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Home , Industrial agriculture, Landscape ecology, Panarchy, Soil resilience

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The architecture of : Systems for sustainable agricultural

A thesis presented to the Honors Tutorial College, Ohio

In partial fulfillment of the requirements for graduation from the Honors Tutorial College with the degree of Bachelor of Science in Environmental and .

Eden Kinkaid 2013

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Contents Introduction: The transformation of ...... 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- ...... 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 as a ...... 41 as indicator of ...... 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 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 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 scenarios ...... 117 The Reference Scenario ...... 120 The Wins Scenario ...... 126 The Agricultural Deserts Scenario ...... 127 The Just World Scenario ...... 129 Kinkaid 3

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 “” ...... 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 ...... 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 exerting top-down effects...... 49 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: of corn near Ames, Iowa...... 65 Satellite image 3: A feedlot in Northern Texas...... 66 Fig. 18: Annual cropping cycle as an adaptive cycle...... 67 Figure 19: Cropping systems along a gradient of ...... 71 Photograph 1: Pepper vines growing up coconut trees in Southern India is one example of intercropping...... 72 Photograph 2: garden in Southern India...... 77 Figure 20: Hierarchical relationship of landscape and site scales, with the driver of socio- economic structures (e.g. subsidies) exerting top-down effects...... 79 Fig: 21: 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 diagram for problem explained in the text...... 150 Fig. 27: Analytical 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 Kinkaid 5

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 processing reliant on fossil fuels, inputs, synthetic nutrients, and increasingly complex . 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 (Foster 1999). The revolves around the goal of eliminating the constraints posed by ecology. First, 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 , but organisms themselves. With this third , 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 change. As we attempt to engineer around the constraints of agriculture - the presence of pests and disease, , the loss of ecosystem services to name a few - we accelerate agricultural systems toward these very Kinkaid 6 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 – , rising , 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 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 of agriculture, to talk about agricultural ecology in the context of market forces or trade liberalization (Fraser 2006), or to assess Kinkaid 7 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 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 Kinkaid 8 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, , 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 of an agroecosystem. This optimization has led to increased production, but this production is supported by increasing amounts of non-renewable fuels, , and . As this technological intervention becomes more and more necessary, the natural capacity of ecosystems to support Kinkaid 11 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 .

If pesticides became suddenly unavailable for whatever reason, or stopped working, we can no longer rely on the ecosystem 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 , 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 of wild crop relatives, agricultural practice has increasingly tended toward greater human control over plants and their environment. Large leaps Kinkaid 12 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 and the growing centrality of market relations; technical changes included improved techniques of , manuring, drainage and management. In contrast, the second agricultural revolution occurred over a shorter period (1830-80) and was characterized by the growth of a industry and revolution in ...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 ) and the more intensive use of chemical inputs – such as and pesticides.

It is this increased use of – 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 expanded to fill military requirements and this produced byproducts that would be used advantageously in chemical fertilizers.” For example, Agent Orange, a defoliant used in the Vietnam

War has since been reconfigured into Monsanto's Roundup, an 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, , inorganic fertilizers, and genetically engineered varieties, Kinkaid 13 into tropical and developing nations (Vandermeer 2011). While production of and did rise, the sustainability of the benefits of the has come under much scrutiny. This “progress” has come at a high energetic cost; a 250% increase in global production was made possible by fifty times the 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 , the availability of and fresh , “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 for managing agriculture in the face of climatic change and uncertainty. The Food and Agriculture Organization (FAO) of the United Nations (UN) has identified (referred to as plant genetic 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 Kinkaid 14

"land clearing, 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 " (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 worldwide have left their multiple local varieties and landraces for genetically uniform, high- yielding 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 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 . Kinkaid 15

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 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 may be a way to increase corn production in the future (Kucharik & Ramankutty 2005). The freshwater needed for is diverted from rivers, , and other sources of . 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 .” 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 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 & 2006). In the future, we can only expect greater strain on these resources, as the demand for food grows and urban areas rapidly expand. Kinkaid 16

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 , and also affects human health, as nearly a hundred different pesticides have been found in 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 . A small number of large, transnational are gaining increasing control over the global agricultural system through , 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, , 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 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” Kinkaid 17

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 ) (“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 , 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 of some 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). Kinkaid 18

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, , and corn yields could drop by 10 percent” (Foster 2009). The impacts of climate change on cereal crops, which are staple 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. , 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 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 Kinkaid 19 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 management. These cascading changes are what have the potential to radically redefine modern .

