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The architecture of ecology: Systems
design for sustainable agricultural
landscapes
A thesis presented to the Honors Tutorial College, Ohio
University
In partial fulfillment of the requirements for graduation from the
Honors Tutorial College with the degree of Bachelor of Science
in Environmental and Plant Biology.
Eden Kinkaid
2013
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Contents
Introduction: The transformation of agriculture ............................................................................. 5
Chapter 1: The history and present state of modern industrial agriculture ................................... 10
Looking toward the past........................................................................................................ 10
The history of modern industrial agriculture .........................................................................11
Trends in modern industrial agriculture ................................................................................ 13
A vulnerable system? ............................................................................................................ 18
Looking toward the future .................................................................................................... 19
Chapter 2: Approaching agriculture as a socio-ecological system ............................................... 21
The Panarchy model ............................................................................................................. 24
Self-Organization .................................................................................................................. 28
Nonlinearity and thresholds .................................................................................................. 30
Understanding a panarchy as a whole ................................................................................... 31
Conclusion ............................................................................................................................ 33
Chapter 3: An agricultural panarchy ............................................................................................. 34
Criteria and parts of a system................................................................................................ 34
Constructing an agricultural panarchy .................................................................................. 37
Chapter 4: The patch ..................................................................................................................... 41
Soil as a landscape ................................................................................................................ 41
Soil organic matter as indicator of soil quality ..................................................................... 42
Soil organic matter dynamics as an adaptive cycle............................................................... 44
Patterns at the scale of the patch ........................................................................................... 46
Drivers at the scale of the patch ............................................................................................ 48
Summary and conclusion ...................................................................................................... 63
Chapter 5: The site ........................................................................................................................ 64
The farm as a landscape ........................................................................................................ 64
The annual cropping season as an adaptive cycle ................................................................. 66
Patterns at the scale of the site .............................................................................................. 68
Drivers at the scale of the site ............................................................................................... 78
Summary and conclusion ...................................................................................................... 84
Chapter 6: The landscape .............................................................................................................. 86
The agricultural landscape .................................................................................................... 86
The concept of the landscape ................................................................................................ 86
Land use history as an adaptive cycle ................................................................................... 90
Patterns at the scale of the landscape .................................................................................... 93
Drivers at the scale of the landscape ................................................................................... 106
Summary and conclusion .....................................................................................................110
Summary of Part I ................................................................................................................110
Chapter 7: Cross-scale interactions ..............................................................................................114
The nature of a panarchy......................................................................................................114
Making predictions of an uncertain future ...........................................................................115
Building scenarios ................................................................................................................117
The Reference Scenario ...................................................................................................... 120
The Agribusiness Wins Scenario ........................................................................................ 126
The Agricultural Deserts Scenario ...................................................................................... 127
The Just World Scenario ..................................................................................................... 129
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The Localization Scenario .................................................................................................. 130
Summary and conclusion .................................................................................................... 132
Chapter 8: The structure of the problem ..................................................................................... 134
Lessons from future scenarios ............................................................................................ 134
Adaptive vs. maladaptive systems ...................................................................................... 136
System traps ........................................................................................................................ 137
Redirecting a maladaptive system ...................................................................................... 141
Chapter 9: The synthesis of form ................................................................................................ 143
Deconstructing the problem ................................................................................................ 143
Understanding the context of design................................................................................... 144
The process of design.......................................................................................................... 146
Diagrams ............................................................................................................................. 149
The constructive diagram as a hypothesis .......................................................................... 153
The adaptive cycle as a constructive diagram ..................................................................... 154
Requirements of “Sustainable Agriculture” ........................................................................ 156
Synthesis ............................................................................................................................. 161
Form .................................................................................................................................... 163
(Re)designing agriculture.................................................................................................... 165
Creating physical form ........................................................................................................ 167
Conclusion: Design, intuition, and logic..................................................................................... 170
The logic of design.............................................................................................................. 170
Connecting science and design ........................................................................................... 172
Confronting an uncertain future .......................................................................................... 173
References ........................................................................................................................... 174
Acknowledgements ............................................................................................................. 186
Appendix ............................................................................................................................. 187
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Table of Figures
Fig. 1: A sustainable agriculture. ................................................................................................. 21
Fig. 2: Agricultural system as a complex adaptive system. ......................................................... 23
Fig. 3: The adaptive cycle relates the state of system variables ................................................... 25
Fig. 4: Phases of the adaptive cycle.............................................................................................. 27
Fig. 5: A panarchy. Note the cross-scale effects of the revolt and remember functions............... 31
Fig. 6: Scalar diagram of a panarchy ............................................................................................ 32
Fig. 7: Spatial relationship of patterns, processes, and drivers. ................................................... 36
Fig. 8: Nestedness of agricultural landscapes............................................................................... 39
Fig. 9: A panarchy of agriculture. ................................................................................................. 40
Fig.10a (left): Soil stratified into layers in a soil profile.
Fig. 10b (right): Heterogeneity in soil microlandscape. .............................................................. 42
Fig. 11: SOM dynamics in an undisturbed ecosystem. ................................................................. 45
Figure 12: The effects of tillage on the adaptive cycle. .............................................................. 46
Figure 13: Hierarchical relationship of patch and site scales, with the driver of farm and soil
management exerting top-down effects. ....................................................................................... 49
Table 1: Treatments simulated through Cropsyst. ........................................................................ 51
Fig. 14: Change in SOM over 100 year simulation period under conventional management...... 53
Fig. 15: Change in SOM over 100 year simulation period under 50 years of conventional
management and 50 years of organic management. ..................................................................... 53
Fig. 16: Change in SOM over 100 year simulation period under 50 years of conventional
management and 50 years of organic management (with twice the organic matter inputs as
treatment 2). .................................................................................................................................. 54
Fig. 17: Change in SOM over 100 year simulation period under organic management. ............. 54
Satellite image 1: A small organic farm outside of Athens, Ohio. ............................................... 65
Satellite image 2: Monoculture of corn near Ames, Iowa. .......................................................... 65
Satellite image 3: A cattle feedlot in Northern Texas. ................................................................. 66
Fig. 18: Annual cropping cycle as an adaptive cycle.................................................................... 67
Figure 19: Cropping systems along a gradient of disturbance. .................................................... 71
Photograph 1: Pepper vines growing up coconut trees in Southern India is one example of
intercropping. ................................................................................................................................ 72
Photograph 2: Forest garden in Southern India. ......................................................................... 77
Figure 20: Hierarchical relationship of landscape and site scales, with the driver of socioeconomic structures (e.g. subsidies) exerting top-down effects. .................................................. 79
Fig: 21: Land use history as an adaptive cycle............................................................................. 91
Fig. 22: Higher level socio-economic processes (i.e. development paradigms, land use decisions)
drive changes at the landscape scale. .......................................................................................... 106
Fig. 23: Hierarchy of scales in an agricultural panarchy. ............................................................112
Fig. 24: Gallopin’s scenarios map onto the adaptive cycle. .......................................................118
Fig. 25: Requirements occur at the form context boundary. ....................................................... 147
Fig. 26: Creation of construction diagram for problem explained in the text. ........................... 150
Fig. 27: Analytical decomposition of the problem into requirements ........................................ 157
Fig. 28: Decomposition of “sustainable agriculture” into categories of requirements............... 160
Table 2: Examples of possible requirements for the problem “sustainable agriculture” ........... 161
Fig. 29: Requirement diagram and form diagram juxtaposed. ................................................... 162
Fig. 30: A panarchy as a constructive diagram. ......................................................................... 162
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Introduction: The transformation of agriculture
Agriculture is the foundation of modern civilization. The practice of agriculture made
possible the first permanent settlement. Today, its mastery supports an incredibly complex global
society. And with the transformation of society, the practice of agricultural has been radically
transformed. What began as the relatively simple process of farming has been transformed into a
global agricultural and food processing industry reliant on fossil fuels, petrochemical inputs,
synthetic nutrients, and increasingly complex technologies. Lewontin captures this
transformation well: “Farming is growing peanuts on the land; agriculture is making peanut
butter from petroleum” (qtd. in Vandermeer 2011).
What has led us to this point in agricultural history? These changes in agriculture were
brought about by several agricultural revolutions (Foster 1999). The history of agriculture
revolves around the goal of eliminating the constraints posed by ecology. First, plants were
domesticated and planted; we no longer had to hunt for them in an unpredictable environment.
Then we realized that we did not have to rely on the bounty of nature, but could engineer fertility
and the other functions of a healthy ecosystem. In current times, we not only engineer
ecosystems, but organisms themselves. With this third revolution, there seem to be no ecological
constraints remaining.
But it is not so clear if this third agricultural revolution will deliver humanity from the
constraints of nature. Ironically enough, our attempts to engineer nature have resulted in the most
complex ecological constraint yet: global climate change. As we attempt to engineer around the
constraints of agriculture - the presence of pests and disease, land degradation, the loss of
ecosystem services to name a few - we accelerate agricultural systems toward these very
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constraints. While we seem to be overcoming these risks at the moment, we are not leaving
ourselves very many options for the future.
And the future is uncertain. Despite the technological genius and scientific insight we
possess, we do not know what the future will hold. There are too many variables to consider –
climate change, rising populations, natural disasters, economic recessions, political revolutions –
and few of them can be modeled or forecasted in a conventional sense. Any one of these
variables could redefine how we practice agriculture. And because our agricultural system is
optimized and vulnerable to external variability, it is likely that this change will result in a rapid
loss of complexity: a collapse.
To avoid such an outcome, we must think critically about the sustainability of agriculture.
What would a sustainable agriculture look like? What is sustainable? The sustainability of a
system refers to its ability to self-maintain, to continue into the future, to meet “the needs of the
present without compromising the ability of future generations to meet their own needs"
(Brundtland 1987). A sustainable agriculture must be sustainable ecologically, economically, and
socially. How can we address all of these realms simultaneously to design a truly sustainable
agriculture?
In order to do so, it is necessary that we think of agriculture as a socio-ecological system:
as an ecological phenomenon, but also as an economic, social, and political one. The “socio” in
socio-ecological systems not only refers to the fact that food provisions are central to the thriving
of civilization, but also to the idea that human systems – politics, economics, design – directly
and indirectly impact and drive changes on the agricultural landscape. Yet it is uncommon, in this
era of large-scale modernization and globalization of agriculture, to talk about agricultural
ecology in the context of market forces or trade liberalization (Fraser 2006), or to assess
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ecological ramifications when analyzing subsidy structures. One major goal of this paper is to
theoretically connect these seemingly disparate realms as parts of a single system.
To understand this system, we must think as historians, scientists, and designers. As
historians, we must seek to understand where we are at this point in history and how we arrived
here; how a system’s past propels it into its possible futures. As scientists, we must describe the
state of the agricultural system – its parts and their interrelations – as well as its dynamics and
behavior. As designers, we must work creatively with the constraints posed by history and
ecology toward a sustainable solution. The complexity of the agricultural system demands
engagement from many angles, and cannot be understood from one discipline alone. For this
reason, it is imperative for the future of humanity, as well as the advancement of science, that the
issue of sustainable agricultural design be approached critically and as a unified whole.
I intend to demonstrate, through this paper, what such a systems approach might look like
and how it would inform our understanding of agricultural systems. In Part I, I will construct and
describe a theoretical model of agriculture. First, I will briefly present the history of modern
industrial agriculture and identify major trends that will play a role in its future (Chapter 1). Then
I will introduce the fundamentals of Systems theory, Complex Adaptive Systems theory, and
Panarchy (Chapter 2). In Chapter 3, I will analyze agriculture at three temporal and spatial scales
through the Panarchy framework, identifying key patterns, processes, and drivers at each scale.
In Chapters 4, 5, and 6, I will look at the three scales in detail, grounding systems theory in the
natural and social sciences. Part II will examine the possible trajectories of agriculture. Chapter
7 will outline cross-scale interaction in Panarchy, and use established relationships between
variables in the agricultural system to design possible future scenarios. Part III will utilize the
system from Part I and the insights from Part II to posit a system of sustainable agricultural
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design. In Chapter 8, I will draw attention to the importance of understanding system structure in
the design process in order to avoid system “traps” that create maladaptive and unsustainable
systems. In the final chapter, I will use a systems design method (Alexander 1971) to derive
design applications from Panarchy theory.
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PART ONE: Defining a system for study
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Chapter 1: The history and present state of
modern industrial agriculture
Looking toward the past
This chapter will briefly present the history and present state of modern industrial
agriculture. The concept of history, as it relates to the self-organization of systems in time and
space, will be a recurring theme in this paper. Specifically, the phenomenon of path dependence
(Cowan & Gunby 1996) - that present possibilities are limited by past decisions through positive
feedback mechanisms - bears relevance on the possible futures of agricultural systems in the face
of change.
The modern industrial agricultural system will be at the center of this analysis. By using
the term “modern industrial agriculture,” I am referring to a specific type of agriculture that
occurs in the United States, Europe, and increasingly, around the globe. In the chapters that
follow, I will make reference to “the agricultural system” which refers to the type of agriculture
described in this chapter. Specifically, I will be looking at agricultural landscape in the United
States. Even within the United States, this is not the only type of agriculture practiced by any
means. However, modern industrial agricultural practices are the “status quo” of agriculture in
the U.S., and will thus compose the target of my analysis.
Overall, trends in modern industrial agriculture have gradually led to increasingly
consolidated systems with low levels of diversity and redundancy. These systems are optimized
for their yield potential at the cost of many other properties of an agroecosystem. This
optimization has led to increased production, but this production is supported by increasing
amounts of non-renewable fuels, petrochemicals, and minerals. As this technological
intervention becomes more and more necessary, the natural capacity of ecosystems to support
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and sustain themselves is being eroded, making the system less adaptable and resilient in the face
of change.
Throughout its history, the agricultural system has been “incrementally adapting” (van
Apeldoorn et al. 2011) to change. These small adaptations have led the system down a particular
pathway that accelerates the system toward vulnerability. Take for instance the use of pesticides.
If pesticides became suddenly unavailable for whatever reason, or stopped working, we can no
longer rely on the ecosystem service of pest regulation (i.e. natural predators) to control pest
populations, because the natural capacity of the system to protect against pests has been
disrupted. In a similar way, if we no longer had the machinery used for tillage, the soil could not
support plant growth. This is because years of tillage destroy soil structure, and the only solution
(other than taking the land out of use and rebuilding the soil) is to keep tilling to artificially
create soil structure. This “incremental adaptation traps,” (van Apeldoorn et. al 2011) where a
behavior was once adaptive, but eventually becomes maladaptive, strongly constrain the possible
futures of the system.
Examining the history of modern industrial agriculture can reveal these kinds of traps and
“accidents of history” that move the system toward certain states. In this case, the system moves
toward degradation and vulnerability.
The history of modern industrial agriculture
The term “modern industrial agriculture” refers to a specific type of food production that
has arisen through a number of technological and cultural developments over centuries.
Beginning with the selective breeding of wild crop relatives, agricultural practice has
increasingly tended toward greater human control over plants and their environment. Large leaps
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in agricultural practice and methods have occurred throughout modern history in the form of
“agricultural revolutions.” Foster (1999) writes:
The first agricultural revolution was a gradual process occurring over several centuries
[17th and 18th], associated with the enclosures and the growing centrality of market
relations; technical changes included improved techniques of crop rotation, manuring,
drainage and livestock management. In contrast, the second agricultural revolution
occurred over a shorter period (1830-80) and was characterized by the growth of a
fertilizer industry and revolution in soil chemistry...The third agricultural revolution was
to occur still later, in the 20th century, and involved the replacement of animal traction
with machine traction on the farm and the eventual concentration of animals in massive
feedlots, together with the genetic alteration of plants (resulting in narrower
monocultures) and the more intensive use of chemical inputs – such as fertilizers and
pesticides.
It is this increased use of technology – mechanical and genetic – that has characterized
the shift to modern industrial agriculture. This shift was made possible by the availability of
cheap fossil fuels after World War II (Pfeiffer 2006). Windham (2007) explains: “While the
ability to make fertilizers had been around since 1909, it was not economically feasible to
produce them until after World War II. During the war, the chemical industry expanded to fill
military requirements and this produced byproducts that would be used advantageously in
manufacturing chemical fertilizers.” For example, Agent Orange, a defoliant used in the Vietnam
War has since been reconfigured into Monsanto's Roundup, an herbicide which is applied to field
crops that have been genetically modified to be resistant to the effects of glyphosate, the active
ingredient (McGrath 2012). In this way, the practices that characterize modern industrial
agriculture arise from a particular history.
While the first and second agricultural revolutions took place in the United States,
Britain, and other parts of Europe, the third agricultural revolution is global in its reach. The
Green Revolution of the 1960's initiated the globalization of agriculture. The goal of the Green
Revolution was to increase global food production by bringing modern technologies, including
hybrid seeds, pesticides, herbicides, inorganic fertilizers, and genetically engineered varieties,
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into tropical climates and developing nations (Vandermeer 2011). While production of rice and
grains did rise, the sustainability of the benefits of the Green Revolution has come under much
scrutiny. This “progress” has come at a high energetic cost; a 250% increase in global grain
production was made possible by fifty times the energy inputs of traditional agriculture (Pfeiffer
2006). An increasing reliance on fossil fuels is but one disquieting trend in the history of modern
agriculture.
Trends in modern industrial agriculture
The shift to modern industrial agriculture has produced numerous trends that threaten the
long-term sustainability of agriculture. Industrial agricultural practices have widespread
implications for biodiversity, the availability of arable land and fresh water, “free” markets, and
the ability of the system to adapt to climate change. The nature and magnitude of these negative
trends are central to the discussion of vulnerability in the agricultural system.
Biodiversity
The existing genetic diversity of food crops is a major resource for managing agriculture
in the face of climatic change and uncertainty. The Food and Agriculture Organization (FAO) of
the United Nations (UN) has identified crop diversity (referred to as plant genetic resources for
food and agriculture or PGRFA) as a vital resource for adapting to climate change (SoWPGR-2
2010). Additionally, the FAO acknowledges that biodiversity contributes to the "resilience of
ecosystems for risk mitigation" and "enhances ecosystem services because those components that
appear redundant at one point in time become important when changes occur" (FAO 2013
Biodiversity and Ecosystem Services). The FAO 2010 Second Report on the State of the World's
Plant Genetic Resources for Food and Agriculture (SoWPGR-2), has highlighted that the "loss of
PGRFA has reduced options for the agricultural sector," with losses being attributed mainly to
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"land clearing, population pressures, overgrazing, environmental degradation and changing
agricultural practices" (SoWPGR-2 2010).
These losses in biodiversity undermine the resilience of ecosystems; low levels of
biodiversity make agricultural systems vulnerable to major disturbances and unable to adapt to
variability. The FAO reports that "Today, 75 percent of the world’s food is generated from only
12 plants and five animal species" (FAO 1999). The modernization, and consequential
homogenization, of global agriculture has led to dwindling uses of landraces (i.e. regionally
adapted varieties) of plants and animals. This shift toward commercial varieties has had large
impacts on the biodiversity of both plant and animal varieties used in agriculture; the FAO
reports that "Since the 1900s, some 75 percent of plant genetic diversity has been lost as farmers
worldwide have left their multiple local varieties and landraces for genetically uniform, highyielding varieties" and that "six breeds [of livestock] are lost each month" (FAO 1999). Specific
statistics vary regionally, but this significantly negative trend represents the state of global
agriculture as a whole.
Soil degradation and loss of arable land
The possibilities for agriculture (i.e. the potential of agriculture to produce an adequate
food supply for a growing population) are limited by ecological, political, and economic factors.
But more fundamentally, the yield potential of agriculture is limited by the availability of arable
land. Pagliai et al. (2004) note: “soil degradation is a major environmental problem worldwide
and there is strong evidence that the soil degradation processes present an immediate threat to
both biomass and economic yields, as well as a long-term hazard to future crop yields.”As will
be discussed in Chapter 4, the methods of modern industrial agriculture have largely negative
impacts on soil, including accelerated rates of erosion and nutrient leaching.
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These practices, along with other short-sighted land use, are contributing to the global
loss of arable land. The International Food Policy Research Institute (IFPRI; Nkonya et al. 2011)
reports: “About 24 percent of global land area has been affected by land degradation. This area is
equivalent to the annual loss of about 1 percent of global land area, which could produce 20
million tons of grain each year, or 1 percent of global annual grain production.” With the global
demand for grains and cereals expected to increase by 40% by 2020 (Kucharik & Ramankutty
2005), a global food supply cannot be sustained alongside losses in arable land and soil
resources.
Water scarcity
As crop yield ceilings are reached on major crops like corn, irrigating arid lands may be a
way to increase corn production in the future (Kucharik & Ramankutty 2005). The freshwater
needed for irrigation is diverted from rivers, aquifers, and other sources of fresh water. As
aquifers are lowered to critical points, this water may no longer be available. Foster (2009)
comments that the unavailability of fresh water for irrigation “poses a threat to global
agriculture, which has become a bubble economy based on the unsustainable exploitation of
groundwater.” Currently, agricultural uses account for 85% of freshwater use in the United
States (Pfeiffer 2006). Increasing the amount of freshwater used in agriculture will tax already
stressed systems.
The diversion and extraction of water resources for agriculture and other uses is results in
the lowering of water tables and aquifers and is causing major rivers to run dry before they reach
the ocean (Pfieffer 2006). Irrigation may also lead to salinization, which makes land unsuitable
for agriculture (Walker & Salt 2006). In the future, we can only expect greater strain on these
resources, as the demand for food grows and urban areas rapidly expand.
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The water crisis is two-fold: on one hand, our sources of water (aquifers) are being
depleted before they can naturally recharge. On the other, existing freshwater sources are being
polluted with chemical run-off and industry wastes. This waste disrupts ecological communities,
and also affects human health, as nearly a hundred different pesticides have been found in
drinking water in the U.S. (Pfeiffer 2006). Neither of these trends is sustainable.
Economic Consolidation
The economic consolidation of agricultural markets also threatens sustainability and food
security. A small number of large, transnational organizations are gaining increasing control over
the global agricultural system through horizontal integration, vertical integration and global
expansion (Howard 2006). Horizontal integration, the process of buying out competitors, has led
to losses in small and medium sized independent seed companies, farms, processors, and
distributors. Vertical integration, the process of taking control of the entire supply chain of an
industry, gives corporations major control over agricultural markets. ConAgra provides a striking
example of vertical integration, as it "distributes seed, fertilizer and pesticides; owns and
operates grain elevators, barges and railroad cars; manufactures animal feed; produces chickens;
[and] processes chickens for sale" (Howard 2006). The combination of horizontal and vertical
integration results in a food system controlled by a few dominant players who participate in all
stages of food production: inventing genetic technologies, selling seed, growing crops, and
transporting, processing, and distributing food products.
The current state of agribusiness reflects this high level of consolidation. Three or four
corporations dominate large percentages of the major food industries in the United States. The
concentration of markets can be measured as four-firm concentration ratios (CR4), measures of
the market share of the four largest companies in an industry (“Four firm concentration ratios”
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2013). CR4 values between 50% and 80% are said have a medium concentration (i.e. the market
is likely to be an oligopoly) while values between 80% and 100% are highly concentrated (i.e.
the market is an oligopoly or monopoly) (“Four firm concentration ratios” 2013) Hendrickson
and Heffernan (2007) calculated the following CR4 values for the major food industries: beef
packing, 83.5%; pork packing, 66%; Broilers, 58.5%; Turkeys, 55%; Soybean crushing, 80%.
The corn seed industry CR2 (the percentage of shares own by the two largest corporations in the
industry) is 58%. Howard (2006) describes the implications of this concentration: "the largest
firms will have a disproportionate influence on not just the price of a commodity, the also the
quantity, quality and location of production."This degree of consolidation and corporate control
has major implications for food security at all scales.
Climate change
A disturbance to the food system is made likely by the changing climate. To what degree
and in what ways the global climate will change are not yet fully known or understood.
However, the climate is expected to become more variable, which would increase variability in
crop yields (Kucharik & Ramankutty 2005). Additionally, global temperatures are expected to
rise by between 1.1 and 6.4oC by 2100 (IPCC 2007). This increase in temperature will certainly
have effects on agriculture. These effects vary regionally. With a one to two degree increase in
low latitudes, the productivity of some cereal crops will decrease. With a three or four degree
increase, the productivity of all cereals will decrease. At mid- and high-altitudes, a one to two
degree increase may increase the productivity of some cereal crops, but a three to four degree
increase would lower the productivity of these crops. With any increase of temperature, there
will be “complex localized negative impacts on small holders, subsistence farmers, and fishers”
(IPCC 2007).
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Furthermore, some suggest that the IPCC estimates are too conservative, and that
warming may occur at twice the rate of change expected (Foster 2009). At any rate,
“experiments at the International Rice Institute and elsewhere have lead scientists to conclude
that with each 1oC (1.8oF) increase in temperature, rice, wheat, and corn yields could drop by 10
percent” (Foster 2009). The impacts of climate change on cereal crops, which are staple foods
globally, are substantial. It is important to realize that this warming cannot be stopped. It may be
able to be slowed, but food systems will have to adapt to this new environment. A failure to do so
will inevitably lead the global food system to collapse.
A vulnerable system?
