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.

Crop models are appropriate and low-cost tools for investigating the effect of agricultural inputs on water and soil resources and crop production. The objective of this study was to evaluate the OILCROP-SUN model for Euroflor hybrid of sunflower in order to gain a suitable base to conserve soil and water resources. This study was conducted as a strip-plot statistical design with randomized complete blocks design with three replications for each treatment. Total biomass, seed weight, seed yield, percentage of seed oil, and seed nitrogen were measured. Using collected field data, OILCROP-SUN model was calibrated and evaluated for different levels of water and nitrogen applications. Six genetic coefficients were then derived from calibration of OILCROP-SUN model for Euroflor hybrid. The results indicated that seed yields were simulated reasonably well for 12 treatments with NRMSE value of 18.5%, and the d-index of 0.92. The d value of different treatments of water and nitrogen for seed nitrogen was 0.93, and for oil production per hectare was 0.91.

The investigation of plant production limiting factors requires extensive and costly research, so the use of models can be noteworthy. Practical models including Aquacrop and DSSAT are used to simulate plant production. In the first year, these models were calibrated for Khuzestan wheat farms in Ramseh pilot and in the second year for two pilots, Ramseh and Hamidiyeh. The normalized root mean square error, agreement index and model efficiency with DSSAT model for grain yield values in the first year were 0. 036, 0. 01, 0. 92 and 0. 76, respectively that Comparison of these values with statistical indices obtained from Aquacrop model indicates less error and more performance index for using DSSAT model. Also, the performance of the model, which is an indicator of the effectiveness in the selection of models, shows that the DSSAT is more efficient than the Aquacrop model. Mean root mean square error and normalized root mean square error of wheat biomass in the DSSAT model were determined to be 0. 23 and 0. 04 respectively that indicates the DSSAT model has higher efficiency and accuracy than the Aquacrop model in simulating grain yield and wheat biomass.

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.