Using crop simulation MODELs is an efficient complement to experimental research. Crop MODELs can also be helpful with respect to decision-making in sustainable farming system. This study was done to estimate genetic coefficients and evaluate performanced DSSAT in prediction of development, growth and yield in wheat. Data from various field experiments for four wheat cultivars Koohdasht, Shiroudi, Tajan and Zagros were used. After estimation of genetic parameters, the MODEL ability were evaluated in simulation of phonological development in days to an thesis, days to maturity, dry matter production at anthesis and physiological maturity, leaf area index at anthesis, accumulation of nitrogen at anthesis and maturity and grain yield. Root Mean Square Error (RMSE) for grain yield was equal to 668 kg. ha-1 which was 18.2 percent of the mean yield. MODEL predictions were appropriate for other crop development and growth characteristics. Therefore, the MODEL can be used for simulation these cultivars.

IntroductionCrop growth simulation MODELs are extensively used for various agricultural studies, including optimal crop selection, irrigation management, and assessing climate change impacts. Among these MODELs, the DSSAT (Decision Support System for Agrotechnology Transfer) is particularly prominent for its ability to simulate growth, yield, and other dynamics for 34 different crops. The DSSAT MODEL integrates various components such as soil, weather, crop management, and genetic factors to provide comprehensive insights into crop performance (Jones et al., 2003). Accurate parameter calibration in this MODEL is crucial for reliable simulations. However, the inherent variability and uncertainty in parameter values pose significant challenges. Uncertainty can arise from various sources, including measurement errors, spatial and temporal variability, and MODEL structure. Addressing these uncertainties is essential to enhance the reliability and accuracy of the MODEL predictions. The Generalized Likelihood Uncertainty Estimation (GLUE) algorithm offers a robust framework for quantifying and incorporating parameter uncertainty into MODEL simulations (Beven & Binley, 1992).In this study, we focus on the application of the GLUE algorithm to the DSSAT MODEL for cotton, aiming to improve the MODEL's predictive accuracy by accounting for parameter uncertainty. We utilize observational data from different irrigation treatments to calibrate the MODEL and evaluate the posterior probability distributions of the parameters.Materials and MethodsThe study used data from a 2009 experiment conducted at the Birjand University research farm. The DSSAT v4.5 MODEL was employed, requiring inputs such as weather, soil properties, and crop management data. Four irrigation treatments (50%, 75%, 100%, and 125% of crop water requirement) were tested to evaluate the GLUE algorithm’s performance in estimating MODEL parameters.Results and discussionThe results demonstrated that the GLUE algorithm effectively estimated the probability distributions of the DSSAT MODEL parameters for cotton. The algorithm’s performance was compared with previous MODELs lacking uncertainty assessments, showing significant improvements in simulation accuracy (Qasemi et al., 2019). The findings highlighted the importance of considering parameter uncertainty for better predictive accuracy and MODEL reliability.ConclusionsThe GLUE algorithm, through Monte Carlo simulations, provides a robust method for assessing and incorporating parameter uncertainty in crop growth MODELs like DSSAT. This approach enhances the MODEL's reliability in predicting crop performance under varying conditions, which is crucial for agricultural planning and management.

Decision Support System for Agrotechnology Transfer (DSSAT) MODEL is able to simulate plant growth, development, and yield that are grown on a uniform surface under simulated management conditions, including changes in soil water, soil carbon, soil nitrogen contents and nitrogen leaching. This study was aimed to investigate the effects of nitrogen on yield and yield components of maize variety SC704 by using this MODEL, and to calibrate CERES-Maize MODEL under 4 levels of nitrogen fertilizer: N1: 25% less than the recommended level, N2: recommended level (200 kg/ha), N3: 50% less than recommended level (260 kg/ha), and N4: 50% more than the recommended level (310 kg/ha). To evaluate the applicability of this MODEL an experiment based on randomized complete block design with three replications was conducted during 2013 at the Research Field of Agriculture Faculty of Islamic Azad University – Karaj Branch. The measured traits, and their simulated values for ear and biomass yields, leaf area index (LAI) and stem dry matter content were compared. The results of the biomass simulation showed that Root Mean Square Error (RMSE) of the four fertilizer levels ranged 2496.48, 2159.24, 2302.43, and 3289.19 kg/ha respectively. For the ear yield, the highest coefficient of determination (R2=0.98) was obtained by N4. In fact, this treatment provided highest accuracy for predicting the yield of maize by the MODEL. For leaf area index, the Willmott Agreement Index (d) varied between 0.77-0.94. This indicates that the MODEL has successfully predicted the variation of leaf area index. Therefore, the MODEL is considered appropriate for simulating growth, development and yield of maize under 4 levels of nitrogen fertilizer. In this case, it is recommended that the MODEL is calibrated and verified, and then, it is applied for research purposes in Karaj climatic conditions.

