Advertisement

Regional Environmental Change

, Volume 18, Issue 4, pp 1185–1199 | Cite as

The socio-economic vulnerability of the Australian east coast grazing sector to the impacts of climate change

  • Erin F. Smith
  • Scott N. Lieske
  • Noni Keys
  • Timothy F. Smith
Original Article

Abstract

Research that projects biophysical changes under climate change is more advanced than research that projects socio-economic changes. There is a need in adaptation planning for informed socio-economic projections as well as analysis of how these changes may exacerbate or reduce vulnerability. Our focus in this paper is on the delivery of time-sensitive socio-economic information that can better support anticipatory adaptation planning approaches. Using a ‘multiple lines of evidence’ approach based on Australian Bureau of Statistics’ data (2010/2011), we examine the socio-economic vulnerability of the grazing sector located on Australia’s east coast. We profile the east coast grazing sector through an overview of the composition of its workforce and the value of grazing commodities produced. We then assess the potential vulnerability of the grazing sector using spatial snapshots of five factors known to shape socio-economic vulnerability in New South Wales and Queensland: (1) reliance on agriculture, (2) geographic remoteness, (3) socio-economic disadvantage, (4) economic diversity and (5) age. Our assessment of the east coast grazing sector reveals six subregions characterised by high potential socio-economic vulnerability to the impacts of climate change. We find high percentages of labour forces employed in agriculture, geographic remoteness and age (high percentages of owner/managers and employees in younger age groups) to be drivers of vulnerability. Finally, we evaluate the ways in which these vulnerabilities may be exacerbated or reduced in light of emerging environmental, economic and social trends. This approach complements demographic projection methods to deliver time-sensitive socio-economic information to support anticipatory adaptation planning.

Keywords

Adaptation Resource dependency Sensitivity Regional planning Broadacre agriculture 

Introduction

Emerging frameworks for climate change adaptation are increasingly emphasising the need for greater systemic flexibility and responsiveness. These frameworks indicate the need to develop a diverse range of adaptation and mitigation options which may be implemented over different timeframes (Haasnoot et al. 2013; Barnett et al. 2014; Manning et al. 2015). Key to enhanced systemic flexibility and responsiveness are shifts from retrospective and incremental adaptation responses towards anticipatory responses (Manning et al. 2015) and transformational adaptation (Park et al. 2012; Rickards and Howden 2012). Developing, planning and implementing measures for climate mitigation and adaptation requires information and tools that (1) facilitate consideration of a broad range of future scenarios and adaptation options, (2) are sensitive to decision-making time frames, (3) incorporate the projected rates of change associated with climatic risks and the socio-economic characteristics of the region/s of interest (Lawrence et al. 2013; Manning et al. 2015) and (4) are context relevant (Dovers and Hezri 2010). Thus, effective planning is dependent upon decision makers’ access to a wide array of time-sensitive information that canvasses the biophysical and socio-economic domains. Arguably, research that projects biophysical changes under climate change (e.g. modelling climate change impacts) is more advanced than research that projects how and in what ways the socio-economic domain may change over the same time scales (Roiko et al. 2012). In light of this research imbalance, our focus in this paper is on the delivery of time-sensitive socio-economic information that can better support anticipatory adaptation planning approaches embedded within effective government adaptation architectures (Jacobs et al. 2016).

Within the climate change adaptation literature, vulnerability is a widely used concept to understand the “propensity or predisposition to be adversely affected” (Mach et al. 2014, p. 128). That is, vulnerability comprises a forward-looking dimension that allows for evaluating possible future harm (Hinkel 2011), which renders the concept highly applicable to anticipatory adaptation planning. Despite its conceptual applicability, questions remain about how to adequately integrate this forward-looking dimension into socio-economic vulnerability assessments of communities, sectors or regions. Notwithstanding this challenge, common among current assessment approaches is a reliance upon population statistics and the identification of variables that co-vary with increased vulnerability (e.g. the development of composite indexes). Although these approaches have been usefully combined with spatial analyses to generate insights about the present-day geographies of vulnerable people and communities (see, for example, Baum et al. 2008; Nelson et al. 2010; Solangaarachchi et al. 2012; Arthurson and Baum 2015), in Australia, projections of how these geographies and the factors represented may change and be associated with future vulnerability are limited.

Demographic projection is one tool that has been used by a small number of researchers to address this knowledge gap and better account for the dynamic nature of socio-economic development (Glavac et al. 2003; Roiko et al. 2012; Loughnan et al. 2013, 2014). This is an important advance if vulnerability assessments are to more effectively support adaptation planning. However, the utility of demographic projection is constrained because it remains based upon value judgements that assume socio-economic factors currently associated with a high risk of harm will continue to be associated with a high risk of harm in the future. For example, older people are understood to have increased physical sensitivity to increased temperatures and reduced adaptive capacity arising from social isolation (e.g. Loughnan et al. 2013); therefore, ageing populations are typically considered to be more vulnerable than populations characterised by younger age profiles. Such relationships will undoubtedly continue to influence socio-economic vulnerability, but equally these relationships—and their association with vulnerability—may be reconfigured by emerging trends associated with healthy, more active lifestyles and people remaining longer in the workforce to maintain income levels (Hajkowicz et al. 2012). These examples point to the need for a broader understanding of the current and future drivers that may exacerbate or (potentially) reduce vulnerability, and the ways in which these drivers will shape socio-economic development. It is into this research space we make a contribution.

In this paper, we provide a snapshot spatial assessment of the socio-economic vulnerability of the grazing sector in three natural resource management (NRM) regions on Australia’s east coast, what we term ‘the east coast grazing sector’.1 First, we review the literature concerning the vulnerability of Australian grazing systems and the research approaches used to date. This review reveals a key research gap: that anticipatory analyses of the socio-economic domain are almost entirely absent. Second, we profile the east coast grazing sector through an overview of the composition of its workforce and the value of grazing commodities produced, followed by a description of our data sources and the research methods for assessing socio-economic vulnerability. In the second half of the paper, we present our assessment of the socio-economic vulnerability of the east coast grazing sector using ‘multiple lines of evidence’ which reveals six subregions characterised by high potential vulnerability to the impacts of climate change. In the final section of the paper, we seek to more comprehensively account for future drivers of vulnerability. We evaluate the ways in which these vulnerabilities may be exacerbated or reduced in light of emerging environmental, economic and social trends that will shape the Australian economy and society in the coming decades (Hajkowicz et al. 2012). In doing so, we seek to broaden the debate about vulnerability to climate change impacts to consider other influences that may shape the dynamics of socio-economic vulnerability in the future.

Climate change impacts and research approaches used to assess the vulnerability of Australian grazing systems

Climate change is projected to impact eastern Australia in the following ways: increasing average temperatures in all seasons, more hot days and fewer frosts, increasing evapotranspiration in all seasons and creating a harsher fire-weather climate. While changes in rainfall patterns are likely to be largely influenced by natural variability, increased intensity of extreme rainfall events is projected. In coastal areas, mean sea level will continue to rise accompanied by an increase in the magnitude of extreme sea level events (Dowdy et al. 2015).2 Like any climate projections, there is uncertainty regarding the degree to which these impacts will manifest at local scales and in which combinations. Despite this uncertainty, the world is committed to some degree of warming due to historical emissions (Collins et al. 2013) rendering some form of adaptation necessary. In turn, researchers have directed their attention towards understanding the implications of climate change for—and adaptation options available to—the Australian agricultural sector.

