Agriculture and Human Values

, Volume 31, Issue 3, pp 371–384 | Cite as

Can resilience thinking provide useful insights for those examining efforts to transform contemporary agriculture?

  • Katrina Sinclair
  • Allan Curtis
  • Emily Mendham
  • Michael Mitchell


Agricultural industries in developed countries may need to consider transformative change if they are to respond effectively to contemporary challenges, including a changing climate. In this paper we apply a resilience lens to analyze a deliberate attempt by Australian governments to restructure the dairy industry, and then utilize this analysis to assess the usefulness of resilience thinking for contemporary agricultural transformations. Our analysis draws on findings from a case study of market deregulation in the subtropical dairy industry. Semi-structured interviews were conducted with dairy producers, their service providers, and industry and government staff. We found the adaptive cycle concept contributed to understanding how deregulation changed industry structures and working practices, how those changes led to feedbacks within the production system and supply chain, and how the industry following deregulation has experienced periods of stability and instability. Regime shifts were associated with an increase in demand for human capital, a degradation of cognitive social capital and a reduction in farm income. Findings identified that were not readily explained by the resilience thinking conceptual framework include a producer’s ability to anticipate and make choices and the change in social and power relationships in the industry.


Australia Dairy industry Market deregulation Resilience theory Transformation 



National competition policy


New South Wales


Social-ecological system


Agricultural producers in developed countries operate in a dynamic and complex environment in which incremental innovation could be regarded as standard practice. However, reduced access to water, declining soil quality, and the underlying irreducible uncertainty of a changing climate are significant contemporary challenges that may mean this “business as usual” approach is no longer sufficient. That is, agricultural producers may need to embrace a more profound or transformative change if they are to secure a future that is desirable, viable, and durable (Bureau of Meteorology 2012; DAFF 2005; Viljoen et al. 2008).

Social-ecological systems (SESs) research recognizes that in some situations adaptation is an inadequate response to changing conditions and that a transformation is required. A systems definition of transformation is a “change in form, appearance, nature or character, especially completely or extensively’ whereas adaptation is simply an ‘adjustment or modification in form, function or structure in response to a changed environment” (Delbridge et al. 1997, p. 2246) that will accommodate maintenance of the current system (Nelson 2011). Bannister and Connolly (2011) maintain that in practice adaptation and transformation reflect the degree to which change occurs along a continuum; in general, there is no clear point at which something ceases to be a minor change and becomes a radical one. It is, however, generally recognized that transformation is the end point or the highest level of the change spectrum. Conceptualizing transformative change is, therefore, difficult, including how to determine the magnitude of change before something is considered completely or extensively changed. Despite these difficulties, a working definition is possible. Transformations are the result of restructuring a system’s components and relationships within and across multiple scales and domains giving rise to an entirely new system based on different assumptions and practices (Nelson 2011; O’Brien 2012).

O’Brien (2012) introduces the idea of deliberate transformation as a potential response to global environmental change. According to O’Brien (p. 4), “deliberate transformations are often carried out with the intention of achieving a particular goal”. In our case it involved an attempt to achieve a more efficient and competitive industry in line with governments’ neoliberal policy.

Resilience thinking offers a framework to understand how the process of change can take place in SESs (Folke et al. 2010). This analytical framework originally emerged from an ecological perspective as outlined by Holling (1973) with a focus on ecological properties that confer resilience; i.e., properties that confer persistence and change in ecosystems (Folke 2006; Perrings 2006; Turner 2010). Resilience thinking takes a complex adaptive systems perspective recognizing the key principles of self-organization, non-linearity, feedback loops, and multiple stable states, which can lead to unanticipated outcomes (Folke 2006). The focus of resilience thinking is on the system as a whole, emphasizing the interrelatedness of the individual components and processes.

The purpose of this paper is to evaluate resilience thinking as a theory conceptually capable of identifying and explaining the changes that follow when governments deliberately attempt to transform an SES. In this paper, the focus is on agriculture in a developed economy. We do this by applying a resilience lens to a case study, namely, deregulation of the Australian dairy market in the year 2000, and the subsequent changes in the subtropical dairy industry, which we identify as an SES. This regional dairy industry is bounded within the subtropical climatic zone of eastern Australia and as such is characterized by warm/wet summers and cool/dry winters. The industry includes dairy producers, dairy industry organizations, milk processors, and government service providers.

We begin by providing background to the Australian dairy industry, market deregulation, and the case study region. A more thorough explanation of the central concepts of resilience thinking is then provided, followed by a description of our research methods. Our analysis uses resilience theory concepts to identify key findings about the nature, extent, and impacts of change in the subtropical dairy industry. We discuss these findings in relation to the current literature on SES change and, in particular, transformative change. We conclude with a summary of the value of resilience thinking as a lens to examine transformation and some lessons learned from this attempt to deliberately transform an agricultural industry.

The Australian dairy industry and market deregulation

In 2011/2012 the Australian dairy industry produced almost 9.5 billion liters of milk with a farm-gate value approaching $4 billion, ranking it third by value of production amongst Australian agricultural industries (Dairy Australia 2012). The Victorian dairy industry, providing 65 % of Australia’s dairy production, is focused primarily on manufactured milk products (e.g., cheese, butter, milk powder) for export whereas the smaller dairy industries in New South Wales (NSW) (11 %) and Queensland (5 %) mostly produce fresh milk for the domestic market (Dairy Australia 2011a).

From 1990 to 2000 the industry experienced a steady decline in farm numbers, a trend that accelerated after 2000 up to 2012 (Dairy Australia 2012). In the decade prior to deregulation the Australian dairy industry also experienced a 50 % increase in milk production. Although that growth has stalled since deregulation in 2000, milk production per farm has continued to increase. At deregulation the average Australian dairy farm produced 0.84 million liters per year, increasing to 1.3 million liters per farm by 2010/2011. The increase in per farm output resulted from an increase in herd size and production per cow (Dairy Australia 2011a; Edwards 2003).

Australia has a history of strong government regulation of the sale of milk and intervention through assistance to the dairy industry. In the 1970s state statutory authorities were established to regulate liquid milk production and quality; restrict interstate trade; and set the price paid to farmers, the margins for processing and distribution, and retail prices (Parker et al. 2000). In 1995, the Australian Government implemented the National Competition Policy (NCP), a package of microeconomic reforms. The NCP promoted market liberalization, which in part required all state governments to reform anti-competitive legislation subject to a public interest/public benefit test (Smith 2001). When the Victorian government applied this test to its dairy industry it failed the test and, as a consequence, Victoria was the first state to deregulate its dairy market. The other Australian states followed the Victorian changes and the Australian dairy industry was deregulated on July 1, 2000. Restrictions governing the sourcing and pricing of milk by state authorities were removed, as were restrictions placed on the movement of milk within and between states, thereby establishing a free and open market for milk.

