Skip to main content

Advertisement

SpringerLink
Log in

Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

The potential impact of the rise in atmospheric CO2 concentration and associated climatic change on agricultural productivity needs assessment. Projecting crop yield changes under climate change requires future climate scenarios as input to crop yield models. It is widely accepted that downscaling of climate data is required to bridge the gap between large-scale global climate models (GCMs) and climate change impact models, such as crop growth models. Regional climate models (RCMs) are often used to dynamically downscale GCM simulations to smaller regional scales, while statistical methods, such as regression-based transfer functions and stochastic weather generators, are also widely employed to develop future climate scenarios for this purpose. The methods used in developing future climate scenarios often contribute to uncertainties in the projected impacts of climate change, in addition to those associated with GCMs and forcing scenarios. We employed climate scenarios from the state-of-the-art RCMs in the North American Regional Climate Change Assessment Program (NARCCAP), along with climate scenarios generated by a stochastic weather generator based on climate change simulations performed by their driving GCMs, to drive the CERES-Wheat model in DSSAT to project changes in spring wheat yield on the Canadian Prairies. The future time horizon of 2041–2070 and the baseline period of 1971–2000 were considered. The projected changes showed an average increase ranging from 26 to 37 % of the baseline yield when the effects of the elevated CO2 concentration were simulated, but only up to 15 % if the elevated CO2 effect was excluded. In addition to their potential use in climate change impact assessment, the results also demonstrated that the simulated crop yield changes were fairly consistent whether future climate scenarios were derived from RCMs or they were generated by a stochastic weather generator based on the simulated climate change from the GCMs that were used to drive the RCMs, in this case, when they were compared for regional averages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abatzoglou JT, Brown TJ (2009) 8-km historical dataset for FPA. Program for Climate, Ecosystem and Fire Applications, Desert Research Institute, Reno, NV. Online accessed on December 29, 2014 at http://cefa.dri.edu/Publications/FPA_8km_historical_report.pdf

  • Aggarwal PK, Mall RK (2002) Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Clim Change 52:331–343

    Article  Google Scholar 

  • Alexandrov VA, Hoogenboom G (2000) Vulnerability and adaptation assessments of agricultural crops under climate change in the southeastern U.S.A. Theor Appl Climatol 67:45–63

    Article  Google Scholar 

  • Asseng S et al (2013) Uncertainty in simulating wheat yields under climate change. Nature Clim Change 3:827–832

    Article  Google Scholar 

  • Baigorria GA, Jones JW, Shin D-W, Mishra A, O’Brien JJ (2007) Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs. Clim Res 34:211–222

    Article  Google Scholar 

  • Baigorria GA, Jones JW, O’Brien JJ (2008) Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model. Agric Forest Meteorol 148:1353–1361

    Article  Google Scholar 

  • Bert FE, Laciana CE, Podestá GP, Satorre EH, Menéndez AN (2007) Sensitivity of CERES-Maize simulated yields to uncertainty in soil properties and daily solar radiation. Agric Syst 94:41–150

    Article  Google Scholar 

  • Betts RA (2005) Integrated approaches to climate-crop modelling: needs and challenges. Phil Trans R Soc B 360:2049–2065

    Article  Google Scholar 

  • Bootsma A, De Jong R (1988) Estimates of seeding dates of spring wheat on the Canadian prairies from climate data. Can J Plant Sci 68:513–517

    Article  Google Scholar 

  • Çaldağ B, Saylan L (2005) Sensitivity analysis of the CERES-Wheat model for variations in CO2 and meteorological factors in northwest of Turkey. Int J Environ Poll 23:300–313

    Article  Google Scholar 

  • Carter TR, Parry ML, Harasawa H, Nishioka S (1994) IPCC technical guidelines for assessing climate change impacts and adaptations. Special Report to Working Group II. Department of Geography, University College London, London

