تحقیقات اقتصاد و توسعه کشاورزی ایران

تحقیقات اقتصاد و توسعه کشاورزی ایران

پیش‌بینی انرژی خروجی و نشر گازهای گلخانه‌ای در تولید بادام‌زمینی: مطالعه موردی شهرستان آستانه اشرفیه استان گیلان

نوع مقاله : مقاله پژوهشی

نویسندگان
اشکان نبوی پله سرائی 1
محمدرضا پاکروان چروده 2
حسن قاسمی مبتکر 3
1 استادیار، گروه مهندسی مکانیک بیوسیستم، دانشکدۀ کشاورزی، دانشگاه رازی، کرمانشاه، ایران
2 استادیار، گروه اقتصاد کشاورزی و توسعه روستایی، دانشکدۀ کشاورزی، دانشگاه لرستان، خرم آباد، ایران
3 استادیار، گروه مهندسی ماشین‌های کشاورزی، دانشکدۀ مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران
10.22059/ijaedr.2021.306056.668924
چکیده
این تحقیق به بررسی انرژی مصرفی، نشر گازهای گلخانه­ای و الگوسازی آن‌ها با بهره­گیری از روش‌های هوش مصنوعی برای تولید محصول بادام­زمینی در شهرستان آستانه‌اشرفیه استان گیلان پرداخته است. داده‌های موردنیاز برای مطالعه از طریق گفتگو و مصاحبه با کشاورزان و کارشناسان منطقه در سال زراعی 1398 به‌دست آمد. نتایج نشان داد، میزان کل انرژی نهاده­ها و انرژی ستانده در تولید محصول بادام‌زمینی به­ترتیب در حدود 19248 و 87210 مگاژول بر هکتار با کارایی 53/4 بوده و بیشترین میزان مصرف نهاده­ها به کودهای شیمیایی با 45% تعلق داشت. همچنین، در فرایند تولید حدود 571 کیلوگرم کربن دی­اکسید بر هکتار منتشر گردید، که سوخت فسیلی با 57 درصد بالاترین سهم انتشار را به خود اختصاص داده است. نتایج الگوسازی نشان داد که در مقایسه با شبکه عصبی مصنوعی، عملکرد سامانه انفیس در پیش­بینی انرژی خروجی و نشر گازهای گلخانه­ای تولید بادام‌زمینی بهتر است.
کلیدواژه‌ها
انرژی
بادام‌زمینی
گازهای گلخانه‌ای
هوش مصنوعی

عنوان مقاله English

Predicting output energy and greenhouse gas emissions in peanut production: A case study in Astaneh-Ashrafiyeh county of Guilan province

نویسندگان English

Ashkan Nabavi-Pelesaraei 1
Mohammad Reza Pakravan-Charvadeh 2
Hassan Ghasemi-Mobtaker 3
1 Assistant Professor, Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Razi University, Kermanshah, Iran
2 Assistant Professor, Department of Agricultural Economics and Rural Development, Faculty of Agriculture, Lorestan University, Khorramabad, Iran
3 Assistant Professor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
چکیده English

Surveying of energy consumption, greenhouse gas emissions and their modeling using artificial intelligence methods in peanut production of Astaneh-Ashrafiyeh county of Guilan province were investigated in this research. The data used in the study were collected using a face-to-face questionnaire in 2019 production period. Results showed the total inputs energy and output energy were 19248 and 87210 MJ ha-1 with energy use efficiency as 4.53, respectively and the highest consumption of inputs was belonged to chemical fertilizers with 45%. Also, in production process, about 571 kgCO2eq. ha-1 was emitted that fuel with 57% had the highest share of emissions. The modeling results showed that in comparison with the results of artificial neural network, the performance of the ANFIS model is better in predicting the output energy and greenhouse gas emissions of peanut production.

