Spatial Ecologies in Viable Agri-Food Supply Chains: Insights from Iceberg Model and Causality Analysis

Abstract

The study explores the role of spatial ecologies in creating viable agri-food supply chains using the Iceberg Model of Systems Thinking and Causality Analysis. By examining the distinct adaptations in rural, urban, and semi-urban areas, this research identifies how spatial variations have influenced the adaptations of agri-food supply chains during COVID-19. Stakeholder interviews provide rich data on the events, patterns, underlying structures, and mental models that drive these adaptations. The analysis reveals that digital connectivity, community initiatives, and hybrid sourcing methods are critical factors in enabling the dynamic reconfigurability of agri-food supply chains to achieve food affordability, quality, and availability. By mapping Causal Loop Diagrams, the study highlights the importance of understanding the spatial dynamics and ecosystem interactions and their effects. These insights are essential for policymakers and practitioners in developing viable agri-food systems through spatially informed strategies.

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Spatial Ecologies in Viable Agri-Food Supply Chains: Insights from Spatial Ecologies in Viable Agri-Food Supply Chains: Insights from
Iceberg Model and Causality Analysis Iceberg Model and Causality Analysis
Kasuni Vidanagamachchi
Western Sydney University
, K.Vidanagamachchi@westernsydney.edu.au
Athula Ginige
Western Sydney University
, a.ginige@westernsydney.edu.au
Dilupa Nakandala
Western Sydney University
, d.nakandala@westernsydney.edu.au
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Vidanagamachchi, Kasuni; Ginige, Athula; and Nakandala, Dilupa, "Spatial Ecologies in Viable Agri-Food
Supply Chains: Insights from Iceberg Model and Causality Analysis" (2024).
ACIS 2024 Proceedings
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2024, Canberra Spatial Ecologies in Viable Agri-Food Supply Chains
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Spatial Ecologies in Viable Agri-Food Supply Chains:
Insights from Iceberg Model and Causality Analysis
Full research paper
Kasuni Vidanagamachchi
School of Computer, Data and Mathematical Sciences
Western Sydney University
Australia
Email: k.vidanagamachchi@westernsydney.edu.au
Athula Ginige
School of Computer, Data and Mathematical Sciences
Western Sydney University
Australia
Email: a.ginige@westernsydney.edu.au
Dilupa Nakandala
School of Business
Western Sydney University
Australia
Email: d.nakandala@westernsydney.edu.au
Abstract
The study explores the role of spatial ecologies in creating viable agri-food supply chains using the
Iceberg Model of Systems Thinking and Causality Analysis. By examining the distinct adaptations in
rural, urban, and semi-urban areas, this research identifies how spatial variations have influenced the
adaptations of agri-food supply chains during COVID-19. Stakeholder interviews provide rich data on
the events, patterns, underlying structures, and mental models that drive these adaptations. The
analysis reveals that digital connectivity, community initiatives, and hybrid sourcing methods are
critical factors in enabling the dynamic reconfigurability of agri-food supply chains to achieve food
affordability, quality, and availability. By mapping Causal Loop Diagrams, the study highlights the
importance of understanding the spatial dynamics and ecosystem interactions and their effects. These
insights are essential for policymakers and practitioners in developing viable agri-food systems through
spatially informed strategies.
Keywords: Viable Agri-Food Supply Chains, COVID-19 disruption, Spatial Ecologies, Iceberg Model of
Systems Thinking, Causal Loop Diagrams, Digital Connectivity.

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1 Introduction
We live in a world full of uncertainties where supply chain operations are disrupted in unprecedented
ways, as evidenced by the recent COVID-19 pandemic. One imminent threat is the impact of climate
change (Vidanagamachchi et al. 2024), which has already begun to disrupt typical agri-food supply
chains (ASCs) globally. ASCs are highly vulnerable to disruptions, as they rely on complex
interconnected networks of producers, processors, distributors, retailers, and consumers, often
spanning vast geographical distances. Due to their critical role in ensuring food security, the world needs
to be better prepared by establishing the ‘viability’ of ASCs in the face of disruptive situations.
It is crucial to understand the purpose of exploring the ‘viability’ of ASCs. ASCs are responsible for the
complex task of efficiently moving food from production to consumption. This process involves
navigating inherent challenges such as minimising costs, balancing supply and demand variations, and
ensuring the quality of food products. To achieve this, ASCs have adopted various strategies, including
lean (focused on cost minimisation), agile (ensuring responsiveness to demand variations), le-agile (a
hybrid of lean and agile), sustainable (balancing economic, social, and environmental factors for long-
term resource continuity), and resilience (the ability to recover from short-term disruptions). However,
during the COVID-19 pandemic, similar to many other industries, ASCs faced significant disruptions,
demonstrating that these strategies alone were not enough to handle prolonged disruptions. This
highlighted the need for establishing viable ASCs capable of surviving while adapting to long-term
disruptions.
