
- GMOs in soybean
- Antibiotics in meat production
- Globalization of information exchange

22. The global meat connection
22
Views about animal
welfare
EU Nitrogen
Peak P

23. Managing Critical Infrastructure (in times of IoT)
23
Developing resilience without loosing too much
efficiency

24. Scientific challenges
1. Defining and understanding the interactions and
interdependencies between components and
subsystems in agrifood/biobased systems.
2. Developing methods for identifying and measuring
early warning for precariousness.
3. Developing methods for managing resilience of
different interrelated systems and for adaptive
governance arrangements.
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25. The role of Agent Based Models (ABM)
 Offer a natural paradigm to represent the dynamics of
complex social systems.
 Represent the micro-behaviours of individual actors, take
interactions in social networks into account, simulate the
emergence of macro-level patterns from these
interactions, and generate feedback from macro-level
phenomena to decision making by individuals (Squazzoni
et al., 2014).
 Individual actors and groups can be situated in a
simulated spatial environment, in and on which the
agents can act and in which autonomous ecological
processes can evolve (Matthews et al., 2007).
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26. The role of Agent Based Models (ABM)
 Can be based on diverse theory, as well as data from a
diversity of data sources
 Can be used for triangulation with the concepts identified
in the analytical framework and the narrative
description.
 These characteristics make ABM an attractive tool to be
used in research on resilience (and complex adaptative
systems and transitions): it is more on understanding
and interaction with practitioners than e.g. optimisation.
(Thanks to Tim Verwaart and Gertjan Hofstede for these
insights on ABM)
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27. Thanks for your
attention
krijn.poppe@wur.nl
www.wur.nl