Modeling Regional Grazing Viability in Outback ...

Modelling Regional Grazing Viability in Outback Australia Using Bayesian Livelihood Networks Kostas Alexandridis and Thomas G. Measham CSIRO Sustainable Ecosystems, Townsville and Gungahlin July 2007

Enquiries should be addressed to: Dr. Kostas Alexandridis Regional Futures Analyst CSIRO Sustainable Ecosystems Davies Laboratory PMB Aitkenvale 4814 Ph: +61 7 4753 8630 Fax: +61 7 4753 8650 Email: [email protected]

Dr. Thomas G Measham Human Geographer CSIRO Sustainable Ecosystems Gungahlin Homestead, Barton Hwy GPO Box 284, Canberra, ACT 2601 Ph: +61 2 6242 1789 Fax: +61 2 6242 1705 Email: [email protected]

ISBN: 978 0 643 09498 7

Copyright and Disclaimer © 2007 CSIRO. This work is subject to copyright. The reproduction in whole or in part for study or training purposes is granted subject to inclusion of an acknowledgment of the source. The Copyright Act 1968 governs any other reproduction of this document. CSIRO Sustainable Ecosystems advises that the information contained in this publication comprises general statements based on scientific research. The views expressed, except where stated otherwise, and the conclusions reached in this publication are those of the author(s) of the reporting pages, and not necessarily those of CSIRO Sustainable Ecosystems. The reader is advised and needs to be aware that any such scientific information and views may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO Sustainable Ecosystems (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly form using this publication (in part or in whole) and any information or material contained in it.

Use of this Report The work contained in this report is an output of a joint research project collaboration between CSIRO Sustainable Ecosystems and Tropical Savannas CRC (Cooperative Research Centre for Tropical Savannas Management). Recommended Citation: Alexandridis, K. and Measham, T.G. (2007). Modelling Grazing Viability in Outback Australia using Bayesian Livelihood Networks. CSIRO Technical Report. Canberra, ACT: CSIRO Sustainable Ecosystems, pp. 65. This publication is available in electronic format from: http://www.cse.csiro.au/publications/

Modelling Regional Grazing Viability in Outback Australia Using Bayesian Livelihood Networks Kostas Alexandridis and Thomas G Measham CSIRO Sustainable Ecosystems, Australia

Executive Summary Outback Australia is characterised by multiple competing trajectories to regional social and economic viability, including a tension between agricultural production and other land uses, reflecting broader social and economic values. However, different regions of the outback experience this tension in different ways. In this context, the concept of sustainable livelihoods represents an important way of conceptualising the health and viability of outback regions and the people who live in them. This concept is receiving increased attention in Australia as a way to understand and address the linkages between social and ecological concerns in rural environments. The scope of the research undertaken and reported in this document is to identify and link key social and economic issues affecting the viability and sustainability of livelihoods in Outback regions. Specifically, the research focuses on enhancing our scientific understanding and filling knowledge gaps pertaining to issues of viability and community health in Outback Australia. Our research integrates cultural, social, and economic dimensions with existing ecological and biophysical understanding of these regions. It also improves existing understanding of the network of relationships among livelihood elements that affect natural resource management and regional viability in general. Specific research objectives include: investigating and exploring advanced methodological and modelling techniques such as probabilistic and social networks; linking qualitative with quantitative approaches for socialecological complex systems; enhancing of the contribution of community-driven decision making on pathways to alternative futures and regional priorities; and understanding regional viability and sustainability of livelihood systems from the “ground-up”. Finally, the researchers’ overarching goal is to assist the Tropical Savannas CRC in fulfilling its unique role in Outback Australia in understanding the multiplicity of environmental, economic, cultural and social dimensions and contributing to the sustainability and management of outback regions. An important issue is understanding the factors that influence sustainable livelihoods in different contexts. These issues are explored through a review of literature on the livelihoods concept in general followed by a detailed case study of the factors affecting grazing livelihoods in the upper Burdekin catchment. The upper Burdekin region is strongly oriented towards pastoralism, with a predominance of owner-operated family-based enterprises. The Final Report, July 2007

i

Modelling Regional Grazing Viability in Outback Australia using Bayesian Livelihood Networks