Subtropical dairy industry and market deregulation

The subtropical dairy region extends along the east coast from Kempsey in the state of New South Wales (31°S) to the Atherton Tablelands in the far north of the state of Queensland (23°S) (Fig. 1). The dairy industry in this region is characterized by family-owned and operated dairy farms producing milk year-round for a highly competitive liquid milk market.
Fig. 1

Location of case study area, northern New South Wales-southeast Queensland within the subtropical dairy region of Australia

Since deregulation, the subtropical dairy industry has experienced an increase in farm exits, though this loss in farms has been substantially higher than in the other dairy regions. To some extent the loss of farms has been offset by increases in the mean farm area, herd size, and cow production as those producers remaining responded to economic forces and policy reforms. Prior to deregulation the average farm in northern NSW and southeast Queensland produced around 20 % less milk than the Australian average farm and this lower level of production compared to the industry average has continued up to 2011 (Dairy Australia 2011b; Spencer 2004).

Subtropical dairy production is predominantly based on a two-pasture system: grazing tropical grasses over summer-autumn and temperate grasses over winter-spring supplemented with grains, hay, and silage. Feedlot-based dairying, in which a total mixed ration is fed out on a designated area, is confined to a small area in southeast Queensland. The pasture-based production system is characterized by a spring peak and winter trough, whereas the feedlot system can produce an even milk supply year-round.

Subtropical dairy systems lack enterprise diversification, which exposes producers to the full impacts of any milk price fluctuations at the farm-gate. In the years prior to deregulation farm-gate milk price averaged 40 cents/liter (c/l) whereas in the 10 years following deregulation the milk price paid has ranged from 35 to 60 c/l (Steve Spencer, personal communication).

The case study is based in northern NSW and southeast Queensland where the industry is concentrated (Fig. 1). Operating as high-cost single enterprise production systems within a limited domestic liquid milk market, the industry was particularly exposed to the unpredictability of the open market system imposed through deregulation.

Resilience thinking

Resilience thinking is founded on the premise that the natural state of a system is one of change rather than stability (Folke 2006; Walker and Salt 2006; Gunderson and Holling 2002). System resilience refers to “the capacity of a system to absorb disturbance and reorganize so as to retain essentially the same function, structure, identity and feedbacks” (Walker et al. 2010, p. 11). Self-organization is a feature of SESs in that humans have the capacity for foresight, reflexivity and to intentionally take action (Walker et al. 2006).

Resilience theory offers a framework to understand the processes of change in SESs. The theory focuses on the dynamics of systems by exploring linkages across time and space, and the interplay between social, economic, and biophysical domains (Darnhofer et al. 2010). The framework is underpinned by six concepts: (1) nonlinearity, alternate regimes, and thresholds; (2) the adaptive cycle; (3) panarchy; (4) adaptability; (5) transformability; and (6) general and specified resilience (Folke 2006; Walker et al. 2004; Walker and Salt 2012).

The first concept, “nonlinearity, alternate regimes, and thresholds,” recognizes that the response to change is non-linear or discontinuous, exhibiting alternating periods of gradual change and order with periods of rapid change and chaos (Gunderson and Holling 2002). Consequently, future conditions or events cannot be precisely predicted (Walker and Cooper 2011). The few key variables that control a system have thresholds, that is, critical levels or limits which if exceeded will move the system to an alternate stable state (or regime) that may be more or less desirable (Walker and Salt 2006; Folke et al. 2004). It is particularly important to be scan for changes in SESs as key variables may change slowly and those changes can go unnoticed for long periods of time. This enables decisions to be made concerning the need for action before it is too late to prevent thresholds being crossed (Walker and Salt 2012). Often biophysical thresholds that can cause a regime shift in ecosystems can be described in terms of a critical limit such as nutrient load (ppm), water table depth (m) or grass cover (%). However, as Walker and Salt (2012) acknowledge, it is much more difficult to determine the thresholds for social variables involved in a regime shift of an SES.

The second and third concepts, the adaptive cycle and panarchy, as descriptive models describe the patterns and processes of change in a system. The adaptive cycle describes a sequence of four development phases: an exploitation phase (growth), a conservation phase (accumulation), a collapse phase (restructuring) and a reorganization phase (renewal) (Walker et al. 2004; Gunderson and Holling 2002) as shown in Fig. 2. The exploitation and conservation phases form the front loop with a disturbance triggering change to the back loop formed by the collapse and reorganization phases (Gunderson and Holling 2002; Walker et al. 2004). The adaptive cycle provides a framework for understanding how the resilience of a system is altered as it changes, adapts, or transforms as it interacts with its environment (Davoudi et al. 2013). Panarchy is an extension of the adaptive cycle, and focuses on the possibilities that may arise through the interactions of multiple adaptive cycles operating across space and time as nested sets (Walker and Salt 2006; Gunderson and Holling 2002). Our analysis presented in this paper focuses on the application of the adaptive cycle as a single interpretive heuristic to understand the progress of change affecting the subtropical dairy industry overall rather than through the lens of multiple and nested sets of adaptive cycles, similar to the application elsewhere (e.g., Allison and Hobbs 2006).
Fig. 2

Deregulation and the adaptive cycle adapted from Gunderson and Holling (2002)

The concepts of adaptability and transformability are particularly useful for considering the role of human and social agency in SESs. Adaptability or adaptive capacity is the ability to make adjustments to an existing system in response to changing circumstances internally and/or externally, and is therefore mainly a function of management practices and decision-making processes (Walker et al. 2004, 2006; Folke et al. 2010). Transformability relates to the capacity to create/enable a “fundamentally new system” when ecological, economic or social conditions make “the existing system untenable” (Folke et al. 2010; Walker et al. 2006). It is, however, likely that there is “overlap in attributes that promote adaptability and transformability” (Walker et al. 2004).

Lastly, there is general resilience, which relates to the ability to absorb disturbances that affect any or all parts of a system. In contrast, specified resilience relates to a targeted part of a system that is resistant to particular disturbances. Frequently, a trade-off occurs or is involved in decisions taken to address a component of a system that is susceptible to a particular threat or to maintain the general resilience of the whole system.

Contemporary resilience thinking is focused on resilience as a measure of persistence, adaptability, and transformability, and the dynamic interplay between these three aspects in response to changing circumstances (Folke et al. 2010; Gallopín 2006). The human dimension of resilience thinking research has broadened to consider the importance of slowly changing characteristics of identity, values, goals, and worldviews as well as other social features such as social and human capital, and social and power relations in constraining or enhancing human action and resilience (Folke et al. 2010; Hatt 2013; Wilson et al. 2013; Nadasdy 2007).