  • Caya D, Laprise R (1999) A semi-implicit semi-Lagrangian regional climate model: the Canadian RCM. Mon Weather Rev 127:341–362

    Article  Google Scholar 

  • Challinor AJ, Wheeler TR, Garforth C, Craufurd P, Kassam A (2007a) Assessing the vulnerability of food crop systems in Africa to climate change. Clim Change 83:381–399

    Article  Google Scholar 

  • Challinor AJ, Wheeler TR, Craufurd PQ, Ferro CAT, Stephenson DB (2007b) Adaptation of crops to climate change through genotypic responses to mean and extreme temperatures. Agric Ecosyst Environ 119:190–204

    Article  Google Scholar 

  • Cheng CS, Li G, Li Q, Auld H (2008) Statistical downscaling of hourly and daily climate scenarios for various meteorological variables in South-central Canada. Theor Appl Climatol 91:129–147

    Article  Google Scholar 

  • Chipanshi AC, Ripley EA, Lawford RG (1997) Early prediction of spring wheat yields in Saskatchewan from current and historical weather data using CERES-Wheat model. Agric Forest Meteorol 84:223–232

    Article  Google Scholar 

  • Christensen JH, Boberg F, Christensen OB, Lucas-Picher P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett 35 (20), L20709. http://dx.doi.org/10.1029/2008GL035694

  • Collins WD, Bitz CM, Blackmon ML, Bonan GB, Bretherton CS, Carton JA, Chang P, Doney SC, Hack JJ, Henderson TB, Kiehl JT, Large WG, McKenna DS, Santer BD, Smith RD (2006) The Community Climate System Model Version 3 (CCSM3). J Clim 19:2122–2143

    Article  Google Scholar 

  • Corte-Real J, Zhang X, Wang XL (1995) Downscaling GCM information to regional scales: a non-parametric multivariate regression approach. Clim Dyn 11:413–424

    Article  Google Scholar 

  • Davidson HR, Campbell CA (1984) Growth rates, harvest index and moisture use of Manitou spring wheat as influenced by nitrogen, temperature and moisture. Can J Plant Sci 64:825–839

    Article  Google Scholar 

  • Deryng D, Conway D, Ramankutty N, Price J, Warren R (2014) Global crop yield response to extreme heat stress under multiple climate change futures. Environ Res Lett 9:034011. doi:10.1088/1748-9326/9/034011

    Article  Google Scholar 

  • Dexter JE, Preston KP, Woodbeck N (2006) Chapter 6: Canadian Wheat. In Popper L, Schafer W, Freund W (eds) Future of flour—a compendium of flour improvement, pp43-62. Verlag Agrimedia, Bergen. Dunne, Germany

  • Easterling WE, Rosenberg NJ, McKenney MS, Jones CA, Dyke PT, Williams JR (1992) Preparing the erosion productivity impact calculator (EPIC) model to simulate crop response to climate change and the direct effects of CO2. Special Issue: Methodology for Assessing Regional Agricultural Consequences of Climate Change. Agric Forest Meteorol 59:17–34

    Article  Google Scholar 

  • Giorgi F (1990) Simulation of regional climate using a limited area model nested in a general circulation model. J Clim 3:941–963

    Article  Google Scholar 

  • Giorgi F, Marinucci MR, Visconti G (1992) A 2 × CO2 climate change scenario over Europe generated using a limited area model in a general circulation model. II: climate change scenario. J Geophys Res 97:10011–10028

    Article  Google Scholar 

  • Giorgi F, Shields-Brodeur C, Bates GT (1994) Regional climate change scenarios over the United States produced with a nested regional model. J Clim 7:375–399

    Article  Google Scholar 

  • Goddard L, Maso SJ, Zebiak SE, Ropelewski CE, Basher R, Cane MA (2001) Current approaches to seasonal to interannual climate predictions. Int J Climatol 21:1111–1152

