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Climatic Change

, Volume 117, Issue 1–2, pp 163–179 | Cite as

Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia

  • A. Potgieter
  • H. Meinke
  • A. Doherty
  • V. O. Sadras
  • G. Hammer
  • S. Crimp
  • D. Rodriguez
Article

Abstract

Climate projections over the next two to four decades indicate that most of Australia’s wheat-belt is likely to become warmer and drier. Here we used a shire scale, dynamic stress-index model that accounts for the impacts of rainfall and temperature on wheat yield, and a range of climate change projections from global circulation models to spatially estimate yield changes assuming no adaptation and no CO2 fertilisation effects. We modelled five scenarios, a baseline climate (climatology, 1901–2007), and two emission scenarios (“low” and “high” CO2) for two time horizons, namely 2020 and 2050. The potential benefits from CO2 fertilisation were analysed separately using a point level functional simulation model. Irrespective of the emissions scenario, the 2020 projection showed negligible changes in the modelled yield relative to baseline climate, both using the shire or functional point scale models. For the 2050-high emissions scenario, changes in modelled yield relative to the baseline ranged from −5 % to +6 % across most of Western Australia, parts of Victoria and southern New South Wales, and from −5 to −30 % in northern NSW, Queensland and the drier environments of Victoria, South Australia and in-land Western Australia. Taking into account CO2 fertilisation effects across a North–south transect through eastern Australia cancelled most of the yield reductions associated with increased temperatures and reduced rainfall by 2020, and attenuated the expected yield reductions by 2050.

Keywords

Emission Scenario Global Climate Model Wheat Yield Lorenz Curve Sowing Date 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

V O Sadras’ research is partially funded by the Grains Research and Development Corporation of Australia, and the Asia-Pacific Network for Global Change Research. D Rodriguez participation was funded by the Department of Agriculture, Fisheries and Forestry as part of the “Developing Climate Change Resilient Cropping and Mixed Cropping/Grazing Businesses in Australia”, funded by Australia’s Farming Future: Climate Change Research Program.