کلیدواژه‌ها English

Energy
Peanut
Greenhouse gas
Artificial intelligence
  1. Azarpour, E., Moraditochaee, M., & Bozorgi, H. R. (2012). Evaluation energy balance and energy indices of peanut production in north of Iran. African Journal of Agricultural Research, 7(16), 2569-2574.
  2. Cochran, W. G., (1963). Sampling Techniques. 2nd Ed., New York: John Wiley and Sons, Inc.
  3. Ekici, B. B., & Aksoy, U. T. (2011). Prediction of building energy needs in early stage of design by using ANFIS. Expert Systems with Applications, 38(5), 5352-5358.
  4. Fei, R., & Lin, B. (2016). The integrated efficiency of inputs–outputs and energy – CO2 emissions performance of China's agricultural sector. Renewable and Sustainable Energy Reviews, 75, 668-676.
  5. Hosseini-Fashami, F., Motevali, A., Nabavi‐Pelesaraei, A., Hashemi, S. J., & Chau, K. W. (2019). Energy-Life cycle assessment on applying solar technologies for greenhouse strawberry production. Renewable and Sustainable Energy Reviews, 116, 1109411, 1-18.
  6. Hosseinzadeh-Bandbafha, H., Nabavi‐Pelesaraei, A., & Shamshirband, S. (2017). Investigations of energy consumption and greenhouse gas emissions of fattening farms using artificial intelligence methods. Environmental Progress & Sustainable Energy, 36(5), 1546-1559.
  7. Hosseinzadeh-Bandbafha, H., Nabavi-Pelesaraei, A., Khanali, M., Ghahderijani, M., & Chau, K. W. (2018). Application of data envelopment analysis approach for optimization of energy use and reduction of greenhouse gas emission in peanut production of Iran. Journal of Cleaner Production, 172, 1327-1335.
  8. Kaul, M., Hill, R. L., & Walthall, C. (2005). Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85(1), 1-18.
  9. Kay, J. W., & Titterington, D. M. (2000). Statistics and Neural Networks. Technometrics, 42(4), 443-447.
  10. Kouchaki-Penchah, H., Sharifi, M., Mousazadeh, H., Zarea-Hosseinabadi, H., & Nabavi-Pelesaraei, A. (2016). Gate to gate life cycle assessment of flat pressed particleboard production in Islamic Republic of Iran. Journal of Cleaner Production, 112, 343-350.
  11. Meul, M., Nevens, F., Reheul, D., & Hofman, G. (2007). Energy use efficiency of specialised dairy, arable and pig farms in Flanders. Agriculture, Ecosystems & Environment, 119(1-2), 135-144.
  12. Mobtaker, H. G., Akram, A., & Keyhani, A. (2012). Energy use and sensitivity analysis of energy inputs for alfalfa production in Iran. Energy for Sustainable Development, 16(1), 84-89.
  13. Mobtaker, H. G., Keyhani, A., Mohammadi, A., Rafiee, S., & Akram, A. (2010). Sensitivity analysis of energy inputs for barley production in Hamedan Province of Iran. Agriculture, Ecosystems & Environment, 137(3-4), 367-372.
  14. Mobtaker, H. G., Mostashari-Rad, F., Saber, Z., Chau, K. W., & Nabavi-Pelesaraei, A. (2020). Application of photovoltaic system to modify energy use, environmental damages and cumulative exergy demand of two irrigation systems-A case study: Barley production of Iran. Renewable Energy, 160, 1316-1334.
  15. Mostashari-Rad, F., Mobtaker, H. G., Taki, M., Ghahderijani, M., Kaab, A., Chau, K. W., & Nabavi-Pelesaraei, A. (2021). Exergoenvironmental damages assessment of horticultural crops using ReCiPe2016 and cumulative exergy demand frameworks. Journal of Cleaner Production, 278, 123788, 1-18.
  16. Mostashari-Rad, F., Nabavi-Pelesaraei, A., Soheilifard, F., Hosseini-Fashami, F., & Chau, K. W. (2019). Energy optimization and greenhouse gas emissions mitigation for agricultural and horticultural systems in Northern Iran. Energy, 186, 115845,
  17. Mousavi-Avval, S. H., Rafiee, S., Jafari, A., & Mohammadi, A. (2011). Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach. Energy, 36(5), 2765-2772.
  18. Nabavi-Pelesaraei, A. (2014). Modeling and optimization of energy consumption and emissions for dominant cultivation patterns of Astaneh-Ashrafiyeh and Langroud cities in Guilan province using expert systems. M.Sc. thesis, University of Tabriz., Tabriz. (In Farsi).
  19. Nabavi-Pelesaraei, A., Abdi, R., & Rafiee, S. (2016). Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems. Journal of the Saudi Society of Agricultural Sciences, 15(1), 38-47.
  20. Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S., & Mobtaker, H. G. (2014). Optimization of energy required and greenhouse gas emissions analysis for orange producers using data envelopment analysis approach. Journal of Cleaner Production, 65, 311-317.
  21. Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S., Shamshirband, S., & Yousefinejad-Ostadkelayeh, M. (2016). Resource management in cropping systems using artificial intelligence techniques: a case study of orange orchards in north of Iran. Stochastic Environmental Research and Risk Assessment, 30(1), 413-427.
  22. Nabavi-Pelesaraei, A., Bayat, R., Hosseinzadeh-Bandbafha, H., Afrasyabi, H., & Berrada, A. (2017). Prognostication of energy use and environmental impacts for recycle system of municipal solid waste management. Journal of Cleaner Production, 154, 602-613.
  23. Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S. S., Hosseinzadeh-Bandbafha, H., & Chau, K. W. (2018). Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. Science of the Total Environment, 631, 1279-1294.
  24. Nemecek, T., Huguenin-Elie, O., Dubois, D., Gaillard, G., Schaller, B., & Chervet, A. (2011). Life cycle assessment of Swiss farming systems: II. Extensive and intensive production. Agricultural Systems, 104(3), 233-245.
  25. Nikkhah, A., Emadi, B., & Firouzi, S. (2015). Greenhouse gas emissions footprint of agricultural production in Guilan province of Iran. Sustainable Energy Technologies and Assessments, 12, 10-14.
  26. Ozkan, B., Akcaoz, H., & Karadeniz, F. (2004). Energy requirement and economic analysis of citrus production in Turkey. Energy Conversion and Management, 45(11-12), 1821-1830.
  27. Pahlavan, R., Omid, M., & Akram, A. (2012). Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37(1), 171-176.
  28. Pathak, H., & Wassmann, R. (2007). Introducing greenhouse gas mitigation as a development objective in rice-based agriculture: I. Generation of technical coefficients. Agricultural Systems, 94(3), 807-825.
  29. Pishgar-Komleh, S. H., Ghahderijani, M., & Sefeedpari, P. (2012). Energy consumption and CO2 emissions analysis of potato production based on different farm size levels in Iran. Journal of Cleaner production, 33, 183-191.
  30. Saber, Z., Esmaeili, M., Pirdashti, H., Motevali, A., & Nabavi-Pelesaraei, A. (2020). Exergoenvironmental-Life cycle cost analysis for conventional, low external input and organic systems of rice paddy production. Journal of Cleaner Production, 278, 121529, 1-16.
  31. Sefeedpari, P., Rafiee, S., Akram, A., Chau, K. W., & Pishgar-Komleh, S. H. (2016). Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach. Computers and electronics in agriculture, 131, 10-19.
  32. Skunca, D., Tomasevic, I., Nastasijevic, I., Tomovic, V., & Djekic, I. (2018). Life cycle assessment of the chicken meat chain. Journal of Cleaner Production, 184, 440-450.
  33. Taghavifar, H., & Mardani, A. (2015). Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network. Journal of Cleaner Production, 87, 159-167.
  34. Taki, M., Mahmoudi, A., Mobtaker, H. G., & Rahbari, H. (2012). Energy consumption and modeling of output energy with multilayer feed-forward neural network for corn silage in Iran. Agricultural Engineering International: CIGR Journal, 14(4), 93-101.
  35. Taseska, V., Markovska, N., Causevski, A., Bosevski, T., & Pop-Jordanov, J. (2011). Greenhouse gases (GHG) emissions reduction in a power system predominantly based on lignite. Energy, 36(4), 2266-2270.
  36. Zangeneh, M., Omid, M., & Akram, A. (2010). Assessment of agricultural mechanization status of potato production by means of artificial neural network model. Australian Journal of Crop Science, 4(5), 372-377.
تحقیقات اقتصاد و توسعه کشاورزی ایران
دوره 53، شماره 1
بهار 1401
صفحه 145-162