Vulnerability assessments have often invoked sustainable livelihoods theory to support the quantification of adaptive capacity based on the availability of capital—social, human, physical, natural, and financial. However, the assumption that increased availability of these capitals confers greater adaptive capacity remains largely untested. We quantified the relationship between commonly used capital indicators and an empirical index of adaptive capacity (ACI) in the context of vulnerability of Australian wheat production to climate variability and change. We calculated ACI by comparing actual yields from farm survey data to climate-driven expected yields estimated by a crop model for 12 regions in Australia’s wheat-sheep zone from 1991–2010. We then compiled data for 24 typical indicators used in vulnerability analyses, spanning the five capitals. We analyzed the ACI and used regression techniques to identify related capital indicators. Between regions, mean ACI was not significantly different but variance over time was. ACI was higher in dry years and lower in wet years suggesting that farm adaptive strategies are geared towards mitigating losses rather than capitalizing on opportunity. Only six of the 24 capital indicators were significantly related to adaptive capacity in a way predicted by theory. Another four indicators were significantly related to adaptive capacity but of the opposite sign, countering our theory-driven expectation. We conclude that the deductive, theory-based use of capitals to define adaptive capacity and vulnerability should be more circumspect. Assessments need to be more evidence-based, first testing the relevance and influence of capital metrics on adaptive capacity for the specific system of interest. This will more effectively direct policy and targeting of investment to mitigate agro-climatic vulnerability.
Agricultural non-point source pollution, common in water supply catchments worldwide, can have significant environmental and human health impacts, and its mitigation poses a challenge for policymakers. We used deliberative multi-criteria evaluation (DMCE) to identify a mix and sequence of policy instruments (or policy design) to address agricultural non-point source pollution using a case study of Cryptosporidium contamination in the Myponga River water supply catchment, South Australia. The major impediments to adoption of on-farm water quality management and benefits for ecosystem services were identified using a landholder survey for use as decision criteria in DMCE. The DMCE approach involved stakeholders in policy design during two community fora held in the catchment. We developed six policy scenarios and quantified their impact on decision criteria. The relative importance of decision criteria was quantified using swing weights and consensus was reached on the preferred policy scenario. The mix, sequence, and targeting of instruments in the preferred policy scenario were refined based on information obtained through the deliberative process. Impediments to adoption included a lack of both information/knowledge and financial resources. The recommended policy scenario involved targeted information, followed by an incentive program, and finally the regulation of a mandatory code of practice for water quality management. Detailed, catchmentspecific context obtained through DMCE was critical for refining an effective mix and sequence of policy instruments. The techniques may be readily used to select and schedule policy instruments for effective mitigation of agricultural non-point source pollution in other drinking water supply catchments elsewhere.
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