پارامتریابی و ارزیابی مدل SSM-iCrop برای پیش‌بینی رشد و نمو، عملکرد دانه، تجمع و غلظت نیتروژن در گندم

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران

2 استاد، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرکان، ایران

3 دانشیار، استاد، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرکان، ایران

چکیده

مقدمه: گندم یکی از مهم‌ترین گیاهان زراعی در ایران است، به‌طوری که امنیت غذایی در این کشور تا حد زیادی به فرآورده‌های حاصل از آرد دانه گندم وابسته است. به‌منظور بررسی رشد و نمو و پویایی نیتروژن در گیاهان زراعی، انجام آزمایش‌های مزرعه‌ای زیادی در طیف وسیعی از زمان‌ها و مناطق اقلیمی ضروری است. اجرای چنین آزمایش‌هایی، سخت، زمان‌بر و پرهزینه است، اما با کمک مدل‌های شبیه‌سازی گیاهی می‌توان در زمان و هزینه‌های ناشی از آزمایش‌های مزرعه‌ای صرفه‌جویی کرد. مطالعه حاضر با هدف پارامتریابی و ارزیابی مدل SSM-iCrop برای پیش‌بینی نمو، سطح برگ، عملکرد زیستی، عملکرد دانه و پویایی نیتروژن در گندم در ایران انجام شد. پارامتریابی و ارزیابی این مدل در شبیه‌سازی تجمع و غلظت نیتروژن برای گندم در ایران تا کنون انجام نشده است.
مواد و روش‌ها: پارامتریابی و ارزیابی مدل شبیه‌سازی SSM-iCrop برای پیش‌بینی مراحل مختلف فنولوژی، تعداد گره، سطح برگ، عملکرد زیستی و دانه، تجمع نیتروژن در بخش هوایی و دانه، و غلظت نیتروژن دانه در گیاه گندم در مناطق مختلف ایران با استفاده از داده‌های جمع‌آوری شده از نتایج مطالعات انجام شده توسط سایر پژوهش‌گران در سال‌ها و مناطق مختلف انجام شد. برای ارزیابی توانایی مدل در پیش‌بینی صفات یاد شده، آماره‌های مختلف شامل مجذور میانگین مربعات خطا (RMSE)، ضریب همبستگی (r) و ضریب تغییرات (CV) بین مقادیر مشاهده شده و شبیه‌سازی شده محاسبه شدند. همچنین، خطوط 1:1 با اختلاف 20± درصد برای نشان دادن میزان انحراف داده‌های شبیه‌سازی شده در مقابل داده‌های مشاهده شده ‌رسم شدند.
یافته‌های تحقیق: نتایج به‌دست آمده از این مطالعه نشان داد که مدل SSM-iCrop با دقت بسیار بالایی می‌تواند زمان وقوع مراحل مختلف نمو گندم، شامل تعداد روز تا سبز شدن، پنجه‌زنی، ساقه‌رفتن، سنبله‌دهی و رسیدگی فیزیولوژیک را پیش‌بینی کند (r = 0.99 و CV = 7.8%). مدل به‌خوبی توانست تعداد گره در ساقه اصلی (r = 0.88 و CV = 11.3%)، حداکثر شاخص سطح برگ در گرده‌افشانی (r = 0.88 و CV = 17.8%)، عملکرد زیستی (r = 0.79 و CV = 11.3%)، عملکرد دانه (r = 0.84 و CV = 12.6%)، تجمع نیتروژن بخش هوایی (r = 0.84 و CV = 12.7%)، تجمع نیتروژن دانه (r = 0.80 و CV = 16.4%) و غلظت نیتروژن دانه (r = 0.66 و CV = 11.3%) را پیش‌بینی کند.
نتیجه‌گیری: با توجه به توانایی بالای مدل SSM-iCrop در پیش‌بینی نمو، تجمع ماده خشک، شاخص سطح برگ، عملکرد دانه، تجمع و غلظت نیتروژن، می‌توان از این مدل برای اهداف مختلف مانند بهبود مدیریت زراعی، تجزیه و تحلیل رشد و عملکرد، تخمین عملکرد پتانسیل، خلأ عملکرد و اثرات تغییرات اقلیمی برای گندم استفاده کرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Parameterization and evaluation of SSM-iCrop model for predicting growth and development, grain yield, accumulation and concentration of nitrogen in wheat

نویسندگان [English]

  • Arezoo Abidi 1
  • Afshin Soltani 2
  • Ebrahim Zeinali 3
1 Ph.D. Student, Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran
2 Professor, Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran
3 Associate Professor, Department of Agronomy, Faculty of Plant Production, Gorgan University of Agricultural Science and Natural Resources, Gorgan, Iran
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Leaf area
  • yield
  • phenology
  • dry matter
  • modeling

مراجع

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