Resilience thinking has made a substantial contribution to understanding the dynamics of ecological systems and to developing principles to manage and govern these ecosystems (Fischer et al. 2009). Well-known ecosystems that have been studied include the Great Barrier Reef in Australia, the Florida Everglades in the USA, and the Kristianstads Vattenrike in Sweden (Gunderson et al. 2006; Hughes et al. 2007).

The resilience framework has also been used as a tool to assess the state of an SES. For example, the Goulburn-Broken catchment, an irrigated agricultural region in Australia, has often been cited as an empirical case for assessing the resilience of an SES, which in this case is a watershed dominated by dairy and horticulture (Walker et al. 2002, 2009; Anderies et al. 2006; Walker and Salt 2006; Kinzig et al. 2006; Walker and Salt 2012; Olsson et al. 2006). In a study of Austrian farms (average 17 ha, 50 % income derived off-farm), Darnhofer (2010) also explored resilience at the farm level and its implications for strategic farm management. Darnhofer et al. (2010) applied the adaptive cycle and panarchy concepts to describe and explain the dynamics and interdependencies between biophysical, economic, and social domains to assess the sustainability at the farm level of the New Zealand kiwi fruit industry as it developed over a 30-year period.

To date the application of resilience thinking to transformative change in an agricultural context appears to have only been applied at a regional scale or farm level. In this paper we apply the resilience thinking conceptual framework to examine an attempt to transform an agricultural industry within a climatic zone across several geographical regions. The application of this theory to examine an industry-scale intervention is also novel. The key contribution of our paper is to assess the value of this theory in providing insights about the processes and outcomes of a recent attempt to transform an agricultural industry.

Research design

We used a case study approach as it allows us to focus on a contemporary event within its real-life context (Robson 2002). By adopting a qualitative, interpretive approach to our study we were able to capture in detail the personal experience, perspective, and understanding of stakeholders, the complexities of the deregulation process, and how time has shaped the subtropical dairy industry (Yin 2009).

Twenty-three face-to-face semi-structured interviews were conducted between August 2011 and March 2012. Participants included thirteen dairy producers, four milk processor representatives, three government agents, and three industry organization representatives. Quotes in this paper are identified using bracketed codes.

The participants were selected through a combination of purposeful and snowballing techniques to obtain a diversity of perspectives and different understandings on the basis that informants had been involved in the industry at least 5 years prior to and again at least 5 years after deregulation. Dairy producers were further selected on the basis of the size and type of production system.

An interview schedule was designed to encourage participants to explore and reflect on the changes they had observed at the property and industry scales in the years before and after deregulation up to the time of interview. The conversations covered broad topic areas in relation to the production system and the relationships with the milk processing sector, industry organization, and government agents. Individual interviews averaged 90 min and were recorded with informed consent and transcribed verbatim.

The transcription data were subsequently examined to provide a description of what occurred in the subtropical dairy industry before and after deregulation. With the aid of the software program QSR NVivo 9, the data were then categorized and coded around themes drawn from the phases of the adaptive cycle (as parent nodes)—conservation (pre-deregulation), collapse (immediate post-deregulation period), and reorganization (up to the time of interview)—structured around three elements—structure, practice, and culture (as child nodes)—to provide a detailed description of the process of change in the industry. We returned to the data again to specifically search for examples of human agency and to identify thresholds—economic, biophysical and, of most interest, social thresholds.

Key findings

We use the resilience thinking concept of the adaptive cycle to frame the progression of the industry from a regulated to a deregulated industry. Within this framework we identify and explain the internal change processes that took place within the industry through the application of key resilience concepts. As we explain below, prior to deregulation the industry was in the conservation phase.

Conservation phase: the industry prior to deregulation

In the decade prior to deregulation the industry functioned as a strongly interconnected and highly ordered industry protected from market influences: the industry operated within the boundaries set by government policy, often to the benefit of producers. “It was a closed industry… there was no real movement… no real change” (milk processor 3). The industry could be viewed as being in the second development phase (conservation phase) in the front loop of the adaptive cycle: slow reduction in farm numbers, no new entrants into the industry, and a gradual increase in production and scale of existing farms (Ashwood 1999).

Dairy production was based on the traditional family farming values of raising a family and working hard together: “making enough money to survive” (industry organizer 1). Producers could readily calculate their income: “I knew I had a 3,000 l quota by 52 (weeks) at 38 (c/l)” (dairy producer 3), making management decisions straightforward and reliable.

As co-operatives, the milk processors were “obliged to take all milk produced… got rid of the surplus [into manufacturing]… everyone seemed fairly happy” (milk processor 2). The choice of processor to whom producers supplied milk was predetermined by regulation and the demand for milk was controlled by the price paid for manufacturing milk products (e.g., butter, cheese, milk powder).

In this period the industry could be viewed as a co-operative in which “everyone was the same” (industry organizer 1); they were all on a level footing, united and supportive, working collectively for the benefit of the industry. Producers were loyal to each other and to their designated milk processing co-operative. In this highly regulated operating environment where everyone was equal there was little incentive to change the status quo.

It was during this period, however, that Australian agricultural policy was shifting from one that supported a protected and controlled market system to one that was open and competitive. This policy change was designed to encourage the dairy industry to reduce its costs and prices, encourage innovation and use resources efficiently. It was agreed by governments in the late 1990s to establish a nationally deregulated dairy market that would commence on July 1, 2000. At the same time within the retail sector the supermarkets began to gradually increase their market share for liquid milk under their generic brand.

Preparing for the collapse phase: preparations for deregulation

The change to a deregulated market system occurred with a set date providing the trigger for change within the industry. The preparation time for the industry was short and, in hindsight, deregulation may have happened “too fast” (industry organizer 1) for the industry to effectively respond, as discussed in the sections below. This period was marked by the industry attempting to build the capacity of its producers and developing a supportive network to meet the deregulation challenge.

The industry encouraged producers to “take stock” of their current situation and explore options for how they may operate in the future. For some this meant exiting the industry “as they couldn’t change their mindset” (industry organizer 1), while for others it was an opportunity to invest and build their business by increasing production, taking on debt, and thus achieving better economies of scale.

The industry organizations provided the connection between government and the industry. They advocated to governments on industry’s behalf and then communicated the outcomes from those negotiations. The industry was fully engaged in ensuring that people knew what was going on. Producer meetings were held on a regular basis and as the industry leader at this time said, “I spent many days on the road and I had some pretty fiery meetings. It’s a pretty big call to take your regulation away” (industry organizer 3).

