Systems thinking: an approach for understanding ‘eco-agri-food systems’. In TEEB for Agriculture & Food: Scientific and Economic Foundations. Geneva: UN Environment. Chapter 2, 17-55.

Full text

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
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2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
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Coordinating lead author: Wei Zhang (International Food Policy Research Institute)
Lead authors: John Gowdy (Rensselaer Polytechnic Institute), Andrea M. Bassi (KnowlEdge Srl / Stellenbosch University),
Marta Santamaria (Natural Capital Coalition) and Fabrice DeClerck (EAT Foundation / Bioversity International)
Contributing authors: Adebiyi Adegboyega (Michigan State University), Georg K.S. Andersson (National University of Río
Negro / Lund University), Anna Maria Augustyn (Groupe de Bruges), Richard Bawden (Western Sydney University), Andrew
Bell (New York University), Ika Darnhofer (University of Natural Resources and Life Sciences, Vienna), John Dearing (University
of Southampton), James Dyke (University of Southampton), Pierre Failler (University of Portsmouth), Leonardo Galetto
(National University of Córdoba / National Research Council, Argentina), Carlos Calvo Hernández (New York University), Pierre
Johnson (Transition & Cooperation), Sarah K. Jones (Bioversity International / King’s College London), Gary Kleppel (State
University of New York), Adam M. Komarek (International Food Policy Research Institute), Agnieszka Latawiec (International
Institute for Sustainability), Ricardo Mateus (University of Lisbon), Alistair McVittie (Scotland’s Rural College), Enrique
Ortega (State University of Campinas), David Phelps (Department of Agriculture and Fisheries, Australia), Claudia Ringler
(International Food Policy Research Institute), Kamaljit K. Sangha (Charles Darwin University), Marije Schaafsma (University
of Southampton), Sara Scherr (EcoAgriculture Partners), Md Sarwar Hossain (University of Bern), Jessica P.R. Thorn (Colorado
State University), Nicholas Tyack (Graduate Institute of International and Development Studies), Tim Vaessen (Foundation for
Sustainable Development), Ernesto Viglizzo (National Research Council, Argentina), Dominic Walker (New York University),
Louise Willemen (University of Twente) and Sylvia L.R. Wood (Université de Québec en Outaouais)
Review editors: Carl Folke (Stockholm Resilience Centre) and Heidi Wittmer (Helmholtz Centre for Environmental Research)
Reviewers: Molly Anderson (Middlebury College), Franz Gatzweiler (International Council for Science) and Jules Pretty
(University of Essex)
Suggested reference: Zhang, W., Gowdy, J., Bassi, A.M., Santamaria, M., DeClerck, F., Adegboyega, A., Andersson, G.K.S.,
Augustyn, A.M., Bawden, R., Bell, A., Darknhofer, I., Dearing, J., Dyke, J., Failler, P., Galetto, L., Hernández, C.C., Johnson,
P., Jones, S.K., Kleppel, G., Komarek, A.M., Latawiec, A., Mateus, R., McVittie, A., Ortega, E., Phelps, D., Ringler, C., Sangha,
K.K., Schaafsma, M., Scherr, S., Hossain, M.S., Thorn, J.P.R., Tyack, N., Vaessen, T., Viglizzo, E., Walker, D., Willemen, L. and
Wood, S.L.R. (2018). Systems thinking: an approach for understanding ‘eco-agri-food systems’. In TEEB for Agriculture &
Food: Scientic and Economic Foundations. Geneva: UN Environment. Chapter 2, 17-55.
2CHAPTER 2
SYSTEMS THINKING:
AN APPROACH FOR UNDERSTANDING
‘ECO-AGRI-FOOD SYSTEMS’
17

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
18
CONTENTS
2.0 Key messages
2.1 Introduction
2.2 Why are systems-based analytical approaches needed?
2.3 A systems approach for the eco-agri-food system
2.4 Conclusion
List of references
SUMMARY
Chapter 2 makes the case for using systems thinking as a guiding perspective for TEEBAgriFood’s development of
a comprehensive Evaluation Framework for the eco-agri-food system. Many dimensions of the eco-agri-food system
create complex analytical and policy challenges. Systems thinking allows better understanding and forecasting of the
outcomes of policy decisions by illuminating how the components of a system are interconnected with one another
and how the drivers of change are determined and impacted by feedback loops, delays and non-linear relationships. To
establish the building blocks of a theory of change, systems thinking empowers us to move beyond technical analysis and
decision-tool toward more integrated approaches that can aid in the forming of a common ground for cultural changes.
FIGURES, BOXES AND TABLES
Figure 2.1 Mapping evidence of policy impact
Figure 2.2 The safe and just space for humanity
Figure 2.3 Photo showing industrial monoculture alongside smallholder agriculture in Tanzania
Figure 2.4 Food systems map that shows how multiple subsystems interact
Figure 2.5 Modiedhigh-level‘systems’diagramofanarchetypaleco-agri-foodsystem 
Figure 2.6 Illustrative Causal Loop Diagram of a generic eco-agri-food system
Box 2.1 Case study: pushing the ecosystem beyond its critical safe boundaries in the Argentine
Pampas during the 20th century
Box 2.2 Case study: the complex reality faced by smallholders farming riverside vegetables in the
dry season, Northern Ghana
Box 2.3 Case study: genetic diversity and the eco-agri-food system
Box 2.4 Case study: what constitutes a “successful” model? The case of soybean industrial
production in Argentina
Box 2.5 Case study: evaluating the impact of fertilizer subsidy policy in Malawi
Box 2.6 Case study: energy subsidy and groundwater extraction for irrigation in India
Box 2.7 Case study: sustainability of coastal agriculture in Bangladesh: Operationalising safe
operating space using social-ecological system dynamics
Box 2.8 Case study: Bayesian networks: a useful tool in applying systems thinking?
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2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
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2.0 KEY MESSAGES
CHAPTER 2
• This chapter makes the case for using systems thinking as a guiding perspective for TEEBAgriFood’s development
of a comprehensive Evaluation Framework for the eco-agri-food system.
• ‘Eco-agri-food systems’ is our collective term for the vast and interacting complex of ecosystems, agricultural
lands,pastures,inlandsheries,labour,infrastructure,technology,policies,culture,traditions,andinstitutions
(including markets) that are variously involved in growing, processing, distributing and consuming food.
• Diverse agricultural production systems grow our crops and livestock and employ more people than any other
economic sector. They are underpinned by complex biological and climatic systems at local, regional and
global levels. These natural systems are overlaid by social and economic systems, which transform agricultural
productionintofoodandnallydeliverittopeoplebasedonmarketinfrastructure,economicforces,government
policies, corporate strategies and consumer and societal preferences. Furthermore, technologies, information
and culture are continually re-shaping production, distribution and consumption, as well as the interactions
among them.
• The global food system is one of the most important drivers of planetary transformation and it is experiencing
multiple failures. Many dimensions of the eco-agri-food system create complex analytical and policy challenges.
In the end, the state of human wellbeing, including the health of people and the planet, is determined by the
diverse interlinked “eco-agri-food systems” and consumer choices made within these systems.
• Eco-agri-food systems are more than production systems. Using one-dimensional metrics such as “per hectare
productivity” ignores the negative consequences and the trade-offs across multiple domains of human and
planetary wellbeing and fails to account for the various dimensions of sustainability.
• Silo approaches are limiting our ability to achieve a comprehensive understanding of the interconnected nature of
the eco-agri-food system challenges. We need a holistic framework that allows the integration of well-understood
individual pieces into a new, complete picture.
• Systems thinking allows better understanding and forecasting of the outcomes of policy decisions by illuminating
howthe components ofasystem are interconnectedwithoneanother. Systemsthinkingidentiesthe drivers
of change as determined and impacted by feedback loops, delays and non-linear relationships. Synergies and
coherence can be gained when evidence is generated and used based on concepts and methods aligned with
systems thinking.
• In the context of TEEBAgriFood, an important role of systems thinking is to identify the main components, drivers,
dynamics and relationships that impact the entire value chain of the eco-agri-food system. This helps make side
effectsandtradeoffsvisible,allowsforidenticationofwinnersandlosers,anduncoverssynergiesthatcanbe
realized through the implementation of public policies or other behaviour interventions.
• To establish the building blocks of a theory of change, systems thinking empowers us to move beyond technical
analysis and decision-tool toward more integrated approaches that can aid in the forming of a common ground
for cultural changes.

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
20
SYSTEMS THINKING
AN APPROACH FOR UNDERSTANDING
‘ECO-AGRI-FOOD SYSTEMS’
2.1 INTRODUCTION
Our crops and livestock arise from diverse agricultural
production systems that employ more people than
any other economic sector globally (ILO 2014). These
production systems are underpinned by complex
biological and climatic systems at local, regional and
global levels. Overlaying these production systems are
social systems, including those involved with agricultural
production and the transformation of crops into food, fuels
and bre. A third layer consists of economic systems,
which deliver agricultural products to people, based
on market forces, available infrastructure, government
policies, and corporate strategies, all of which interact
with consumer preferences and broader societal norms.
Many of the interactions, both within and across systems,
involve “externalities” (positive or negative), described in
economicsasthecostorbenetthataffectsapartywho
did not choose to incur that cost or benet (Buchanan
and Stubblebine 1962). Furthermore, technologies,
information, divergent views, and culture are continually
re-shaping production, distribution, and consumption
modes, as well as the interactions among them. In the
end, the state of many dimensions of human wellbeing,
including the health of people and of the planet, are
affected by the diverse interlinked food systems and
the consumer choices made within these systems. In
this report, the eco-agri-food system refers to the vast
and interacting complex of ecosystems, agricultural
lands, pastures, inland sheries, labour, infrastructure,
technology, policies, culture, traditions, and institutions
(including markets) that are variously involved in growing,
processing, distributing and consuming food.
The global food system, one of the most important drivers
of planetary transformation (Rockström et al. 2009a;
Rockström et al. 2009b; Ehrlich and Ehrlich 2013), is
“failing”, and the “business-as-usual” model is not working
(Vivero-Pol 2017; IFPRI 2016; IAASTD 2009; Rosin et al.
2012a; Rosin et al. 2012b). The Global Food Policy Report
(IFPRI 2016, p.6) points out the failures of the current
food system:
On the one hand, it feeds more than 6 billion people—more
than many in earlier decades and centuries would have
believed possible. On the other hand, it leaves nearly 800
million people hungry. It does not provide all people with
a healthy, safe, and nutritious diet; many of those who get
sufcient calories are still malnourished. The food system
does not generate adequate livelihoods for millions of people
employed in the food system. And in a context of scarce and
degraded natural resources and advancing climate change, it
is not environmentally sustainable.
Humans are the main driver of change in the epoch in
which we live, the new geological era some refer to as the
Anthropocene (Rockström et al. 2009a; Steffen et al. 2011;
Steffen et al. 2015). Much of this transformation has been
driven by the commercialization of production and the
mechanization of agriculture globally (see Box 2.1 for
an example), but failure by markets and governments to
address externalities that affect social and environmental
integrity have also contributed to the problem. The
negative impact of human activity on the natural world has
reached crisis levels. Terrestrial vertebrate populations
declined by an astonishing 58 per cent between 1970
and 2010 (WWF 2016). Invertebrate populations show a
global decline of about 45 per cent over the past 40 years
(Dirzo et al. 2014). Similar declines have been documented
for marine species (McCauley et al. 2015). Much of the
declines in wildlife is attributed to habitat loss, pollution
and over-exploitation associated with food production
systems (Rockström et al. 2009a; Godfray et al. 2010;
Amundson et al. 2015). Livestock production is the largest
source of anthropogenic alteration to global phosphorus
and nitrogen cycles. Since the 1950s, surpluses in these
nutrients have increased by a factor of four and ve,
respectively (Bouwman et al. 2013). Excess quantities
of these nutrients entering waterways are the leading
causes of freshwater and marine eutrophication and the
emergence of dead zones affecting aquatic life. Soil loss
and terrestrial nutrient depletion are also accelerating
(Baveye et al. 2016).
Furthermore, the expansion of industrial agriculture in
many cases has had adverse social consequences for
human communities (Ehrlich and Ehrlich 2013). Land-
insecure smallholders, family farmers and peri-urban
CHAPTER 2

