Introduction
Wheat (Triticum aestivum L) is one of the most important cereal crops. A model is a tool that helps us interpret and understand the world we live in. Crop modeling, a branch of agricultural science, has been around for about 40 years, with the development of powerful and efficient computers playing a major role in its advancement. These models usually enable the determination of management options and can be used to explore a wide range of management strategies at low costs. Plant models have proven to be useful tools for estimating crop yield, integrating a comprehensive set of values under physiological conditions, and evaluating crop management options. One essential feature of simulation models is the accurate prediction of crop phenology. The key phenological stages required for simulating the physiological processes of growth and yield in wheat are as follows: Days from planting to emergence, days from emergence to tillering, days from tillering to stem elongation, days from stem elongation to booting, days from booting to heading, days from heading to anthesis, days from anthesis to physiological maturity, and days from physiological maturity to harvest. The aim of this research is to evaluate the SSM-Wheat model for predicting the phenological stages and yield of wheat.
 
Materials and Methods
The necessary information for simulating the phenological stages and yield of wheat was obtained from experimental data on 7 planting dates (November 1st, November 10th, November 20th, November 30th, December 10th, December 20th, and December 30th) and four bread wheat genotypes (line N9-93 and the varieties Araz, Taktaz, and Arman) over two cropping years, 2019-2020 and 2020-2021. Data on the phenological stages and yield of wheat were used to evaluate the model. Daily meteorological data (maximum and minimum temperatures, precipitation, and solar radiation) were collected and defined in the model. Soil characteristics parameters were considered based on the model's default data.
 
Results and Discussion
The evaluation of the SSM-Wheat model showed that this model accurately simulates the phenological stages, including days to emergence, days to stem elongation, days to heading, days to flowering, and days to physiological maturity, as well as grain yield across different planting dates and wheat varieties under the climatic conditions of Gonbad. However, the model did not accurately predict days to tillering, biological yield, and harvest index. The highest coefficients of determination (R²) were obtained for days to heading (0.79), days to flowering (0.77), and days to physiological maturity (0.92). Accurate prediction of crop phenology is a crucial feature of simulation models. Yield in crop simulation models is largely regulated by the timing of developmental stages. The poor prediction of biological yield and harvest index may be due to water stress and high temperatures during the grain-filling and maturity stages, which caused leaf drop and reduced biomass, leading to incomplete harvest by the combine. Delayed planting and low temperatures during the early growth stages in areas with very cold winters, due to inadequate plant establishment and growth to withstand autumn frosts, and high temperatures in hot and dry regions during the late growth stages, especially the grain-filling period, can reduce plant yield. Grain weight is a key component of final grain yield and is influenced by environmental stresses, particularly water stress.
 
Conclusion
The evaluation of the SSM-Wheat model showed that this model was acceptable in simulating phenological stages, including days to emergence, days to stem elongation, days to heading, days to flowering, days to physiological maturity, and grain yield across different planting dates and cultivars under the climatic conditions of Gonbad Kavous. However, the model poorly predicted days to tillering, biological yield, and harvest index. It seems necessary to revalidate and refine the model accuracy using data from diverse experiments, and if the results of this review are confirmed, to incorporate them into the model equations. Nevertheless, this model can be used in field management planning, such as selecting planting dates, suitable cultivars, analyzing crop yield, and its limitations. Obviously, models become effective only when applied with an analysis of physiological and ecological conditions, based on experimental measurements and observations from the system.
 







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