The viability of a supply chain is defined as “the ability of a supply chain (SC) to maintain itself and
survive in a changing environment through a redesign of structures and replanning of performance with
long-term impacts” (Ivanov 2020, p. 1). This concept of viability differs from supply chain resilience,
which typically pertains to a single supply chain within an organisation. Viability, on the other hand, is
achieved at a much larger ecosystem level and requires the coordination and collaboration of multiple
stakeholders (Ivanov et al. 2023). Despite the unprecedented challenges of the COVID-19 pandemic,
ASCs around the world have demonstrated a remarkable capacity for adaptation and survival, which is
worth exploring to better design future supply chains.
Digital technologies play a vital role in establishing supply chain viability. Previous studies have explored
the viability concept in manufacturing and services domains (Ali and Gossaye 2023; Chervenkova and
Ivanov 2023; Schleifenheimer and Ivanov 2024), yet its application to the ASC domain remains limited
(Balezentis et al. 2023). The adaptations that occurred in Sri Lanka's ASCs during COVID-19, as
highlighted by Vidanagamachchi and Ginige (2023) and Vidanagamachchi et al. (2024), demonstrate
that different adaptations have occurred in different spatial ecologies-urban, semi-urban and rural.
Spatial ecology examines how space affects individual species' dynamics and the structure, diversity,
and stability of multi-species communities (Fletcher and Fortin 2018). Hence, in the context of this
study, spatial ecologies focus on behaviours and decision-making of ASC stakeholders across different
geographic environments, considering the availability of resources (such as water, land, or inputs) and
services (such as logistics, technology, and market access).
Vidanagamachchi et al. (2024) propose a blueprint for a viable agri-food supply chain ecosystem that
can dynamically reconfigure in a digital environment. However, understanding the dynamics of spatial
ecologies is crucial for developing interventions that contribute to viable agri-food supply chain
ecosystems. Hence, this study utilises a system thinking approach to explore the effect of spatial
dynamics in enabling the viability of ASCs. To further understand the ASC adaptations that occurred in
different spatial ecologies (urban, rural, semi-urban), this research conducted interviews with ASC
stakeholders in Sri Lanka to examine their responses to COVID-19 disruption. The data were analysed
thematically using a systems-thinking approach, which incorporated the Iceberg Model and Causal Loop
Diagrams (CLDs) to explore four levels of causality.
The Iceberg Model offers four levels for unpacking a problem: events, patterns and trends, structure and
mental models. In the context of ASC, events represent the visible adaptations triggered by COVID-19;
patterns reflect the behaviours and trends that emerged over time; structures are the natural conditions
or the man-made rules, regulations, and policies that shape these behaviours. Mental models consist of
the values, beliefs and assumptions that influence ASC stakeholders’ actions and decisions. This
approach provided a clearer understanding of the adaptations observed and helped to uncover the
underlying causalities. Specifically, the structural-level causalities and feedback loops reveal crucial
leverage points for intervention to build more effective systems (Maani and Cavana 2007). Additionally,

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this methodology identifies the underlying dynamics, feedback loops, and spatial variations that drive
adaptations in ASCs.
Leveraging on the insights gained, this study aims to uncover the underlying factors that contribute to
the viability of ASCs across diverse spatial ecologies. As the concept of viability is still in its early stages
of investigation, further exploration of its essential attributes is crucial for future-proofing agri-food
supply chains. The findings from this research will offer valuable guidance to policymakers and
practitioners in enhancing the viability of these supply chains in the face of future disruptions.
2 Related Work
2.1 Understanding of Supply Chain Viability vs Resilience
Since the COVID-19 pandemic, researchers have increasingly recognised the importance of supply chain
viability, which extends the concept of supply chain resilience. Ivanov et al. (2023, p. 2402) provide
more clarity into the difference between resilience and viability by stating two directions on how viability
extends resilience: “(1) Resilience is the ability to recover after a disruption (e.g., an earthquake) and
return to some baseline, initial state. Viability is the ability to operate and continue to serve
markets/customers with products and services in the presence of disruptions and long-term crises (i.e.,
the ability to survive in the long term) through adaptation and reconfiguration of changing states
dynamically. (2) Resilience considers the economic performance of individual supply chains. Viability
integrates resilience and sustainability through a combination of economic and societal components.