As stated, these major trends in agriculture have serious implications for the
sustainability - and ultimately, the feasibility- of modern industrial agriculture. Biodiversity loss,
soil degradation, water scarcity, economic consolidation and climate change all represent areas of
risk for agriculture. The dialogue surrounding the future of energy recognizes that a society
heavily reliant on fossil fuels is a major risk. For agriculture, this means that without a major
energy down-scaling in the coming years, the food system will become continually more
vulnerable. Vulnerable to what? To any kind of disturbance or shock – for instance, a regional or
national pest outbreak, an unpredictable climate, a large scale loss of electricity, or a financial
downturn. The risk (not only the probability of a negative outcome, but the magnitude of that
outcome) for any one of these is increasing; emerging pesticide resistances (Whalon et al. 2008),
the reality of global climate change, and the salient possibilities of natural disasters and terrorist
attacks all pose threats the feasibility of modern industrial agriculture. As reliance on fossil fuels
continues to increase, our capacities as a society - which most certainly must include natural
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resources such as biodiversity and arable land - are becoming limited. These limitations will
certainly constrain the possible futures of agriculture.
What form the future of agriculture will take is unclear. The non-linearity of complex
adaptive systems undermines scientific prediction. What can be predicted is unpredictability. The
ability to predict and identify risk and manage a system's outcomes is at the basis of both
economics and agriculture. Based on available knowledge we can anticipate disturbances like
hurricanes or droughts. However, we lack the tools to predict large scale systemic disturbance.
The changing climate provides a particularly compelling example; not only can we not very
precisely predict coming changes to the climate, but we also cannot predict how these changes
will ripple though other systems, including agriculture, economics, and natural resource
management. These cascading changes are what have the potential to radically redefine modern
life.
Looking toward the future
The possibility of a large-scale collapse in agriculture cannot be ignored. The human
suffering that would result is justification enough to carefully examine the vulnerabilities of the
food system. While the possibility of such a collapse is at the center of this discussion of
agriculture, I do not intend to predict a particular outcome; the form and magnitude of a collapse
is highly variable. In speculating about collapse, I am not arguing that an agricultural apocalypse
is imminent, but that a decrease in system complexity can be anticipated as a product of internal
systems dynamics, driven by changes in external variables. Instead I hope to illuminate the
possibility of such an outcome, and examine how this outcome may be connected to a particular
history that is produced by ecological and socio-economic drivers, which can be observed and
modeled scientifically. By observing trends, behaviors, and interactions between key variables,
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we can come to understand the relationships between them and illuminate the structure of the
agricultural system as a whole.
A systems approach to agriculture can expose the vulnerabilities of agriculture and the
food system as products of history and system dynamics. By understanding the history of a
system, we can deduce the relationships and feedback mechanisms that operate within the system
and drive it into the future. With this history in mind, the following chapters will build upon the
idea of agriculture as a socio-ecological system and outline a system for further analysis.
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Chapter 2: Approaching agriculture as a socioecological system
Navigating the multidimensionality of agricultural systems
The above discussion of agriculture demonstrates its inherent multi-dimensionality and
complexity; agriculture exists at the nexus of ecology, society, and economy, at the interface of
human and natural systems (see Fig. 1). As such, a full understanding of agriculture cannot be
had in any one discipline alone. Systems thinking requires that we reframe the way that natural
and human systems are perceived and studied. Firstly, a systems approach requires that human
and natural systems are considered as overlapping socio-ecological systems. Secondly, these
systems are not static, but dynamic entities that change and evolve through time. Thus systems
are not in “equilibrium states” but move between multiple-steady states or basins of attraction
(Holling et al. 2002). These two assumptions are fundamental to the study of complex adaptive
systems. As such, Complex Adaptive Systems theory aims to understand and describe the
structure of complex systems, their dynamics and how they adapt and change.
Fig. 1: A sustainable agriculture. A sustainable agriculture must be sustainable in terms of
ecology, equity, and economy.
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In order to treat agriculture as a complex adaptive system, it is necessary to define
complex adaptive systems more fully, and to explore their implications for how we understand
ecological systems. In the next section, I will introduce the broad theoretical elements of
complex adaptive systems, and in doing so, will outline criteria of complex adaptive systems,
which will be used to define a theoretical model of agriculture in the following chapter.
Complex adaptive systems and agriculture
The term “complex adaptive system” refers to a system with certain components and
types of interactions among these components. Levin (1999) identifies three essential
components of a complex adaptive system (CAS): (1) “diversity and individuality of
components,” (2) “localized interactions among the components,” and (3) “an autonomous
process which uses outcomes of those local interactions to select a subset of those components
for replication or enhancement.” The characteristics of a complex adaptive system also include
self-organization, feedback relationships among components, non-linear dynamics, heterogeneity
and structural hierarchies, which will be discussed in this chapter. Using these criteria, Levin
(1999) concludes that “to varying degrees, corporations, whole economies, ecosystems and the
biosphere represent other examples of complex adaptive systems.”
This approach will consider the agroecosystem as a complex adaptive system. While
economics and corporations are of critical importance to the future of agriculture, as are the
dynamics of the biosphere, in this scheme, both realms are considered drivers of ecological
change on agricultural landscapes (Fig. 2; Case 1). If this were a study of agricultural
economics, I might consider the opposite: ecological dynamics (in the form of agricultural
yields) as a driver in markets (Case 2). A study concerned with the biosphere would also likely
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cite agriculture as a driver, a source for inputs of C and N into the biosphere (Case 3). Thus,
selecting the ecosystem as the system for study is a choice of priority; I am focusing on one point
in a network created by the overlap of many systems. By taking the agricultural ecosystem as
my focus, I hope to illuminate the interrelations between ecological systems, economic systems,
and ultimately, societal systems. In order to address the multi-dimensional question of
sustainability, all of these systems must be considered.
Fig. 2: Agricultural system as a complex adaptive system. The focus on ecology as a CAS is a
choice of priority over analysis of the systems of the economy and biosphere.
This kind of interdisciplinary approach to agriculture is possible within the framework of
Complex Adaptive Systems theory, as well as its outgrowths, resilience thinking and Panarchy.
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Specifically, the Panarchy model has been used to describe the structure and dynamics of
complex and multi-dimensional socio-ecological systems, including agriculture (van Apeldoorn
et al. 2011) and natural resource management systems (Holling et al. 2002). The framework of
Panarchy will structure the following discussion of agriculture, and will serve as an
organizational structure for scenarios of the future of agriculture and society. Its implications for
understanding agricultural landscapes will also be integrated in the discussion of sustainable
design in Chapter 9. The remainder of this chapter will consist of a discussion of the Panarchy
model, which will be threaded throughout this analysis of agricultural systems.
The Panarchy model
Panarchy is a framework for describing the structure, behavior, and interaction of
complex adaptive systems. The generalizable model maps the interaction of three variablessystem wealth, connectedness, and resilience- through phases of a system's “life” or “cycle.” The
interaction of these variables defines the state of the system and its possible futures and thus
drives a system through what is called the adaptive cycle. The adaptive cycle is the pattern
produced by the interactions of these variables (Fig. 3), which follows a progression from
renewal and growth to consolidation and collapse. This predetermined course is produced
internally in the system through the process of self-organization, which inevitably leads to a
critical state where it is vulnerable to collapse. The system reduces in complexity and begins this
process once more.
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Fig. 3: The adaptive cycle relates the state of three system variables: wealth (or capacity),
connectedness, and resilience. Sourced from ecologyandsociety.org
Wealth, connectedness, and resilience are three general variables in the adaptive cycle. In
some cases, they lend themselves to empirical measurement (for instance, the measure of system
wealth in the form of accumulated biomass or soil organic matter), and in others, they are more
difficult to measure. In particular, connectedness and resilience are rather abstract. As defined by
the Panarchy model, system wealth is the potential in a system; Holling et al. (2002) explain:
“the system must be productive, must acquire resources and accumulate them, not for the
present, but for the potential they offer for the future.” System wealth, therefore, is the amount of
resources in a system, which can limit or expand future capabilities, or possible states, of the
system.
“Second,” Holling et al. (2002) explain, “there must also be some sort of shifting balance
between stabilizing and destabilizing forces reflecting the degree and intensity of internal
controls and the degree of influence of external variability.” This balance is the degree of
connectedness that a system exhibits. In the case of modern industrial agriculture, production
relies heavily on inputs (e.g. fertilizers, pesticides) and fairly stable conditions. It is highly
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connected because it attempts to deal with external variability through increasingly rigid internal
controls (e.g. pesticides). A disruption of this internal regulation would majorly disrupt
agricultural yields. Because the system is optimized in terms of yield, it cannot adapt well to
novel or variable conditions.
The third dimension of a system is the property of resilience, the ability of a system to
absorb shock and maintain its characteristic structure and processes. Holling et al. (2002) explain
that “the resilience of the system must be a dynamic and changing quantity that generates and
sustains both options and novelty, providing a shifting balance between vulnerability and
persistence. Resilience is directly related to system wealth and connectedness, as a system’s
ability to respond to a disturbance is influenced by available resources (i.e. wealth; e.g.. nutrients
in a system, seeds in a seed bank, social capital, economic capital), as well as the stability of the
system internally (i.e. connectedness).
Throughout the adaptive cycle, these variables vary, giving rise to four characteristic
phases of system behavior (Fig. 4): alpha (the reorganization phase), r (the exploitation phase), K
(the conservation phase), and omega (the collapse phase). The phases usually occur in this order,
though there are a few exceptions, which will be discussed in Chapter 8. Each phase is
characterized by a particular relationship among the three variables of wealth, connectedness,
and resilience.
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Fig. 4: Phases of the adaptive cycle. Sourced from Holling et al. 2002.
In alpha, following a collapse, wealth is freely available, as it has been released from a
locked-up system. Connectedness is low; there are not strong relationships relations between the
system's parts. These two qualities make a system in the alpha phase resilient (i.e. able to absorb
shock without turning into another system altogether). The process of swidden agriculture, or
slash-and-burn, takes advantage of these conditions; nutrients released from burns support
growth (high wealth), while diverse and short-lived establish communities buffer major losses
(low connectivity and high resilience).
After reorganization comes the r, or exploitation, phase. Wealth is less available in this
phase because it has become distributed throughout the system. Connectivity remains low and
resilience is high. Imagine a forest developing after a disturbance, e.g. a fire. After the collapse
(omega) brought about by this disturbance, reorganization begins; energy is released for new
growth. Seeds germinate from the seed bank and restore a plant community. For many years
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following such a disturbance, all of these organisms are competing for limited resources,
including space. As time passes, this pioneering or exploitation phase gives way to the K phase.
Eventually, competition plays out and the forest community becomes more “mature;” it
become less dynamic, more homogenous, and resources become tightly coupled in a decreasing
number of organisms. This is the K, or conservation, phase, which is characterized by high levels
of concentrated wealth (in the form of biomass), high connectivity (fewer parts becoming more
and more tightly coupled), and low resilience. As the system continues to move in this direction,
it reaches a critical point (referred to as late K), where the system cannot be sustained. Low
resilience makes the system unable to absorb smaller and smaller shocks, which results in a rapid
loss of complexity and organization through collapse (omega).
It may seem that explaining system dynamics through the adaptive cycle is teleological in
some sense, as it assumes that systems must pass through certain phases in their “development”
and are progressing toward a particular outcome. However, an understanding of self-organization
in systems clarifies this misunderstanding.
Self-Organization
The mechanism that moves systems through the adaptive cycle is the process of selforganization. Self-organization is an emergent principle in systems. Meadows (2008) defines
self-organization as the “the capacity of a system to make its own structure more complex.” In
other worlds, it is a system's ability to “learn, diversify, complexify, [and] evolve” (2008).
Holling et al. (2002) explain the mechanism of self-organization as the creation and
reinforcement of pattern. A common example of a simple, physical process of self-organization
is the creation of a snowflake. A seed crystal begins the process, upon which other crystals form.
The formation of each crystal is determined by the specific physical environment created by the
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last crystal. In other words, the snowflake is created through a linear set of reactions; it has a
history. The processes to follow, the possible futures of the snowflake, are guided by that history
in a very fundamental way (Murphy 2006). Likewise, the present state of all systems is the
product of a particular history.
This concept of systems as products of history, which are capable of adapting and
evolving, opens up new directions for scientific inquiry. A diverse set of phenomena can be
studied with scientific rigor in search of general principles. Meadows (2008) argues that
“science, a self-organizing system itself, likes to think that all of the complexity in the world
must arise, ultimately, from simple rules.” Panarchy is an attempt to describe how systems work,
theoretically and empirically, and to illuminate these basic principles. Using these basic systems
“rules,” such diverse phenomena as the evolution of life, the workings of financial markets, the
development of technology, and the functioning of ecosystems can be understood in a new light
(2008).
Mechanisms of self-organization
How systems “move” through this history or temporal progression is explained, in part,
through feedback relationships inherent in any complex adaptive system. Feedbacks are “control
mechanisms” in systems (Meadows 2008). They either maintain the current state of the system
(balancing/negative feedbacks), or accelerate it toward another state (runaway/positive
feedbacks). Predator-prey relationships demonstrate a balancing (negative) feedback, where an
increase in a prey population is held in balance by an increase in the population of predators
(Vandermeer 2011). On the other hand, glacial melting, a product a global warming, accelerates
itself (through a positive/runaway feedback) by reducing the area of the earth that is white (i.e.
reflective of light), thus leading to a greater planetary absorption of heat (Curry et al. 1995). The
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heat effect becomes stronger, which further accelerates melt, and so on, until the ice is melted,
and the system changes into a new state (iceless).
These feedback relationships are the mechanism behind the internal dynamics of a
system. Feedbacks make systems non-linear, which makes their behavior hard to predict.
However, it is possible to look at feedback relationships structurally and understand the
relationships and connections between parts of a system. The operating of feedback mechanisms
can thus help explain how systems work and change through time. These feedbacks can be used
to identify what phase (alpha, r, K, omega) a system is likely to be in at a given time and where it
is likely to go next.
Nonlinearity and thresholds
Though we may know where a system is in the adaptive cycle, it may still be difficult to
model or predict its behavior into the future. This is because complex adaptive systems are nonlinear and contain thresholds. Thresholds are critical points in system variables. Incremental
change up to a certain point may produce linear or no results, but after crossing a threshold, the
system rapidly changes (Walker and Salt 2006). While a steady state model of ecosystems
assumes that ecosystems are equilibrium systems, the Panarchy model assumes that ecosystems
have multiple-steady states. In other worlds, a system may have mechanisms to maintain an
equilibrium state up to a point, but when pushed too far, the system will accelerate toward a new
equilibrium, which may be very different from the original system. The point at which the
system processes change from balancing or linear to accelerating is a system threshold.
Examples of ecological thresholds are numerous (see the Resilience Alliance threshold database
for examples; “Thresholds and alternate states in ecological and social-ecological systems: A
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Resilience Alliance / Santa Fe Institute database”); the eutrophication of lakes (Carpenter et
al.1999) and the salinization of dairy lands in Australia (Walker and Salt 2006) are two examples.
Understanding a panarchy as a whole
The previous sections have described the properties and internal mechanisms of complex
adaptive systems. The Panarchy model relies on these concepts to explicate the interactions
between different scales of analysis within a system. These mechanisms drive change in a
panarchy, which is composed of adaptive cycles nested in space and time (Fig. 5). A panarchy is
composed of “hierarchical” scales; at the bottom are the smallest and fastest cycles. As you move
up in a panarchy, the adaptive cycles at each scale become larger and slower. In essence, a
complex adaptive system is composed of many different adaptive cycles operating at different
speeds and spatial scales.
Fig. 5: A panarchy. Note the cross-scale effects of the revolt and remember functions. Sourced
from ecologyandsociety.org.
A system’s phase in an adaptive cycle has implications for behavior at a particular scale
of analysis. It also has consequences for the entire system. This is one of the central ideas of
Panarchy; systems are composed of multiple “layers” of adaptive cycles operating across
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temporal and spatial scale, which are nested and interacting. For instance, there are discrete
dynamics (and cycles) that occur at the scale of a pine needle, versus a tree crown, versus a tree,
versus a stand, versus a forest, and so on (Holling et al. 2002; Fig. 6). While these discrete levels
can be identified analytically, they all exist simultaneously and compose one another. Changes or
disturbance in one scale is oftentimes not limited to that scale. For instance, a forest fire destroys
the stand and all the scales “below” it. An outbreak of a pest that feeds preferentially on new
growth may begin on new needles, spread to a tree crown, and, over time, defoliate an entire
stand (a scale “above” it) (Holling 2001). When change occurs at the lowest scale of the
panarchy and ripples into higher scales, the system is said to “revolt” (Holling 2001) Revolt
introduces novelty into the system. After a disturbance, higher levels of the panarchy can impose
artifact structures (in the form of the “remember” function) onto the system, maintaining some
continuity to the system. Thus the Panarchy model contains conservative forces to maintain
systems and limit their possibilities, but also processes for introducing innovation which is
fundamental to system change and transformation.
Fig. 6: Scalar diagram of a panarchy (Holling et al. 2002). Notice how the adaptive cycles
operate at different spatial and temporal scales.
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This structure of analysis becomes particularly important when trying to understand
systemic change. When vulnerability (i.e. high wealth, high connectedness, low resilience)
occurs at the same time at two or more scales, they system may experience cascading change
Because each scale operates at significantly time scales, this is not often the case. However,
when different levels are held in a vulnerable phase (K or late K), the potential for large scale
collapse continues to increase as long as the system is held in that state. As the system moves
further and further toward late K, it becomes more and more difficult (if not impossible on
human timescales) to return to a previous state in the system. This is when a shift to another state
can occur. Systemic change is not limited to collapse within systems; innovation can also move
“up” in a system. Change at one level, be it positive or negative, can lead to changes at other
levels in the panarchy.
Conclusion
An understanding of the properties of complex adaptive systems is integral to
understanding the agricultural system across many scales. The concepts of multiple-steady states,
adaptive cycles nested in time and space, non-linearity, and feedbacks will resurface throughout
this paper. In the next chapter, I will use ideas from complex adaptive systems theory and
Panarchy theory to construct a theoretical model of agriculture for analysis.
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Chapter 3: An agricultural panarchy
Criteria and parts of a system
In order to look at agriculture, or any other phenomenon, through the Panarchy lens, a
system must be constructed for analysis. As a complex adaptive system, a theoretical model of
agriculture must be composed of independent parts, interrelations among those parts, and some
sort of mechanism of selection to move the process forward in time (Levin 1999). Agricultural
landscapes, which operate according to the principles of ecology, meet these criteria.
Additionally, the system must possess feedback mechanisms which result in self-organization of
the system. As a Panarchy, this system must be composed of at least three “levels” that differ in
temporal and spatial scale and interact across scale.
This hierarchy creates the potential for cross-scale interaction, while patterns and
processes at each scale produce local interactions and dynamics. Pattern and process at each
scale have self-organizing and self-reinforcing relationships. Walker and Salt (2006) describe:
“Very importantly, the processes that produce these patterns are in turn reinforced by those
patterns- that is, the patterns and the processes are self-organizing. This is a key aspect of
complex adaptive system.” The creation of pattern on the landscape is especially relevant in
human-dominated systems; Holling et al. (2002) describe: “humans develop self-organized
patterns more intensively and over much larger ranges of scale than other organisms do. We
conjecture that those self-organized patterns are as important for evolution as Darwinian natural
selection, and as important for sustainable development as the market.” Despite the
acknowledged importance of pattern in shaping and constraining systems in the Panarchy model,
there is little literature on what pattern is and what role it plays in a panarchy. A major goal of
this paper is to explicate what this role is, and how it can be leveraged in systems design.
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Within the field of landscape ecology, where Panarchy has its roots, pattern is defined as
the spatial configuration of elements on a landscape (Turner et al. 2001). Properties of landscape
pattern include composition (i.e. proportion of landscape element) and connectivity (i.e. spatial
configuration of landscape element) (Zaccarelli et al. 2008). At the scale of the landscape, pattern
is the source of heterogeneity or “patchiness.” This heterogeneity produces landscape processes,
which in turn, create and reinforce pattern on the landscape.
To illustrate this point, consider a large amount of rain falling to the ground. After the
ground becomes saturated, water will begin to flow in sheets across the ground. Eventually, the
water will begin to flow in streams, as this movement is more thermodynamically favorable
(Bejan & Zane 2012). As it does so, it will produce gullies in the soil. When the next rainfall
event occurs, rain will flow into this gully and be transported in it. Thus landscape process (e.g.
the movement of water) create landscape pattern (e.g. the network of gullies), and in turn,
becomes constrained by those patterns. Each time a large rainfall event occurs, this pattern will
be reinforced by more erosion. At the level of the landscape, these processes have produced the
rivers and streams that snake across the landscape.
Ecosystem processes, in this analysis, refers to processes that occur on the landscape that
are connected to patterns at the same scale. At each scale, the adaptive cycle serves as a proxy for
the sum of these ecosystem processes. Thus the adaptive cycle represents the interaction of
pattern and process within the landscape. The trajectory of the system may also be affected by
external influence. These “drivers” are the third element in this analysis. The term “drivers”
refers to processes that occur at the next scale “up” in a panarchy (fig.7). Drivers affect both
pattern and process at the level below, and as a consequence, the behavior of the system at that
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scale. They are processes that are qualitatively different than the processes that occur at the scale
upon which they are acting.
Fig. 7: Spatial relationship of patterns, processes, and drivers.
Consider the example of desertification. The state of the ecosystem (i.e. a grassland or
pasture) is produced by the interaction between patterns (e.g. distribution and abundance of
vegetation) and processes (e.g. hydrology, moisture regimes) on the landscape. The process of
desertification, however, is produced by a disturbance in this relationship; changes in plant
communities and distribution lead to changes in available moisture and hydrology, which
accelerate the system into a “desertified” state. Kefi et al (2007) suggest that shifts into a
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desertified state can be anticipated given the spatial dynamics of vegetative patches; in other
words, a threshold exists regarding the distribution of vegetation. Though changes in this key
variable cause this shift, these changes were brought about by “off-site” drivers. Pasture
management (e.g. removal of native vegetation, overstocking of cattle), drove changes in plant
communities, and consequently, system processes. Management decisions are a different sort of
process that evapo-transpiration, or biogeochemical cycling and impact a different amount of
area While these drivers act on the same landscape, they are “off site” in the sense that they are
qualitatively and quantitatively different than processes at the scale upon which they are acting.
Constructing an agricultural panarchy
With these criteria in mind, we can begin to construct a theoretical model of the
agricultural system. System boundaries must be defined; each scale must be discrete and
bounded. Without boundaries, a system would be impossible to comprehend, analyze, and
manage. However, we must recognize that boundaries are constructed for the purpose of analysis
and that they do not reflect "real" boundaries in the world (Meadows 2008). In any systems
model, there must be a balance of boundedness and openness in the system.
The next step in creating a structure for analysis is to identify at least three scales that are
relevant to a discussion of agriculture. I am interested in the multiple scales at which agriculture
takes place. Brussaard (1994) notes: “agroecosystems can be studied at different hierarchical
levels from agronomic (field scale) through microeconomic (farm scale), and ecologic
(watershed or landscape scale) to the macroeconomic (national or regional scale).” Most
intuitively, agriculture occurs at the scale of the farm. The size of a farm, whether it is at the
scale of a family farm or a commercial producer, is not what is of interest here; what is of
interest is that this scale is where agriculture is a “practice,” a process of designing and managing
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agro-ecosystems. In other words, this is the site of food production; it is henceforth referred to as
the site scale. At each scale, we must ask: (1) what patterns are relevant to the ecology of this
scale?; (2) what ecological processes occur at this scale?; (3) what kind of external drivers
impact the landscape? At the scale of the site, plant communities form patterns on the landscape.
Site processes include cultivation, growth, harvest, decomposition, the movement of water,
cycling of nutrients, etc. Drivers at this scale, which inform the composition of landscape pattern,
and consequently, the processes it produces, include subsidy structures, available knowledge and
technology, and other socio-economic factors related to farming.
Agriculture, as a human and ecological system, does not occur only at this scale. The site
scale will provide the middle level of the panarchy used in this analysis. Below this scale are the
workings of soil and its organisms. This scale focuses on the communities (particularly below
ground communities) that make agriculture possible. Relevant patterns below ground include soil
structure and the trophic structure of soil food webs. Processes include decomposition, nutrient
cycling, cation exchange, etc. This scale is connected to the site scale through soil-plant
feedbacks and agricultural management practices (e.g. tillage, fertilizer use, pesticide use, cover
cropping, etc.). All of these elements together form the state of the soil ecosystem.
If we look beyond the site scale, to part of the agricultural system that is “larger,” the
agricultural landscape comes into focus. Again, this scale is not defined by certain dimensions,
but can be thought of as the culmination of different land uses that form a patchwork or mosaic.
The different land uses create heterogeneity or pattern on the landscape. Processes at this scale
fall into the category of land use change. What produces these patterns and processes are landuse decisions, which are guided by higher-level processes, e.g., capitalism and the socioeconomic and institutional climate of a region (Lambin et al. 2001)
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Finally, a very important element of panarchy is that these scales, though they are
discrete, are nested in space and time (fig. 8). This means that changes in a scale affect other
scales. In each of the following three chapters, I will begin with a description of the landscape at
that scale (fig. 9 provides a preview). I will then describe how the adaptive cycle at the scale can
be understood through a key variable, which links together major system processes. Finally, I
will describe relevant patterns on the landscape. I will then examine drivers at each scale, and
reflect on the implications of landscape change for the sustainability of agriculture. In Part II, I
will demonstrate how these discrete scales interact and function as a whole, as a panarchy.
Fig. 8: Nestedness of agricultural landscapes. The patch composes the site and the site composes
the landscape.
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Fig. 9: A panarchy of agriculture. Adaptive cycles at the landscape, site, and patch, are land use
history, the annual cropping cycle, and soil organic matter accumulation, respectively. These
landscape elements will be discussed at length in the following three chapters.