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, Kinkaid 20 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 socio- ecological 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. Kinkaid 22

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 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 Kinkaid 23 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. Kinkaid 24

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 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 variables- system 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. Kinkaid 25

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 Kinkaid 26 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 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 , 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. Kinkaid 27

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 and restore a plant . For many years Kinkaid 28 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, 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 self- organization. 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 Kinkaid 29 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 Kinkaid 30 heat effect becomes stronger, which further accelerates melt, and so on, until the 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 non- linear 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 Kinkaid 31

Resilience Alliance / Santa Fe Institute database”); the eutrophication of lakes (Carpenter et al.1999) and the salinization of 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 Kinkaid 32 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 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. Kinkaid 33

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.

Kinkaid 34

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. Kinkaid 35

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 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 Kinkaid 36 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 or pasture) is produced by the interaction between patterns (e.g. and of ) and processes (e.g. , 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 Kinkaid 37 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 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 Kinkaid 38 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 land- use decisions, which are guided by higher-level processes, e.g., capitalism and the socio- economic and institutional climate of a region (Lambin et al. 2001) Kinkaid 39

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.

Kinkaid 40

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.

Kinkaid 41

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 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). Kinkaid 42

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 Kinkaid 43 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 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 , and soil aggregate formation and stabilization (Brady and Weil 2004). In addition, the cycling of (in the form of SOM) is coupled with the cycling of soil . 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.). Kinkaid 44

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 , 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). Kinkaid 45

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. ) 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. Kinkaid 46

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 Kinkaid 47 al. 2002). Favorable soil structure is necessary for agriculture as soil structure is connected to a number of processes important to agriculture including decomposition, “ movement and retention, erosion, crusting, nutrient , root penetration, and crop yield” as well as

“ such as runoff, surface and ground water 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 particles with organic and inorganic substances.” Aggregation occurs when particles form ionic bonds with one another, 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, 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. Kinkaid 48

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 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 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 , 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 Kinkaid 49 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 Kinkaid 50

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 composed of 40% , 45% loam, and 15% with a bulk density of 1.44 g/cm3 (bulk density was calculated by the model from the ).

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Treatment 1

Year Rotation Amendments Tillage

2000-2100 Corn 128 KgN/ha anhydrous ammonium injected pre-planting dust mulching to 10 cm Corn 128 KgN/ha anhydrous ammonium injected pre-planting dust mulching to 10 cm Soy none none

Treatment 2 Year Rotation Amendments Tillage 2000-2050 Corn 128 KgN/ha anhydrous ammonium injected pre-planting dust mulching to 10 cm Corn 128 KgN/ha anhydrous ammonium injected pre-planting dust mulching to 10 cm Soy none none 2050-2100 Corn 16.125 short tons dry beef (3.5% N)/ha none Corn 16.125 short tons dry beef manure (3.5% N)/ha none Soy 16.125 short tons dry beef manure (3.5% N)/ha none

Treatment 3 Year Rotation Amendments Tillage 2000-2050 Corn 128 KgN/ha anhydrous ammonium injected pre-planting dust mulching to 10 cm Corn 128 KgN/ha anhydrous ammonium injected pre-planting dust mulching to 10 cm Soy none none 2050-2100 Corn 32.250 short tons dry beef manure (3.5% N)/ha none Corn 32.250 short tons dry beef manure (3.5% N)/ha none Soy 32.250 short tons dry beef manure (3.5% N)/ha none

Treatment 4 Year Rotation Amendments Tillage 2000-2100 Corn 32.250 short tons dry beef manure (3.5% N)/ha none Corn 32.250 short tons dry beef manure (3.5% N)/ha none Soy 32.250 short tons dry beef manure (3.5% N)/ha none 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.

Kinkaid 56

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 is more sustainable in terms of its effects on SOM. From the results of these Kinkaid 57

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 under the plow layer and by changing the shape Kinkaid 58

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, , 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 .

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, Kinkaid 59

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 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- to bacterial- dominance (de Vries & Bardgett 2012). In undisturbed ecosystems, like , 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 bacterial- dominance (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. Kinkaid 60

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 bacterial- dominant communities (Thiele-Bruhn et al. 2012). Additionally, fungal communities are more resilient to drought than bacteria-dominated systems (Thiele-Bruhn et al. Kinkaid 61

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 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 (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 Kinkaid 62

(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 Kinkaid 63

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 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. Kinkaid 65

Satellite image 1: A small organic farm outside of Athens, Ohio. The farm produces for the local farmer’s market.

Satellite image 2: Monoculture of corn near Ames, Iowa. Kinkaid 66

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. Kinkaid 67

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. Kinkaid 68

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 agro- ecosystem radically changes state. The lack of vegetation encourages leaching, runoff, and soil erosion. Conservation tillage, the practice of leaving standing or stubble Kinkaid 69

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 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. Kinkaid 70

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 system (C), perennial tree crops are planted, with annuals cultivated around them until they grow to maturity (agroforestry). In the case of 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 Kinkaid 71

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- 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 Kinkaid 72

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 Kinkaid 73

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 , 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 (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 “ 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, Kinkaid 74

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 Kinkaid 75

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, , fodder, mulch, fibres, medicines) or services (e.g. soil fertility maintenance and , 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. Kinkaid 76

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 movement, a system of design the builds on 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, , herbs) and disturbance is low. Kinkaid 77

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 – , , and markets – make forest gardens sustainable, but not Kinkaid 78

necessarily “maintainable” (1994). Similarly, Jacke’s edible food forests may not have the 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.