aim of the upper Burdekin case study is to improve understanding of the factors influencing outback livelihoods through a participatory Bayesian Belief Network approach. In the context of regional outback Australia, this report redefines the concept of sustainable livelihoods as a system of livelihood elements that contribute uniquely, collaboratively and conjunctively to the viability of the region, communities and individuals. In other words, the notion of sustainable livelihoods adopted for the scope of this research, moves away from collective capital accumulation, and represents a more fundamental, generative and emergent mechanism for social, economic and environmental system integration. The report demonstrates the methodological and technical elements of a Bayesian livelihoods network for grazing systems in outback Australia. We define a Bayesian livelihoods network as a probabilistic network of relationships among livelihood elements present in a subjective system of heuristic inference. We use qualitative, participatory and communitydriven information to construct a livelihoods network involving issues of viability and sustainability in grazing livelihoods. Beyond the social science basis of our approach we are demonstrating the use of advanced Bayesian network techniques for representing such systems. We describe the model construction process and analyse key drivers of probabilistic elicitation of livelihood elements as graph nodes in the network. We examine different types of nodes and their probabilistic distributions that emerge from (a) self-reported perceptions and inductive inference of citizens and community members; (b) objectively verified elements of the physical and environmental drivers of the livelihoods system, and; (c) heuristically inferred relationships amongst key members and determinants of sustainable grazing livelihoods. We discuss the importance of social science and qualitative research to inform quantitative and inductive paradigms of probabilistic and cognitive inference, using innovative, bottom-up approaches. Finally, the report presents recommendations for future research and the potential role of heuristics in representing dynamic concepts of structure and form in social systems. Fortunately, many outback farming households have a degree of choice in how they go about maintaining a viable livelihood that includes off farm work, diversification into other sectors and financial investments. The drivers affecting these decisions are many and complex. Understanding these is the key to making improved decisions in the long term. The drivers of choices and outcomes presented in a modelling framework must be robust enough to function across a number of theoretical and empirical cases. Such a robust framework renders the use of a heuristic and logical interpretation of actions and outcomes as an essential mechanism of livelihoods representation.

ii

Final Report, July 2007


Table of Contents: 1. Introduction............................................................................................................................... 1 1.1. Aims of this research.......................................................................................................... 1 1.2. Literature review................................................................................................................ 1 1.2.1. The livelihoods concept .............................................................................................. 1 1.2.2. Factors influencing livelihood strategies .................................................................... 4 2. Livelihoods and Bayesian inference: introduction and rationale.............................................. 6 2.1. Case study description ....................................................................................................... 7 2.1.1. Regional characteristics .............................................................................................. 7 2.1.2. Interviews and data collections ................................................................................ 12 2.2. Relevance of the livelihoods concept to outback Australia ............................................. 16 3. The interplay between social systems and inferential heuristics ........................................... 20 3.1. Qualitative social science as a constructive mechanism ................................................. 20 3.2. From qualitative social science to quantitative network construct ................................ 21 4. From measurement to structure: BOLNet .............................................................................. 21 4.1. From theoretical to empirical construct .......................................................................... 21 4.2. Methodological framing of the Bayesian livelihoods network approach ........................ 23 4.3. Qualitative interviews as basis of network structure ...................................................... 24 5. Analysis of qualitative livelihood characteristics .................................................................... 27 5.1. Frequencies and livelihood elements responses ............................................................. 27 6. Probabilistic assessment and composition of BOLNet ............................................................ 31 6.1. The role of network composition and function ............................................................... 31 6.2. Challenges for probabilistic inference ............................................................................. 31 6.3. The BOLNet inference framework ................................................................................... 32 6.4. Construction of BOLNet quantitative components ......................................................... 37 6.4.1. Climatic conditions ................................................................................................... 37 6.4.2. Beef cattle quantity and prices................................................................................. 41 6.5. Probabilistic assessment of the livelihoods network (learning) ...................................... 44 6.5.1. Climate learning components .................................................................................. 44 6.5.2. Beef prices learning components ............................................................................. 46 7. Livelihood network structure sensitivity analysis ................................................................... 48 7.1. Network connectivity ....................................................................................................... 49 7.2. Network centrality ........................................................................................................... 50 7.3. Network structural balance ............................................................................................. 51 7.4. Network critical path ....................................................................................................... 52 7.5. Network structure revisited ............................................................................................. 54 8. Future work and challenges .................................................................................................... 54 8.1. Parameterizing posterior livelihood network distributions ............................................. 54 8.2. Preliminary BOLNet survey analysis ................................................................................ 54 8.3. Pathways to posterior livelihood network assessment ................................................... 57 9. Conclusions and next steps ..................................................................................................... 57 10. Acknowledgments ................................................................................................................. 58 11. Bibliography .......................................................................................................................... 59