Climate change is “expected to have a severe and costly impact” on Australia’s agricultural sector (Garnaut 2008, p. 121; see also Reisinger et al. 2014). To understand the grazing sector’s vulnerability to these impacts, researchers have deployed modelling approaches to forecast biophysical changes and/or the economic implications of changed environmental conditions. Results reported in this growing body of literature indicate that CO2 fertilisation is expected to have a positive impact on pasture growth arising from improved water- and nitrogen-use efficiencies, but these benefits may be counteracted by lower rainfall (Cobon et al. 2009; Whish et al. 2014). Accordingly, under a range of climate scenarios, pasture growth and livestock-carrying capacities are projected to be negatively impacted (Cobon et al. 2009; Whish et al. 2014), the abundance and distribution of plant and animal pests may be altered (White et al. 2003) and changes in rainfall and temperature may result in shorter growing seasons (Moore and Ghahramani 2013). In some cases, these analyses of the projected biophysical impacts of climate change have been extended to consideration of the projected economic impacts. In their study of broadacre agriculture in southern Australia, Moore and Ghahramani (2013) examined the potential economic impact of shorter growing seasons. Under these conditions, by 2050, farm profitability is expected to decline on average by 7%.

Together, these biophysical and economic projections are expected to reconfigure annual production cycles and shift agricultural production zones (Reisinger et al. 2014). Thus, the future of the grazing sector will be shaped by the ways in which climate change impacts manifest at local scales (White et al. 2003; Harle et al. 2007; McKeon et al. 2008; Webb et al. 2012; Reisinger et al. 2014; Whish et al. 2014) and the adaptation responses deployed by individual graziers and the wider grazing industry. Yet, when adaptation responses are considered in the studies reviewed above, inadequate attention is given to graziers’ propensity (or capacity) to alter their farming practices which may mediate their vulnerability. For example, White et al. (2003) consider the influence of switching cattle breeds on the vulnerability of the beef industry to changed distributions of cattle tick, citing only one dated study (from the 1980s) as evidence that beef producers are prepared to change their cattle breeds. Similarly, Ghahramani and Moore’s (2013) simulations of the effectiveness of a range of on-farm adaptation options (e.g. higher soil fertility, confinement feeding) to protect against reduced farm profits include no analysis of the likely uptake of these adaptations. It may be argued that these research approaches are simply not intended for the purposes of projecting farmers’ future preferences and practices. Yet, understanding these on-farm socio-economic dynamics and how they may change in the future alongside climate projections is integral to devising effective anticipatory adaptation responses for the grazing sector.

Social researchers have applied a range of concepts and research approaches to understand better the socio-economic determinants of the vulnerability of Australian grazing systems. These approaches include the operationalisation of allied concepts such as adaptive capacity and resource dependency (Crimp et al. 2010; Marshall 2010, 2011; Marshall et al. 2014; Brown et al. 2015) and the development of indices based upon the rural livelihoods framework (Crimp et al. 2010; Nelson et al. 2010). Crimp et al. (2010), Brown et al., 2010, 2015assess the adaptive capacity of livestock producers, graziers and natural resource managers respectively using a participatory, self-assessment process. In workshop settings, small groups of farmers and natural resource managers were asked to select indicators for each of the five capitals (human, social, financial, physical and natural) comprising the rural livelihoods framework and then score each indicator according to the extent with which they thought it supported adaptation to climate change. In the Crimp et al. (2010) and Brown et al. (2015) studies, financial capital (e.g. farm profitability, access to government funding/drought relief, cost of land, ability to raise capital) was perceived to be the biggest constraint on farmers’ ability to respond effectively to climate change. South East Queensland graziers perceived the following factors to support their adaptive capacity: (1) their high education levels, (2) social networks (e.g. industry and farmer groups), (3) access to markets and infrastructure and (4) the topographical characteristics of the region (Brown et al. 2015).

Marshall (2010) adopts a different approach, drawing on the resilience literature to conceptualise adaptive capacity as comprising: (1) the perception of risk associated with change; (2) the ability to plan, learn and reorganise; (3) the proximity to the thresholds of coping; and (4) the level of interest in change (see also Marshall and Marshall 2007). Results reveal that, in general, graziers perceive themselves to have high levels of adaptive capacity (Marshall 2010), but that it varies between individual graziers resulting in differing levels of vulnerability (Marshall et al. 2014). Marshall and colleagues provide further insight into graziers’ vulnerability by incorporating the concept of resource dependency (Marshall, 2010, 2011; Marshall et al. 2014). Resource dependency comprises social, economic and environmental components which “describe the nature and magnitude of the sensitivity of resource users to changes in resource condition or access as a result of climate change” (Marshall 2011, p. 1106; see also Marshall et al. 2007). Results demonstrate that (1) graziers are highly resource dependent and, therefore, especially sensitive to climate change because of limited social, economic and environmental flexibility (Marshall 2011); (2) their adaptive capacity is positively correlated with aspects of resource dependency, including attachment to place, financial security, having a strategic approach to business and employability (Marshall 2010); and (3) differences in individuals’ levels of resource dependency also contribute to their varying levels of vulnerability (Marshall et al. 2014). Thus, graziers’ vulnerability to climate change arises from a complex interaction between their adaptive capacity and dependence upon the grazing resource, which means that graziers who are the most sensitive to climate change (i.e. the most resource dependent) are not always the most vulnerable because high levels of adaptive capacity may offset high levels of resource dependence (Marshall et al. 2014).

Where Marshall and colleagues’ work is focused at the level of individual graziers, Nelson et al. (2010) provide a national-level study of the vulnerability of Australian rural communities using large geographic areas (statistical divisions, the largest and most stable spatial unit within each state/territory used by the Australian Bureau of Statistics (ABS) prior to 2011) as the unit of analysis. They conceptualise vulnerability as the intersection between exposure-sensitivity and adaptive capacity. In the case of the exposure-sensitivity dimension, they include the forward-looking aspect of vulnerability by modelling changes in rainfall, pasture growth and farm incomes. In contrast, their treatment of adaptive capacity is based upon a static assessment using data from national agricultural surveys.

Together, these studies examining the socio-economic aspects of climate change vulnerability offer an insightful complement to the biophysical and farm profitability projections. Notably, they reveal the highly variable nature of farmers’ and rural communities’ vulnerability to climate change (Crimp et al. 2010; Nelson et al. 2010; Marshall et al. 2014) and the utility of combining socio-economic analyses alongside biophysical projections (Nelson et al. 2010). However, while the three studies utilising the rural livelihoods framework (Crimp et al. 2010; Nelson et al. 2010; Brown et al. 2015) incorporate information about projected changes in climate, none of them include assessments of how graziers’/rural communities’ adaptive capacity may be reconfigured across similar timescales. Similarly, the works by Marshall and colleagues do not include this forward-looking dimension. Although adaptive capacity may be described as the necessary preconditions for adapting to change (Gallopín 2006) and, therefore, understanding present-day adaptive capacity is relevant, when used alone, static assessments do not capture the dynamic nature of social and economic change in rural communities and agricultural industries. Thus, there is considerable scope for methods of analysis that more comprehensively account for the potential socio-economic futures of the grazing sector specifically and the futures of the localities in which graziers are located more broadly (e.g. changes in local labour markets, prevalence of socio-economic disadvantage, health and lifestyle trends).3 Our contention here is that inclusion of an anticipatory component for the socio-economic aspects examined in vulnerability assessments contributes to filling a critical knowledge gap in the current body of knowledge about the vulnerability of the Australian grazing sector, as well as to the practice of vulnerability assessments more broadly. Before presenting the results and analysis from our socio-economic vulnerability assessment, we provide a profile of our region of interest, the east coast grazing sector.