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Chapter 4: The patch
Soil as a landscape
Agriculture is fundamentally reliant on the nutrients and processes contained within the
soil. This scale of analysis considers the soil as a physical and biological landscape. Its patterns
and processes happen on a small scale (nanometers to meters) and change relatively quickly (i.e.
on a weekly-monthly basis).
To the untrained eye, soil may seem like a relatively homogenous medium. Depending on
the scale of analysis, this is more or less true. For instance, if one collected a small shovel full of
soil and examined it, it may seem roughly uniform, save for a scattering of earthworms and some
larger aggregates of soil. However, at a scale larger or smaller, heterogeneity is introduced. For
instance, a larger sample of soil (e.g. in a soil pit; fig. 10a) reveals that soil is stratified into
distinct horizons that differ in texture, aggregation, pH, color, and chemical composition (Brady
& Weil 2004). On a microscopic scale, heterogeneity is introduced through microclimates (fig.
10b), which become an important aspect of soil, as organisms with a wide variety of
physiological needs must find suitable habitat in which to live. Chemical processes in the soil
also introduce heterogeneity (e.g. through the leaching of clays and minerals to deeper soil
horizons) (Brady & Weil 2004).
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Fig.10a (left): Soil stratified into layers in a soil profile. Photograph sourced from www.
earthquake.usgs.gov Fig. 10b (right): Heterogeneity in soil microlandscape. Diagram sourced
from www.vro.dpi.vic.gov.au.
In the case of the soil landscape, this heterogeneity (i.e. pattern) includes physical
elements such as compaction, burrows, and channels and biological ones including soil structure,
and soil food webs. These patterns create different environments for life and consequentially,
different flows of energy (i.e. processes).This chapter will describe these patterns, how they are
connected to soil processes, and how both pattern and process can be understood through the
adaptive cycle. The latter half of this chapter will look at how off-site drivers, in this case
agricultural management, affect the adaptive cycle, pattern, and process at this scale. Soil
structure and food webs will be considered patterns on the soil landscape. The accumulation and
loss of soil organic matter (SOM) will be used as a proxy for soil processes, including cation
exchange, water retention, decomposition, and biogeochemical cycling.
Soil organic matter as indicator of soil quality
Many qualities of a soil can be measured; measures of pH, compaction, cation exchange
capacity, bulk density, water potential, soil C, and soil N provide information about the type of
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soil under study and its relative quality (i.e. its potential to support productive above and below
ground communities). All of these properties have different implications for the growth and
development of plant, animal, and microorganism communities. For instance, a low pH can limit
the available of essential plant nutrients like P and Ca, and determine, in part, what plant
communities will be successful at that site. A high bulk density limits the ability of roots to
penetrate to deeper soil horizons, which may make them less able to access minerals and water
(Brady and Weil 2004). These qualities can be described in aggregation through the concept of
“soil quality.”
Because many soil properties are connected to the organic matter content of the soil
(SOM), SOM can be used as a measure of overall soil quality (Brady and Weil 2004). Soil
organic matter (SOM) is defined as “the organic fraction of the soil that includes plant and
animal residues at various stages of decomposition, cells and tissues of soil organisms, and
substances synthesized by the soil population” (Brady and Weil 2004). As organic matter (e.g.
plant and animal biomass, exudates) decomposes in the soil, a small fraction of it is stabilized
into recalcitrant humic compounds. These complex and very large molecules make up SOM.
SOM contributes to cation and anion exchange capacity (i.e. the ability of the soil to hold
nutrients), water holding capacity, thermal regulation, plant nutrition, and soil aggregate
formation and stabilization (Brady and Weil 2004). In addition, the cycling of soil carbon (in the
form of SOM) is coupled with the cycling of soil nitrogen. Because of SOM has a direct
relationship with these key aspects of a soil, it is an indicator of the overall quality of a soil. As
such, it can serve as a proxy for other ecosystem processes (e.g. cation exchange, water retention,
biogeochemical cycling, etc.).
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Changes in SOM have far-reaching implications for both landscape process and
landscape pattern. As SOM content decreases, assumptions can be made about other soil
properties, given SOM’s relationship with these properties. Landscape process (e.g.
decomposition, cation exchange, nutrient cycling, water retention, thermal properties) and
landscape pattern (e.g. soil structure and soil food webs) are coupled with SOM, and changes in
any of these elements inevitably affect plant communities. For this reasons, it is important to
consider the effects of agriculture on SOM, as changes at this scale may “scale up” into the site
and landscape scales. Maintaining SOM, and along with it, the overall health of soils, is a major
concern of agricultural sustainability. Accordingly, the adaptive cycle of SOM accumulation will
connect landscape pattern, process, and drivers at this scale.
Soil organic matter dynamics as an adaptive cycle
In a natural (i.e. non-agricultural) system, SOM remains relatively constant. Large scale
soil disturbances, which would destroy SOM, are uncommon, and limited to events like
landslides and glaciations. SOM is constantly being broken down by fungi and bacteria;
however, a dynamic equilibrium exists between the decomposition and humification of organic
matter and the breakdown of the humic compounds that compose SOM. Understood through the
adaptive cycle, SOM accumulation in a “undisturbed” ecosystem (non-tilled) is a process of
gradual accumulation punctuated by a large scale disturbance and release of carbon (see fig. 11).
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Fig. 11: SOM dynamics in an undisturbed ecosystem. SOM accumulates until a large scale
disturbance (e.g. glaciation) released the stored carbon into the atmosphere. The system
reorganizes and resumes building SOM. This cycle occurs on geological time scales. Adapted
from Holling et al. (2002). Original image sourced from www.adaptivekm.com
However, the introduction of semi-annual, annual, and bi-annual tillage in most
agricultural lands leads to the rapid loss of carbon (i.e. SOM) held in the soil. Additionally, plant
residues and other sources of carbon (e.g. manures) are uncommonly added back onto the fields
to replenish soil C. This combination of losses through tillage and fewer OM additions leads to
diminishing stores of SOM. For this reason, van Apeldoorn et al. (2011) argue that annual
agriculture short-circuits the process of the adaptive cycle, cycling between r (the exploitation
phase, where benefit is reaped from released SOM) and alpha (the “free state” of resources after
tillage) (fig. 12). With no new inputs into the system, SOM is increasingly diminished after each
cycle.
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Figure 12: The effects of tillage on the adaptive cycle.van Apeldoorn et al. (2011) describe how
tillage changes the adaptive cycle by cycling between the alpha and r phases. The cycle is
accelerated to an annual or biannual basis. Sourced from ecologyandsociety.org
This gradual loss of SOM after continual large scale disturbances (e.g. tillage,
clearcutting, harvest) has been documented in longitudinal soil studies (see for instance, Richter
& Markewitz 2001). The negative trends created by agricultural management in agricultural soils
can also be simulated through agro-ecological modeling software. The effect of agricultural
management on SOM dynamics will be examined in more detail in the discussion of drivers at
this scale.
Patterns at the scale of the patch
Soil structure and soil food webs, the patterns at this scale, are both integral aspects of
soil quality, and are directly and indirectly connected to the dynamics of SOM.
Soil structure
According to Bronick and Lal (2004), “Soil structure refers to the size, shape and
arrangements of solids and voids, continuity of pores and voids, their capacity to retain and
transmit fluids and organic and inorganic substances, and ability to support vigorous root growth
and development.” Soil structure controls the movement of water, gases, and nutrients (Rillig et
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al. 2002). Favorable soil structure is necessary for agriculture as soil structure is connected to a
number of processes important to agriculture including decomposition, “soil water movement
and retention, erosion, crusting, nutrient recycling, root penetration, and crop yield” as well as
“externalities such as runoff, surface and ground water pollution and CO2 emissions.” (Bronick
and Lal 2004). Holding these relationships in mind, it is important to examine soil structure and
how it is affected by agricultural management.
Aggregation
The structure of a soil is determined by aggregation of soil particles. Bronick and Lal
(2004) define aggregates as “secondary particles formed through the combination of mineral
particles with organic and inorganic substances.” Aggregation occurs when particles form ionic
bonds with one another, forming a microaggregate. Microaggregates may also form when
microbial exudates are released around decomposing particulate organic matter (Bronick & Lal
2004). Thus, aggregation is dependent on a number of variables, including “the environment, soil
management factors, plant influence, and soil properties such as mineral composition, texture,
SOC [soil organic carbon] concentration, pedogenic processes, microbial activities,
exchangeable ions, nutrient reserves, and moisture availability” (Bronick and Lal 2004).
Out of the variables that affect soil structure, SOC is the most significant. Six et. al
(2000) identify a significant relationship between loss of SOM and loss of soil structure. This is
because SOM is a primary binding agent in aggregate formation and stabilization ((Brady and
Weil 2004). Bronick and Lal (2004) describe: “ the SOC creates regions of heterogeneity in the
soil, leading to “hot spots” of aggregation.” Conversely, soil structure affects the rate of
decomposition and SOM accumulation. Thus soil structure and SOM dynamics have a positive
feedback relationship.
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Soil food webs
The biodiversity of soil organisms is integral to soil functioning and the provision of
ecosystem services (e.g. water filtration, food production). These services include:
decomposition and nutrient turnover, bioturbation, greater nutrient efficiency, and disease
suppression (Thiele-Bruhn et al. 2012). Microbial activity also contributes to soil structure by
stabilizing aggregates (Brussard 1994, Bronick & Lal 2004). The functions that biodiversity
serves at the scale of the patch also “scale up” into the site and landscape; Thiele-Bruhn et al.
(2012) reflect on the function soil biodiversity plays on a larger scale:
These services are of high and increasing relevance since C sequestration in soil, nutrient
mobilization and turnover, and biotransformation of organic pollutants are indispensible
from the perspectives of global change, sustainable soil fertility and nature conservation.
As such, soil biodiversity, which is discussed in this section as soil food webs, is an important
pattern at this scale. The structure and composition of soil food webs are connected to processes
at this scale (e.g. nutrient turnover, N mineralization, N fixation, decomposition, etc.) as well as
SOM accumulation. Belowground communities majorly contribute to the breakdown of organic
matter into SOM and, “vice versa, the amount and quality of SOM determines the number and
activity of soil biota” (Thiele-Bruhn et al. 2012). This feedback relationship means that increases
in SOM will lead to increases in belowground activity and abundance, which will feedback into
further SOM accumulation. Conversely, losses in SOM will lead to losses in the abundance and
activity of microorganisms, which will, in turn, slow SOM accumulation.
Drivers at the scale of the patch
The drivers on the soil landscape are elements of agricultural and soil management (fig.
13). This includes practices such as tillage, the application of fertilizers, pesticides, and
herbicides, and the use of cover crops. In this section, I will explore how agricultural and soil
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management affects the adaptive cycle at this scale, as well as the landscape patterns of soil
structure and soil food webs.
Figure 13: Hierarchical relationship of patch and site scales, with the driver of farm and soil
management exerting top-down effects. Patterns at the scale of the patch include soil structure
and soil food webs. Plant communities (here depicted as monoculture) are the pattern considered
at the scale of the site.
Agriculture management and its impacts on SOM
As previously mentioned, agricultural management creates SOM dynamics that are
distinct from those in undisturbed soils. While tillage leads to better plant growth, it also hastens
decomposition and releases carbon into the atmosphere. Under conventional methods, this
carbon is usually not replaced through manuring or the use of cover crops/green manures. In the
following section, I will explore the relationship between conventional and organic agricultural
management techniques and their effects on SOM through an agroecological modeling software,
Cropsyst (Stöckle & Nelson 1998b).
Agricultural management and SOM
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The following simulations were created through Cropsyst, an agro-ecological model that
simulates agricultural management effects on soil properties. Specifically, CropSyst is a model
designed as a management tool for agricultural systems (Stöckle & Nelson 1998b). As such,
Cropsyst simulates the effects of agricultural practices (e.g. tillage, irrigation, nitrogen
applications, organic matter applications) on key soil variables. This allows for the comparision
of conventional agriculture practices, with “less intensive” practices (i.e. organic agriculture).
Four treatments were simulated: one conventional treatment, two conventional-organic
treatments, and one organic treatment (Table 1). Treatment 1 simulated current conventional
practices in large-scale agriculture, and thus provides an idea of the current trends in SOM.
Treatment 4 utilizes organic nitrogen, simulating an organic alternative to the production of the
same crop rotation. Treatments 2 and 3 represent shifts from conventional practices to organic
ones, in order to simulate the recovery of SOM after its initial degradation. All treatments shared
the same time frame (100 years), crop rotation (corn-corn-soy) and weather data. The soil profile
was identical for all simulations and was composed of a 15 cm plow layer. The soil was a loam
composed of 40% sand, 45% loam, and 15% clay with a bulk density of 1.44 g/cm3 (bulk density
was calculated by the model from the soil texture).
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Treatment 1
Year
Rotation Amendments
Tillage
2000-2100
Corn
Corn
Soy
128 KgN/ha anhydrous ammonium injected pre-planting
128 KgN/ha anhydrous ammonium injected pre-planting
none
dust mulching to 10 cm
dust mulching to 10 cm
none
Rotation
Corn
Corn
Soy
Corn
Corn
Soy
Amendments
128 KgN/ha anhydrous ammonium injected pre-planting
128 KgN/ha anhydrous ammonium injected pre-planting
none
16.125 short tons dry beef manure (3.5% N)/ha
16.125 short tons dry beef manure (3.5% N)/ha
16.125 short tons dry beef manure (3.5% N)/ha
Tillage
dust mulching to 10 cm
dust mulching to 10 cm
none
none
none
none
Rotation
Corn
Corn
Soy
Corn
Corn
Soy
Amendments
128 KgN/ha anhydrous ammonium injected pre-planting
128 KgN/ha anhydrous ammonium injected pre-planting
none
32.250 short tons dry beef manure (3.5% N)/ha
32.250 short tons dry beef manure (3.5% N)/ha
32.250 short tons dry beef manure (3.5% N)/ha
Tillage
dust mulching to 10 cm
dust mulching to 10 cm
none
none
none
none
Rotation
Corn
Corn
Soy
Amendments
32.250 short tons dry beef manure (3.5% N)/ha
32.250 short tons dry beef manure (3.5% N)/ha
32.250 short tons dry beef manure (3.5% N)/ha
Tillage
none
none
none
Treatment 2
Year
2000-2050
2050-2100
Treatment 3
Year
2000-2050
2050-2100
Treatment 4
Year
2000-2100
Table 1: Treatments simulated through Cropsyst. Treatment 1 is a simulation of conventional
agriculture. Treatments 2 and 3 simulate transitions from conventional to organic agriculture.
Treatment 4 is a simulation of organic agriculture.
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CropSyst decomposition calculations
Default settings were used to determine biomass left on site for each crop. Of
remaining corn and soy biomass after harvest, 90% remained lying in the field as
surface residue, and 10% remained in the field as standing biomass/stubble. Bovine
manure (3.5% N, 35% carbon of dry weight biomass) was incorporated into the soil in
organic treatments.
The methods for calculating residue decomposition are included in the
appendix.
Results
Changes in SOM in each treatment are presented in figures 1-4. In treatment
1, SOM decreased from 6.02% to 4.14%, a 31.2 % loss, over the 100 year interval.
This loss was more rapid in the first 50 years (r2 = .9414) than in the last 50 years (r2 =
.9864). In treatment 2, SOM decreased during the first 50 year interval at the same
rate (r2 =.9413 ) as the first 50 years of treatment 1; however, during the second 50
year interval, SOM increased (r2= .3268), reaching 4.79% at the end of the 100 year
interval. In treatment 3, SOM decreased at the same rate as the first 50 year interval
of treatments 1 and 2, and increased during the second interval (r2= .5384), reaching
5.29% at the end of the 100 year interval. In treatment 4, SOM decreased over the
100 year interval (r2 = .9867) to a final SOM content of 5.63%.
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Fig. 14: Change in SOM over 100 year simulation period under conventional
management.
Fig. 15: Change in SOM over 100 year simulation period under 50 years of
conventional management and 50 years of organic management. The arrow is
positioned at 2050, where the management regime changes.
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Fig. 16: Change in SOM over 100 year simulation period under 50 years of
conventional management and 50 years of organic management (with twice the
organic matter inputs as treatment 2). The arrow is positioned at 2050, where the
management regime changes.
Fig. 17: Change in SOM over 100 year simulation period under organic management.
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Discussion
Effects of management on SOM accumulation
These results demonstrate clear differences in how conventional (as described
by treatments 1, 2, and 3) and organic (as described in treatments 2, 3, and 4) practices
affect SOM content. In the case of conventional practices, SOM continually declines.
The rate of loss is faster at the beginning of the treatment and becomes more gradual
as the simulation continues. Under organic practices (treatment 4), a net loss of SOM
is observed, but this loss is much more gradual than in the conventional treatment.
SOM content in treatment 1 decreased by 1.88% vs. .39% in treatment 4.
In the two combinational treatments (treatments 2 and 3), the conventional
interval demonstrates the same negative trend as in treatment 1. However, the two
organic treatments that follow each 50 year conventional interval had a positive rate of
change and lead to SOM accumulation. In treatment 2, SOM recovered by .2% over
50 years. In treatment 3, where inputs were doubled, SOM recovered by .7%. This
lack of proportional change seems to indicate that there are non-linear dynamics at
play in the process of SOM loss and accumulation. In summary, SOM loss occurred
much faster than its recovery, which is a time and resource intensive process.
Additionally, the simulations show the gains leveling off. If this is an accurate
representation of SOM accumulation, restoring SOM to its initial value may be even
more difficult and ultimately made unfeasible by economic and other constraints.
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Is there an SOM critical point?
SOM plays a crucial role in determining soil quality, and consequentially,
structure, aggregation, and productivity of the plant community situated on the soil. In
the literature, there is a consensus that a threshold exists (~2% SOC or 3.4% SOM) at
which soil structure and quality becomes seriously compromised by a lack of SOM
(Loveland and Webb, 2003). However, in their treatment of the subject, Loveland and
Webb (2003) found little quantitative evidence of such a threshold. They also found
that soils with SOM content under this value could be as productive as soils above the
value with synthetic nitrogen amendments. This observation gets at a significant issue
of conventional agriculture, which is that synthetic inputs mask the effects of soil
quality degradation. Increasing inputs of synthetic nitrogen solve the loss of fertility,
while pesticides, fungicides, and nematicides buffer losses of the soil’s ability to
suppress pathogens and pests through biodiversity and management regimes (Abawi
and Widmer 2000). Ultimately, the conventional approach to managing soil fertility
cannot be sustained.
Limits of the model
Like any ecological model, CropSyst is limited by our knowledge of processes
and relationships in ecosystems, as well as our ability to model their complexity. One
major limitation of the model is that it cannot simulate polyculture. It is unlikely that
any model could reliably model systems with such a variance in space, time, and
interactions among components. However, these results do demonstrate that organic
farming is more sustainable in terms of its effects on SOM. From the results of these
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simulations, it does not appear that organic farming can sustain SOM content. This
may or may not be due to the limitations in the model that only allow for the
simulation of organic farming as conventional farming without synthetic nitrogen. In
other words, the treatments simulate “industrial organic” farming, which still relies on
monocultures and external inputs. In reality, organic farmers often employ a variety of
techniques and practices that are likely to have different effects on SOM. These
practices include cover cropping, reduced and no-till farming, intercropping, and
composting.
Despite these limitations, it is clear from the simulations that modern industrial
processes are depleting SOM, and as a consequence, undermining soil aggregation and
structure, and changing underground communities.
Agricultural management and soil structure
Agricultural management is a driver of changes in soil structure. The loss of
soil structure is a form of soil degradation (Bronick and Lal 2004). Modern
management practices, including tillage and the lack of carbon-containing inputs, can
undermine aggregate formation, leading to degraded soils. Tillage has direct (e.g.
breaking up aggregates) and indirect (e.g. reducing fungal communities that contribute
to structure) effects on soil structure.
As discussed earlier in this chapter, the maintenance of soil structure is
fundamental to agricultural sustainability. In their study of these properties, Pagliai et
al. (2004) found that conventional plowing disrupted soil structure by promoting the
formation of soil crusts and hardpans under the plow layer and by changing the shape
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of macropores. These changes lead to reduced water movement and rooting through
the soil profile.
While conventional management techniques directly (through tillage) and
indirectly (through the lack of organic matter inputs) have negative impacts on soil
structure, it has been demonstrated that alternative tillage regimes and practices like
composting and manuring improve soil structure (Pagliai et al. 2004). Managing for
soil structure entails using composts, manure, mulching and residue management,
cover crops, and agroforesty, and managing for biodiverse soil communities (Bronick
and Lal 2004). Soil structure has implications for sustainability at many scales,
including carbon sequestration, water quality, biodiversity, and sustainable food
production (Bronick and Lal 2004).
For these reasons, it is of great importance that the connections between soil
structure, agricultural sustainability, and agricultural management are examined. The
degradation of soil presents a major risk to agricultural sustainability. Pagliai et al.
(2004) explain how soil structure can reveal information about vulnerability and
degradation:
The characterization of the soil pore system gives essential indications about
soil quality and vulnerability in relation to degradation events mainly
connected with human activity. The quantification of the shape, size,
continuity, orientation, and irregularity of pores allows the prediction of the
changes that can be expected following soil structural modifications induced
by management practices, or following soil degradation due to compaction,
formation of surface crusts, etc.…the quantification of the damage caused by
degradation processes also makes it possible to predict the risk of soil erosion.
These are risks that must be critically examined and avoided. The signals of soil
degradation must be seriously considered before degradation reaches a threshold level,
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resulting in damage that is irreversible on meaningful economic and human
timescales. Losses in soil structure present a major source of vulnerability in the
agricultural system. Soil structure, especially in the absence of fossil fuel technologies
and inputs, is a major variable in sustainable agricultural production which cannot be
ignored.
Agricultural management and soil food webs
Agricultural management is a driver in the composition of soil food webs.
Conventional agriculture management (i.e. industrial agricultural practices) alters food
webs through modification and simplification.
Changes in food webs
One of the most prominent changes that occurs in soils under cultivation is the
shift in underground communities structure from fungal-dominance to bacterialdominance (de Vries & Bardgett 2012). In undisturbed ecosystems, like forests, fungal
hyphae, a network of fine fungal filaments, thread through the soil. Tillage, the
breaking up and overturning of soil, damages these hyphae. Additions of synthetic
nitrogen also shift the community away from fungal-dominance to bacterialdominance (de Vries & Bardgett 2012). The use of fungicides may also limit fungal
communities in soil. On the other hand, soil bacteria thrive in cultivated soils rich in
nitrogen (de Vries & Bardgett 2012). Tillage stimulates decomposition and microbial
activity. The application of inorganic N fertilizers provides ample food for soil
bacteria.
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This shift from fungal-dominant to bacterial-dominant communities has
implications for soil structure, nutrient cycling, and plant growth. Arbuscular
mychorrizal fungi play a particularly important role in these processes. Firstly, AMF,
which form symbiotic relationships with plant roots, produce Glomalin, a compound
that binds aggregates together and strongly contributes to soil structure (Rillig et al.
2002). Tillage not only leads to compaction and poor soil structure through mechanical
means, but by sharply reducing AMF colonization, it also undermines soil structure
biochemically.
Secondly, AMF inoculation of crop roots has been demonstrated to reduce
phosphate leaching (Verbruggen 2012, Thiele-Bruhn et al. 2012), as well as N
leaching and N2O losses (Thiele-Bruhn et al. 2012). Fungal-dominant systems are
characterized by slower nutrient turnover, better nutrient recycling, and thus tighter
nutrient cycles (i.e. lower losses due to leaching). Conversely, bacteria may actually
increase P leaching (Thiele-Bruhn et al. 2012). There is evidence of a trade-off
between leaching and yield, however, with tighter nutrient cycles decreasing leaching,
but also decreasing yield (Verbruggen 2012).
Overall, the properties and characteristics of fungal-dominated communities
contribute to the biophysical and ecological sustainability. More research is required to
learn how these systems affect yield. Because their nutrient cycles are slower and
tighter, fungal-dominant systems are better able to self-regulate than bacterialdominant communities (Thiele-Bruhn et al. 2012). Additionally, fungal communities
are more resilient to drought than bacteria-dominated systems (Thiele-Bruhn et al.
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2012). In the absence of nutrient inputs, or in the face of climate change, these
properties will contribute substantially to agricultural feasibility and sustainability.
Temporal dynamics
Aside from changing dominant underground communities, management also
has temporal effects on underground communities. For example, the sudden removal
of biomass from crop fields leads to a decrease in bactivorous and fungivorous
nematodes, which cannot reproduce under these soil conditions. Consequently, in the
spring, they are inactive when residues are incorporated back into the soil, and cannot
perform their vital function of nitrogen mineralization (Ferris et al. 2004). Cover
cropping can positively affect this drop off in population, leading to better N
mineralization in the spring (Ferris et al. 2004). The annual cropping cycle likely leads
to similar temporal cycles in other soil organisms.
Simplification
Changes in the soil food web are accompanied by the simplification (i.e. loss
of species and/or tropic levels) of food webs. The application of pesticides, fungicides,
nematicides, etc., have direct and indirect effects on species richness (Thiele-Bruhn et
al. 2012). In general, below-ground communities become less diverse because of the
high levels of disturbance. These conditions favor species that can tolerate disturbance
and excludes, to varying degrees, those which cannot. This loss of species may
translate into losses of trophic levels, which may result in pest and pathogen outbreaks
which may scale up to the site and landscape scales. For example, Briar et al. (2007)
found that conventional agricultural plots had higher populations of root lesion
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(parasitic) nematodes. They note that plant parasitic nematodes and bacterivorous
nematodes can tolerate disturbance, while fungivorous and predatory nematodes
cannot. By providing an environment suited for plant-predatory nematodes, but not
their predators, agricultural systems become vulnerable to outbreaks of these
destructive pests.