    Article  Google Scholar 

  • Godwin DC, Singh U (1998) Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In: Tsuji GY et al (eds) Understanding options for agricultural production. System approaches for sustainable agricultural development. Kluwer, Dordrecht, the Netherlands, pp 55–77

    Chapter  Google Scholar 

  • Gordon C, Cooper C, Senior A, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168

    Article  Google Scholar 

  • Hansen JW, Ines AVM (2005) Stochastic disaggregation of monthly rainfall data for crop simulation studies. Agric Forest Meteorol 131:233–246

    Article  Google Scholar 

  • Hansen JW, Challinor A, Ines A, Wheeler T, Moron V (2006) Translating climate forecasts into agricultural terms: advances and challenges. Clim Res 33:27–41

    Article  Google Scholar 

  • Hayhoe HN (2000) Improvements of stochastic weather data generators for diverse climates. Clim Res 14:75–87

    Article  Google Scholar 

  • He Y, Wei Y, DePauw R, Qian B, Lemke R, Singh A, Cuthbert R, McConkey B, Wang H (2013) Spring wheat yield in the semiarid Canadian prairies: effects of precipitation timing and soil texture over recent 30 years. Field Crops Res. doi:10.1016/j.fcr.2013.05.013

    Google Scholar 

  • Herndl M, White JW, Graeff S, Claupein W (2008) The impact of vernalization requirement, photoperiod sensitivity and earliness per se on grain protein content of bread wheat (Triticum aestivum L.). Euphytica 163:309–320

    Article  Google Scholar 

  • Hoogenboom G, Tsuji GY, Pickering NB, Curry RB, Jones JW, Singh U, Godwin DC (1995) Decision support system to study climate change impacts on crop production. In: Climate change and agriculture: analysis of potential international impacts. ASA Special Publication No. 59, Madison, WI, p 51–75

  • Hoogenboom G, Jones JW, Porter CH, Wilkens PW, Boote KJ, Batchelor WD, Hunt LA, Tsuji GY (eds) (2003) Decision Support System for Agrotechnology Transfer Version 4.0. Volume 1: Overview. University of Hawaii, Honolulu, HI

  • Iglesias A (1995) Modelling the effects of climate change and climate variability on crops at the site scale. Effect on maize. In: Harrison, P. A., R. E. Butterfield, T. E. Downing (eds.) Climate change and agriculture in Europe. Assessment of Impacts and Adaptations. Research Report No. 9, University of Oxford, U.K., pp. 223–231

  • Iizumi T, Yokozawa M, Nishimori M (2010) Probabilistic evaluation of climate change impacts on paddy rice productivity in Japan. Clim Change 107:391–415. doi:10.1007/s10584-010-9990-7

    Article  Google Scholar 

  • Iizumi T, Takayabu I, Dairaku K, Kusaka H, Nishimori M, Sakurai G, Ishizaki NN, Adachi SA, Semenov MA (2012) Future change of daily precipitation indices in Japan: a stochastic weather generator-based bootstrap approach to provide probabilistic climate information. J Geophys Res 117, D11114. doi:10.1029/2011JD017197

    Article  Google Scholar 

  • Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric Forest Meteorol 138:44–53

    Article  Google Scholar 

  • Ines AVM, Hansen JW, Robertson AW (2011) Enhancing the utility of daily GCM rainfall for crop yield prediction. Int J Climatol 31:2168–2182

    Article  Google Scholar 

  • IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z et al (eds) Contribution of Working Group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Google Scholar 

  • Jame YW, Cutforth HW (1996) Crop growth models for decision support systems. Can J Plant Sci 76:9–19

    Article  Google Scholar 

  • Jones JW, Tsuji GY, Hoogenboom G, Hunt LA, Thornton PK, Wilkens PW, Imamura DW, Bowen WT, Singh U (1998) Decision Support System for Agrotechnology Transfer; DSSAT v3. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer, Dordrecht, the Netherlands, pp 157–177