What’s at stake? Profile of the east coast grazing sector

We used the ABS’ employment and value of agricultural commodities produced (VACP) classifications to define the east coast grazing sector in the Fitzroy, Northern Rivers and Hunter-Central Rivers natural resource management regions. (See Smith et al. 2016 and Online Resource 1 for details about how these data were compiled). The employment data were obtained from the 2011 Australian Census of Population and Housing, while the VACP data were obtained from the 2010/2011 Australian Agricultural Census. Thus, the vulnerability assessment reported in this paper represents a snapshot embedded within the prevailing economic and climatic conditions at the time these data were generated. In the 12 months prior to the two ABS censuses from which data were drawn, there was a turnaround in the extreme drought conditions experienced throughout much of Australia during the first decade of the twenty-first century (the Millennium drought). During 2010/2011, the east coast grazing sector received well above average rainfall, conditions that led to a reduction in the sales of beef cattle for slaughter as farmers rebuilt their herds (Thompson and Martin 2012). While overall climatic conditions improved, farmers had to contend with severe flooding events, and most damages were to farm and public infrastructure rather than in the form of direct stock losses (Thompson and Martin 2012). From an economic perspective, between 2009/2010 and 2010/2011, farm cash income for beef producers increased on the back of increased beef cattle prices. Across the same time period, farm cash income remained unchanged for Queensland dairy farmers in line with steady milk prices, while it declined for New South Wales dairy farmers following reduced milk prices (Martin et al. 2012). While this contextual backdrop influenced the ABS data upon which this vulnerability assessment is based, the utility of the approach stems from the way in which it provides a low-cost, rapid assessment process that can direct attention towards subregions that require more specific and contextualised investigations (Smith et al. 2016).

The grazing sector dominated agricultural employment and the VACP in each of the regions. In Northern Rivers and Hunter-Central Rivers, the percentage of the agricultural workforce employed in the grazing sector was the same as the national agricultural workforce (55%), while in Fitzroy, a much higher percentage of agricultural workers was employed in the grazing sector (79%). With respect to the VACP, the grazing sectors in each of the regions made larger contributions to the regional VACP (Fitzroy, 68%; Northern Rivers, 50%; Hunter-Central Rivers, 51%) than the national grazing sector (38%) made to the total VACP in Australia.

A more nuanced understanding of the importance of the grazing sector in each region may be obtained by disaggregating the grazing sector into subsectors, and then situating these data within the wider state and national contexts. Figure 1, which also serves to locate the study in Australia, shows the composition of the grazing sector in each region, as well as the composition of the national grazing sector. These data demonstrate the dominance of beef cattle farming within each region’s grazing sector.
Fig. 1

The composition of the grazing sector in Australia and three natural resource management regions (Australian Bureau of Statistics 2011, 2012)

Situating the regional data within the Australian and the respective state data provides an indication of the relative importance of the east coast grazing sector and each region’s grazing sector (see Online Resource 1 for detailed data). Cumulatively, the east coast grazing sector employed 10% of Australia’s grazing sector workforce in 2011 and contributed 8% of Australia’s gross value of grazing commodities produced in 2010–2011. At the state level, the Fitzroy grazing sector employed almost one-fifth (19%) of Queensland’s grazing workforce, while Northern Rivers and Hunter-Central Rivers employed 13 and 8%, respectively, of New South Wales’ grazing workforce. With regards to the production of grazing commodities, Fitzroy contributed almost one-fifth (18%) of Queensland’s value of grazing commodities, while Northern Rivers and Hunter-Central Rivers contributed more modest amounts to the New South Wales grazing sector (12 and 8%, respectively). At the grazing subsector level, the three regions employed 20% of the national beef cattle farming workforce and contributed 15% of the national value of cattle/calves slaughterings and disposals. At the state level, Fitzroy produced one-fifth (20%) of Queensland’s value of cattle/calves slaughterings and disposals, while Northern Rivers and Hunter-Central Rivers combined contributed almost 40% of the value of New South Wales’ milk production and 30% of the value of cattle/calves slaughterings and disposals.

In sum, although the east coast grazing sector represented small percentages of Australia’s total grazing workforce and the total value of grazing commodities produced, disaggregating the grazing sector in each region reveals that some subsectors contribute high percentages to the state-wide employment and VACP of the grazing sector. This is particularly the case for the Fitzroy and Northern Rivers grazing sectors. If adverse economic or environmental conditions impact these regions, up to 19% of Queensland’s grazing workforce and 13% of New South Wales’ grazing workforce may be affected. Similarly, up to 18% of Queensland’s and 12% of New South Wales’ total value of grazing commodities may be at stake. In the next section, we briefly outline the methods used to assess socio-economic vulnerability.

Methods

The potential vulnerability of the grazing sector in each region was assessed using spatial snapshots of five factors known to shape socio-economic vulnerability in New South Wales and Queensland: (1) reliance on agriculture, (2) geographic remoteness, (3) socio-economic disadvantage, (4) economic diversity and (5) age (Smith et al. 2015). Each factor was represented in ArcGIS using readily available data from the ABS. Table 1 summarises the factors, indicators and their interpretation for assessing socio-economic vulnerability (see Online Resource 1 for further information about the data sources used).
Table 1

Factors, indicators and data sources used to assess socio-economic vulnerability

Factor

Indicator

Categories

Interpretation

   

When compared to other areas of a given region…

Reliance on agriculture

Percentage of the labour force employed in agriculture

Four- or five-class equal interval (20%) classification scheme

…areas characterised by high percentages of the labour force employed in agriculture may have high potential vulnerability

Geographic remoteness

ABS’ Remoteness Structure

Five categories: (1) major cities, (2) inner regional Australia, (3) outer regional Australia, (4) remote Australia and (5) very remote Australia (Pink 2013a)

…areas that are more remote may have high potential vulnerability

Socio-economic disadvantage

ABS’ Index of Relative Socio-economic Advantage and Disadvantage

Five categories: scores 1–2, 3–4, 5–6, 7–8, 9–10. High scores indicate areas with relatively high levels of advantage and relatively low levels of disadvantage (Pink 2013b).

…areas characterised by high levels of socio-economic disadvantage may have high potential vulnerability

Economic diversity

Hachman Index

Five categories: 0.01–0.20; 0.21–0.40; 0.41–0.60; 0.61–0.80, 0.81–1.00. Hachman scores closer to 1 indicate a more diverse economy (Thomsen et al. 2012).

…areas with low economic diversity may have high potential vulnerability

Age

Age profiles of the grazing sector workforces

Six age groups were created: (1) 15–24 years, (2) 25–34 years, (3) 35–44 years, (4) 45–54 years, (5) 55–64 years and (6) 65 years and older. Then, four age cohorts were calculated: (1) owner/managers 65 years and older, (2) owner/managers aged 25–54 years,a (3) employees 65 years and older and (4) employees aged 25–54 years.a

…grazing workforces with high percentages of workers aged 65 years or older may have high potential vulnerability arising from the increased physical sensitivity of older people. …grazing workforces with high percentages of workers aged 25–54 years may have high potential vulnerability because working aged adults may have reduced adaptive capacity arising from adverse impacts on their business property combined with adverse social impacts with their having dependent children

aClemens et al. (2013) conclude that adults of ‘working age’ were disproportionately affected by the 2010/2011 Queensland flood and cyclone disasters when compared to older people. In particular, they identify people under 55 years as more likely to have been exposed to and affected by property damage than older adults. They hypothesise that this finding may reflect the “greater likelihood that this age group participates in the labour force, owns an income-producing property, and is financially responsible for dependents”. Here, we use the age cohort 25–54 years to reflect those in the grazing workforce who are likely to have this combination of commitments. While Clemens et al. (2013) do not separate the impacts of the flood and cyclone disasters on the grazing sector from the broader Queensland population, Smith et al. (2015) report Clemens et al.’s age analysis is more nuanced than typical approaches that tend to focus on the vulnerability of people at either end of the age spectrum and is therefore one of the best available approaches to consider the influence of age on vulnerability to environmental hazards

The characteristics of the data necessitated some customisation of the scale at which the regional maps were created for each factor. Reliance on agriculture and socio-economic disadvantage were represented at the ABS’ geographic unit statistical area 1 (SA 1).4 Economic diversity was represented at the ABS’ statistical area 2 (SA 2) because this is the smallest scale at which place of work data are available. The age profiles were presented using subregional aggregations of SA 2s, and geographic remoteness was represented using the ABS’ remoteness structure 2011. Customising the maps in these ways was possible because in this approach the maps remain disaggregated for the purposes of transparency and to allow for alternative interpretations by different user groups (Smith et al. 2016).

