Evaluation of SSM-Wheat Model in Simulating the Growth and Development of Wheat (Triticum aestivum L.) Cultivars in Different Planting Densities

IntroductionWheat (Triticum aestivum L.) is one of the most important and widely consumed crops in the world. Changing the density towards an optimal density can alter the ratio of soil evaporation to plant transpiration in such a way that water use efficiency improves. One of the branches of crop science and crop physiology is crop modeling. Quantifying the growth and development of a crop in response to environmental conditions in a system is called modeling, which helps the user make better decisions about crop management. One of the simple models of crops is the SSM model, which provides a simple simulation for estimating yield and phenological stages of various crops. Models have the ability to be used with physiological and ecological analysis based on research and empirical observations. The aim of this experiment is to evaluate the SSM-Wheat model under different density conditions and late-season drought stress. Materials and Methods This experiment was conducted in the cropping year 2021-2022 at the Gorgan Agricultural Research Station. The factorial experiment included factors such as plant density at six levels (200, 250, 300, 350, 400, and 450 seeds per square meter) and genotype at six levels (N-93-9, Taktaz, Araz, Arman, Kalateh, and Tirgan). In this study, the SSM-Wheat model was used to simulate the growth and development of bread wheat. The meteorological data file, including precipitation, total sunshine hours, average relative humidity, average temperature, and average maximum temperature, was collected daily and defined in the model. The parameters related to soil characteristics were considered from the base data of the model. To evaluate the model, the coefficient of determination (R2), root mean square error (RMSE), normalized root mean square error (nRMSE), and the 1:1 line were used. Results and DiscussionThe results of the model, using statistics based on the differences between simulated and observed values, including the coefficient of determination, root mean square error, normalized root mean square error, and the 1:1 line, showed that the model was able to accurately estimate the main phenological stages of days to emergence, days to flowering, and days to physiological maturity. The highest coefficient of determination was obtained for days to emergence, days to physiological maturity, and days to flowering, at 0.97, 0.77, and 0.71, respectively. The root mean square error (RMSE) for these traits was 2.8, 4.9, and 8.8, respectively. However, the traits "days to tillering" and "days to stem elongation" were estimated with lower accuracy, with a coefficient of determination and RMSE of 0.44 and 15.2 for days to tillering, and 0.17 and 6.8 for days to stem elongation, respectively. The results suggest that with an increase in maximum, minimum, and average temperature, and annual precipitation, the number of days required to reach each phenological stage decreases, which is logical. The maximum and minimum model-predicted values for grain yield were 410.4 and 547.6 grams per square meter, respectively, with a mean of 467.8 grams per square meter. The coefficient of determination and root mean square error for grain yield were 0.63 and 35.3, respectively. The distribution of simulated and observed points for the main phenological stages of days to emergence, days to flowering, and days to physiological maturity, as well as grain yield, fell within the 1:1 line range, indicating the model's high accuracy in predicting yield. ConclusionIn general, the results showed that the SSM-Wheat model was useful in simulating the main stages of wheat phenology and its performance under different conditions in different cultivars. The evaluation of the model using statistical indices of the coefficient of determination and root mean square error also confirmed the model's strength. Overall, the present study confirmed that SSM-Wheat is a simple, robust, and transparent model suitable for agricultural applications aimed at improving technical decision-making in crop management. In general, according to the results obtained for the SSM-Wheat model, it can be used for correct management in terms of the density and suitable cultivars of wheat cultivation in the field and its performance analysis in Gonbad Kavus weather conditions.