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Modelling and evaluating the impacts of climate change on three major crops in south-eastern Australia using regional climate model simulations

  • Bin WangEmail author
  • De Li Liu
  • Jason P. Evans
  • Fei Ji
  • Cathy Waters
  • Ian Macadam
  • Puyu Feng
  • Kathleen Beyer
Original Paper

Abstract

The use of regional climate models (RCMs) to localise results from coarse-resolution global climate models has recently attracted more interest in agricultural impact studies. Theoretically, it has advantages over global climate models in terms of realisation of future climate projections. However, there are few studies that have used dynamical downscaling results to assess climate change impacts on Australian cropping systems. In this study, we used post-processing bias-corrected climate data from the NSW/ACT Regional Climate Modelling (NARCliM) project to drive the Agricultural Production Systems SIMulator (APSIM) for modelling and evaluating the response of three major crops (wheat, canola and lupin) to projected climate conditions under various farm management practices in south-eastern Australia. Our results showed that historical crop yields from APSIM simulations forced by RCM output tended to underestimate yields from simulations forced by observations due to large biases in the NARCliM simulations. Therefore, bias correction was used to correct the APSIM outputs before conducting future impact analysis. The bias-corrected results showed that ensemble-mean yields based on 12 RCMs were projected to increase over the study area under the A2 emission scenario. However, the magnitude of yield increase depended on the time periods, crop type and location. Multiple linear regression models showed that the changes of radiation, rainfall and temperature and elevated CO2 concentration could explain 60–78% of crop yield changes. It is interesting to note that residue incorporation and nitrogen application had a large effect on percentage yield increases for wheat due to future climate change, but had limited effects on the response of lupin and canola yields. Our study suggests that sufficient bias-correction method is needed when using NARCliM outputs in crop models. Although uncertainties (e.g. the choice of emission scenarios and RCMs) still exist in our study, the results are of central importance in the development of high-yield adaptive strategies for local farmers and policy makers in south-eastern Australia.

Notes

Acknowledgements

This work was supported by NSW Department of Primary Industries (NSW DPI) and partially funded by NSW Office of Environment and Heritage (OEH) for the project entitled “Assessment of climate change impacts on major crops and identification of farming management option in southern NSW” (NSW DPI: RDE959) as a part of the Climate Change Fund supported research led by OEH. Useful conversations were held with Anthony Coward of NSW OEH.