iii


List of Tables: Table 1: Factors affecting livelihoods strategies and examples from literature ........................... 5 Table 2: Key spatial and demographic characteristics of the upper Burdekin region, and comparisons with Burdekin, Queensland and Australia regions. Primary data source: enumerated population aggregates across census collection districts from ABS (2001). ..................................................................................................................... 12 Table 3: Key attributes of interview subjects used in the construction of the BOLNet. ............. 14 Table 4: Types of nodal states in BOLNet: black dots represent the presence of the state in the current version of BOLNet, and grey dots represent alternative and feasible configurations for BOLNet construction. ....................................................................... 35

List of Figures: Figure 1: The classic sustainable livelihoods development framework (adapted from DFID, 2001). ............................................................................................................................... 2 Figure 2: The IDS analysis framework of sustainable livelihoods (adapted from Scoones, 1998). ............................................................................................................................... 4 Figure 3: Regional extent and land use of the BOLNet study area ............................................... 8 Figure 4: Australian IBRA bioregions and their respective commodity-oriented classification and use approximated by Holmes (1997). ....................................................................... 9 Figure 5: Geographic distribution of Aboriginal culture groups in the Burdekin region. Source: AIATSIS (2003). The geographic extent of the upper Burdekin region almost coincides with Gugu-Badhum traditional Country. ............................................ 10 Figure 6: Population distribution in the Burdekin Region. Data source: census collection districts of ABS (2001). The overwhelming majority of the upper Burdekin region has a very low population density. ................................................................................ 11 Figure 7: Example of a Burdekin rangeland property ................................................................. 13 Figure 8: The interplay between choice and outcome and a two-dimensional sustainable livelihoods relational continuum. The context of the outback Australia falls within the upper left quadrant of the two-dimensional continuum. ....................................... 18 Figure 9: The theoretical approach and the conceptual framework for the construction of the Bayesian livelihoods network used for the BOLNet model. .................................... 23 Figure 10: The BOLNet Bayesian Outback Livelihoods Network structure as a result of qualitative analysis. The signs adjacent to directed arcs denote the type of qualitative structural relationship in the model. ........................................................... 27 Figure 11: Combined numeric representation of the interview responses (left) and the number of coded references (right) per thematic network node, by respondent role (grazier vs. town folk). The nodes are ranked by lowest significance in the total number of responses and references. ........................................................................... 28 Figure 12: Combined numeric representation of the interview responses (left) and the number of coded references (right) per thematic network node, by respondent gender (male vs. female). The nodes are ranked by lowest significance in the total number of responses and references. ........................................................................... 29 iv



Figure 13: Combined numeric representation of the total number of words coded per thematic network node, by respondent gender (male vs. female) in the left subgraph, and by respondent role (grazier vs. town folk) in the right subgraph. The nodes are ranked by lowest significance in the total number of words coded. ............ 30 Figure 14: The prior Bayesian distributions of the BOLNet Bayesian Outback Livelihoods Network model for the upper Burdekin basin. .............................................................. 36 Figure 15: Three major climatic factors for the upper Burdekin region: (a) Mean annual evaporation, (b) Mean annual temperature, and (c) Mean annual rainfall. Higher values of evaporation and temperature and lower values of rainfall denote more extreme drought conditions. ......................................................................................... 38 Figure 16: Schematic and probabilistic representation of the climatic network in the upper Burdekin Region. Real geographical data for the UB climatic conditions were used to parameterize the scales and relationships of data. The network cluster estimates a unified climatic index (CI), that links further to a model of likelihoods. This form of CI is an additive form. ................................................................................ 39 Figure 17: Schematic and probabilistic representation of the climatic network in the upper Burdekin Region. Real geographical data for the UB climatic conditions were used to parameterize the scales and relationships of data. The network cluster estimates a unified climatic index (CI), that links further to a model of likelihoods. This form of CI is a multiplicative form. ......................................................................... 40 Figure 18: Schematic and probabilistic representation of the Burdekin outback livelihood networks’ climatic cluster, prior to the data training and learning. The probabilistic relationships shown in the network are derived from empirical probability distributions: Extreme Value Distributions for temperature and evaporation, and normal distribution for rainfall....................................................................................... 41 Figure 19: Probability density estimation (Gaussian) of number of beef cattle sold domestically and for live exports. The first row (left and right) of the subgraphs display the total and exported mean annual quantities of beef cattle for the upper Burdekin region respectively. Based on this information a Gaussian kernel estimation was performed on the percentage of exported to total beef cattle sold (lower left subgraph). The time series for the beef sales data are shown in the lower right subgraph. ..................................................................................................... 43 Figure 20: Seasonal (weekly) variation of beef cattle export values in the Northern Queensland region (in cents/Kg). Lines display the weekly and annual means, and the minimum and maximum annual prices. .................................................................. 44 Figure 21: EM-learned probabilistic configuration of the additive climatic z-cluster component. .................................................................................................................... 45 Figure 22: Gradient-learned probabilistic configuration of the additive climatic z-cluster component. .................................................................................................................... 45 Figure 23: EM-learned probabilistic configuration of the multiplicative climatic z-cluster component. .................................................................................................................... 46 Figure 24: Gradient-learned probabilistic configuration of the multiplicative climatic zcluster component. ........................................................................................................ 46


