نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری، گروه زراعت، دانشکده تولیدات گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران
2 استاد، گروه زراعت، دانشکده تولیدات گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرکان، ایران
3 دانشیار، استاد، گروه زراعت، دانشکده تولیدات گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرکان، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction
Wheat is the most important crop in Iran, and food security in this country largely depends on products made from wheat grain flour. To comprehensively study growth, development, and nitrogen dynamics in crops, extensive field experiments across diverse climatic regions and time period are required; however, conducting such experiments is challenging, time-intensive, and costly. Using plant simulation models, it is possible to save time and costs from 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, the parameterization and evaluation of this model for simulating nitrogen accumulation and concentration in wheat in Iran have not been conducted.
Materials and methods
In this study, the SSM-iCrop simulation model was employed to parameterize and evaluate its accuracy in predicting various phenological stages, leaf area, biological and grain yield, nitrogen accumulation in above-ground biomass and grain, and grain nitrogen concentration of 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 line was drawn with ±20% difference 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 applications, including improving crop management, analysing growth and yield, estimating potential yield, assessing yield gaps, and examining the impacts of climate change on wheat.
کلیدواژه‌ها [English]
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