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
21
settlers are being pushed off land they have traditionally
cultivated in many parts of the world, in the face of
commercialization and the purchase of large tracts of land
by foreign or absentee investors (De Schutter 2011; Rulli
et al. 2013; Thorn et al. 2015). Many such cases have been
documented in Latin America (Arancibia 2013; Carrizo and
Berger 2012; Lapegna 2013; 2017; Leguizamón 2014a). In
addition to a host of social impacts, such displacement
leads to the loss of the local, experiential knowledge that
is essential for site-appropriate agricultural production
practices. Locally adapted cultivars and breeds may be
lost, reducing agricultural biodiversity.
Seeking an ecologically sustainable and socially fair
transition out of the current crisis has become an issue
of utmost priority (Vivero-Pol 2017). Multiple voices have
called for a paradigm shift in the structure and operation of
the global food system (IAASTD 2009; Watson 2012; Rosin
et al. 2012b), although the values, narratives, economic
and moral foundations of that new aspirational and
inspirational paradigm have not yet been fully developed
(Vivero-Pol 2017). The application of systems thinking to
understanding and managing the complexity of the global
eco-agri-food system is an important step in achieving
this transformation (Bosch et al. 2007; UNEP 2011). In
this report, TEEBAgriFood sets out to evaluate the reality
of today’s highly complex “eco-agri-food” systems. By
making the invisibles (externalities) visible, the society will
be better positioned to take into account the impacts of
activities that have previously been ignored.
Traditionally, scientists have assessed or analysed
components or subsystems of the eco-agri-food
system in individual studies. The goal has been to
improve the eciency of each component, based on
theassumptionthatthiswill also improvetheeciency
of the whole system. However, little attention has been
paid to connecting the pieces of this puzzle to achieve
a comprehensive understanding of what takes place
in reality. Indeed, a holistic framework that allows the
integration of these pieces into a new, full, picture has
thus far been lacking. Using money as the common unit,
economists have focused on aspects that can be readily
identied, traded and monetized. However, this has left
social and environmental impacts along value-chains
insuciently considered or valued, especially if they are
nancially invisible. By emphasizing evidence-based
choices, political decision makers have relied on best
estimates and expert knowledge, taking into account
only those pieces of the puzzle that are well researched
and leaving out much local, traditional and indigenous
knowledge.Moreover,thelackofinformationowbetween
scientists, practitioners and policy makers exacerbates
these shortcomings, contrary to increased emphasis upon
evidence-based policy (Pretty et al. 2010). Despite evidence
of the interconnectedness of challenges across sectors,
thecurrentpoliticalandscienticincentivestructuresdo
not reward integrated approaches that address linkages,
time delays and feedback loops, which cut across multiple
sectors and disciplines, to seek shared solutions. The
consequences, trade-offs and impacts left unaddressed,
too frequently work against achieving sustainability in the
eco-agri-food system overall.
As population and inequity increase worldwide, critical
questions arise regarding how we can produce and
distribute food of high nutritional quality to feed a growing
global population in a sustainable manner (Foresight
2011). Future policy decisions will increasingly pit
multiple domains of ecological sustainability, economic
development, and human well-being against one another,
but this growing complexity cannot be a cause for inaction.
Systems thinking, which focuses on the identication
of interrelationships between components, is urgently
neededtohelpusndareaswheresynergiesarepossible
and where interventions will have the most impact, as
well as identify where trade-offs must be recognized and
negotiated.
The ambition of the TEEBAgriFood evaluation is to
improve the conditions for integrated decision-making
for a more sustainable eco-agri-food system. This can
only be convincingly done by taking a systems approach
to understand how the eco-agri-food system functions
within natural and social systems, while at the same
time considering cultural narratives and the need for
transformational change. To achieve this, the contributions
of natural and social capital to the eco-agri-food system
need to be made visible. This implies not only focusing on
production processes, but also on multiple interactions,
feedback loops, and pathways by which the environment
and agriculture contribute to human health and well-being.
This calls for redoubling efforts to uncover the values of
services of nature and roles of social capital not accounted
forinthemarketeconomy(TEEB2015)andthefullbenets
and costs of the eco-agri-food system across all stages
of the value chain. We must recognize that the notion of
developing a “full” picture is in itself value-laden, critically
dependent on what is included (hinging on the nature of
knowing and knowledge), what matters to whom, and how
we structure, reason, connect and interpret what we see
(our underlying perspective or worldview, epistemic beliefs
and assumptions). Considering such factors requires
discovery of and appreciation for the epistemological views
of different social actors, which are inherently value-laden,
in order to form a common ground for cultural changes.
The health of our planet and its population depends
on bringing together all components of the eco-agri-
food system for study and decision-making within an
integrated framework. We need a framework where we
can understand that dzud1 in Mongolia, protectionism
in Europe, political change in the U.S., corporate take-
1 A Mongolian term for summer drought followed by a severe winter,
generally causing serious loss of livestock.

over of family agriculture in Australia, or land grabbing
in Africa all affect the quantity and quality of food on
global markets, the stability of impoverished states, and
the functioning of ecosystems in seemingly unconnected
parts of the world. We need a framework that can capture
how the increasing demand for red meat in Asia could
degrade soils in Australia, lead to extinction of yet-to-be-
discovered insects, and contribute to the socio-economic
collapse of small rural towns. Globalization has created
an interconnected global community. We now need a
systems-based framework that can help us connect the
dots and understand the relationships across multiple
sectors, disciplines and perspectives for improved
decision-making. Any framework will have limitations, but
the one contained in this report was created with the intent
to capture as many factors as possible in order to achieve
a more holistic understanding and accurate evaluation of
the eco-agri-food system.
Understanding the complexity of the eco-agri-food
system and its importance for both the health of people
and the planet requires systemic analysis based on a
comprehensive evaluation framework. This chapter
articulates the need for using systems thinking as a
guiding perspective for TEEBAgriFood’s development of
such an Evaluation Framework.
While the empirical evidence of the challenges faced
by the eco-agri-food system and the consequences of
failing to take a systems view are elaborated in Chapter
3, Chapter 4, and Chapter 5, this chapter explores the
role of systems thinking in achieving a more sustainable
eco-agri-food system, by lending conceptual support for
the development and application of the TEEBAgriFood
Evaluation Framework (Chapter 6, Chapter 7 and Chapter
8). Going beyond the Framework to explore other building
blocks of a theory of change and its applications is
discussed in Chapter 9 and Chapter 10.
In this chapter, following the introduction, Section 2.2
explains why we need systems-based analytical tools. An
eco-agri-food system is more than just a production system.
Its multiple dimensions create complex analytical and
policy challenges that require inclusive conceptualizations
and analytical tools. Section 2.3 introduces what systems
thinking has to offer, and explains how a systems
approach, including conceptualization, investigation
and quantication, can contribute to informed decision-
making by integrating the key components of the eco-agri-
food system, i.e. their economic, social, health, ecosystem,
and environmental dimensions. It also demonstrates the
application of a systems approach in understanding the
eco-agri-food system and evaluating options for future
changes to the system. Finally, Section 2.4 concludes with
key messages.
Box 2.1 Case study: pushing the ecosystem beyond its critical safe boundaries in the Argentine Pampas during the
20th century
The Pampas of Argentina are a large and complex sand dune system that formed during the last era of Pleistocene
glaciations and later semi-desertic episodes. Humans only colonized the region during the last century, but their action
was powerful enough to push the ecosystem beyond its safe operating boundaries and trigger two catastrophic events:
one during the rst half of the century,and the other during the second half. Deforestation and de-vegetation, over
grazing and over cropping plus a non-suitable tillage technology, in interaction with extremely dry and windy conditions
of the 1930s and 1940s, caused a large dust-bowl episode that led to severe dust storms, cattle mortality, crop failure,
farmer bankruptcy and rural migration (Viglizzo and Frank 2006). During the second half of the century, improved rainfall
conditions favoured the conversion of abandoned lands into grazing lands and croplands. At the same time, recurrent
episodesofoodingaffectedtheareabetween1970and2017,moredrasticallyinthehighlyproductivelowlandsofthe
area.Thecongurationofduneswithrespecttoslope,andthelackofasuitableinfrastructure,impededwaterremoval
and favoured its accumulation. The expansion of the cultivation frontier with annual crops provoked a rapid rise in the
watertable,whichdramaticallyincreasedtheseverityofoodsduringhumidperiods.Bothecologicalcollapsesduring
the 20th century were the result of a complex interaction of geological conguration, climate variability and human
intervention. Over cropping likely surpassed critical ecological thresholds in the area and this, in turn, triggered both the
dustbowlandtheoodingevents.On theotherhand,naturalfeedback mechanismsactivatedbysucheventshelped
with the stabilization and recovery of the affected lands.
2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
22

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
23
2.2 WHY ARE SYSTEMS-
BASED ANALYTICAL
APPROACHES NEEDED?
2.2.1 Eco-agri-food systems are more
than production systems
Agriculture and food systems have typically been
evaluated based on their yield, with much research
focusing on increasing productivity, rather than on more
holistic, integrative natural resources management (NRM),
and even less on equitable food access and nutritional
security (IAASTD 2009). Using one-dimensional metrics
such as “per hectare productivity” is highly problematic
as it ignores the negative consequences (i.e. externalities
of individuals’ choices/activities and of policies) and
the trade-offs across multiple domains of human
and planetary wellbeing corresponding to the various
dimensions of sustainability. Eco-agri-food system and
sustainability challenges are tightly linked (Liu et al.
2015); however, these are most often studied in isolation.
This isolation is a reason for the failure of food systems
to provide healthy diets to the global population, and a
major driver of pushing us beyond multiple planetary
boundaries (Rockström et al. 2009).
The world has experienced an extraordinary growth
in crop yield since the 1960s due to investments in
crop research and infrastructure, and thanks to market
development and government support (Pingali 2014).
While human populations more than doubled during
1960-2010, the Green Revolution enabled a threefold
increase in the production of cereal crops, with only a
30 per cent increase in cultivated land area (Wik et al.
2008). The share of undernourished people decreased
from 24 per cent in 1990-91 to 13 per cent by 2012
(FAO 2015; Thorn et al. 2016a). However, this singular
focus on yields has had important environmental costs.
The IPCC estimated that roughly one-fth of the total
anthropogenic emissions of greenhouse gases during
the 1990s originated from land use changes (Goldewijk
andRamankutty2004).Theintensicationofagriculture
has had negative consequences with regard to water
availability, soil degradation, and chemical runoff, with
impacts beyond the areas cultivated (Burney et al. 2010).
Part of these externalities have been “internalized” within
agriculture as manifested in the slowdown in yield growth
observed since the mid-1980s, which can be attributed,
in part, to the degradation of the agricultural resource
base. But much of the externalities remain unaddressed.
These environmental costs are widely recognized as
a threat to the long-term sustainability and replication
of the Green Revolution success (IAASTD 2009; Webb
2009; Pingali and Rosegrant 1994). Some authors have
pointed out that the environmental consequences were
not caused by the Green Revolution technology per se,
but rather by the policy environment that promoted
overuse of inputs and the injudicious expansion of
cultivation into areas that could not sustain high levels
of intensication (Pingali 2014). Seppelt et al. (2014)
show that the peak-rate years (dened as the year of
maximum resource appropriation rate) for many of the
world’s major resources are synchronized (i.e., occurring
at approximately the same time in the history of human
civilization), suggesting that multiple planetary resources
have to be managed simultaneously when assessing the
likelihood of successful adaptation of the global society
to physical scarcity.
The overemphasis on productivity has also imposed
signicant costs on human health and contributed to
inequity. By 2013, several of the top risk factors driving
disease globally were related to diet (GBD 2013 Risk
Factors Collaborators 2015). Current food systems
over-produce products of low nutritional value and even
harmful foods such as sugary drinks, driven by political
and corporate interests (Mintz 1985; Richardson 2009),
whilesignicantlyunder-producingmanybenecialfoods
such as seeds and nuts, fruits and vegetables, as noted
in the Global Burden of Disease report (GBD 2013 Risk
Factors Collaborators 2015).
In addition to the direct food consumption channel,
human health can also be negatively affected by the
environmentally-mediated impacts of food production.
For example, 20 per cent of premature mortality due to
air pollution is derived from agricultural activities and
biomass burning. Clearing forests for agriculture adds
another 5 per cent to these mortality gures (Lelieveld
et al. 2015). Highly hazardous pesticide use is still
widespread across the globe, contributing to a range of
health problems such as reduced fertility of male farm
workers (Aktar et al. 2009; Roeleveld and Bretveld 2008)
and increased incidence of fetal conditions and perinatal
death (e.g. Maertens 2017; Regidor et al. 2004; Taha and
Gray. 1993). Negatu et al. (2017) found that the expansion
of commercial farming in the last decade in Ethiopia has
led to a 6- to 13-fold increase in the use of pesticides, which
has had an adverse impact on the respiratory health of
workers exposed to these pesticides. In Argentina, recent
evidence suggests that herbicides (including glyphosate,
adjuvants and the metabolite AMPA) have teratogenic
and genotoxic effects on mammals and humans and are
linked to diverse pathologies and diseases (e.g. Beuret et
al. 2005; Avila-Vazquez et al. 2017).
Importantly, increasing crop production has not
guaranteed increased food security or even availability of
nutritious food (Smith 2013). Currently, almost one fourth
of total food production is wasted, an amount that could
feed four times the number of the hungry people in the
world (FAO 2011). Food waste is not just an issue linked
to ineciency; it raises important questions of equity