Each factor and its corresponding map are considered one line of evidence for potential socio-economic vulnerability. Areas within each region where multiple lines of evidence intersect suggest higher potential vulnerability than areas in which fewer lines intersect. Areas of potential high vulnerability are then compared to the spatial distribution of the grazing sector which was indicated by the (1) percentage of the gross value of grazing commodities produced and (2) percentage of the labour force employed in grazing. The commodity and employment classifications and data sources used were the same as those used to define the grazing sector (see Fig. 1 and Online Resource 1). The percentage of the labour force employed in grazing was mapped at SA 1 (Australian Bureau of Statistics 2011). The gross value of grazing commodities produced was mapped at SA 2, the smallest geography at which the data are available (Australian Bureau of Statistics 2012).

Results

Our socio-economic vulnerability assessment revealed six grazing subregions across the three NRM regions: (1) southern Fitzroy, (2) northern/central Fitzroy, (3) northwest Northern Rivers, (4) southwest Northern Rivers, (5) northwest Hunter-Central Rivers and (6) northeast Hunter-Central Rivers (Fig. 2). This multiple lines of evidence approach enables comparative analyses: (1) among grazing subregions across the east coast grazing sector and (2) between the two grazing subregions within each NRM region. We present our findings from each of these analyses in turn.
Fig. 2:

Location of grazing subregions: a) Fitzroy; b) Northern Rivers; c) Hunter-Central Rivers and high potential vulnerability

The Australian east coast grazing sector

Across the east coast grazing sector, the two Fitzroy subregions were characterised by the highest potential vulnerability. When compared to the other four subregions, southern Fitzroy and northern/central Fitzroy were potentially the most vulnerable on four out of five lines of evidence (Table 2). They were (1) the most remote subregions, entirely located in areas categorised as remote or very remote; (2) characterised by very low economic diversity (Hachman scores 0.01–0.20); and (3) characterised by the largest percentages of employees and owner managers aged 25–54 years. In addition, southern Fitzroy was the most reliant on agriculture (up to 100% of the labour force employed in agriculture) (see Online Resource 2).
Table 2

Summary of subregions with high potential vulnerability for each factor

 

Reliance on agriculture

Geographic remoteness

Socio-economic disadvantage

Economic diversity

Age profiles

65 years and older

25–54 years

Owner/managers

Employees

Owner/managers

Employees

First (i.e. highest potential vulnerability)

Southern Fitzroy

Northern/central Fitzroy

NORTHWEST NR

Northern/central Fitzroy

Northeast HCR

Northwest HCR

Northern/central Fitzroy

Southern Fitzroy

Southern Fitzroy

Southern Fitzroy

SOUTHWEST NR

Northern/central Fitzroy

Second

SOUTHWEST NR

NORTHWEST NR

Northeast HCR

Northwest HCR

NORTHWEST NR

SOUTHWEST NR

Southern Fitzroy

NORTHWEST NR

Northern/central Fitzroy

Northwest HCR

Northeast HCR

Northern/central Fitzroy

Northwest HCR

Northeast HCR

Northwest HCR

Northeast HCR

Third

Northwest HCR

Southwest NR

Northwest HCR

Southwest NR

Southern Fitzroy

Southwest NR

Northeast HCR

Northwest NR

Southern Fitzroy

Fitzroy subregions = italics; Hunter-Central Rivers subregions = bold; Northern Rivers subregions = all caps

In contrast to the two Fitzroy subregions, in the Northern Rivers and Hunter-Central Rivers subregions, fewer lines of evidence intersected, which suggests lower potential vulnerability (see Online Resources 2, 3 and 4). There were also intra-regional differences in potential vulnerability; these differences are examined below.

Fitzroy (Queensland)

The two Fitzroy subregions contributed similar percentages of the regional value of grazing commodities (approximately 33%) and employed similar numbers of people, although the structure of the workforces differed. In northern/central Fitzroy, the number of owner/managers was approximately the same as the number of employees, while in southern Fitzroy, there was twice as many owner managers as there was employees.

The potential vulnerability of northern/central Fitzroy and southern Fitzroy was similar but shaped by different influences. Southern Fitzroy was more reliant on agriculture and its grazing workforce was characterised by higher percentages of owner/managers and employees aged 65 years or older. In contrast, northern/central Fitzroy was characterised by higher levels of socio-economic disadvantage and its workforce had a higher percentage of owner/managers aged 25–54 years. Otherwise, the subregions were comparable in terms of geographic remoteness, economic diversity (specialised local economies) and the percentages of employees aged 25–54 years (see Online Resource 2).

Northern Rivers (New South Wales)

The two Northern Rivers subregions displayed similar levels of potential vulnerability and made similar sized contributions to the value of grazing commodities produced in the region (~ 30%), but the southwest grazing sector employed approximately 300 more people than the sector in the northwest. However, the factors that shaped their socio-economic vulnerability differed. The northwest Northern Rivers subregion was characterised by higher levels of socio-economic disadvantage (deciles 1–6) than in the southwest (deciles 1–10), and the influence of geographic remoteness may be exacerbated in the northwest by the lack of a large regional centre such as Armidale located in the southwest. In contrast, southwest Northern Rivers was more reliant upon agriculture (up to 80% of the labour force was employed in agriculture) than northwest Northern Rivers (up to 60% of the labour force was employed in agriculture). However, in both cases, the percentage of the labour forces employed in grazing approached equality with the percentage of the labour forces employed in agriculture, which suggests there are limited alternative agricultural employment opportunities should the grazing sector experience a downturn. With regards to the age profiles of the grazing workforces, both subregions had high percentages of owner/managers and employees aged 65 years or older, but the southwest workforce was characterised by higher percentages of owner/managers and employees aged 25–54 years than in northwest Northern Rivers (see Online Resource 3).

Hunter-Central Rivers (New South Wales)

Northwest Hunter-Central Rivers was characterised by higher potential vulnerability than northeast Hunter-Central Rivers. This assessment primarily reflects the lower economic diversity in the northwest subregion when compared to the northeast and differences in the age profiles of the grazing workforces. With the exception of Scone, an urban centre in the northwest, the northwest subregion was characterised by specialised local economies (Hachman scores 0.01–0.20), while the economies in the northeast were moderately diverse.

In the case of the grazing workforce age profiles in northwest and northeast Hunter-Central Rivers, the percentages of owner/managers aged 65 years or older were at least four times larger than the total national workforce, suggesting high potential vulnerability. There was a similar pattern for grazing employees aged 65 years or older. In the context of the six east coast grazing subregions, the Hunter-Central Rivers subregions were ranked second and third in terms of vulnerability associated with older workforces. Similarly, owner/managers and employees aged 25–54 years were comparatively low compared to the total national workforce and the other grazing subregions. This suggests lower potential vulnerability. However, the workforce in the northwest subregion had higher percentages of owner/managers and employees aged 25–54 years than the northeast subregion and, therefore, may be more vulnerable. The influences of reliance on agriculture, geographic remoteness and socio-economic disadvantage were similar in both subregions (see Online Resource 4).

Discussion: anticipating future drivers of socio-economic vulnerability in the east coast grazing sector

The central premise of this article is that static assessments of socio-economic vulnerability are not sufficient for anticipatory adaptation planning processes. Thus, in this section, we extend our snapshot assessment by evaluating how the socio-economic vulnerability of the east coast grazing sector may be exacerbated or reduced in light of emerging environmental, economic and social trends that are anticipated to significantly alter governance processes, business models and social systems throughout Australia (Hajkowicz et al. 2012). In doing so, we seek to broaden the debate about vulnerability to climate change impacts to consider other influences that may reconfigure the dynamics of socio-economic vulnerability.

