Gradually developing climatic and weather anomalies due to increasing concentration of atmospheric greenhouse gases can pose threat to farmers and resource managers. There is a growing need to quantify the effects of rising temperature and changing climates on crop yield and assess impact at a finer scale so that specific adaptation strategies pertinent to that location can be developed. Our work aims to quantify and evaluate the influence of future climate anomalies on winter wheat ( Triticum aestivum L.) yield under the Representative Concentration Pathways 6.0 and 8.5 using downscaled climate projections from different General Circulation Models (GCMs) and their ensemble. Marksim downscaled daily data of maximum (TMax) and minimum (TMin) air temperature, rainfall, and solar radiation (SRAD) from different Coupled Model Intercomparison Project GCMs (CMIP5 GCMs) were used to simulate the wheat yield in water and nitrogen limiting and non-limiting conditions for the future period of 2040-2060. The potential impact of climate changes on winter wheat production across Oklahoma was investigated. Climate change predictions by the downscaled GCMs suggested increase in air temperature and decrease in total annual rainfall. This will be really critical in a rainfed and semi-arid agro-ecological region of Oklahoma. Predicted average wheat yield during 2040-2060 increased under projected climate change, compared with the baseline years 1980-2014. Our results indicate that downscaled GCMs can be applied for climate projection scenarios for future regional crop yield assessment.
KeywordsWheatClimate ChangeMarksimGCMsDownscaling
USDA (2017) World Agricultural Supply and Demand Estimates Report (WASDE). United States Department of Agriculture. http://www.usda.gov/oce/commodity/wasde/latest.pdf
USDA-NASS (2017) United States Department of Agriculture Data and Statistics. http://www.nass.usda.gov/Quick_Stats/Lite/
Godfray, H.C.J., Crute, I.R., Beddington, J.R., et al. (2010) Food Security: The Challenge of Feeding 9 Billion People. Science, 327, 812-818. https://doi.org/10.1126/science.1185383
Olmstead, A.L. and Rhode, P.W. (2011) Adapting North American Wheat Production to Climatic Challenges, 1839-2009. Proceedings of the National Academy of Sciences of the United States of America, 108, 480-485. https://doi.org/10.1073/pnas.1008279108
IPCC (2012) Summary for Policymakers. In: Field, C.B., Barros, V., Stocker, T.F., Qin, D., Dokken, D.J., Ebi, K.L., Mastrandrea, M.D., Mach, K.J., Plattner, G.-K., Allen, S.K., Tignor, M. and Midgley, P.M., Eds., Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, New York, NY, 582 p.
Hansen, J.W. and Indeje, M. (2004) Linking Dynamic Seasonal Climate Forecasts with Crop Simulation for Maize Yield Prediction in Semi-Arid Kenya. Agricultural and Forest Meteorology, 125, 143-157. https://doi.org/10.1016/j.agrformet.2004.02.006
Maraun, D., Wetterhall, F., Ireson, A.M., et al. (2010) Precipitation Downscaling under Climate Change: Recent Developments to Bridge the Gap between Dynamical Models and the End User. Reviews of Geophysics, 48, 1-34. https://doi.org/10.1029/2009RG000314
Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R. and White, L.L. (2014) Climate Change 2014: Impacts, Adaptation, and Vulnerability: Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, New York, NY, 1132 p.
Lobell, D.B. and Field, C.B. (2007) Global Scale Climate—Crop Yield Relationships and the Impacts of Recent Warming. Environmental Research Letters, 2, Article ID: 011002. https://doi.org/10.1088/1748-9326/2/1/014002
Lobell, D.B., Sibley, A. and Ivan Ortiz-Monasterio, J. (2012) Extreme Heat Effects on Wheat Senescence in India. Nature Climate Change, 2, 186-189. https://doi.org/10.1038/nclimate1356
Long, S.P., Ainsworth, E.A., Leakey, A.D.B., Nosberger, J. and Ort, D.R. (2006) Food for Thought: Lower-Than-Expected Crop Yield Stimulation with Rising CO2 Concentrations. Science, 312, 1918-1921.