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
24
and ethics in the global food system. This is especially
problematic in countries where subsistence farming was
replacedbyintensiedcommercialfarming.Forexample,
Sierra Leone now exports food while people experience
hunger locally (IFPRI et al. 2012). The food justice
movement has pointed out that women farmers and other
marginal groups continue to experience land insecurity
and lack of access to production resources. The case
study presented in Box 2.2 highlights the increasingly
interconnected and systemic nature of a “wicked problem”
and the converging issues that support and hinder socio-
ecological resilience in agricultural landscapes.
Box 2.2 Case study: the complex reality faced by smallholders farming riverside vegetables in the dry season, Northern
Ghana
In the semi-arid Guinea-Savannah zone of Upper West and East region of Northern Ghana, smallholders frequently have to
contendwithweatheructuations,climateextremes(Tallet al.2014),andhazardssuchasooding,droughtandstorms
(Lopez-Marrero 2010; Barrett 2013). All of these factors present risks to agriculture (Harvey et al. 2014), such as failed
food and seed stores, crop loss, and infrastructural damage. The region is home to the nation’s highest rural population
of predominantly Dagaare and Fare-Fare agro-pastoralists (84 per cent in the Upper West) - 28 per cent higher than the
rural average of 56 per cent and 8 per cent higher than the national average (FAO 2008). However, the current speed and
magnitude of climate change undermines farmers’ ability to employ traditional methods to cope with variability (Harvey
et al. 2014; IFAD 2015). Their vulnerability is exacerbated by the fact that these farmers, like many other smallholders,
tend to live in marginal environments (e.g. river banks, slopes or close to industrial lands); depend mostly on rain-fed
agriculture; farm small parcels of land; and often lack risk mitigation tools, such as regulated long-term credit, cash
reserves, reliable weather forecasts, early warning systems, farming inputs or storage infrastructure. Non-climatic
stressorscompound this risk,includingmarketprice uctuation,under-orover-utilization ofsyntheticpesticidesand
fertilizers, and lack of information about appropriate application of inputs. Other issues include limited availability of
organic inputs to boost soil fertility, increasing scarcity of land associated with population growth, and lack of labour due
to worker migration to Southern urban centres (Tall et al. 2014).
Vulnerability is particularly high during the dry season, which typically runs from November – April, when cereal production
comes to a halt due to the lack of rainfall, food stocks run low and demand for labour in the south is high (Laube et al.
2012). Many agricultural producers “sit idle” during this time, but in recent years, vegetable cultivation has increasingly
become an important rural activity (including cultivation of chilli pepper, onion, garden egg, tomato, okra, cabbage, and
sweetpotato).Vegetablesarespaceecient,commonlyintercroppedwithotherstaplescropslikecassava,mangoand
banana, have a high nutritional value and cash crop value, and are growing in demand in urban and rural areas (James et
al.2010;Cernansky2015).Dryseasonvegetablefarmingsupportsbiodiversityintermsoflandscapecongurationand
land management (Norfolk et al. 2013). Many farmers maintain the landscape surrounding the area in cultivation with
patches of native trees, thereby increasing species diversity and heterogeneity as compared to monocropped landscapes
(FernandesandNair1986).Landmanagementdecisionscanalsobeneton-farmbiodiversity.Forexample,farmersuse
mulch to retain soil moisture and promote decomposition, which in turn supports below-ground microbial communities.
Concurrently,biodiversitybenetsdryseasonvegetablefarming.Thatis,treessurroundingfarmshousepopulationsof
birds and insects, which in turn support crop productivity through pollination and seed dispersal (Jha and Vandermeer
2010). Biodiversity around farms further provide provisioning ecosystem services such as medicinal and aromatic plants
and fodder (James et al. 2010).
Despite these benets, expanding dry season vegetable cultivation faces challenges. Current methods of irrigation
arelabour andtimeintensive –withfarmers spending4.5hoursperdayllingup to350handheld bucketstocollect
water from riverbanks. The river water is reportedly contaminated, given multiple use requirements for washing, limited
sanitation,livestockand the inuenceofupstreamdams on turbidity andvelocity.Labourproductivityis hindered by
limited health services, the continued presence of the parasite Dracunculus medinensis(guineaworm),andpoorltration
and monitoring of water quality. External international drivers, e.g. European agricultural subsidies, are reducing the
export markets for smallholder farmers (Laube et al. 2012). Concurrently, farmers suggest that changing climatic
conditionstheyhaveobserved,suchashighertemperaturesandhumidity,havestronglyinuencedpestincidenceon
crop production (NPAS 2012). Thorn et al.(2016b)conrmedthis, showingthatinhotter,drierclimaticconditions,the
proportional abundance of ground- and vegetation-dwelling Hemiptera increases, particularly the economically damaging
Phytophage, Homoptera auchenorrhyncha cicadellidae, and there is a greater risk of seed predation due to the presence of
more granivores. However, the same factors have led to an observed greater abundance of long-tongued pollinators, from
whichfarmersmaybenetduetomoreecientpollendispersalanddecomposition.
24

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
25
This case study highlights the increasingly interconnected converging issues that support and hinder socio-ecological
resilience in agricultural landscapes. This complexity creates challenges in how best to balance needs in a changing
climate. The need for more clarity is evident in current disagreements in national Ghanaian institutions, some of which
advocate for more cultivation of vegetables, while others argue against it. To understand what interventions may enhance
smallholder adaptive capacity and sustainability of crop production for environmental services, biodiversity and food
security, a systems approach that analyses the interrelations between human and non-human systems across temporal
and spatial scales is needed. The TEEBAgriFood Evaluation Framework can help by identifying the total range of impacts
and externalities for vegetable cultivation in this scenario, helping the actors involved to choose the best-suited means
ofcropproductionforthesespeciccircumstances.
2.2.2 The many dimensions of the eco-
agri-food system create complex analytical
and policy challenges
The eco-agri-food system is dynamic, complex
and multifunctional, referring to the inescapable
interconnectedness of agriculture’s different roles and
functions (IAASTD 2009). The concept of multifunctionality
recognizes agriculture as a multi-output activity producing
not only products (including food, feed, bres, agrofuels,
medicinal products and ornamentals), but also human
health effects, livelihoods and employment opportunities,
environmental services, landscape amenities, and a
source of cultural heritages (IAASTD 2009; Robertson et al.
2014). An important attribute that underpins agriculture’s
multifunctionality is biodiversity. Agricultural biodiversity
is a key component of farming systems and breeding
systems worldwide, and results in nutritious foods that
are culturally acceptable and often adapted to local and
low-input agricultural systems (see, for example, Box 2.1).
Biodiversity is also a source of important traits for breeding
climate-tolerant, nutritious crops and animal breeds in the
future (Bioversity International 2017). This central role
of farm and landscape diversication in transforming
agricultural and food system has been highlighted in the
2016 International Panel of Experts on Sustainable Food
Systems report (IPES-Food 2016).
The multiple dimensions of the eco-agri-food system create
complex analytical and policy challenges (EEA 2017).
Efforts to alter one aspect of the system (e.g. reducing
environmental pressures) will very likely produce impacts
elsewhere (e.g. affecting employment, investments and
earnings). This can also mean that interventions produce
signicant unexpected feedback and side effects. In
addition, food systems do not operate in isolation from
other systems such as those involving energy, mobility,
and wider society, which in turn shape the context in which
thefoodsystemoperates.Theuseofsimpliedindicators
(i.e. productivity per hectare or GDP of the agricultural
sector), focused on selected measurable variables, can
lead to poor decisions (EEA 2017). Drawing from reviews
of empirical evidence, the case studies presented in
Box 2.4 (Argentina), Box 2.5 (Malawi) and Box 2.6 (India)
demonstrate how agricultural policies affected the many
interconnected aspects of economy and society.
Agricultural policy, through its effect on price and availability
of food, is known to be an important determinant of health
(Pekka et al. 2002; Zatonski and Willett 2005; Birt 2007;
Jackson et al. 2009; Hawkesworth et al. 2010; Wallinga
2010; Nugent 2011). However, health has largely been left
out of consideration in agricultural policies (Dorward and
Dangour 2012; Fields 2004; Hawkesworth et al. 2010), and
tension between agricultural and nutritional/health policies
is commonplace, and not only in the EU (Aguirre et al. 2015;
Popkin 2011). The 2013 European Common Agricultural
Policy reform liberalized the EU sugar market in 2017,
abolishing sugar quotas and lowering EU commodity (or
wholesale)sugarprices signicantly.Scholars and public
health research centres had projected that these changes
would have the potential to increase sugar consumption
(UKCRC-CEDAR 2015), particularly among the lowest
socioeconomic groups (Aguirre et al. 2015), while causing
substantial losses in sugar exporting by African, Caribbean
andPaciccountries(Richardson2009).
Policies that seem reasonable in one sector or for providing
a solution to one problem can cause unintended adverse
effects on other sectors, or over a longer time horizon or
larger spatial scale. For example, in the Nagchu Prefecture
of Tibetan Autonomous Region in China, the enforcement
of a conservation area with the aim to restore degraded
habitat has resulted in the eviction of semi-nomadic
pastoralists who have depended for centuries on the
land for grazing livestock, with adverse impacts on their
livelihoods (Yeh et al. 2015).
Encouragement of high-eciency irrigation can directly
reduce the water use per area and the total water use of
a given system. However, the reduction of existing costs
of purchasing or pumping water affect the economic
productivity of water, which can lead to other changes.
First, crops that were previously unprotable or even
agronomically unfeasible may become lucrative, increasing
the share of water-intensive crops in the overall cropping
system, and increasing the average water use per area.
Secondly, the overall area planted with crops may expand.
This increase in planted area can again lead to an increase
in global water use. These system responses to improved
technology can create rebound effects, where gains in
eciency are offset by expanded use. In some cases,
global consumption may increase overall, in what is known

as the Jevons Paradox. The extent to which a system
rebounds will depend in large part upon the strength of
system feedbacks (the balancing loops) and the new
equilibria they create – at what point increased water and
pumpingcostsinhibitfurtherintensication,ordepressed
prices inhibit further expansion.
These examples show that systems thinking is needed
to improve evaluation and impact assessment before
policies or technologies are put in place. An analytical
framework capable of integrating subsystems and
showing connections between them will improve our
understanding of the consequences of choices in
quantitative and qualitative terms, across the whole eco-
agri-food system. This framework will furthermore help to
gather the information needed to make better decisions by
agents involved across the value chain. Without systems
thinking, we will continue to fail to consider the “what ifs”.
For example, in any theoretical scenario, what would have
been the impact of investing in infrastructure, irrigation,
extension and research had the government not spent most
of its agricultural support budget on subsidies? What would
have been the overall societal impact if more government
resources had been used to implement ecosystem-based
approaches, instead of agro-chemical input subsidies?
Ideology and culture affect how we understand issues
around food (Rosin et al. 2012a, 2012b). Food is a vital
part of community, family and tradition, and encompasses
many non-economic dimensions that are important for
individuals and society, but it is often evaluated as just
another thing to be bought and sold (Rosin et al. 2012a;
Vivero-Pol 2017). Pretty (2012) called for developing new
alternative models of agricultural and food systems that
are culturally embedded and meaningful. Such models
would put food at the centre of economies and societies,
and ensure that food is produced in ways that improve the
environmental systems of the planet.
Box 2.3 Case study: genetic diversity and the eco-agri-food system
An essential component of the global eco-agri-food system is the genetic diversity of crops and livestock. These genetic
resources, including both the diversity of cultivated varieties as well as the wild relatives of crops (“crop wild relatives”)
and livestock, are a key form of natural capital, and the conservation and use of agrobiodiversity is essential for the
development of a more sustainable and resilient global food system.
In a way, the improved crops we grow are supported by the entire “genepool” of cultivated and wild diversity to which
we can turn to mitigate pest epidemics and stressors like climate change through the breeding of new crop varieties.
However, the development of improved varieties has at the same time led to a narrowing of crop diversity as farmers
abandon traditional varieties, and as wild lands containing crop wild relatives are cleared for development. Without
considering the important role of genetic diversity within the eco-agri-food system, we run the risk of disaster.
Nowhere are the dangers of low genetic diversity more pronounced than in the case of the banana, where a single, clonal
variety dominates production for the global export market: the Cavendish. Similar to the Gros Michel, an older variety
that was almost completely wiped out by a fungus known as the Panama disease (or Fusarium wilt), the Cavendish is
currentlyfacinganewfungaldisease,BlackSigatoka(Pseudocercosporajiensis),inadditiontoamutatednewstrain
of Fusarium wilt. Currently, banana plantations are sprayed with fungicides up to 45 times on an annual basis (Vargas
2006) at great economic and environmental cost. The wild relatives of the cultivated banana are a valuable source of
resistance genes, and have been used to breed cultivars resistant to Black Sigatoka (Wu et al. 2016). However, wild
banana populations are declining due to the direct and indirect effects of climate change (Emshwiller et al. 2015).
To ensure the long-term viability of banana production, crop diversity needs to be maintained. As this is costly and a
global public good, the most adequate strategy is to manage on a global scale, through collaboration between countries.
Thisrequiresthatgovernmentsinvestinconservingcropvarietiesingenebanks(andinfarmers’elds)aswellascrop
wild relatives in their natural habitats, work to reduce further loss of agricultural diversity, and facilitate the use of these
genetic resources. An example of how this can be partially accomplished is the International Musa Germplasm Transit
Centre (ITC), home to the world’s largest collection of banana varieties, both cultivated and wild. The ITC has distributed
thousands of banana samples over the past 30 years to users in more than 100 countries, as its holdings fall under the
jurisdiction of the Multilateral System of the International Treaty on Plant Genetic Resources for Food and Agriculture,
which was adopted in 2001 and currently includes more than 100 participating countries.
Similar initiatives are undertaken for other crops; notwithstanding, the challenge of eroding genetic diversity remains
huge and is exacerbated by the increasing industrialization of agricultural systems (IPES-Food 2016).
26
2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’

