Evaluation of DSSAT-Nwheat Model across a Wide Range of Climate Conditions in Iran

Document Type : Scientific - Research


Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran


Crop models can integrate the complex interactions of soil properties, climatic conditions, crop management practices, and crop genetic characteristics. One of the main aspects of crop simulation models is the possibility to use them across various environmental and management conditions, provided that they have been evaluated under a wide range of growing conditions. The Decision Support System for Agrotechnology Transfer (DSSAT) modeling platform is leading crop modeling system that is widely applied in various environments. Testing crop models under various temperature environments are essential to apply models to climate impact studies. The objective of this study was the testing and evaluation of DSSAT-Nwheat model across a wide range of climate conditions in Iran.
 Materials and Methods
Nwheat model, which recently integrated into DSSAT, was evaluated for four wheat cultivars using observations from field experiments included a wide range of climate and management. Cultivars were Shahriyar, Pishtaz, Tajan, and Chamran cultivated in cold, temperate, humid and tropical regions in Iran, respectively. The locations represent four different wheat mega-environments, a concept used by wheat breeders for testing cultivars. The management information used at each site was obtained from the Seed and Plant Improvement Institute. Daily weather data, management events, and soil characteristics imported to DSSAT. The performance of the DSSAT-Nwheat during the calibration and evaluation was assessed using different statistics, Root Mean Square Error (RMSE), Normalized Root Mean Square Error (nRMSE), Willmott’s index (d), and coefficient of determination (R2) of a 1:1 regression line. A sensitivity analysis was conducted using 30 years of observed weather data from Tabriz, Mashhad, Gorgan, and Ahwaz. For the sensitivity analysis scenarios, the temperaturewas increased by 3, 6, and 9°C, and atmospheric CO2 concentration levels were set at 360, 540, and 720 ppm.
 Results and Discussion
Evaluation results showed that DSSAT-Nwheat model simulated planting to anthesis and planting to maturity accurately with RMSE values less than four days, nRMSE less than 3%, and d index close to one. Also, evaluation of grain yield showed that RMSE varied from 568 kg ha-1 for Tajan cultivar up to 933 kg ha-1 for Chamran cultivar. In general, nRMSE and d index for grain yield were less than 20% and higher than 0.8, respectively, which showed good calibration accuracy. In DSSAT-Nwheat model, the specific heat stress function explains heat stress effects during grain filling on grain yield in cultivars. Chamran cultivar is somewhat resistant to end season heat stress, so the DSSAT-Nwheat model underestimated in the warm regions. Because the cultivars differ regarding resistance to the end season heat stress, crop models need to consider cultivar-specific tolerance to heat stress to better simulate temperature effects on wheat cropping systems. The response of the model to the increase in temperature was different in regions and levels of CO2 concentrations. Elevated atmospheric CO2 concentrations lessened some of the adverse effects of high temperature. Therefore, the sensitivity analysis of DSSAT-Nwheat model to temperature variations and elevated atmospheric CO2 concentrations showed that the model could be used in studies of climate change impacts on wheat production. This model can be employed to explore the integrated effects of temperature, atmospheric CO2concentrations, water, nutrients, and agronomic management practices in a range of wheat growing environments.
The results of this study showed that the DSSAT-Nwheat model had reliably good performance under a wide range of management and environmental conditions. This calibrated model can now be used for assessing impacts of various agronomic management strategies and decisions in wheat cropping systems under current and anticipated climate change. But more importantly is the calibration method and using a large number of climatological data to calibrate.


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