تحلیل پوششی داده‌ها در شرایط عدم قطعیت برای بهبود انرژی مصرفی باغات کیوی استان مازندران

نوع مقاله : مقالات پژوهشی

نویسندگان

• مهدی صباغی المشیری ¹
• مرتضی تاکی ¹
• مصطفی مردانی نجف‎ آبادی ²
• افشین مرزبان ¹

¹ گروه مهندسی ماشین‌های کشاورزی و مکانیزاسیون، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، ملاثانی، ایران

² گروه اقتصاد کشاورزی، کشاورزی و منابع طبیعی رامین خوزستان، خوزستان، ایران

https://doi.org/10.22067/jead.2021.68026.1008

چکیده

تحلیل انرژی ﯾﮑﯽ از روشﻫﺎی ﺑﺴـﯿﺎر ﻣﻔﯿـﺪ و موثر به منظور بررسی وضعیت انرژی مصرفی در تولیدات کشاورزی است. در این راستا محققان زیادی در ایران و سایر کشورهای جهان از تحلیل پوششی داده­ها (DEA) به منظور بهینه­سازی جریان انرژی مصرفی در انواع محصولات کشاورزی استفاده کرده­اند ولی بدلیل عدم قطعیت در داده­های مورد استفاده در این روش، استفاده از الگوهایی که توانایی کنترل این عدم قطعیت را دارا باشند، ضروری است. در این راستا، به منظور تعیین کارایی باغات کشت کیوی استان مازندران و لحاظ عدم قطعیت داده­های ورودی، از مدل تحلیل پوششی داده­های استوار (RDEA) و فازی بازه­ای (FIDEA) استفاده شد. داده­های مورد نیاز با توزیع و تکمیل پرسشنامه و مصاحبه حضوری و با استفاده از روش نمونه­گیری تصادفی در سال 98-1397 جمع­آوری شد. نتایج نشان داد که میانگین کارایی فنی کل مزارع کیوی در مدل RDEA در سه سطح احتمال انحراف از محدودیت 10، 50 و 100 درصد به ترتیب برابر با 0/93، 0/96 و 0/98 درصد است. نتایج مدل FIDEA نشان داد که چنانچه سطح محافظت از مدل در مقابل داده‌های نامطمئن (پارامتر α) کاهش یابد، میانگین کارایی مزارع کیوی افزایش می­یابد. بیش­ترین میزان صرفه‎جویی انرژی مربوط به نهاده سموم شیمیایی و کم­ترین میزان صرفه‎جویی مختص نهاده‎های کود‎های شیمیایی و الکتریسیته می‎باشد. در این راستا برگزاری دوره‎های آموزشی در زمینه کاربرد درست و بهینه سموم شیمیایی و همچنین افزایش تکنولوژی استفاده از ادوات توزیع سم و کود می‎توانـد باعث بهبود کارایی و صرفه‎جویی در میزان مصرف نهاده‎ها و انرژی در تولید این محصول در منطقه مورد مطالعه شود.

کلیدواژه‌ها

• بهینه‌سازی انرژی
• تحلیل پوششی داده‌های استوار
• کارایی

20.1001.1.20084722.1400.35.2.3.5

موضوعات

• اقتصاد کشاورزی

عنوان مقاله [English]

Data Envelopment Analysis in Uncertainty Conditions to Improve the Energy Consumption in Mazandaran Kiwi Orchards

نویسندگان [English]

• M. Sabaghi Alamshiri ¹
• M. Taki ¹
• M. Mardani ²
• A. Marzban ¹

¹ Department of Agricultural Machinery and Mechanization, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani

² Department of Agricultural Economics, Agricultural Sciences and Natural Resources University of Khuzestan, Khuzestan, Iran

چکیده [English]