Asseng, S., Ewert, F., et al. (2015) Rising Temperatures Reduce Global Wheat Production. Nature Climate Change, 5, 143-147. https://doi.org/10.1038/nclimate2470
Gooding, M.J., Ellis, R.H., Shewry, P.R. and Schofield, J.D. (2003) Effects of Restricted Water Availability and Increased Temperature on the Grain Filling, Drying and Quality of Winter Wheat. Journal of Cereal Science, 37, 295-309. https://doi.org/10.1006/jcrs.2002.0501
Wahid, A., Gelani, S., Ashraf, M. and Foolad, M.R. (2007) Heat Tolerance in Plants: An Overview. Environmental and Experimental Botany, 61, 199-223. https://doi.org/10.1016/j.envexpbot.2007.05.011
Lobell, D.B., Schlenker, W. and Costa-Roberts, J. (2011) Climate Trends and Global Crop Production Since 1980. Science, 333, 616-620. https://doi.org/10.1126/science.1204531
Vocke, G. and Mir, A. (2013) U.S. Wheat Production Practices, Costs, and Yields: Variations across Regions. EIB-116, US Department of Agriculture, Economic Research Service, August 2013. https://www.ers.usda.gov/webdocs/publications/43783/39923_eib116.pdf?v=41516
Brisson, N., Gary, C., Justes, E., et al. (2003) An Overview of the Crop Model STICS. European Journal of Agronomy, 18, 309-332. https://doi.org/10.1016/S1161-0301(02)00110-7
Jones, J.W., Porter, C.H., Hoogenboom, G., et al. (2003) The DSSAT Cropping System Model. European Journal of Agronomy, 18, 235-265. https://doi.org/10.1016/S1161-0301(02)00107-7
Keating, B.A., Carberry, P.S., et al. (2003) An Overview of APSIM, a Model Designed for Farming Systems Simulation. European Journal of Agronomy, 18, 267-288.
Stockle, C.O., Donatelli, M. and Nelson, R. (2003) CropSyst, a Cropping Systems Simulation Model. European Journal of Agronomy, 18, 289-307. https://doi.org/10.1016/S1161-0301(02)00109-0
Zhang, X.C. (2005) Spatial Downscaling of Global Climate Model Output for Site-Specific Assessment of Crop Production and Soil Erosion. Agricultural and Forest Meteorology, 135, 215-229. https://doi.org/10.1016/j.agrformet.2005.11.016
Zhang, X.C., Nearing, M.A., Gabrecht, J.D. and Steiner, J.L. (2004) Downscaling Monthly Forecasts to Simulate Impacts of Climate Change on Soil Erosion and Wheat Production. Soil Science Society of America Journal, 68, 1376-1385. https://doi.org/10.2136/sssaj2004.1376
Hoogenboom, G., et al. (2015) Decision Support System for Agrotechnology Transfer (DSSAT). DSSAT Foundation, Prosser, Washington DC. http://dssat.net/
Edwards, J.T., Smitha, E.L., Hunger, R.M., et al. (2011) “Duster” Wheat: A Durable, Dual-Purpose Cultivar Adapted to the Southern Great Plains of the USA. Journal of Plant Registrations, 6, 37-48. https://doi.org/10.3198/jpr2011.04.0195crc
Jones, C.D., Ritchie, J.T., Kiniry, J.R., Godwin, D.C. and Otter, S.I. (1983) The CERES Wheat and Maize Models. Proceedings of the International Symposium on Minimum Data Sets for Agrotechnology Transfer, Patancheru, India, 21-26 March 1983, 95-100.
Ritchie, J.T. and Otter, S. (1985) Description and Performance of CERES-Wheat: A User-Oriented Wheat Yield Model. In: Willis, W.O., Beltsville, M.D., et al., Eds., ARS Wheat Yield Project, ARS-38, Natural Technology Information Service, Springfield, Missouri, 159-175.
Castaneda-Vera, A., Leffelaar, P.A., álvaro-Fuentes, J., Cantero-Martínez, C. and Mínguez, M.I. (2015) Selecting Crop Models for Decision Making in Wheat Insurance. European Journal of Agronomy, 68, 97-116. https://doi.org/10.1016/j.eja.2015.04.008
Thornton, P.E., Thornton, M.M., Mayer, B.W., Wei, Y., Devarakonda, R., Vose, R.S. and Cook, R.B. (2016) Daymet: Daily Surface Weather Data on a 1-Km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee.
Brock, F.V., Crawford, K.C., Elliott, R.L., Cuperus, G.W., Stadler, S.J., Johnson, H.L. and Eilts, M.D. (1995) The Oklahoma Mesonet: A Technical Overview. Journal of Atmospheric and Oceanic Technology, 12, 5-19.
