Estimation of Water Requirement and Wheat (Triticum aestivum L.) Yield under the Impact of Climate Change

Crop production is directly dependent on climatic conditions, and climate determines the sources of production and productivity of agricultural activities. Therefore, long-term forecasting of climate variables and taking the necessary measures to mitigate the adverse effects of climate change have been considered by many researchers around the world. Climate change affects water requirement and crop yields in the future, so it is important to study changes in meteorological parameters and their impact on water requirement and crop yields in each region. So, in this study, the effect of climate change on the yield and water requirement of wheat in Qazvin synoptic station was investigated.

In this study, the results of the scenarios were compared with the data of Qazvin station for wheat crop by statistical error criteria including Explanation coefficient statistics (R2), root mean square error (RMSE) and maximum error (ME). For evaluation, from the general circulation models in the LARS-WG model (EC-Earth, GFDL-CM3, HadGEM2-ES, MIROC5, MPI-ESM-MR) and the scenarios of RCP2.6, RCP4.5 and RCP8.5 in the baseline 1986-2015 was used. Yield and water requirement of wheat in the baseline and future periods 2021-2040, 2041-2060, 2061-2080 and 2081-2100 were calculated with Aqua Crop software.

 The coefficient of explanation for the maximum and minimum temperatures simulated with the LARS-WG model shows that the simulated data and the synoptic station data are highly correlated. An explanation coefficient greater than 90% indicates that more than 90% of the variance in the minimum and maximum temperature data of the synoptic station is described by the LARS-WG model data. The value of RMSE at the minimum and maximum temperature is less than 3 °C, which indicates the low temperature deviation simulated with the LARS-WG model compared to the actual temperature. The ME index value was obtained for the minimum temperature equal 6.93 °C and for the maximum temperature equal 7.76 °C. The coefficient of explanation for the precipitation simulated with the LARS-WG model shows that the simulated data and the data of the synoptic station do not have a high correlation and the coefficient of explanation decreases to less than 0.5. The values of RMSE and ME were 33.28 mm and 183.10 mm, respectively. The results show that the model is more accurate in simulating minimum and maximum temperatures than precipitation. In a study, Goudarzi et al. (2015) investigated the performance of LARS-WG and SDSM microscopic exponential models in simulating climate change in the catchment area of Lake Orumieh. The results showed that both models are more accurate in simulating temperature than precipitation, which is consistent with the results of the present study. The average wheat yield for the baseline was 7.67 (tons/ha). The yield average will increase in future periods, which is the highest in the HadGEM2-ES model with the RCP8.5 scenario and the period 2081-2100. Water requirement was obtained in the baseline 127.14 mm. The water requirement average will decrease in future periods.

The simulation results of the LARS-WG model in the baseline showed that the model has more accurate in the simulation of minimum temperature (Tmin) and maximum temperature (Tmin) than precipitation. This study findings have also showed that the temperature will increase in future periods. Precipitation changes were seen as both decreasing and increasing trend. The yield increased in future periods, which is the highest in the HadGEM2-ES model with the RCP8.5 scenario and the period 2081-2100. The water requirement decreased in future periods.