Box 2.4 Case study: what constitutes a “successful” model? The case of soybean industrial production in Argentina
In the last three decades, export-driven industrialized farming was promoted by the Argentinian government as the main
model of production and as an agricultural development strategy especially in regard to GM soybeans (Pengue 2005;
Teubal et al. 2008; Delvenne et al. 2013; Leguizamón 2014a; b; Torrado 2016). Favourable international market forces
and globalization further aided this trend (Harvey 2003, Pengue 2005; Leguizamón 2014a; Cáceres 2015). This neo-
extractivist developmental model (Gudynas 2009; 2014) is heavily dependent on modern technologies and inputs in
monoculture-dominated large-scale production systems, as well as the extraction of natural resources (Pengue 2005;
Teubal 2006; Cáceres 2015).
However, on what terms is the ‘‘success’’ demonstrated in this case understood? Argentina’s industrial agriculture model
could be understood as successful within the scope of neoliberalism, and as regards a few “winners”, namely, large-
scale farming and agribusiness corporations. Argentina ranks third in the world in the production and export of GM
soybeans with ca. 20 million hectares under production and an output of 56 million metric tons during the 2014/15
season (Torrado 2016). Soybean has become the most important crop in Argentina (Pengue 2005; Aizen et al. 2009;
Cáceres2015; Leguizamón 2016;Torrado2016; Lapegna2017),withrecord harvestsandprots(Leguizamón 2014a,
2016;Lapegna 2017). The government also beneted tremendouslyfromresultingexport tax revenues (Leguizamón
2014a, 2016; Torrado 2016; Lapegna 2017).
However,thebenetsofthismodelbecomelesscertain(ornegative)whenotherperspectivesandcriteriaareconsidered.
A large body of studies has documented that neoliberal policies supporting the expansion of industrial agriculture have
generated negative environmental and social impacts. Social inequity is clearly evidenced. For instance, the country is
producing “food” for over 300 million people but more than 30 per cent of its population (40 million people) lives below
national poverty line (García Guerreiro and Wahren 2016). Moreover, industrial agriculture is one of the main drivers
of land use change (Zak et al. 2004; 2008; Gasparri and de Walroux 2015); displacement of other crops important for
domestic consumption (Teubal et al. 2005; Aizen et al. 2009); deforestation and forest fragmentation (Torrella et al.
2011; 2013; Hoyos et al. 2013; Piquer-Rodríguez et al. 2015); fresh water pollution (Pizarro et al. 2016a; b); and reduction
of native plant populations and appearance of invasive species (Vila-Aiub et al. 2008; Binimelis et al. 2009; Martínez-
Ghersa 2011; Ferreira et al. 2017). As a result of forest loss, production of vital resources such as wood, grass and hay
for domestic animals, honey, and bres have been considerably reduced (Trilloet al. 2010; Arias Toledo et al. 2014;
Leguizamón 2014a), creating substantial negative impacts on subsistence farmers and indigenous people (Cáceres
2015; Leguizamón 2016; Cabrol and Cáceres 2017; Lapegna 2017). In the land rush for industrial crop cultivation (e.g.
soybean), violence against indigenous and peasant families for land control escalated (Carrizo and Berger 2012; 2014;
Arancibia 2013; Lapegna 2013, 2017; Leguizamón 2014a; b; Berger and Carrizo 2016).
Studies have also documented the negative social-ecological impacts of fumigation, particularly with glyphosate.
Even though glyphosate is considered a less toxic alternative for weed control than some of its precursors, its use is
controversial as there is increasing evidence of possible profound eco-toxicological effects of this herbicide on the eco-
agri-food system (Bourguet and Guillemaud 2016; Cuhra et al. 2016). For example, there have been recent reports in
Argentina of direct negative glyphosate effects on freshwater phytoplankton, bacterioplankton and periphyton (Peruzzo
et al. 2008; Vera et al. 2010; Pizarro et al. 2016a; b); soils, microorganisms and fungi (Druille et al. 2013; 2016; Okada et al.
2016); invertebrates (Casabé et al. 2007; Mugni et al. 2011), amphibians (Lajmanovich et al. 2003; 2017; Attademo et al.
2014; Mariel et al. 2014); reptiles (Burella et al.2017)andsh(Ballesteroset al. 2017a; b; Bonansea et al. 2017). In wild
mammals, domestic mammals and humans, recent evidence indicates that the herbicide glyphosate (with adjuvants
and the metabolite AMPA) has teratogenic and genotoxic effects and shows associations with diverse pathologies and
diseases (Beuret et al. 2005; Carrizo and Berger 2012; 2014; Arancibia 2013; Avila-Vazquez et al. 2017).
Looking across the multiple tradeoffs derived from the model, Leguizamón (2014a; 2014b; 2016) pointed out a fundamental
conictbetween thenarrativeof‘‘success’’of theArgentineanGM soybeanboomandsocio-ecological sustainability.
Systemic analysis is needed to evaluate alternative models of the eco-agri-food system, providing a comprehensive
picture of performance, while considering different economic, environmental, health, and social indicators.
27

Box 2.5 Case study: evaluating the impact of fertilizer subsidy policy in Malawi
This case study presents a review of the empirical evidence regarding the impact of an inorganic fertilizer input subsidy
program implemented in Malawi between 2005 and 2010. Smallholder farmers dominate agriculture in Malawi and about
70 per cent of the population depends on agriculture for their livelihood, with maize being the major crop (Denning et al.
2009). Traditionally, most farmers used little or no inorganic fertilizers due to high costs. Also, before the intervention
maize yield response to inorganic fertilizer was low, due to low soil organic matter and poor response of traditional
varieties (Ngwira et al. 2012). Due to variable maize prices on the market, the purchase of fertilizer input was seen as risky
and unattractive (Dorward and Chirwa 2011).
Starting in the 2005/06 growing season, the Malawian government implemented an ambitious program countrywide,
which offered subsidized fertilizer and improved maize seeds through a voucher system, with vouchers distributed
through district traditional authorities.
Despitesomequestionsregardingspecicgures,thereisaconsensusthatthesubsidyprogramincreasedagricultural
productivity, with bumper harvests in 2005/06 and 2006/07. While this enhanced food security for individual households,
the overall impact was uneven. As Sibande et al. (2015) found, only the richest 40 per cent of participating households
achieved food security as a result of the subsidy programs, with 60 per cent remaining food insecure. It was also found
thatmale-headedhouseholdsweremorelikelytobefoodsucientcomparedtofemale-headedhouseholds(Dorwardand
Chirwa 2011). This gendered effect was partly due to the fact that land ownership was a requirement for participation. In
a survey by Holden and Lunduka (2013), 40 per cent of sampled households reported a positive effect on their children’s
health, with another 65 per cent indicating that children’s school attendance improved. However, Lunduka et al. (2013)’s
review study suggested that the subsidy program might not have improved the overall food security. While national
povertyratesdecreasedby2.7percent,itwasmostlytheurbanpoorwhobenetedfromlowerfoodprices(Arndtet al.
2016).
At their peak in 2008/09, subsidy costs accounted for 80 per cent of the public budget to agriculture and 16 per cent of
the total national budget (Dorward and Chirwa 2011). This had effects on other areas, with reduced budget allocated to
infrastructures such as roads and irrigation, as well as to extension and research (Arndt et al. 2016).
Importantly, the various studies, which sometimes reached contradictory conclusions (indicated by the “+/-” sign in
Figure 2.1),showthattheimpactofsuchavastsubsidyprogramis often dicult to assess and quantify (indicated
by question marks). This is partly due to differences in timing and methods of data collection. Even when the intended
outcome is observed, distributional effects may or may not be positive (the yellow triangle sign in the Figure indicates
where such distributional effects may rise). A subsidy program as broad as this one has impacts beyond agricultural
practices and food supply. It can improve children’s health and school attendance, for instance. Yet, the impact is often
heterogeneous,e.g.unevenlydividedintermsofbenetsbetweenmale-andfemale-headedhouseholds,richandpoor
households, or urban and rural households. Such a program may inadvertently reinforce existing inequalities. The
interdependencies in an eco-agri-food system are complex and trade-offs need to be carefully weighed.
One interesting question is whether redirecting government budgets from simply providing inorganic fertilizer to
alternative approaches that are focused more on ecosystem functions and sustainable land management would have
helped to avoid some of the documented unintended negative effects while improving productivity in the long run, and
what other unanticipated changes might emerge. Uptake of such techniques remains low in Malawi, and outcomes for
food security and income are mixed. But their appeal may grow if external driving forces such as climate change put even
more pressure on energy supply and crop yields.
28

Figure 2.1 Mapping evidence of policy impact (Source: authors)
Input
market
Diversification,
conservation
agriculture,
agroforestry
Soil
quality
Deforestation Energy
and water
Government
budget
Subsidy on
fertilizers
Chemical
fertilizer
use
Crop
yields
Household
income Poverty
Household
food
security
Health and
education
Food prices
Crime and
social
impact
Government
expenditure on
infrastructure
(irrigation, road,
extension, etc.)
Food import
+
-
-
-
-
+
+
-
-
+
+
+
-
+
+
+
-
+
+/-?
+/-?
+?
+?
-
+/-?
+
+/-?
Distribution of household
types (e.g. head gender,
selection of beneficiary, rural
vs. urban poor, net producing
vs. net consuming household)
Hypothesized
positive outcome
sought by policy
Box 2.6 Case study: energy subsidy and groundwater extraction for irrigation in India
Groundwater irrigation in India covers more than 86 million hectares (ha) out of 192 million ha of gross cropland (GoI
2013). However, agriculture in India is trapped in a complex cycle of groundwater depletion and dependence on energy
subsidies (Shah et al. 2008). The government subsidizes electricity costs for pumping ground water to encourage greater
agricultural productivity, which has encouraged farmers to continue drilling deeper and pumping more. The subsidies are
oftenpricedataattariff,ifatall,andthegroundwaterisseldomeffectivelyregulated.Asaresult,farmerslackmonetary
incentivestosavewateror use it eciently (Narayanamoorthy 2004). The resulting crisis ingroundwaterresources,
especially in northwestern India (Rodell et al. 2009), had ripple effects on smallholder farmers, rural communities, and the
environment. Despite effort by the government to formulate groundwater regulations and pass state laws, enforcement
has largely been ineffective.
Systems thinking is useful for looking at the impact of energy subsidies in India. For instance, several feedback loops
exist between the energy subsidies, national imperatives for economic development, food security, the overexploitation
of groundwater and consequences for rural livelihoods. At the political-institutional level, energy subsidies have
threatened the viability of State Electricity Boards: their capacity is physically stretched by irrigation pumping, and their
capacityas organizationsisunderminedas therearelimited incentivesforeciency.Energysubsidieshaveaffected
rural populist politics in that political efforts to regulate water are hindered. Proliferation of pumps has also jeopardized
the power supply in several states, with implications for regional and urban power services. The energy subsidies have
also incentivized farmers to choose water-intensive crops such as rice over less demanding ones, which reinforce the
rising demand for irrigation water.
29