    Chapter  Google Scholar 

  • Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:35–265

    Article  Google Scholar 

  • Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over Europe using a nested regional climate model. I: assessment of control climate, including sensitivity to location of lateral boundaries. QJR Meteorol Soc 121:1413–1449

    Google Scholar 

  • Jones RG, Noguer M, Hassell DC, Hudson D, Wilson SS, Jenkins GJ, Mitchell JFB (2004) Generating high resolution climate change scenarios using PRECIS, Met Office Hadley Centre, Exeter, UK, 40pp

  • Kim SJ, Flato GM, Boer GJ, McFarlane NA (2002) A coupled climate model simulation of the Last Glacial Maximum. I. Transient multi-decadal response. Clim Dyn 19:515–537

    Article  Google Scholar 

  • Kim SJ, Flato GM, Boer GJ (2003) A coupled climate model simulation of the Last Glacial Maximum. II. Approach to equilibrium. Clim Dyn 20:635–661

    Google Scholar 

  • Ko J, Ahuja L, Kimball B, Anapalli S, Ma L, Green TR, Ruane AC, Wall GW, Pinter P, Bader DA (2010) Simulation of free air CO2 enriched wheat growth and interactions with water, nitrogen, and temperature. Agric Forest Meteorol 150:1331–1346

    Article  Google Scholar 

  • Laprise R (2008) Regional climate modelling. J Comput Phys 227:3641–3666

    Article  Google Scholar 

  • Li CS (2000) Modeling trace gas emissions from agricultural ecosystems. Nutr Cycl Agroecosyst 58:259–273

    Article  Google Scholar 

  • Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistanct quantile matching. J Geophys Res 115, D10101. doi:10.1029/2009JD012882

    Article  Google Scholar 

  • Mavromatis T, Jones PD (1999) Evaluation of HadCM2 and direct use of daily GCM data in impact assessment studies. Clim Change 41:583–614

    Article  Google Scholar 

  • McCaig TN, DePauw RM, Clarke JM, McLeod JG, Fernandez MR, Knox RE (1996) AC Barrie hard red spring wheat. Can J Plant Sci 76:337–339

    Article  Google Scholar 

  • Mearns LO, Schneider SH, Thompson SL, McDaniel LR (1990) Analysis of climate variability in general circulation models: comparison with observations and changes in variability in 2 × CO2 experiments. J Geophys Res D 95:20469–20490

    Article  Google Scholar 

  • Mearns LO, Giorgi F, McDaniel L, Shields C (1995) Analysis of daily variability of precipitation in a nested regional climate model: comparison with observations and doubled CO2 results. Global Planet Change 10:55–78

    Article  Google Scholar 

  • Mearns LO, Rosenzweig C, Goldberg R (1996) The effect of changes in daily and interannual climatic variability on CERES-Wheat: a sensitivity study. Clim Change 32:257–292

    Article  Google Scholar 

  • Mearns LO, Rosenzweig C, Godlberg R (1997) Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty. Clim Change 35:367–396

    Article  Google Scholar 

  • Mearns LO, Gutowski WJ, Jones R, Leung L-Y, McGinnis S, Nunes AMB, Qian Y (2009) A regional climate change assessment program for North America. Eos 90:311–312

    Article  Google Scholar 

  • Mearns LO, Arritt R, Biner S, Bukovsky MS, McGinnis S, Sain S, Caya D, Correia J Jr, Flory D, Gutowski W, Takle ES, Jones R, Leung R, Moufouma-Okia W, McDaniel L, Nunes AM, Qian Y, Roads J, Solan L, Snyder M (2012) The North American Regional Climate Change Assessment Program: overview of phase I results. Bull Am Meteorol Soc 93:1337–1362

    Article  Google Scholar 

  • Michalakes J, Dudhia J, Gill D, Henderson T, Klemp J, Skamarock W, Wang W (2004) The Weather Research and Forecast Model: Software Architecture and Performance. Proceedings of the 11th ECMWF Workshop on the Use of High Performance Computing in Meteorology, 25-29 October 2004, Reading, U.K. Online accessed on September 17, 2014 at http://www.wrf-model.org/wrfadmin/docs/ecmwf_2004.pdf