Increased scarcity of and competition for natural resources and the implications for agricultural production

Natural resources that are essential to human survival and lifestyles are becoming increasingly scarce, challenging the future quality of life of growing populations. Overcoming these challenges will be facilitated by innovation in the private and public sectors and within communities more broadly (Hajkowicz et al. 2012). The implications of these trends on the east coast grazing sector present opportunities and challenges. On the one hand, increased demand and changing diets (e.g. demand for increased meat consumption) from the rising middle classes in developing countries offers new markets for east coast graziers. On the other hand, increased demand for these products places additional pressure on agricultural inputs such as water and energy, potentially inflating production costs. In addition, increased demand for these resources from non-agricultural users will likely heighten competition and further challenge agriculture’s status as a privileged user of water (Smith and Pritchard 2014). If not successfully negotiated, these dynamics may exacerbate the vulnerability of all the grazing subregions, but may be especially heightened in Fitzroy owing to its locational disadvantage relative to Hunter-Central Rivers and (to a lesser extent) Northern Rivers. Thus, the challenges posed by increased production costs may require substantially greater levels of innovation in production systems and access to markets in Fitzroy.

Changing climatic conditions: shifting production zones suited to grazing

The world’s ecological habitats and biodiversity are in decline as a result of human activities. Continued population growth, industrialisation and climate change will place more pressure on the world’s natural habitats, plant and animal species (Hajkowicz et al. 2012). Altered climatic conditions will reconfigure local biodiversity and change growing conditions that will produce spatially differentiated consequences for agricultural production and the economic value generated. These changes may force or allow for farm system diversification (or even transformation). In a recent study of the future suitability for grazing in the east coast NRM regions, the areas most suited to grazing in Fitzroy are anticipated to shift and contract south and east; in Hunter-Central Rivers (now renamed Hunter Local Land Services Region), suitable areas for grazing will contract in the west, but increase in some eastern areas (Hosking et al. 2014a, b).5 Thus, some currently productive grazing regions may become marginal and, therefore, increasingly vulnerable, while vulnerability in other regions may decrease as they become more important to regional and local economies. These trends may benefit southern Fitzroy and northeast Hunter-Central Rivers, while disadvantaging northern/central Fitzroy and northwest Hunter-Central Rivers.

The subsequent disruption to predator-prey and plant-insect relationships arising from changed climatic conditions may exacerbate agricultural pests and weeds, demanding innovative management systems, the success of which will be shaped by the availability of workers with the relevant training and skills. These challenges may be more difficult to overcome in the subregions with lower percentages of younger workers who may have a higher propensity for technology adoption than older workers, namely both of the Hunter-Central Rivers’ subregions and northwest Northern Rivers.

Recalibrated export markets and changing consumer food preferences

Marked shifts in the world’s economy (e.g. west to east and north to south) are projected to recalibrate export markets, trade relations, business models and cultural ties. These economic shifts are accompanied by growing middle class populations in Asia and South America who will have changing consumer preferences (Hajkowicz et al. 2012). Increased economic ties with Asia (and South America) offer the grazing sector new and growing markets for high protein foods such as meat and dairy products. The capacity of graziers to take advantage of these opportunities will be shaped (in part) by climatic changes rendering particular localities more (or less) suitable for high protein commodities (see above).

New economic relations may also create opportunities for the grazing sector through increased levels of foreign investment enabling new business models. Such investment will likely concentrate in particular areas where prevailing conditions are more conducive to investment needs—meaning that other areas will be bypassed—potentially exacerbating (or creating) disadvantage (Pritchard and Tonts 2011) and, in turn, exacerbating vulnerability. Grazing subregions already characterised by high levels of disadvantage, such as northwest Northern Rivers and northwest Hunter-Central Rivers, will likely have the most to gain (or lose).

Shifts in the world’s economy will also create challenges for the grazing sector in developed countries such as Australia. Developing nations are likely to increase their contributions to world commodity supplies, potentially placing further pressure on Australian producers to tightly manage production costs to remain competitive in the global market place. In turn, these dynamics may be more acutely experienced in the more remote subregions located further from transportation networks, processing supply chains and infrastructure (e.g. northern/central Fitzroy).

The changing composition of agricultural labour markets

Australia’s population is ageing which has implications for retirement models and the composition of labour markets (Hajkowicz et al. 2012). In the context of wider agricultural change (e.g. new production systems, precision farming technologies, interaction with new markets), successful adaptation to variability in environmental and economic conditions will depend upon how well graziers are able to source the required skills from the available labour markets.

The local labour markets in each of the grazing subregions are likely to become older at rates similar to the Australian population (Regional Australia Institute 2014). However, a more detailed study found that the rural populations in Northern Rivers and Hunter-Central Rivers areas have aged faster than the wider Australian population since 1981 (Smailes et al. 2014).6 These trends, partly shaped by continued youth out-migration from Australia’s rural and regional areas, may exacerbate the vulnerability of the grazing sector because it may become increasingly reliant upon older workers who are likely to be have increased physical sensitivity to climate changes (e.g. increased temperatures) (Vaneckova et al. 2008). These dynamics may be less abrupt for both Fitzroy subregions and southwest Northern Rivers because they had higher percentages of younger workers and owner/managers than the other subregions.

Notwithstanding this potential for increased vulnerability arising from changes in labour market composition, longer lifespans and more active older people may make available to graziers new, flexible labour markets for seasonal work. In addition, tapered retirement models that enable people to remain in the workforce for longer, together with sea change and tree change migration patterns, may bring new workers with marketing and business skills (for example) from non-agricultural sectors into closer proximity of grazing subregions upon which the sector may capitalise. A potential downside of these dynamics is that innovation and creativity may be constrained because younger workers may be confronted with increased labour market competition (see The Creative Class, Florida 2002).

Increased connectivity: opportunities for capitalising on new technologies, information and market access

Individuals, communities, governments and businesses are becoming increasingly connected through technologies that are re-shaping the ways in which people interact, work, conduct business and access services and information (Hajkowicz et al. 2012). The implications of these trends for the east coast grazing sector concentrate around opportunities for marketing, collaboration, information sharing, access to markets and potential production efficiencies.

Virtual technologies have the potential to enable graziers—particularly those located in more remote areas—to access new information and advice across multiple scales. The immediacy of information will also be improved (e.g. weather forecasts), enabling precision farming techniques to be tested and implemented with subsequent benefits for productivity and cost efficiencies. Such technologies have the potential to reduce the differential between urban and remote regions, thereby supporting the continued viability of grazing districts located further from essential infrastructure, regional centres and transport hubs (e.g. northern/central and southern Fitzroy). Of course, the influence of virtual/digital technologies will be spatially variegated, dependent upon the provision of the necessary infrastructure (e.g. Australia’s national broadband network). Thus, existing vulnerabilities may be further entrenched in grazing districts that do not receive access to infrastructure essential for reaping the benefits of increased connectivity, and capitalising upon new technologies may be more difficult in districts already characterised by high levels of disadvantage compared to already advantaged districts (e.g. northwest Northern Rivers vs southwest Northern Rivers).

Opportunities for developing high value-added products and agri-tourism

As incomes grow and people’s discretionary expenditure increases, demand for services and experiences over products will increase. At the same time, people are increasingly expecting environmentally responsible and ethical ‘feel good’ products (Hajkowicz et al. 2012). The implications for the grazing sector of these trends also present as opportunities and challenges. On the one hand, increased expectations among younger people may contribute to high youth out-migration, as well as difficulties with attracting highly skilled workers as they pursue higher paying employment opportunities outside of the agricultural sector. In turn, skill levels among local labour markets may be reduced. On the other hand, expectations among the wider population may provide opportunities for the grazing sector to develop high value-added, ethical products that will allow consumers to trace food items to the source farm. The grazing sector in high amenity areas may also be able to diversify into agricultural experience offerings (e.g. home stays, farm work experience programmes); other areas may be able to leverage off broader tourism marketing campaigns such as ‘Outback Eventures’ in Queensland (Outbacknow 2013), a campaign that encourages urban residents to visit regional Australia to attend events.