Viability refers not only to individual supply chains but also to intertwined supply networks and
ecosystems”. Hence, resilience focuses on a system's ability to recover and return to normal conditions
after short-term disruptions; viability emphasises the capacity to withstand by adapting to prolonged,
long-term disruptions. Supply chain viability also provides short-term resilience.
Researchers have highlighted how different industries, such as pharmaceutical (Schleifenheimer and
Ivanov 2024) automobile (Chervenkova and Ivanov 2023) and manufacturing (Ali and Gossaye 2023),
demonstrated viability throughout the pandemic. Viability is not limited to interactions between entities
in one industry; it enables interactions between entities in multiple industries, enabling more synergy
and sustainability (Ivanov et al. 2023).
Ivanov's work and contributions from other scholars have been pivotal in shaping the discourse around
supply chain viability. However, there is still limited research focused on exploring viability within the
agri-food sector. The following sections will explore the current literature on agri-food supply chain
viability research and identify emerging themes in this field of study.
2.2 Agri-Food Supply Chain Viability
Balezentis et al. (2023) provide theoretical foundations for ASC viability by exploring related literature
and acknowledging the scarcity of research in this area. Vidanagamachchi and Ginige (2023) and
Vidanagamachchi et al. (2024) identify viability within the broader context of the agri-food supply chain
(ASC) ecosystem in COVID-19 disruption. Vidanagamachchi et al. (2024) explore the applicability of
ASC viability as a strategy to face potential climate change disruptions, where ASC stakeholders are
interconnected and capable of dynamically reconfiguring in response to evolving disruptive
circumstances. The studies have uncovered the viability traits of ASCs through their adaptations in the
production (cultivation and harvesting), logistics (collection from farmers, processing, storage, and
transportation to consumer-accessible locations), and consumption (retail purchases, including last-
mile deliveries and final consumption) phases of the supply chain.
Vidanagamachchi et al. (2024) highlight how less popular channels (e.g., home gardens, community
sharing) came to the surface when conventional channels (e.g., supermarkets) failed during the COVID-
19 disruption. This diversity of agri-food supply channels allowed consumers to maintain access to food
during prolonged disruptions, showcasing a symbiotic existence among those channels
(Vidanagamachchi et al. 2024). Symbiotic existence refers to the dynamic interactions between various
channels of fresh agri-food sourcing, where the behaviours of four types of agri-food channels—M1
(home gardening), M2 (food sharing), M3 (farmers' markets), and M4 (markets with intermediaries)—
demonstrate mutual dependence and interconnection that supports long-term survival of ASCs in Sri
Lanka.
Further, this symbiotic existence extends not only within a single industry but also across industries
where “industrial symbiosis” occurs (Ivanov et al. 2023). Industrial symbiosis is achieved when the
waste or by-products of one supply chain process become inputs for another, reflecting the principles of

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sharing and the circular economy. To facilitate this, interconnected supply networks (ISN) (Ivanov and
Dolgui 2020) capable of dynamic reconfiguration are essential. For ASCs, digital technologies enable
this interconnection, and the resulting information-sharing plays a significant role in ensuring viability.
2.3 Role of Digital Inter-connection in ASCs for Viability
Digital interconnectivity is paramount in viable ASCs because the information visibility it facilitates
enables dynamic reconfiguration and adaptations in real-time. Vidanagamachchi et al. (2024) present
a blueprint for a dynamically adaptive ASC ecosystem enabled by digital technologies. The integration
of these digital technologies enables efficient data collection throughout supply chains, creating a data-
rich environment that enhances market knowledge, supply-demand management, multi-product supply
chains, waste reduction, consumer insights, and more. For instance, the seamless transportation of food
from surplus regions to areas with high demand minimises waste, while multi-product transportation
optimises resource use (Nakandala et al. 2016). Furthermore, consumers have greater access to
agricultural knowledge and market information, empowering them to make informed purchasing
decisions and even participate as prosumers through digital technologies. The primary channels for
disseminating this information include social media and various knowledge platforms. In addition,
research and development (R&D) plays a critical role in establishing robust systems for planning,
forecasting, coordination, aggregation, and collaboration. As a result, the blueprint opens up new
business opportunities in the agri-food supply domain while enabling viability.
