Evaluation of Yield Changes and Length of Dryland Wheat Phenological Stages under RCP Scenario Using DSSAT and AquaCrop Models in Western Iran

Document Type : Research Paper


1 PhD student in Agricultural Meteorology, Science and Research Branch, Islamic Azad University Tehran, Iran

2 - Associate Professor of Agricultural Meteorology, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Associate Professor of Meteorology, Department of Earth Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran

4 Assistant Professor of Water Science and Engineering, Department of Water Science and Engineering, Bu-Ali Sina University, Hamadan, Iran


The impact of climate change as the most important factor affecting agriculture, especially rainfed cultivation has challenged the management of these resources. This study tries to investigate the effect of climate change on the yield and length of the dryland wheat phenological stages in western Iran. For this purpose, two downscaling models, SDSM and LarsWG, were used to simulate the climate over the next 30 years. To model the performance and phonological stages, two models of AquaCrop and DSSAT in the base period and the future period were used, considering the three RCP climate scenarios of 2.6, 4.5, and 8.5. The results showed that the AquaCrop model has better performance and less error than DSSAT. So that the value of the coefficient of determination between observed and simulated data in the base period with AquaCrop model in Kermanshah, Sanandaj, and Ilam stations are 0.86, 0.64, and 0.89, respectively; and RMSE coefficient values are 198.6, 274.6 and 192 kg/ha, respectively. While, in the DSSAT model, the coefficient of determination is 0.90, 0.11, and 0.82, respectively, and the RMSE coefficient is 219.9, 288.1, and 238 kg/ha, respectively. The general results show that in LarsWG downscale model with AquaCrop and DSSAT agronomic model, the lowest yields are allocated to Kermanshah, Sanandaj, and Ilam in 8.5, 4.5, and 8.5 scenarios, respectively, and the highest yields are obtained in 2.6, 2.6, and 4.5 scenarios; which indicates a decrease in performance in the scenario of rising temperature and rising carbon dioxide. However, in the SDSM downscale model, the highest yield of dryland wheat is mainly in scenarios 4.5 and 8.5, and the lowest yield will be in scenario 2.6, which is different from the results of the LarsWG model. According to these results, it can be stated that the type of downscale model and crop model can be effective in the obtained results.


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