Conclusion

Effective anticipatory adaptation planning for climate change is dependent upon decision makers’ access to a wide array of time-sensitive information that canvasses the biophysical and socio-economic domains. Detailed climate projections like those recently released by CSIRO for Australian NRM regions make a vital contribution to planning at scales at which decisions are made. Regional climate projections need to be paired with forward-looking analyses of local socio-economic development factors to guide comprehensive and sustainable strategies to effectively adapt to climate change impacts. In this article, we have attempted to address this pressing need for socio-economic research by situating a snapshot spatial socio-economic vulnerability assessment of the Australian east coast grazing sector within a series of economic, social and environmental trends forecast to shape socio-economic life in the coming decades. While integrating regional climate projections with the multiple lines of evidence approach for assessing socio-economic vulnerability to climate change impacts is beyond the scope of this paper, doing so is a critical area for future research. The approach used here should be viewed as a useful addition to the practitioner’s and researcher’s methodological toolkits for broadening the debate about vulnerability to climate change impacts and for considering other influences that may shape the dynamics of socio-economic vulnerability in the future.

Our findings have several implications. First, they can aid prioritisation of adaptation options suited to the drivers of socio-economic vulnerability in each region. Across the east coast grazing sector, there were differences in overall potential socio-economic vulnerability. The two Fitzroy subregions were characterised by the highest potential vulnerability as indicated by the intersection of four out of five lines of evidence, while in the Northern Rivers and Hunter-Central Rivers subregions, fewer lines of evidence intersected, indicating lower relative potential vulnerability. Notwithstanding these differences in overall potential vulnerability, the assessment revealed common drivers of socio-economic vulnerability in all NRM regions, namely high percentages of the labour forces employed in agriculture and geographic remoteness. In the absence of many alternative agricultural employment opportunities outside of the grazing sector, useful adaptation strategies may emerge from strategic partnerships with regional- and state-led entities developing agricultural experience offerings. Likewise, strategies that focus on strengthening value chain and marketing linkages are likely to be applicable in all the regions to overcome the implications of locational disadvantage. The implications of socio-economic disadvantage and economic diversity on regional socio-economic vulnerability suggest the need for consideration of different factors when developing adaptation strategies. In Northern Rivers and Hunter-Central Rivers, consideration of the implications of high levels of socio-economic disadvantage is warranted, while in Fitzroy, the implications of low economic diversity require attention.

Second, the influence of age upon socio-economic vulnerability in the grazing subregions highlights new ways that age might be utilised in vulnerability assessments. Consistent with existing interpretations of the influence of age on vulnerability to climate change impacts, the high proportions of the grazing workforces aged 65 years and older contributed to socio-economic vulnerability in all three regions. However, while the age of the grazing workforces in the Fitzroy subregions contributed to their overall socio-economic vulnerability, it did so in ways not typically associated with agriculture. The implications of age to the agricultural sector typically focus upon the challenges associated with an ageing farming population (Barr 2014). In Fitzroy, age contributes to socio-economic vulnerability primarily through the high percentages of owner/managers and employees in younger age groups. This finding suggests that the ways in which age is included in vulnerability assessments requires more nuanced treatment than simply focusing upon people 65 years and older. Equally, it is possible that the agricultural sector’s older age profile—when compared to the general Australian population—may contribute to greater resilience and adaptive capacity due to the acquisition of extensive experience and knowledge during a wide range of climatic and economic conditions over farmers’ lifetimes. Examination of the relationships between age, sector and vulnerability in the context of specific hazards (e.g. floods vs droughts) offers a fruitful line for future inquiry.

A third implication of our findings and analysis is that consideration of how cross-sectoral drivers may coalesce to shape future vulnerability provides a dynamic perspective of climate risk. The application of multi-disciplinary trends expected to shape Australia’s future to a sector-specific socio-economic vulnerability assessment provides a dynamic perspective of climate risk, which may be readily applied beyond the regions and sector examined in this paper. In doing so, this approach complements demographic projections and static assessments of socio-economic vulnerability to include a broader understanding of the complex interactions inherent to socio-ecological systems. This approach includes current and future drivers that may exacerbate or reduce vulnerability, and the ways in which these drivers will shape socio-economic development. Understanding these interactions and the implications for socio-economic vulnerability is critical to contemporary adaptation decision-making in a wide range of different problems and geographic contexts.

Footnotes

  1. 1.

    Australia’s 55 mainland NRM regions are part of a regionalised framework for environmental governance that gained momentum during the 1980s and 1990s. These arrangements re-configured the relationships between government, business and civil society for environmental governance by devolving responsibility for NRM to community-based regional entities (Wallington and Lawrence 2008; Gunningham 2009). The structural and governance arrangements differ between states and territories as a result of individual bilateral agreements between the federal government and the respective state/territory government. These organisations also differ markedly in terms of their environmental and demographic characteristics, operational capacities and the land area for which they are responsible (Moore and Rockloff 2006; Robins and Dovers 2007; Robins and Kanowski 2011).

  2. 2.

    The Commonwealth Scientific and Industrial Research Organisation’s (CSIRO) climate change projections cited here are the most comprehensive projections ever released for Australia and were developed specifically for NRM regions. Up to 40 global climate models were used for the simulations. Detailed projections are available for four of the Intergovernmental Panel on Climate Change’s (IPCC) Representative Concentration Pathways (RCPs). Further information is available online at http://www.climatechangeinaustralia.gov.au/en/.

  3. 3.

    This knowledge gap is not unique to assessments of the vulnerability of the grazing sector. For example, a recent systematic review of research examining the impact of climate change on the transmission of Ross River Virus reveals that studies seldom include non-climatic factors, particularly socio-economic factors (e.g. migration patterns, population growth and urbanisation). Consequently, identifying vulnerable communities and formulating comprehensive and sustainable programmes to manage Ross River Virus transmission is inhibited (Yu et al. 2014).

  4. 4.

    Statistical areas are the geographic units used in the ABS’ Australian Statistical Geography Standard (effective from July 2011). SA 1s represent regions with populations in the range of 200–800. SA 2s are the next largest geographic unit which represents regions with populations in the range of 3000–25,000.

  5. 5.

    No directly comparable analysis is available for the Northern Rivers (now renamed the North Coast Local Land Services Region).

  6. 6.

    The Smailes et al. (2014) study spanned only the states of New South Wales, Victoria and South Australia. No comparable analysis is available for the Fitzroy NRM region.

Notes

Funding information

This article received funding from the Department of Climate Change and Energy Efficiency as part of the Natural Resource Management Climate Change Impacts and Adaptation Research Grants Program, under the Natural Resource Management Planning for Climate Change Fund—A Clean Energy Future Initiative (Australia). The views expressed herein are not necessarily the views of the Commonwealth of Australia, and the Commonwealth does not accept responsibility for any information or advice contained herein.

Supplementary material

10113_2017_1251_MOESM1_ESM.docx (33 kb)
Online Resource 1 (DOCX 33 kb)
10113_2017_1251_MOESM2_ESM.docx (1.1 mb)
Online Resource 2 (DOCX 1077 kb)
10113_2017_1251_MOESM3_ESM.docx (979 kb)
Online Resource 3 (DOCX 979 kb)
10113_2017_1251_MOESM4_ESM.docx (922 kb)
Online Resource 4 (DOCX 921 kb)