v


Figure 25: E-M learning of Gaussian probability density estimation thresholds of domestic farm-gate beef cattle prices in the Burdekin region, based on time-weighted data series (1978-2005).......................................................................................................... 47 Figure 26: E-M learning of Gaussian probability density estimation thresholds of live export beef cattle prices in the Burdekin region, based on time-weighted price data series (1978-2005). ................................................................................................................... 48 Figure 27: Joint E-M learning of Gaussian probability estimation threshold of both domestic (80%) and export (20%) beef cattle prices in the Burdekin region, based on time-weighted data series (1978-2005). ................................................................... 48 Figure 28: Livelihood network cutpoints for the BOLNet model. The nodes displayed with large blue circles indicate critical nodal cutpoints. The rest of the nodes are denoted with small red circles. ...................................................................................... 50 Figure 29: Variation of the degree of centrality among livelihood elements of the BOLNet model. Larger node sizes denote higher degree of centrality in the network. ............. 51 Figure 30: Variation of the degree of fragmentation among livelihood elements of the BOLNet model. The degree of fragmentation is classified in four colour-coded categories. Higher fragmentation indicates sensitivity to changes and lack of robustness. ..................................................................................................................... 52 Figure 31: Visual results of the critical path (length) algorithm computation. The general direction of nodal connections is shown with the thick grey arrows. ........................... 53 Figure 32: Preliminary profile of the BOLNet survey respondents (n=17). ................................. 55 Figure 33: Livelihood elements average importance ratings. Preliminary data from BOLNet survey responses (n=17). ............................................................................................... 56 Figure 34: Livelihood elements average strength ratings. Preliminary data from BOLNet survey responses (n=17). ............................................................................................... 56

vi



1. Introduction Outback Australia is characterised by multiple competing trajectories to regional social and economic viability, including a tension between agricultural production and postproductivist modes of rural occupancy, reflecting broader social and economic values (Argent, 2002; Holmes, 2002). This has been proposed as a multifunctional transition, “(…) leading to greater complexity and heterogeneity in rural occupancy at all scales” (Holmes, 2006). However, different regions across the outback experience this transition in different ways. In this context, the livelihoods concept, as defined by Chambers and Conway (1992) and discussed by Maru (in Stafford Smith et al., 2003) represents an important way of conceptualising the health and viability of outback regions and the people who live them. The concept of sustainable livelihoods is receiving increased attention in Australia as a way to understand and address the linkages between social and ecological concerns in rural environments (Black, 2005; Stafford Smith et al., 2003). An important issue concerns understanding the factors that influence sustainable livelihoods in different contexts. These themes are explored through a review of literature on the livelihoods concept in general followed by a detailed case study of the factors affecting grazing livelihoods in the upper Burdekin catchment. The upper Burdekin region is strongly oriented towards pastoralism, with a predominance of owner-operated family-based enterprises. The aim of the upper Burdekin case study is to improve understanding of the factors influencing outback livelihoods through a participatory Bayesian Belief Network approach.