A number of interviewees spoke about the importance of having a strong leader to advocate on the industry’s behalf to government and for the industry to be united going into deregulation. The industry leader during this period had “immense influence on the industry… He had a real presence… He was extremely well respected. He was there at the right time” (industry organizer 1).

Producer preparations varied from a ‘business as usual’ and ‘hoping it would fall over’ approach to a detailed preparation of the whole production system. One producer continued to buy quota while another producer stopped purchasing quota, “built a new dairy and we did up everything we could like machinery and fences…so that the farm was in a sound condition” (dairy producer 13). But as one producer said, “as with most people we had no real understanding of what the repercussions of deregulation were going to be,” which is to be expected when producers had never had to worry about the milk price they would receive and who would take their milk.

Collapse phase: the industry early post-deregulation

The period immediately after deregulation was characterized by chaos and instability and in terms of the adaptive cycle this period epitomizes the destructive release phase in the adaptive cycle. The industry underwent extensive rapid and radical change in structure and practice as it navigated its way in the new market environment.

In effect, “all hell broke loose” (dairy producer 13) after July 1, 2000. Milk prices dropped from 50 to 33 c/l (a 2–3 c/l fall significantly affects producers’ bottom line), the supermarkets “flexed their muscles” and the milk processors tendered “some unbelievable low prices.” Those dairy producers who chose to remain in the industry increased milk output as friends and neighbors exited the industry. “It was a very gloomy period… The support structure fell apart” (dairy producer 12). “We stood and we watched our fellow farmers… who can survive and who can’t… it was horrible… there was nothing fair about it” (dairy producer 2).

The response by most producers was to increase production in the belief that the solution to deregulation was simply an economic one: increase milk volume and dilute overhead costs. They found, however, that increasing the stocking rate impacted their whole production system. “Where they could comfortably milk 100 cows, 150 cows became a bit of a nightmare” (milk processor 3). As one producer who had increased his herd size by 50 % said, “I was spending about 13 h a day at the milking shed. It was pretty hard on all the physical resources of the farm.” In pushing production per cow too high, this producer said, “we burnt the cows out faster” and had to buy in replacement cows; in feeding high rates of grain “the cows suffered from acidosis so we had to reduce the amount fed.”

What was unexpected was that as the number of farm exits accelerated, the milk processors found themselves “screaming out for milk” (milk processor 4) as demand exceeded supply. Processors began actively pursuing supply, competing against each other to attract producers by offering 5-year contracts with unlimited supply. Ironically, producers were now being encouraged to produce as much milk as possible, which required most to take on debt to improve dairy infrastructure and to employ labor to assist in production.

Early post-deregulation producers were so busy that they no longer had time to meet and share information so decision-making became a process of “learning on the go.” With many farm exits the local face-to-face networks of producers were degraded or dissolved leaving producers more isolated and with less support.

Reorganization phase: the industry 10 years after deregulation

In the decade since deregulation the industry sits within the adaptive cycle’s reorganization phase, operating under new assumptions and a new structure. As the industry continues to respond to a fully commercialized and privatized supply chain there have been periods of uncertainty and unpredictability punctuated by periods of certainty and predictability. When milk was in short supply the processors introduced 5-year contracts with unlimited supply at highly favorable prices. This enabled producers to confidently borrow money to grow their business in the knowledge that the debt could be repaid by the end of the contract. However, with the next supply contract the situation had changed completely with supply now in excess of demand. The new processor supply contracts were of shorter duration, the milk price was much lower and could be varied within the contract time, and each producer was allocated a specific volume with severe penalties for exceeding that quota. In this situation producers were unwilling to make any further investments in their business.

The two major supermarkets set the conditions for the two major (now globally owned) milk processors who in turn set the operating conditions for subtropical dairy producers. The retail sector with its demand for large volumes of milk for its generic brand now has the power to exploit others in the supply chain. Without a manufacturing base to take any milk surplus to requirements the processors introduced new milk supply contracts as a tool to ensure that producers produce only supply enough milk to meet processor’s daily demand.

The post-deregulation changes in the industry meant that producers could no longer assume that processors would accept all the milk they produced in any season. Prior to deregulation the processors accepted the challenges (and risks) associated with the seasonal milk production that is characteristic of pasture-based systems. Demand for fresh milk by consumers is relatively constant and in the post-deregulation environment processors have sought to sign producers to contracts that require the supply of a set volume of milk throughout the year (known as flat-line production). Achieving flat-line production is difficult and costly in largely pasture-based systems, which experience a peak in milk production in spring (pasture quality and quantity is high) and a trough in autumn (pasture quality and quantity is low).

Producers see themselves as individual businesses looking for a competitive “edge over their neighbor.” They “talk less to each other because they are a little more suspicious of each other” (industry organizer 1) especially because they may supply different processors. Producers have also had to learn to negotiate their supply contracts and deliver a product that meets market specifications in terms of volume, milk components, and quality standards.

Processors employ their own field officers who have a broad role in the delivery of processor policy that ensures producers meet milk volume and quality standards. The government agents are now closely aligned with implementing government priorities assisting in the delivery of programs that are strategic to government. It remains for private consultants to provide a one-on-one service to producers.

In summary, the industry exhibits the attributes of an industry that is now fully deregulated as it responds to the supply/demand for milk. It has taken on a new identity. Dairy producers operate as individual businesses that must meet processor supply contracts and, commonly, have other sources of income to enhance their resilience. The major processors are fully privatized and, as they strive to maintain their profit margins, they place stringent conditions on producers. The supermarkets hold substantial market power as a result of the increasing shelf space allocated to their generic milk brands. Producers continue to leave the industry as they find it increasingly difficult to meet the changing supply requirements of a competitive open market system.

The adaptive cycle

The section above illustrates that in the attempt to restructure the subtropical dairy industry the phases of the adaptive cycle were useful in describing and analyzing system changes. In the decade prior to deregulation the industry appeared to be in a late conservation phase. The industry operated in a closed market environment in which competition and novelty were not essential attributes, progress was slow, and actors were highly interconnected. The removal of market regulatory controls on July 1, 2000, shocked the industry to such an extent that it entered into a short-lived chaotic collapse phase. In the collapse phase the industry was released from previous constraints; close relations between stakeholders were broken, existing industry structures were no longer appropriate, existing management practices were no longer suitable and the supply chain was open to stakeholder exploitation. Within 2–3 years the industry moved into a reorganization phase during which the industry restructured.