Introduction 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. Conclusion 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.

A successful hybrid maize (Zea mays L. ) seed production program depends on conformity and synchrony of growth and developmental stages of the parental inbred lines with environmental conditions. Crop simulation MODELs plays a key role in managing such synchrony by providing the simulation of the growth stages occurrence time. To evaluate the power of the DSSAT-CSM-CERESMaize MODEL to simulate the growth and developmental stages of B73 maize inbred line, an experiment was performed as split plot in randomized complete block design with four replications in Karaj, Iran, in 2013. The experimental factors were including planting date and plant densities in five and four levels, respectively. Time to reach any of the developmental stages of B73 maize inbred line including emergence (VE), tassel initiation (TI), silk appearance as the crop flowering (R1), completion of fertilization or beginning of the seed filling (R2) and physiological maturity (R6) were recorded. Then, the genetic coefficients used in the MODEL including P1, P2, P5 and PHINT were determined based on generalized likelihood uncertainty estimation using GLUE software. These genetic coefficients were 307, 0. 33, 970 and 70, respectively. The normalized root of error mean square (nRMSE) values for the recorded five growth stages were calculated as 7. 857, 14. 0, 7. 141, 3, 607 2. 687, respectively, which show the MODEL can simulate the growth stages of B73 maize inbred line using the new specific genetic coefficients. Overall, the results of current research showed that the CERES-Maize MODEL which already developed to simulate the growth and development of maize hybrid cultivars can be efficient and accurate to simulate the production of maize hybrid seed only if the specific genetic coefficient of each parental inbred line is used.

Background and objectives: Crop simulation MODELs are widely used in the analysis of cropping systems, climate change and crop management methods. It is a good tool for completing and developing the results of field trials to evaluate new cultivars and new management systems. The aim of this research was to simulate the phenological stages and yield of different bread wheat cultivars in climatic conditions of Gorgan city using DSSAT-Nwheat MODEL.Materials and methods: In this study, in order to evaluate the performance of the DSSAT-Nwheat MODEL, the data drived from a two-year experiment (Growing seasons 2019-2020 and 2021-2020), in which four new bread wheat cultivars were studied under seven sowing dates as split plot based on randomized complete block design (RCBD). Seven sowing dates (from 1 November to 31 December, 10-day intervals) were placed in main plots and four bread wheat genotypes (including Arman, Araz, Taktaz and N-93-9) were placed as subplots. The data derived from the first year and the second year were used for calibration and validation of the MODEL, respectively. In addition to field data, daily meteorological data, management events, soil characteristics and geographical coordinates were provided to DSSAT 4.7 software. After determining the genetic coefficients of each genotype, the MODEL was calibrated for different traits and subsequently the same coefficients were used to validate the MODEL. Using statistical indices, the simulated values of the MODEL were tested with the observed values.Results: The results showed that the phenological stages including day to anthesis and day to maturity were simulated with root mean squared error (RMSE) equal to four days, and normalized root mean square error (nRMSE) less than 3%. RMSE for grain yield and biological yield were 416 kg ha-1 and 1000 kg ha-1, respectively, and nRMSE values were between 7-8%. In water productivity based on grain yield and biological yield, nRMSE values were 6.21% and 7.53%, respectively, and RMSE values were 0.93 kg ha-1 mm-1 and 2.91 kg ha-1 mm-1, respectively. In all the simulated traits, the Willmott's agreement indices (d) and the coefficient of determination (R2) were in the acceptable range, which showed the proper performance of the DSSAT-Nwheat MODEL for simulating these traits in different bread wheat cultivars.Conclusion: The results of this study showed that the DSSAT-Nwheat MODEL had proper performance for simulating phenological stages, grain yield, biological yield and water productivity in four cultivars including Araz, Arman, Taktaz and N-93-9. The nRMSE values for all studied traits were between 6-8%. The cultivars studied in this study are the latest cultivars released for the northern warm and humid agro-climatic zone, Iran, in the next few years, they will occupy a large area of wheat cultivation in Golestan province. Therefore, it seems that the results of this study can be used in the decisions of wheat cultivation systems, different effects of agricultural management and current and future climate change in Golestan province.

