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.
Highlights
Wheat (Triticum aestivum L.) is a primary staple crop worldwide, with a projected global production of ~740 million metric tons in 2017 [1]
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
This paper describes a methodology for rapid synthesis of GCM-based, spatially explicit, high resolution future weather data inputs for the Decision Support System for Agrotechnology Transfer (DSSAT) crop model, for cropland area across wheat growing regions of Oklahoma on a seamless temporal scale
Summary
Wheat (Triticum aestivum L.) is a primary staple crop worldwide, with a projected global production of ~740 million metric tons in 2017 [1]. According to the 2017 estimates of the United States Department of Agriculture (USDA), the U.S ranks fourth in wheat production by country with a projected production of 49.64 million metric tons. Achieving sustainable and equitable food security and profitability of crop production in future relies on better understanding of climate and changes in greenhouse gas concentration [3]. Atmospheric CO2 concentrations recorded at Mauna Loa, Hawaii show an increasing trend for CO2. Measured at 313 ppm, at the time of writing this paper the atmospheric concentration has crossed 408 ppm (https://www.esrl.noaa.gov/gmd/ccgg/trends/). Climate variability and extremes have multifold consequences and damage the economy as well as natural systems, and could result in pronounced deleterious impacts on food security in less developed regions of the world
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