References

  1. Arthurson K, Baum S (2015) Making space for social inclusion in conceptualising climate change vulnerability. Local Environ Int J Justice Sustain 20:1–17.  https://doi.org/10.1080/13549839.2013.818951 Google Scholar
  2. Australian Bureau of Statistics (2011) Census of population and housingGoogle Scholar
  3. Australian Bureau of Statistics (2012) Agricultural census 2010–11. http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/7101.0main+features2Dec%202012
  4. Barnett J, Graham S, Mortreux C, Fincher R, Waters E, Hurlimann A (2014) A local coastal adaptation pathway. Nat Clim Chang 4:1103–1108.  https://doi.org/10.1038/nclimate2383 CrossRefGoogle Scholar
  5. Barr N (2014) New entrants to Australian agricultural industries: where are the young farmers? Rural Industries Research and Development Corporation, CanberraGoogle Scholar
  6. Baum S, Horton S, Choy DL (2008) Local urban communities and extreme weather events: mapping social vulnerability to flood. Aust J Reg Stud 14:251Google Scholar
  7. Brown PR, Nelson R, Jacobs B, Kokic P, Tracey J, Ahmed M, DeVoil P (2010) Enabling natural resource managers to self-assess their adaptive capacity. Agric Syst 103:562–568.  https://doi.org/10.1016/j.agsy.2010.06.004 CrossRefGoogle Scholar
  8. Brown PR, Hochman Z, Bridle KL, Huth NI (2015) Participatory approaches to address climate change: perceived issues affecting the ability of South East Queensland graziers to adapt to future climates. Agric Hum Values 32:689–703.  https://doi.org/10.1007/s10460-015-9584-0
  9. Clemens SL, Berry HL, McDermott BM, Harper CM (2013) Summer of sorrow: measuring exposure to and impacts of trauma after Queensland’s natural disasters of 2010–2011. Med J Aust 199:552–555.  https://doi.org/10.5694/mja13.10307 CrossRefGoogle Scholar
  10. Cobon DH, Stone GS, Carter JO, Scanlan JC, Toombs NR, Zhang X, Willcocks J, McKeon GM (2009) The climate change risk management matrix for the grazing industry of northern Australia. Rangel J 31:31–49.  https://doi.org/10.1071/RJ08069 CrossRefGoogle Scholar
  11. Collins M, Knutti R, Arblaster J, Dufresne J-L, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 1092–1136Google Scholar
  12. Crimp SJ, Stokes CJ, Howden SM, Moore AD, Jacobs B, Brown PR, Ash AJ, Kokic P, Leith P (2010) Managing Murray-Darling Basin livestock systems in a variable and changing climate: challenges and opportunities. Rangel J 32:293–304.  https://doi.org/10.1071/rj10039 CrossRefGoogle Scholar
  13. Dovers SR, Hezri AA (2010) Institutions and policy processes: the means to the ends of adaptation. Wiley Interdiscip Rev Clim Chang 1:212–231.  https://doi.org/10.1002/wcc.29 CrossRefGoogle Scholar
  14. Dowdy A, Abbs D, Bhend J, Chiew F, Church J, Ekstrom M, Kirono D, Lenton A, Lucas C, McInnes K, Moise A, Monselesan D, Mpelasoka F, Webb L, Whetton P (2015) East Coast cluster report, climate change in Australia. Projections for Australia’s Natural Resource Management Regions. CSIRO and Bureau of Meteorology, AustraliaGoogle Scholar
  15. Florida R (2002) The rise of the creative class: and how it’s transforming work, leisure, community and everyday life. Perseus Book Group, New YorkGoogle Scholar
  16. Gallopín GC (2006) Linkages between vulnerability, resilience, and adaptive capacity. Glob Environ Chang 16:293–303.  https://doi.org/10.1016/j.gloenvcha.2006.02.004 CrossRefGoogle Scholar
  17. Garnaut R (2008) The Garnaut climate change review: final report. Cambridge University Press, MelbourneGoogle Scholar
  18. Ghahramani A, Moore AD (2013) Climate change and broadacre livestock production across southern Australia. 2. Adaptation options via grassland management. Crop Pasture Sci 64:615–630.  https://doi.org/10.1071/CP13195 CrossRefGoogle Scholar
  19. Glavac SM, Hastings PA, Childs IR (2003) Demographic projection as a tool for analysing trends of community vulnerability. Aust J Emerg Manag 18:9Google Scholar
  20. Gunningham N (2009) Environment law, regulation and governance: shifting architectures. J Environ Law 21:179–212.  https://doi.org/10.1093/jel/eqp011 CrossRefGoogle Scholar
  21. Haasnoot M, Kwakkel JH, Walker WE, ter Maat J (2013) Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world. Glob Environ Chang 23:485–498.  https://doi.org/10.1016/j.gloenvcha.2012.12.006 CrossRefGoogle Scholar
  22. Hajkowicz SA, Cook H, Littleboy A (2012) Our future world: global megatrends that will change the way we live. The 2012 revision. CSIRO, Canberra.  https://doi.org/10.4225/08/584ee9706689b Google Scholar
  23. Harle KJ, Howden SM, Hunt LP, Dunlop M (2007) The potential impact of climate change on the Australian wool industry by 2030. Agric Syst 93:61–89.  https://doi.org/10.1016/j.agsy.2006.04.003 CrossRefGoogle Scholar
  24. Hinkel J (2011) Indicators of vulnerability and adaptive capacity: towards a clarification of the science-policy interface. Glob Environ Chang 21:198–208.  https://doi.org/10.1016/j.gloenvcha.2010.08.002 CrossRefGoogle Scholar
  25. Hosking C, Mills M, Lovelock CE (2014a) Climate change and agriculture: a study for the Fitzroy Basin Association. https://www.terranova.org.au/repository/east-coast-nrm-collection/climate-change-and-agriculture-a-study-for-the-fitzroy-basin-association. Accessed 4 May 2015
  26. Hosking C, Mills M, Lovelock CE (2014b) Climate change and agriculture: a study for the Hunter Local Land Services. https://www.terranova.org.au/repository/east-coast-nrm-collection/climate-change-and-agriculture-a-study-for-the-hunter-local-land-services/. Accessed 4 May 2015
  27. Jacobs B, Lee C, Watson S, Dunford S, Coutts-Smith A (2016) Adaptation planning process and government adaptation architecture support regional action on climate change in New South Wales, Australia. In: Leal Filho W (ed) Innovation in climate change adaptation. Springer, Berlin, pp 17–29.  https://doi.org/10.1007/978-3-319-25814-0_2
  28. Lawrence J, Reisinger A, Mullan B, Jackson B (2013) Exploring climate change uncertainties to support adaptive management of changing flood-risk. Environ Sci Pol 33:133–142.  https://doi.org/10.1016/j.envsci.2013.05.008 CrossRefGoogle Scholar
  29. Loughnan ME, Tapper NJ, Phan T, Lynch K, McInnes JA (2013) A spatial vulnerability analysis of urban populations during extreme heat events in Australian capital cities. National Climate Change Adaptation Research Facility, Gold CoastGoogle Scholar
  30. Loughnan M, Tapper N, Phan T (2014) Identifying vulnerable populations in subtropical Brisbane, Australia: a guide for heatwave preparedness and health promotion. ISRN Epidemiol 2014:12.  https://doi.org/10.1155/2014/821759 CrossRefGoogle Scholar
  31. Mach KJ, Planton S, von Stechow C (2014) Annex II: glossary. In: Pachauri RK, Meyer LA (eds) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. Intergovernmental Panel on Climate Change, GenevaGoogle Scholar
  32. Manning M, Lawrence J, King D, Chapman R (2015) Dealing with changing risks: a New Zealand perspective on climate change adaptation. Reg Environ Chang 15:581–594.  https://doi.org/10.1007/s10113-014-0673-1 CrossRefGoogle Scholar
  33. Marshall NA (2010) Understanding social resilience to climate variability in primary enterprises and industries. Glob Environ Chang 20:36–43.  https://doi.org/10.1016/j.gloenvcha.2009.10.003 CrossRefGoogle Scholar
  34. Marshall NA (2011) Assessing resource dependency on the rangelands as a measure of climate sensitivity. Soc Nat Resour 24:1105–1115.  https://doi.org/10.1080/08941920.2010.509856 CrossRefGoogle Scholar
  35. Marshall NA, Marshall PA (2007) Conceptualizing and operationalizing social resilience within commercial fisheries in northern Australia. Ecol Soc 12:1CrossRefGoogle Scholar