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
30
Many responses have arisen in the wake of the socio-ecological challenges associated with energy subsidies
in agriculture in India. Most of these include various groundwater management proposals. Some, like the strategy
implemented in West Bengal, involve virtually no subsidy on power, because the state has metered all its tubewells
and the government now charges farmers at near-commercial rates (Shah et al. 2012). Other regions have focused on
ndingasecond-bestmiddlegroundthattstherealitiesofthestatelevelpoliticaleconomyandphysicalconditions.
One such effort is the JyotigramschemeintroducedinGujaratwhichchargesfarmersaatratetariff,whileimposing
explicit rationing of high-quality power (Shah et al. 2012). Some are focused on improving irrigation eciency and
transitioningawayfromoodirrigation(Fishmanet al. 2015). Others have focused on the important role of collective
action in order to restrict highly water-consumptive crops where state capacity to control groundwater use is limited
(Meinzen-Dick et al. 2016). Whether the effort is aimed at correcting distortions rooted in the economic or human
behaviour domain, a systems view is necessary to ensure that we look beyond the immediate steps or consequences
and consider broader scales and dynamics.
2.2.3 Conceptualizing a sensible operating
space for the eco-agri-food system
How can the overall viability and sustainability of any
eco-agri-food system be assessed? Much of the current
research that attempts to look beyond simple productivity
as the only meaningful measure of agricultural production
has focused on the biophysical impacts of production
systems on the environment. Many studies have looked
at how to close the ‘yield gap’ (i.e. raise yields in less
productive systems vis-a-vis industrial agriculture)
(Harvey et al. 2014; Campbell et al. 2014) by examining
the impact of conservation strategies on agricultural
productivity (Branca et al. 2012). It is widely accepted that
for human activities to be sustainable, we must respect
the ecological constraints on what we can do on and with
planet Earth (Clift et al. 2017).
Rockström et al. (2009a; 2009b) dened ‘safe operating
space for humanity’ in terms of a set of planetary
boundaries. The concept has signicantly inuenced
the international discourse on global sustainability
(Dearing et al. 2014) by using nine interlinked biophysical
(hereafter referred to as ecological) boundaries at the
planetary scale that global society should remain within,
if it is to avoid ‘‘disastrous consequences for humanity’’.
Raworth (2012)’s extension of the Planetary Boundary
concept to include social objectives, such as health,
gender equality, social equality, and jobs, in the context of
sustainability policy and practice has produced a heuristic
with an explicit focus on the social justice requirements
underpinning sustainability (see Figure 2.2) (Raworth
2012). Raworth’s approach brings planetary boundaries
together with social boundaries, creating a safe and just
space between the two, in which humanity can thrive. The
concept of “safe and just operating spaces” has since
been used to guide analysis of regional social-ecological
systems in a variety of situations and contexts (for
example, in China by Dearing et al. (2014), and in coastal
Bangladesh as described in Box 2.7).
On the one hand, the eco-agri-food system, which is
bounded by the same overarching (global) ecological
and biophysical constraints and shares the same social
foundations as human development, must operate
within a “safe and just space for humanity”. Dening
this space for a given system obviously depends on the
values and worldviews held, but systems thinking can
play a role in fostering conceptualization and cultural
narratives that better appreciate the social and natural
foundations of sustainability. On the other hand, the
performance of eco-agri-food systems plays a critical
role in determining if humanity can thrive within planetary
and social boundaries. Systems thinking again can offer
conceptual guidance on the methodologies of analysis
and governance.

Figure 2.2 The safe and just space for humanity (Source: adapted from Raworth 2012)
food
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income
health education
resilience
voice
jobsenergy
social
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Box 2.7 Case study: sustainability of coastal agriculture in Bangladesh: Operationalising safe operating space using
social-ecological system dynamics
The safe operating space concept offers a new basis for negotiating trade-offs for sustainable development in the face
of growing challenges. Using the safe operating space concept to evaluate the complex dynamics (e.g. feedbacks,
nonlinearity) of social-ecological systems, in this case, of agriculture in coastal Bangladesh, involved three research
steps: i) analysis and understanding of the co-evolution (drivers, trends, changes points, slow and fast variables) of social-
ecological systems involved (Hossain et al. 2015; 2016a), ii) unravelling the dynamic relationships (e.g. interactions,
feedbacks and nonlinearity) between social and ecological systems (Hossain et al. 2016b), and iii) simulation and
exploration of the social-ecological system dynamics by generating eight ‘what if’ scenarios based on well-known
challenges (e.g. climate change) and current policy debates (e.g. subsidy withdrawal) (Hossain et al. 2017).
Coastal agricultural production doubled in Bangladesh (1.5–3.0 Mt) from 1972 to 2010 due to technological innovation
and fertilizer input. The ecosystem, however, has degraded since the 1980s due to increasing temperatures and salinity
levels (in both soil and water), rising sea levels and rising ground water levels (Hossain et al. 2015, Hossain et al. 2016a).
Recordedstatisticsconrmthatthisareaisoneofthemostvulnerabletoclimatechange(Maplecroft2010;Ahmedet al.
1999)andisalsounderstressbecauseoflandusechange,waterscarcity,oods,salinityriseandurbanization(Hossain
et al. 2015; ADB 2005). Projections show that the detrimental effects of climate change in the area are likely to continue,
as rice and wheat yields decrease due to temperature increases (MoEF Bangladesh 2005). In such a context, it is highly
important to know the proximity of the social-ecological system to tipping points and the chances of stepping outside
the safe operating space if a ‘perfect storm’ of social-ecological failings is to be avoided.
31

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
32
PriortoemployingsystemdynamicmodellingtoexplorethesafeoperatingspaceintheBangladeshidelta,wedened
the safe operating space in relation to the envelope of variability, environmental limit and impacts on society, assuming
that, outside the envelope of variability for crop production, income and GDP, the society will move out from the safe
operating space, posing danger to humanity. Eight ‘what if’ scenarios were formulated based on well-known challenges,
current policy debates and stakeholder consultations on the Bangladesh delta in relation to issues such as climate
change (debate of 2°C and 3.5°C temperature rise in Paris agreement), sea level rise, withdrawal of subsidy according to
World Trade Organization by 2023 and withdrawal of water in the upstream of Ganges delta. Model simulation results for
the period 2010s to 2060s revealed that a 3.5°C temperature increase over the period would be dangerous for the social-
ecological systems, especially when combined with sea level rise, withdrawal of water and withdrawal of subsidies.
Based on the simulated results, we suggest that agricultural development in Bangladesh can stay within the safe
operating space by managing feedback (e.g. by reducing production costs) and the “slow” biophysical variables (e.g. by
remaining below a 2°C temperature increase), and revising national policies regarding agricultural subsidies. This case
study highlights the value of modelling complex social-ecological systems in data scarce regions and demonstrates
how we can operationalise sustainability science concepts (e.g. tipping points, limits to adaptation) in real world social-
ecological systems.
2.2.4 Currently applied conceptualisations
and analytical tools are limiting
‘Silo analysis’ not only limits a comprehensive
understanding of the complexity of the eco-agri-food
system, but is also a consequence of the limited
availability of data and means to investigate the eco-
agri-food system as an integrated complex whole. In this
section, we provide some examples of the limitations of
the currently applied conceptualizations and analytical
tools, which contributed in part to today’s challenges with
regard to the eco-agri-food system. We also highlight how
synergies and coherence can be gained when evidence is
generated using concepts and methods that are aligned
with systems thinking (Tallis et al. 2017).
Treating natural capital using the tools of national income
accounting
To understand the limitations of current approaches
to assessing the value of natural capital, it is helpful to
understand the origins of these approaches. The current
system of economic accounting was developed in the
1930s, particularly in the U.S. and U.K. with the creation
of the concept of Gross National Product (GNP). GNP
was cast as a way to understand “return on investment”
that depended on maintaining capital stocks (Solow
1956). This enabled the macro economy to be analysed
as if it were one big rm. An important impact of this
conceptual development was that it redirected the
concerns of economic theory and economic policies
away from questions of income distribution towards
production, especially through improving eciency and
ensuring the optimal allocation of productive inputs.
When employed for long enough, indicators like GNP
can ultimately change underlying perceptions of values,
becoming valued attributes in their own right (Haider et al.
2015) (see the earlier Argentinian case study in Box 2.1).
Although indicators are formulated to measure what we
value, in practice the opposite often happens – we come
to value what we measure (Meadows 1998).
An important advancement in income accounting was
the realization that capital stock should include the
contribution of the services of nature (‘natural capital’)
(Dasgupta and Mäler 2000). In 2012, nearly a century after
the rise of GNP as a metric, the UN established the System
of Environmental Economic Accounting - Experimental
Ecosystem Accounting (SEEA-EEA) (UN et al. 2014).
Alongside it emerged the concepts of ‘green accounting’
(Serafy 1996) and ‘inclusive wealth’ (UNU-IHDP and UNEP
2014).
The Inclusive Wealth Report describes four kinds of capital:
manufactured or physical, natural, human, and social
(UNU-IHDP and UNEP 2014). Each of these capitals
is involved in agriculture and all are linked in complex
ways. For example, while it may be technologically
possible to replace human capital (e.g. farm workers)
with manufactured capital (e.g. machinery), this may
have negative consequences on social capital (e.g.
social networks). As Daly (1996) pointed out, the notion
of ‘capital’ implies that one type of capital can be
substituted by another type of capital, a viewpoint that
hassignicantshortcomings.Indeed,theultimatesource
of all manufactured capital is the natural world and its
essential services are not substitutable.
Georgescu-Roegen (1984) argued that land, labour, and
capital are funds, not stocks. Funds must be maintained
by preserving the conditions that enable them to be
perpetuated. Especially in the eco-agri-food system, this
seems a more appropriate concept. Ecosystem services
such as soil fertility and other vital soil characteristics
must be maintained to sustain the output of crops in
the long run. Labour (agricultural workers) must also
be maintained through health care and the supporting
institutions of family and communities. This way of

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
33
thinking emphasizes the importance of social capital
in the economic process. Social capital is particularly
important in the eco-agri-food system, whose success
depends directly on the supporting functions of family
and community (e.g. via the provision of information
or appropriate inputs, or labour sharing). Many aspects
of industrial agriculture work against sustainability by
undermining the social structure that supports farm
workers (Lobao and Stofferahn 2008; Goldsmith and
Martin 2006) and by drawing down the funds supporting
ecosystems services like water quality and availability,
pollination and pest control insects, and soil nutrient
cycling (Kimbrell 2002).
Awareness is growing that a new way to capture
interdependencies and assess trade-offs is required. As
Imhoff (2015, p.5) writes in the report on a “Biosphere
Smart Agriculture in a True Cost Economy”:
“In the face of a rapidly overheating climate, collapsing
sheries, degraded soil, depleted water resources, vanishing
species, and other challenges directly related to agriculture,
we can no longer afford to pursue a flawed accounting
system.”
The Millennium Ecosystem Assessment (MA), The
Economics of Ecosystems and Biodiversity (TEEB), and
the Intergovernmental Platform for Biodiversity and
Ecosystem Services (IPBES) are known for their focus
on the importance of ecosystems to human well-being
and to economic activity. These efforts document the
importance of natural capital to economic activity, and the
cost of environmental degradation on society. Yet, in view
of the magnitude of the continuing deterioration of many
ecosystems and social institutions, we must take the
concept of biodiversity and ecosystem services and the
many dimensions of human wellbeing further by looking
at how these issues might be addressed. One of the most
salient problems is the diculty of operationalizing the
broad vision of these initiatives; that is, incorporating
complexity and interdependence with a systems approach.
Because the dependencies and impacts are indirect,
interconnected, and complex, seemingly reasonable sector-
based policies can lead to unintended consequences that
make the whole system (along with its stakeholders) worse
off.Akeystepistorstbroadenouranalyticalframework
to allow for the conceptualization and evaluation of the
far-reaching implications of various options to manage the
eco-agri-food system, in order to inform decision-making,
and to improve the existing standards and guidance (e.g.
IFC Environmental and Social Safeguards, EIA and SEA
directives of the EU).
Beyond single numeraires for evaluating multi-dimensional
challenges
Over the past few decades environmental accounting
has matured and standardized. Researchers across
disciplines can now refer to a set of common methods to
measure nature’s services. However, like any accounting
methodology, environmental accounting is based on
simplications of reality that affect which variables are
included, the numbers produced, and their relevance. In the
course of reaching consensus on how to construct natural
resource accounts or how to estimate environmental
services, conceptual diculties have been glossed over
or ignored entirely. Most importantly, in many empirical
applications the ecosystem services narrative reduces
the value of nature to merely monetary terms that can be
quantiedandbroughtintocost-benetcalculations.
Nature is perceived and valued in starkly different and
often conicting ways, and embracing such diversity
can aid transformative practices aiming at sustainable
futures (Pascual et al. 2017). In the context of eco-agri-
food system, food has different meanings to different
people, including, for example, calorie production,
income generation, ways of living, and cultural heritage.
Developed within the context of the IPBES, the inclusive
valuation of nature’s contributions to people (NCP) aims
to improve decision making using a pluralistic approach
to recognize the diversity of values (Pascual et al. 2017).
Appropriateindicatorsthatreectthecomplexityofthe
eco-agri-food system are needed. Haider et al. (2015)
proposed four principles to guide researchers and
practitioners when looking at complex systems. First,
indicators are integral parts of a wider monitoring and
management system and they provide the key tool by
which different elements of the monitoring and evaluation
process can be logically connected as attributes change
over time. Second, indicators should be designed and
used with a suite of other assessment tools and as a
coherent part of a wider monitoring system. Even though
the use of a single index can provide information (such
as GDP), the complex nature of social-ecological systems
means that such an index will never adequately capture
measures of sustainability. On the other hand, many
environmental monitoring programs combine various
types of indicators into uncoordinated simple lists with
little hierarchical or interactive structure (Gardner 2010).
Indicators can only have relevance to management and
decision-making processes within complex systems if
they are used in coherent and interactive ways, and in the
context of a particular aim or objective. Third, it is essential
to understand how different indicators relate to the wider
system that is being monitored. Finally, indicators, and
the monitoring and management systems to which they
are linked, should be designed through a participatory
process that involves the key stakeholders who are
responsible for or inuenced by the system attributes
that the sustainability indicators are trying to represent.
Participatory approaches to monitoring sustainability
are particularly important in developing countries, where
engagement in the design and execution of monitoring
programs by local stakeholders may empower them to

