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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.

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• TITLE: Climate Change Impact on Wheat Production in the Southern Great Plains of the US Using Downscaled Climate Data

  AUTHORS: Kundan Dhakal, Vijaya Gopal Kakani, Evan Linde

  KEYWORDS: Wheat, Climate Change, Marksim, GCMs, Downscaling

  JOURNAL NAME: Atmospheric and Climate Sciences, Vol.8 No.2, April 10, 2018

  ABSTRACT: 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.