University of Guilan Cereal Research 2252-0163 14 4 2025 01 20 Parameterization and evaluation of SSM-iCrop model for predicting growth and development, grain yield, accumulation and concentration of nitrogen in wheat Parameterization and evaluation of SSM-iCrop model for predicting growth and development, grain yield, accumulation and concentration of nitrogen in wheat 379 395 8376 10.22124/cr.2025.28834.1842 FA Arezoo Abidi Ph.D. Student, Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran Afshin Soltani Professor, Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran 0000-0002-6941-4047 Ebrahim Zeinali Associate Professor, Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran Journal Article 2024 10 29 Introduction Wheat is one of the most important crops in Iran, with national food security heavily dependent on products derived from wheat grain flour. To comprehensively study growth, development, and nitrogen dynamics in crops, extensive field experiments across diverse climatic regions and time periods are required; however, conducting such experiments is challenging, time-intensive, and costly. Crop simulation models offer a way to reduce the time and expenses associated with field experiments. This study aims to parameterize and evaluate the SSM-iCrop model for predicting key phenological stages, leaf area, biological and grain yield and nitrogen dynamics in wheat in Iran. To date, this model has not been parameterized or evaluated for simulating nitrogen accumulation and concentration in wheat in Iran. Materials and methods In this study, the SSM-iCrop simulation model was employed to parameterize and evaluate to predict various phenological stages, node number, leaf area, biological and grain yield, nitrogen accumulation in above-ground biomass and grain, and grain nitrogen concentration in wheat across different regions in Iran by using data collected from the results of studies conducted in different years and regions by other researchers. To evaluate the ability of the model in predicting the aforementioned traits, statistical indicators including root mean square error (RMSE), correlation coefficient (r) and coefficient of variation (CV) were calculated between observed and simulated values. Additionally, 1:1 lines with ±20% difference were drawn to show the deviation of the simulated data against the observed data. Research findings The findings indicated that the SSM-iCrop model accurately predicted various phenological stages, including the number of days to emergence, tillering, stem elongation, heading, and physiological maturity (r = 0.99, CV = 7.8%). The model also performed well in predicting the number of nodes on the main stem (r = 0.88, CV = 11.3%), maximum leaf area index at anthesis (r = 0.88, CV = 17.8%), biological yield (r = 0.79, CV = 11.3%), grain yield (r = 0.84, CV = 12.6%), nitrogen accumulation in the above-ground biomass (r = 0.84, CV = 12.7%), grain nitrogen accumulation (r = 0.8, CV = 16.4%), and grain nitrogen concentration (r = 0.66, CV = 11.3%). Conclusion Given the high predictive accuracy of the SSM-iCrop model, it can be used for a range of purposes, including improving crop management, analysing growth and yield, estimating potential yield, assessing yield gaps, and examining the impacts of climate change on wheat. Introduction Wheat is one of the most important crops in Iran, with national food security heavily dependent on products derived from wheat grain flour. To comprehensively study growth, development, and nitrogen dynamics in crops, extensive field experiments across diverse climatic regions and time periods are required; however, conducting such experiments is challenging, time-intensive, and costly. Crop simulation models offer a way to reduce the time and expenses associated with field experiments. This study aims to parameterize and evaluate the SSM-iCrop model for predicting key phenological stages, leaf area, biological and grain yield and nitrogen dynamics in wheat in Iran. To date, this model has not been parameterized or evaluated for simulating nitrogen accumulation and concentration in wheat in Iran. Materials and methods In this study, the SSM-iCrop simulation model was employed to parameterize and evaluate to predict various phenological stages, node number, leaf area, biological and grain yield, nitrogen accumulation in above-ground biomass and grain, and grain nitrogen concentration in wheat across different regions in Iran by using data collected from the results of studies conducted in different years and regions by other researchers. To evaluate the ability of the model in predicting the aforementioned traits, statistical indicators including root mean square error (RMSE), correlation coefficient (r) and coefficient of variation (CV) were calculated between observed and simulated values. Additionally, 1:1 lines with ±20% difference were drawn to show the deviation of the simulated data against the observed data. Research findings The findings indicated that the SSM-iCrop model accurately predicted various phenological stages, including the number of days to emergence, tillering, stem elongation, heading, and physiological maturity (r = 0.99, CV = 7.8%). The model also performed well in predicting the number of nodes on the main stem (r = 0.88, CV = 11.3%), maximum leaf area index at anthesis (r = 0.88, CV = 17.8%), biological yield (r = 0.79, CV = 11.3%), grain yield (r = 0.84, CV = 12.6%), nitrogen accumulation in the above-ground biomass (r = 0.84, CV = 12.7%), grain nitrogen accumulation (r = 0.8, CV = 16.4%), and grain nitrogen concentration (r = 0.66, CV = 11.3%). Conclusion Given the high predictive accuracy of the SSM-iCrop model, it can be used for a range of purposes, including improving crop management, analysing growth and yield, estimating potential yield, assessing yield gaps, and examining the impacts of climate change on wheat.

https://cr.guilan.ac.ir/article_8376_e6c506f538f9378a1ada7a92f5d5130c.pdf