Animal disease control and value chain practices: Incorporating
economics and systems thinking approaches
Karl M. Rich, ILRI
5th Food Safety and Zoonoses Symposium of Asia Pacific, Global Health Institute 2018,
Chiang Mai, Thailand, 6-7 July 2018

Outline
• An overview of economics and animal health
• The role of value chains – and where disease threats lurk
• What we currently miss from a value chain perspective
and why it matters
• Where systems thinking can help us (and what it is)
• Some insights and examples
• Towards a systems based paradigm for risk analysis

Motivation
What is economics?

Motivation
It might be best to explain first what economics is
NOT:
• It is NOT just about money…
• It is NOT about accounting …
• It is NOT just about costs …
• It is NOT about getting the number for your grant
application …
• And … it doesn’t have to be boring …

Motivation
• Economics highlights how and why people make
choices under conditions of scarcity and the
results of such choices on society.
• It is about assessing tradeoffs: how do we
reconcile unlimited wants with limited resources?
• It is about behavior and incentives: faced with
tradeoffs, why do people do what they do?

Where does economics fit into the animal
health world?
Economics should provide a framework for helping
with improved decision-making
• Increased cost-effectiveness: optimization of spending
decisions and resource allocation
• Understanding of drivers, incentives, and constraints of
decision-makers
• Insights into potential impacts of different policies

What have vets learned from economics?
• Supply-demand theory
• Elasticity
• Economics in animal production and farm
management decisions
• International trade/policy

What are the gaps?
• But this doesn’t mean all is well…
• While our toolkits have expanded, the
fundamental appreciation of what economics is
as a discipline is missing.
• As noted earlier, economics is not just about “the
number” or getting a “better number” – it’s
about choices under scarcity and the behavior
induced when there are tradeoffs.

What are the gaps?
• Much of animal health economics has focused on the impact
of disease.
• That is, disease X costs $Y million per year in terms of costs
associated with:
• Animal sickness or death
• Veterinary costs of control
• International trade costs
• Other indirect costs
• But what does this really tell us? How does this change
mindsets or behavior among farmers?

What are the gaps?
• Also, you might ask: “well, our models capture what policymakers should
do, right?”
• Yes, but they also approach the world from a command-and-control
perspective: if X then Y must happen
• The problem is that there are always people behind those decisions,
whether in policy, on the farm, or somewhere in between that influence
how well those choices are implemented.
• And, those people have their own incentives, their own tradeoffs they
face.
• Move from normative to positive approaches, and to systems

Value chains – an overview
• A value chain is “the full range of activities which are required
to bring a product or service from conception, through the
intermediary phases of production, delivery to final
consumers, and final disposal after use.” (Kaplinsky 2000:121)
• More than a diagram of actors and flows – value chains
(should) highlight the broader context and reasons for
observed behavior
• A platform for identifying risk hotspots and critical control
points in animal health settings

A “simple” value chain
Farm
Intermediaries
Processors
Retailers
Products
Products
Products
Products

A “simple” value chain
Farm
Intermediaries
Processors
Retailers
Products
Products
Products
Products
Inputs
Products/services
Products/services
Products/services

A “simple” value chain
Farm
Intermediaries
Processors
Retailers
Products
Products
Products
Products
Inputs
Gov’t
Products/services
Products/services
Products/services
Products/services

A “simple” value chain
Farm
Intermediaries
Processors
Retailers
Products
Products
Products
Products
Inputs
Gov’t
Products/services
Products/services
Products/services
Products/services
Banks
Other
service
providers
Products,
services,
information
Products,
services,
information
Products,
services,
information
$$$
$$$
$$$, info.,
services
$$$
$$$
$$$, info.,
services
$$$, info.,
services

A “simple” value chain
Farm
Intermediaries
Processors
Retailers
Products
Products
Products
Products
Inputs
Gov’t
Products/services
Products/services
Products/services
Products/services
Banks
Other
service
providers
Products,
services,
information
Products,
services,
information
Products,
services,
information
$$$
$$$
$$$, info.,
services
$$$
$$$
$$$, info.,
services
$$$, info.,
services
Governance of
transactions? Who
coordinates?
Dynamics of
transactions - how do
they change over space
and time (across/within
nodes)
Constraints in
transactions – cash
flow, transactions costs
How does the VC
influence disease risk?
How do diseases and
behaviors taken
influence the VC?