From the government’s neoliberal perspective the industry was now in a more desirable state: the free market system acting as the mechanism to drive competition and efficiency in the industry. From the perspective of many producers they have had to give up some economic efficiency so they can meet the strict volume and quality conditions set in contracts by the milk processors. The processors are similarly affected as they meet the stringent conditions set out by the supermarkets in their tender contracts. As they attempt to maintain their profit margins, producers now manage a system that carries increased risk to the physical environment and to animal health and welfare because of higher stocking rates and production per cow. By competing against each other rather than supporting one another, producers have lost social capital, an essential part of their well-being.

Non-linearity, multiple stable states, thresholds

Deregulating the market system was the ‘disturbance’ intentionally used by government to restructure the Australian dairy industry. Prior to this ‘disturbance’ the subtropical dairy industry operated within a structural framework that followed a slowly evolving path of change as producers gradually increased milk output and the scale of their operations. Conversely, since deregulation producers operate in a dynamic market environment that requires a production system that has the capacity to rapidly alter the level of milk output to accommodate the market’s changing demand for fresh milk.

In our study we identified farm viability as a key controlling variable that was crucial to system change at the farm level. The substantial reduction in income associated with the shock fall in farm gate milk prices from 50 to 33 c/l can be linked to decisions to exit the industry, expand to achieve economies of scale, or wait to see what transpired. Those producers who chose to rapidly expand milk output by increasing their herd size and production per cow unexpectedly found a deficit in their farm management skills required to manage a more intensive and complex system in a subtropical environment. This rapid increase in demand for human capital can be linked to the emergence of animal health and welfare issues and the degradation of pastures and soil pugging associated with grazing at a higher stocking rate under prolonged wet conditions.

Not so immediate or obvious was the breakdown in social relations between producers in place-based communities and between producers and processors: the stock of social capital as a key variable. This gradual degradation in cognitive social capital can be linked to the increased competitiveness amongst producers and the increasingly strictly commercial nature of the relationship between producer and processor. In our Discussion section we explore the nature of some of the thresholds in these identified key variables and how these multiple thresholds combined to create a fundamentally new system.

In the period between deregulation of the dairy market in 2000 and the timing of our interviews in 2011 there have been profound changes in the structure, practices, and culture of the industry. The profound changes that have taken place in the industry have been in response to the feedbacks and interplay across the social, economic, and biophysical domains and, as a result, the industry has emerged with a new identity.


Adaptability relates to the capacity of actors to make decisions and adjust practices in response to changes in their existing system. Transformability is more than adaptability. It relates to an actor’s capacity to make the profound changes necessary to create a fundamentally new system when conditions make the existing system unsustainable.

Prior to deregulation the industry based on traditional family farms possessed high stocks of social capital. Social networks were strong, centered on the local milk processing co-operative. Producers loyally supported by government service providers and processor field staff met regularly to share information. Within a regulated industry of set milk prices and quotas there was little incentive for producers to make substantial changes to their often inefficient practices. Production systems characterized by low stocking rates and low production per cow meant management decisions were uncomplicated and predictable. Further, financial management was also uncomplicated and predictable as farm income could be readily calculated where there was a guaranteed milk price. In this operating environment, the typically low level of human capital among producers was adequate: producers could rely on their experience and shared knowledge of other producers to manage their production system.

This changed for many producers with deregulation, in that some producers’ skill levels and, hence, stock of human capital, were tested and often found deficient. Production intensification meant that farm management decision-making became far more complex as a change in one management practice often led to feedbacks in other parts of the production system. Producers had to understand the principles of grazing interval and intensity to maximize pasture regrowth and persistence under a higher stocking rate, the fertilizer requirements for a more intensive grazing system, the interaction between feeding grain supplements and grazing pasture to avoid substitution, and how to allocate pasture to avoid pasture wastage. While some producers demonstrated their ability to adapt by learning from their mistakes the level of knowledge and understanding required in cow nutrition, for example, required a high level of training or the payment to private nutritionists as government services and those of the processor field services were withdrawn. In addition, many producers had to improve their financial management skills so they could fully comprehend the implications of the complex arrangements set out in processor milk supply contracts and to understand the financial complexities of operating a business subject to volatile input costs and milk price.

Post-deregulation producers began to operate in a highly contested commercial environment where they were competing with fellow producers to supply the processors. In this environment social capital degraded as support networks began to break down with producers unwilling to meet and share their knowledge. Instead they sought to gain any advantage over their fellow producers. There was also intense competition between producers and processors with processor field staff placing their loyalty with the processor.

In response to the challenges of deregulation producers upgraded their management competencies including: financial skills to negotiate milk contracts; nutritional skills to manage the complex interaction between the feedbase and livestock; and human resource management skills needed to employ, manage, and retain increased numbers of employees. Although dairy farms remain family-owned, producers have developed the capacity to operate as individual businesspersons managing their operation according to business principles. While producers were developing their capacity to operate in a deregulated environment, their social networks degraded and the high levels of trust between producers declined.

Specific and general resilience

In managing for a specific threat actors may simply focus all their efforts on one part of the system. In so doing they may reduce the system’s capacity to cope with a range of other disturbances resulting in a loss of overall general system resilience.

In preparing for deregulation it was believed that increasing productivity would be the key to maintaining producer profitability in a post-deregulation environment. Producers, however, soon found that increasing milk output impacted every aspect of their production system. The existing milking shed and laneways were found to be inadequate for the increased herd sizes, producers lacked the knowledge of nutrition to sustain increased milk production per cow, and the increased stocking rates (often too expensive to expand by buying additional land) exposed pastures to damage from stock trafficking paddocks in extreme wet weather events.

In response to these negative feedbacks, and reflecting considerable adaptive capacity, producers upgraded farm infrastructure, sought advice from consultant nutritionists and reduced their herd sizes to ease the pressure on their pastures and laneways. As a group, those who remained in the industry were able to develop a substantially different production system that enabled them to become more responsive to market signals. Many producers also took actions to reduce exposure to fluctuating milk prices by establishing an off-farm income stream, including having one partner work off-farm and investing surplus funds in real estate or the share market.


The benefit of applying a resilience thinking framework was in enabling the researchers to comprehensively describe and explain the complex dynamic changes and interactions that took place across time as the dairy industry moved from a system that functioned effectively in a closed market to one that had to successfully operate in an open market. We believe that deregulation led to a transformation of the subtropical dairy industry by substantially changing the culture of the industry, as well as the structure and working practices of the production system, supply chain, industry organization, and the government agencies. In response to deregulation the industry did not simply make adjustments to its structure and working practices to maintain the status quo. It made radically different changes. Importantly, these changes in structure and working practices were accompanied by changes in perspectives, attitudes, beliefs, and values.