1.1. Aims of this research There were two main aims of this research. The first was to provide a review of literature on the livelihoods concept, with consideration of its potential application to outback Australia. The second was to develop a Bayesian Belief Network model of factors affecting livelihoods in the upper Burdekin grazing communities. More broadly, the research has sought to explore the concept of sustainable livelihoods in the upper Burdekin as a case study of a broader program of research supported by the Tropical Savannas CRC under the heading of Outback Livelihoods, complementing other case studies conducted as part of this program. The Outback Livelihoods concept is also the focus of research conducted by the Desert Knowledge CRC, which demonstrates the currency of interest in this area. A key mechanism for linking the different case studies of Outback Livelihoods conducted in the tropical savannas was a two day workshop held in Townsville in April 2007 which. This forum highlighted how these different research projects form part of broader body of work which is well suited to an integrated publication drawing from and integrating the case study reports such as the one presented here.

1.2. Literature review 1.2.1. The livelihoods concept Current use of the livelihoods concept can be traced to the World Commission on Environment and Development (1987) which drew attention to the social and environmental pressures on resource poor households in areas under ecological stress. Final Report, July 2007

1


From this first use, a group of key dimensions have characterised the employment of this concept. It evolved in a development context with the multiple goals of improving equity and prosperity for poor communities whilst improving the ‘environmental balance sheets’ for the better off (Chambers and Conway, 1992; Scoones, 1998). It has been primarily focussed at the household level and has been strongly associated with diversification as a means of increasing prosperity (Hussein and Nelson, 1998). Importantly, use of the term ‘livelihood’ became popular in the development literature as a more encompassing alternative to terms such as income, subsistence and employment (Ellis, 2000). A livelihood encompasses such things as cash income, non-cash exchanges, self produced items, property rights and social relations (Ellis, 1998). Drawing on the work of Chambers and Conway (1992) and others, Scoones (1998, p. 5) summarised the Institute for Development Studies (IDS) definition of a livelihood as: “A livelihood comprises the capabilities, assets (including material and social resources) and activities required for a means of living. A livelihood is sustainable when it can cope with and recover from stresses and shocks, maintain or enhance its capabilities and assets, while not undermining the natural resource base.” Two of the most widely used representations of livelihoods are the Department for International Development’s (DFID) Sustainable Livelihoods Framework and a version of the Framework as presented by the IDS. The DFID framework (Figure 1) summarizes the main components and issues with respect to livelihoods. It does not provide an exhaustive list of the issues to be considered and it should be adapted to meet the needs of different circumstances (DFID, 2001). DFID’s framework was designed particularly to understand the livelihoods of the poor.

Figure 1: The classic sustainable livelihoods development framework (adapted from DFID, 2001).

2



The IDS’s framework for the analysis of sustainable rural livelihoods is shown in Figure 2. The focal points of this analysis is to provide a context to work out which combination of livelihood resources result in the ability to follow which combination of livelihood strategies with which outcomes. Institutional processes which mediate the ability to carry out such strategies and achieve or not achieve such outcomes are also seen to be important (Scoones, 1998). The IDS framework was also designed with specific reference to poverty reduction, rural development and environmental management. Considering the IDS definition, with its emphasis on maintaining and enhancing prosperity and reducing environmental impacts through diversification, the concept of livelihoods is inherently linked to the notion of resilience. Resilience has multiple definitions but our use of it here refers to the ability of a system to maintain its essential structure and function in the face of disturbance or change (Holling, 1973; Levin, 1998). This has been particularly relevant in rural areas where farming alone is frequently insufficient as a means of living. In such contexts, a portfolio of market and non-market activity enhances the resilience of hazard-prone households by spreading risk and increasing options for substitution between diverse livelihood components. Options are increased by nurturing social networks of kin and community so that livelihood diversity has both economic and social dimensions, meaning that it relates more to the notion of well-being than merely income alone (Bezemer and Lerman, 2004; Ellis, 2000; Smith et al., 2001). On the other hand, this represents only one dimension of diversification and resilience, the one that concerns the study of function and form, but not space and time. Spatial and temporal diversification represents an equally important part of regional dynamics and sustainability transitions, however they are beyond the scope of the study presented in this report. Though most research on livelihoods has primarily focussed at the household level, to be environmentally and socially sustainable livelihoods need to maintain or enhance ‘the local and global assets on which livelihoods depend’ and have ‘net beneficial effects on other livelihoods’ (Chambers and Conway, 1992, p. 1). For these reasons the concept has also been linked to community-based natural resource management. For example, Marschke and Berkes (2005) studied how Cambodian villages have to grapple with resource degradation in order to improve livelihoods within their community. In another example, the livelihoods concept has been employed to assist with the development of a community vision in response to shared problems associated with land degradation in Nicaragua (Vernooy et al., 2003). Other authors have applied the concept at a regional scale, looking at ways that the processes of globalisation can work to exclude people from achieving sustainable livelihoods by exploiting low wages and lower levels of regulation (Shucksmith et al., 2003).