Implications for the future of modern industrial agriculture
This simplification affects the resilience of the soil community, as a more
complex food web beings with it “more links in the food web, more organismal
interactions, greater functional redundancy, and, potentially, more stability of
function” (Ferris et al. 2004). Additionally, these changes in food webs undermine the
ability of the system to self-regulate as “with intensification, self-regulation of
functions through biodiversity is replaced by regulation through chemical and
mechanical inputs” (Thiele-Bruhn et al. 2012). Through intensification, resilience is
undermined, causing the system to become overly connected to human management
practices.
Conventional management practices clearly undermine SOM accumulation,
soil structure, and below ground diversity. Because both patterns at this scale are
coupled with (i.e. have positive feedback relationships with) SOM, they change in the
same direction and reinforce this change. Deterioration of all of these properties of a
soil leads to losses in ecosystem services. As such, in the absence of tillage and
chemical inputs, agricultural soils will be less equipped to prevent leaching of
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nutrients, support plant growth, buffer pest and pathogen outbreaks, and ultimately,
produce sufficient agricultural yields.
Summary and conclusion
Agriculture at the scale of the patch happens across small spatial and temporal
scales. Heterogeneity in the soil landscape drives changes in its ecological structure
and function and vice versa. At the scale of the patch, the agricultural landscape can be
understood as a product of the interactions between patterns (e.g. soil structure, soil
food webs), processes (e.g. nutrient cycling, leaching, water retention), the adaptive
cycle (i.e. SOM accumulation) and off-site drivers (e.g. agricultural management) that
create changes on the soil landscape.
Modeling the “future” of soil can provide data on key soil variables, including
SOM. Using the trends produced in agroecological management simulations, it is
possible to make inferences about other soil properties, given their relationship and
coupling with SOM. The data trends in the simulation of modern industrial agriculture
(Simulation1), produced by modern management practices (tillage, nitrogen) have
clearly negative impacts on SOM, and as a consequence, on soil structure and food
web diversity. When considered through the lens of panarchy and resilience thinking,
it can be said that soils are losing ecological robustness, and, as a direct consequence,
are becoming more vulnerable to disturbance, shock, and collapse.
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Chapter 5: The site
The farm as a landscape
Agriculture happens, in the most tangible sense, on farms. These farms vary in
size, configuration, and agricultural practices. A market farmer might produce food on
a few acres or less, and support his or her family with the help of a few seasonal
employees (Satellite image 1). A sixth generation farmer in Iowa might plant 500 acres
or more in corn or soybeans annually (Satellite image 2). Some sites of modern food
production challenge the definition of farm altogether; take for instance feedlots that
extend for miles and house thousands of animals (Satellite 3). These different sites
create different landscape pattern, and will have different impacts on ecological
functioning. Whatever the size of the operation, at this scale of analysis, I am
concerned with the sites of modern food production. For the sake of simplicity; and
because livestock have been removed from the farm in industrial agriculture, I will
focus on crop production and exclude the raising of livestock. First, I will look at the
major ecological dynamic of this scale: the annual cycle. I will then discuss two types
of pattern, monoculture and polyculture, and explore their implications for ecosystem
processes. I will conclude by identifying drivers at this scale, with subsidy structures
as my main focus.
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Satellite image 1: A small organic farm outside of Athens, Ohio. The farm produces
vegetables for the local farmer’s market.
Satellite image 2: Monoculture of corn near Ames, Iowa.
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Satellite image 3: A cattle feedlot in Northern Texas. Image in the bottom left is a
close up of one cattle pen.
The annual cropping season as an adaptive cycle
The growing season provides the most relevant ecological cycle at this scale of
analysis (fig. 18). Each year, the successional cycle is renewed, when the soil is
disturbed and planted. In the spring, fields are tilled and planted (a; reorganization).
This process hastens decomposition and creates a disturbance in the soil. In
conventional agriculture, nitrogenous fertilizers are added before or soon after a crop
is planted (Ferris et al. 2003). These disturbed conditions, coupled with high system
wealth, create ideal conditions for the r phase. Throughout the next several months,
crops collect nutrients from the soil and produce biomass, moving toward the
conservation (K) phase. These resources become concentrated in the crop’s yield.
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At the end of the season, biomass considered yield is removed from the field.
The remaining biomass is left standing, tilled under, or taken off site for other uses
(e.g. animal feed, biofuels). The removal of biomass creates a “collapse” (omega) in
the system. Left over biomass (e.g. roots), decompose, releasing nutrients into the soil.
Because there is no living plant biomass to take up these nutrients, they may be
leached out of the system. Hydrological cycles are also altered, increasing runoff and
erosion. Come spring, the soils are again tilled (alpha), renewing the cycle.
Maintaining this cycle of renewal throughout the growing season allows for the
productivity of a monoculture.
Fig. 18: Annual cropping cycle as an adaptive cycle. Adapted from Holling et al.
(2002). Original image sourced from www.adaptivekm.com
This annual cycle is characteristic of all conventional agriculture, and some
alternative (e.g. organic) production. The fact that most major food crops are annuals
(e.g. corn, wheat, rice, soybeans, most vegetables) makes this somewhat unavoidable.
As such, the form of agricultural plant communities is tied to this annual cycle.
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Agricultural communities have major implications for landscape pattern. Of particular
interest here is how agricultural communities (e.g. monocultures and polycultures)
function ecologically, how they are connected to the adaptive cycle at this scale, and
how both pattern and process are informed by higher level processes (i.e. off-site
drivers). Drivers at this scale, including subsidy structures, may change or reinforce
outcomes on the farm landscape.
Patterns at the scale of the site
Plant communities: monocultures
At the scale of the farm, the structure of plant communities in space and in
time is central in determining the dynamics of an agro-ecosystem. Species identity is
an important consideration; for instance, a field of corn, a nitrogen hungry species,
will function differently than a field planted with cotton, as these plants have different
physiological needs (i.e. water, space, nutrients) and support different above and
below ground communities. Not only do species have different physiological needs
which impact their environment, they also serve different ecological functions. This
idea informs the common practice of rotating plantings of corn with plantings of
soybeans; the soybeans provide nitrogen (through their association with symbiotic
fungi) for the next crop of corn and boost yields.
While rotations serve to somewhat alleviate the fertility problem in many
agricultural fields, the annual cycle remains unchanged. After harvest, the agroecosystem radically changes state. The lack of vegetation encourages leaching, runoff,
and soil erosion. Conservation tillage, the practice of leaving standing or stubble
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biomass on fields, alleviates these concerns to some extent, but they are not solved.
Thus the annual cycle of monocultures undermines soil fertility in two major ways; as
seen in the last chapter, this kind of farming does not contribute to the accumulation
of SOM, as most biomass is taken offsite, and secondly, outside of the growing
season, non-vegetated earth is exposed, leading to the physical loss of topsoil by the
elements.
Agro-biodiversity in monocultures
Monocultures are characterized by low levels of diversity and simplified
communities (Vandermeer 2011). The selection of a single crop is the first limitation
on diversity. Cultivating this crop in a large, continuous planting eliminates refugia
and habitat for non-agricultural species. Additionally, the application of pesticides,
herbicides, and fungicides eliminates potentially harmful and helpful species.
Above ground, plant competition is controlled through the application of
herbicides. Because of the homogeneity of these agro-ecosystems, they do not attract
many animal or insect species, and those that do live within them are generalists that
are tolerant of disturbance, or pests. Monocultures present a lure to pest species, which
maximize on the homogeneity and availability of resources for growth and
reproduction. By creating an environment perfectly suited to pests, and eliminating
natural predators, monocultures are a significant risk. At present, this risk is managed
by chemical inputs. However, resistance is growing to these inputs (Whalon et al.
2008), and in the future, they may not be available.
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While the goal of any agricultural enterprise is to cultivate a particular food
crop at the expense of other vegetation, the kind disturbance that monoculture
engenders on ecosystem functioning is by no means inevitable. The prevalence of
monocultures, along with the other elements of modern industrial agriculture, can be
understood a product of a particular historical and technological trajectory. Alternative
forms of cropping, refered to generally as polyculture, may be able to provide for
human food needs without the levels of disturbance present in industrial agriculture.
Plant communities: polyculture
Polyculture refers to the growing of more than one crop simultaneously.
Polyculture can take many forms; the following diagram presents some possible forms
of polycultures. Polyculture can take place on different time scales and create variable
disturbance regimes. Polyculture may take place in the annual cycle, as rows or
clusters of annual crops are interspersed with other annuals, biennials, or perennials
(B1 and B2). In an agroforestry system (C), perennial tree crops are planted, with
annuals cultivated around them until they grow to maturity (agroforestry). In the case
of shade grown coffee, coffee trees are interspersed in naturally occurring forest
species, taking advantage of the benefits of biodiversity. This forest structure may also
be mimicked in food forests (D) plantings of perennial plants in a vertically layered
design.
Because each of these systems is quite different, I will overview their elements
in distinct categories - intercropping, agroforestry, and food forests – on a scale of
annual to semi-annual to perennial agriculture. I will compare their provision of
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ecosystems services with monoculture (this is primarily the comparison made in the
literature), including pest/pathogen suppression, biodiversity, and yields.
Figure 19: Cropping systems along a gradient of disturbance. A. monoculture B1. row
intercropping B2. clustered intercropping C. agroforestry and D. forest garden. The
lighter shading in C and D represents areas of perennial (i.e. undisturbed) plantings.
Intercropping
Intercropping is the practice of growing two crops in an integrated planting.
This can take the form of simple and regular spatial arrangements between two crops
e.g. alternating rows or hills (Chabi-Olaye et al.), or more complex, and irregular
arrangements like in agro-forestry and food forests. Cropping pattern may be
influenced by microclimactic or topographic features. Newsham and Thomas (2011)
describe such a system among Ovambo farmers in North Central Namibia, who make
planting decisions based on the identity “land units,” categorizations of land based on
local agro-ecological knowledge. This system has been passed down through local
knowledge for generations. Conversely, in a study of the performance of intercropped
sorghum and groundnut cultivars in Ethiopia, Tefera and Tana (2002) conclude that
new knowledge must be created to make intercropping feasible. They find that the
configuration of crops, as well their varieties, affect the outcomes of intercropping. In
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order to establish a successful intercropping regime, potential crops will have to be
screened for their compatibility and success in the field. Thus intercropping has a
history in traditional agro-ecological knowledge, and is contemporarily being
evaluated for its relevance to modern food production, especially on increasingly
degraded lands.
Photograph 1: Pepper vines growing up coconut trees in Southern India is one
example of intercropping. Photograph taken by Kinkaid (2011).
Benefits of intercropping
The benefits of intercropping include weed suppression (Postma & Lynch
2012), decrease pest damage (Postma & Lynch 2012; Chabi-Olaye et al. 2005),
overyielding (Postma & Lynch 2012; Tefera & Tana 2002), and higher Land Use
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Efficiency (LER; Chabi-Olaye et al. 2005; Tefera & Tana 2002). Overyielding and
increased LER are due to complimentary interactions between species that are
intercropped.
Interactions between crops
Postma and Lynch (2012) propose that above and below ground niche
complementarity play a role in overyielding in the “three sisters” polyculture of maize,
bean, and squash. Aboveground, the maize provides physical support on which beans
climb, while the small bean leaves can “occupy gaps in the maize canopy” (Postma &
Lynch 2012). The squash plants, which grow close to the ground, provide a shade
cover that holds in soil moisture (Postma & Lynch 2012). The different architectures
of maize, bean, and squash, allow for better space utilization (Land equivalency ratio
[LER]; Vandermeer 2011) than maize plants alone (i.e. to achieve the same yield as an
acre of intercropped maize, corn, and beans, more than one acre of monocultures of
these species would be required). Belowground, differences in root structure may
cause “niche differentiation by allowing different species to explore distinct soil
domains with varying intensity” (Postma & Lynch 2012). They also hypothesize that
root exudates and nitrogen fixation may add to complementarity, and that the
increased distance between roots in the polyculture lessens competition for soil
nutrients (Postma & Lynch 2012).
The theory behind intercropping is that “biodiversity is thought to have
important ecosystem functions, which include greater productivity and resource
utilization” (Postma & Lynch 2012). For decades, ecologists (see Tilman 1999,
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Picasso et al. 2008) have studied the relationship between plant diversity and system
productivity. Their findings support the diversity-productivity hypothesis (Tilman et
al. 1996), which predicts that diversity allows communities to utilize resources “more
fully.” In experimental plots, Tilman (1999) observed higher total biomass production
in polycultures over monocultures. It is important to note that most of the biomass
came from one plant (often the largest). However, when all yields from the plot are
considered, polycultures have a higher productivity and LER than monocultures of the
constituent species.
This overyielding, combined with a polyculture’s positive effect on biophysical
sustainability, makes it a viable option for agriculture, especially for farming on
marginal lands. Tefera and Tana (2002) suggest that intercropping may not only
produce better on these lands, but offer sustainable livelihoods for poor farmers, who
can use smaller yields from less productive crops for subsistence or animal fodder.
Thus intercropping has the potential to increase productivity per unit land, especially
on marginal lands, as well as contribute to a diverse set of human needs, including
food, fiber, medicine, and income. Intercropping systems vary from annual row crops
to perennial food forests. In any of these forms, intercopping can increase the ability
of agroecosystems to be productive and contribute human and animal needs.
Agro-forestry
Agroforestry refers to “growing trees, crops and sometimes animals in an
interacting combination,[which] create land-use systems that are structurally and
functionally more complex” (Silva et al. 2011). Agroforestry is a land use strategy
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that can take many forms. At its simplest, an agroforestry system may consist of rows
of annual crops planted between trees. At its most complex, an agroforestry system
may mimic the structure of a forest with tree, shrub, and herb crops and may include
animals like pigs, cows, chickens, and ducks. Recognizing this diversity in
agroforestry systems, Torquebiau (2000) proposes six categories of agroforestry
systems: “crops under tree cover, agroforests, agroforestry in a linear arrangement,
animal agroforestry, sequential agroforestry and minor agroforestry techniques.”
These categories occur along the gradient of disturbance presented in figure 19.
The benefits of any kind of agroforestry system are diverse. Torquebiau (2000)
describes: “agroforestry advantages can be described as the provision of multiple
products (e.g. food, wood, fodder, mulch, fibres, medicines) or services (e.g. soil
fertility maintenance and erosion control, microclimate improvement, biodiversity
enhancement, watershed protection) by the trees.” Thus agroforestry contributes to
biophysical sustainability (soil and water protection), biodiversity, and produces
multiple crops at once with a higher land use efficiency. Depending on the markets a
farmer has access to, agroforestry systems may meet their need for both ecological
sustainability and economic viability.
At one end of the disturbance gradient, agroforestry systems attempt to mimic
natural forested ecosystems. In his study of such systems, Silva et al. (2011) concludes
that these system maintain soil physical properties in a similar way as naturally
occurring forests. As such, these “forest gardens,” may provide a model for a
sustainable perennial agriculture.
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Forest Gardens
Nuburg et al. (1994) describe forest gardens as “rain fed polycultures…[that]
usually have the appearance of a forest because of the predominance of perennial
species. In comparision with other agroecosystems these forest gardens share similar,
but not identical structural and functional characteristics of the local natural forest.”
While Nuberg et al.’s (1994) study focuses on traditional gardens in Sri Lanka, forest
gardens, or “food forests,” are gaining popularity today in the Permaculture
movement, a system of design the builds on agroecology and agroforestry. In Jacke’s
(2005) words, an edible food forest is “an edible ecosystem, a consciously designed
community of mutually beneficial plants and animals intended for human food
production.” They also provide for other human needs, including, fiber, animal fodder,
fuel, and medicine (2005). Forest gardening is a very different approach from planting
a monoculture; instead of holding the system at the beginning of the succession
sequence, the agroecosystem is managed between the r and k phases. As such, crops
are perennial (i.e. nuts, fruits, herbs) and disturbance is low.
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Photograph 2: Forest garden in Southern India. This man explained that this small
(~1/4 acre) garden was significantly more profitable than other food production
operations in the area. Photo taken by Kinkaid (2011).
This kind of polyculture is thought to be more sustainable than other forms of
agriculture. As a polyculture, it has all the benefits of intercropping: reduced soil
erosion, reduced pest pressure, a higher land-use efficiency, and niche
complementarity. Also, it is perennial, so it does not need to be managed as intensively
as a monoculture or annual polyculture (Jacke 2005). In their analysis of forest
gardens, Nuberg et al. (1994) rate the biophysical sustainability (a category composed
of soil resources and biodiversity) as very high compared to other types of agriculture.
In the same analysis, Nuberg et al. (1994) identify the weaknesses of this kind
of food production. While it is biophysically sustainable, stable, equitable, and
promotes autonomy, it is not commercially viable. In other words, external structures –
infrastructure, credit, and markets – make forest gardens sustainable, but not
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necessarily “maintainable” (1994). Similarly, Jacke’s edible food forests may not have
the economies of scale to compete in large markets, but may be successful in niche or
local markets. Ultimately, the sustainability of these enterprises is undermined by
external drivers. These drivers tend to reinforce “conventional” ways of farming that
are less sustainable and less efficient. As such, these drivers play a major role in
shaping agricultural landscapes, and determining what kinds of agriculture are feasible
and desirable.
Drivers at the scale of the site
The previous section has examined how patterns on the farm (various
configurations of plant communities) influence ecological processes and the provision
of ecosystem services. In this section, I will identify off-site drivers at the scale of the
site, and their effects on the patterns, processes, and the adaptive cycle at this scale
(fig. 20). At this scale, these drivers are larger socio-economic structures and processes
which produce the “socio-economic situatedness” of a farm.
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Figure 20: Hierarchical relationship of landscape and site scales, with the driver of
socio-economic structures (e.g. subsidies) exerting top-down effects. Pattern at the
scale of the site is depicted as a monoculture. Pattern at the scale of the landscape is
depicted as patterns of land use and development.
The socio-economical situatedness of the farm
At the scale of the site, the most prominent pattern under examination in this
analysis is plant community structure (e.g. monoculture and polyculture). The
composition and continuity of this pattern in time and space affect above and below
ground biodiversity at the site, and, as a direct consequence, affect the ecological
services provided by biodiversity. Given the negative effects monoculture has on
agricultural resources, e.g. soil, water, and beneficial organisms, it is rather
counterintuitive that modern industrial agriculture is the most “efficient” method of
agriculture in history. The conditions that make this a reality are complex and
historically situated.
When viewed in this larger context, the historically produced “economical
situatedness” of the farm comes into focus. When we consider an individual farmer’s
decision to plant a high-input intensive monoculture, or a perennial polyculture, or
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anything in between, we must consider her values and goals. For instance, a farmer
who is trying to maximize her profit in a commodity market will likely plant a
monoculture. A farmer who is targeting a smaller, more diverse market, might
experiment with intercropping or polyculture, as would a farmer concerned with
building SOM or offering habitat to the natural predators of crop pests. Though the
farmer’s values influence these decisions, these decisions are never made in isolation,
as a farm is situated in a larger socio-economic context. Pascual & Perrings (2007)
describe that management practices and their effects (e.g. agrobiodiversity change)
“can be seen as an investment/disinvestment decision made in the context of a certain
set of preferences, ‘value systems’, moral structures, endowments, information,
technological possibilities, and social, cultural, and institutional conditions.” These
elements in the “institutional or meso-economic environment” (Pascual &Perrings
2007) drive changes in the agricultural landscape, including agrobiodiversity loss,
through the aggregation of individual farmer’s decisions.
A farmer’s situatedness in this meso-economic context is strongly and directly
related to the structure of agricultural subsidies. These subsidies have a significant
influence on what crops are grown and how they are grown. Over time, agricultural
subsidies in the U.S. have transformed from a price-control support system for farmers
to a set of policies that favor large scale industrial growers, the discounting of
agricultural lands, land degradation, and overproduction (Windham 2007). At the
landscape level, this history has been unfolding in tandem with the history of
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industrial agriculture and has produced the current food system and many of its
vulnerabilities.
History of agricultural subsidies
Windham (2007) describes that agricultural subsidies were first created when
overproduction flooded agricultural markets in the 1920’s, which lowered crop prices.
In order to buffer the effects of the declining prices of the Great Depression, programs
were established under the New Deal that allowed farmers to take out a “non-recourse
loan” from the government and withhold their storable crops from the market until
prices improved. If prices did not improve, farmers could give their crop to the
government as a payment for the loan (2007).
This philosophy was successful because it prevented the market from being
flooded and assured that farmers received reasonable prices for their crops. This
program continued until 1973, when the structure of the subsidy system changed.
Windham (2007) describes the new system of “deficiency payments” and their effects
on the commodity market: “instead of keeping commodity crops out of a falling
market, the new deficiency payments were paid directly to the farmers and this
encourages farmers to sell their grain at any prices because the government would
make up the difference.”This system had a new goal: to drive down the cost of food,
overproduce, feed foreign markets, and accumulate food as a source of political power
(Windham 2007).
Effects of agricultural subsidies
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The shift in the goals and structure of the subsidy system has wide reaching
effects on agricultural markets and the agricultural landscape. The “deficiency
payment” system rewards high yields at the cost of conservation and incentivizes
continuous cropping over fallowing and cover cropping (Windham 2007). By
focusing on yield above any other property of a farm, it promotes intensive, high input
monocultures.
The more subtle and startling implications of the new subsidy structure is that
it is designed to benefit large scale industrial operations over small farms. According
to Windham (2007), “the United States Department of Agriculture confirms that over
two-thirds of the farm subsidy payments,” which are funded by tax dollars, “go to the
top ten percent of subsidy recipients” and that the “bulk of the money goes to
enormous, politically savvy and powerful agricultural operations [and] sixty percent of
all farmers receive no aid at all.” The most recent Farm Bill (The Food, Conservation,
and Energy Act of 2008) does include provisions to organic farmers and the use of
renewable energy. However, much of the focus on renewable energy is aimed at
biofuel production, which appears to have largely negative effects on ecosystems and
the provision of ecosystem services (Landis et al. 2008).
The markedly different circumstances surrounding the industrial commodity
farmer, who is funded by billions of tax dollars and supported by chemical inputs, and
the organic farmer, who has little to no federal aid, are responsible for the illusion that
industrial food production is somehow more efficient and less expensive than any
alternative. In reality, the difference in energy input per dollar of output – 18,000 btu
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in industrial agriculture and 6,800 btu in organic (Windham 2007) - should dispel this
myth immediately. However, the massive subsidization of industrial agricultural and
fossil fuel inputs creates the illusion of a price difference between organic and
industrial food. Because industrial food is cheap, it must be efficiently produced.
Other drivers on the agricultural landscape
Agricultural subsidies play a major role in promoting industrial food
production by providing incentives to use these practices, which inform individual
farmer’s decisions through a “meso-economic environment.” Subsidies are not the
only part of this context, though they are a significant aspect that is directly and
indirectly related to other limitations that farmers experience and with which they
must contend. For instance, trade liberalization has been linked to lower crop diversity
(Fraser 2006). Additionally, the availability of capital, labor, credit, knowledge,
technology, and extension services also influence how a farmer farms. The availability
of these resources is oftentimes connected to the larger structures surrounding
agriculture, and how an individual farmer’s decisions fit into this structure. Through
socio-economic structure like subsidies, the dominance of modern industrial
agriculture creates differential access to farmers using alternative practices. In this
way, the USDA and increasingly the global food system, are overinvested in industrial
agriculture such that alternatives are becoming less feasible without novel microeconomic structures like local and specialty markets.
Whether or not these micro-economic and localized systems can develop
enough to support the country and globe after a collapse in the industrial system is
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uncertain. What is clear is that agricultural policy in the U.S. has long been
disinvesting in these types of systems and narrowing opportunities of possible
agricultural futures. As industrial agriculture degrades more land and resources, the
issue of global food security is becoming more salient. Ironically, policy has
reinforced the same practices that arguably created the problem; in a world with
dwindling resources, we must have an agriculture that can “feed the world,” yet the
continuation of that agriculture is accelerating the global food system toward
degradation and increasing vulnerability. Pascual and Perrings (2007), referring to the
increasing use of a narrow range of technologies, comment:
at one level this can make the system more stable in the sense that there is less
variation in the producer’s economic activities following minor perturbations,
but conversely, it may also reduce the capacity of that system to absorb greater
environmental or economic shocks, such as sudden and unexpected commodity
price changes.
While industrial agriculture may provide less variable yields, which would seem to
contribute to the goal of “feeding the world,” it does this at the risk of entire system.
The connectedness of the system, its tight regulation by internal controls, makes it less
able to respond to external changes. Taken all together, it seems that “our nation’s food
supply may in fact be on a collision course with itself” (Windham 2007).
Summary and conclusion
Analysis at the scale of the site considers the farm as a landscape. Its most
relevant ecological dynamic, which is connected to many other ecological processes,
is the annual cropping cycle. This system displays the behavior of an adaptive cycle.
The annual cycle is closely connected to the most prominent pattern at this scale: plant
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communities. Monocultures and polycultures have different effects on ecosystem
processes and patterns above and below this scale. External forces from the “higher”
scale of the landscape drive changes on the farm landscape. In modern industrial
agriculture, the policies and economic structures that influence this scale drive the
landscape toward vulnerability by undermining the ecological robustness and
ecosystem services of agro-ecosystems. They also promote the economic and spatial
consolidation of agricultural assets. These trends in agriculture at the scale of the site
make the system increasingly vulnerable to shocks and potentially, collapse. Because
decisions at the site inform the composition of the larger agricultural landscape, this
vulnerability has the potential to “scale up” to the next level in the panarchy: the
agricultural landscape.
