Adaptation to Climate Change in Regional Australia: A Decision‐Making Framework for Modelling Policy for Rural Production

Abstract

A decision‐making framework was developed and applied in regional Australia to identify adaptation issues arising in agricultural systems and rural production as a consequence of climate change. Australian agriculture is very susceptible to the adverse impacts of climate change, with major shifts in temperature and rainfall projected. An advantage of the framework is that it provides a suite of tools to aid in the formulation of strategies for sustainable regional development and adaptation. The decision‐making framework uses a participatory approach that integrates land suitability analysis with uncertainty analysis and spatial optimisation to determine optimal agricultural land use (at a regional scale) for current and possible future climatic conditions. It thus provides a robust analytic approach to (i) recognise regions under threat of productivity declines, (ii) identify alternative cropping systems better adapted to likely future climatic conditions and (iii) investigate policy actions to improve the sub‐optimal situations created by climate change. The decision‐making framework and its methods were applied in a case study of the South West Region of Victoria.

A Modelling Framework for Optimisation of Commodity Production by Minimising the Impact of Climate Change

“…This technique creates a pair-wise comparison matrix, providing answers to questions, such as "How important is criterion A relative to criterion B?" Pair-wise comparison is a robust technique for capturing preferences, as experts can compare all factors against each other repeatedly, but only two factors at each time step, and thus can make a more reliable judgement based on a self-optimising procedure-as (Sposito et al 2010) distinct from merely using a passive survey or questionnaire. The AHP Weight Derivation routine from the software package Idrisi32 (© Clark University, USA) was used to automatically calculate criterion weights and also produce a consistency ratio measure for expert preferences.…”

Evaluation of Deterministic and Complex Analytical Hierarchy Process Methods for Agricultural Land Suitability Analysis in a Changing Climate

“…Only the highest and lowest suitability classes are detailed in this particular table due to the complexity and size of this particular AHP decision tree model. Further presentations of a simpler AHP decision tree model as applied to suitability analysis in Victoria, Australia, can be found in Sposito et al [28]. For actual application of the model there are several defined suitability classes, from high to very low, not documented within the table.…”

“…Pairwise comparison can be a rigorous technique for capturing expert preferences or opinions, as comparisons of each factor is done against one another, thus making more reliable judgements [28]. This technique improves consistency amongst criteria weights.…”

“…For this study a bounded transition zone sinusoidal membership function is used, initially described by Burrough [20] as 'model 2' (Figure 2). Pairwise comparison can be a rigorous technique for capturing expert preferences or opinions, as comparisons of each factor is done against one another, thus making more reliable judgements [28]. This technique improves consistency amongst criteria weights.…”

Realising an eScience Platform to Support Climate Change Adaptation in Victoria

“…Models relevant to each of the eight commodities were initially developed in the ESRI ArcView 3.3 Model builder environment [23]. The input data for each model corresponded to climate data (current or future), was provided in raster format, and soil information data (pH, depth to bedrock, slope, electric conductivity, soil depth), and was provided in the ESRI shapefile format.…”