Under a regulated market system the industry was centered on producer co-operatives with all actors in the industry there to support the tradition of family farming and ensure that the common good of the industry prevailed. Deregulation reflected a very different culture with an emphasis on competition, efficiency, private enterprise, profit margins, and the individual family unit. The pressures of an open market system triggered producers to view their operation as a business and to adopt new practices and attain new skills to enable them to increase farm productivity. The milk processing sector is no longer supported by producer co-operatives that used to accept all milk produced. It is now a highly competitive corporatized sector with strict producer supply contracts to ensure that supply equals the daily demand. Membership of the peak industry organization is now voluntary, and that body now offers a range of services to complement its traditional advocacy role. Where once government agents provided a free advisory service to producers following deregulation, those government services have been withdrawn and private service providers now work with producers on a fee-for-service basis.

Our case study also provided insights into what others have identified as potential limitations to resilience thinking (Davidson 2010; Cote and Nightingale 2012). In particular, we reflect on concerns that resilience thinking falls short in adequately acknowledging the capacity for proactive human agency and in identifying social thresholds.

People have the capacity to reflect on the past and to imagine the future, to improvise, to anticipate change, and to choose how they will respond: to act or not (Davidson 2010; Davoudi 2012). In our case study there is evidence of producers thinking, making decisions, and taking action pre-deregulation. Producers used words such as “I expected,” “we believed,” “we tried to be proactive,” “we investigated,” “our thinking,” “we thought,” and “we had no real understanding” signifying to us that producers tried to predict or imagine what the future could be like. In preparing for deregulation the industry held regular meetings to keep producers fully informed and it seems that producers who accessed this information reached different decisions about what they should do with their quota (a purchased milk supply entitlement used in the regulated market system). Some producers chose not to purchase more quota, others sold their quota while others continued buying quota up to deregulation.

As a result of human agency it seems there is likely to be greater variability in the behavior of the social system compared to the biophysical component of an SES. It seems that those setting out to effect change in an SES will need to be mindful that human agency will most likely contribute to increased complexity, including the potential for detrimental or maladaptive outcomes (Davidson 2013).

According to resilience thinking, for a transformation to occur, the thresholds of a few key variables would need to be exceeded. In our case study the key variables at the farm level were producer incomes (immediately declined post-deregulation), management expertise required by producers (much higher post-deregulation), and the level of cognitive social capital (gradually declined post-deregulation).

Although Walker and Salt (2012, p. 75) acknowledge that “thresholds in the social and economic domains might be harder to identify” than those in the ecological domain, we revisited our data to find out if we could recognize thresholds in the key variables. It is particularly important to be able to identify those critical levels given the discussion in the literature about the importance in thinking about what the future state of a system may look like. Perhaps if the key actors could have foreseen where those thresholds would be crossed, they could have considered whether that change was desirable and if not, what action they might take to avoid crossing to an undesired state.

One of the key variables in the transformation of the subtropical dairy industry was the management expertise required by producers. In this case, a threshold was approached when many producers responded to the challenges of deregulation by expanding cow numbers and increasing milk output per cow. This intensification of their production systems challenged producers’ knowledge and skills to manage animal health and welfare issues in larger herd sizes, to provide a balanced ration (forage and supplements), and to manage their pastures under high stocking rates. These steps towards more intensified production led to feedbacks that were mostly unanticipated including sick animals, poor pasture regrowth, and sudden and unexpected falls in milk production. To effectively manage these negative feedbacks, producers needed to develop new skills in herd and pasture management. With hindsight we can identify that these feedbacks occurred and that they were a key element in determining the viability of individual producer enterprises. Having said that, these thresholds were specific to each enterprise and even with hindsight, it is difficult to determine at what point in time these feedbacks affected a substantial number of producers or required a determined, focused response by a substantial proportion of producers. We can see from this example how crossing one threshold can induce multi-dimensional feedbacks that can amplify the change and increase the potential for other thresholds to be crossed within a system, resulting in interacting thresholds and cascading change as we can see when producers’ financial viability was threatened (Kinzig et al. 2006; Resilience Alliance 2010). Indeed, as Nelson (2009, p. 2) states, “It is only through hindsight that the true complexity of drivers, functional relationships, feedback and the range of possible outcomes become apparent”.

The resilience framework also provided insights into what and how change took place within the subtropical dairy industry and led to a better understanding of why the industry changed to the extent it did when deregulated. Framing the progression of the industry through the deregulation process using the four-phased adaptive cycle was very useful and applicable to other agricultural industries as Darnhofer et al. (2010) have shown for the New Zealand kiwi fruit industry. While the adaptive cycle was relatively simple to apply, other concepts were not as easy requiring some interpretation by the authors. Identifying social thresholds was not obvious. For example, the change in social capital, involving a slow breakdown in social networks and trust between producers, clearly resulted in a regime shift, but it would be difficult and counterproductive to try to pinpoint exactly when social relations became so degraded that they had passed a critical threshold. We were unable to clearly differentiate between adaptability and transformability although we could recognize that changes in capital stocks were necessary for transformative change. While the vagueness of some of the resilience concepts presented some difficulties, the multi-dimensional nature of the framework was useful in its application to agriculture and assessing for transformative change at an industry level.


Dairy deregulation and its subsequent impact on the subtropical dairy industry provide an illustration of deliberate transformative change in contemporary agriculture. The transformation of this dairy industry was characterized by a complex web of change as the industry self-organized across time and across multiple domains—economic, social and biophysical. There were noted changes in the structure, working practices of the production system, supply chain and industry organization as the industry reorganized in response to deregulation. Notable also were the changes in the values and attitudes of many producers and in the social relationships between stakeholders as they sought advantage in the new market environment. There was also a change in the structural relationships within the supply chain (big business versus small business) that resulted in the retail sector exerting overwhelming power in the supply chain, an outcome that has substantially influenced the future state of the subtropical dairy industry.

If a resilience lens had been applied prior to deregulation, some of those unintended consequences may have been anticipated. Steps could have then been considered that might have enabled the actors to avoid crossing thresholds leading to less desirable long-term outcomes for the industry and its stakeholders. For instance, rather than leaving the key actors to prepare for deregulation as best they could, the state and Australian governments could have established a reference group that included the key stakeholders (e.g., producers, processors, supermarket, consumers, government) and supported them through a facilitated process (with input from social scientists and other technical experts) to develop and explore possible futures for the industry under deregulation. Could such a process have anticipated that under deregulation there would be such a far-reaching re-alignment of power in the supply chain? While it is possible that it is only with hindsight that we can understand the range of possible outcomes of deregulation, our view is that a sound process could have foreshadowed this possibility.