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Chapter 6: The landscape
The agricultural landscape
At each scale of analysis, I have defined a landscape at that scale (e.g. soil as a
landscape, farm as a landscape). As mentioned in previous chapters, these scaless are
not spatially defined per se but characterized by a set of relationships; by a system. As
such, both the patch and site scale are agricultural landscapes. However, by using the
term “agricultural landscape” in this section, I am referring to a particular
configuration of ecosystems and human systems on a “kilometers wide” scale (Wiens
and Milne 1989). In other words, I am looking at agriculture as one land use among
many in a land use mosaic.
The concept of the landscape
The concept of the landscape has undergone reinterpretations since its
inception. In a strictly geographic sense, Turner et al. (2001), define landscape as “an
area that is spatially heterogeneous in at least one factor of interest.” In a more
holographic explanation, they describe:
Most of us have an intuitive sense of the term landscape; we think of the
expanse of land and water that we observe from a prominent point and
distinguish between agricultural and urban landscapes, lowland and
mountainous landscapes, natural and developed landscapes. Any of us could
list components of these landscapes, for example, farms, fields, forests, and the
like. (Turner et al. 2001)
In this sense, a landscape is a mosaic of ecosystems and land uses.
While the term “landscape” may intuitively evoke this kind of aerial or
“kilometers wide” scale, this focus may be a historical product of the study of
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landscape ecology, which was initially oriented towards the role of humans on the
landscape (Wiens & Milne 1989). Wiens and Milne (1989) argue that the ultimate goal
of landscape ecology is not to study this scale of landscape per se, but to examine
“how landscape elements or patches are configured in relation to one another in an
overall mosaic and how such landscape structure influences a wide variety of
ecological patterns and processes.” With this goal in mind, they argue that “there is
nothing in this perspective that restricts it to human modified landscapes or areas
scaled to the human level of perception. Considerations of mosaic patterns and their
effects should be scaled to the organisms and phenomena being investigated and the
questions being asked.” Using this perspective, they examine the landscape of a
species of beetle, and how landscape elements affect its behavior. This refocusing to
the elements of the landscape – patterns and processes – allows for a greater
investigation of the principles of landscape ecology. This perspective allows us to
view the soil and the farm as a landscape, alongside the “kilometers wide” scale of the
agricultural landscape.
With the issue of scale set aside, we can view landscapes at many scales of
analysis. However, this concept of the landscape is still limited in a sense. While it
provides for a broad sense of ecological processes at this scale, it does not necessarily
capture the human systems that interact with the landscape to create these patterns.
Nevah (2005) imagines a landscape ecology that is concerned with an
integrated view of human and ecological systems. This “Total Human Ecosystem”
(Nevah 2005) is composed of a biosphere (i.e. unmanaged ecosystems), a
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technosphere (i.e. a landscape whose dynamics are technologically based; Nevah
describes industrial agriculture as more technospheric than biospheric), and infosphere
(i.e. the realm of human ideas and cultural information). Together, these spheres form
an integrated landscape that extends beyond ecosystems and land use configuration to
the realm of politics- the ideas, logics, and powers that interact with ecological and
technological systems- to form the a whole landscape. Tress & Tress (2001) also offer
a holistic concept of the landscape in their multidimensional transdisciplinary concept
of the landscape, which includes the landscape as a spatial entity, a mental entity, a
temporal dimension, the nexus of nature and culture, and a complex system.
Ultimately, these approaches attempt to highlight the historical nature of the
landscape, and how this history, which is embedded in ecological patterns and
processes, is informed by culture. As such, they do not depart from the “original”
conception of landscape ecology, but build upon it to illuminate the interconnections
between nature and culture.
Relying on each of these concepts of the landscape to varying extents, this
analysis has looked at agricultural landscapes at three scales. At the level of the soil,
the landscape most closely resembled the first definition: a landscape as a
heterogeneous medium upon which ecological dynamics are built. The second scale of
analysis, the site, took farming as an ecological, technological, and economic activity
occurring on a unit of land. The final scale of analysis, the agricultural landscape, will
resemble the third definition as it maps ideas and information onto the landscape. In
this chapter, I will consider the higher level socio-economic and political processes
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that define the landscape as a whole and examine how these processes drive changes
in agriculture.
While, the landscape is created from below – from the smaller sites and
ecosystems that compose it – it is also the product of higher level processes. These
processes are socio-cultural in nature; they are collective decisions that are mediated
by governmental/political/economic institutions and their norms (e.g. development
paradigms, property rights, the free market, democracy, etc.). These “top-down”
elements are embedded in culture. As such, they make up the “big and slow” cycle in
the panarchy.
The decisions that define the agricultural landscape are embedded in the
history of the landscape. Through the landscape, we can view an ecological legacy as
well as a cultural one. A period of political instability in a country could lead to the
liquidation of its natural assets (Fraser & Stringer 2009), changing patterns on the
landscape, and as a consequence, a landscape’s function. An embargo could redefine
the agricultural practices of an entire country (Pfeiffer 2006). A decision to use land in
a particular way at some point in the past may carry with it a “socio-political inertia”
that pushes the landscape toward irreversible degradation (Anderies et al. 2006).
Because of the strong connections between cultural structures and the
landscape, land use history – and the decisions, politics, and paradigms that create it is considered a driver at the landscape level. These drivers influence landscape pattern
(i.e. how land is used and how different land uses are connected or made separate).
The configuration and connectivity of land use types is of ecological importance at
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this level. At this scale of analysis, I will examine the land use history in the U.S.
(mostly in the eastern and mid-western regions), specifically looking at trends in
urbanization and the built environment, in order to consider the effect of changing
land use on agriculture. I am choosing this focus for two reasons: (1) the needs of the
growing population will require more urbanized landscapes and greater agricultural
productivity, and (2) these needs seem to constrain each other; development of urban
and suburban landscapes directly and indirectly impacts agricultural landscapes. At
this scale, the adaptive cycle is the history of land use (specifically the clearing of
forests for agricultural lands and subsequent urbanization). Through this history, we
can come to understand the various socio-ecological process and patterns that structure
the landscape mosaic of which agriculture is a part.
Land use history as an adaptive cycle
The adaptive cycle at this scale is the process of land use change. Fig. 21
shows U.S. land use history as an adaptive cycle. This presentation is rather broad, as
it looks at a general landscape trend. This trend is the replacement of native habitat
with agricultural lands (and other human or technospheric landscapes). Before
European settlement, much of the north-eastern United States was old-growth forest
(“Forest Resources of the United States” 2013). This is not to say that the entire
landscape was pristine, as native peoples surely managed the landscape to their own
ends, but that the majority of the eastern United States was forested. As settlements
expanded, these natural resources were liquidated (omega phase; Z1) for timber and
other provisions. What was a previously forested ecosystem became reorganized
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(alpha; A1) into an agricultural one. Since settlement, land has been increasingly
cleared for agriculture and other land uses(r to K phase; r1-K2). The end of this cycle,
the omega phase, will occur when a threshold is crossed (late K) where the amount of
natural habitat is insufficient to support agro-ecosystems. This large scale loss of
ecological integrity will cause the agricultural system to collapse ecologically (omega
phase, K2). It is not entirely clear how this system might reorganize after such a
collapse (A2).
Fig: 21: Land use history as an adaptive cycle. Adapted from Holling et al. (2002).
Original image sourced from www.adaptivekm.com
Landscape pattern
Like any of the landscapes discussed thus far, the agricultural landscape is
composed of a set of patterns and the processes that create and sustain those patterns.
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Just as the patterns at the scale of the site (i.e. plant communities) support different
ecosystem processes, the configuration of sites in a larger landscape creates certain
ecological dynamics at the landscape level. Conditions at the site, or locality, create
cumulative effects at the scale of the landscape. For this reason, pattern at this scale
will refer to the patchwork of land uses that compose the landscape. I will then look at
how these patterns interact to produce ecological dynamics and environmental impacts
with particular attention to agricultural ecosystems.
Environmental Impacts of land-cover/land-use change
Change on the landscape, and the creation and reinforcement of landscape
pattern, occurs through land-cover and land-use change. The effects of land-cover (the
“biophysical attributes of the earth’s surface”) and land-use change (the “human
purpose or intent applied to these attributes”) touch many different aspects and scales
of ecological functioning (Lambin et al. 2001). At the scale of the biosphere, landcover/land-use change significantly contributes to the amount of carbon dioxide in the
atmosphere, and consequentially, global climate change (Lambin et al. 2001; Turner et
al. 1992, Meyer & Turner 1994). Other environmental impacts include: loss of
biodiversity (Lambin et al. 2001), local and regional climate change (Lambin et al.
2001), losses in arable land (Meyer & Turner 1994), trace gas emissions (Meyer &
Turner 1994); hydrological change (Meyer & Turner 1994), soil loss, degradation, and
sedimentation (Meyer & Turner 1994). Given the significant impacts that landcover/land-use change has on agricultural resources (e.g. soil, water, biodiversity, a
stable climate), land use patterns will be examined as key patterns at the scale of the
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landscape. Following the identification of land use patterns and their significance for
agricultural sustainability, I will outline drivers of land-cover/land-use change at the
scale of the landscape.
Patterns at the scale of the landscape
The landscape is composed of a mosaic of land uses. What land uses compose
this mosaic, and in what proportions they compose it, is a product of a region’s
historical trends, socio-economic context, and culture. In order to examine the ways in
which the landscape is constructed, in this section, I will examine two “opposing”
patterns of landscape use, both of which focus on patterns of development and
urbanization. How these patterns resolve the pressures of human needs and population
growth on ecosystems is of much importance to the integrity of ecosystems, including
agricultural ecosystems, and bears on the future of sustainable agriculture. In order to
examine how these landscape patterns are created and reinforced, I will first define
two oppositional development paradigms that guide the “development” of natural and
semi-natural lands into built environments.
Philosophies guiding development
In their treatment of urban development, Camagni et al. (2002) distinguish
between two general attitudes toward urbanization. The first, “an optimistic ‘neo-free
market’ approach” holds that urban planning schemes should not interfere with the
market mechanisms that spur on development. The second approach, which is
described as “a pessimistic ‘neo-reformist’ approach” supports such intervention as
necessary, claiming that planning is needed so as not to waste resources in hasty or ill-
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informed planning decisions. The first takes a normative stance that market-driven
development is desirable or “correct,” while the second values efficient space and
energy utilization (as well as the cultural aspects of a city center) as desirable and
proper pathways for development (Camagni et al. 2002). These development
paradigms determine the spatial configuration and extent of development on the
landscape, and are thus major drivers in landscape pattern. These paradigms are one
form of the higher level socio-economic drivers on the landscape; they address how
development should proceed, and in doing so, create and reinforce patterns on the
landscape.
Patterns of urbanization
These two general attitudes towards urban and suburban development produce
a gradient of land use patterns. Development operating under the first paradigm (i.e.
market driven development) is likely to take the form of suburban growth or “sprawl,”
while the second (city planning) more likely results in urban in-filling (i.e. developing
vacant land in urban centers) and more resource conscious uses of space (Camagni et
al. 2002). This section will look at two patterns at opposite sides of the spectrum:
sprawl and Vandermeer and Perfecto’s (2005) “planned mosaic.”
Sprawl
Sprawl refers to a pattern of development characterized by “low density
development, extending to the extreme edge of the metropolitan region and location in
a random, ‘leapfrog’ fashion, segregated in specialized mono-functional land uses, and
largely dependent on the car” (Camagni et al. 2002). This pattern of development is
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connected to suburban living. Changing lifestyles, as well as undesirable
characteristics of the city (e.g. crime, air pollution, noise pollution), have driven the
development of suburbs.
How do suburban developments contribute to sprawl? That nature of suburban
development and its spatial relationship to urban centers contribute to sprawl in a
number of ways. First, suburban development is characterized by its low density
compared to city living. Thus more land per capita is consumed by suburb dwellers
(Camagni et al. 2002). Suburb dwellers also use more energy for transport to and from
the city center (Camagni et al 2002). Development also takes place along the corridors
of transport to and from cities (linear development), while extension forms of
development occur at the fringe of the city between the city center, exurbs, and
suburbs (Camagni et al. 2002). Thus the creation of suburbs leads to development that
extends beyond the actual site of a suburban housing development.
The consequences of sprawl from a city planning perspective are numerous.
Sprawl consumes much more land than urban development; Blair (2004) provides the
following example: “of the 9224 km2 of urbanized land in Ohio, 7186 km2 are
occupied by communities with fewer than 50,000 residents, whereas only 2038 km2
are occupied by more populous communities.” Another perspective is offered by
Camagni et al. (2002), who identify the effects of sprawl on the city as social and
cultural center: “the European city, the very place of social interaction, innovation, and
exchange, risks weakening this fundamental role as a result of the cumulative effects
of decentralization tendencies, increasing specialization of land uses and social
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segregation.” Additionally, they identify other effects of fringe development, including
high costs of infrastructure and energy, increasing social segregation, traffic
congestion, and environmental degradation (2002).
Environmental and ecological impacts of sprawling development
By definition, urbanization impacts the ecology of an area in which it takes
place. The removal of trees and other vegetation, construction of buildings, and paving
of large areas, changes plant and animal communities, as well as hydrology,
microclimate, and air quality. While these issues arise with urbanization, urban sprawl
exacerbates these conditions. This is because sprawl is more diffuse and consumptive
of land. One study, looking at the economic cost of sprawl, found that “the planned
form of development saved around 20-45% of land resources, 15-25% of the costs for
providing local roads, and 7-15% for water and drains” (Camagni et al. 2002). These
economic costs are also spatial costs; sprawl takes up more land, creates more
impervious surfaces, and disturbs more ground area in the construction of above and
belowground infrastructure. Ultimately, this means that more habitat is disturbed and
converted into urban and semi-urban land uses.
This ecological disturbance created by urbanization has increasingly been the
focus of ecological studies. While ecology has traditionally focused on “undisturbed”
and “natural” environments (Blair 2004), it has had to adapt to this modern landscape
upon which humans have altered most of the land on the planet in some way or
another. As such, the ecology of urbanized areas has come under focused study in the
last twenty years (Blair 2004). A subset of these studies has looked at how native
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species adapt to varying degrees of urbanization. This idea of an urbanization gradient
is significant for the discussions of urban planning. By studying ecological processes
along such a gradient, we can understand the complex effects of urbanization on
ecosystems.
In one such study, Blair (2004) examined the effects of urban sprawl on birds.
He found that species had differential abilities to adapt to urbanization. He identified
three categories of responses to urbanization: “urban avoiders,” “suburban adaptable”
and “urban exploiters” (Blair 2004). Birds in the first category were typically
woodland species that were restricted to undisturbed lands. Birds in the second
category seemed to thrive in suburbs and were not present at less disturbed levels on
the gradient. Urban exploiters, some of which were introduced species, were found at
more urbanized sites on the gradients. Species diversity peaked at suburban sites in the
middle of the gradient. At the continental level, Blair (2004) observed homogenization
of bird populations finding that “in many instances, local extinction of endemic
species is followed by local invasion by ubiquitous species. Apparently, it is not a
serendipitous circumstance that House Sparrows (Passer domesticus) can be found
begging for French fries outside of McDonald’s restaurants anywhere in the world.”
In a similar study, Di Mauro et al. (2007) examined the effects of urbanization
on generalist butterflies. Their findings were similar to Blair’s (2004); diversity
steadily decreased along the rural-suburban-urban gradient. They concluded that the
urban matrix may limit diversity, along with other factors, including pollution, access
to water, and patch size (2007). They also examined how butterfly gardens along the
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gradient affected diversity and abundance of the species. While the metropolitan
gardens cannot support the butterfly’s entire lifecycle, they may serve as “stepping
stones” for the species (2007). These designated habitat areas may play an
increasingly important role in urban biodiversity and city planning in the future. This
kind of study sheds light on the urban environment as an ecosystem, and suggests that
it may be possible to meet human needs in cities, while supporting biodiversity.
Though these studies are limited to two groups of species, they may be
generalizable in the sense that at higher rates of disturbance, we can expect to see
larger proportions of generalist, r-type species and smaller proportions of specialist
and endemic species that cannot tolerate disturbance. Thus increasing urbanization
results in losses in diversity at the local, regional, and continental scales. With the loss
of these species, we lose the cultural value they might possess, as well as the services
and functions they provide in the context of the ecosystem.
Impacts of urbanization on agricultural lands
It is clear that urbanization has landscape level effects on biodiversity and
ecological integrity. But do these effects impact agricultural systems? Urbanization
and development have direct and indirect effects on agricultural lands and agricultural
productivity. These effects are socio-ecological in nature; they pertain to both natural
resources and the physical/biological environment and the socio-economic aspects of
agriculture.
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Direct effects
Land-use change relating to development and urbanization has a direct impact
on agricultural lands through the conversion of agricultural land to other uses. Wu et
al. (2011) describe: “From 1982 to 2003, the total developed area in the United States
increased by 48%, whereas the total cropland acreage decreased by 12%.”
Additionally, the American Farmland Trust reports that “between 2002 and 2007,
4,080,300 acres of agricultural land were converted to developed uses—an area nearly
the size of Massachusetts” (“Threatened Farmland” 2012). While the spread of urban
and suburban development onto agricultural land may not pose problems in the near
future, some speculate that there may be a threshold or a “critical mass of farmland for
agricultural sector viability (Wu et. al 2011) This not only refers to the physical
resources needed to produce food, but also elements needed for social and economic
viability, which are certainly part of agricultural sustainability. More research is
needed to determine whether or not such thresholds exist and how they may vary
regionally and nationally. Wu et al. (2011) suggest differential effects of urbanization
based on such a threshold: “in rural communities that have already experienced a high
degree of urbanization, continuing urban sprawl may indeed threaten agriculture as a
viable way of living.” This comment alludes to the indirect effects of urbanization on
agriculture, which are socio-economic in nature.
Indirect effects
The indirect effects of urbanization affect the social and economic elements of
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agriculture. These effects can be understood in terms of the system variables
wealth, connectedness, and resilience.
Wealth
By temporarily, and oftentimes, irreversibly, developing agricultural lands,
potential resources are “removed” from the system. By developing prime agricultural
lands into urban and suburban environments, the “potential” of the agricultural system
relies on a decreasing land area, and consequently, decreasing resources, to maintain
its productivity. In this sense, development has negative impacts on natural capital.
Urbanization also has seemingly negative effects on social capital in farming
communities. Because urbanization fragments the agricultural landscape, it can also
disconnect farmers from much needed support and services. Wu et al. (2011) describe
the benefits of being part of a large farming community: “it allows a farm to operate
more productively in sourcing inputs, and in accessing information, technology, and
needed institutions. For example, farmers depend on neighboring farmers for many
services, including equipment sharing, land renting, custom work, and joint irrigation
projects.” They note that this social capital may “be conducive to innovations and new
business formation” (Wu et al. 2011). The disruption of the local networks and
farming communities presents farmers from working together to build economies of
scale, and makes them less able to compete against large scale growers.
These changes in system wealth, toward consolidation (i.e. the concentration of
farmland) and centralization (i.e. the fragmentation of local networks and the growth
of regional and national ones) suggest that they system may be moving toward a
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vulnerable state. This is made more evident when the connectedness of the system is
considered.
Connectedness
The fragmentation of local farming communities and economies has economic
impacts that reach beyond the success or failure of a single farmer. Wu et al. (2011)
describe another threshold in agricultural productivity that occurs at the level of the
landscape:
As the number of farmland acres drops below a threshold, the nearest
processor or shipper may close its business because of an insufficient supply of
output, and farmers may face additional transportation costs or lower output
prices. This suggests that, … at an aggregate level, there may exist a critical
mass of farmland below which the vertically linked nonfarm sectors may have
to shut down, raising the cost of farming. (Wu et al. 2011)
This loss of local processors results in the consolidation of the industry. This
concentration leads to a reliance on cheap fossil fuel transport for profitability. This
trend toward spatial and economic consolidation increases the risk of the system
becoming overly connected.
The development of agricultural lands can also lead to increased connectedness
at the scale of the site though agricultural intensification. Wu et al. (2011) note that
losses in farmland are “partially offset by increasing intensity,” which involves the use
of fertilizers, pesticides, and less extensive plantings.
Resilience
The trends in wealth and connectedness at the scale of the landscape make the
agricultural system increasingly rigid, as it comes to rely more heavily on a narrow set
of resources and technologies for its productivity and feasibility. In order to be
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resilient, to be able to absorb shock without collapsing, the agricultural system must be
more flexible. In other words, we must maintain options for the system. For example,
if we maintain the minimum amount of cropland needed to support agricultural
productivity (i.e. if agricultural productivity is optimized) and much of the remaining
suitable crop land is developed, we will not be able to adapt to any changes in the
future (i.e. irreversible land degradation, climate change) that might makes these crop
lands unsuitable or unproductive. In a similar manner, if we rely on an infrastructure
built on the subsidization of fossil fuels, a disruption to this system may frustrate the
processing and distribution of food, and consequentially, food access. Neither of these
centralized or “optimized” solutions is resilient.
Above all else, the loss of biodiversity threatens the resilience of the system.
The loss of endemic and specialist species and replacement with invasive, generalist,
and development tolerant species impacts agriculture in the same way it impacts other
ecosystems. If a locality or region cannot support wildlife, farmers lose the ecological
services that these species can provide. Foremost among these services are pollination
and insect control, services provided by butterflies, bees, moths, birds, and bats that
are indispensible to sustainable food production. As these species are lost, the
agricultural ecosystem, and other ecosystems, become increasingly less robust and
resilient.
Toward a new development paradigm
It is clear that market-driven development has serious environmental and
ecological impacts that extend to agricultural landscapes. If sustainability and
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conservation are goals at the scale of the landscape, land use planners must be aware
of the magnitude and extent of the disturbance they are creating on the landscape. This
level of disturbance will determine what species can live in the developed
environment and how a site will function ecologically as a result of the development.
Farmers must be aware of this disturbance as well, as agro-ecosystems exist
within a gradient of disturbance and are sources of disturbance to varying degrees.
This is to say that it matters what is outside the boundaries of a farm; landscape
elements outside the physical boundaries of the farm will influence its functioning. In
the first case, consider the example of a site that extends up to a stream. If a farmer
plants all the way up to the stream, erosion and chemical runoff is likely. The effect
moves downstream, where sedimentation and nitrogen affect stream properties for the
length of the waterway. The “Dead Zone” of anoxic water in the Gulf of Mexico
provides convincing evidence of this lack of boundedness at the level of the landscape
(Pfeiffer 2006). Conversely, the surrounding landscape matrix may influence the site;
a forest surrounding a field will increase humidity and break the wind (Fraser &
Stringer 2009). The essence of these effects is that agricultural land is not distinct from
the rest of the landscape, and that a meaningful conservation plan must integrate and
resolve different land uses, including agricultural, urban, residential, and natural
environments.
A land use mosaic
Development planning and management are the alternative to market-driven
sprawling development. While sprawl is driven by the market and the potential for
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profits, a holistic landscape plan must be driven by more than economic growth. As
urban sprawl extends over the landscape, it homogenizes it; the original landscape is
developed into repetitious units of suburban development. Native vegetation is
replaced with ornamentals, turf, and pavement, while endemic species are replaced
with “urban exploiting” invaders (Blair 2004). The landscape becomes fragmented by
an urbanized landscape with one main purpose: growth. Meanwhile, the capacity of
the land to support other functions is undermined and destroyed.
This pathway is not inevitable; rather it is the product of a particular
development paradigm that results in collective land use decisions. Other pathways to
development are surely possible, and can provide more services and functions than the
homogenous urban landscape. Vandermeer and Perfecto (2005), in a discussion of
rainforest conservation, sketch out one such possible trajectory, a “planned mosaic.”
They describe the planned mosaic as
a diverse mosaic of land uses, ranging from protected forests to managed
forests to plantations to sustainable agriculture, a mosaic where decisions about
land use are tied to the capabilities of the land and the needs of people, not to
the requirement of profit or repayment of past accumulated debt, or even the
desires of northern conservationists.
For tropical agriculture, this means only farming on soils that can sustain productivity
and allowing poorer soils to regenerate into forest. This kind of development is patchy
and integrated; some rainforest is protected, some is cleared, but the protected parts
are connected by other semi-natural habitats (i.e. polyculture, pastures) that can serve
as corridors for rainforest species. This creates a much less extreme gradient between
rainforest and development than in industrial agriculture where “isolated islands of
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pristine rain forest [are] surrounded by biological deserts of pesticide-drenched
modern agriculture” (Vandermeer &Perfecto 2005).
Thus a “planned mosaic” is a multi-functional landscape. It meets
conservation, material, settlement, and food needs. Development is based on local
need, rather than national and foreign markets, and occurs in the most suitable place,
given available information. This simple idea – using land in a way that aligns with its
natural capacity – could prevent undesirable outcomes on the landscape. Take for
example, the case of the Goulburn Broken Catchment in Australia. The land has been
used historically for dairy farming, and has relied on irrigation for productivity. In
recent years, due to the removal of native vegetation and the salinization of the soil by
irrigation water and a rising water table, the area has become less productive. Anderies
et al. (2006) capture the one-sided approach of the management of this issue:
The catchment community responded to the crisis by asking “what must be
done to keep irrigated dairy running?” rather than “Is irrigated dairy a
reasonable use of natural resources in the GB, given inevitable biohydrological
changes that will reduce the ability of the system to cope with minor change?
This community did not consider the “natural capacities” approach to development,
and is inevitably faced with an ecological, as well as economic, crisis. In summary, the
need for this kind of development is not limited to regions containing tropical rain
forests; conservation of biodiversity, soil resources, and ecological services are a
concern everywhere.