References

  1. Adhikari P, Ale S, Bordovsky JP, Thorp KR, Modala NR, Rajan N, Barnes EM (2016) Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-cotton model. Agric Water Manag 164(Part 2:317–330Google Scholar
  2. Alexandratos N, Bruinsma J (2012) World agriculture towards 2030/2050: the 2012 revision. ESA Working paper No. 12–03. FAO, RomeGoogle Scholar
  3. Anwar MR, Liu DL, Farquharson R, Macadam I, Abadi A, Finlayson J, Wang B, Ramilan T (2015) Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia. Agric Syst 132:133–144Google Scholar
  4. Asseng S, Ewert F, Rosenzweig C, Jones J, Hatfield J, Ruane A, Boote K, Thorburn P, Rötter R, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal P, Angulo C, Bertuzzi P, Biernath C, Challinor A, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt L, Ingwersen J, Ozaurralde R, Kersebaum K, Müller C, Naresh Kumar S, Nendel C, O'Leary G, Olesen J, Osborne T, Palosuo T, Priesack E, Ripoche D, Semenov M, Shcherbak I, Steduto P, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White J, Williams J, Wolf J (2013) Uncertainty in simulating wheat yields under climate change. Nat Clim Chang 3(9):827–832Google Scholar
  5. Bakker AMR, Bessembinder JJE, de Wit AJW, van den Hurk BJJM, Hoek SB (2014) Exploring the efficiency of bias corrections of regional climate model output for the assessment of future crop yields in Europe. Reg Environ Chang 14(3):865–877Google Scholar
  6. Balkovič J, van der Velde M, Skalský R, Xiong W, Folberth C, Khabarov N, Smirnov A, Mueller ND, Obersteiner M (2014) Global wheat production potentials and management flexibility under the representative concentration pathways. Glob Planet Chang 122:107–121Google Scholar
  7. Cammarano D, Stefanova L, Ortiz BV, Ramirez-Rodrigues M, Asseng S, Misra V, Wilkerson G, Basso B, Jones JW, Boote KJ, DiNapoli S (2013) Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US. Reg Environ Chang 13(1):101–110Google Scholar
  8. Cammarano D, Rivington M, Matthews KB, Miller DG, Bellocchi G (2017) Implications of climate model biases and downscaling on crop model simulated climate change impacts. Eur J Agron 88(Supplement C:63–75Google Scholar
  9. Chen C, Wang E, Yu Q (2010) Modelling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agric Water Manag 97(8):1175–1184Google Scholar
  10. Colette A, Vautard R, Vrac M (2012) Regional climate downscaling with prior statistical correction of the global climate forcing. Geophys Res Lett 39(13)Google Scholar
  11. Dalgliesh N, Wockner G, Peake A (2006) Delivering soil water information to growers and consultants, Proceedings of the 13th Australian Agronomy Conference, 10–14 September 2006, Perth, Western AustraliaGoogle Scholar
  12. Dettori M, Cesaraccio C, Duce P (2017) Simulation of climate change impacts on production and phenology of durum wheat in Mediterranean environments using CERES-wheat model. Field Crop Res 206(Supplement C:43–53Google Scholar
  13. Di Luca A, Evans JP, Ji F (2017) Australian snowpack in the NARCliM ensemble: evaluation, bias correction and future projections. Clim Dyn 1–28Google Scholar
  14. Evans JP, Ekström M, Ji F (2012) Evaluating the performance of a WRF physics ensemble over South-East Australia. Clim Dyn 39(6):1241–1258Google Scholar
  15. Evans J, Ji F, Lee C, Smith P, Argüeso D, Fita L (2014) Design of a regional climate modelling projection ensemble experiment–NARCliM. Geosci Model Dev 7(2):621–629Google Scholar
  16. Evans JP, Argueso D, Olson R, Di Luca A (2017) Bias-corrected regional climate projections of extreme rainfall in south-east Australia. Theor Appl Climatol 130(3):1085–1098Google Scholar
  17. Fita L, Evans JP, Argüeso D, King A, Liu Y (2017) Evaluation of the regional climate response in Australia to large-scale climate modes in the historical NARCliM simulations. Clim Dyn 49(7):2815–2829Google Scholar
  18. Fitzgerald G, Tausz M, O'Leary G, Mollah M, Tausz-Posch S, Seneweera S, Mock I, Löw M, Partington D, McNeil D (2016) Elevated atmospheric [CO2] can dramatically increase wheat yields in semi-arid environments and buffer against heat waves. Glob Chang Biol 22(6):2269–2284Google Scholar
  19. Hawkins E, Osborne TM, Ho CK, Challinor AJ (2013) Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agric For Meteorol 170:19–31Google Scholar
  20. Holzworth DP, Huth NI, Zurcher EJ, Herrmann NI, McLean G, Chenu K, van Oosterom EJ, Snow V, Murphy C, Moore AD (2014) APSIM–evolution towards a new generation of agricultural systems simulation. Environ Model Softw 62:327–350Google Scholar
  21. Hwang S, Graham WD, Geurink JS, Adams A (2014) Hydrologic implications of errors in bias-corrected regional reanalysis data for west central Florida. J Hydrol 510(Supplement C:513–529Google Scholar