3


Contexts, conditions and trends

Livelihood resources

Institutional Processes & Organisational structures

Livelihood strategies

Sustainable Livelihood Outcomes

Livelihood

Policy History

Natural capital

Politics

Economic/ financial capital

Macro-economic conditions

Human capital

Terms of trade Climate

Institutions and Organisations

Social Capital

Agricultural intensification extensification

2. Poverty reduced

Livelihood diversification

3. Well-being and capabilities improved Sustainability

Migration

Agro-ecology Demography

And others…

4. Livelihood adaptation, vulnerability and resilience enhanced 5. Natural resource base sustainability ensured

Social differentiation

Contextual analysis of conditions and trends and Assessment of policy setting

1. Increased numbers of working days created

Analysis of livelihood resources : trade-offs, combinations, sequences, trends

Analysis of institutional/organisational influences on access to livelihood resources and composition of livelihood strategy portfolio

Analysis of livelihood strategy pathways

Analysis of outcomes And trade-offs

Figure 2: The IDS analysis framework of sustainable livelihoods (adapted from Scoones, 1998).

1.2.2. Factors influencing livelihood strategies From early interest in the Livelihoods concept there has been a focus on understanding the ‘determinants’ of livelihoods and the activities and strategies that they are built on (Chambers and Conway, 1992; Ellis, 2000). Based on broad social and economic factors such as income, education and socialisation, it has been noted that many livelihoods are predetermined to a large degree by the circumstances beyond individual control. For these reasons, a key focus amongst the development literature has been to provide opportunities where they may not otherwise exist. Central to the livelihoods approach is to consider the assets of any given household and develop a portfolio of activities that build on those in order to survive and to improve standard of living (Chambers and Conway, 1992). As such, application of the concept has been intrinsically linked to livelihood diversification. Diversification

The reasons for livelihood diversification tend to fall into two broad categories: necessity and choice (Ellis, 2000). Necessity refers to the involuntary rationales for diversification, including social distress such as loss of access to land, personal distress such as declining health, environmental deterioration leading to declining yields, civil disasters such as conflict, and natural disaster such as floods and droughts. By contrast, choice refers to proactive reasons for diversification. These may include seeking out seasonal work opportunities and investing in non-farm businesses for perceived benefits, along with personal interest and skills development. Another way of characterising the different degrees of choice is the notion of ‘coping’ or ‘accumulation’ of livelihood diversity. An improved understanding of how

4



different motivations relate to these broad categories of diversification is an important area for further study (Bezemer et al., 2005). In developing countries, key determinants of income diversification focus on entryconstraints to various livelihood strategies. For example, lack of capital and low level skills are key constraints for poor households in many cases, restricting both on-farm and off-farm livelihood activities (Abdulai and Crolerees, 2001; Smith et al., 2001; Woldenhanna and Oskam, 2001). However these factors are not restricted to developing countries. Access to capital has also been found to be a major constraint amongst marginal farmers in affluent countries. For example, in Belgium lack of capital has been observed to greatly restrict the options for onfarm diversification activities, making off-farm activities the most accessible strategy to marginal farmers (Meert et al., 2005). Other factors affecting the mix of livelihood activities include the roles of education, such as levels of literacy, age, gender and social networks, in explaining the difference between those embracing traditional agriculture and labour intensive resource harvesting (e.g. gathering of forest products) and non-traditional strategies such as skilled roles in service industries, (Abdulai and Crolerees, 2001; Smith et al., 2001). More general factors include the availability of physical infrastructure, particularly roads, as well as historical settlement patterns (Smith et al., 2001). Diversification is also a frequently used strategy for risk management in agriculture in developed countries (Katchova, 2005). A possible downside to diversification is a potential ‘discounting’ effect on farm values for diversified farms, which can vary depending on the nature of the farm and the nature of the diversification. In the past, a positive relationship between farm size and diversification has been observed, such that larger farms tend to be more diversified (Pope and Prescott, 1980). However, more recently it has been observed that smaller family farms have employed a range of diversification strategies including off-frm income either by choice or by necessity to remain on the farm (Mascarenhas, 2001; Meert et al., 2005). From the literature reviewed for this research a series of general factors affecting livelihoods strategies is proposed in Table 1. Table 1: Factors affecting livelihoods strategies and examples from literature General factors influencing household livelihoods

Available financial resources

Available skills

Level of education

Family size and structure


Comments

Financial entry barriers restrict livelihood options Different levels of skills are required for wage labour, contract services and self employment opportunities, emphasising the importance of training schemes to improve rural livelihoods. Households with higher levels of education are more likely to participate in non-farm sectors as part of their livelihood activity mix. Larger families tend to be more likely to engage in non-farm income. Gender roles affect options for livelihood activities but sometimes these shift in female-headed households.