The presence of urban sprawl on the American landscape indicates that a
“planned mosaic” is not likely a paradigm guiding development. In order to reverse
the trends produced by urbanization and create a more integrated landscape, it is
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important to understand what forces are guiding land use decisions. These forces are
drivers in a landscape’s history and its future and play a major role in land use change
at the scale of the landscape.
Drivers at the scale of the landscape
The amazing magnitude and extent to which humans have altered the earth and
the impacts of these modifications make land-use change an important area of
research. While the fact that the landscape is radically changing is evident, the reasons
for these changes are not. While the paradigms guiding development offer a simplified
notion of how development changes landscapes, the processes that produce landscape
change are much more complex. These drivers include land use decisions and higher
level socio-economic processes (fig. 22).The drivers of landscape change are not
easily targeted; they are extremely complex and operate at many scales.
Fig. 22: Higher level socio-economic processes (i.e. development paradigms, land use
decisions) drive changes at the landscape scale.
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The formulation of global, generalized theories about land use change has not
been a realistic goal due to different socio-economic and cultural contexts around the
world. Lambin et al. (2001) and Vandermeer and Perfecto (2005) caution against
broad generalizations (e.g. deforestation is a result of high population and/or poverty),
as they contribute to “myths” about land-use change that are often mobilized in
politically charged development projects. Besides the political implications of
identifying drivers of undesirable environmental change, the subject introduces
methodological questions (e.g. establishment of independent variables) (Lambin et al.
2001). Despite these challenges, landscape ecologists have put forth a number of
forces that appear to have a role in land-use change and, more often than not,
environmental decline.
Neo-malthusian approaches
Long part of the public imagination, the neo-malthusian approach to landscape
change asserts that populations (in particular, impoverished ones) are to blame for the
overharvesting of natural resources, like the rainforests (Lambin et al., Vandermeer
and Perfecto 2005). This view takes the stance that deforestation is due to the
overpopulation and lack of ecological understanding of poor peasants in the tropics.
Lambin et al. (2001) and Vandermeer & Perfecto (2005) express serious criticism of
this idea and assert that it is a gross oversimplification of a complex web of
interactions. This oversimplification is also politically loaded. Lambin et al. (2001)
argue that population and poverty certainly do have a role in deforestation and other
kinds of environmental damage, but this role is oversimplified and exaggerated.
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Despite these criticisms, it seems intuitive that overpopulation poses
environmental problems. The IPAT formula (impact on environment or resource=
population x affluence x technology) attempts to quantify the impact of population
(and other key variables), but is frustrated by interdependencies among its variables
(Lambin et al. 2001). Meyer and Turner (1994) suggest that population is not a
meaningful indicator of land use change generally, but may prove significant when
comparing regions of similar socio-economic conditions.
The intersections of population and deforestation are further complicated by
another variable: poverty. For landless peasants faced with poverty, it may seem that
cutting down the rainforest and settling there is their “only choice.” A broader view of
the phenomenon would introduce the role of plantations, migration, and “free trade” in
the destruction of that patch of rainforest (Vandermeer and Perfecto 2005). Ultimately,
this pattern of deforestation in the tropics seems to be related to settlement pattern.
Settlement pattern, in turn, may be related to changing economic opportunities
(Lambin et al. 2001).
Socio-economic and Marxist approaches
According to Lambin et al. (2001), “political and economic explanations focus
on differential power and access enforces by dominant social structures as the
centerpiece of land-use change.” They look to the “restricted options created by
poverty” and “unchecked state and corporation concentration of wealth” as drivers in
deforestation and other undesirable land-use changes. The context that produces
deforestation, for example, is not simply poverty, but “changing socio-economic
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conditions, mediated by institutional factors” (2001). The factors include “free trade”
policies that create large, transitory worker populations on banana plantations
(Vandermeer & Perfecto 2005). Similarly, Meyer and Turner (1994) identify socioeconomic organization and economic development as important factors in changing
landscapes. Together, these socio-economic factors produce situations in which
deforestation occurs.
21st century context
Other factors relating to environmental degradation and landscape change are
connected to technology, modern institutions, and the reach of globalization. Meyer
and Turner (1994) present such an argument: “the runaway or careless use of
technology is primary to environmental degradation, though population increase may
exacerbate the problems created.” The institution of global capitalism is noted as a
possible driver as well (Meyer & Turner 1994; Lambin et al. 2001). Lambin et al.
conclude that many of the aforementioned factors (e.g. technology, capitalism) are
connected to globalization, noting that “rapid land use changes often coincide with the
incorporation of a region into an expanding world economy. Global forces
increasingly replace or rearrange the local factors determining land uses, building new,
global cause-connection patterns in their place.” In the case of agricultural landscapes,
industrial farming may be used to produce commodity crops for export. Ultimately, it
seems that land use change is a context driven process; all of these a factors contribute
to particular circumstances in a locality or region that drive changes on the landscape.
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Summary and conclusion
The agricultural landscape is composed of a patchwork of land uses. These
land uses form landscape pattern at this scale. The patterns examined here, urban
sprawl and the “planned mosaic” (Vandermeer & Perfecto 2005), have different effects
on biodiversity and ecological functioning, and consequently, the sustainability of
agriculture. The landscape as a whole is defined by its patterns, as well as its processes
- land-use history and land-use decisions- which are produced by higher level socioeconomic processes, like capitalism, political intuitions, and cultural conventions.
Summary of Part I
The previous three chapters have described three scales at which agriculture
takes place: the patch, the site, and the agricultural landscape (fig.23). In the patch,
patterns (i.e. soil structure and food webs) are connected to processes (i.e. nutrient
cycling, decomposition, and the movement and retention of water). At this scale, SOM
serves as a proxy for these processes because it is coupled to many properties of the
soil. Changes in the landscape are brought about by the off-site driver of agricultural
and soil management. Industrial agriculture undermines soil resilience by destroying
soil structure, simplifying soil food webs, and utilizing SOM more quickly than it is
replaced.
At the scale of the site, farming is a practice of managing agro-ecological
systems. The design, i.e. pattern, of these sites is connected to site processes like
pest/pathogen dynamics, animal and insect movement and primary production. The
annual cycle is the most important ecological cycle at this scale. It influences
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community structure and agricultural management practices. Off site drivers like
subsidy structures and commodity markets also impact the communities on this
landscape. Industrial agricultural practices undermine resilience this scale by relying
on low diversity monocultures.
At the scale of the landscape, farming is a land use among many others. Land
use patterns, i.e. sprawl and the land use mosaic, create different gradients upon which
ecological dynamics take place. The cycle at this landscape is land use history.
Changes in land use history and landscape pattern/process are brought about by land
use decisions, which are the product of higher level socio-economic processes. The
resilience of the agricultural landscape is undermined at this scale by poor land-use
planning, land degradation, and fragmentation of natural habitats by development. The
following diagram unites these scales into a panarchy.
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Fig. 23: Hierarchy of scales in an agricultural panarchy. Lower levels exert bottom up
effects on higher levels because they compose those higher levels. Higher levels exert
influence through top-down effects in the form of drivers.
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PART TWO: Imagining Agricultural
Futures
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Chapter 7: Cross-scale interactions
The nature of a panarchy
In the last three chapters, I have described in detail three levels of analysis of
agricultural landscapes. In doing so, I have fulfilled one criterion of a panarchy: that a
panarchy must consist of at least three scales that differ quantitatively and
qualitatively in their dimensions, patterns, and processes. However, identifying these
levels is only the beginning of understanding what a panarchy is and why Panarchy is
an important tool in understanding how complex systems adapt and change.
What is revolutionary about Panarchy theory is that it presents a dynamic
hierarchy; change can move both up and down a system’s levels (Holling 2001).
Change can come from below, when collapse or novel reorganization prompts a
“revolt” through the system, changing the higher levels that are dependent on
processes at lower scales. Conversely, an organizing structure can come from above,
by driving change on the lower landscapes. After collapse, reorganization at all levels
may be informed by remnant higher level structures (the “remember function”). The
importance of the “revolt” and “remember” functions in a panarchy is that they
describe how change can happen at one level and cascade through an entire system.
How a system might interact across scales is unpredictable. However,
Panarchy theory makes generalizations about possible interactions, based on internal
system dynamics (i.e. the adaptive cycle). In this way, a particular outcome cannot be
predicted per se, but shifts in system behaviors and properties can be anticipated.
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These shifts occur when vulnerability occurs at multiple levels in the panarchy. This
allows a collapse at one level to ripple into another.
The previous chapters have offered a picture of what collapse at each scale
could look like: loss of structure and biological communities in soil, decimation of a
monoculture by pests or pathogens at the site, and a large scale loss of ecological
integrity or crashing agricultural markets at the level of the landscape. In this chapter, I
will explore how these outcomes might interact to produce a collapse that touches the
entire agricultural system.
It is impossible to accurately model the future of agriculture, or any other
complex system. It may be possible to calculate the probability of a particular negative
outcome, but these predictions are undermined by the non-linearity of complex
systems. Panarchy theory takes a different approach, attempting to look at a system’s
future through its history, through the dynamics between system variables -wealth,
connectedness, and resilience- that produce its present state and drive the system “into
the future.” The state of these three system variables are represented in the adaptive
cycle. Thus, the state of the adaptive cycle at each scale of analysis in a panarchy
provides an idea of possible futures, of vulnerabilities and opportunities, in the system.
Making predictions of an uncertain future
Given the non-linear and seemingly unpredictable nature of complex adaptive
systems, how are we to best prepare for the future, which may come to be defined by
vast ecological, economic, and societal change? While modeling technologies may fall
short of this weighty task, the practice of scenario building may offer the flexibility
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and awareness needed to meet these imminent challenges. Cumming (2007) explains:
“[Scenarios] shift the focus of research and management from making singular
predictions and developing single ‘best’ strategies to exploring uncertainties and
assessing the outcomes of alternative policies.” Scenarios do not have the same
tendency to become “locked in” as a policy regime may, as they propose multiple
courses of action that change with new knowledge. The purposes of a policy measure
and scenario building are fundamentally different; while the former proposes a
solution based on available knowledge, the latter is an exercise aimed to “explore
uncertainties and identify knowledge gaps, shedding light on the possible
consequences of decisions and a range of possible trajectories that encompass both
‘good’ and ‘bad’ outcomes” (Cumming 2007). Additionally, while effective scenario
building exercises utilize scientific knowledge, they also rely on the values of their
creators and society at large.
Scenario building is being used in a diversity of fields, including the design of
sustainable transportation systems (Shiftan et al. 2003), metropolitan areas
(Barbanente et al. 2002), energy conservation plans in industry (Saxen & Vrat 1992),
public relations (Sung 2007), small business planning (Foster 1993), disaster relief
(Machlis & McNutt 2010), global biodiversity (Cumming 2007), climate change
(Hannah et al. 2002), and global futures (Gallopin 2002). These practices create
narrative models, which attempt to expose areas of risk, opportunity, and uncertainty
in a system. They rely on scientific and practical knowledge, but also the goals of the
managers of the system.
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Given this quality – the synthesis of human values and scientific knowledge scenarios are particularly useful tools in Panarchy theory. The systems that panarchy
addresses – those at the nexus of ecology and society – are co-created by ecological
laws and cultural practices. For this reason, it is necessary to weigh both “scientific”
and “social” outcomes through a hybrid narrative about the future.
When mapped onto a panarchy (see Gallopin 2002 and fig. 24), scenarios build
upon our understanding of systems and how they behave and change. As “logical
narratives dealing with possibly far reaching changes,” (Gallopin 2002), scenarios can
move beyond linear understandings of change to complex, non-linear, and systemic
transformation. Most importantly, scenarios may expose the desirability and feasibility
of alternative paths into the future and identify “branching points at which human
actions can significantly affect the future” (Gallopin 2002). Rasmussen (2005) goes so
far as to suggest that scenario building may “give people a memory of the future,” and
create a context and meaning for the future through storytelling. This appeal to
people’s desire for a meaningful future is certainly a strong undercurrent in
envisioning and working toward sustainable and equitable society.
With these opportunities in mind, I will present a number of scenarios
representing possible futures of the agricultural system and examine their implications
for a sustainable future.
Building scenarios
Scenario building begins with a “reference scenario,” which captures the major
trends which are likely to define future outcomes and presents the state of a system of
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interest (Gallopin 2002). From here, narratives are constructed, using different
outcomes in key variables and drivers. In a way, these narratives self-organize around
the feedback relationships of the system. In his global futures scenarios, Gallopin,
maps his narratives onto the adaptive cycle, creating scenarios that represent different
system trajectories. The categories Gallopin creates will be briefly presented in the
following paragraphs because they will structure scenarios of agricultural futures in
this chapter.
Fig. 24: Gallopin’s scenarios mapped onto the adaptive cycle. Sourced from Gallopin
et al. 2002.
Gallopin begins his scenario building exercise with a Reference Scenario,
which is characterized by a rising population, accelerating environmental degradation,
(habitat destruction, biodiversity loss, accumulation of toxins), and a growing gap
between the rich and poor of the world. This scenario is positioned in the K-phase of
the adaptive cycle. All of the other scenarios begin at this point.
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In the Policy Reform scenario, population growth is slowed from the
Reference Scenario. Policy measures reduce poverty, hunger, the gap between rich and
poor, and global carbon dioxide emission. This scenario forestalls collapse, keeping
the system in the K-phase. However, the changes are superficial and collapse is
inevitable.
The Breakdown and Fortress scenarios offer dark possible futures. In the
Breakdown scenario, social order falls away as the tensions of inequity and war
intensify. Physical and civil infrastructure crumbles. On the adaptive cycle, the system
moves through omega into a poverty trap, a state of low wealth where the system
cannot reorganize. In the Fortress scenario, elites continue to manage and hoard most
of the world’s resources. Conditions worsen for most of the population. The rich hide
in “fortresses,” while the rest of the people suffer in a degraded environment and
crumbling society. In this scenario, the system is held in a resilience trap in late K; it
refuses to collapse because the powers that be are investing massive amounts of
energy and resources into maintaining it. There is no clear resolution to either of these
scenarios; strife and societal breakdown will characterize the state of the world until
something novel evolves.
Gallopin offers two optimistic futures in his Ecocommunalism and New
Sustainability Paradigm scenarios. In the former, local networks and simple
technologies replace large and complex social structures. After a collapse of the
“status quo,” these sustainable communities give the system a modular structure and
avoid entering the K-phase. The New Sustainability Paradigm also envisions a
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sustainable future, but one that embraces a new paradigm of global development, the
goals of which are to further education and equity. This pathway would be taken if
civilization opted to scale down and reroute the global system altogether. On the
adaptive cycle, this is represented by exiting this cycle, without a collapse, and
entering into a new one.
These different scenarios, and the systems behaviors behind them - collapse
into a poverty state, sustained resilience in a maladaptive system, novel
reorganization, and conscious scaling down - represent pathways that the future of
agriculture could follow. Through scenario building, it is possible to weigh the current
trends in agriculture against these outcomes. Using the scales of analysis and trends at
each scale that I have established, I will sketch out these futures. These possible
futures will be grounded in knowledge from the natural and social sciences, and
inevitably appeal to human values. This synthesis of scientific and social knowledge
will provide the necessary context for evaluating potential solutions to the problems of
industrial agriculture. A discussion of how we can design a sustainable agricultural
system in light of these possible futures will form Part 3 of this paper.
The Reference Scenario
The introduction to this paper summarized some of the key trends that will
play a role in the future of agriculture. These trends include biodiversity loss, soil and
land degradation, water scarcity and contamination, economic consolidation, and
climate change. The future of agriculture is also affected by a rising population and
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changing patterns of consumption (Kucharik & Ramankutty 2005). These trends will
form the drivers in the following future scenarios
The global population is on the rise, growing at higher rates in developing
countries. The United Nations estimates that the global population will reach between
7.4 and 10.6 billion people by 2050 (Kucharik & Ramankutty 2005). This rise in
population will be accompanied by a rise in per capita food consumption, particularly
in developing countries (Kucharik & Ramankutty 2005). By 2020, the demand for
cereals will increase by 40% (Kucharik & Ramankutty 2005). Kendall and Pimental
(1994) report “When both population and food consumption rate increases are
accounted for, it is estimated that food production will need to triple by the year 2050”
(qtd. in Kucharik & Ramankutty 2005).
To put this challenge into perspective, consider the potential for higher
production in corn, one of the world’s major crops. Though corn yields have risen
continually over the last 50 years, Kucharik & Ramankutty (2005) point out that the
gains are dwindling; “it is clear that the spectacular gains of the 1960s are over for the
most part—only a few localized regions show signs of significant growth and these are
regions where current yields are below the average when compared to the rest of the
Corn Belt.” These yields will be boosted for the most part by irrigation, which reduces
yield variability substantially (Kucharik & Ramankutty 2005). However, with outflow
of the Ogallalla aquifer and other sources of irrigation water exceeding annual inflow,
water scarcity will inevitably affect the feasibility of irrigated agriculture (Pfeiffer
2006). While gains are decreasing, variability is increasing; Kucharik & Ramankutty
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(2005) suggest that “as farmers near potential corn yield ceilings across the U.S. Corn
Belt, they are potentially at a higher risk for catastrophic losses.”
Variability in corn yields is likely to increase for other reasons. Kaufmann and
Snell (1997) “estimated that roughly 19% of the variability in corn yield observed was
due to climate variables, and about 74% of the variability can be explained by social
variables (e.g., capital, labor, fertilizers, pesticides, etc.)” (qtd. in Kucharik &
Ramankutty 2005). Thus corn yields will be significantly affected by the concurrence
of climate change and peak oil. Ultimately, Kucharik & Ramankutty (2005) report, “it
may take a second coming of another agricultural revolution to boost yields so that
average annual increases in grain production can keep up with an escalating demand
for food.”
The global need to increase food production threefold by 2050 is in conflict
with other goals of agriculture. For example, the production of corn and other grasses
for biofuels utilized 37.9 million hectares of land in 2007 and is on the rise (Landis et
al. 2008). Biofuels are heralded as a source of clean energy, which is much needed in
the era of peak oil and climate change. However, production of these crops for
biofuels negatively affects the provision of ecosystem services (Landis et al. 2008),
and cannot realistically be maintained alongside a threefold increase in food
production, food production must become more intensive, or take place on more land.
As gains from agricultural technology are appearing to level off, it is likely that more
land will come under cultivation. This will further stress fragmented and simplified
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ecosystems. Food production cannot be sustained alongside the destruction of
ecosystems, which offer vital services to agriculture.
In summary, the present state of agriculture is under many kinds of pressures.
Global food needs push agriculture toward higher production and intensification,
which undermines the land’s ability to support agriculture. The benefits of modern
industrial agriculture seem to be leveling off, and are becoming more variable.
Dwindling resources (e.g. water, fossil fuels) as well as emerging pesticide (Whalon
2008), insecticide, and herbicide resistance (Manalil et al. 2011), threaten to
destabilize these yields. Climate change introduces uncertainty into agricultural
production. Given the narrow reliance on a few crop varieties and chemical and
technological inputs, climate change will likely pose serious challenges to the
continuation of modern industrial agriculture.
The Agricultural Policy Reform Scenario
In the agricultural policy reform scenario, measures are taken to reduce the
negative impacts of industrial agriculture. These policies are aimed at reducing
negative environmental impacts (e.g. erosion, run off, surface and ground water
pollution), and create a meso-economic environment in which small scale farmers
have more autonomy and potential for success.
In this scenario, agricultural subsidies are a major focus of policy reform.
Incentives are created and strengthened to reduce soil erosion and run off through
practices like conservation tillage and the planting of perennial grassy margins along
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waterways. New policies focus on strengthening incentives for environmentally
sustainable farming.
In contrast, environmentally degrading farming practices are deincentivized
through policy measures that punish landowners for the pollution of waterways. In
order to receive federal aid, landowners must submit a plan stating how they will
counteract run-off and water pollution.
To combat the consolidation of agricultural operations, a committee in
Washington proposes a set of anti-trust measures, specifically targeted at agribusiness.
These measures would inhibit corporations from forming ogliopolies and monopolies
and create a “level playing field” for small farmers. Additionally, after much pressure
from public interest groups, the president introduces a bill into Congress that will cap
CO2 emissions in industry, including agriculture.
After subsidies are reformed and evaluated, their impact on environmental
degradation is assessed. A failure to reform the basic structure of subsidies (the
“deficient payment” system and yield based payments) result in conservation
measures backfiring, leading to more environmental degradation. While conservation
measures physically protect soil, the overall structure of subsidies encourage low
diversity and high input monocultures, which simplify above and below ground
communities1. Though anti-pollution laws are somewhat revolutionary, they are
difficult to enforce and ignored depending on the political climate in the U.S.
Environmental Protection Agency. Additionally, the ability of large agribusiness
corporations to pay pollution fines and continue their business undermines the purpose
1
Windham 2007
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of deincentivizing pollution. Large agribusiness corporations continue to possess
disproportionate control over food production and agricultural markets2.
The largely partisan effort to limit the power of agribusiness corporations fails
in both chambers of Congress, conceivably due to oppositional support from
agribusiness lobbyists and interest groups3. Climate change legislation also receives
criticism from industry leaders, lobbyists, and Congress members. The legislation
eventually passes, though its impacts are significantly watered down in Congress and
undermined during the rule-making process.
In summary, policy reform is “too little too late” to prevent large scale
environmental degradation and lessen the impacts of climate change. Superficial
changes to subsidy structures and unenforceable policy measures fail to address the
real issues of industrial agriculture. In terms of system variables, wealth becomes
concentrated in both the monocultures vulnerable to pest invasion (at the level of the
site) and the highly consolidated agricultural industry (at the level of the landscape).
Industrial agriculture becomes more dependent on inputs – fertilizer, machinery,
pesticides, technology at the site and oil at the level of the landscape – it becomes less
adaptable when faced with a shock. A shock to the system may send it into collapse
and radically change the state of the agricultural system. At the scale of the site, a
farmer might rather suddenly have to learn how to operate with limited gas or
fertilizers. At the scale of the landscape, we would be faced with an obsolete
2
3
Howard 2006
Food and Agriculture interest groups contributed $89,675,179 to the 2012 election cycle
(“Agribusiness: Top Contributors to Federal Candidates, Parties, and Outside Groups,” 2012).
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agricultural infrastructure and will have to find ways to produce and distribute food
with limited access to oil and technology.
While these measures might slightly forestall collapse, they cannot avert it.
These kinds of policies identify system thresholds (e.g. the amount of CO2 in the
atmosphere we should not surpass, levels of water and air pollution that are tolerable
or safe, the maximum amount of political consolidation in a free market, etc.) and
sometimes work to slow down the approach to them, they do not change track; the
threshold is eventually crossed. After crossing such a threshold and changing rapidly,
the system would have to swiftly reorganize to avert widespread hunger and social
strife.
The Agribusiness Wins Scenario
In the Agribusiness Wins Scenario, the U.S. government fails to regulate the
agricultural industry. Markets become increasingly consolidated. Wealth and
increasing amounts of resources come under the power of these corporations. Dollars
pour into political campaigns by corporate interest groups that oppose regulatory
policy. Agribusiness giants come to wholly own the marketplace. Small farms are
made less competitive, as they cannot compete with larger firms’ economies of scale.
Additionally, billions of dollars of government subsidies go to industrial agriculture
corporations every year, further increasing their advantage and artificially driving
down the cost of food4.
4
Windham 2007
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Business continues as usual, but it takes more and more energy to sustain it.
System shocks are numerous. Pesticide5, insecticide, and herbicide resistances6 create
annoyances, and at their worst, produce catastrophic outbreaks. Worsening
environmental degradation threatens the system at all levels (e.g. loss of topsoil and
fertility at the patch, disappearance of ecosystem services [e.g. pollination] at the site,
and impacts of climate change at the landscape).
The system in this scenario is held in a Resilience Trap (Holling et al. 2002).
The system is maladaptive and ridden with vulnerabilities, but it does not collapse; it
is being held in late-K by a massive amount of inputs. As this intensifies, it no longer
makes sense energetically or economically for the system to operate, but it continues
to do so, because there is no other option; the system is “locked in.” Connectedness
continues to increase as the system consolidates, and wealth becomes more and more
concentrated. The food system may remain in such a state for months, years, or
decades. The agricultural infrastructure remains in place, but food shortages are
increasingly common. Food is more accessible to some than others. This inequality
produces a disgruntled lower class, and eventually creates unrest in the middle class.
This system is sustained until the resources propping up the system run out, or until
some kind of revolution takes place.
The Agricultural Deserts Scenario
Eventually, both the Policy Reform Scenario and the Agribusiness Wins
Scenario collapse. The Agricultural Deserts Scenario is one possible way the
5
6
Whalon et al. 2008
Manalil et al. 2011
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agricultural system might reorganize (or fail to reorganize), depending on the severity
of environmental degradation and resource scarcity.
In this scenario, feedbacks in the food system accelerate the system toward
vulnerability. Loss of SOM and topsoil make inputs more and more vital to food
production, which further degrades the soil. This cycle does not end until industrial
agriculture collapses, which may be due to an exhaustion of the resources needed for
its continuation (e.g. oil, fresh water, arable land). After such a collapse, the system
must reorganize in an environment with scarce resources.
Farmers must produce food without inputs, but also without natural fertility
and with hampered ecosystem services like pollination, detoxification, decomposition,
and nutrient cycling. Soil wealth, in the form of SOM is degraded. Resources are
scarce, and so is the knowledge needed to farm without these inputs. At the scale of
the landscape, fragmentation undermines the provision of ecosystem services.
Agricultural infrastructure becomes obsolete.