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
34
better manage their own resources (Haider et al. 2015).
Moreover, a participatory approach can also encourage a
culture of learning, which is paramount to the success of
adaptive management (Cundill and Fabricius 2009).
The limitations of comparative static approaches
“Comparative statics” provide a way to evaluate the effects
of a change in policy or a production practice by using two
‘snapshots’, one before and one after a change. However,
there are limits to such comparative static analyses when
dealing with dynamic and evolving systems. These types
of comparisons are usually made based on the assumption
that variables remain constant and will not change in a
signicantwayinthefuture,i.e.the‘allotherthingsbeing
equal’ principle. This assumption is highly problematic
when considering complex adaptive systems, which are
driven by emergence and characterized by change.
Moreover, a snapshot approach does not look at the
dynamic interaction of elements within a system, so it
may not be representative of the full effects of a change.
Some interdependencies might be poorly captured and
others overlooked because they are deemed irrelevant or
because their effects only become apparent over the long-
term.
Thecaseofgeneticallymodiedorganisms(GMOs)crops
is instructive. As Hakimoct (2016) summarizes:
“The promise of genetic modication was twofold: By making
crops immune to the effects of weed killers and inherently
resistant to many pests, they would grow so robustly that
they would become indispensable to feeding the world’s
growing population, while also requiring fewer applications
of sprayed pesticides.”
These claims were based on several studies that seemed
to convincingly show that GMOs increased yields,
required fewer chemical inputs, and had no adverse
effects on human health. GMOs were rst allowed in
the United States and Canada some 20 years ago, but
were subsequently banned in most countries in Europe.
These political choices led to an unintentional but
useful controlled experiment assessing GMOs effect
on production, biodiversity, and human and soil health,
amongst other factors. According to Hakimoct (2016),
the U.S. and Canada showed no discernible gain in crop
yields per acre compared to Western Europe. Another
unexpected outcome was that herbicide use increased in
the U.S. By comparison, Europe’s major producer, France,
reduced its use of herbicides and pesticides during the
same period. Other unexpected impacts emerged in the
social sphere. In India, many studies have recognized
the adverse social impacts of GMOs stemming from the
inability of smallholder cotton farmers to repay loans,
which leads to a loss of autonomy and control over food
production. These effects have been associated with
farmer suicides, the loss of crop genetic diversity and
decline in the number of locally adapted varieties.
The debate about GMOs is not conclusive, in part due
to a lack of long-term studies and comprehensive
assessments of impacts on ecosystem services, social
dynamics, and human health. For example, we lack
an understanding of how GMOs affect the long-term
evolution of herbicide and insecticide resistance in crops,
impact predators and pollinators, affect irrigation needs
and seed distribution policies, and how GMOs perform
under variable precipitation (Romeu-Dalmau et al. 2015).
To better understand the effect of GMOs, a systems
approach would improve our understanding of the
interdependencies and trade-offs involved, and thus the
situations, contexts and conditions where GMOs would
be appropriate or not.
The limitations of efciency as policy objective
The goal of eciency is a central concept in economic
policy and in research to improve agricultural production.
It is not only an essential part of microeconomic theory,
but also a driving force in market economies. Businesses
strive to create their products at the lowest possible
cost, arguably to avoid wasting scarce resources, but
also by externalizing a number of costs linked to the
environmental and social impact of their activities. It is
largely taken for granted that it is an objective criterion
and not a value judgment, but as Bromley (1990) pointed
out,eciencyisavalue-ladenideology—partofashared
system of meaning and comprehension.
The picture from Tanzania in Figure 2.3 shows the
stark difference between plots planted in industrial
monoculture versus smallholder agriculture (<0.5ha) (see
Figure 2.3).Usingmeasuresofeciencyandprotability,
the industrial system might look preferable, but what
effects are left out? Taking a systems view encourages
policy makers to consider a larger spatial and temporal
boundary, and to assess the impact of alternatives on a
broader set of policy considerations, such as employment
of smallholder farmers, destruction of the family
farming-based system, loss of local knowledge, impact
on bio-diverse multifunctional landscapes, and effects
on connectivity, ood buffers, habitats, and personal
relationships.

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
35
Figure 2.3 Photo showing industrial monoculture alongside smallholder agriculture in Tanzania (Source:
Bourne 2009)
As Bromley (1990) pointed out, eciency is only one
possible policy goal with no particular claim to being more
importantthananyother.Eciencyisusuallyinterpreted
as ‘allocative eciency’, i.e. focusing on allocating
productive inputs among alternative uses in order to
maximizeoutput.However,thisisonlyonewaytodene
eciency.Insystemsthinkingtheconceptencompasses
the eciency of ecosystems functioning, or eciency
in the allocation and preservation of social capital to
improve the well-being of society. It should also include
the notion of ‘adaptive eciency’2, where the focus is
on practices and processes that will enable a system to
adapt to changes. This is a core message from resilience
thinking: prepare for the unexpected, for example through
diversication, maintenance of redundant resources
that can be mobilized quickly, and focusing on (social)
learning through on-going experimentation (Folke et al.
2010; Walker and Salt 2012).
The limitations of marginal analysis and discounting
Marginal analysis is a key decision-making tool in many
businesses. It is the process of identifying the relative
benetsandcostsof alternativedecisions byexamining
the incremental change in revenue over costs caused by
a one-unit change in inputs or outputs. The eco-agri-food
system has signicant implications for sustainability
2  Dened by North (2010) as a society’s effectiveness in creating
institutions that are productive, stable, fair, and broadly accepted-
-and, importantly, exible enough to be changed or replaced in
response to political and economic feedback.
and equity, and limiting evaluations to the yardstick of
‘value addition’ does not address important equity and
resilience issues (TEEB 2015). Marginal analysis does not
capture the cumulative effects of small decisions. Kahn
(1966) described the “tyranny of small decisions” as a
situationwhere small, seemingly insignicant decisions
accumulate and result in an undesirable long-run outcome.
Such situations abound in environmental issues. For
example, as noted by Odum (1982), the marshlands
along the coast of Massachusetts and Connecticut in
the U.S. were reduced by 50 per cent between 1950 and
1970 because of small incremental decisions made by
landowners.
Discounting is another thorny issue in economic valuation
and one that illustrates the divide between an individual
perspective and the perspective of “human society”
(Gowdy et al. 2010). Ecosystem services that support food
production become more important as external inputs
increase in cost or become scarcer. Even if individuals
demonstrate preference for current over future benets
(i.e. discounting the future), that does not necessarily
mean that this is appropriate for social decisions (Quiggin
2008). The question of which time frame to use is also
critical. Scenario analysis of diverse plausible futures,
established envisioned desirable and undesirable futures,
and backcasting are approaches increasingly gaining
traction as a planning approach to address possible
future trajectories along varied time horizons over decadal
periods. This diverts from traditional economic planning
of four- to seven-year time horizons.

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
36
2.3 A SYSTEMS
APPROACH FOR THE ECO-
AGRI-FOOD SYSTEM
2.3.1 Origins and evolution of Systems
Thinking
Systems Thinking (ST) is an approach that allows better
understanding and forecasting of the outcomes of our
decisions, across sectors, economic actors, over time
and in space (Probst and Bassi 2014). It places emphasis
on the system, made of several interconnected parts,
rather than its individual parts. Originating from Systems
Theory, ST is transdisciplinary, cutting across social,
economic and environmental dimensions. Further, it aims
at identifying and understanding the drivers of change as
determined and impacted by feedback loops3, delays and
non-linear relationships.
ST supports the integration of information through
the explicit representation of causal relations. It uses
feedbacks, delays, and non-linearity, three crucial
properties of real systems, to describe these relations
(Sterman 2000). The strengths of some causal relations are
determined, among other factors, by cultural norms. New
causalrelationsmayemergeinspecicsettings,requiring
the application of a systems approach customized at the
local level. To navigate through complexity, ST supports
the identication of the main mechanisms underlying
the performance of a system through the creation of a
cognitive map, such as the Causal Loop Diagram (CLD),
described in more detail in Section 2.3.3.
ST is general in scope, meaning it can be applied to
several topics and types of systems, and focuses on
the integration of drivers of change across elds. As a
result, it builds on other applications of Systems Theory.
Examples include systems biology, ecology, and systems
engineering.
There are several methodologies and tools that support
the implementation of ST. In general, the identication
of the components of a system and of the relationships
among these components represents the so-called soft
side of Systems Theory; attempts to quantify these
linkages and forecast how their strength might change
over time represents the hardsideoftheeld(Probstand
Bassi 2014).
Both applications have greatly evolved over time,
originating from Wiener’s (1948) book “Cybernetics” in the
3 “Feedback is a process whereby an initial cause ripples through a chain
of causation ultimately to re-affect itself” (Roberts et al. 1983, p.16).
homonymouseld,Odum’s(1960)articletitled“Ecological
potential and analog circuits for the ecosystem”, Forrester’s
(1961; 1969) publications on industrial and urban dynamics
(respectively) in the eld of System Dynamics, Lorenz’s
(1963) work on chaos theory, von Bertalanffy’s (1968) work
and book titled “General System Theory” in the context of
biology, to cite a few examples.
Over time, advances have been made both in systems
science (e.g. Complex Adaptive Systems, coined by
the Santa Fe Institute) and applications of ST to public
policymaking (e.g. The Limits to Growth, published by the
Club of Rome (Meadows et al. 1972)) and the subsequent
expansionoftheeldofSystemDynamics(seeChapter7).
When seeking to implement ST, the soft side is
characterized by seeking to understand and map system
complexity. This is achieved through the creation of
system maps, also called Causal Loop Diagrams (CLD),
Bayesian networks (see Box 2.8 for an example), and
mind maps, to cite a few examples. These approaches,
together with additional techniques to harvest expert
opinion (e.g. Delphi Analysis), allow for the creation of a
shared understanding of how a system works, which in
turn helps to identify effective entry points for (human)
intervention, such as public policies. When this is done
using a participatory approach, it helps bring stakeholders
together, creating the required building blocks for the co-
creation of a shared and effective theory of change.
The hard side of ST is represented by several simulation
methodologies and models, as presented in more depth
in Chapter 7. These methodologies and models offer
different ways of unpacking complexity (UNEP 2014).
For instance, models can be bottom-up (e.g. Agent-
Based Modelling, systems engineering models, Partial
Equilibrium Models) or top-down (e.g. General Equilibrium
Models, System Dynamics). Models may focus on the
understanding of the behaviour of agents, and how these
interact with one another, or on explaining the drivers
of structural change in the system. Hybrid approaches
also exist, where various models are integrated into
nested models, or fully incorporated into an integrated
model (Probst and Bassi 2014; UNEP 2011). Overall,
we nd that the modelling eld is rapidly evolving, and
there is increasing literature on complex systems and
on approaches to tackle complexity. We believe that
the TEEB Evaluation Framework, built on ST, can help in
both: i) identifying what should be included in modelling
exercises, to provide useful inputs to decision making,
and ii) determining what models to use (if in isolation or
in conjunction with others) and, more importantly, how to
interpret their results (according to their strengths and
limitations).
In the current report, our perspective embraces the notion
(and associated behaviours) of embeddedness within the
dynamicowsandcyclesofnature,andtherebysupports