Examples from literature

Barrett et al. (2001) Block and Webb (2001) Meert et al. (2005)

Woldenhanna and Oskam (2001) Abdulai and CroleRees (2001)

Reardon et al. (1992)

5


Breadth of social networks

Property size Access to physical infrastructure Population changes

Seasonal conditions Access to land

Choice Policies and institutional factors

Larger social networks can assist access to new livelihood activities

Smith et al. (2001)

Larger properties have more opportunities to diversify on farm. Smaller properties tend to rely more on off-farm diversification for survival.

Pope and Prescott (1980) McNally (2001)

Access to infrastructure such as roads or ports influences opportunities for livelihood diversification.

Smith et al. (2001)

Can include population growth due to settler schemes; emigration as part of rural population decline in developed countries Coping with or preparing for adverse seasonal factors such as droughts are major rationale for diversification. Pre-existing land assets can be available for additional uses (e.g. farm stay tourism) whilst limited access to land through lack of capital or tenure restrictions restricts diversification options. Choice can include proactive reduction of risk or selecting the least unattractive option in a difficult times. Agricultural adjustment schemes and welfare policies support programs for industry change and attempt to influence livelihood decisions.

Black (2005) Koczberski and Curry (2005) Valentine (1993) Ellis (1998) Koczberski and Curry (2005)

Ellis (2000) Chaplin et al (2004) Vandermeulen et al. (2006)

2. Livelihoods and Bayesian inference: introduction and rationale This paper aims to provide theoretical and methodological support for alternative pathways for participatory, community-driven and flexible approximations of sustainable livelihoods systems, with emphasis on the grazing systems in outback Australia. We aim to demonstrate the usefulness, flexibility and emerging heuristic properties of Bayesian networks as innovative mechanisms that capture the systemic complexity of real-world elements of livelihoods and viability, as well as the depth and breadth of cross-linkages and feedbacks existing within such a livelihoods system. Bayesian networks have been gaining recognition in addressing many real-world environmental problems (Cain et al., 1999; Liu and Wellman, 2004). For example, Bayesian techniques have been used in Environmental Impact Analysis (Perdicoúlis and Glasson, 2006), multi-objective evaluation and/or optimization problems (Dorner et al., 2007), landscape pattern and classification (Brett, 2006) including remote sensing and satellite imagery classification problems (Kiiveri et al., 2001; Park and Stenstrom, 2006), point- and nonpointpollution detection and estimation methods (Dorner et al., 2007; Moreno et al., 2005; Sadiq et al., 2006), various natural processes and flows (Barros, 2005; Robertson and Wang, 2004) including water quality and quantity problems (Borsuk et al., 2003; Stow et al., 2003), estimation of wildlife populations (Marcot et al., 2001), agent-based, artificial neural networks and other dynamic and intelligent modelling techniques (Busetta and Ramamohanarao, 1998; Hélie et al., 2006; Lei et al., 2005; Maier and Dandy, 2000), to name a few. Some of the important advantages of Bayesian networks involve the use of the causal independence assumption1 (Heckerman and Breese, 1994; Sanscartier and Neufeld, 2005), and the potential

1

According to the causal independence assumption, a factorization of the conditional joint probability of a variable can be achieved, “given” the state of its parent variables or nodes. This factorization is not causally dependent, in the sense that any associative (e.g., “associated to”, “related to”, “similar to”, “connected to”, etc) or logical (e.g., 6