2.4 Spatial Ecologies within the ASC Ecosystem
2.4.1 ASC Ecosystem: A Systemic View
Rebs et al. (2019) provide a framework of systems perspective on sustainable supply chain management,
which consists of an economic system (the supply chain functions) at the core and the social system
consisting of the government, NGOs, and communities surrounding it, an overarching ecological system
with environmental factors such as biodiversity, natural resources which provides resources for
production. Ivanov (2020) provides a framework for a viable supply chain (VSC) ecosystem consisting
of economy and governance, society, nature and digital and physical forms of supply chains. These
digital and physical forms of inter-connected supply chains form intertwined supply networks (Ivanov
2020), a concept that parallels earlier ideas presented by Ginige (2004). As informed by Ivanov (2020)’s
VSC framework and the viable agri-food supply chain (VASC) ecosystem framework presented by
Vidanagamachchi and Ginige (2023), the technological and information system interacts with social and
economic systems, enabling the viability and dynamic reconfigurations of ecosystem actors.
Figure 1: VASC ecosystem: a systemic view
Hence, based on the frameworks presented in Rebs et al. (2019), Ivanov (2020) and Vidanagamachchi
and Ginige (2023), Figure 1 visualises a systemic view of the interconnected Viable ASC ecosystem
(interrelationships between each system are shown with dotted arrows). The food system is a subsystem

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of the broader economic system in which the agri-food supply chain stakeholders are interconnected
and aim to achieve the most desired cost, quality, and availability for the consumer.
2.4.2 Applicability of Spatial Ecologies in ASCs
‘Spatial ecology’ (placed in the environmental system in Figure 1) examines the interplay between the
spatial distribution of ecological elements and the processes influencing those patterns. It emphasises
how the arrangement of entities, such as individuals, communities or populations, across space affects
and is affected by ecological interactions and dynamics (Fletcher and Fortin 2018). Although not
explicitly addressed in the current ASC literature, the principles of spatial ecology can be applied to
improve ASCs, promoting viability, which can be explained under the following key areas.
Place-based production: Place-based production recognises the unique ecological and geographical
features of locations (Vermunt et al. 2020). Spatial ecology reveals that environmental factors such as
climate, soil, and water availability determine what can be sustainably produced in a specific region.
Understanding such spatial dynamics is crucial for designing efficient agri-food supply chains and
guiding decisions on sourcing, transportation, and regional specialisations. For example, short-food
supply chains are primarily associated with place-based production. Marsden et al. (2000) highlight the
importance of understanding "how supply chains are built, shaped, and reproduced over time and
space," emphasising the influence of local factors.
Spatial relationships and sustainable food systems: López-García and González de Molina
(2021) emphasise the need to address the geographical separation between production and
consumption in food systems, a concept known as the "metabolic rift." It argues that sustainable food
systems require re-localisation and the reorganisation of activities to close these spatial loops. They
suggest that City Region Food Systems (CRFS) bridge rural and urban areas, promoting coordinated
policymaking and stakeholder engagement across these spaces for more sustainable food flows. Further,
Vidanagamachchi and Ginige (2023) and Vidanagamachchi et al. (2024) also highlight that there has
been a clear separation between production areas (rural) and urban consumption areas, which has been
a key reason for the food unavailability problem during COVID-19 in urbanised areas.
Supply chain connectivity and resilience: In spatial ecology, 'connectivity' refers to the links
between habitat patches that enable organism movement (Fletcher and Fortin 2018). Further, the
concept of telecoupling highlights how geographically distant regions are interconnected through flows
of materials, energy, information, and people, creating interdependencies with significant social,
economic, and environmental impacts (Coenen et al. 2023; de Castro et al. 2022; Liu et al. 2019).
Similarly, agri-food systems can be seen as networks where the flow of goods mirrors these ecological
movements, offering insights into identifying vulnerabilities and enhancing resilience (Chiffoleau et al.
2020; Nakandala and Lau 2019). Spatial analysis can identify weak points or bottlenecks in supply
chains, such as those vulnerable to extreme weather or political instability, where disruptions could have
severe consequences. Understanding these spatial dependencies is essential for designing supply chains
that can better withstand shocks and adapt to stresses.
2.5 Research Question
According to the literature review, the adaptations that occurred in ASCs during the COVID-19 are well-
documented. However, while it is evident that spatial ecologies have influenced these adaptations, the
causal relationships driving them have not been systematically identified so far. Identifying them is
important to better prepare to manage future disruptions and ensure the viability of diverse spatial
ecological food systems. Thus, the research question explored in this study is: What are the causal
relationships between spatial ecologies and agri-food supply chain adaptations during COVID-19, and
how can these relationships inform strategies to ensure the viability of diverse spatial ecological food
systems in the face of future disruptions?
3 Research Approach and Modelling Methodology
This section explains the theoretical background and the research approach followed in this study.