References

  1. ABARE (2007) Australian commodities. In: Wright, A. (ed), December quarter, 07.4, ISSN 1321–7844, Australian Bureau of Agricultural Resource and Economics, Canberra, Australia, p. 752.Google Scholar
  2. Alcamo J, van den Born GJ, Bouwman AF, de Haan BJ, Klein Goldewijk K, Klepper O, Krabec J, Leemans R, Olivier JGJ, Toet AMC, de Vries HJM, van der Woerd HJ (1994) Modeling the global society-biosphere-climate system: part 2: computed scenarios. Water Air Soil Pollut 76:37–78CrossRefGoogle Scholar
  3. Allan RJ (2000) El Niño and the Southern Oscillation: Multiscale variability and its impacts on natural ecosystems and society. In: Diaz HF, Markgraf V (eds) ENSO and climatic variability in the last 150 years. Cambridge Univ. Press, Cambridge, UK, pp 3–55Google Scholar
  4. Anwar MR, O’Leary G, McNeil D, Hossain H, Nelson R (2007) Climate change impact on rainfed wheat in south-eastern Australia. Field Crop Res 104:139–147CrossRefGoogle Scholar
  5. Asseng S, Cao W, Zhang W, Ludwig F (2009) Crop physiology, modelling and climate change: impact and adaption strategies. In: Sadras VO, Calderini DF (eds) Crop physiology: applications for genetic improvement and agronomy. Academic, San Diego, pp 511–543Google Scholar
  6. Asseng S, Foster I, Turner NC (2011) The impact of temperature variability on wheat yields. Global Change Biol 17:997–1012CrossRefGoogle Scholar
  7. Fischer RA (2009) Farming systems of Australia: Exploiting the synergy between genetic improvement and agronomy. In Crop physiology: applications for genetic improvement and agronomy. Academic Press, San Diego, pp 23–54Google Scholar
  8. Folland CK, Karl TR, Christy JR, Clarke RA, Gruza GV, Jouzel J, Mann ME, Oerlemans J, Salinger MJ, Wang SW (2001) Observed climate variability and change. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) Climate change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom, p 881Google Scholar
  9. Hammer GL (1987) Effects of climatic variability and possible climatic change on reliability of wheat cropping - a modelling approach. Agr Forest Meteorol 41:123–142CrossRefGoogle Scholar
  10. Hamon WR (1961) Estimating potential evapotranspiration. J Hydraul Div ASCE 87:107–120Google Scholar
  11. Hansen JW, Potgieter A, Tippett MK (2004) Using a general circulation model to forecast regional wheat yields in northeast Australia. Agr Forest Meteorol 127:77–92CrossRefGoogle Scholar
  12. Harch BD, Correll RL, Meech W, Kirkby CA, Pankhurst CE (1997) Using the Gini coefficient with BIOLOG substrate utilisation data to provide an alternative quantitative measure for comparing bacterial soil communities. J Microbiol Methods 30:91–101CrossRefGoogle Scholar
  13. Howden SM and Crimp S (2005) Assessing dangerous climate change impacts on Australia’s wheat industry. In: MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand. ISBN: 0-9758400-2-9., December 2005., pp. 170–176.Google Scholar
  14. Howden SM, Meinke H, Power B, McKeon GM and Mssanzi (2003) Risk management of wheat in a non-stationary climate: frost in Central Queensland, p. 17–22.Google Scholar
  15. Howden SM, Soussana JF, Tubiello FN, Chhetri N, Dunlop M, Meinke H (2007) Adapting agriculture to climate change. Proc Natl Acad Sci 104:19691–19696CrossRefGoogle Scholar
  16. Hulme M, Jiang T, Wigley T (1995) SCENGEN: a climate change scenario generator. software user manual, version 1.0. Climatic Research Unit, University of East Anglia, Norwich, United Kingdom, p 38Google Scholar
  17. IPCC (2001) Climate change 2001: the scientific basis. Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UKGoogle Scholar
  18. IPCC (2007) Climate change 2007: the physical science basis; summary for policymakers. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  19. Jeffrey SJ, Carter JO, Moodie KM, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Model Software 16:309–330CrossRefGoogle Scholar
  20. Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ (2003) An overview of APSIM, a model designed for farming systems Simulation. Eur J Agron 18:267–288CrossRefGoogle Scholar
  21. Ludwig F, Asseng S (2006) Climate change impacts on wheat production in a Mediterranean environment in Western Australia. Agric Syst 90:159–179CrossRefGoogle Scholar
  22. Ludwig F, Asseng S (2010) Potential benefits of early vigor and changes in phenology in wheat to adapt to warmer and drieer climates. Agr Syst 103:127–136CrossRefGoogle Scholar
  23. Ludwig F, Milroy SP, Asseng S (2009) Impacts of recent climate change on wheat production systems in Western Australia. Clim Chang 92:495–517CrossRefGoogle Scholar
  24. Luo QY, Bellotti W, Williams M, Bryan B (2005a) Potential impact of climate change on wheat yield in South Australia. Agric Forest Meteorol 132:273–285CrossRefGoogle Scholar
  25. Luo QY, Bryan B, Bellotti W, Williams M (2005b) Spatial analysis of environmental change impacts on wheat production in Mid-Lower North, South Australia. Clim Chang 72:213–228CrossRefGoogle Scholar
  26. Luo QY, Jones RN, Williams M, Bryan B, Bellotti W (2005c) Probabilistic distributions of regional climate change and their application in risk analysis of wheat production. Clim Res 29:41–52CrossRefGoogle Scholar
  27. Luo QY, Bellotti W, Williams M, Cooper I, Bryan B (2007) Risk analysis of possible impacts of climate change on South Australian wheat production. Clim Chang 85:89–101CrossRefGoogle Scholar
  28. Luo Q, Bellotti W, Williams M, Wang E (2009) Adaptation to climate change of wheat growing in South Australia: analysis of management and breeding strategies. Agric Ecosyst Environ 129:261–267CrossRefGoogle Scholar