This poverty state is biological, but also social. The lack of agricultural
infrastructure and food security is a source of social tension and strife. Governments
attempt to recreate the modern industrial food system and put remnant infrastructure
back into use. This reorganization of the food system is unsuccessful, because the
resources that fed the industrial agricultural system are no longer available.
The system is in a Poverty Trap (Holling et al 2002). After collapse, a system
may be too degraded to reorganize successfully. The system remains in a state of
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collapse that cannot proceed into reorganization. This state will continue until a new
resource or wealth is discovered to move the system into reorganization.
The Just World Scenario
In the Just World Scenario, a new paradigm reshapes the food system.
Technology is embraced, but directed toward achieving domestic and global equality
and food access. The right to patents seed and other life forms is overturned and these
resources become part of the Commons. In the developing world, grassroots
movements successfully oppose the import of genetically modified seed into
developing countries. Hybrid seed and “terminator” seed7 are abandoned for locally
sourced, open pollinated seed. In Europe and the U.S., growing public concern leads
to mandatory labeling of GMO’s. An absence of strong consumer buy in for GM crops
results in a quickly deflating market.
Alternative energy sources are capitalized upon, leading their industries to
become competitive with the contracting oil industry. Growing investments in the
private and public sphere advance these technologies, and make them accessible to the
American people.
The U.N. takes a new approach to dealing with food shortages and hunger
around the world. Instead of sending food aid in the form of grains and genetically
modified seed8, projects are launched globally to establish sustainable perennial and
7
“terminator” seed refers to “genetic use restriction technology,” which is used by Monsanto to ensure
that the plants that grow from their seeds will not produce viable seed. This way, farmers cannot “steal”
their “intellectual property” and must repurchase their seeds every year. They also may prevent
transgenes from entering the environment through seed. (“Genetic use restriction technologies”)
8
“Humanitarian aid.” (2013)
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annual agriculture9. Locally appropriate solutions are favored over one-size-fits-all
solutions.
A growing sense of community autonomy and self-reliance creates strong local
networks that disconnect from the global capitalist system. While global connections
are certainly maintained, they system does not become overly connected. Wealth
becomes less concentrated and is circulated around the globe. The global marketplace
is composed of thriving regional economies and governed by equity and fair trade
principles.
In the terms of the adaptive cycle, in this scenario, the global food system exits
into a new cycle. It is still global in scale, but maintains different relationships among
the variables of wealth, connectedness, and resilience.
The Localization Scenario
In the Localization Scenario, local and regional food systems replace the
current national/global food system. After the food system collapses (its possible
collapse trajectories include the Policy Reform, Agribusiness Wins, and Agricultural
Deserts scenarios), reorganization is constrained by limited infrastructure and fossil
fuel derived resources. Local economies come to be self-sufficient for the most part,
with regional systems meeting some food and material needs. Because agricultural
inputs and machinery are no longer available, organic, low input farming becomes the
9
See, for example, the World Watch Insitute’s Sustainable Agriculture Program (“Sustainable
agriculture program,” 2013)
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norm. Additionally, agricultural subsidies have become obsolete, making organic
agriculture the most economical practice10.
At the scale of the patch, natural fertility (wealth) is maintained for its
importance in sustainable food production. Soil conservation practices are also used to
prevent soil erosion. At the scale of the site, plantings are diversified (connectedness)
in order to provide for local needs, and to take advantage of the benefits of
intercropping (e.g. soil conservation and pest control). Farmers and gardeners
experiment with perennial food systems (i.e. agroforestry and food forests). A
significant amount of food production occurs in urban spaces and on backyard scales.
Local and organic food production, and the self-sufficiency that comes with it,
become a source of pride for people. Community gardens and cooperatives connect
gardeners of all ages, ethnicities, and backgrounds in a shared community goal11. This
shared sense of community and support ease some of the inevitable tensions during the
transition12.
The agricultural system at the level of the landscape looks very different. It is
modularized, with local markets clustered within regional markets (connectedness and
resilience). As a whole, the landscape mosaic is more integrated; areas of food
production are diversified, semi-perennial, and are integrated into natural habitat.
Perennial and low disturbance areas provide corridors between natural habitats13.
10
The difference in energy input per dollar of output: 18,000 btu in industrial agriculture and 6,800 btu
in organic (Windham 2007)
11
Okvat & Zautra 2011
12
Okvat & Zautra 2011
13
Turner et al. 2001
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After the collapse brought about by the other scenarios, the system reorganizes
into a qualitatively and quantitatively different system. The system moves into the r
phase, where wealth is distributed, connectedness is low, and resilience is high.
Because it maintains its diversified communities (at the patch and site) and its
modularity (at the landscape), the system does not move toward late K. It remains
between r and K as long as it is maintained and adaptively managed.
Summary and conclusion
These scenarios represent pathways that the agricultural system might take in
the future. At the present state of the agricultural system (i.e. as it approaches late K),
there are not many options, and many of these options result in systemic collapse.
Acknowledging this narrow range of options can help us to prepare for an uncertain
future. Furthermore, scenario building can inform our attempts to mitigate agricultural
collapse, or rebuild the system following a collapse.
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PART THREE: Designing a sustainable
agriculture
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Chapter 8: The structure of the problem
Lessons from future scenarios
Scenario building is a powerful tool in planning for an uncertain future because
it presents a number of divergent possible worlds that might not come about in a linear
policy process. These futures are premised on the trends and variables that are
identified as important today; in the case of agriculture, rising population, changing
lifestyles, climate change, economic consolidation, biodiversity loss, land degradation,
water scarcity and peak oil are major variables that will certainly play a role in
agriculture in the years and decades to come. How will of all these variables interact to
produce a single future? That we cannot fully know or predict; the process of scenario
building is meant to flesh out this uncertainly with logical, scientifically based
possibilities.
That being said, scenarios are formed with our best knowledge of the
relationships between variables, patterns in trends, and lessons from history. With
these variables in mind, we can create futures where these relationships, patterns, and
lessons play out. At best, these are hypotheses; each scenario follows an assumption
about how the system might behave, and attempts to “model” the state of the other
variables given this behavior.
While these hypotheses have value in and of themselves as scientific models
(i.e. accounts of relationship between variables of interest), they are not of much use
unless they are held in the context of human values. So how are these scenarios to
inform our course of action as individuals and a society; since none of these scenarios
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is necessarily a prediction or forecast in a typical sense - since they do not attempt to
predict the future per se - what is it that they can offer? To answer this questions, we
must engage our values and our visions of a desirable future.
The purpose of this exercise rests on the assumption that as a society, we
would rather live in a world that is equitable and peaceful and that can provide for our
material needs, rather than one defined by warfare, inequality, and scarcity. The
breakdown scenarios, Agribusiness Wins and Agricultural Deserts, depict a system
that becomes trapped in a maladaptive cycle and can no longer adapt to change. This
rigidity has real consequences: stress on an already vulnerable food system, decreasing
food access, food insecurity, and ultimately, social strife and warfare. It is reasonable
to expect any of these outcomes from a sudden and prolonged collapse in the food
system.
In contrast, the Just World and Localization scenarios present futures wherein
agriculture becomes sustainable. In the first scenario, agriculture disconnects from
non-renewable resources, averting the slow decline of a post-peak oil economy.
Political motives shift as well, taking power away from corporations and enhancing
community resilience. In the Localization scenario, collapse occurs, but localities and
regions adapt. The food system is stabilized in the r-K range, and does not continue
growing, consolidating, and optimizing. The outcomes of either scenario could be
considered a movement toward sustainable agriculture.
What makes one future pathway sustainable, and another unsustainable?
Perhaps this distinction seems intuitive or self-explanatory. What is it that is being
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sustained? In general terms, the sustainability of a system refers to its ability to
continue, or be maintained, indefinitely into the future. This chapter will examine what
makes a system capable of sustaining itself and what makes a system adaptive or
maladaptive. I will then discuss “traps” wherein systems become maladaptive and
opportunities for change and transformation, in the context of agricultural futures
sketched out in the previous chapter.
Adaptive vs. maladaptive systems
This discussion of agriculture is framed by Complex Adaptive Systems theory.
What makes complex adaptive systems special is their capacity to change, learn,
adapt, and self-organize; these systems are not static or linear, but are composed of
particular histories that enable and constrain possible futures. As a system is driven
through the adaptive cycle, these possibilities become more and more narrowly
defined. As the system moves into K, it becomes increasingly invested in
conservation, and less able to adapt to change. When a system is held in this
vulnerable state, it becomes maladaptive; instead of adapting to change, it becomes
increasingly invested in maintaining itself against external variability.
When a system begins investing in a narrow set of future possibilities and
internally regulating against external variability, its possible futures culminate into a
single outcome: collapse. These systems fall into an “incremental adaptation trap;”
(van Apeldoorn et al. 2011) small adaptations and optimizations make the system less
able to deal with change in the long term. Take for instance the use of synthetic
fertilizers. While they solve the problem of soil fertility on the short term, they create
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feedbacks (e.g. loss of natural fertility) that lock farmers into using them. Thus the
process of soil degradation continues to accelerate. If a time came when fertilizers
were not available, the system could not buffer the loss of synthetic fertility and would
not be capable of maintaining food production at previous levels.
This is but one example of the “system traps” that reinforce themselves,
preventing change and driving systems toward vulnerability. These traps reveal
feedback mechanisms in systems; a seemingly simple decision or policy measure may
set off a chain of reactions that is, to varying degrees, irreversible (Dupouey et al.
2002). Recognizing these traps, and maneuvering out of them, requires a systems
perspective that is often missing from policy and decision making at all levels.
Each of the maladaptive scenarios in the previous chapter – Policy Reform,
Agribusiness Wins, and Agricultural Deserts – were maladaptive, in part, because they
were trapped in these self-reinforcing cycles that push the system toward vulnerability.
In the Reference Scenario, the current state of agriculture, the system is already
“locked in” to some extent through incremental adaptation. The outcome of the system
in each subsequent scenario, whether it is maladaptive, or adaptive, depends on
whether or not these trap mechanisms accelerated or were disengaged.
System traps
Escalation
All three of the breakdown scenarios exhibit escalation, a feedback loop
governed by the rule “I’ll raise you one” (Meadows 2008). The nuclear arms race is
one such loop; if one country gets a weapon of mass destruction, another will, then the
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other will increase their stock, and the other will increase theirs, and so on (Meadows
2008). A simple rule leads rapidly to a dangerous situation. In the case of agriculture,
this feedback loop operates in terms of the system’s connectedness. External
variability – in the climate, markets, etc. – increases, and the system responds by
becoming more connected, by relying even more heavily on internal regulation in the
form of pesticides, fertilizers, irrigation, subsidies etc. This response is quite myopic;
instead of disengaging the loop and becoming more resilient in the face of variability,
it leads the system in the opposite direction: toward vulnerability. The feedback loop
cannot go on indefinitely. Eventually, the resources needed to buffer the system
against external variability will run out. This is results in collapse in all of the
scenarios.
Resilience traps: Success to the successful
If the system is managed to be held in a maladaptive state, it is in a resilience
trap. This state has all the characteristics of late K (high connectedness, high
concentration of wealth), but remains resilient because of the high inputs of resources
put into maintaining the system (Holling et al. 2002). How does a system get into this
state in the first place? Meadows (2008) identifies the trap that leads to this outcome
as “success to the successful.” She describes: “if the winners of a competition are
systematically rewarded with the means to win again, a reinforcing loop is created by
which, if it is allowed to proceed uninhibited, the winners eventually take all, while
the losers are eliminated.” This is the case in the Agribusiness Wins scenario; because
corporations have control over the policy that regulates them, they make a market that
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rewards them. Through the “revolving door” phenomenon – “when key industry
personnel seek employment in government regulatory entities and vice versa”
(Meghani &Kuzma 2010)- they have a disproportionate influence over policy
formulation and enforcement. The market becomes increasing consolidated into their
hands. Perverse subsidies distort the market and shift the costs of food production onto
the public, giving the corporations an economic advantage. Eventually, they gain
control of vast amount of society’s resources, and manipulate them to their gain.
Positive feedbacks reinforce this state. Holling et al. (2012) describe this trap in the
context of agriculture: “in agro-industry…command and control have squeezed out
diversity and power, politics, and profit have reinforced one another.” This is the
outcome in cases like Gallopin’s fortress scenario, as well as in dictatorships (Holling
et al. 2002).
Poverty Trap
When it finally collapses, the “remember” function of the system may prevent
reorganization, leading to a poverty trap (Holling et al. 2002). The effect that this
escalation had on natural resource stocks – liquidation – may make it impossible for
the system to reorganize. Overgrazed savannahs and failed states are examples of
these impoverished states (Holling et al. 2002). We saw this trap in Agricultural
Deserts, where resources were too scarce and degraded to support agriculture. This
poverty trap will continue until a new resource is discovered, or resources regenerate.
On meaningful human time scales, this impoverished state may be irreversible.
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Policy Traps
There are a number of traps that policy measures reinforce because policy
makers are unaware of the structure of the systems they are attempting to regulate.
Foremost among these is the trap of “seeking the wrong goal” (Meadows 2008). If
policy measures are meant to optimize one part of a system, e.g. agricultural yields or
GDP, they do so at the expense of the rest of the system (Meadows 2008). Meadows
(2008) warns that if we, as a society, define our goals as producing agricultural yield
or GDP, that is what the system will produce. We must consider if these goals reflect
the welfare of the system as a whole.
Another common policy trap is the drift to low performance (Meadows 2008).
This trap creates a positive feedback loop between decreasing system performance and
decreasing expectations for its performance. Thus policy measures are often
inadequate, and we settle for more pollution in the air and water than we would like,
or more corporate control over agriculture than we think is desirable.
Perhaps the most serious and common mistake that policy makers and
managers make is what Meadows (2008) calls “shifting the burden to the intervener.”
This trap arises when “a solution to a systemic problem reduces (or disguises) the
symptoms, but does nothing to solve the underlying problem.” These kinds of
interventions cause “the self-maintaining capacity of the system to atrophy or erode”
and set up a “destructive reinforcing feedback loop.” This trap occurs throughout the
agricultural system; the use of pesticides to treat the lack of biodiversity functions and
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the use of fertilizer to mask the problem of soil degradation are two examples. Both
mask the problem temporarily, but ultimately contribute to the problem.
All of these system traps make designing effective policy measures difficult.
More often than not, Meadows (2008) describes, policy attempted to move a system in
one direction ends up sending it in the opposite direction. This is because, as a society,
we do not understand how to work with and think in systems.
Redirecting a maladaptive system
These traps direct systems toward escalation and conservation. They operate
through feedback mechanisms that act like a chain reaction when triggered. Policy and
management actions are prone to backfire when the structure of a problem is not
considered systematically. Meadows (2008) explains that “leverage points,” places
where a system can be changed, are generally counter-intuitive and that policy
measures often push the system into an unintended direction. Being able to recognize,
understand and avoid these kinds of traps is fundamental to creating a system that can
maintain its self-regulating capacities, resilience, and sustainability.
The last two scenarios (Just World and Localization) present two possible
futures in which some measure of sustainability is achieved. In the Just World
scenario, the globe continues to become connected, but technology is harnessed for
social good. Locally appropriate renewable energy contributes to global equity. In the
Localization scenario, the food system is modular and localized; it does not accelerate
toward consolidation and growth. System thresholds are recognized and avoided. In
other words, this system is managed with an understanding of whole system dynamics.
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This systems level understanding is necessary for developing sustainable solutions to
the problems facing agriculture.
The structure of the problem
At this time, there is no reason to believe that a sustainable agriculture is
infeasible, or incapable of meeting global food needs (Chappell and LaValle 2011).
This illusion is produced by the history and politics of a particular industry which is
overinvested in the practices that such a belief would inevitably justify. A systems
analysis of agriculture illuminates this history, as it examines the patterns,
mechanisms, and ideas that produce it. In other words, a systems understanding of
agriculture can illuminate the structure of the problem (agricultural sustainability). A
systems perspective constructs the “web of causality” (Vandermeer and Perfecto 2005)
that produces the state of the agricultural system; it looks at the relationships and
interactions between parts of a system. Vandermeer and Perfecto (2005) explain: “it is
quite pointless to try to identify a single entity with the web of causality as the ‘true
cause.’ The true cause is the web itself.” If we can understand the nature of the
problem as a complex web of interactions, as a structural entity, we may be able to
“solve it” by manipulating its structure, by changing these relationships. The potential
to design a solution to the problem is fundamentally connected to our ability to
perceive the structure of the problem and to imagine and orchestrate its
transformation.
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Chapter 9: The synthesis of form
Deconstructing the problem
The problem of agricultural sustainability, like many problems in the modern
world, is inherently complex. This complexity frustrates attempts to model systems
and predict their behaviors and is a barrier to designing effective solutions. In a rapidly
globalizing world, human systems are likely to become even more complex (Ralston
& Wilson 2006). How are we, as the “managers” of these systems, to deal with this
complexity? How can we understand these systems analytically, as scientists, but also
prescriptively, as policy makers and designers?
The first part of this paper has demonstrated one way of understanding
agricultural systems scientifically through the construction of a theoretical model of
agriculture. This model utilizes knowledge from studies of complex adaptive systems
to draw out relevant variables and their relationships (e.g. patterns, processes, drivers)
at multiple scales of the agricultural landscape. The purpose of creating such a system,
like any model, is to reveal something about the structure and functioning of the
system and to be able to predict –to some degree– how it will behave and change.
The bulk of this paper has focused on how theories of complex adaptive
systems can be applied to, and help illuminate, the problem of industrial agriculture.
As Chapter 7 implied, this theoretical knowledge must be translated into practice if it
is to aid society in navigating the challenges ahead. Though it is not necessarily within
the scope of scientific inquiry to be prescriptive, this scientific understanding can be a
tool in approaching the issue of sustainable agriculture. In particular, it is critical that
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this knowledge informs the way in which we design and manage socio-ecological
systems.
Most designers and managers approaching the problem of sustainable
agriculture are not equipped with this theoretical framework, nor scientific and
systems-level knowledge. As Meadows (2008) suggests, this lack of awareness of
system structure, thresholds, and leverage points often leads to decisions that make the
problem worse. In order to create solutions to the problems facing agriculture,
designers must be equipped with a model of agriculture that illuminates its complex
nature. By understanding this context, the “web of causality” (Vandermeer & Perfecto
2005) surrounding agriculture, design may begin to effectively address the problem of
agricultural sustainability.
Understanding the context of design
Illuminating this complex web of interactions – the agricultural system – is a
serious undertaking; the first part of this paper has attempted to identify and connect
the most relevant parts of this system in order to provide a holographic understanding
of agricultural landscapes. The construction of such a system is not an endpoint, but an
instrument that can inform scientific inquiry (i.e. experimental work), as well as policy
formulation and decision making. Ultimately, the purpose of this paper is to use this
model to derive implications for design from Panarchy theory. As such, this final
chapter will explore potential connections between Panarchy theory and sustainable
design.
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My goal in creating this model is to reduce and simplify the agricultural
system; by identifying key variables at each scale, the system becomes organized into
categories of interaction (i.e. pattern, process, drivers), and is thus more manageable
that it would be without these distinctions. However, these categories are not
exhaustive; they are merely summaries or proxies of the diverse phenomena that
compose agricultural systems. As such, this system is an attempt to clarify and distill
the incredibly complex context in which agriculture is situated. It necessarily does this
incompletely.
The challenge of understanding and managing this complexity is central to the
process of design. How are we to design solutions to problems when we cannot model
phenomena completely? How can we create functional designs when we are not fully
informed of the context in which the solutions must function? Christopher Alexander
(1971), architect and theorist, describes this problem: “we wish to design clearly
conceived forms which are well adapted to some given context…the number of
variables has increased, the information confronting us is profuse and confusing…the
very thoughts we have, as we try to help ourselves, distort the problem and make it too
unclear to solve.” This increasing complexity and “cognitive burden actually make it
harder and harder for the real causal structure of the problem to express itself in this
process [design]” (1971). Understanding the structure of the problem, then, seems to
be fundamental to the process of effective design.
Without an understanding of the structure of the problem we seek to resolve,
design often fails to meet its goals. Designing any kind of solution without an
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awareness of thresholds, intra-system relationships, and the overall response a system
will have to a proposed change, is likely to fail, or even worsen the problem it sought
to solve (Meadows 2008). In the previous two chapters, I have stressed the importance
of understanding system dynamics in order to pose solutions to complex problems. In
Chapter 7, the future of the agricultural system rested on what actions were taken, and
how these actions were connected to system variables and to the thresholds, traps, and
leverage points described in Chapter 8. While I have spent considerable attention
describing these traps and the maladaptive actions that lead a system into them, I have
not addressed a systematic way to avoid them, or to design adaptive solutions. In this
chapter, drawing heavily on the design processes outlined by Christopher Alexander, I
will attempt to address these questions as they relate to sustainable agriculture. The
remainder of this chapter will walk through the design process, foremost as it is
described in Alexander’s Notes on the Synthesis of Form (1971). Through this design
process, I aim to illuminate and clarify the applications of Panarchy and system
thinking to the design of sustainable agricultural systems.
The process of design
For Alexander (1971), the fundamental goal of design is to create forms that
“fit” with their contexts; Alexander describes: “any state of affairs in the ensemble
which derives from the interaction between form and context, and causes stress in the
ensemble is a misfit.” Stress in this case is defined rather loosely as a “state of affairs
that is somehow detrimental to the unity and well-being of the system” (1971). At the
interface of form and context are “requirements” for design (fig. 25); the ability of a
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design to address these requirements determines whether or not the form is a “fit” or a
“misfit” with its context. In this way, design is structured around these requirements
(the form-context boundary) which indirectly structure form; form is not created
directly, but is arises out of the requirements of design.
Fig. 25: Requirements occur at the form context boundary. These examples show how
fit and misfit occur at this boundary, at the interface of form and context.
This distinction, though it may seem subtle, it very important in understanding
Alexander’s method. This is a distinction between two rather different approaches to
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design: (1) designing a form “from scratch” and (2) using the context of design (i.e the
environment in which the form must function) to define the form the design will take.
He reasons that no designer could create a form that would fit its context by “luck” or
“coincidence,” but that an effective design must be methodically built from a set of
requirements. The requirements are areas of potential misfit in the design. They can be
quantitative (e.g. the need for low material cost in the construction of affordable
housing) or qualitative (e.g. the need for comfortable living spaces within those
houses) (Alexander 1971). In both of these cases, a high capital cost, or lack of
comfortable living spaces, makes the design unsuccessful, or a misfit. In a sense,
requirements can be understood as goals of a design in a sense. These goals (e.g. low
cost, comfortable living spaces) are sub-requirements of the fundamental requirement
of design: “goodness of fit,” (Alexander 1971) or the lack of misfit in a design. So
these requirements are not goals in the sense that they are chosen as desirable
outcomes per se, but are required for the functionality of a design. As such “low cost”
and “comfortable living spaces” are sub-categories of the larger goal/requirement,
“goodness of fit.” In order to detect where misfit might occur, and to formulate
requirements that will prevent misfit, the designer must be aware of, to the fullest
extent possible, the structure of the problem and how it relates to the larger context
into which it must “fit.”
Of course, this is a large task for one mind. Given the complexity of the world,
Alexander admits that it is impossible to fully know and understand the context in
which a problem occurs. For instance, it is unrealistic, if not impossible, for one
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person to hold in mind all of the nuances and subtleties of the context of modern
industrial agriculture while attempting to design a “solution” to it. However, he also
suggests that there is a way to organize this information systematically, which can give
the designer a better handle on the information. By constructing systems that
represent the problem, the designer can synthesize new information about that
problem and its possible solutions. At the center of this method is the use of diagrams.
Diagrams
Diagramming is a major part of Alexander’s method. Alexander defines a
diagram as “any pattern, which, by being abstracted from a real situation, conveys the
physical influence of certain demands or forces” (1971) He draws a distinction
between form diagrams and requirement diagrams. Form diagrams present the
physical structure or “patterns of organization” of the object of study, whereas
requirement diagrams “summarize function or constraints” and serve “principally as a
notation for the problem, rather than for the form.” (1971) He provides the following
examples (as diagrams of a racecar) to illustrate these two categories:
Let us consider extreme examples of a requirement diagram and a form
diagram for a simple object. The mathematical statement F=kv2 expresses the
fact that under certain conditions, the energy lost by a moving object because
of friction depends on the square of its velocity. In the design of a racing car, it
is obviously important to reduce this effect as far as possible; and in this sense,
the mathematical statement is a requirement diagram. At the other extreme, a
water color perspective view of a racing car is also a diagram. It summarizes
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certain physical aspects of the car’s organization and is therefore, a legitimate
form diagram. (Alexander 1971)
He goes onto explain that neither of these two diagrams are particularly useful
in the search for form. A useful diagram must express information about both form and
requirement; it must express the structure of the problem, but also identify the
constraints or requirements of a solution.
Alexander calls such a useful diagram a constructive diagram. A constructive
diagram is “a bridge between requirements and form” (Alexander 1971). It is meant to
unite these properties into a single graphical representation. By doing so, it presents
the structure of the problem, the constraints of the solution, and thus, defines a form
graphically. The following diagram provides a simple example of a constructive
diagram (fig. 26).
Fig. 26: Creation of construction diagram for problem explained in the text.
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The design “problem” in this case is the flooding of a river. The diagram on the
left is a form diagram of the river under study. The diagram in the middle builds on
this form diagram and shows, where it is shaded, the areas where the river overflows,
and how far water moves up the bank. This diagram shows the hydrological processes
of the river, and thus composes a requirement of the problem. This requirement could
be expressed “flooding at various parts of the river must be managed,” “x m3 of flood
water must be absorbed,” etc. In other words, in order to design an effective solution,
this requirement must be identified and addressed. The diagram on the right shows the
solution to the flooding, a corridor of vegetation, shaded. Notice how the form of the
restorative corridor emerges directly out of this constructive diagram, out of the
overlay of requirement and form.