3.1 Theoretical Background: Systems Thinking
Given the complexity of agri-food systems, understanding the causalities of adaptations of ASCs during
COVID-19 disruptions requires a systems perspective. Systems thinking is a discipline for understanding
the complexity underlying business, economic, scientific and social systems, and it offers a structured
approach to analysing the dynamics of complex problems (Bosch et al. 2007). This study utilises tools
in systems thinking to explore the problem of interest.

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3.2 Research Approach
The study deploys a qualitative approach to analyse the data collected from ASC stakeholders in Sri
Lanka.
Study context: The study was carried out in Sri Lanka to capture the causalities of the adaptation
strategies of ASC stakeholders to the COVID-19 disruption. Sri Lanka underwent a longer-term
disruption than most other countries due to the economic and political instability that followed COVID-
19 (George et al. 2022).
Materials & Methods: Stakeholder interviews were conducted with farmers (05), transporters (02),
retailers (06), consumers (27), agri-tech entrepreneurs (03), and policymakers (02) in Sri Lanka. The
interviews captured the country's adaptations in critical spatial ecologies, urban, semi-urban, and rural
areas, by selecting consumers, farmers, and retailers covering diverse areas.
The interviews were transcribed and analysed using ATLAS.ti software following the thematic analysis.
The interview responses were used to identify causal expressions, such as “X caused Y,” those causalities
were organised based on two key tools in systems thinking: the iceberg of systems thinking and causal
loop diagramming (CLD). Vensim PLE software was used to develop CLDs.
3.2.1 Iceberg Model
The Iceberg model, also known as the four levels of systems thinking (Maani and Cavana 2007), provides
a solid foundation and clarity for understanding observed problems. It helps to identify the underlying
structures and mental models that drive observable events and patterns within supply chains. Often, we
only see the surface symptoms of a problem but not the underlying causes that led to it. The iceberg
model helps outline the various levels of causality in a systematic manner.
The model has four levels, with only the top level (events) visible. This level includes the visible signs or
symptoms of a problem. Below this level are recurring themes or trends (patterns) that emerge from a
series of events, allowing for a deeper understanding of the system's behaviour over time. The structure
level represents the underlying relationships and interconnections within the system, including
feedback loops, causal relationships, and the arrangement of system components. The base is made out
of beliefs, assumptions, and values that are often unspoken but shape how people think and act within
the system. These mental models influence the structures, patterns, and events observed. Home
gardening is a production adaptation that was visible during COVID-19. Figure 2 depicts the
applicability of the iceberg model for the home gardening adaptation (explained in 4.1).
Figure 2: Iceberg model of system thinking (application to the event home gardening)

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3.2.2 Causal Loop Diagrams
A causal loop diagram (CLD) is a tool for revealing the causal relationships among a set of variables (or
factors) operating in a system (Maani and Cavana 2007). The process of deriving causalities from
interview data involved the following seven steps:
Step 1- The interview transcripts were classified into three spatial segments- urban, semi-urban, and
rural- based on the interviewees' geographic locations.
Step 2- Behavioural patterns of adaptations in these spatial areas were mapped over time (before,
during, and after the COVID-19 pandemic) in graphical form to capture changes and trends.
Step 3- These adaptations were categorised and coded based on their type - production, logistics, or
consumption.
Step 4- Interview responses were analysed to identify factors influencing the increase or decrease in
these adaptation patterns. Causal expressions were formulated from these responses (e.g., "X caused
Y").
Step 5- The causal expressions were then analysed within each spatial segment, focusing on the factors
that influenced these adaptations.
Step 6- Structural and mental model-related causalities were grouped meaningfully to reflect deeper
insights into how factors interacted within and across segments.
Step 7- Finally, CLDs were constructed to map the causal relationships and feedback loops.
CLDs are a systems thinking tool for visualising the circular cause-and-effect relationships within a
system. In a CLD, the causality between two variables is represented by an arrow pointing towards the
effect. A plus sign (+) on an arrow signifies that an increase in one variable leads to an increase in the
other, while a minus sign (-) implies opposite effects. CLDs map the feedback loops within the system,
highlighting the reinforcing and balancing loops that influence supply chain dynamics. In a reinforcing
loop, the causal relationships amplify change in one direction. In a balancing loop, the causal
relationships maintain stability or resist change, often keeping a system within a specific range. Based
on the causalities extracted from interviews, the study looked at the underpinning causalities of selected
adaptations.
3.2.3 Iceberg Model and Causality Mapping
Figure 3 shows the framework that underpins the organisation of the causalities. Figure 3 was
constructed based on the nature of the disruption and the adaptations that emerged due to COVID-19.