Climate change is rapidly degrading the conditions of crop production. For instance, increasing salinization and aridity is forecasted to increase in the most parts of the world. This research was conducted in two regions of Yazd province with 10 separate experiments in the form of a randomized complete block design with three replications. Experimental factors included 5 promising modified lines in Yazd Salinity Research Center with Titicaca cultivar.The results of calibration and validation of CROPGRO MODEL with DSSAT software were evaluated as favorable for quinoa and the 30-year seasonal analysis of the MODEL for the city of Yazd showed that the optimal planting dates for lines 3, 4, 5 and 6 are the first of August, the end of July, and the middle of It is August and the end of July because it is the shortest period of growth. Considering that this MODEL can integrate the complex interactions of soil properties, climatic conditions, management practices and genetic characteristics of the product, it leads to a better understanding of the complex interactions between factors affecting the growth and development of this plant, so it can be used to develop studies on the aspect Different types of quinoa ecophysiology should be used in research departments.

The purpose of this study was to calibrate and evaluate DSSAT-CANEGRO MODEL using field data from two datasets for cultivar CP57-614, in Khuzestan. The experimental plan was performed at three levels of irrigation water (full and deficit irrigation) with three replicates in a completely randomized block design during cultivation years of 85-86 and 94-95. First of all, MODEL calibration was done to find out the important input parameters by GLUE method. DSSAT-CANEGRO MODEL consists of 20 Genetic parameters. In order to reduce some parameters, parameterization was conducted using field data. The comparison between predicted and measured data showed that the MODEL efficiency was 0. 69 to 0. 75 for aerial dry mass, 0. 67 to 0. 7 for stalk dry mass and 0. 18 to 0. 25 for sucrose. The results indicated that the sucrose prediction by CANEGRO MODEL is weak as compared to other parameters. This is due to measuring sucrose at the end of grown season.

Simulation MODELs are suitable tools for predicting the effects of different management scenarios and selecting the most appropriate solutions in agricultural production systems. In this study, after evaluating the efficiency of the DSSAT MODEL, the effect of water table management on rice growth and yield was investigated. The required field experiments were performed under a randomized complete block design with four irrigation treatments and three replications during a rice growing season in a research farm at the Sari Agricultural Sciences and Natural Resources University. Irrigation treatments included conventional or flooding irrigation (control) with water height of 5 cm above the soil surface (I1), water table control at soil level (I2), water table control at 5 cm below soil surface (I3) and water table control at 15 cm below soil surface (I4). During rice growing season and at harvest, leaf area index, shoot weight, plant height, number of tillers, biological yield and grain yield were measured. The data of I1 treatment were used for calibration and the data of other treatments were used for validation of the MODEL. In both calibration and validation processes, the DSSAT MODEL showed a good performance for predicting phenological dates, leaf area index, biological yield and grain yield. In the calibration and validation stages, root mean square error (NRMSE) values were in the range of 0.7-7.6% and 1-7.6%, respectively, and Wilmot agreement index (d) values were in the range of 0.73-0.99 and 0.82-0.99, respectively. Effects of irrigation treatments were significantly different on plant height, number of tillers per hill, leaf area index, grain yield and biological yield. Among different treatments, the highest grain yield was 5584 kg ha-1, related to the control treatment. Grain yield in I2, I3 and I4 treatments was 4.7, 4.6 and 39.2% lower than that in the control treatment, respectively. Water use efficiency in I1, I2, I3 and I4 treatments was 0.48, 0.65, 0.83 and 0.73 kg m-3, respectively. Based on the results, in order to maintain rice production while saving water, it is recommended to control the water table at a depth of 5 cm below the soil surface.