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علی کعب محمد شریفی حسین مبلی

چکیده

اين بررسي با‏ هدف ارزیابی چرخه‌حیات تولید نیشکر (Saccharum officinarum L.) و هم‌چنین مدل‌سازی و پیش‌بینی میزان انتشار شاخص‌های زیست‌محیطی بر اساس نهاده‌های ورودی در مزارع پلنت و مزارع راتون به‌طور جداگانه با استفاده از شبکه عصبی مصنوعی در استان خوزستان بررسي شد. اطلاعات لازم براي انجام اين مطالعه به‌شکل 92 پرسش‌نامه و بررسي حضوري از شرکت کشت و صنعت امام خمینی (ره) استخراج شد. دروازه مزرعه و یک‌تن محصول تولیدی به‌ترتیب به‌عنوان مرز سامانه و واحد عملکردي انتخاب شدند. گروه‌های اثر مورد مطالعه شامل تخلیه مواد غیرآلی، اسیدی شدن، اختناق دریاچه‌ای، پتانسیل گرمایش جهانی، نقصان لایه ازن، مسمومیت انسان‌ها، مسمومیت آب‌های سطحی، مسمومیت آب‌های آزاد، مسمومیت خاک، اکسیداسیون فتوشیمیایی بود. به‌منظور ارزیابی اثرات زیست‌محیطی از نرم‌افزار Simapro V8.0.3.14 استفاده شد. نتایج حاصل از اثرات زیست‌محیطی نشان داد که در بخش‌های گروه‌های اثر مورد مطالعه، الکتریسیته، کود نیتروژن و ماشین‌های کشاورزی بیشترین تأثیر بر انتشار آلایندگی در تمام گروه‌ها داشته‌اند. مجموع شاخص زیست‌محیطی نیشکر تولید شده در مزارع پلنت برابر با 45/0 EcoX به‌ازای یک تن نیشکر تولیدی محاسبه گردید که حدود 32 درصد بالاتر از مجموع این شاخص در مزارع راتون می‌باشد. نتایج مدل‏سازی نشان داد که بهترين ساختار براي شبکه عصبي جهت پیش‌بینی آلاینده‌های زیست‌محیطی تولید نیشکر، ساختار 10- 5- 10- 9 برای مزارع پلنت و ساختار10- 6- 9- 7 برای مزارع راتون تخمين زده شد. R2 برای شاخص مسمویت انسان‌ها و مسمویت آب‌های آزاد با 960/0 کمترین مقدار، و برای شاخص اسیدی شدن و اختناق دریاچه‌ای با 992/0 بیشترین مقدار در مزارع پلنت و هم‌چنین در مزارع راتون، R2 برای شاخص اختناق دریاچه‌ای با 985/0کمترین مقدار، و برای شاخص اسیدی شدن 995/0بیشترین مقدار، محاسبه گردید. بنابراين شبکه عصبي می‌تواند به‌خوبی ميزان نشر آلاینده‌های زیست‌محیطی را در مزارع نیشکر پیش‌بینی و مدل‌سازی کند.