The findings from our case study stress the importance of establishing processes that enable key actors to apply a resilience lens before and during an attempted transformation in SESs. In practice these actors need to proactively manage the transformation by drawing on their capacities to imagine the future, to anticipate change, and to choose how to respond. In so doing, these actors will look for key controlling variables that may enable or hinder a transformative change, be aware of the potential for thresholds to reach critical levels, be ready to respond to anticipated and unanticipated feedbacks, and avoid maladaptive outcomes in favor of improved outcomes for them and other actors.

In an era of climate change the challenges facing agriculture in countries such as Australia will undoubtedly mean that some industries and their producers will need to take transformative action to secure a sustainable future. While this empirical case study can offer some lessons for those considering transformative change, it is clear that such fundamental change will have a profound impact on all those involved. It will challenge their values, beliefs, attitudes, and perspectives. It is likely that there will be winners and losers in the process, which highlights the importance of an approach that builds on good governance principles of inclusiveness and fairness (Lockwood et al., 2010).

The lessons from this case study can provide some guidance for agricultural practitioners who may be considering or facing transformative change. In such contexts, a key strategy is to develop an understanding of the dynamics of complex systems together with an awareness of key controlling variables, and the need to recognize and respond to unintended consequences. In preparing the system for change it is helpful to recognize the benefit of those with visionary leadership that can drive the change process. Another strategy involves building social and human capital and allowing new skills to emerge. This will facilitate diversity, enterprise, and innovation: essential elements for a system transformation. Developing multiple options for action as part of future planning can facilitate the development of a more flexible approach to system change. Because outcomes are rarely predictable for those navigating transformative change, it is vital that an active adaptive management approach is adopted that nurtures experimentation and learning. Navigating transformative change is a turbulent process that some agricultural industries and their producers will be facing, especially as a result of climate change. There are clear benefits for actors within those industries to enter that turbulent period equipped with the above strategies.