  36. Marshall NA, Stokes CJ, Webb NP, Marshall PA, Lankester AJ (2014) Social vulnerability to climate change in primary producers: a typology approach. Agric Ecosyst Environ 186:86–93.  https://doi.org/10.1016/j.agee.2014.01.004 CrossRefGoogle Scholar
  37. Martin P, Thompson T, Phillips P, Bowen B. (2012) Farm performance: Broadacre and dairy farms, 2009-10 to 2011-12 Agricultural Commodities. 2: 129–164.Google Scholar
  38. McKeon GM, Flood N, Carter J, Stone G, Crimp S, Howden M (2008) Simulation of climate change impacts on livestock carrying capacity and production: report for the Garnaut climate change review. Queensland Environmental Protection Agency, BrisbaneGoogle Scholar
  39. Moore AD, Ghahramani A (2013) Climate change and broadacre livestock production across southern Australia. 1. Impacts of climate change on pasture and livestock productivity, and on sustainable levels of profitability. Glob Chang Biol 19:1440–1455.  https://doi.org/10.1111/gcb.12150 CrossRefGoogle Scholar
  40. Moore SA, Rockloff SF (2006) Organizing regionally for natural resource management in Australia: reflections on agency and government. J Environ Policy Plan 8:259–277.  https://doi.org/10.1080/15239080600915600 CrossRefGoogle Scholar
  41. Nelson R, Kokic P, Crimp S, Meinke H, Howden SM (2010) The vulnerability of Australian rural communities to climate variability and change: part II—integrating impacts with adaptive capacity. Environ Sci Pol 13:18–27.  https://doi.org/10.1016/j.envsci.2009.09.007 CrossRefGoogle Scholar
  42. Park SE, Marshall NA, Jakku E, Dowd AM, Howden SM, Mendham E, Fleming A (2012) Informing adaptation responses to climate change through theories of transformation. Glob Environ Chang 22:115–126.  https://doi.org/10.1016/j.gloenvcha.2011.10.003 CrossRefGoogle Scholar
  43. Pink B (2013a) Socio-economic indexes for areas (SEIFA) 2011: technical report. Australian Bureau of Statistics, CanberraGoogle Scholar
  44. Pink B (2013b) Australian statistical geography standard (ASGS): volume 5—remoteness structure. Australian Bureau of Statistics, CanberraGoogle Scholar
  45. Pritchard B, Tonts M (2011) Market efficiency, agriculture and prosperity in rural Australia. In: Tonts M, Siddique M (eds) Globalisation, agriculture and development: perspectives from the Asia-Pacific. Edward Elgar Publishing Ltd, England, pp 29–53Google Scholar
  46. Regional Australia Institute (2014) Talking point: an ageing (regional) Australia and the rise of the super boomer. Regional Australia Institute, CanberraGoogle Scholar
  47. Reisinger A, Kitching RL, Chiew F, Hughes L, Newton PCD, Schuster SS, Tait A, Whetton P (2014) Australasia. In: Barros VR, Field CB, Dokken DJ, Mastrandrea MD, Mach KJ, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, Maccracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part B: regional aspects. Contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 1371–1438Google Scholar
  48. Rickards L, Howden SM (2012) Transformational adaptation: agriculture and climate change. Crop Pasture Sci 63:240–250.  https://doi.org/10.1071/CP11172 CrossRefGoogle Scholar
  49. Robins L, Dovers S (2007) NRM regions in Australia: the ‘haves’ and the ‘have nots’. Geogr Res 45:273–290.  https://doi.org/10.1111/j.1745-5871.2007.00460.x CrossRefGoogle Scholar
  50. Robins L, Kanowski P (2011) ‘Crying for our country’: eight ways in which ‘Caring for our Country’ has undermined Australia’s regional model for natural resource management. Aust J Environ Manag 18:88–108.  https://doi.org/10.1080/14486563.2011.566158 CrossRefGoogle Scholar
  51. Roiko A, Mangoyana R, McFallan S, Carter R, Oliver J, Smith T (2012) Socio-economic trends and climate change adaptation: the case of South East Queensland Australasian. J Environ Manag 19:35–50.  https://doi.org/10.1080/14486563.2011.646754
  52. Smailes P, Griffin T, Argent N (2014) Demographic change, differential ageing, and public policy in rural and regional Australia: a three-state case study. Geogr Res 52:229–249.  https://doi.org/10.1111/1745–5871.12067 CrossRefGoogle Scholar
  53. Smith EF, Pritchard B (2014) Water reform in the 21st century: the changed status of Australian agriculture. In: Dufty-Jones R, Connell J (eds) Rural change in Australia: population, economy and environment. Ashgate, Surrey, pp 169–186Google Scholar
  54. Smith EF, Keys N, Lieske SN, Smith TF (2015) Assessing socio-economic vulnerability to climate change impacts and environmental hazards in New South Wales and Queensland, Australia. Geogr Res 53:451–465.  https://doi.org/10.1111/1745-5871.12137
  55. Smith EF, Lieske SN, Keys N, Smith TF (2016) Rapid regional-scale assessments of socio-economic vulnerability to climate change. Environ Res Lett 11:1–11.  https://doi.org/10.1088/1748-9326/11/3/034016
  56. Solangaarachchi D, Griffin AL, Doherty MD (2012) Social vulnerability in the context of bushfire risk at the urban-bush interface in Sydney: a case study of the Blue Mountains and Ku-ring-gai local council areas. Nat Hazards 64:1873–1898.  https://doi.org/10.1007/s11069-012-0334-y CrossRefGoogle Scholar
  57. Thompson T, Martin P (2012) Australian beef: financial performance of beef cattle producing farms, 2009–10 to 2011–12. ABARESGoogle Scholar
  58. Thomsen DC, Smith T, Stephenson C (2012) Sustainability indicators: annual sustainability trends for the sunshine coast. Report prepared for the Sunshine Coast Council, Queensland, Australia. Sustainability Research Centre, University of the Sunshine Coast, Sippy DownsGoogle Scholar
  59. Vaneckova P, Hart MA, Beggs PJ, De Dear RJ (2008) Synoptic analysis of heat-related mortality in Sydney, Australia, 1993–2001. Int J Biometeorol 52:439–451.  https://doi.org/10.1007/s00484-007-0138-z CrossRefGoogle Scholar
  60. Wallington TJ, Lawrence G (2008) Making democracy matter: responsibility and effective environmental governance in regional Australia. J Rural Stud 24:277–290.  https://doi.org/10.1016/j.jrurstud.2007.11.003 CrossRefGoogle Scholar
  61. Webb NP, Stokes CJ, Scanlan JC (2012) Interacting effects of vegetation, soils and management on the sensitivity of Australian savanna rangelands to climate change. Clim Chang 112:925–943.  https://doi.org/10.1007/s10584-011-0236-0 CrossRefGoogle Scholar
  62. Whish GL, Cowley RA, Pahl LI, Scanlan JC, MacLeaod ND (2014) Impacts of projected climate change on pasture growth and safe carrying capacities for 3 extensive grazing land regions in northern Australia. Trop Grasslands - Forrajes Trop 2:151–153CrossRefGoogle Scholar
  63. White N, Sutherst RW, Hall N, Whish-Wilson P (2003) The vulnerability of the Australian beef industry to impacts of the cattle tick (Boophilus microplus) under climate change. Clim Chang 61:157–190.  https://doi.org/10.1023/A:1026354712890 CrossRefGoogle Scholar
  64. Yu W, Dale P, Turner L, Tong S (2014) Projecting the impact of climate change on the transmission of Ross River virus: methodological challenges and research needs. Epidemiol Infect 142:2013–2023.  https://doi.org/10.1017/S0950268814000399 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Erin F. Smith
    • 1
  • Scott N. Lieske
    • 2
  • Noni Keys
    • 3
  • Timothy F. Smith
    • 4
  1. 1.Sustainability Research Centre, ML28University of the Sunshine CoastMaroochydore DCAustralia
  2. 2.School of Earth and Environmental SciencesUniversity of QueenslandBrisbaneAustralia
  3. 3.University of the Sunshine CoastMaroochydore DCAustralia
  4. 4.Faculty of Arts Business and LawUniversity of the Sunshine CoastMaroochydore DCAustralia

Personalised recommendations