جزئیات مقاله

کلمات کلیدی

پتانسیل‌گرمایش جهانی, شاخص بوم‌شناخت, مدل‌سازی, مزارع پلنت, مزارع راتون

مراجع
Antanasijević, D., Pocajt, V., Ristić, M., and Perić-Grujić, A., 2015. Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks. Energy 84: 816-824.
Auer, J., Bey, N., and Schäfer, J.M., 2017. Combined life cycle assessment and life cycle costing in the Eco-Care-Matrix: A case study on the performance of a modernized manufacturing system for glass containers. Journal of Cleaner Production 141: 99-109.
Cochran, W.G., 1977. The estimation of sample size. Sample Technology 3: 72-90.
Elhami, B., Akram, A., and Khanali, M., 2016b. Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) approaches. Information Processing in Agricultures 3(3): 190-205.
Elhami, B., Khanali, M., and Akram, A., 2016a. Combined application of artificial neural networks and life cycle assessment in lentil farming in Iran. Information Processing in Agricultures 4: 18–32.
FAO., 2016. Food and Agricultural Organization Statistical Yearbook, http://www.fao.org.
Ghaderpour, O., Rafiee, SH., and Sharifi, M., 2018. Life cycle assessment of alfalfa production and prediction of emissions using multi-layer Adaptive Neuro-Fuzzy Inference System in Bukan Township. Journal of Agricultural Machinery 8(1): 119-136. (In Persian)
González-García, S., Bacenetti, J., Negri, M., Fiala, M., and Arroja, L., 2013. Omparative environmental performance of three different annual energy crops for biogas production in Northern Italy. Journal of Cleaner Production 43: 71-83.
Guinée, J. B., Gorrée, M., Heijungs, R., Huppes, G., de Koning, K.R.A., Wegener Sleeswijk, A., Suh, S., Udo de Haes, H., Bruijn, H., Duin, R.V., and Huijbregts, M.A.J., 2002. Handbook on life cycle assessment. Operational guide to the ISO standards. Kluwer, Dordrecht, the Netherlands.
Haroni, S., Shiekhdavoodi, M.G., and Kiani, M., 2015. Modeling of energy consumption and greenhouse gas emissions in the sugarcane production process in ratoon farms using artificial neural networks. A case study in Debel Khazai Agro-industry in Iran. Iranian Journal of Agricultural Machinery 4(2): 11-19. (In Persian with English Summary)
Iriarte, A., Rieradevall, J., and Gabarrell, X., 2010. Life cycle assessment of sunflower and rapeseed as energy crops under Chilean conditions. Journal of Cleaner Production 18(4): 336-345.
ISO., 2006. 14040 International standard. Environmental Management–Life Cycle Assessment–Principles and Framework, International Organisation for Standardization, Geneva, Switzerland.
Kaab, A., Sharifi, M., and Mobli. H., 2019a. Analysis and optimization of energy consumption and greenhouse gas emissions in sugarcane production using data envelopment analysis. Iranian Journal of Biosystem Engineering 50(1):19–30. (In Persian with English Summary)
Kaab, A., Sharifi, M., Mobli, H., Nabavi-Pelesaraei, A., and Chau, K.W., 2019b. Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production. Science of the Total Environment 664:1005–19.
Khanali, M., and Hosseinzadeh-Bandbafha, H., 2017. Evaluation of energy flow and environmental impacts of greenhouse production of medicinal plants with life cycle assessment approach case study of Aloe vera plant. Iranian Journal of Biosystem Engineering 48(3): 361-377. (In Persian with English Summary)
Khanali, M., Mobli, H., and Hosseinzadeh-Bandbafha, H., 2017. Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks. Environmental Science and Pollution Research 24(34): 26324-26340.
Khoshnevisan, B., Rafiee, S.H., Omid, M., and Mousazadeh, H., 2013. Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production. Energy 58: 588-593.
Khoshnevisan, B., Rafiee, S.H., Omid, M., Mousazadeh, H., and Clark, S., 2014. Environmental impact assessment of tomato and cucumber cultivation in greenhouses using life cycle assessment and adaptive neuro-fuzzy inference system. Journal of Cleaner Production 73: 183-192.
Kiani, M.K.D., Ghobadian, B., Tavakoli, T., Nikbakht, A., and Najafi, G., 2010. Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol-gasoline blends. Energy 35: 65-69.
Kouchaki-Penchah, H., Sharifi, M., Mousazadeh, H., and Zarea-Hosseinabadi, H., 2016a. Life cycle assessment of medium-density fiberboard manufacturing process in Iran. Journal of Cleaner Production 112: 351-358.
Kouchaki-Penchah, H., Sharifi, M., Mousazadeh, H., Zarea-Hosseinabadi, H., and Nabavi-Pelesaraei, A., 2016b. Gate to gate life cycle assessment of flat pressed particleboard production in Islamic Republic of Iran. Journal of Cleaner Production 112: 343-350.