  1. Allison, H., and R. Hobbs. 2006. Resilince, adaptive capacity, and the “Lock-in Trap” of the Western Australia Agricultural Region. Ecology and Society, 9(1):3. Accessed 1 Dec 2013.
  2. Anderies, J.M., P. Ryan, and B.H. Walker. 2006. Loss of resilience, crisis, and institutional change: Lessons from an intensive agricultural system in southeastern Australia. Ecosystems 9(6): 865–878.CrossRefGoogle Scholar
  3. Ashwood, A. 1999. Nationalisation of the NSW dairy industry. Wollongbar, NSW: Wollongbar Agriculture Institute.Google Scholar
  4. Bannister, F., and R. Connolly. 2011. Trust and transformational government: A proposed framework for research. Government Information Quarterly 28(2): 137–147.CrossRefGoogle Scholar
  5. Bureau of Meteorology. 2012. State of the climate 2012. Australia: Bureau of Meteorology, Australian Government.Google Scholar
  6. Cote, M., and A.J. Nightingale. 2012. Resilience thinking meets social theory: Situating social change in socio-ecological systems (SES) research. Progress in Human Geography 36(4): 475–489.CrossRefGoogle Scholar
  7. DAFF. 2005. Australian agriculture and food stocktake. Canberra: Department of Agriculture, Fisheries and Forestry, Australian Government.Google Scholar
  8. Dairy Australia. 2011a. Australian dairy industry in focus 2011. Melbourne: Dairy Australia.Google Scholar
  9. Dairy Australia. 2011b. Dairy 2011 situation and outlook. Melbourne: Dairy Australia.Google Scholar
  10. Dairy Australia. 2012. Australian dairy industry in focus 2012. Melbourne: Dairy Australia.Google Scholar
  11. Darnhofer, I. 2010. Strategies of family farms to strengthen their resilience. Environmental Policy and Governance 20(4): 212–222.CrossRefGoogle Scholar
  12. Darnhofer, I., J. Fairweather, and H. Moller. 2010. Assessing a farm’s sustainability: Insights from resilience thinking. International Journal of Agricultural Sustainability 8(3): 186–198.CrossRefGoogle Scholar
  13. Davidson, D.J. 2010. The applicability of the concept of resilience to social systems: some sources of optimism and nagging doubts. Society & Natural Resources 23(12): 1135–1149.CrossRefGoogle Scholar
  14. Davidson, D.J. 2013. We still have a long way to go, and a short time to get there: a response to Fikret Berkes and Helen Ross. Society & Natural Resources 26(1): 21–24.CrossRefGoogle Scholar
  15. Davoudi, S. 2012. Resilience: a bridging concept or a dead end? Planning Theory & Practice 13(2): 299–333.CrossRefGoogle Scholar
  16. Davoudi, S., E. Brooks, and A. Mehmood. 2013. Evolutionary resilience and strategies for climate adaptation. Planning, Practice & Research 28(3): 307–322.CrossRefGoogle Scholar
  17. Delbridge, A., J.R.L. Bernard, D. Blair, S. Butler, P. Peters, and C. Yallop (eds.). 1997. The Macquarie dictionary, 3rd ed. Sydney: Macquarie Library.Google Scholar
  18. Edwards, G. 2003. The story of deregulation in the dairy industry. Australian Journal of Agricultural and Resource Economics 47(1): 75–98.CrossRefGoogle Scholar
  19. Fischer, J., G.D. Peterson, T.A. Gardner, L.J. Gordon, I. Fazey, T. Elmqvist, A. Felton, C. Folke, and S. Dovers. 2009. Integrating resilience thinking and optimisation for conservation. Trends in Ecology & Evolution 24(10): 549–554.CrossRefGoogle Scholar
  20. Folke, C. 2006. Resilience: The emergence of a perspective for social-ecological systems analyses. Global Environmental Change 16(3): 253–267.CrossRefGoogle Scholar
  21. Folke, C., S.R. Carpenter, B. Walker, M. Scheffer, T. Chapin, and J. Rockstrom. 2010. Resilience thinking: integrating resilience, adaptability and transformability. Ecology and Society 15(4):20. Accessed 1 Dec 2013.
  22. Folke, C., S.R. Carpenter, B. Walker, M. Scheffer, T. Elmqvist, L. Gunderson, and C.S. Holling. 2004. Regime shifts, resilience, and biodiversity in ecosystem management. Annual Review of Ecology Evolution and Systematics 35: 557–581.CrossRefGoogle Scholar
  23. Gallopín, G.C. 2006. Linkages between vulnerability, resilience, and adaptive capacity. Global Environmental Change 16(3): 293–303.CrossRefGoogle Scholar
  24. Gunderson, L.H., S.R. Carpenter, C. Folke, P. Olsson, and G. Peterson. 2006. Water RATs (Resilience, Adaptability, and Transformability) in lake and wetland social-ecological systems. Ecology and Society 11(1):16. Accessed 1 Dec 2013.
  25. Gunderson, L.H., and C. Holling. 2002. Panarchy: understanding transformations in human and natural systems. Washington, DC: Island Press.Google Scholar
  26. Hatt, K. 2013. Social attractors: A proposal to enhance “resilience thinking” about the social. Society & Natural Resources 20(1): 30–43.CrossRefGoogle Scholar
  27. Holling, C.S. 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4: 1–23.CrossRefGoogle Scholar
  28. Hughes, T.P., et al. 2007. Adaptive management of the Great Barrier Reef and the Grand Canyon World Heritage Areas. AMBIO: A Journal of the Human Environment 36(7): 586–592.CrossRefGoogle Scholar
  29. Kinzig, A.P., P.A. Ryan, M. Etienne, H.E. Allison, T. Elmqvist, and B.H. Walker. 2006. Resilience and regime shifts: assessing cascading effects. Ecology and Society 11(1):20. Accessed 1 Dec 2013.
  30. Lockwood, M., J. Davidson, A. Curtis, E. Stratford, and R. Griffith. 2010. Governance principles for natural resource management. Society & Natural Resources 23(10): 986–1001.CrossRefGoogle Scholar
  31. Nadasdy, P. 2007. Adaptive co-management and the gospel of resilience. In Adaptive co-management: collaboration, learning, and multi-level governance, ed. D. Armitage, F. Berkes, and N. Doubleday, 208–227. Vancouver: UBC Press.Google Scholar
  32. Nelson, D.R. 2009. Conclusions: transforming the world. In Adapting to climate change: Thresholds, values, governance, ed. W.N. Adger, I. Lorenzoni, and K.L. O’Brien, 491–500. Cambridge: Cambridge University Press.Google Scholar
  33. Nelson, D.R. 2011. Adaptation and resilience: Responding to a changing climate. Wiley Interdisciplinary Reviews: Climate Change 2(1): 113–120.Google Scholar
  34. O’Brien, K. 2012. Global environmental change II: from adaptation to deliberate transformation. Progress in Human Geography 36(5): 667–676.CrossRefGoogle Scholar
  35. Olsson, P., L.H. Gunderson, S.R. Carpenter, P. Ryan, L. Lebel, C. Folke, and C.S. Holling. 2006. Shooting the rapids: Navigating transitions to adaptive governance of social-ecological systems. Ecology and Society 11(1):18. Accessed 1 Dec 2013.
  36. Parker, A., K.B. Woodford, and E. Woods. 2000. Deregulation and restructuring in the Queensland dairy industry. Occasional Paper 7(2). Brisbane, Australia: School of Natural and Rural Systems Management, University of Queensland.Google Scholar
  37. Perrings, C. 2006. Resilience and sustainable development. Environment and Development Economics 11(4): 417–427.CrossRefGoogle Scholar
  38. Resilience Alliance. 2010. Assessing resilience in social-ecological systems: workbook for practitioners. Version 2.0.
  39. Robson, C. 2002. Real world research: a resource for social scientists and practitioner-researchers. Oxford: Blackwell.Google Scholar
  40. Smith, S. 2001. Deregulation and National Competition Policy and its effect on rural and regional areas. Briefing paper 7/01. Sydney, Australia: NSW Parliament.Google Scholar
  41. Spencer, S. 2004. Dairy—now and then: the Australian dairy industry since deregulation. Melbourne, Australia: Report for the National Competition Council.Google Scholar
  42. Turner II, B.L. 2010. Vulnerability and resilience: Coalescing or paralleling approaches for sustainability science? Global Environmental Change 20(4): 570–576.CrossRefGoogle Scholar
  43. Viljoen, C., P. Price, S. Lovett, and L. O’Connor. 2008. Healthy soils for sustainable farms program report. Canberra: Australian Government.Google Scholar
  44. Walker, B., S. Carpenter, J. Anderies, N. Abel, G. Cumming, M. Janssen, L. Lebel, J. Norberg, G.D. Peterson, and R. Pritchard. 2002. Resilience management in social-ecological systems: A working hypothesis for a participatory approach. Conservation Ecology 6(1):14. Accessed 1 Dec 2013.
  45. Walker, B., C.S. Holling, S.R. Carpenter, and A. Kinzig. 2004. Resilience, adaptability and transformability in social-ecological systems. Ecology and Society 9(2):5. Accessed 1 Dec 2013.
  46. Walker, B., L. Gunderson, A. Kinzig, C. Folke, S. Carpenter, and L. Schultz. 2006. A handful of heuristics and some propositions for understanding resilience in social-ecological systems. Ecology and Society 11(1):13. Accessed 1 Dec 2013.
  47. Walker, B., N. Abel, J.M. Anderies, and P. Ryan. 2009. Resilience, adaptability, and transformability in the Goulburn-Broken Catchment, Australia. Ecology and Society 14(1):12. Accessed 1 Dec 2013.
  48. Walker, J., and M. Cooper. 2011. Genealogies of resilience: From systems ecology to the political economy of crisis adaptation. Security Dialogue 42(2): 143–160.CrossRefGoogle Scholar
  49. Walker, B., and D. Salt. 2006. Resilience thinking: Sustaining ecosystems and people in a changing world. Washington, DC: Island Press.Google Scholar
  50. Walker, B., and D. Salt. 2012. Resilience practice: Building capacity to absorb disturbance and maintain function. Washington, DC: Island Press.CrossRefGoogle Scholar
  51. Walker, B., J. Sayer, N.L. Andrew, and B. Campbell. 2010. Should enhanced resilience be an objective of natural resource management research for developing countries? Crop Science 50: S10–S19.CrossRefGoogle Scholar
  52. Wilson, S., L.J. Pearson, Y. Kashima, D. Lusher, and C. Pearson. 2013. Separating adaptive maintenance (resilience) and transformative capacity of social-ecological systems. Ecology and Society 18(1):22. Accessed 1 Dec 2013.
  53. Yin, R.K. 2009. Case study research: design and methods, 4th ed. Thousand Oaks, CA: Sage Publications.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Katrina Sinclair
    • 1
  • Allan Curtis
    • 2
  • Emily Mendham
    • 3
  • Michael Mitchell
    • 4
  1. 1.New South Wales Department of Primary IndustriesWollongbarAustralia
  2. 2.Institute for Land, Water and SocietyCharles Sturt UniversityAlburyAustralia
  3. 3.National Centre for Groundwater Research and TrainingCharles Sturt UniversityAlburyAustralia
  4. 4.School of Geography and Environmental StudiesUniversity of TasmaniaHobartAustralia

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