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900

    Article  Google Scholar 

  • Nakicenovic N, Swart R (eds) (2000) Special report on emissions scenarios. A special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part 1. A discussion of principles. J Hydrol 10:282–290

    Article  Google Scholar 

  • Oettli P, Sultan B, Baron C, Vrac M (2011) Are regional climate models relevant for crop yield prediction in West Africa? Environ Res Lett 6:014008. doi:10.1088/1748-9326/6/1/014008

    Article  Google Scholar 

  • Palosuo T, Kersebaum KC, Angulo C, Hlavinka P, Moriondo M, Olesen JE, Patil RH, Ruget F, Rumbaur C, Takac J, Trnka M, Bindi M, Caldag B, Ewert F, Ferrise R, Mirschel W, Saylan L, Siska B, Roetter R (2011) Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur J Agron. doi:10.1016/j.eja.2011.05.001

    Google Scholar 

  • Parry M, Rosenzweig C, Iglesias A, Fischer G, Livermore M (1999) Climate change and world food security: a new assessment. Global Environ Change 9:51–67

    Article  Google Scholar 

  • Parry M, Rosenzweig C, Livermore M (2005) Climate change, global food supply and risk of hunger. Phil Trans R Soc B 369:2125–2138

    Article  Google Scholar 

  • Polley HW (2002) Implications of atmospheric and climatic change for crop yield and water use efficiency. Crop Sci 42:131–140

    Article  Google Scholar 

  • Porter CH, Jones JW, Adiku S, Gijsman AJ, Gargiulo O, Naab JB (2014) Modeling organic carbon and carbon-mediated soil processes in DSSAT v4.5. Oper Res 10:247–278

    Google Scholar 

  • Qian B, Gameda S, Hayhoe H, De Jong R, Bootsma A (2004) Comparison of LARS-WG and AAFC-WG stochastic weather generators for diverse Canadian climates. Clim Res 26:175–191

    Article  Google Scholar 

  • Qian B, Hayhoe H, Gameda S (2005a) Evaluation of the stochastic weather generators LARS-WG and AAFC-WG for climate change impact studies. Clim Res 29:3–21

    Article  Google Scholar 

  • Qian B, Hayhoe H, and Gameda S (2005b) Developing daily climate scenarios for agricultural impact studies. 85th American Meteorological Society Annual Meeting, January 8-14, 2005, San Diego, California. Online accessed on December 29, 2014 at https://ams.confex.com/ams/Annual2005/techprogram/paper_83278.htm

  • Qian B, Gameda S, Hayhoe H (2008) Performance of stochastic weather generators LARS-WG and AAFC-WG for reproducing daily extremes of diverse Canadian climates. Clim Res 37:17–33

    Article  Google Scholar 

  • Qian B, De Jong R, Gameda S (2009a) Multivariate analysis of water-related agroclimatic factors limiting spring wheat yields on the Canadian prairies. Europ J Agronomy 30:140–150

    Article  Google Scholar 

  • Qian B, De Jong R, Warren R, Chipanshi A, Hill H (2009b) Statistical spring wheat yield forecasting for the Canadian prairies provinces. Agric Forest Meteorol 149:1022–1031

    Article  Google Scholar 

  • Qian B, Gameda S, de Jong R, Falloon P, Gornall J (2010) Comparing scenarios of Canadian daily climate extremes derived using a weather generator. Clim Res 41:131–149

    Article  Google Scholar 

  • Qian B, De Jong R, Yang J, Wang H, Gameda S (2011) Comparing simulated crop yields with observed and synthetic weather data. Agric Forest Meteorol 151:1781–1791