Kylili, A., Christoforou, E., and Fokaides, P.A., 2016. Environmental evaluation of biomass pelleting using life cycle assessment. Biomass Bioenergy 84: 107-117.
Lin, P.L., Petway, J., and Settele, J., 2017. Train artificial intelligence to be fair to farming, nature international Journal of Science 334-552.
Manfredi, M., and Vignali, G., 2014. Life cycle assessment of a packaged tomato puree: a comparison of environmental impacts produced by different life cycle phases. Journal of Cleaner Production 73: 275-284.
Mila, I., Canals, L.M., Burnip, G.M., and Cowell, S.J., 2006. Evaluation of the environmental impacts of apple production using life cycle assessment (LCA): Case study in New Zealand. Agriculture, ecosystems and environment 114(2): 226-238.
Milutinović, B., Stefanović, G., Dekić, PS., Mijailović, I., and Tomić, M., 2017. Environmental assessment of waste management scenarios with energy recovery using life cycle assessment and multi-criteria analysis. Energy 137:917-926.
Ministry of Jihad-e-Agriculture of Iran., 2018. Center for Information and Communication Technology - Ministry of Agriculture, amar.maj.ir. (In Persian)
Mirhaji, H., Khojastehpour, M., Abbaspour-Fard, M.H., and Mahdavi-Shahri, S.M., 2012. Environmental impact assessment of sugarbeet (Beta vulgaris L.) production with life cycle assessment method (Case study: South Khorasan province farms). Journal of Agroecology 4(2): 112-120. (In Persian with English Summary)
Mollafilabi, A., 2019. Comparison of environmental impacts for rice (Oryza sativa L.) agroecosystems in the first and second planting patters by using life cycle assessment (Case study: Sari county). Journal of Agroecology 10(4): 949-964. (In Persian with English Summary)
Mousavi-Avval, S.H., Rafiee, SH., Sharifi, M., and Hoseynpour, S., 2015. Energy and environmental life cycle assessment of canola production in Mazandaran province of Iran by applying two different approaches. Iranian Journal of Biosystem Engineering 46(3): 265-274. (In Persian with English Summary)
Mousazadeh, H., Keyhani, A., Javadi, A., Mobli, H., Abrinia, K., and Sharifi, A., 2011. Life cycle assessment of a solar assist plug-in hybrid electric tractor (SAPHT) in comparison with a conventional tractor. Energy Convers Manag 52(3): 1700-1710.
Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S.S., Hosseinzadeh-Bandbafha, H., and 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 631–632: 1279–94.
Nasrollahi-Sarvaghaji, S., Alimardani, R., Sharifi, M., and Taghizadeh Yazdi, M.R., 2016. Comparison of the environmental impacts of different municipal solid waste treatments using life cycle assessment (LCA) (Case study: Tehran). Iranian Journal of Health and Environment 9(2): 273-88. (In Persian with English Summary)
Nemecek, T., Dubois, D., Huguenin-Elie, O., and Gaillard, G., 2011. Life cycle assessment of Swiss farming systems: I. Integrated and organic farming. Agricultural Systems 104(3): 217-232.
Renno, C., Petito, F., and Gatto, A., 2016. ANN model for predicting the direct normal irradiance and the global radiation for a solar application to a residential building. Journal of Cleaner Production 135: 1298-1316.
Renouf, M.A., Wegener, M.K., and Pagan, R.J., 2010. Life cycle assessment of Australian sugarcane production with a focus on sugarcane growing. The International Journal of Life Cycle Assessment 15(9): 927-937.
Romero-Gámez, M., Audsley, E., and Suárez-Rey, E.M., 2012. Life cycle assessment of cultivating lettuce and escarole in Spain. Journal of Cleaner Production 73:193-203.
Safa, M., and Samarasinghe, S., 2011. Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”. Energy 36: 5140-5147.
Soleymani, M., Keyhani, A., and Omid, M., 2017. Life cycle assessment of ethanol produced from sugarcane molasses in Iran. Journal of Agricultural Engineering 40(2): 13-27. (In Persian with English Summary)
Sugarcane Research Institue of Iran., 2018. Annual Statistics Report, www.sugarcane.ir (In persian)
Yousefi, R., 2011. Agricultural Mechanization, First Edition. Publication of Applied Higher Education Institution of Jihad Agriculture, Iran. p.361. (In Persian)
ارجاع به مقاله
کعبع., شریفیم., & مبلیح. (2019). ارزیابی چرخه‌حیات و برآورد انتشار آلاینده‌های زیست‌محیطی در تولید نیشکر (Saccharum officinarum L.) با استفاده از شبکه عصبی مصنوعی. بوم شناسی کشاورزی, 12(1), 87-106. https://doi.org/10.22067/jag.v12i1.76629
نوع مقاله
علمی - پژوهشی