  22. Jeffrey SJ, Carter JO, Moodie KB, Beswick AR (2001) Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ Model Softw 16(4):309–330Google Scholar
  23. Ji F, Ekström M, Evans JP, Teng J (2014) Evaluating rainfall patterns using physics scheme ensembles from a regional atmospheric model. Theor Appl Climatol 115(1):297–304Google Scholar
  24. Ji F, Evans JP, Argueso D, Fita L, Di Luca A (2015) Using large-scale diagnostic quantities to investigate change in East Coast Lows. Clim Dyn 45(9):2443–2453Google Scholar
  25. Ji F, Evans JP, Teng J, Scorgie Y, Argüeso D, Di Luca A (2016) Evaluation of long-term precipitation and temperature Weather Research and Forecasting simulations for southeast Australia. Clim Res 67(2):99–115Google Scholar
  26. Katharina W, Neil H, Peter C, Enli W (2015) How model and input uncertainty impact maize yield simulations in West Africa. Environ Res Lett 10(2):024017Google Scholar
  27. Kirkegaard JA, Conyers MK, Hunt JR, Kirkby CA, Watt M, Rebetzke GJ (2014) Sense and nonsense in conservation agriculture: principles, pragmatism and productivity in Australian mixed farming systems. Agriculture. Ecosyst Environ 187(Supplement C):133–145Google Scholar
  28. Liu DL, Anwar MR, O'Leary G, Conyers MK (2014a) Managing wheat stubble as an effective approach to sequester soil carbon in a semi-arid environment: spatial modelling. Geoderma 214:50–61Google Scholar
  29. Liu M, Rajagopalan K, Chung SH, Jiang X, Harrison J, Nergui T, Guenther A, Miller C, Reyes J, Tague C, Choate J, Salathé EP, Stöckle CO, Adam JC (2014b) What is the importance of climate model bias when projecting the impacts of climate change on land surface processes? Biogeosciences 11(10):2601–2622Google Scholar
  30. Liu DL, O’Leary GJ, Christy B, Macadam I, Wang B, Anwar MR, Weeks A (2017a) Effects of different climate downscaling methods on the assessment of climate change impacts on wheat cropping systems. Clim Chang 144(4):687–701Google Scholar
  31. Liu DL, Zeleke KT, Wang B, Macadam I, Scott F, Martin RJ (2017b) Crop residue incorporation can mitigate negative climate change impacts on crop yield and improve water use efficiency in a semiarid environment. Eur J Agron 85:51–68Google Scholar
  32. Liu DL, Wang B, Evans J, Ji F, Waters C, Macadam I, Yang X, Beyer K (2018) Propagation of regional climate model biases to biophysical modelling can complicate the assessment of climate change impact in agricultural system. Int J Climatol (in submission)Google Scholar
  33. Lychuk TE, Hill RL, Izaurralde RC, Momen B, Thomson AM (2017) Evaluation of climate change impacts and effectiveness of adaptation options on crop yield in the Southeastern United States. Field Crop Res 214(Supplement C:228–238Google Scholar
  34. Macadam I, Pitman AJ, Whetton PH, Liu DL, Evans JP (2014) The use of uncorrected regional climate model output to force impact models: a case study for wheat simulations. Clim Res 61:215–229Google Scholar
  35. Macadam I, Argüeso D, Evans JP, Liu DL, Pitman AJ (2016) The effect of bias correction and climate model resolution on wheat simulations forced with a regional climate model ensemble. Int J Climatol 36:4577–4591Google Scholar
  36. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772Google Scholar
  37. O'Leary GJ, Christy B, Nuttall J, Huth N, Cammarano D, Stöckle C, Basso B, Shcherbak I, Fitzgerald G, Luo Q (2015) Response of wheat growth, grain yield and water use to elevated CO2 under a Free-Air CO2 Enrichment (FACE) experiment and modelling in a semi-arid environment. Glob Chang Biol 21:2670–2686Google Scholar
  38. Olson R, Fan Y, Evans JP (2016) A simple method for Bayesian model averaging of regional climate model projections: application to southeast Australian temperatures. Geophys Res Lett 43(14):7661–7669Google Scholar
  39. Osborne T, Rose G, Wheeler T (2013) Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation. Agric For Meteorol 170:183–194Google Scholar
  40. Pascal O, Benjamin S, Christian B, Mathieu V (2011) Are regional climate models relevant for crop yield prediction in West Africa? Environ Res Lett 6(1):014008Google Scholar
  41. Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99(1):187–192Google Scholar
  42. Probert ME, Dimes JP, Keating BA, Dalal RC, Strong WM (1998) APSIM's water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems. Agric Syst 56(1):1–28Google Scholar
  43. Ramarohetra J, Pohl B, Sultan B (2015) Errors and uncertainties introduced by a regional climate model in climate impact assessments: example of crop yield simulations in West Africa. Environ Res Lett 10(12):124014Google Scholar
  44. Raymundo R, Asseng S, Robertson R, Petsakos A, Hoogenboom G, Quiroz R, Hareau G, Wolf J (2017) Climate change impact on global potato production. Eur J AgronGoogle Scholar