Introduction: One of the principal requirements for sustainable agriculture is efficient energy use. Energy use in agriculture has been increasing in response to the growing global population, limited arable land and desire for higher living standards. It should be noted that agriculture contributes significantly to atmospheric GHG emissions, with 10-12% of the net global CO₂ (carbon dioxide) emissions. The scientific community believes global warming will pose one of the major environmental challenges in the future, with the bulk of GHG originating from fossil fuel consumption. Kiwifruit is an economically important fruit crop in northern Iran, because the northern region of Iran is a suitable, natural habitat for kiwifruit cultivation. The high kiwifruit production in Iran has reached a point that Iran is now well-known on global markets and in recent years this fruit has contributed a large share to agricultural exports. More recently, Mazandaran horticulturists have been encouraged to produce more kiwifruit. Increased production leads to greater energy consumption by Iranian kiwifruit orchards due to the added application of inputs, such as fertilizers and fuel. Besides, where there is no clear energy consumption pattern in agricultural production, especially fruit orchards, a lot of energy dissipates in the fruit production cycle. Therefore, it seems necessary to provide a model for the energy consumption of kiwifruit orchards in Mazandaran Province to prevent excessive energy utilization. Energy analysis is one of the methods has been used to evaluate the status of agricultural production. In this regard, many researchers have used Data Envelopment Analysis (DEA) for optimization the energy consumption in agricultural productions. DEA is recognized as a methodology widely used to evaluate the relative efficiency of a set of decision-making units (DMUs) involved in a production process. Although DEA is a powerful tool to measure efficiency but the uncertainty in the applied data in this model is inevitable and there is need to use different models that be able to control this uncertainty.
Materials and Methods: In this study, in order to determine the efficiency of kiwifruit orchards in Mazandaran province and in terms of uncertainty of input data, the Robust Data Envelopment Analysis model (RDEA) and Fuzzy Interval Data Envelopment Analysis model (FIDEA) were used. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. The required data were collected by distributing and completing a questionnaire and face-to-face interview using random sampling method in 1397-98.
Results and Discussion: The results showed that the average technical efficiency of all kiwi fields in RDEA model at three levels of probability include: 10, 50 and 100% is equal to 0.93, 0.96 and 0.98%, respectively. The results of FIDEA model showed that if the level of parameter α (optimal use of production factors) increases, the average efficiency of kiwi fields will increase. The highest energy savings are related to chemical pesticides and the lowest amount of savings is related to the chemical fertilizers and electricity inputs, respectively. So, holding the training courses on the correct and optimal use of production inputs from an economic and managerial point of view and improving the level of knowledge of farmers and factors involved in kiwi production in Mazandaran province can improve the efficiency and save energy consumptions.
Conclusion: Evaluating the performance of many activities by a traditional DEA approach requires precise input and output data. However, input and output data in real-world problems are often imprecise or vague. To deal with imprecise data, this study uses RDEA and FIDEA approaches as a way to quantify vague data in DEA models. It is shown that the approaches can be a useful tool in DEA models without introducing additional complexity into the problem. A case study of kiwifruit orchard units is presented to illustrate the reliability and flexibility of the models. As a result, efficiency decreases as the constraint violation probability increased. Additionally, the RDEA approach provides both a deterministic guarantee about the efficiency level of the model, as well as a probabilistic guarantee that is valid for all symmetric distributions.

کلیدواژه‌ها [English]

