What actually confers adaptive capacity? Insights from agro-climatic vulnerability of Australian wheat.

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.

Meta-modeling soil organic carbon sequestration potential and its application at regional scale.

Upscaling the results from process-based soil-plant models to assess regional soil organic carbon (SOC) change and sequestration potential is a great challenge due to the lack of detailed spatial information, particularly soil properties. Meta-modeling can be used to simplify and summarize process-based models and significantly reduce the demand for input data and thus could be easily applied on regional scales. We used the pre-validated Agricultural Production Systems sIMulator (APSIM) to simulate the impact of climate, soil, and management on SOC at 613 reference sites across Australia's cereal-growing regions under a continuous wheat system. We then developed a simple meta-model to link the APSIM-modeled SOC change to primary drivers, i.e., the amount of recalcitrant SOC, plant available water capacity of soil, soil pH, and solar radiation, temperature, and rainfall in the growing season. Based on high-resolution soil texture data and 8165 climate data points across the study area, we used the meta-model to assess SOC sequestration potential and the uncertainty associated with the variability of soil characteristics. The meta-model explained 74% of the variation of final SOC content as simulated by APSIM. Applying the meta-model to Australia's cereal-growing regions reveals regional patterns in SOC, with higher SOC stock in cool, wet regions. Overall, the potential SOC stock ranged from 21.14 to 152.71 Mg/ha with a mean of 52.18 Mg/ha. Variation of soil properties induced uncertainty ranging from 12% to 117% with higher uncertainty in warm, wet regions. In general, soils in Australia's cereal-growing regions under continuous wheat production were simulated as a sink of atmospheric carbon dioxide with a mean sequestration potential of 8.17 Mg/ha.

Impact of agricultural management practices on soil organic carbon: simulation of Australian wheat systems.

Quantifying soil organic carbon (SOC) dynamics at a high spatial and temporal resolution in response to different agricultural management practices and environmental conditions can help identify practices that both sequester carbon in the soil and sustain agricultural productivity. Using an agricultural systems model (the Agricultural Production Systems sIMulator), we conducted a high spatial resolution and long-term (122 years) simulation study to identify the key management practices and environmental variables influencing SOC dynamics in a continuous wheat cropping system in Australia's 96 million ha cereal-growing regions. Agricultural practices included five nitrogen application rates (0-200 kg N ha(-1) in 50 kg N ha(-1) increments), five residue removal rates (0-100% in 25% increments), and five residue incorporation rates (0-100% in 25% increments). We found that the change in SOC during the 122-year simulation was influenced by the management practices of residue removal (linearly negative) and fertilization (nonlinearly positive) - and the environmental variables of initial SOC content (linearly negative) and temperature (nonlinearly negative). The effects of fertilization were strongest at rates up to 50 kg N ha(-1) , and the effects of temperature were strongest where mean annual temperatures exceeded 19 °C. Reducing residue removal and increasing fertilization increased SOC in most areas except Queensland where high rates of SOC decomposition caused by high temperature and soil moisture negated these benefits. Management practices were particularly effective in increasing SOC in south-west Western Australia - an area with low initial SOC. The results can help target agricultural management practices for increasing SOC in the context of local environmental conditions, enabling farmers to contribute to climate change mitigation and sustaining agricultural production.

A combined site proximity and recreation index approach to value natural amenities: an example from a natural resource management region of Murray-Darling Basin.

This paper presents a hedonic property price model of rural land in a natural resource management region of the Murray-Darling Basin in Australia. In traditional hedonic models, the marginal value of environmental amenities is estimated using distance to or size of the environmental asset. The approach applied in this study offers the potential for a richer set of information, where environmental assets are described in terms of their 'recreational attractiveness'. The level of recreational attractiveness is represented as latent variables that are based on park facilities and recreational activities offered at each site. For a property in the study area that is 1 km away from the River Murray, moving half a kilometre closer will increase the property price by $245,000, holding every other variable constant at the mean. This value is magnified by $27,000 if the house is in an area where there is high river recreational attractiveness and drops by $14,000 if river recreational attractiveness is low. By including recreational quality indices in a typical hedonic framework that is corrected for spatial dependency, the model is able to capture how individuals value environmental amenities around their homes based on the site's natural characteristics as well as recreational services.

Comparing spatially explicit ecological and social values for natural areas to identify effective conservation strategies.

Consideration of the social values people assign to relatively undisturbed native ecosystems is critical for the success of science-based conservation plans. We used an interview process to identify and map social values assigned to 31 ecosystem services provided by natural areas in an agricultural landscape in southern Australia. We then modeled the spatial distribution of 12 components of ecological value commonly used in setting spatial conservation priorities. We used the analytical hierarchy process to weight these components and used multiattribute utility theory to combine them into a single spatial layer of ecological value. Social values assigned to natural areas were negatively correlated with ecological values overall, but were positively correlated with some components of ecological value. In terms of the spatial distribution of values, people valued protected areas, whereas those natural areas underrepresented in the reserve system were of higher ecological value. The habitats of threatened animal species were assigned both high ecological value and high social value. Only small areas were assigned both high ecological value and high social value in the study area, whereas large areas of high ecological value were of low social value, and vice versa. We used the assigned ecological and social values to identify different conservation strategies (e.g., information sharing, community engagement, incentive payments) that may be effective for specific areas. We suggest that consideration of both ecological and social values in selection of conservation strategies can enhance the success of science-based conservation planning.