  45. Reyenga PJ, Howden SM, Meinke H, McKeon GM (1999) Modelling global change impacts on wheat cropping in south-east Queensland, Australia. Environ Model Softw 14(4):297–306Google Scholar
  46. Ruan H, Feng P, Wang B, Xing H, O’Leary GJ, Huang Z, Guo H, Liu DL (2018) Future climate change projects positive impacts on sugarcane productivity in southern China. Eur J Agron 96:108–119Google Scholar
  47. Ruiz-Ramos M, Rodríguez A, Dosio A, Goodess CM, Harpham C, Mínguez MI, Sánchez E (2016) Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century. Clim Chang 134(1):283–297Google Scholar
  48. Scally J, Schwarzman R, Stirling K, McGuinness S (2016) Riverina Murray Agricultural Industries Final Report (http://www.planning.nsw.gov.au/Plans-for-Your-Area/Regional-Plans/Riverina-Murray/~/media/E170FEF15B7E41E6821C995058C4EE57.ashx). Accessed Jan 2016
  49. Semenov MA, Shewry PR (2011) Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Sci Rep 1:66.  https://doi.org/10.1038/srep00066 Google Scholar
  50. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR technical note NCAR/TN-475+STRGoogle Scholar
  51. Sultan B, Guan K, Kouressy M, Biasutti M, Piani C, Hammer G, McLean G, Lobell D (2014) Robust features of future climate change impacts on sorghum yields in West Africa. Environ Res Lett 9(10):104006Google Scholar
  52. Tai AP, Martin MV, Heald CL (2014) Threat to future global food security from climate change and ozone air pollution. Nat Clim Chang 4(9):817–821Google Scholar
  53. Teixeira EI, de Ruiter J, Ausseil A-G, Daigneault A, Johnstone P, Holmes A, Tait A, Ewert F (2018) Adapting crop rotations to climate change in regional impact modelling assessments. Sci Total Environ 616-617(Supplement C):785–795Google Scholar
  54. Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456:12–29Google Scholar
  55. Vanuytrecht E, Raes D, Willems P, Semenov MA (2014) Comparing climate change impacts on cereals based on CMIP3 and EU-ENSEMBLES climate scenarios. Agric For Meteorol 195:12–23Google Scholar
  56. Verrillo F, Badeck F-W, Terzi V, Rizza F, Bernardo L, Di Maro A, Fares C, Zaldei A, Miglietta F, Moschella A, Bracale M, Vannini C (2017) Elevated field atmospheric CO2 concentrations affect the characteristics of winter wheat (cv. Bologna) grains. Crop Pasture Sci 68(8):713–725Google Scholar
  57. Wang J, Wang E, Li Liu D (2011) Modelling the impacts of climate change on wheat yield and field water balance over the Murray–Darling Basin in Australia. Theor Appl Climatol 104(3–4):285–300Google Scholar
  58. Wang B, Chen C, Liu DL, Asseng S, Yu Q, Yang X (2015) Effects of climate trends and variability on wheat yield variability in eastern Australia. Clim Res 64(2):173–186Google Scholar
  59. Wang B, Liu DL, Asseng S, Macadam I, Yang X, Yu Q (2017) Spatiotemporal changes in wheat phenology, yield and water use efficiency under the CMIP5 multimodel ensemble projections in eastern Australia. Clim Res 72(2):83–99Google Scholar
  60. Wheeler T, Von Braun J (2013) Climate change impacts on global food security. Science 341(6145):508–513Google Scholar
  61. Yang Y, Liu DL, Anwar MR, Zuo H, Yang Y (2014) Impact of future climate change on wheat production in relation to plant-available water capacity in a semiaridenvironment. Theor Appl Climatol 115(3–4):391–410Google Scholar
  62. Yang C, Fraga H, Ieperen WV, Santos JA (2017) Assessment of irrigated maize yield response to climate change scenarios in Portugal. Agric Water Manag 184(Supplement C:178–190Google Scholar
  63. Yu Q, Li L, Luo Q, Eamus D, Xu S, Chen C, Wang E, Liu J, Nielsen DC (2014) Year patterns of climate impact on wheat yields. Int J Climatol 34(2):518–528Google Scholar
  64. Ziska LH, Bunce JA, Shimono H, Gealy DR, Baker JT, Newton PC, Reynolds MP, Jagadish KS, Zhu C, Howden M (2012) Food security and climate change: on the potential to adapt global crop production by active selection to rising atmospheric carbon dioxide. Proc R Soc Lond B Biol Sci, rspb20121005 279:4097–4105Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Bin Wang
    • 1
    Email author
  • De Li Liu
    • 1
    • 2
  • Jason P. Evans
    • 2
  • Fei Ji
    • 3
  • Cathy Waters
    • 4
  • Ian Macadam
    • 2
    • 3
  • Puyu Feng
    • 1
    • 5
  • Kathleen Beyer
    • 3
  1. 1.NSW Department of Primary IndustriesWagga Wagga Agricultural InstituteWagga WaggaAustralia
  2. 2.Climate Change Research Centre and ARC Centre of Excellence for Climate ExtremesUniversity of New South WalesSydneyAustralia
  3. 3.Department of Planning and EnvironmentNSW Office of Environment and HeritageSydneyAustralia
  4. 4.NSW Department of Primary IndustriesOrange Agricultural InstituteOrangeAustralia
  5. 5.School of Life Sciences, Faculty of ScienceUniversity of Technology SydneyUltimoAustralia

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