    Article  Google Scholar 

  • Qian B, Huffman T, De Jong R (2012) Simulated crop productivity on different soils in Canada. In Kang M, Dumont Y, Guo Y (eds) Proceedings 2012 I.E. 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications. IEEE Press. pp 312-315. DOI: 10.1109/PMA.2012.6524851

  • Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17:182–190

    Article  Google Scholar 

  • Richardson CW. Wright DA (1984) WGEN: a model for generating daily weather variables. US Department of Agriculture, Agricultural Research Service, ARS-8. USDA, Washington, DC

  • Ritchie JT (1972) Model for predicting evaporation from a row crop with incomplete cover. Water Resour Res 8:1204–1213

    Article  Google Scholar 

  • Ritchie JT, Godwin DC, Otter-Nacke S (1988) CERES-Wheat. A simulation model of wheat growth and development. Texas A&M Univ Press, College Station, USA

    Google Scholar 

  • Ritchie JT, Singh U, Godwin DC, Bowen WT (1998) Cereal growth, development and yield. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding Options for Agricultural Production. System Approaches for Sustainable Agricultural Development, vol 7. Kluwer Academic Publisher in cooperation with ICASA, Dordrecht, The Netherlands, pp 79–98

    Chapter  Google Scholar 

  • Rosenzweig C, Parry ML, Fischer G, Frohberg K (1993) Climate change and world food supply. Research Report No. 3, Environmental Change Unit. Oxford University Press, Oxford, U. K

    Google Scholar 

  • Rosenzweig C, Parry ML (1994) Potential impact of climate change on world food supply. Nature 367:133–138

    Article  Google Scholar 

  • Rosenzweig C et al (2013) The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agric Forest Meteorol 170:166–182

    Article  Google Scholar 

  • Rötter RP, Carter TR, Olesen JE, Porter JR (2011) Crop-climate models need an overhaul. Nat Clim Chang 1:175–177. doi:10.1038/nclimate1152

    Article  Google Scholar 

  • Ruane AC, McDermid S, Rosenzweig C, Baigorria GA, Jones JW, Romero CC, Cecil D (2014) Carbon-temperature-water change analysis for peanut production under climate change: a prototype for the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). Global Change Biol 20:394–407

    Article  Google Scholar 

  • Semenov MA, Wolf J, Evans LG, Eckersten H, Iglesias A (1996) Comparison of wheat simulation models under climate change. II. Application of climate change scenarios. Clim Res 7:271–281

    Article  Google Scholar 

  • Semenov MA, Shewry PR (2011) Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Scientific Reports 1:66. doi:10.1038/srep00066

    Article  Google Scholar 

  • Semenov MA, Stratonovitch P, Alghabari F, Gooding MJ (2014) Adapting wheat in Europe for climate change. J Cereal Sci 59:245–256

    Article  Google Scholar 

  • Smith WN, Grant BB, Desjardins RL, Kroebel R, Li C, Qian B, Worth DE, McConkey BG, Drury CF (2013) Assessing the effects of climate change on crop production and GHG emissions in Canada. Agric Ecosyst Environ 179:139–150

    Article  Google Scholar 

  • Soil Landscapes of Canada Working Group (2006) Soil Landscapes of Canada v3.1. Agriculture and Agri-Food Canada (Digital Maps and Database at 1:1 million scale). Online accessed on December 29, 2014 at http://sis.agr.gc.ca/cansis/nsdb/slc/index.html

  • Southworth J, Pfeifer RA, Habeck M, Randolph JC, Doering OC, Gangadhar Rao D (2002) Sensitivity of winter wheat yields in the Midwestern United States to future changes in climate, climate variability, and CO2 fertilization. Clim Res 22:73–86

    Article  Google Scholar 

  • Terink W, Hurkmans RTWL, Torfs PJJF, Uijlenhoet R (2010) Evaluation of a bias correction method applied to downscaled precipitation and temperature reanalysis data for the Rhine basin. Hydrol Earth Syst Sci 14:687–703