for using nonparametric and often non-numeric estimation of probabilities for uncertain propositions involving key probabilistic relationships inferred from qualitative or semiquantitative data (Griffin and Steel, 2004; Lenk, 1984; Tapia and Thompson, 1978). The latter ability of the Bayesian networks becomes a key feature as the amount and completeness of the information available in the parameter-space of inference decreases. In other words, the presence of highly uncertain relationships and incomplete information in the knowledge and variable base, increases the significance of nonparametric and nonnumeric estimation of key Bayesian coefficients (priors and posteriors). The next logically inferred methodological jump has been realized in recent years, via the use of Bayesian network techniques to bridge two relatively distinct scientific methods: qualitative and quantitative methods and analyses. The combined use of quantitative and qualitative sets of relationships under the same modelling and methodological regime (Bayesian Networks), mainly achieves : a. Bringing together quantitative natural, environmental and resource management sciences with social, economic and cognitive sciences. b. Allowing for qualitative assessments (including beliefs, narratives, stories, etc.) to be translated to subjective (Bayesian) probabilities, in the same way that quantitative (frequentist) probabilities can

2.1. Case study description 2.1.1. Regional characteristics Our study analyses the livelihood elements of a small regional grazing system in the upper Burdekin region of Northern Queensland (Figure 3). It is located west of Townsville and represents a sub-catchment of the Burdekin River. The region includes the town of Charters Towers, the Clarke River, and a section of the Burdekin River. Part of the dry tropics climatic zone, the region is representative of many commonly encountered grazing systems throughout central and northern Australia (Figure 4). In essence, it consists of a number of relatively large cattle grazing properties, relatively robust and resilient grazing enterprises (a.k.a., cattle stations or cattle ranches) and relatively limited resource availabilities, service access and remoteness. There are a number of detached, small communities included in the region. These communities, often by-products of highly productive, yet no longer extant mining operations, are experiencing gradual decline in growth, infrastructure and opportunities over time. Often, the communities are struggling to retain their elusive regional identities, especially in the face of an emerging and growing world of inter-connectedness, cross-scale linkages, and global processes. Similarly, more often than not, the contrast between social and community dynamics and the growing competitive economic pressures of the grazing enterprises are apparent in many dimensions: from the spatial configuration and infrastructure networks, to the temporal evolution of changes in the systemic composition, to the lack of coherent discussion and dialogue among community members, local and regional stakeholders, and collective institutions. Contrasts such as the needs of communities and small towns to grow and develop their unique regional identity on one hand, and the competitive pressures for

“or”, “and”, “sum”, “min”, “max”, “almost equal to”, etc.) proposition can play the role of the conditional operant for the factorization.


7


intensification of grazing enterprises on the other, are characteristic examples of the social and economic tensions present in these regions. Furthermore, a number of resource limitations exist, ranging from the climate and natural landscape conditions (dry tropics and savanna ecosystems in outback Australia), remoteness (distance to main cities and infrastructure, lack of extensive social and economic networks), service disparities (limitations on governance, institutional and regional service provisions), and economic opportunities (labour availability, capital intensity, extensive market infrastructure).

Figure 3: Regional extent and land use of the BOLNet study area

8



Figure 4: Australian IBRA bioregions and their respective commodity-oriented classification and use approximated by Holmes (1997).

The geographic extent of the upper Burdekin region is defined for this study based on catchment boundaries. Of interest to this research is that the biophysical boundary very closely matches traditional Gugu-Badhum traditional Country as can be seen in Figure 5 (AIATSIS and Sinclair Knight Mertz Pty Ltd, 2003). With unique cultural values and the dominant land use of grazing, the upper Burdekin is a distinct region with relatively homogenous boundaries. Outside of the grazing areas there is also some relatively unique volcanic outcrop country which represents significant habitat for migratory bird species.


9


Figure 5: Geographic distribution of Aboriginal culture groups in the Burdekin region. Source: AIATSIS (2003). The geographic extent of the upper Burdekin region almost coincides with Gugu-Badhum traditional Country.

Finally, the upper Burdekin region has a relatively uniform distribution of population density as shown both in Figure 6 and in the ABS (2001) data.

10



Figure 6: Population distribution in the Burdekin Region. Data source: census collection districts of ABS (2001). The overwhelming majority of the upper Burdekin region has a very low population density.

The key demographic characteristics of the enumerated population across the census collection districts of the upper Burdekin region are shown in Table 2 (see also, ABS, 2001). In comparison with the entire Burdekin area/catchment, the state of Queensland and the entire Australian continent, the upper Burdekin region presents some unique characteristics. Firstly, the proportion of the population in the economically active age group is significantly lower than that in any other aggregation (by 2-3%), an indicator of potential problems with the availability of labour in the area. Secondly, the youngest population class (30 C), relatively high evaporation rates (above 1850 mm) and relatively low rainfall rates (