Trends in wheat yields under representative climate futures: Implications for climate adaptation
Introduction
Australian agriculture has developed to cope with a climate that is highly variable, spatially and temporally. This has influenced the choice of farming systems, management practices, productivity, product quality and costs (Howden et al., 2013). Against a backdrop of longer-term climatic trajectories, the unpredictability of Australia's weather patterns is projected to increase with climate change (CSIRO and Bureau of Meteorology, 2016). While temperatures are projected to increase with climate change, projections in rainfall vary between global climate models (GCMs) (Flato et al., 2013). These changes are projected to vary considerably between regions (CSIRO and Bureau of Meteorology, 2015). Overall, it is highly likely that the agricultural sector will need to increase its level of adaptation if it is to better manage the major uncertainties and other challenges ahead in order to ultimately maintain, or genuinely achieve, more efficient, profitable and sustainable production systems (Stokes and Howden, 2010, Whetton et al., 2012, Vermeulen et al., 2013, Prober et al., 2017).
Many climate change impact studies in agriculture have used either a single GCM (Bassu et al., 2011, Cullen et al., 2009, Anwar et al., 2007) or ensembles of GCMs (Asseng et al., 2013, Vermeulen et al., 2013). These studies incorporated projected climate parameters into agricultural models, which to broadly describe what Vermeulen et al. (2013) call impact approaches. These use statistical or mechanistic models that attach probabilities to possible outcomes under the given range of scenarios. Multi-model ensemble simulations generally provide more robust information than any single model (Randall et al., 2007). However, different GCMs produce different climate projections, presenting a range of plausible future climates. There is considerable disagreement regarding the selection of specific models for future impact studies, making it difficult to justify using a reduced sub-set of climate projections (CSIRO and Bureau of Meteorology, 2015).
One approach is to use a small set of best performing GCMs based on their ability to replicate features of the current climate, particularly for specific regions. There are more than 40 Global Circulation Models (GCM) used in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) (CSIRO and Bureau of Meteorology, 2015). The benefits of using a subset selection of GCMs reduces the extraneous computations involved with modelling projected climate change using all of the GCMs used in CMIP5. However, selection can be influenced by bias, leading to inconsistencies across studies and confusion among policymakers (Ruane and McDermid, 2017). Furthermore, the exclusion of GCMs deemed of lower reliability might exclude the consideration of low likelihood, but high impact future regional climates of real significance to adaptation planning. To address these shortcomings, Whetton et al. (2012) developed the Representative Climate Futures (RCF) method in impact and adaptation assessment for the selection of GCMs to represent projected climate change across specified regions. This involved classifying projected changes from the full suite of climate models into classes and assigning relative likelihoods based on the number of climate models falling within those classes (Clarke et al., 2011).
The aim of the present study was to apply the RCF methodology to model the range of climate change impacts across the wheat producing regions of southern Australia, comparing these impacts and variability on future yields between regions and RCFs. The implications of the results for climate change adaptation are discussed.
Section snippets
Sample sites
The Representative Climate Futures (RCF) approach was modelled at 10 sites within four Natural Resource Management (NRM) regions across southern and eastern Australia. Information on location and agro-ecological zone is presented in Fig. 1 and Table 1. All sites modelled were in the ‘Temperate Seasonally Dry Slopes and Plains’ agro-ecological zone (Williams et al., 2002), apart from Moree, which was defined as ‘sub-humid, subtropical slopes and plains (Williams et al., 2002). Sites were
Representative climate futures
The RCF analysis resulted in 8 GCMs selected for RCP 4.5 and 9 GCMs selected for RCP 8.5. All GCMs projected temperature increases for the Year 2090, but varied in their respective rainfall outputs (Table 4, Table 5). Under RCP 4.5 for 2090, the GCMs for all ‘worst’ cases projected hotter and much drier climates across all the NRMs. The ‘most-likely’ and ‘best’ cases were variable across the NRMs. The GCMs projected a drier climate under the ‘most-likely’ and ‘worst’ cases for Southern and SW
Discussion
The RCF subset projected a hotter and much drier climate for Southern and SW Flatlands West under its ‘most-likely’ and ‘worst cases’ under RCP 4.5 and a much hotter and much drier climate under RCP 8.5. These projections resulted in overall declines for wheat yield, ranging from 26% under RCP 4.5 to 80% under RCP 8.5 by 2090. Rainfall change presented the strongest signal to change in wheat yield, which was consistent with other studies (Hochman et al., 2017, Luo et al., 2005, Anwar et al.,
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