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 ...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.展开更多
Climate change is expected to affect the agricultural systems, such as crop yield and plant disease occurrence and spread. To be able to mitigate against the negative impacts of climate change, there is a need to use ...Climate change is expected to affect the agricultural systems, such as crop yield and plant disease occurrence and spread. To be able to mitigate against the negative impacts of climate change, there is a need to use early warning systems that account for expected changes in weather variables such as temperature and rainfall. Moreover, providing such information at high spatial and temporal resolutions can be useful in improving the accuracy of an early warning system. This paper describes a methodology that can be used to produce high spatial and temporal resolutions of minimum temperature, maximum temperature and rainfall in an agricultural area. We utilize MarkSim GCM, a weather file generator that incorporates IPCC based climate change models to downscale the weather variables at monthly intervals. An ensemble of 17 GCM models is used within the RCP 8.0 emission scenario within the latest model based CMIP5. We first assess the usability of the model, by comparing results produced to what has been recorded at weather station level over a vast region. Then, we estimate the correction factors for model results by implementing a linear regression that is used to assess the relationship between the variables and the deviation of model outputs to the weather station data. Finally, we use kriging geostatistical technique to interpolate the weather data, for the year 2010. Results indicated that the model overestimated the results of maximum temperature, while underestimating the result of minimum temperature. Variability in the recorded weather variables was also evident, indicating that the response variables such as plant disease severity dependent on such weather information could vary in the area. These datasets can be useful especially in predicting the occurrence of plant diseases, which are affected by either rainfall or temperature.展开更多
文摘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.
文摘Climate change is expected to affect the agricultural systems, such as crop yield and plant disease occurrence and spread. To be able to mitigate against the negative impacts of climate change, there is a need to use early warning systems that account for expected changes in weather variables such as temperature and rainfall. Moreover, providing such information at high spatial and temporal resolutions can be useful in improving the accuracy of an early warning system. This paper describes a methodology that can be used to produce high spatial and temporal resolutions of minimum temperature, maximum temperature and rainfall in an agricultural area. We utilize MarkSim GCM, a weather file generator that incorporates IPCC based climate change models to downscale the weather variables at monthly intervals. An ensemble of 17 GCM models is used within the RCP 8.0 emission scenario within the latest model based CMIP5. We first assess the usability of the model, by comparing results produced to what has been recorded at weather station level over a vast region. Then, we estimate the correction factors for model results by implementing a linear regression that is used to assess the relationship between the variables and the deviation of model outputs to the weather station data. Finally, we use kriging geostatistical technique to interpolate the weather data, for the year 2010. Results indicated that the model overestimated the results of maximum temperature, while underestimating the result of minimum temperature. Variability in the recorded weather variables was also evident, indicating that the response variables such as plant disease severity dependent on such weather information could vary in the area. These datasets can be useful especially in predicting the occurrence of plant diseases, which are affected by either rainfall or temperature.
