Affiliations: [a] Physical Geography Department, Faculty of Geography, University of Tehran, Tehran, Iran
| [b] Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, China
| [c] Department of Water Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
| [d] Department of Agroecology, Environmental Sciences Research Institute, Shahid Beheshti University, G.C., Tehran, Iran
Abstract: Climate has a significant effect on social and economic activities, and currently is a major problem, especially in agricultural yields. This study used two types of climatic and agricultural data. To simulate the climate for the next 30 years (2021-2050) from daily temperature and precipitation data for the base period 1986-2015, Reanalysis Atmospheric Data (NCEP) as observational predictors data and CanESM2 Atmospheric General Circulation Model data with two scenarios RCP 2.6 and RCP 8.5 were used as large-scale predictors. The data is related to the Rasht Rice Research Center field experiments. The results abstained from simulations showed that in future climate conditions, the average temperature would be 0.7 to 0.9°C, and precipitation would be 20 to 70 mm in the study area based on both emission scenarios compared to the base period (1986-2015) increases. The effect of climate change on the rice yield on the planting date of June 5, especially in the eastern parts of the region, is unfavourable in the future. At the regional level, in all planting dates, the length of the rice growth period in the future period (2021-2050) will decrease by 2 to 4 days compared to the base period. The planting date treatment of 5 May with a density level of 50 plants per square meter, a nitrogen fertiliser level of 195 kg per hectare with an intermittent irrigation regime (8-day cycle) is the most suitable adaptation strategy to reduce the negative effects of climate change and increase rice yield in the entire surface of the coastal area in the Caspian Sea.
Keywords: Climate change, Exponential microscale, Cultivation pattern, Rice, APSIM model