Challenges
What are we missing (at our peril)?
• Temporal changes: how value chains evolve over time matters
(seasonally, short-term, long-term)
• Spatial changes: where the value is matters and how it co-evolves
with the landscape
• Actor dynamics: HH decisions or incentives are not static – cash
flow is an important determinant of behavior
Implications – we miss second-round or feedback effects that could
undermine the success of a mitigation option (why we keep fighting the
same battles over and over …)

Systems approaches to value chains
We need to stop thinking linearly and embrace the
complexity of animal health systems for what they are!
From linear to systems …
From generic to spatial …

Systems approaches to value chains
• Systems approaches hold promise as a means to unravel
these dynamics and identify leverage points for change.
• Systems thinking – a systematic means to understand
and embrace the interdependence of the world we live
in (Senge)
• Principles: commitment to learning, challenging one’s own
thinking, collective insights
• It is NOT just drawing stakeholder maps and making
connections!!
• System dynamics (SD) – a platform for modelling and
simulating complex systems, including agricultural value
chains (Rich et al. 2011)

Why system dynamics for value chains?
• Systems matter – the world is interconnected (bottom-up vs. top-bottom)
• Behind systems are people, each with their own values, value systems,
constraints
• Unintended consequences, behavior, delays, and incentives  potential
for feedback effects within/between groups
• Impacts on risk pathways and how they might change (and ways to
anticipate)
• A theoretically coherent way to look at systems  behavioral archetypes
derived from causal behavior not just a bunch of flowcharts

Why SD and value chains for animal disease
analysis?
• Animal disease outbreaks take place in a systems context, with the risk and
spread of disease contingent on measures taken throughout the chain.
• “Weak links” in the chain may accentuate disease risk, but analysis is
needed to understand who these stakeholders, how they interact with
others, and why they behave as they do (and how things change over time
and space).
• Systems thinking vs. silo thinking
• SD models – a way to capture these interactions between systems and
health (and contextual drivers)
– Qualitative: archetypes of system behavior based on feedback loops
– Quantitative: simulation approach to quantify impacts (ex-ante)

DISEASE
ENVIRONMENT
TECHNOLOGY
Traditional veterinary view
E
MARKETS

DISEASE
ENVIRONMENT
TECHNOLOGY
MARKETS
BEHAVIOR
CASH
FLOW
HH DYNAMICS
(GENDER)
A systems view

Challenges in applying systems thinking
• System dynamics: great in theory/conceptually, difficult to
do in practice
• Major constraint: data
• Difficult to develop good survey platforms
• Difficult to get people to think dynamically
• VCs: movies not pictures

Possible solutions – participatory processes
• Participatory processes: a potentially important
solution
– Data collection in challenging environments
– Model validation
– Joint ownership of model (“buy-in”)
• Group model building: a tested method to build
systems models through participatory means
(Vennix 1996; Richardson and Andersen 1997;
Hovmand 2014)

What is group model building?
• A participatory process aimed at:
– Identifying and prioritizing the key problems in the system
– The causes of these problems
– The consequences of these problems
• SD principles and language (stocks/flows/feedbacks) are used to facilitate
this discussion
• Iterative model development and construction based on this process
• Use of primary/secondary data to complement and triangulate
stakeholder information
• Goals: joint learning, fostering consensus, stakeholder buy-in

Source: Lie et al. (2017)

Spatial group modeling – extending systems
thinking further
• SGMB: a new way to analyze complex systems incorporating
space.
• The “where” of the system matters as much as the “what”,
“how”, and “why”
• Key characteristics:
– Grounding problems spatially
– Identifying spatial and temporal changes and their co-evolution
– Using maps and participatory GIS concepts to facilitate model and
system building through physical platform LayerStack (Rich et al. 2018;
Mumba et al. 2017)
– Insights into risk analysis

Applications – ECF control in Zambia
• ECF – an important livestock disease in East Africa, including
Zambia.
• Recent field work (Mumba 2018) highlighted importance of ECF
relative to other government priorities (e.g. FMD)
• Little known about drivers/context of control and how this differs
across space.
• How to identify and quantify impact of interventions that would
both improve communal involvement in the chain and reduce
disease?

Frequency of animal diseases in Zambia
0
10
20
30
40
50
60
70
% of Disease/year
Percentage
Source: Mumba et al (2018)

Cattle sales patterns in Zambia
• Jan: School fees, festive
season, food reserves
dry
• April: Schools open,
Easter holiday
• Aug-Schools open
• Sept-Dec: Agriculture
inputs, Xmas
0
5
10
15
20
25
30
35
40
Monthly cattle sales
Source: Mumba et al (2018)

Towards a systems based paradigm for risk
analysis
Hazard
identification
Risk assessment Risk management
Risk communication

Towards a systems based paradigm for risk
analysis
Hazard
identification
Risk assessment Risk management
Risk communication
VC actor
risk
behavior
VC actor
risk
behavior
VC actor
risk
behavior

Towards a systems based paradigm for risk
analysis
Hazard
identification
Risk assessment Risk management
Risk communication
VC actor
risk
behavior
VC actor
risk
behavior
VC actor
risk
behavior
Systems
models
to inform
Systems
models
to inform
Systems
models
to inform

This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
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