Kinkaid 79

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 farmer’s decision to plant a high-input intensive monoculture, or a perennial polyculture, or Kinkaid 80

anything in between, we must consider her values and goals. For instance, a farmer who is trying to maximize her 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 Kinkaid 81

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 Kinkaid 82

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 Kinkaid 83

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 micro- economic 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 Kinkaid 84

uncertain. What is clear is that 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 Kinkaid 85

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 Kinkaid 87

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 Kinkaid 88

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 , 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 Kinkaid 89

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 , 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 Kinkaid 90

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 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 Kinkaid 91

(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. Kinkaid 92

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, land- cover/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 land- cover/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 Kinkaid 93

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 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- Kinkaid 94

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 Kinkaid 95

connected to suburban living. Changing lifestyles, as well as undesirable characteristics of the city (e.g. crime, , 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 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 tendencies, increasing specialization of land uses and social Kinkaid 96

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 , and paving of large areas, changes plant and animal communities, as well as hydrology, microclimate, and air quality. While these issues arise with urbanization, 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 , 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 Kinkaid 97

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 , were found at more urbanized sites on the gradients. 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 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 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 Kinkaid 98

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 Kinkaid 100

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 .

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 , custom work, and joint irrigation projects.” They note that this social capital may “be conducive to 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 Kinkaid 101

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 Kinkaid 102

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 , farmers lose the ecological services that these species can provide. Foremost among these services are 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 Kinkaid 103

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 Kinkaid 104

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 (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 Kinkaid 105

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 , 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 Kinkaid 106

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. Kinkaid 107

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. 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 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. Kinkaid 108

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 “” 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 Kinkaid 109

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 socio- economic 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. Kinkaid 110

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 socio- economic 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 . The annual cycle is the most important ecological cycle at this scale. It influences Kinkaid 111

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.

Kinkaid 112

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. Kinkaid 115

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 Kinkaid 116

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), (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. Kinkaid 117

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 Kinkaid 118

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,

(, 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. Kinkaid 119

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 Kinkaid 120

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 Kinkaid 121

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 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 Kinkaid 122

(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 . 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 are appearing to level off, it is likely that more land will come under cultivation. This will further stress fragmented and simplified Kinkaid 123

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), , 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 ), 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 Kinkaid 124

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 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 Kinkaid 125

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 Howard 2006

3Food and Agriculture interest groups contributed $89,675,179 to the 2012 election cycle (“Agribusiness: Top Contributors to Federal Candidates, Parties, and Outside Groups,” 2012). Kinkaid 126

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 Kinkaid 127

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 Whalon et al. 2008 6 Manalil et al. 2011 Kinkaid 128

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 Kinkaid 129

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 seed and other life forms is overturned and these resources become part of the . In the developing world, 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 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) Kinkaid 130

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) Kinkaid 131

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. 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 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 Kinkaid 132

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 and a society; since none of these scenarios Kinkaid 135

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 Kinkaid 136

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 Kinkaid 137

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 Kinkaid 138

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 Kinkaid 139

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 Kinkaid 141

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. Kinkaid 142

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 Kinkaid 144

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. Kinkaid 145

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 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 Kinkaid 146

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, 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 Kinkaid 147

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 Kinkaid 148

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 Kinkaid 149

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 Kinkaid 150

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 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. Kinkaid 151

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 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 ) 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 Kinkaid 152

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 Kinkaid 153

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 Kinkaid 154

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. Kinkaid 155

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 Kinkaid 156

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, sub- categories.

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 Kinkaid 157

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 Kinkaid 158

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 Kinkaid 159

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. Kinkaid 160

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, Kinkaid 161

connectedness, and resilience, (W, C, R), specific requirements can be derived. Some examples of these requirements are provided in table 2.

Scale of Analysis Wealth Connectedness Resilience Patch Pattern "living soil" complex food web fungal food web Process SOM accumulation self-regulating nutrient cycles functional redundancy Drivers cover crops, organic inputs rotation Site Pattern farming communities polyculture distributed risk Process resource self-sufficiency integrated pest management localization Drivers "beyond yield" subsidies modularity land-use mosaic Landscape Patterns undisturbed habitats corridors clumped distribution Process conservation "natural capacity planning" adaptation Drivers equity anti-monopoly policy 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 “sub- system,” i.e. the system variables of wealth, connectedness and resilience. Kinkaid 162

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). Kinkaid 163

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 Kinkaid 164

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 Kinkaid 165

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 (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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