Elsevier

Agricultural Systems

Volume 164, July 2018, Pages 1-10
Agricultural Systems

Trends in wheat yields under representative climate futures: Implications for climate adaptation

https://doi.org/10.1016/j.agsy.2017.12.007Get rights and content

Highlights

  • The Representative Climate Futures (RCF) method allowed for a locally representative subset of Global Climate Models (GCMs) to be selected;

  • This method addressed any potential bias in selecting individual GCMs for agricultural productivity modelling;

  • Wheat yields declined under RCP 8.5 for the ‘most-likely’ case across most wheat producing regions, but increased for some sites under RCP 4.5;

  • Agriculture faces significant adaptation challenges under some of the ‘most-likely’ scenarios and all of the ‘worst’ case scenarios; and

  • The results provide insights for specific wheat producing regions, where the risks of maladaptation under future climate change may be higher.

Abstract

Underestimating the impacts of climate change on agricultural production could lead to complacency about the potential adaptation challenges. This study used a Representative Climate Futures (RCF) approach to model projected wheat yields under climate change in Australia. It simulated the range of impacts, resulting from a subset of individual Global Climate Models (GCMs), on wheat production in the major wheat regions of Australia. The study used RCFs that represented ‘most-likely’, ‘best’ and ‘worst’ cases across multiple Representative Concentration pathways (RCPs). Median wheat yields modelled for the South West Australia projected declines between 26% and 38%, under a ‘most-likely’ case for RCP 4.5 by 2090, and between 41% and 49%, under a ‘most-likely’ case for RCP 8.5. Median wheat yields declined under RCP 8.5 for the ‘most-likely’ case across the majority of wheat producing regions, with a range of 1% to 49%. Greater declines were projected under the ‘worst’ cases of hottest and driest climates. However, the ‘best’ cases of least warm and wetter climates projected an increase in median wheat yield, a range of 2% to 87%. Variability also changed from the baseline under all projected RCFs and across all regions, with a standard deviation of up to 2.46 t/ha under the ‘most likely’ case at a site in south-eastern Australia. These likely shifts in the size and reliability of yields, combined with concurrent climate change impacts on other factors, mean that agriculture faces significant adaptation challenges, particularly under some of the ‘most-likely’ scenarios and all of the ‘worst’ case scenarios. Further work is required to explore how scenarios in one region relate to those in other regions and thus the overall outcome at the continental scale.

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.,

References (49)

  • F. Ludwig et al.

    Climate change impacts on wheat production in a Mediterranean environment in Western Australia

    Agric. Syst.

    (2006)
  • Q. Luo et al.

    Quantitative and visual assessments of climate change impacts on South Australian wheat production

    Agric. Syst.

    (2003)
  • Q. Luo et al.

    Potential impact of climate change on wheat yield in South Australia

    Agric. For. Meteorol.

    (2005)
  • R. Nelson et al.

    The vulnerability of Australian rural communities to climate variability and change: part I—Conceptualising and measuring vulnerability

    Environ. Sci. Pol.

    (2010)
  • R. Nelson et al.

    The vulnerability of Australian rural communities to climate variability and change: part II – integrating impacts with adaptive capacity

    Environ. Sci. Pol.

    (2010)
  • S.E. Park et al.

    Informing adaptation responses to climate change through theories of transformation

    Glob. Environ. Chang.

    (2012)
  • S.M. Prober et al.

    Informing climate adaptation pathways in multi-use woodland landscapes using the values-rules-knowledge framework

    Agric. Ecosyst. Environ

    (2017)
  • S. Asseng et al.

    The impact of temperature variability on wheat yields

    Global Change Biology

    (2011)
  • S. Asseng et al.

    Uncertainty in simulating wheat yields under climate change

    Nat. Clim. Chang.

    (2013)
  • K. Chenu et al.

    Environment characterization as an aid to wheat improvement: interpreting genotype–environment interactions by modelling water-deficit patterns in North-Eastern Australia

    J. Exp. Bot.

    (2011)
  • J.M. Clarke et al.

    December. providing application-specific climate projections datasets: CSIROs climate futures framework

  • CSIRO and Bureau of Meteorology

    Climate Change in Australia Information for Australia's Natural Resource Management Regions: Technical Report

    (2015)
  • CSIRO and Bureau of Meteorology

    State of the Climate 2016, Australian Government and Bureau of Meteorology

  • B.R. Cullen et al.

    Climate change effects on pasture systems in south-eastern Australia

    Crop Pasture Sci.

    (2009)
  • Cited by (14)

    • Are the planning targets of liquid biofuel development achievable in China under climate change?

      2021, Agricultural Systems
      Citation Excerpt :

      To estimate the potential of liquid biofuel production in the future, the impact of climate change cannot be ignored. Global climate models (GCMs) are the primary tool for understanding how the global climate may change in the future (Kara et al., 2016; Srivastava et al., 2019; Taylor et al., 2018). However, because of the model structure, parameterization, and spatial resolution, large uncertainties arise from GCMs (Wada et al., 2013).

    • Projected temperature increases may require shifts in the growing season of cool-season crops and the growing locations of warm-season crops

      2020, Science of the Total Environment
      Citation Excerpt :

      Mediterranean ecosystems are characterized by two distinct climatic seasons with warm/dry summers and mild/wet winters, and support some of the most diverse and productive agricultural systems. However, warming in the already hot/dry summers of Mediterranean ecosystems may create particularly large adaptation challenges for these agricultural systems (Taylor et al., 2018). Mediterranean type ecosystems occur in five regions of the world including the Mediterranean Basin, California, central Chile, the western Cape region of South Africa and Southwest and South Australia.

    • Linking climate change and socioeconomic development to urban land use simulation: Analysis of their concurrent effects on carbon storage

      2020, Applied Geography
      Citation Excerpt :

      The existing study indicates that the promotion of potential agricultural productivity relies on the coordination of water and heat conditions and their consistent change trends (Ruan et al., 2018). Therefore, under S1 and S3 scenarios, inconsistent water and heat coordination and excessive inter-annual climate fluctuations will have a significant negative impact on the growth of crops and trees, which make the demands for cultivated land, grassland and woodland increase in order to meet the needs of residents for agricultural products and livestock products, thereby leading to more carbon loss (Taylor, Cullen, D'Occhio, Rickards, & Eckard, 2018). The S2, however, has significant increase of grassland in the future, and will be more effective to mitigate regional carbon loss due to the stimulatory photosynthesis induced by high precipitation and autotrophic respiration inhibited by low temperature (Sun & Mu, 2018).

    • Assessment of the sustainability of different cropping systems under three irrigation strategies in the North China Plain under climate change

      2020, Agricultural Systems
      Citation Excerpt :

      The Agricultural Production Systems SIMulator (APSIM) has been extensively validated for wheat and maize in the NCP (Zhao et al., 2018). Previous studies have shown that the APSIM model was able to predict the crop biomass, growth, grain yield and crop water uptake of wheat and maize in response to water supply (Sun et al., 2016; Taylor et al., 2018; Yang et al., 2018). Thus, this model can be used for impact assessments in the NCP.

    View all citing articles on Scopus
    View full text