Philip Kokic

Managing Murray-Darling Basin livestock systems in a variable and changing climate: challenges and opportunities.

The key biophysical impacts associated with projected climate change in the Murray–Darling Basin (MDB) include: declines in pasture productivity, reduced forage quality, livestock heat stress, greater problems with some pests and weeds, more frequent droughts, more intense rainfall events, and greater risks of soil degradation. The most arid and least productive rangelands in theMDBregion may be the most severely impacted by climate change, while the more productive eastern and northern grazing lands in theMDBmay provide some opportunities for slight increases in production. In order to continue to thrive in the future, livestock industries need to anticipate these changes, prepare for uncertainty, and develop adaptation strategies now. While climate change will have direct effects on livestock, the dominant influences on grazing enterprises in the MDB will be through changes in plant growth and the timing, quantity and quality of forage availability. Climate change will involve a complex mix of responses to rising atmospheric carbon dioxide levels, rising temperatures, changes in rainfall and other weather factors, and broader issues related to how people collectively and individually respond to these changes. Enhancing the ability of individuals to respond to a changing climate will occur through building adaptive capacity. We have, via secondary data, selected from the Australian Agricultural and Grazing Industries Survey, built a national composite index of generic adaptive capacity of rural households. This approach expresses adaptive capacity as an emergent property of the diverse forms of human, social, natural, physical and financial capital from which livelihoods are derived. Human capital was rated as ‘high’ across the majority of theMDBcompared with the rest of Australia, while social, physical and financial capital were rated as ‘moderate’ to ‘low’. The resultant measure of adaptive capacity, made up of the five capitals, was ‘low’ in the northern and central-west regions of the MDB and higher in the central and eastern parts possibly indicating a greater propensity to adapt to climate change in these regions.

From rainfall to farm incomes—transforming advice for Australian drought policy. II. Forecasting farm incomes

Australian drought policy is focussed on providing relief from the immediate effects of drought on farm incomes, while enhancing the longer term resilience of rural livelihoods. Despite the socioeconomic nature of these objectives, the information systems created to support the policy have focussed almost exclusively on biophysical measures of climate variability and its effects on agricultural production. In this paper, we demonstrate the ability of bioeconomic modelling to overcome the moral hazard and timing issues that have led to the dominance of these biophysical measures. The Agricultural Farm Income Risk Model (AgFIRM), developed and tested in a companion paper, is used to provide objective, model-based forecasts of annual farm incomes at the beginning of the financial year (July-June). The model was then used to relate climate-induced income variability to the diversity of farm income sources, a practical measure of adaptive capacity that can be positively influenced by policy. Three timeless philosophical arguments are used to discuss the policy relevance of the bioeconomic modelling. These arguments are used to compare the value to decision makers of relatively imprecise, integrative information, with relatively precise, reductionist measures. We conclude that the evolution of bioeconomic modelling systems provides an opportunity to refocus the analytical support for Australian drought policy towards the rural livelihood effects that matter most to governments and rural communities.

From rainfall to farm incomes—transforming advice for Australian drought policy. I. Development and testing of a bioeconomic modelling system

In this paper we report the development of a bioeconomic modelling system, AgFIRM, designed to help close a relevance gap between climate science and policy in Australia. We do this by making a simple econometric farm income model responsive to seasonal forecasts of crop and pasture growth for the coming season. The key quantitative innovation was the use of multiple and M-quantile regression to calibrate the farm income model, using simulated crop and pasture growth from 2 agroecological models. The results of model testing demonstrated a capability to reliably forecast the direction of movement in Australian farm incomes in July at the beginning of the financial year (July-June). The structure of the model, and the seasonal climate forecasting system used, meant that its predictive accuracy was greatest across Australias cropping regions. In a second paper, Nelson et al. (2007, this issue), we have demonstrated how the bioeconomic modelling system developed here could be used to enhance the value of climate science to Australian drought policy.

Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches

Anthropogenic climate change has already been shown to effect the frequency, intensity, spatial extent, duration and seasonality of extreme climate events. Understanding these changes is an important step in determining exposure, vulnerability and focus for adaptation. In an attempt to support adaptation decision-making we have examined statistical modelling techniques to improve the representation of global climate model (GCM) derived projections of minimum temperature extremes (frosts) in Australia. We examine the spatial changes in minimum temperature extreme metrics (e.g. monthly and seasonal frost frequency etc.), for a region exhibiting the strongest station trends in Australia, and compare these changes with minimum temperature extreme metrics derived from 10 GCMs, from the Coupled Model Inter-comparison Project Phase 5 (CMIP 5) datasets, and via statistical downscaling. We compare the observed trends with those derived from the “raw” GCM minimum temperature data as well as examine whether quantile matching (QM) or spatio-temporal (spTimerQM) modelling with Quantile Matching can be used to improve the correlation between observed and simulated extreme minimum temperatures. We demonstrate, that the spTimerQM modelling approach provides correlations with observed daily minimum temperatures for the period August to November of 0.22. This represents an almost fourfold improvement over either the “raw” GCM or QM results. The spTimerQM modelling approach also improves correlations with observed monthly frost frequency statistics to 0.84 as opposed to 0.37 and 0.81 for the “raw” GCM and QM results respectively. We apply the spatio-temporal model to examine future extreme minimum temperature projections for the period 2016 to 2048. The spTimerQM modelling results suggest the persistence of current levels of frost risk out to 2030, with the evidence of continuing decadal variation.

99.999 per cent certainty humans are driving global warming: new study

Credit: Skeptical Science under CC BY 3.0 licence Published in the journal Climate Risk Management last week, our research is the first to quantify the probability of historical changes in global temperatures and examine the links to greenhouse gas emissions using rigorous statistical techniques. Our new CSIRO work provides an objective assessment linking global temperature increases to human activity, which points to a close to certain probability exceeding 99.999 per cent. Our work extends existing approaches undertaken internationally to detect climate change and attribute it to human or natural causes. The 2013 Intergovernmental Panel on Climate Change Fifth Assessment Report provided an expert consensus that: