کاربست رهیافت میانگین‌گیری مدل بیزی (BMA) برای تخمین عملکرد گندم در استان گلستان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران

2 استادیار/گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران

چکیده

گندم محصولی استراتژیک در امنیت غذایی است لذا پیش‌بینی دقیق عملکرد آن جهت برنامه‌ریزی‌ و اتخاذ سیاست‌های مناسب در جهت واردات یا صادرات امری مهم محسوب می‌شود. مدل‌های زراعی با استفاده از داده‌های اقلیمی، خاک، نوع ارقام و مدیریت زراعی، رشد، نمو و عملکرد گیاهان زراعی را پیش‌بینی می‌کنند. هدف از این پژوهش بهبود دقت برآورد عملکرد گندم با استفاده از ترکیب مدل‌های توصیه شده در استان گلستان و به‌کمک میانگین‌گیری مدل بیزی (BMA) است. در این مطالعه ابتدا، توانایی مدل‌های شبیه سازی DSSAT، CropSyst و SSM-Wheat در برآورد عملکرد گندم در استان گلستان مورد ارزیابی قرار گرفت. طبق نتایج، مدل DSSAT با داشتن مقادیر جذر میانگین مربعات خطا (RMSE) برابر با 290 کیلوگرم در هکتار، ضریب تبیین (R2 ) برابر با 96%، جذر میانگین مربعات خطای نرمال شده (NRMSE) برابر با 34/6 و مقدار راندمان مدل (EF) برابر با 9/0، نسبت به دو مدل دیگر برآورد دقیق‌تری از عملکرد گندم می‌دهد. در مرحله بعد به‌کمک رهیافت BMA، ترکیب‌های دوتایی و سه تایی از سه مدل‌ زراعی گرفته‌شد که منجر به تولید چهار مدل جدید شد. با مقایسه عملکرد حاصل از این مدل‌ها نسبت به مقدار عملکرد محاسبه شده توسط مدل‌های منفرد، مشاهده شد که ترکیب مدل‌ها موجب بهبود دقت در برآورد عملکرد می‌شود. به‌‌طوریکه مقدار جذر میانگین مربعات خطا در مدلC23 (حاصل ترکیب مدل‌های DSSAT و SSM-Wheat) نسبت به مقدار عملکرد محاسبه‌شده توسط بهترین مدل منفرد (DSSAT)، %30 کاهش یافت و به مقدار 202 کیلوگرم در هکتار رسید. همچنین مقدار ضریب تبیین  در مدل C23 به 97% رسید. بنابراین ترکیب مدل‌ها موجب بهبود دقت در برآورد عملکرد گندم می‌شود.

کلیدواژه‌ها

عنوان مقاله [English]

Application of Bayesian Model Averaging (BMA) Approach to Estimating Wheat Yield in Golestan Province

نویسندگان [English]

  • Arezoo Kazemi 1
  • Zahra Aghashariatmadari 2
1 Irrigation & Reclamation Engineering Deptpartment University of Tehran Karaj, Iran.
2 Assistant Prof., Irrigation & Reclamation Engrg. Dept. University of Tehran Karaj, Iran.
چکیده [English]

Wheat is a strategic crop for food security, so accurate forecasting of its performance is important for planning and adopting appropriate policies for import or export. Crop models use climatic, soil, cultivar type and crop management data to predict crop growth, development and yield. The purpose of this study was to improve the accuracy of estimating wheat yield using a combination of recommended crop models in Golestan province using Bayesian model averaging (BMA). In this study, first, the ability of DSSAT, CropSyst and SSM-Wheat models was evaluated to estimate wheat yield in Golestan province. According to the results, the DSSAT model with root mean square error (RMSE) equal to 290 kg ha-1, coefficient of determination (R2) equal to 96%, mean square root of normalized error (NRMSE) equal to 6.34 and the efficiency of the model (EF) equal to 0.9 has the most accurate estimation as compared to two other models. In the next step, using the BMA approach, binary and ternary combinations were taken from three crop models, which led to the production of four new models. Comparing the performance of BMA models with individual models, showed that the combination of models improves the accuracy of estimation. Thus, the amount of RMSE in the C23 model (which is the result of combining DSSAT and SSM-Wheat models (was reduced by 30% compared to the amount of yield calculated by the best single model (DSSAT) and reached 202 kg ha-1. Also, the value of R2 in the C23 model reached 97%. Therefore, the combination of models improves the accuracy, estimating wheat yield.

کلیدواژه‌ها [English]

  • DSSAT
  • SSM-Wheat
  • CropSyst
  • Crop models
  • EM algorithm

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تحقیقات آب و خاک ایران
دوره 53، شماره 9
آذر 1401
صفحه 2045-2059

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