The key events are the adaptations that emerged in response to the disruption. Those events result from
stakeholder actions (behaviours) repeated over the period concerned. For them to take action, what they
already know and believe affects significantly as hidden forces. For example, from a consumer
perspective, their primary need (food availability) was challenged during the COVID-19 disruption.
From their experience, they believe (mental model) they can either buy from alternative places or grow
at home. Depending on the constraints (structures) they have (such as land availability, resource
availability, information availability, and geographical locations), they took favourable actions. Hence,
the existing structures either amplify or constrain the intensity of behaviours.
Figure 3: Causal flow in agri-food system adaptations: from mental models to observable outcomes

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In the same way, the events (adaptations) at the tip of the iceberg that were successfully implemented
and helped solve the issue influence (feedback) the mental model of the stakeholders of ways to address
the issue by developing a positive impression over time about that particular adaptation and the
strategies that worked. These tend to change the structure by creating conducive environments
(reinforce) for such adaptations to evolve/ expand.
The framework in Figure 3 effectively provided the base for translating interview data into causal chains
and successfully identifying stakeholder mental models, behaviours over time, underlying structures,
and observable events (adaptations) in various spatial ecologies, as discussed in Section 4 below.
4 Results and Discussion
Vidanagamachchi and Ginige (2023) show that there have been multiple adaptations in the production,
logistics and consumption stages of the supply chain. Following is the fundamental phenomenon that
happened due to COVID-19 disruption as a series of causalities.
Covid-19
à
Disruptions
à
Food Shortage
à
Adaptations
à
Sufficient Food.
The issue at hand is the food shortage, leading to necessary adaptations that helped people find sufficient
food and restore balance in the situation. This scenario is being studied at the event level. According to
consumer interviews, home gardening is one of the key adaptations in all spatial ecologies, urban, rural,
and semi-urban. However, the scale of home gardening differed significantly across the three spatial
ecologies, and the production methods used also varied based on factors such as the availability of land,
resources and others. The scenario below explores how the adaptation of home gardening can be
explored further based on the iceberg and causality framework presented above.
4.1 Application of Iceberg Model to Home Gardening Adaptation (Event)
Figure 2 illustrates the various factors that impact the widespread adoption of home gardening during
the pandemic, using the iceberg model to demonstrate levels of systems thinking. Home gardening is a
form of production adaptation that has taken place in urban, semi-urban, and rural areas to varying
degrees and in different ways. Table 1 outlines these behaviours, identifying the specific structural
elements and mental models that led them. Additionally, it provides example quotations from
interviews, breaking down the influencing factors found under different levels. It further provides the
causal chains that are demonstrated by quotations.
According to Table 1, people believe that growing is the most reliable source of fresh food when other
purchasing channels are inaccessible. Based on that mental model, people look for ways to start home
gardens. As per the interviews, people from different areas approached gardening differently. Those are
due to the constraints/ encouragements provided by the structure. At the structure level, what shapes
the gardening decision are the land area available, availability of farming knowledge, availability of time
to dedicate to gardening, capital available to buy inputs and tools, accessibility to technology, to acquire
farming knowledge or space utilisation techniques, innovative methods of farming such as polyhouse,
information about crop varieties, weather.
As per the analysis, the factors vary across different spatial environments. However, Figure 4 illustrates
a typical pattern defining the structure of the three ecologies. Urban areas exhibit high accessibility to
digital technologies, infrastructure, service levels, capital investments, digital literacy, and population
density. On the other hand, rural areas are characterised by factors such as high land availability,
farming knowledge, natural resources, labour availability, and community engagement; semi-urban
areas can be characterised as having moderate levels of all these structural elements.
Figure 4: Structural factors influenced the adaptation of home gardening in spatial areas

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Level
Explanation
Quotations
Causalities
Event
Home gardens
became a major
source of food
during COVID-
19
“After lockdown, we saw a huge rise
in the number of home gardens
when stores were inaccessible.” (A
consumer)
During COVID-19 only we started
growing something at home, at least
some crops such as tomatoes or
green chillies, because we couldn’t
go out (A consumer)
During Covid, we did a lot of
gardening; even if we didn’t have
enough space, we grew in pots. (A
consumer)
Lockdown Restrictions à
Reduced market access for
purchasing food àIncrease
in home gardening
Pattern
Increase in
home gardens
during COVID-
19.
"During the lockdown, we expanded
the number of varieties of vegetables
at the home garden to fulfil our day-
to-day food requirement" (A
consumer)
Lockdown restrictionsà
Expansion/ increase of
home gardensà Food
availability
Structure
Land area,
Knowledge
"We had enough land and
knowledge to start a home garden."