This rather straightforward example demonstrates how the constructive
diagram (the diagram in the middle) gives rise to the form that will be the solution to
the problem. Alexander provides another simple example. The problem he is
addressing in this example is traffic congestion at an intersection (1971). To diagram
this problem, he first draws the intersection and then overlays arrows of various
widths on each of the lanes which indicate the amount of traffic in each lane. Because
this diagram tells us something about form (i.e. current form of the road) and
something about the requirements of design (i.e traffic flow), it gives rise to a formal
solution; the lanes with more traffic flow, designated by their width, are the ones that
need to be literally widened. Thus the constructive diagram produces, through the
combination of form and requirement, the answer to the design problem; the process
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of overlaying requirements onto the form directly shows the form the solution is to
take (i.e. how the road should be widened).
Thus the constructive diagram is an important tool in the process of design. It
merges a formal description of the problem with the problem’s requirements, which
represent constraints of the design. In doing so, the diagram further illuminates the
context of design; it presents the specific requirements needed to achieve “goodness of
fit.” Most importantly, it gives rise to the form that will be the solution to the problem
of design. For simple problems, like the flooding or traffic examples, these diagrams
are the solution; the constructive diagram defines the form that solves the problem.
In more complex contexts, however, it is not so apparent how a diagram would
give rise to the solution. Take our problem of sustainable agriculture for example; the
context of this problem is extremely complex, operating over multiple spatial and
temporal scales. Furthermore, the problem of “agricultural sustainability” is a different
kind of problem than “traffic congestion” or “flooding.” This problem is not explicitly
spatial; the problem of “agricultural sustainability” has different kinds of requirements
than one would encounter in the design of an intersection or a riverbank, some of
which are rather abstract. It is difficult to imagine how one would diagram these
abstractions spatially -and how form could arise from them - while maintaining
analytical rigor and clarity.
However, I do not think that the complexity of sustainable agriculture excludes
it from the design process as described by Alexander. In fact, I think that his method
neatly compliments the approach to agricultural sustainability that has been developed
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thus far in this paper. In order to illustrate how his method contributes to the
discussion of complex adaptive systems and agriculture, it is necessary to further
investigate the concept of the constructive diagram, and to revisit the structure of the
adaptive cycle and panarchies, as described in chapter 2.
The constructive diagram as a hypothesis
For Alexander, the constructive diagram is not just a static representation or
notation of a problem; it actually contributes to the designer’s knowledge of a
problem. He writes: “the designer never really understands the context fully. He may
know, piece-meal, what the context demands of the form. But he does not see the
context as a single pattern – a unitary field of forces. If he is a good designer the form
he invents will penetrate the problem so deeply that it not only solves it, but
illuminates it” (1971). What does it mean to illuminate a problem? It means that
design may solve a problem in a radically new way that spurs on a whole new way of
thinking about that problem; such a design is “based on a hunch which actually makes
it easier to understand the problem” (1971). In this sense, Alexander sees designs as
hypotheses. He writes:
Each constructive diagram is a tentative assumption about the nature of the
context. Like a hypothesis, it relates an unclear set of forces to one another
conceptually; like a hypothesis, it is usually improved by clarity and economy
of notation. Like a hypothesis, it cannot be obtained by deductive methods, but
only by abstraction and invention. (Alexander 1971)
This description calls to mind the “hypothesis” that forms the foundation of
this paper: that the adaptive cycle and Panarchy can be used to describe agriculture in
a meaningful way. But these theoretical constructs are hypotheses themselves;
panarchy is a “hunch” about how systems work that developed out of empirical studies
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of socio-ecological systems. Applying panarchy to socio-ecological systems is a
process of testing this hypothesis. Upon applying this framework to socio-ecological
systems, these systems are seen in a radically new way. So in the broadest sense, the
adaptive cycle (and the panarchy which it composes) reflects the qualities of a
constructive diagram. However, in order to validate this connection, it is necessary to
examine the adaptive cycle as both a form and requirement diagram.
The adaptive cycle as a constructive diagram
The adaptive cycle forms the centerpiece of the Panarchy model. Recall that a
panarchy is constructed of adaptive cycles operating in nested spatial and temporal
scales. In this sense, a panarchy is a formal description of a system. This formal
description - that systems are composed of hierarchical, nested scales- can be
expressed graphically in a number of ways. The following diagrams, featured in
chapters three and six, describe this structure.
Fig. 8: The nestedness of agricultural landscapes.
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Fig. 20: Hierarchy of scales in an agricultural panarchy. Lower levels exert bottom up
effects on higher levels because they compose those higher levels. Higher levels exert
influence through top-down effects in the form of drivers.
These diagrams, as formal notations of the system (i.e. form diagrams), do not
say much about system processes, or the requirements for design. It is not readily
apparent, from either of these diagrams, what a design for a sustainable agriculture
should look like, or what elements it should include. In short, these diagrams are not
constructive, because they say nothing about requirements for design; they only
present the hierarchical structure of the problem.
In order to derive a form from these diagrams, it is necessary that they
incorporate the requirements of design, i.e. the constraints to the solution. This set of
requirements, which Alexander refers to as “the program,” will structure the solution
by giving rise to some kind of form, just as the constructive diagrams of the river and
intersection did. However, this form, because of the complex context into which it
must “fit,” may not be as well defined as the solutions to the flooding and traffic
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congestion; because a panarchy is an abstract representation of socio-ecological
systems, the “form” will likely not be explicitly spatial, but exist in an abstracted
physical space. In order to understand how the adaptive cycle may serve as a
constructive diagram, it is first necessary to identify the requirements of “sustainable
agriculture” and map them onto the structure of the problem as described by these
hierarchical form diagrams.
Requirements of “Sustainable Agriculture”
In order to get at the requirements of a context or problem, Alexander proposes
a method for design that is built upon an analytical “decomposition” of the problem at
hand; he calls this decomposition “the program” (Alexander 1971) The program is a
graphical representation of the problem and the requirements that compose it. A
designer comes to understand the program by creating a system of analytical
categories and dividing a problem into smaller and smaller, hierarchically nested, subcategories.
In a simple example, Alexander (1971) describes the process of decomposing
the requirements of a tea kettle. While the design of a tea kettle may seem rather
simple, there are a number of requirements for a well-designed tea kettle that must be
considered. Alexander divides these requirements into two major categories,
“function” and “economics.” He then divides “function” into the sub-categories of
“production,” “safety,” and “use,” and divides “economics” into “capital” and
“maintenance.” Under these sub-categories, he organizes 21 specific requirements of a
tea kettle, e.g. “must be able to withstand the temperature of boiling water,” “it must
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not be unstable on the stove when it is boiling,” “the material it is made of must not
cost too much,” etc. These specific requirements fall under the sub-categories of
function > use, function >safety, and economics > capital, respectively. This
organization of requirements gives rise to a simple tree diagram, which is a
requirement diagram (fig. 27).
Fig. 27: Analytical decomposition of the problem into requirements; adapted from
Alexander (1971).
This simple process can be extrapolated to more complex design problems. In
the case our problem, “sustainable agriculture,” I derived three categories of
requirements – pattern, process, and drivers – that occurred at three hierarchically
nested scales: the patch, the site and the landscape. Under these categories of
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requirements, we must design solutions that meet the overarching requirement of the
problem (“goodness of fit”); that is to say that patterns, processes, and drivers must be
designed in such a way that they are sustainable in order to produce a sustainable
agricultural landscape as a whole.
Next, we must develop sub-requirements for these categories. What is required
of patterns, processes, and drivers, such that they are sustainable? Here it is important
to remember the scenarios from Chapter 7. Ultimately, a sustainable agriculture cannot
go down the same path as the current system; it cannot enter into the K phase and be
held in a state of vulnerability. One of the sustainable futures, the Localization
scenario, situated a sustainable agriculture and food system perpetually between the r
and K phases. Because we are interested in designing a system that is maintained in
this phase, it is necessary to pay attention to the variables that produce the phases of
the adaptive cycle. That is to say that the requirements of design at any scale are
defined by the ecological constraints described by the system variables – wealth,
connectedness, and resilience – of the adaptive cycle.
These variables can be considered requirements of the problem “sustainable
agriculture” because they represent system processes that lead the system toward
particular outcomes. They also possess thresholds that when crossed, eliminate the
possibility of a sustainable agriculture. For instance, when wealth becomes
concentrated in a few large corporations, policies that would support sustainable
agriculture are undermined in favor of policies that support industrial practices, due to
the “success to the successful” feedback loop (Meadows 2008). Increasing reliance on
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internal controls (i.e. increasing connectedness) undermines the system’s ability to
sustain itself. If a system’s resilience is lowered to a critical point, the system may
collapse altogether. In short, these variables express ecological constraints to which
agro-ecosystems are subject.
The requirements needed to keep the system between the r and K phases can
be defined generally as (1) “the system cannot tend toward the concentration of
wealth characteristic of the K-phase,” (or, stated positively, “the system must maintain
and circulate wealth), (2) “the system must maintain a level of internal regulation (i.e.
a certain degree of connectedness) that maintains its ability to adapt to external
variability” and (3) “the system must retain its resilience and redundancy.” These
general requirements can be further decomposed into specific requirements at each
scale. For instance, at the scale of the patch, the requirement “self-regulating nutrient
cycles” falls under the requirement of connectedness, because the ability of nutrient
cycles to self-regulate is connected to the internal stability of the system. Maintaining
this ecological service avoids the “chemical treadmill” (Vandermeer 2011) that makes
agro-ecosystems overly connected. Likewise, “adequate SOM” is a requirement of
system wealth; degradation of soil can send the system into a poverty trap. “Functional
redundancy in ecological communities” is a requirement of system robustness, or
resilience. The following diagram presents the structure of this tree of requirements at
any one scale.
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Fig. 28: Decomposition of “sustainable agriculture” into categories of requirements:
pattern, process, and drivers; and wealth (W), connectedness (C), and resilience (R).
Notice that the category of “drivers” is above those of “pattern” and “process.”
This is because drivers occur at the scale “above” the one they affect. Thus patterns
and processes at the scale of the patch are constrained by the drivers at the next scale,
namely, agricultural management. These drivers are subordinate to patterns and
processes at the scale in which they occur (e.g. the driver “agricultural management”
is a product of plant communities (pattern) and the annual cycle (process) at the scale
of the site).
Given this relationship, we can arrange the requirement diagrams at each scale
hierarchically (Fig. 28). Under each sub-system of the requirements of wealth,
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connectedness, and resilience, (W, C, R), specific requirements can be derived. Some
examples of these requirements are provided in table 2.
Scale of Analysis
Patch
Pattern
Process
Drivers
Site
Pattern
Process
Drivers
Landscape
Patterns
Process
Drivers
Wealth
Connectedness
Resilience
"living soil"
SOM accumulation
cover crops, organic inputs
complex food web
self-regulating nutrient cycles
rotation
fungal food web
functional redundancy
adaptive management
farming communities
resource self-sufficiency
"beyond yield" subsidies
polyculture
integrated pest management
modularity
distributed risk
localization
land-use mosaic
undisturbed habitats
conservation
equity
corridors
"natural capacity planning"
anti-monopoly policy
clumped distribution
adaptation
sustainability paradigm
Table 2: Examples of possible requirements for the problem “sustainable agriculture.”
Synthesis
This requirement diagram serves to “probe the context” (Alexander 1971) of
the problem of sustainable agriculture. It illuminates the constraints of design (in this
case, ecological constraints), from which we can derive specific requirements of
design. When organized into a hierarchy, the structure of the problem (i.e. sustainable
agriculture), which is composed of discrete scales, is made apparent (fig. 29). This
combined diagram demonstrates the structure of the problem, as a nested any
hierarchical system, and also provides information about the constraints in each “subsystem,” i.e. the system variables of wealth, connectedness and resilience.
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Fig. 29: Requirement diagram and form diagram juxtaposed.
Fig. 30: A panarchy as a constructive diagram. Note that it expresses the requirements
for design (wealth, connectedness, and resilience define the phases) and the
hierarchical nature of the problem, making it both a requirement and form diagram,
i.e. a constructive diagram. Sourced from Holling et al. (2002).
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The content of these two diagrams can be merged and expressed as a set of
adaptive cycles in a panarchy. A panarchy captures the hierarchical, nested nature of
these systems, as well as the ecological constraints which form the “phase space” of
the adaptive cycle (fig.30). It also captures the interaction of the system across scale.
In summary, if we want an agricultural system that remains between the r and
K phases, there is a certain set of relationships that must be maintained between these
variables of the system. This particular set of relationships forms the constraints, or
requirements, of design. These requirements are structured hierarchically in a
panarchy. Thus the panarchy diagram is both a form and a requirement diagram; it is a
constructive diagram. As such, we should be able to derive implications for design
from Panarchy theory.
Form
What are these implications, and how are they derived from the panarchy
diagram? In Alexander’s concept of the constructive diagram, the diagram directly
gives rise to form, as in the example of the flooding of the river, or the congestion of
the intersection. However, no clear design for agriculture emerges from this diagram.
This is because the diagram is not explicitly spatial; it is an abstraction of space. The
hierarchy is composed of landscapes, which represent physical realities, but also
conceptualizations. The spatial description of a panarchy (i.e. its shape) is derived
from the interaction of the three system variables- wealth, connectedness, and
resilience- plotted on three axes (refer to fig. 3). The state of the system, as defined in
this “phase space” is dependent on the interaction of these variables. So in an abstract
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sense, the system is defined spatially by the interactions of these variables because the
state, or “form,” of the system is constrained by them.
Fig. 3: The adaptive cycle relates the state of three system variables: wealth (or
capacity), connectedness, and resilience. Sourced from ecologyandsociety.org
Thus, form does arise from this diagram. It is not the kind of “first-order” form
that comes out of the intersection or river examples. Instead, the adaptive cycle
provides information about the interface, or relationship, between the form (a design)
and its context (ecological constraints). In other words, in the design of a sustainable
agriculture, it is clear (if we accept the premises of Panarchy) that any form we create
must maintain a certain relationship with system wealth, connectedness, and
resilience. Furthermore, these constraints must be managed in a hierarchical structure.
Alexander (1971) explains:
The hierarchical composition of these diagrams will then lead to a physical
object whose structural hierarchy is the exact counterpart of the functional
hierarchy established during the analysis of the problem; as the program
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clarifies the component sources of the form’s structure, so its realization in
parallel, will actually begin to define the form’s physical components and their
hierarchical organization.
In this sense, the adaptive cycle, as a diagram, does illuminate form; it presents
a system of relationships that must define a sustainable agriculture. Holling et al.
(2002) note that “sustainability is maintained by relationships among a nested set of
adaptive cycles arranged as a dynamic hierarchy in space and time – the panarchy.”
This concept of sustainability makes the connections between sustainability and
“goodness of fit” explicit: both are concerned with how the components of a
hierarchical structure fit/are adaptive or misfit/are maladaptive. In this way,
sustainability and “goodness of fit” can be seen as the same goal: the avoidance of
stress in a system. From this systemic or abstract form -this hierarchy of relationships we can derive the actual, physical forms that agriculture is to take.
(Re)designing agriculture
Perhaps it is rather obvious that agricultural systems are subject to the
constraints posed by the laws of ecology. However, the design of modern industrial
agriculture seems to ignore or deny these constraints. A steady stream of inputs
(wealth) support food production on lands that are becoming increasingly degraded
and unproductive. More and more energy is put into regulating these systems
(connectedness), which are becoming increasingly vulnerable to external variability
and the ecological constraints variability exposes. Additionally, agro-ecosystems are
becoming less diverse; the direct and indirect simplification of species eliminates the
redundancy in the system, making it less adaptable (resilience). In all, these agro-
Kinkaid 166
ecosystems resemble more technospheres than biospheres (Naveh 2005). But that does
not remove the constraints posed by ecology.
The modern industrial agro-ecosystem is arranged around one organizing
principle: maximizing yield. In this scheme, technological developments seem to be
the only constraint. The failure of industrial agriculture to acknowledge ecological
constraints is most certainly the cause of “stress” within the system, and what
Alexander would call its “misfit” with the goals of a sustainable agriculture. He warns
that “design is not an optimization problem” (Alexander 1971). So it may be that
industrial agro-ecosystems are not “designed” at all, at least not in the sense that
Alexander uses the word.
In order to truly “design” (in Alexander’s usage) agricultural ecosystems, the
goal must be shifted away from optimization toward Alexander’s concept of
“goodness of fit.” This “goodness of fit” is achieved when the solution or object of
design is not in conflict with its context; the goal of design is creating a system that is
not “stressed” by its environment. Within the frame of Complex Adaptive Systems
theory, this means that wealth, connectedness, and resilience cannot drive the system
into the K-phase, where it becomes increasingly stressed by external variability and
vulnerable to disturbance. Such a system would have to be dynamically balanced
between the r and K phases in order to avoid late K, where the system accelerates
toward vulnerability.
As the previous section has demonstrated, the adaptive cycle provides a
framework for this type of design. The structure and specific requirements of
Kinkaid 167
sustainable agricultural design outlined above can be used as criteria for evaluating
possible physical designs for agro-ecosystems. Some of these requirements translate
rather easily into physical designs (e.g. polyculture, corridors, land use mosaic), while
others do not readily suggest a physical form (e.g. “living soil,” adaptive management,
conservation, equity). In Alexander’s terms, some properties of a system seem
“patternlike” (the first set of elements, which give rise to more clear physical
components), while others are “piecelike” (the second set of elements, which do not
provide such clear instruction) (1971). However, he argues that this is an artificial
division and that all components of a system are at once patterns and pieces, or units,
and that they are above all else, part of a structure of components. It is this hierarchical
structure of components that gives rise to form.
Creating physical form
This discussion of agricultural design has been largely theoretical. I have not
aimed to define what an agricultural landscape might look like per se, but instead, to
identify constraints, and develop a set of criteria for sustainable agricultural
landscapes. This is because a set of clear, systematic, and analytically derived criteria
seem to be missing from most discussions of sustainable agriculture. Running parallel
to this discussion are other fields of design, including Permaculture (Holmgren 2002)
and Regenerative Design (“Regenerative Development”), that attempt to articulate a
similar founding principle: that agriculture and design should be approached
systematically and ecologically. Neither of these design philosophies is firmly
Kinkaid 168
grounded in a scientific discipline, and, at this point in their development, remain open
to personal interpretation and practice.
This is not to say that these design philosophies do not play a valuable role in
this discussion; they play a vital one. While Alexander’s approach provides criteria for
design and an abstract form from which design can be derived, it does not provide
physical forms per se. Systems of design like Permaculture are composed of specific
practices that contribute to agricultural sustainability. While one system lacks physical
form, the other has physical form but lacks an organizational structure. As such, the
approach laid out in this paper (a combination of Complex Adaptive Systems theory
and Alexander’s design methods) and the practices and strategies described by
Permaculture, and to some extent, Regenerative Design, nicely complement one
another. In fact, the most applied aspect of the analytical decomposition of agriculture,
the table of specific requirements, contain elements of Permaculture; discussions of
Permaculture are focused around living soils, polycultures, self-sufficiency, integrated
pest management, and other physical aspects of agriculture, but also address more
abstract “invisible structures” like equity and monetary systems (Holmgren 2002). As
such, Permaculture may be helpful in fleshing out the physical design of sustainable
agricultural landscapes where Alexander’s method leaves off.
Ultimately, the usefulness of Alexander’s method is to illuminate and organize
the requirements of sustainable design. Without clearly defined requirements, there is
no way to move forward with the design of sustainable agricultural landscapes.
Holding Alexander’s framework in mind, as well as the insights of Panarchy theory,
Kinkaid 169
the designer is better equipped to address the myriad challenges of sustainable design.
Within this framework, various practices and strategies, like those presented in
Permaculture, can be understood as contributing to a single form and an overarching
goal: “goodness” of fit,” or sustainability.
Kinkaid 170
Conclusion: Design, intuition, and logic
The logic of design
The process of analyzing sustainable agriculture that is employed in this paper
has resembled, in many ways, Alexander’s theory of design. It has also drawn heavily
from scientific studies of complex adaptive systems and ecology. Central to both of
these approaches is the interplay of structure and function, pattern and process. I have
attempted to organize a theoretical scaffolding around these concepts, particularly as
they relate to agricultural landscapes.
With this theoretical model in place, I have demonstrated what it can tell us
about agricultural sustainability. Through the construction of various future scenarios
(Chapter 7), I manipulated the “inputs” to the model (e.g. trends, events, surprises),
and described their likely outputs, given recognized relationships between its
variables. In Chapter 8, I looked further into the structural elements and mechanisms
driving these systems into the future. In the final chapter, I aimed to demonstrate how
the systems understanding of agriculture developed in this paper might influence how
we design agricultural ecosystems.
Though these different aspects of my argument have been grounded in
academic literatures and empirical studies, the thesis remains quite theoretical.
Perhaps it is not clear why the topic of sustainable agriculture design must be engaged
in this abstract sense. After all, “if theory cannot be expected to invent form, how it is
likely to be useful for a designer?” (Alexander 1971).
Kinkaid 171
As chapter 9 concluded, theory does not invent form; rather, it provides a
structure, a set of constraints, for form. How does it do this? In the case of Panarchy
theory, this structure is expressed by the adaptive cycle; the adaptive cycle unites the
“relevant” aspects of systems into one “pattern or a unitary field of forces.”
(Alexander 1971) It does so by connecting system processes and patterns to the system
variables of wealth, connectedness and resilience. In doing so, specific system
processes are abstracted into general principles of how systems behave and change.
This level of abstraction views particular systems in a larger resolution so that they
can be understood through the logic of systems generally, rather than as specific kinds
of systems (e.g. temperate grassland ecosystem, capitalist economy).
When we attempt to recreate a natural system in terms of logic – in a
theoretical manner – we attempt to reproduce these relationships. In doing so, we can
predict the outcomes – based on systems “rules” we derive – of manipulations or
changes in the system. By recreating this system of relations, the designer reconstructs
the internal “logic” of the system. This logic can then operate independently from the
actual system. Thus theory replaces an endless supply of disconnected empirical
observation with general principles which have predictive power. Alexander (1971)
writes:
While it is true that a great deal of what is generally understood to be logic is
concerned with deduction, logic, in its widest sense, refers to something far
more general. It is concerned with the form of abstract structures, and is
involved the moment we make pictures of reality and then seek to manipulate
these pictures so that we may look further into the reality itself. It is the
business of logic to invent purely artificial structures of elements and relations.
Sometimes one of these structures is close enough to a real situation to be
Kinkaid 172
allowed to represent it. And then, because the logic is so tightly drawn, we gain
insight into the reality which was previously withheld from us.
In other words, theoretical models are attempts to recreate the logic of a system in
order to predict its outcomes. If this model is “a good one,” it can actually tell us more
than we knew when we made the model. It can expose new areas of inquiry,
unforeseen consequences, and unrecognized relationships. Just as a constructive
diagram illuminated the context of a problem in a novel way, a systems perspective
can provide a new context to a problem, enabling more comprehensive and holistic
solutions.
In this sense, design is by no means intuitive; if design is to be effective, it
must be analytical, at least initially. Alexander’s method demonstrates this; the
deductive and analytical phase of design sets the constraints for creative solutions to
the problem of interest. Then, these solutions must be evaluated in the terms of the
system constructed as part of the design process. Only then can a solution be designed
that addresses the problem as a whole system.
Connecting science and design
Though it may not appear so on the surface, science has much to offer design.
Alexander comments: “Scientists try to identify the components of existing structure.
Designers try to shape the components of new structures. The search for the right
components, and the right way to build the form up from these components, is the
greatest physical challenge faced by the designer” (Alexander 1971). Thus the
foundations that scientific work provides are indispensible to the synthesis of design
solutions.
Kinkaid 173
In the search for solutions to the problems of agriculture, we must engage both
science and design. As scientists, we can study agricultural ecosystems, their parts,
and dynamics. As designers, we can synthesize this knowledge into the form that
resolves the problems of modern industrial agriculture. The quest for sustainability is
one that touches all disciplines of knowledge. Without the synthesis of these
disciplines – without understanding the full context of the problem – it is unlikely that
a solution will be found.
Confronting an uncertain future
The complex challenges facing agriculture will certainly demand vigilant
action in the coming decade if we are to achieve, as a globe, a sustainable, equitable,
and stable food system. This action must take place on the ground, through the design
and creation of sustainable agricultural landscapes, as much as it must occur in the
minds of agricultural practitioners, academics, and laypeople alike. As agriculturalists,
we must critically consider the direction in which we are headed and reevaluate our
goals. As academics, we must engage the incredibly important questions that lie
outside of traditional disciplinary boundaries. In general, we must learn to think in
systems – to understand how our actions contribute to something beyond ourselves, to
think ecologically – in order to grasp and address the complex problems that threaten
our very survival. Only then might it be possible to understand the fullest meaning of,
and work toward, sustainability.
Kinkaid 174
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Acknowledgements
I would like to acknowledge my thesis advisor, Dr. Ted Bernard, for his significant
intellectual contributions and guidance throughout the process of this project. I would
also like to thank Dr. Jared DeForest for serving as my tutor and assisting with the
research that went into Chapter 4. Thank you to Sarah Minkin for editing my work
throughout the process. And finally, thanks to Dr. Harvey Ballard, and the Honors
Tutorial College for their unconditional support and enthusiasm.
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Appendix 1: Cropsyst decomposition calculation (Stockle & Nelson 1998b).
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