ارزیابی و مدل‌سازی روند مصرف انرژی، عملکرد و میزان انتشارات گلخانه‌ای در تولید نخودآبی استان اصفهان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مکانیزاسیون کشاورزی، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه کشاورزی و منابع طبیعی رامین، اهواز، ایران

2 دانشیار گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

3 استادیار گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

این مطالعه به­ منظور بررسی و مدل­سازی میزان انرژی مصرفی و انتشارات گازهای گلخانه­ای در کشت نخود آبی در استان اصفهان توسط مدل پرسپترون چند لایه­ای شبکه­ی عصبی مصنوعی اجرا گردید. میزان هر یک از نهاده­های مصرفی در تولید محصول، از 110 تولیدکنند­ه­ی نخود آبی به شکل تصادفی توسط پرسش‌نامه جمع­آوری گردید. کل انرژی مصرفی، عملکرد محصول و نسبت انرژی در تولید نخود آبی به ترتیب برابر با 18/33211 مگاژول بر هکتار، 36/2276 کیلوگرم بر هکتار و 02/1 محاسبه گردید. کود نیتروژن با 9808 مگاژول بر هکتار بیشترین میزان انرژی مصرفی را به خود اختصاص داد. کل انتشارات گازهای گلخانه­ای برابر 20/965 کیلوگرم معادل کربن دی‌اکسید بر هکتار محاسبه گردید که الکتریسیته و سوخت دیزل به ترتیب با 36% و 34% بیشترین سهم را از کل انتشارات گلخانه­ای داشتند. مدل شبکه­ی عصبی مصنوعی با آرایش 2-7-13 به عنوان بهترین مدل برای پیش­بینی عملکرد و کل انتشارات گلخانه­ای شناخته شد. بر اساس این مدل، مقدار ضریب تبیین در پیش‌بینی عملکرد محصول و کل انتشارات گلخانه­ای به ترتیب برابر با 929/0 و 979/0 تعیین شد. نتایج تحلیل حساسیت مدل نیز نشان داد که نهاده­ی ماشین­های کشاورزی بیشترین اثر را بر عملکرد و میزان انتشارات گلخانه­ای داشته است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Assessment and Modeling of Energy Consumption, Yield and Greenhouse Gas Emissions of Irrigated Chickpea Production in Isfahan Province

نویسندگان [English]

  • Behzad Elhami 1
  • Asadollah Akram 2
  • Majid Khanali 3
1 Ph.D. Student, Department of Agricultural Machinery Engineering, Ramin Agriculture and Natural Resources University of Ahvaz, Ahvaz, Iran
2 Associate Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
3 Assistant Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

This study was conducted to investigate and model the energy consumption and greenhouse gas emissions of irrigated chickpea cultivation in Isfahan province using multilayer perceptron artificial neural network (ANN). The amount of each consumed inputs in production were collected from 110 producers of chickpea randomly by a questionnaire. The total energy consumption, product yield and energy ratio in chickpea production were calculated as 33211.18 MJ/ha, 2276.36 kg/ha, and 1.02, respectively. Nitrogen fertilizer with 9808 MJ/ha had the highest amount of consumed energy. Total greenhouse gas (GHG) emissions were calculated 965.20 kg CO2eq. ha-, in which, electricity and diesel fuel had the highest amount of total GHG emissions with 36% and 34%, respectively. An ANN model with 13-7-2 topology was recognized as the best model for prediction of yield and total GHG emissions. Based on this ANN model, the values of determination coefficient in prediction of yield and total GHG emissions were determined as 0.929 and 0.979, respectively. The results of sensitivity analysis of the model showed that agricultural machinery inputs had the highest impact on yield and total GHG emissions.

کلیدواژه‌ها [English]

  • electricity
  • Greenhouse emissions
  • Sensitivity analysis
  • Diesel fuel
  • nitrogen fertilizer
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