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
37
the analysis and understanding of a whole system rather
than its parts or subsystems (Meadows 2008; Sterman
2000). Analysing the underlying structure of the system
allows for plausible inferences about its past and future
behaviour (Coyle 2000), which are useful for policy
formulation and evaluation.
2.3.2 Applying Systems Thinking to the
eco-agri-food system
TEEBAgriFoodmakesuseofscienticadvancesinrelevant
disciplines, and argues for better integration of knowledge
across sectors and actors. In addition, the study emphasizes
the importance of sharing results of analysis effectively in
order to better inform decision-making. We argue that using
ST and related tools can help all actors in the eco-agri-food
system to better plan for the future. Applications of ST can
alreadybefoundinmanyothereldswithinboththeprivate
and public sector; together with an emphasis on Learning
Organizations (Senge 1990) we can better understand
how socioeconomic and ecological systems, as well as
organizations and institutions, learn and evolve over time.
The TEEBAgriFood Evaluation Framework is inspired by ST
and attempts to capture impacts of production, processing
and distribution, and consumption throughout the system,
keeping in mind of the drivers and contexts of the eco-
agri-food system, and important properties of the system
such as dynamics, scales, and feedbacks. By doing so, the
Framework can help identifying what should be included in
more comprehensive modelling approaches.
The eco-agri-food system involves many components, or
subsystems, which interact dynamically and give rise to
unpredictable properties that emerge at different levels
of organization - so-called emergent properties - which
aretheessentialreasonforstudyingsystemsintherst
place. We are accustomed to dealing with complicated
systems, composed of many different parts which
interact linearly, and whose behaviour thus follows a
precise logic and repeats itself in a patterned way. These
complicated systems are therefore predictable. Complex
systemsaredominatedbydynamicsthatareverydicult
to predict. These dynamics are the result of multiple
interactions between variables that do not always follow
a regular pattern, and are driven by various feedback
loops. As a result, their interplay can lead to unexpected
consequences. The rapidly evolving environment in which
we live requires responses based on careful analysis of
alternative intervention options, especially when multiple
and simultaneous challenges emerge. Decisions that do
not consider the complex dynamics underlying the true
causes of a problem risk unintended consequences or
side effects.
Today’s challenges are increasingly complex, and it will be
necessary to apply systems thinking if we are to improve
our abilities to address the challenges. In an analysis of the
top 100 questions for global agriculture and food security,
Pretty et al. (2010) identied a series of interlinked and
overarching challenges for this century, grouped into: i)
climate change and water, ii) biodiversity and ecosystem
services, iii) energy and resilience, iv) social capital and
gender, v) governance, power and policy making, vi)
food supply chains, and vii) consumption patterns. They
demonstrate the intertwining nature of agricultural and
food systems, and show that solutions will have to come
from more than one sphere of political, technological and
economic life (Pretty et al. 2010; Pretty 2012).
An improved global food system requires radical change
to its organization (Rosin et al. 2012a; IPES-Food 2016).
In reviewing the literature of recommendations for
reconguringtheglobalfoodsystem,Rosinet al. (2012b)
highlighted that the transformational recommendations
allinvolvesignicantshiftsinthestructureandoperation
of the global food system. One example of structural
change in the model of agriculture called upon by the
International Panel of Experts on Sustainable Food
Systems is to diversify farms and farming landscapes
IPES-Food (2016). The environmental limits of our food-
related activities must be respected; the functions of
the ecosystems in which food is produced must be
maintained; the multiple outputs of agriculture and its
multiple roles must be considered. Take conservation for
example. The aforementioned recommendation implies
a recognition of the multiple and often non-monetary
and cultural incentives for conservation in agricultural
landscapes of different actors. Changes in food production
systems must ensure that the environmental, social,
and human health qualities inherent to food production
and consumption, including but not limited to economic
benets, are valued and therefore maintained. A radical
shift in our treatment of food is called for, both in terms
of the values we attach to food, and in our imaginings of
morejustandexiblesystems.
Using systems thinking requires a shift in fundamental
beliefs and assumptions that constitute what are referred
to as our ‘worldviews’. These are essentially intellectual
and moral foundations for the way we view and interpret
reality. This in turn requires a shift in our beliefs about
the nature of knowledge and the processes of knowing.
For instance, when it comes to judgments about what
constitutes improvements to the way land is farmed,
ourworldviewsreectourviewsonthenatureofhuman
values, particularly as they relate to ethics and aesthetics
(Bawden 2005).
Complexity theorists have long recognized the
importance of cultural narratives, what Sahlins (1996)
refers to as “cosmologies.” These are belief systems so
ingrained in language and customs that they are hard to
recognize. Researchers are making headway in applying
the general principles of systems thinking to a variety
of social problems involving sustainability (Newell et

al. 2009; Dyball and Newell 2014), and are moving from
focusing solely on individual behaviour to emphasizing
the importance of cultural institutions and society’s
assumptions about which policies are feasible and which
are not. Behavioural economists and psychologists
have made progress in identifying patterns of individual
behaviour relevant to policy formulation. Much more
work remains in order to understand how transformation
towards sustainability can be triggered and supported by
policy at societal level.
Increasingly,variouseldsofpolicyandcorporatepractice
recognize the necessity of ST and systems approaches in
solving today’s interconnected and complex challenges.
For instance, the development community is moving
towardmore comprehensive—orsystemslevel—thinking
as it looks at issues of poverty, hunger, and malnutrition
(Fan 2016). International development organizations
such as UNDP, the World Bank, USAID, CIDA, and Japan
International Cooperation Agency have shifted to systems
concepts-based (FASID 2010), holistic, and integrated
approaches (FHI 360 2016) for the design, delivery and
evaluation of development programs. The conservation
community is also moving in this direction. The Nature
Conservancy (TNC), for example, recently stated that
creating “systemic change” (creating or strengthening
the social, economic, political, and cultural systems that
comprise and sustain a socio-ecological system) should
be the focus of interventions (TNC 2016). Furthermore,
more cross-sector and cross-disciplinary initiatives are
emerging, aiming to promote integrated approaches and
collaborative work that breaks silos. Among them, the
Bridge Collaborative (TNC 2017) envisions global health,
development and environment communities jointly
solvingtoday’scomplex,interconnectedchallenges,rst
by recognizing the interconnectedness of the challenges
each of the three communities face.
These examples show how ST is increasingly embraced
because it takes a holistic view of the world and allows
for the discovery of interactions (Röling and Jiggins
1998). While system science has been around for more
than six decades, to meaningfully embrace the systems
approach requires fundamental changes in the way we
view and analyse problems and design solutions, as well
as the type of institutions we create and use to do this.
The TEEBAgriFood study offers a tool, in the form of an
Evaluation Framework, to help us advance towards this
type of change.
Box 2.8 Case study: Bayesian networks: a useful tool in applying systems thinking?
One of the key challenges in operationalising systems thinking is the integration of interdisciplinary knowledge to
provide robust models for decision-making. McVittie et al. (2015) used Bayesian Networks (BN) to develop an ecological-
economic model to assess the delivery of ecosystem services from riparian zone management on agricultural land. Also
known as belief networks (or Bayes nets for short), BN belong to the family of probabilistic graphical models (GMs),
which use graphical structures to represent knowledge about an uncertain domain (Ben-Gal 2007). For example, the
interface between terrestrial and aquatic ecosystems contributes to the provision of important ecosystem benets
includingcleanwaterandreducedoodrisk,andisheavilyinuencedbylandusedecisionsandpolicy.Aparticipatory
workshop gathered scientic and policy stakeholders to explore the linkages across these ecosystems and their
ecosystem services. This yielded extremely complex connections that would have presented a considerable modelling
challenge. The use of a BN allowed the capture of elements of this complexity whilst focusing on the key interactions
betweenunderlying ecosystemprocessesandthe deliveryofecosystem service benets.Anattractive featureofthe
BNapproachisthatitcancombinequantitativeandqualitativedatatoproduceprobabilisticoutcomesthatreectthe
uncertainty of complex natural processes.
AsecondelementindevelopingtheBNmodelwastheintegrationofvaluesforthebenetsofthewaterqualityandood
risk services. These values can be monetary or non-monetary and as such can be derived using a variety of approaches
(e.g. stated preference valuation, participatory workshops, multi-criteria analysis). The utility or value associated with
different outcomes is in turn used to indicate the optimal management option.
Although the BN is a promising interdisciplinary and participatory decision support tool, there remains a need to
understandthetrade-offbetweenrealism,precisionandthebenetsofdevelopingjointunderstandingofthedecision
context (McVittie et al. 2015). Important issues such as feedback loops and spatial and temporal factors are also not
easily incorporated into BNs.
38

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
39
Figure 2.4 Food systems map that shows how multiple subsystems interact (Source: adapted from the
Nourish initiative n.d.)
Farmers
Land & Soil
Family
& Friends
Community
Region
National
Global
Civic
Engagement
Lobbying
BIOLOGICAL
SYSTEM
SOCIAL
SYSTEM
ECONOMIC
SYSTEM
Agriculture
Ground Water
Government
& Policy
Food
Literacy
Biodiversity
Biodiversity
Transport
Chemicals
Sunlight
Food
Wholesalers
Food
Companies
Farmers Markets
and CSAs
Grocery
Stores
Restaurants
Land Use
Climate Change
Pollution
Animal Welfare
Wellness
Care
Prevention
Food Safety
Food Security
Trash Waste Waste
Nutrients
Water Money
Food
Money
Food
Labor
Seed Know-how
Social Network
Media/Advertising
Access
Education
Food Culture
Farming
Commercial
Consumer
HEALTH
SYSTEM
Regulations
Taxes
Subsidies
Ownership
Trade
POLITICAL
SYSTEM
D
E
M
A
N
D
S
U
P
P
L
Y
FARMING
SOCIAL
ENVIRONMENTAL
ECONOMIC
I N P U T S
Systems can be represented in multiple ways. Figure 2.4,
for example, shows a holistic representation of food
systems used by the Nourish initiative. They can also be
described verbally, through mathematical equations, or
by simulation approaches such as those commonly used
in climate modelling and land use analysis (Malczewski
2004). These diverse approaches are used by systems
scientists to simulate how systems function and,
foremost, to improve our capacity to describe systems,
and eventually predict system changes and outcomes
caused by interventions.
Figure 2.4shows material owswithinthefoodsystem,
but also ows of money and knowledge. Importantly,
representedbytheguresofhumans,itshowshowmany
dynamics are driven by individual and societal choices,
rather than impersonal ‘principles’ or ‘laws of nature.’
Indeed, next to biological, economic and social systems,
the political system is drawn separately to highlight its
role in the food system. Understanding the food system
byonlyaccountingfortheeconomicowsfailstoaccount
for other important driving factors.
To highlight the fact that many different dimensions
are involved in the eco-agri-food system and complex
interconnections and feedback loops drive the relation
between them, a slightly modied version of the
“simplistic” system diagram of an archetypal eco-agri-

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
40
food system is used in Figure 2.5. It illustrates the
key components and linkages to be considered when
assessing the eco-agri-food system, including the context
in which the value chain is embedded, as well as some of
the key system features discussed above. These include:
Value chain perspective and its macro contexts
The eco-agri-food system value chain encompasses
all actors and activities involved in food production,
processing, distribution, and consumption. Within the
social and natural subsystems, the stages of an eco-
agri-food value chain are tightly intertwined. Demand,
production, and distribution of food all form closed loops
that are simultaneously and heavily dependent on external
inuence as well as on internal dynamics. These are
represented in Figure 2.5 by the four stages of the value
chainappearing horizontally in the middleof the gure.
These stages are connected by two-way arrows showing
(simplistically)examplesofowsbetweencapitalstocks
and the value chain in both directions.
Because value chains include activities from food
production, postharvest through to consumers, they
provide useful lenses for viewing the broader eco-agri-
food system and identifying entry points for policies and
interventions to improve system performance (Gelli et al.
2015). It is essential to understand the broader macro-
level context, or enabling environment, within which the
value chain operates, including policy and governance,
political and economic context, culture, gender, equity,
climate and environment (Hawkes et al. 2012). Biophysical
structureandprocessbothimpactandareinuencedby
the eco-agri-food system; as are ecosystem functions
and integrity. Whether these contexts are exogenous or
endogenous to the system depends on the time horizon
over which decisions are made.
Figure 2.5 Modified high-level ‘systems’ diagram of an archetypal eco-agri-food system (Source: adapted
from authors of Chapter 1)
NATURAL CAPITAL
Agricultural
Production
Manufacturing
and Processing
Distribution, Marketing
and Retail
Household
Consumption
AGRICULTURE & FOOD VALUE CHAIN
► Biomass growth
► Fresh water
► Pollination
► Pest control
► Nutrient cycling
► Ecosystem restoration
► Deforesation and habitat loss
► Greenhouse Gas Emissions
► Pollution
► Labour
► Knowledge
► Wages
► Working conditions
► Nutritious food
► Social networks
► Land access/
tenure
► Cultural
knowledge
► Food security
► Opportunities for
co-operation and
community
activities
► Income
► Profits and
rents
► Taxes
► Machinery and
infrastructure
► Energy, fuel, fertilizers
and pesticides
► Research and
Development; IT
► Finance
POLITICAL, ECONOMIC, CULTURAL, AND INSTITUTIONAL CONTEXT (OR STRUCTURAL CONDITIONS)
FEEDBACK
LOOPS
FEEDBACK
LOOPS
BIOPHYSICAL STRUCTURE AND PROCESS/ECOSYSTEM FUNCTIONS AND INTEGRITY