Donatelli, M., Bellocchi, G. and Fontana, F. (2003) RadEst3.00: Software to Estimate Daily Radiation Data from Commonly Available Meteorological Variables. European Journal of Agronomy, 18, 363-367. https://doi.org/10.1016/S1161-0301(02)00130-2
Kunkel, K.E., et al. (2013) Regional Climate Trends and Scenarios for the U.S. National Climate Assessment. Part 3. Climate of the Midwest U.S. NOAA Technical Report NESDIS, 142-143.
Pierce, D.W., Barnett, T.P., Santer, B.D. and Gleckler, P.J. (2009) Selecting Global Climate Models for Regional Climate Change Studies. Proceedings of the National Academy of Sciences of the United States of America, 106, 8441-8446. https://doi.org/10.1073/pnas.0900094106
Gleckler, P.J., Taylor, K.E. and Doutriaux, C. (2008) Performance Metrics for Climate Models. Journal of Geophysical Research: Atmospheres, 113, D06104. https://doi.org/10.1029/2007JD008972
Semenov, M.A. and Stratonovitch, P. (2010) Use of Multi-Model Ensembles from Global Climate Models for Assessment of Climate Change Impacts. Climate Research, 41, 1-14. https://doi.org/10.3354/cr00836
Taylor, K.E., Stouffer, R.J. and Meehl, G.A. (2012) An Overview of CMIP5 and the Experiment Design. Bulletin of the American Meteorological Society, 93, 485-498. https://doi.org/10.1175/BAMS-D-11-00094.1
Estes, L.D., Bradley, B.A., Beukes, H., et al. (2013) Projected Climate Impacts to South African Maize and Wheat Production in 2055: A Comparison of Empirical and Mechanistic Modeling Approaches. Global Chang Biology, 19, 3762-3774. https://doi.org/10.1111/gcb.12325
Jones, P.G. and Thornton, P.K. (2013) Generating Downscaled Weather Data from a Suite of Climate Models for Agricultural Modeling Applications. Agricultural Systems, 114, 1-5. https://doi.org/10.1016/j.agsy.2012.08.002
Mathukumalli, S.R., Sengottaiyan, V., Dammu, M., et al. (2016) Prediction of Helicoverpa armigera Hubner on Pigeonpea during Future Climate Change Periods Using MarkSim Multimodel Data. Agricultural and Forest Meteorology, 228-229, 130-138. https://doi.org/10.1016/j.agrformet.2016.07.009
Zhang, X.C. (2012) Cropping and Tillage Systems Effects on Soil Erosion under Climate Change in Oklahoma. Soil Science Society of America Journal, 76, 1789-1797. https://doi.org/10.2136/sssaj2012.0085
Qian, B.D., De Jong, R., Huffman, T., Wang, H. and Yang, J.Y. (2016) Projecting Yield Changes of Spring Wheat under Future Climate Scenarios on the Canadian Prairies. Theoretical and Applied Climatology, 123, 651-669. https://doi.org/10.1007/s00704-015-1378-1
Smith, W.N., Desjardins, R.L., Grant, B.B., et al. (2013) Assessing the Effects of Climate Change on Crop Production and GHG Emissions in Canada. Agriculture, Ecosystems & Environment, 179, 139-150. https://doi.org/10.1016/j.agee.2013.08.015
Hoogenboom, G., Tsuji, G.Y., Pickering, N.B., Curry, R.B., Jones, J.W., Singh, U. and Godwin, D.C. (1995) Decision Support System to Study Climate Change Impacts on Crop Production. In: Rosenzweig, C., Ed., Climate Change and Agriculture: Analysis of Potential International Impacts, American Society of Agronomy, Madison, WI, 51-75.
Asseng, S., Ewert, F., et al. (2013) Uncertainty in Simulating Wheat Yields under Climate Change. Nature Climate Change, 3, 827-832. https://doi.org/10.1038/nclimate1916
Challinor, A.J., Wheeler, T.R., Craufurd, P.Q., Slingo, J.M. and Grimes, D.I.F. (2004) Design and Optimisation of a Large-Area Process-Based Model for Annual Crops. Agricultural and Forest Meteorology, 124, 99-120. https://doi.org/10.1016/j.agrformet.2004.01.002