(A consumer)
Availability of land area,
Knowledge à Ability to start
a garden.
Labour/ time
"My family looked after the garden,
especially when we had more time
at home." (A consumer)
Availability of time à Ability
to start a garden
Knowledge
"We knew how to grow certain
vegetables, so it was easy to start a
garden." (A consumer)
Knowledge à Ability to start
a garden
Capital
"Having a small budget limited our
ability to grow more food." (A
consumer)
Small budget à Limited
ability to grow more food
Government
policies
"Government also launched a home
gardening programme, and they
also distributed seeds through local
councils." (A consumer, farmer)
Government/policy support
à Encouragement to do
gardening
Technology
"We watched YouTube tutorials on
how to grow vegetables in small
spaces." (A consumer)
Limited space àNeed for
knowledgeàYouTube/ use
Technologyà Farming
knowledge
Mental
Model
Buying and
growing are the
ways to obtain
food.
"During COVID, if we couldn't buy,
we had to grow our own food. It was
the only way."(A consumer)
COVID-19
lockdownàLimited
buyingà Need to grow
foodàFood availability
Table 1: Iceberg of Home Gardening Adaptation During the Pandemic
The structure has significantly impacted ASC stakeholder actions, resulting in certain patterns, such as
home gardening, food sharing, online purchasing, and door-to-door deliveries, to repeat in the face of
COVID-19 disruption in response to the food shortage issue.
The interviews with various stakeholders, including consumers, retailers, farmers, policymakers, and
agri-tech entrepreneurs, helped reveal connections within the three spatial ecologies at a systemic level.
The iceberg model has been used in other adaptations, and the identified patterns and behaviours over
time, structures, and mental models have been used to derive causal relationships to create causal loop
diagrams to explain the overall system's behaviour.
4.2 The Impact of the Link Between Rural and Urban Areas on the Adaptations
Figure 5 illustrates how the COVID-19 pandemic disrupted the rural food system in Sri Lanka and its
impact on urban and semi-urban food systems. The mapping aims to show how structural differences
in ecologies have influenced the food security of each area, leading to specific adaptations with the
government's intervention. There is an abundance of land in rural areas, enabling the production

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2024, Canberra Spatial Ecologies in Viable Agri-Food Supply Chains
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(commercial farming) of an ample harvest to sustain urban and semi-urban areas. Conversely, limited
land availability in urban areas results in minimal food production.
Figure 5: CLD: Adaptations related to rural areas and linkage with other areas
Reinforcing Loops: The closure of farmer's markets due to the COVID-19 pandemic has prompted
consumers to expand their home gardens. Reinforcing loop 1 (R1) relates to “home gardening and food
availability,” which shows how home gardening increases food availability for rural consumption,
leading to excess harvest, reinforcing more home gardening through sharing with communities and
selling. Land availability, farming knowledge, and government support positively influence home
gardening.
Reinforcing loop 2 (R2) implies that selling excess harvest generates income, encouraging further
investment in rural production (e.g., better seeds and tools), leading to more excess harvest and income.
Balancing Loops: During the COVID-19 disruption, the transportation of food between regions was
halted, leading to a severe shortage of fresh food in urban areas and a subsequent increase in prices. The
interviews revealed that the government issued special permits to truck owners, allowing them to
transport food throughout the country. B1 loop reveals that the government intervention enabled the
transportation of food from rural areas to urban markets, stabilising food availability and prices in urban
areas.
Further, the selling excess harvest to mobile sellers balancing loop (B2) shows that when the transport
of food from rural to urban was halted, there was an abundance of food in rural areas, leading to a
reduction in fresh food prices in rural areas. When the prices are low, the mobile trucks buy more of this
excess harvest, which helps stabilise rural food prices by preventing food oversupply in rural markets.
In addition, when fresh food prices are low in rural areas, it discourages further production, prevents
overproduction, and helps stabilise rural food prices over time, as depicted in B3.
Furthermore, urban and semi-urban areas witnessed distinct technology adaptations in response to the
disruption, as discussed in 4.3.

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4.3 Impact of Technology on Adaptations in Different Spatial Ecologies
Figure 6 depicts a causal loop diagram of the adaptations that happened due to digital technologies,
especially around urban areas. The objective of this diagram is to show that the urban adaptations to
COVID-19 are different to those of Rural. And semi-urban adaptations have not been explicitly drawn
here. However, that includes adaptations from rural and urban areas, where home gardening, sharing,
community activity, and use of technology are to a moderate degree, depending on the structural factors
identified in Figure 4. Urban areas with high population densities and limited land lack opportunities
for farming. However, due to advancements in technology and digital literacy among urban populations,
more technology adaptations have helped them overcome the food shortage issue.