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
41
An inclusive conception of an economy’s capital assets
Following the Inclusive Wealth Report (UNU-IHDP and
UNEP 2014), the eco-agri-food system relies on the use
of different types of capital, including: i) produced capital
(roads, buildings, machines, and equipment), ii) human
capital (skills, education, health), iii) social capital (or
the “networks together with shared norms, values and
understandings that facilitate cooperation within or
among groups” (Healy and Côté 2001), and iv) natural
capital (sub-soil resources, ecosystems, the atmosphere).
Other durable assets, such as knowledge, institutions,
culture, religion – more broadly considered as social
capital - are considered enabling assets, assets that
enable the production and allocation of the other three
types mentioned before.
These types of capital are represented in Figure 2.5
by the four outer boxes at the top and bottom of. From
these boxes, arrows surround the value chain stages,
representing the underpinning role of these capitals
for the value chain. The eco-agri-food system not only
depends on these capitals for various reasons along
the value chain, but also, in turn, impacts these capitals,
contributing to positive or negative change in quality,
availability, and distribution across spatial and temporal
scales.
Analysis of flows: impacts and dependencies on capitals
Theowsofsupplyfromeachofthefourtypesofcapital
(natural, social, human and produced) into the activities
across the value chain are represented in Figure 2.5 by
vertical arrows ‘inputting’ toward each value chain stage.
Examples of these inputs for the production stage include:
i) inputs from natural capital such as energy, land fertility
(e.g. nutrients and organic carbon), genetic diversity,
water, and pollination services, ii) inputs from produced
capital, such as machinery (e.g. tractors), agrochemicals
and irrigation infrastructure, iii) inputs from human capital,
such as labour, skills, and land management practices,
and iv) inputs from social capital, such as knowledge and
cultural practices. Among the examples provided above,
some are unique inputs that contribute to a single stage
ofthevaluechain(e.g.nutrientcyclingisusedasinowin
the production stage), while others contribute to multiple
stages across the value chain (e.g. fresh water is relevant
to all stages of the value chain).
As a result of the activities developed in each stage of
the value chain, outputs can have a positive or negative
impact on society by affecting different types of capitals.
These are represented in Figure 2.5 by vertical arrows
‘out-owing’ from the value chain towards the different
capital types. Each stage of the value chain generates
potential positive outputs, such as wages, food or carbon
sequestration that lead to broader societal impacts,
such as nutrition and food security (related to crop yield
and income), social equity and human health (including
nutrition and access to clean water). However, adverse
or negative outputs can also arise, such as air and water
pollution (e.g. from the use of chemical fertilizers and
pesticides), and biodiversity loss (e.g. through habitat
loss/fragmentation and agrochemical use); these
negative outputs can also have health and social impacts.
System connections: feedback loops and cascading effects
A cascading effect can be noted between inputs and
outputs, both within a single value chain stage and
across the whole value chain. For instance, all stages
require water, which is inuenced by various uses (e.g.
for irrigation and sanitation) and by the use of chemical
inputs and waste (e.g. fertilizers and pesticides). If water
is not properly managed, systemic consequences may
emerge, where the consumption and contamination of
water in one stage may affect all the others (processing
and distribution and consumption), and also reach beyond
the value chain to affect society.
Feedback loops should be highlighted across the value
chain. Impacts on human health may raise awareness
among the public about the impacts of unsustainable
production, and thus lead to changes in consumer
preferences, such as a shift to fair-trade or organic
products. Subsequent changes in production practices
and processing and distribution standards could improve
the quality of food and reduce environmental impacts,
resulting in mitigated or reduced health impacts.
A second feedback loop also emerges when considering
the full value chain of the eco-agri-food system. The
various stages of the value chain share inputs, which are
affected by the outputs of all the stages of the eco-agri-
food system. Tight interconnections pertain especially
to the natural, human and social capital. In fact, with key
natural resources being impacted at every stage of the
value chain, and being used at each stage (e.g. water
quantity and quality, air quality), the performance of the
eco-agri-foodsystemisinuencedbyeveryactivitywithin
its boundaries. Care must be taken when the various
stages are dislocated in space, i.e. when natural resources
are not shared across the value chain within the same
landscape. This is not necessarily an advantage, nor a
sign of resilience. Indeed, the lack of direct connections
across the stages of the value chain may lead to an
overexploitation of natural resources, because this
unsustainable use could go unnoticed or unaccounted for
alongperiodoftime.Itisessentialtocarefullydenethe
system boundary, both spatially and temporally, to ensure
the sustainability of the system.
Actors and their influence
Therearemany and varied actors inuencing and being
affected by the eco-agri-food system, which are described

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
42
in more detail in Chapter 9. These include, among
others, governments, NGOs, individuals (different than
consumers already considered), nancial institutions,
other businesses and sectors, and research and academia,
which in turn formulate, shape, or implement actions that
inuence and are affected by the system. These actors
determine the performance of the different stages of the
value chain, through regulations, nancial requirements
or engagement policies, campaigns, knowledge and
innovations, etc.
2.3.3 An illustrative Causal Loop Diagram
of a generic eco-agri-food system model
A causal loop diagram (CLD), i.e. a map of the system,
is a way to represent and explore the interconnections
between the key indicators in a sector or system. A CLD is
thus an integrated map representing the dynamic interplay
of different system dimensions and exploring the circular
relations or feedbacks between the key elements—the
mainindicators—thatconstitutea given system (Probst
and Bassi 2014).
CLDs make feedback loops visible, and thus the processes
‘whereby an initial cause ripples through a chain of
causation ultimately to re-affect itself’ (Roberts et al. 1983,
Probst and Bassi 2014). Two types of feedback loops
exist, positive (or reinforcing) feedback loops that amplify
change, and negative (or balancing) feedback loops that
counter and reduce change. Regardless of the complexity
of the system analysed and of the CLD created, only a
handful of feedback loops may be responsible for most
of a system’s behaviour (Probst and Bassi 2014). Thus, if
thesedominatingfeedbackloopscanbeidentied,entry
points for effective intervention, or policy levers, can also
be detected.
The creation of a CLD has several purposes. First, it is a
means to elicit and integrate a team’s ideas, knowledge
and opinions. Second, it requires the explicit discussion
and dening of the components and boundaries of the
analysis. Third, it allows all the stakeholders to achieve
basic-to-advanced understanding of the analysed issue’s
systemic properties (Sterman 2000).
Shared understanding is crucial for solving problems that
inuenceseveralsectorsorareasofinuence.Whenthe
process of creating a CLD involves broad stakeholder
participation, all parties involved need a shared
understanding of the factors that generate the problem
and those that could lead to a solution. As such, the
solution should not be imposed on the system, but should
emerge from it. In this context, the role of feedbacks is
crucial. It is often the very system we have created that
generates the problem, due to external interference or
to a faulty design, which shows its limitations as the
system grows in size and complexity. In other words, the
causes of a problem are often found within the feedback
structures of the system.
Figure 2.6 represents a stylized CLD to illustrate some
generic relations and system dynamics of the eco-agri-
food system. This CLD highlights selected feedback loops
that are generally thought to be responsible for the trends
observed in the last decades. This CLD does not attempt to
comprehensively capture all elements and relationships.
It is presented for illustrative purposes to highlight the
emphasis on indicators, their interconnections, and the
feedback loops that these interconnections form. For
instance, we capture the impact of deforestation on water
(as an ecosystem service that supports agriculture) as an
example of ecosystem service change that resulted from
land use choices, but other important elements such as
the effects on specic species (currently lumped under
biodiversity) are not included here.
Specically, one of the key drivers of the eco-agri-food
system is food demand, which is primarily driven by
population and income and also by different industries that
convert agricultural production to products beyond food,
such as biofuels, additives, livestock feed etc. An increase
in demand for these items can lead to the expansion
of agriculture land, growth in employment and income,
and hence more food demand. This circular relationship
represents a positive, or reinforcing (R1) feedback loop,
which leads to growth. Further, an expansion of agricultural
land would lead to higher food production (all else equal),
whichwouldhavetwomaineffects.Therstone(a)would
increase access to food and nutrition, having a positive
impact on human health and population (R2) and on labour
productivity and income (R3). Two more reinforcing loops
arethereforeidentied,leadingtomorefooddemandand
land conversion. The second effect (b) emerges over time,
withtheaccumulationofprotsandwiththeimprovement
of knowledge and technology. This generally leads to an
increase in mechanization and the use of fertilizers and
pesticides, leading to higher land productivity. This in turn
has three main effects, it increases production in terms
of higher yield per hectare (R4 and R5); it lowers food
prices, which increases food demand (R6); and reduces the
amount of land required (B1), all else equal.
At this stage, the eco-agri-food system in Figure 2.6 is
dominated by reinforcing loops, and shows a trend of
growth over time. The increase of population and thus
demand, leads to the expansion of agricultural land,
improved employment and income, as well as increased
nutrition, potentially leading to increased population. When
this growth is coupled with an increase in land productivity
and a reduction in food prices, we generally expect growing
demand,productionandprots.

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
43
Figure 2.6 Illustrative Causal Loop Diagram of a generic eco-agri-food system (Source: authors)
population
food
demand
settlement
land
food
production
agriculture
land water
availability
land
productivity
fertilizers and
pesticides
labor
income
nutrition
labor
productivity
human
health food
price
water
quality
food safety
mechanization
profits
<labor>
deforestation
precipitation
water
demand
carbon capture
and
sequestration
R
<deforestation>
quantity
vs. quality
age cohorts,
income cl asses
and gender
subsidies
taxation
culture
distributional
impacts,
equity
governance and
institutions
just
transition
biodiversity
R
B
B
B
B
B
B
R
R
R
key determinants of equity
and distributional impacts
positive
negative
On the other hand, several balancing loops, which
constrain growth, also emerge. First, with the adoption
of mechanization, labour intensity declines. This leads to
higherproductionandprotsforproducers,butlowersthe
potential growth of employment and income (B2), possibly
leading to growing inequality. Further, the use of fertilizers
and pesticides has negative impacts on water quality
(B3) and food safety (B4), two factors that negatively
affect human health, and hence labour productivity and
population. Finally, the expansion of agricultural land,
and the growth of population (and hence the expansion
of settlement land) might take place at the expense
of forest or vegetation cover. The loss of biodiversity,
carbon storage and sequestration with increased carbon
emissions can further negatively impact human health
(B5), the hydrological cycle, and possibly the productivity
of agricultural land (e.g. due to sedimentation, runoff of
fertile topsoil or erosion) (B6).
As a result, the growth observed historically (and
determined by reinforcing loops) is the cause for the
emerging challenges (represented by balancing loops)
being faced by the eco-agri-food system: increased
reliance on fertilizers and pesticides, more frequent
water shortages, an increasing trend of deforestation and
growing health impacts (primarily related to the quality of
food and nutrition). A silo approach considering individual
actors and relying solely on economic indicators would
not make visible the emergence of these side effects.
2.4 CONCLUSION
The fact that components or subsystems of the eco-
agri-food system are interconnected and interdependent
is undisputed. This chapter builds on that observation
to make the case for systems thinking as a guide for
the conceptualization and analysis of the eco-agri-food
system, on which the subsequent chapters of this report
offer a concrete attempt to advance.
The many dimensions of the eco-agri-food system
create complex analytical and policy challenges. A

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
44
rst step toward a necessary paradigm shift is a re-
assessment of how we conceptualise and interpret the
problems of the global food sector and how we choose
methods to analyse them. To conceptualise what
constitutes a sensible operating space for the eco-agri-
food system, we draw on the concept of “safe and just
operating spaces for humanity” (Rockström et al. 2009a;
2009b; Raworth 2012; 2017), emphasizing that we
must respect the planetary boundary (e.g. biophysical
constraints) while simultaneously addressing social
and development objectives (such as health, gender
equality, social equality, and jobs). A sustainable eco-
agri-food system can only be achieved if the social and
environmental dimensions are also taken seriously, in
addition to the economic dimension. Silo approaches
are limiting our ability to achieve a comprehensive
understanding of the interconnected nature and the
many challenges we face. We therefore need a holistic
framework allowing the integration of well-understood
individual pieces into a new, complete picture. Indeed,
synergies and coherence can be gained when evidence
is generated and used based on concepts and methods
aligned with systems thinking.
The shortcomings of current approaches also include the
limited availability of data and methods for the analysis
of the eco-agri-food system as a complex system. In this
chapter we use several examples to explain the limitations
of currently applied conceptualizations and analytical
tools. We call for expanding the analytical boundary
and adopting analytical tools guided by an integrated
approach based on systems thinking.
This chapter offers a conceptual representation for the
eco-agri-food system, presenting a general overview of the
key components and linkages that need to be examined in
order to understand the dynamics of the system, as well as
the contexts within which the eco-agri-food system value
chain is embedded. A stylized Causal Loop Diagram is
presented to illustrate some generic relations and system
dynamics of the eco-agri-food system. The key elements,
dynamics,andrelationshipswillbeeshedoutinChapter
3, Chapter 4 and Chapter 5. The TEEBAgriFood Evaluation
Framework presented in Chapter 6 advances on such
analysis by attempting to examine all potential impacts
and consequences of the respective subsystems.
“Transformability,” dened as “the capacity to create a
fundamentally new system when ecological, economic,
or social structures make the existing system untenable,”
is about shifting development into new pathways and
even creating novel ones (Folke 2006, Folke et al. 2010,
Walker et al. 2004). Implementing the TEEBAgriFood
Evaluation Framework for the eco-agri-food system puts
us in a much better position in the transformative process
to understand the full set of impacts of externalities,
costs and benets, particularly on the public goods
affected, and thereby identies what changes would be
required for a more balanced and equitable development
approach. Further, empowered by systems thinking, the
TEEBAgriFood Framework’s contribution goes beyond
technical analysis by contributing to actively enlisting
support for systemic transformations across the
stakeholder continuum (see Chapter 9). Systems thinking
adopted for the eco-agri-food system can aid forming a
common ground for cultural changes through promoting
more integrated approaches.

2. Systems Thinking: An approach for understanding ‘eco-agri-food systems’
45
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