• Robust Data Envelopment
• Energy Optimization
• Efficiency

مراجع

• Anonymous, Agricultural Jihad Organization, Deputy for Horticultural Affairs, 2000. Available at http://horticulture.maj.ir.
• Babaei M., Paknezhad H., Mardani M., and Salarpour M. 2013. Investigating the efficiency of Jahrom district crops using interval data envelopment analysis, Journal of Operational Research and Its Applications (Journal of Applied Mathematics) 9(4): 43-53.
• Banaeian N., Zangeneh M., and Omid M. 2010. Energy use efficiency for walnut producers using Data Envelopment Analysis (DEA), Australian Journal of Crop Science 4(5): 359-362.
• Bolandnazar E., Rohani A., and Taki M. 2020. Energy consumption forecasting in agriculture by artificial intelligence and mathematical models, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42(13): 1618-1632.
• Despotis D.K., Maragos E.K., and Smirlis Y.G. 2006. Data envelopment analysis with missing values: An interval DEA approach, European Journal of Operational Research 140: 24–36.
• Emami Meybodi A. Principles of measuring efficiency and productivity Publications of the Institute of Business Studies and Research. (In Persian)
• Ghasemi Vernamkhasti M., Hashemi A., and Hashemi M. 2014. Investigation of energy indicators and optimization of its consumption in peach production Case study: Saman city in Chaharmahal and Bakhtiari province, Agricultural Machinery 5(1): 206-216. (In Persian)
• Guo P., and Tanaka H. 2001. Fuzzy DEA: A perceptual evaluation method, Fuzzy Sets and Systems 119: 149–160.
• Kitani O. 1999. Energy and Biomass Engineering. In "CIGR Handbook of Agricultural Engineering 5: 330. St. Joseph, MI: ASAE.
• Mandal K.G., Saha K.P., Gosh P.L., Hati K.M., and Bandyopadhyay K.K. 2002. Bioenergy and economic analysis of soybean-based crop production systems in central India, Biomass Bioenergy 23: 337-345.
• Mansoorian N. 2005. Study of energy efficiency in agriculture of Iran (Case study of Khorasan province). P. 1-11. Proceedings of the 5th Biennial Conference on Agricultural Economics of Iran, 7-9 Sep. 2005. Sistan and Baluchestan- Zahedan University, Iran.
• Mardani A., Zavadskas E.K., Streimikiene D., Jusoh A., and Khoshnoudi M. 2017. A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency, Renewable and Sustainable Energy Reviews 70: 1298-1322.
• Mardani M., Abdeshahi A., Ghorbani M.R., and Zabari Y. 2019. Evaluation of the ability of analysis models of interval fuzzy data and stable in determining the efficiency of broiler breeding units in Khuzestan province, Journal of Agricultural Economics 13(3): 56-29. (In Persian with English abstract)
• Mardani M., and Salarpour M. 2015. Measuring technical efficiency of potato production in Iran using robust data envelopment analysis, Information Processing in Agriculture 2(1): 6-14.
• Mardani M., and Ziaee S. 2016. Determining the efficiency of irrigated wheat fields in Neishabour city under uncertainty, Journal of Agricultural Economics and Development 3(2): 147-136. (In Persian)
• Mardani Najafabadi M., and Taki M. 2020. Robust data envelopment analysis with Monte Carlo simulation model for optimization the energy consumption in agriculture. Energy Sources Part A; Recovery, Utilization and Environmental Effects. ttps://doi.org/10.1080/15567036.2020.1777221.
• Mohammadi A., Rafiee S., Mohtasebi S., and Rafiee H. 2010. Energy inputs–yield relationship and cost analysis of kiwifruit production in Iran, Renewable Energy 35(5): 1071-1075.
• Nabavi-Plesaraei A., Rafiee S., Hosseinzadeh-Bandbafha H., and Shamshirband S. 2016. Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks, Journal of Cleaner Production 133: 924-931.
• Ozkan B., Akcaoz H., and Fert C. 2004. Energy input-output analysis in Turkish agriculture, Renewable Energy 29: 39-51.
• Ozkan B., Akcaoz H., and Karadeniz F. 2004. Energy requirement and economic analysis of citrus production in Turkey, Energy Conversion and Management 45(11-12): 1821-1830.
• Pergola M., D'Amico M., Celano G., Palese A., Scuderi A., Vita G., and Inglese P. 2013. Sustainability evaluation of Sicily's lemon and orange production: an energy, economic and environmental analysis, Journal of Environmental Management 128: 674-682.
• Raei Jadidi M., Homayounifar M., Saboohi Sabouni M., and Kheradmand V. 2010. Investigation of energy efficiency and productivity in tomato production (Case study: Marand city), Journal of Agricultural Economics and Development (Agricultural Sciences and Industries) 24(3): 363-370. (In Persian)
• Sajjadifar H., Asali M., and Fathi B. 2015. Measuring energy efficiency using coating analysis method with undesirable outputs, Journal of Planning and Budgeting 20(4): 55-70. (In Persian)
• Soheilifard F., Taki M., and Van Zelm R. 2020. Impact of energy flow optimization on the mitigation of environmental consequences and costs in greenhouse cucumber production, Environmental science and pollution research: https://doi.org/10.1007/s11356-020-11219-8.25-Soltanali H., Nikkhah A., and Rohani A. 2017. Energy audit of Iranian kiwifruit production using intelligent systems, Energy 139: 646-654.
• Taki M., Ajabshirchi Y., Abdi R., and Akbarpour M. 2012. Energy efficiency analysis of greenhouse cucumber crop by data envelopment analysis method, case study (Shahreza city - Isfahan province), Journal of Agricultural Machinery 2(1): 28-37. (In Persian)
• Taki M., Ajabshirchi Y., and Ghobadifar A. 2016. Using non-parametric mathematical model to optimize energy consumption and greenhouse gas emissions in irrigated wheat cultivation, Journal of Environmental Science and Technology 18: 77-89. (In Persian with English abstract)
• Taki M., Rohani A., Soheili-Fard F., and Abdeshahi A. 2018. Assessment of energy Consumption and modeling of output energy for wheat production by neural network (MLP and RBF) and Gaussian process regression (GPR) models, Journal of Cleaner Production 172: 3028–3041.
• Torabi S., and Ghorbani M. 2015. Efficiency of traditional dairy farms: implication and strategist for their promotion in Mazandaran province (application of Fuzzy Data Envelopment analysis), Iranian Animal Science. 46(4): 445-456. (In Persian with English abstract)
• Tsionas E.G. 2003. Combining DEA and stochastic frontier models: An empirical Bayes approach, European Journal of Operational Research 147: 499-510.
• Vahedi A. 2019. Study of efficiency and optimization of energy consumption of broiler units in Alborz province with data envelopment analysis approach, Iranian Biosystem Engineering 50(2): 475-488. (In Persian with English abstract)
• Yong T., and Chunweki K. 2003. A hierarchical AHP/DEA methodology for the facilities layout design problem, European Journal of Operational Research 147(2): 128-136.
• Yu J.R., Tzeng Y.C., Tzeng G.H., Yu T.Y., and Sheu H.J. 2004. A fuzzy multiple objective programming to DEA with imprecise data, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12: 591-600.
• Zarini R.L., Yaghoubi H., and Akram A. 2013. Energy use in citrus production of Mazandaran province in Iran, African Crop Science Journal 21(1): 61-65.