Figure 6: CLD: Technology adaptations related to urban areas
Reinforcing Loops: R1, R2, and R3 loops show how increasing one factor (e.g., knowledge sharing,
community platform use, online ordering) positively influences food availability, which feeds back into
further increasing the initial factor. This happens when traditional supermarket access is limited.
Balancing Loop: B1 shows how the system stabilises food availability when traditional supermarket
access is limited. When the government started issuing transport and fuel permits, businesses and truck
owners adapted to delivering or selling food to doorsteps, as identified before. However, when sufficient
food was available, and the COVID-19 situation was balanced, the government discontinued issuing the
permits, implying that the system did not spiral out of control and that food availability is maintained
through mobile sellers during times of crisis.
Hence, it is evident that in the face of disruption, different spatial ecologies behaved in different ways
shaped by the structural factors inherent to each of those spatial ecologies. According to Figure 3, the
dotted arrows (d,e) indicate that once an intervention is implemented, based on its response over time,
it will influence changing the mental model and the structure. Changing the structure will result in
different behaviours. How do we change the structure of spatial ecological agri-food ecosystems to be
more viable?
4.4 Identifying Leverage Points
Leverage points are the places to intervene in a system where a small change could lead to a significant
shift in behaviour (Wright and Meadows 2009). As this study observed in the CLDs above, when the
signs of a food shortage started happening, the government first intervened by launching a home garden
programme to encourage people to self-grow, then issuing permits to truck owners to transport food
items and providing them with fuel permits where food has been identified as a primary and urgent need
of people. The impact it created in addressing the issue was immense. Government policies are effective
ways of intervening in a system so that it can change the structure.
Marambe and Silva (2020) outline all the policy interventions that took place in Sri Lanka during the
first 60 days of COVID-19 to minimise the effect of food shortage within the country. Beyond policy

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interventions, technology has played a vital role by providing alternative ways of finding food for urban
populations and selling the harvests of rural farmers. Although the adoption of technology among rural
farmers was minimal, younger farmers, one among the participants, started selling through Facebook,
highlighting the potential of technological integration.
When new technology is introduced into a supply chain, it can reshape both its structure and behaviour.
For example, mobile applications can revolutionise communication by enabling features such as mobile
ordering or real-time inventory checks. These changes in communication directly impact the flow of
materials across the supply chain, from production to consumption. Over time, this leads to new
behaviour patterns and the emergence of different channels for interaction, fostering more efficient or
innovative connections between consumers and businesses. Given that production, logistics, and
consumption phases must be seamlessly interconnected, creating an environment that allows ASC
stakeholders to adapt and reconfigure is critical for sustaining these technological advancements.
5 Conclusion
The study highlights the critical role of spatial ecologies—urban, semi-urban, and rural—in shaping the
adaptations within agri-food supply chains, as analysed through system thinking tools such as the
Iceberg Model and Causal Loop Diagrams. The findings demonstrate that spatial dynamics, such as land
availability, technology, and infrastructure, play a fundamental role in influencing behaviours and
adaptations, especially during disruptions such as the COVID-19 pandemic. Restrictions on movement
and access to food sources led to varied behaviours across different locations while still maintaining the
core Production-Logistics-Consumption (PLC) chain.
Adaptations were more effective when digital technology was integrated into the spatial domain,
allowing for greater resilience in the face of external shocks. Digital tools facilitated communication,
resource distribution, and knowledge sharing, proving essential for the ongoing viability of food systems.
Spatial ecologies influence how food systems respond, and the integration of digital technology helps
bridge the gaps between different environments, enabling better adaptation strategies.
This initial analysis explores agri-food supply chain viability in a country disrupted by COVID-19 and an
economic crisis, revealing how adaptations were shaped by the country’s diverse spatial ecologies.
Future research can build on these insights to compare adaptations in other regions, helping design
viable agri-food supply chain ecosystems. To create viable agri-food systems, well-designed digital must
be adaptable, user-friendly, and cater to the diverse needs of stakeholders in various spatial contexts.
Policy and business interventions, combined with spatial awareness and digital applications, are
essential for ensuring the long-term viability of agri-food supply chains.
Thus, a coordinated approach that includes spatial dynamics, technology, and policy will be vital to
maintaining food security in an increasingly uncertain global environment. This paper significantly
contributes to the understanding and application of spatial ecologies in planning viable agri-food
systems and extending the theory on supply chain viability.
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Copyright © 2024 Kasuni Vidanagamachchi, Athula Ginige and Dilupa Nakandala. This is an open-
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