  29. Luo QY, Bellotti W, Hayman P, Williams M, Devoil P (2010) Effects of changes in climatic variability on agricultural production. Clim Res 42:111–117CrossRefGoogle Scholar
  30. Meinke H, Howden M and Nelson R (2006) Integrated assessments of climate variability and change for Australian agriculture – connecting the islands of knowledge. In: Proc 3rd Biennial meeting of the Int Environmental Modelling and Software Soc., Environmental Modelling and Software Soc., Burlington, VTGoogle Scholar
  31. Mitchell JFB, Johns JC, Eagles M, Ingram WJ, Davis RA (1999) Towards the construction of climate change scenarios. Clim Change 41:547–581CrossRefGoogle Scholar
  32. Orrell D (2007) The future of everything. The science of prediction. Thunder’s Mouth Press, New YorkGoogle Scholar
  33. Orrell D, Smith L, Barkmeijer J, Palmer T (2001) Model error in weather forecasting. Nonlinear Processes Geophys 8:357–371CrossRefGoogle Scholar
  34. Passioura JB (1996) Simulation models: science, snake oil, education, or engineering? Agron J 88:690–716CrossRefGoogle Scholar
  35. Penm J (2002) Australian commodities - economic overview. In: Wright, A. (ed), Australian commodities, ABARE Canberra, p. 654Google Scholar
  36. Potgieter AB, Hammer GL, Butler D (2002) Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO. Aust J Agr Res 53:77–89CrossRefGoogle Scholar
  37. Potgieter AB, Everingham Y, Hammer GL (2003) On measuring quality of a commodity forecasting from a system that incorporates seasonal climate forecasts. Int J Climatol 23:1195–1210CrossRefGoogle Scholar
  38. Potgieter AB, Hammer GL, Meinke H, Stone RC, Goddard L (2005) Three putative types of El Nino revealed by spatial variability in impact on Australian wheat yield. J Climate 18:1566–1574CrossRefGoogle Scholar
  39. Potgieter AB, Hammer GL and Doherty A (2006) Oz-Wheat: a regional-scale crop yield simulation model for Australian wheat, Information Series, Queensland Department of Primary Industries & Fisheries, Brisbane, Australia. (ISSN 0727–6273), p. 20Google Scholar
  40. Pratley J (2003) Principles of field crop production. Oxford University Press, Melbourne, p 550Google Scholar
  41. Rodriguez D, Sadras VO (2007) The limit to wheat water use efficiency in eastern Australia. I. Gradients in the radiation environment and atmospheric demand. Aust J Agric Res 58:287–302CrossRefGoogle Scholar
  42. Rodriguez D, deVoil P, Power B, Cox H, Crimp S, Meinke H (2011) The intrinsic plasticity of farm businesses and their resilience to change. Aust example’ Field Crop Res 124(2):157–170CrossRefGoogle Scholar
  43. Rotmans J, Hulme M, Downing TE (1994) Climate change implications for Europe: an application of the ESCAPE model. Glob Environ Chang 4:97–124CrossRefGoogle Scholar
  44. Sadras VO, Bongiovanni R (2004) Use of Lorenz curves and Gini coefficients to assess yield inequality within paddocks. Field Crop Res 90:303–310CrossRefGoogle Scholar
  45. Sadras VO, Monzon JP (2006) Modelled wheat phenology captures rising temperature trends: shortened time to flowering and maturity in Australia and Argentina. Field Crops Res 99:136–146CrossRefGoogle Scholar
  46. Sadras VO, Rodriguez D (2007) The limit to wheat water use efficiency in eastern Australia. II. Influence of rainfall patterns. Aust J Agric Res 58:657–669CrossRefGoogle Scholar
  47. Santer BD, Wigley TML, Schlesinger ME, Mitchell JFB (1990) Developing climate scenarios from equilibrium GCM results, Max-Planck-Institut-für-Meteorologie, Hamburg, Germany, p 29Google Scholar
  48. Stone RC, Nicholls N, Hammer G (1996) Frost in northeast Australia: trends and influences of phases of the southern oscillation. J Climate 9:1896–1905CrossRefGoogle Scholar
  49. van Oort PAJ, Zhang T, de Vries ME, Heinemann AB, Meinke H (2011) Correlation between phenology prediction error and temperature in rice (Oyza Sativa L.). Agr Forest Meteorol 151:1545–1555CrossRefGoogle Scholar
  50. Wang J, Wang EL, Luo QY, Kirby M (2009) Modelling the sensitivity of wheat growth and water balance to climate change in Southeast Australia. Clim Chang 96:79–96CrossRefGoogle Scholar
  51. Weiner J, Solbrig OT (1984) The meaning and measurement of size hierarchies in plant populations. Oecologia 61:334–336CrossRefGoogle Scholar
  52. Whetton PH, Katzfey JJ, Hennessy KJ, Wu X, McGregor JL, Nguyen KC (2001) Developing scenarios of climate change for Southeastern Australia: an example using regional climate model output. Climate Res 16:181–201CrossRefGoogle Scholar
  53. Xu C-Y, Singh VP (2001) Evaluation and generalization of tempertaur-based methods for calculating evaporation. Hydrolog Process 15:305–319CrossRefGoogle Scholar
  54. Zachariahs C (2011) Australia flood, cyclone disasters to cost economy $9.4 Billion, Swan says. In: Zachariahs, C. (ed), Bloomberg News Pty. Ltd., 4th April 2011Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • A. Potgieter
    • 1
  • H. Meinke
    • 2
    • 6
  • A. Doherty
    • 3
  • V. O. Sadras
    • 4
  • G. Hammer
    • 1
  • S. Crimp
    • 5
  • D. Rodriguez
    • 1
  1. 1.Queensland Alliance for Agriculture and Food Innovation (QAAFI)University of QueenslandToowoombaAustralia
  2. 2.University of TasmaniaTasmanian Institute of Agriculture (TIA)HobartAustralia
  3. 3.Department of AgricultureFisheries and Forestry (DAFF)ToowoombaAustralia
  4. 4.South Australian Research and Development Institute, Waite CampusUrrbraeAustralia
  5. 5.Agricultural Systems, CSIRO Ecosystem SciencesCanberraAustralia
  6. 6.Centre for Crop Systems AnalysisWageningen UniversityWageningenThe Netherlands

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