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2012) Bias correction of regionalclimate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456–457:12–29

    Article  Google Scholar 

  • Toure A, Major DJ, Lindwall CW (1994) Comparison of five wheat simulation models in southern Alberta. Can J Plant Sci 75:61–68

    Article  Google Scholar 

  • Tsvetsinskaya EA, Mearns LO, Mavromatis T, Gao W, McDaniel L, Downton MW (2003) The effect of spatial scale of climatic change, scenarios on simulated maize, winter wheat, and rice production in the southeastern United States. Clim Change 60:37–71

    Article  Google Scholar 

  • von Storch H, Zorita E, Cubasch U (1993) Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. J Clim 6:1161–1171

    Article  Google Scholar 

  • Wang H, Cutforth H, McCaig T, McLeod G, Brandt K, Lemke R, Goddard T, Sprout C (2009) Predicting the time to 50 % seedling emergence in wheat using a Beta model. NJAS-Wageningen J Life Sci 57:65–71

    Article  Google Scholar 

  • Wang H, Flerchinger GN, Lemke R, Brandt K, Goddard T, Sprout C (2010) Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada. Can J Soil Sci 90:37–53

    Article  Google Scholar 

  • Wang H, He Y, Qian B, McConkey B, Cutforth H, McCaig T, McLeod G, Zentner R, DePauw R, Lemke R, Brandt K, Liu T, Qin X, White J, Hunt T, Hoogenboom G (2012) SHORT COMMUNICATION: climate change and biofuel wheat: a case study of southern Saskatchewan. Can J Plant Sci 92:421–425

    Article  Google Scholar 

  • Wilby RL (1998) Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices. Clim Res 10:163–178

    Article  Google Scholar 

  • Wilks DS, Wilby RL (1999) The weather generation game: a review of stochastic weather models. Prog Phys Geogr 23:329–357

    Article  Google Scholar 

  • Winkler JA, Palutikof JP, Andresen JA, Goodess CM (1997) The simulation of daily temperature time series from GCM output. Part II: sensitivity analysis of an empirical transfer function methodology. J Clim 10:2514–2532

    Article  Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216

    Article  Google Scholar 

  • Yang JY, Huffman EC, Drury CF, Yang XM, De Jong R (2011) Estimating the impact of manure nitrogen losses on total nitrogen application on agricultural land in Canada. Can J Soil Sci 91:107–122

    Article  Google Scholar 

Download references

Acknowledgments

This study was partially supported by The Sustainable Agriculture Environmental Systems (SAGES) Initiative financed by the Government of Canada and the provincial and territorial governments. We are indebted to Mr. Guilong Li of Environment Canada for his help in extracting and verifying RCM data. We wish to thank the North American Regional Climate Change Assessment Program (NARCCAP) and Dr. Linda O. Mearns for providing the RCM data and advice. NARCCAP is funded by the National Science Foundation (NSF), the U.S. Department of Energy (DoE), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Environmental Protection Agency Office of Research and Development (EPA). We are indebted to two anonymous reviewers for their comments that helped in improving this paper. This is ECORC contribution No. 15-003.

Author information

Authors and Affiliations

  1. Eastern Cereal and Oilseed Research Centre, Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada

    Budong Qian, Reinder De Jong & Ted Huffman

  2. Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food Canada, Swift Current, Saskatchewan, Canada

    Hong Wang

  3. Greenhouse and Processing Crops Research Centre, Agriculture and Agri-Food Canada, Harrow, Ontario, Canada

    Jingyi Yang

Authors
  1. Budong Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reinder De Jong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ted Huffman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jingyi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Budong Qian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qian, B., De Jong, R., Huffman, T. et al. Projecting yield changes of spring wheat under future climate scenarios on the Canadian Prairies. Theor Appl Climatol 123, 651–669 (2016). https://doi.org/10.1007/s00704-015-1378-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-015-1378-1

Keywords

Search

Navigation