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Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study

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Abstract

In the recent centuries, one of the most important ongoing challenges is energy consumption and its environmental impacts. As far as agriculture is concerned, it has a key role in the world economics and a great amount of energy from different sources is used in this sector. Since researchers have reported a high degree of inefficiency in developing countries, it is necessary for the modern management of cropping systems to have all factors (economics, energy and environment) in the decision-making process simultaneously. Therefore, the aim of this study is to apply Multi-Objective Particle Swarm Optimization (MOPSO) to analyze management system of an agricultural production. As well as MOPSO, two other optimization algorithm were used for comparing the results. Eventually, Taguchi method with metrics analysis was used to tune the algorithms’ parameters and choose the best algorithms. Watermelon production in Kerman province was considered as a case study. On average, the three multi-objective evolutionary algorithms could reduce about 30 % of the average Greenhouse Gas (GHG) emissions in watermelon production although as well as this reduction, output energy and benefit cost ratio were increased about 20 and 30 %, respectively. Also, the metrics comparison analysis determined that MOPSO provided better modeling and optimization results.

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References

  • Al Jadaan O, Rajamani L, Rao C (2008) Non-dominated ranked genetic algorithm for Solving multiobjective optimization Problems. In: NRGA, J. Theor. Appl. Inform. Technol, Citeseer

  • Anonymous (2011) Simapro software version 7.1

  • Anonymous (2013) Annual Agricultural Statistics. Ministry of Jihad-e-Agriculture of Iran

  • Bakhshoodeh M, Thomson KJ (2001) Input and output technical efficiencies of wheat production in Kerman. Iran Agric Econ 24:307–313

    Article  Google Scholar 

  • Bolandnazar E, Keyhani A, Omid M (2014) Determination of efficient and inefficient greenhouse cucumber producers using Data Envelopment Analysis approach, a case study: Jiroft city in Iran. J Clean Prod 79(1):108–115

    Article  Google Scholar 

  • Boshrabadi HM, Villano R, Fleming E (2008) Technical efficiency and environmental-technological gaps in wheat production in Kerman province of Iran. Agric Econ 38:67–76

    Article  Google Scholar 

  • Chiang T-A, Che Z (2015) A decision-making methodology for low-carbon electronic product design. Decis Support Syst 71:1–13

    Article  Google Scholar 

  • Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization evolutionary computation. IEEE Trans 8:256–279

    Google Scholar 

  • Coello CC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, Berlin

    Google Scholar 

  • Deb K, Gupta H (2005) Searching for robust Pareto-optimal solutions in multi-objective optimization. Lect Notes Comput Sci 3410:150–164

    Article  Google Scholar 

  • Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Lect Notes Comput Sci 1917:849–858

    Article  Google Scholar 

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II evolutionary computation. IEEE Trans 6:182–197

    Google Scholar 

  • Deb K, Zope P, Jain A (2003) Distributed computing of pareto-optimal solutions with evolutionary algorithms. In: Evolutionary multi-criterion optimization. Springer, Berlin, pp 534–549

    Book  Google Scholar 

  • Duan C, Wang X, Shu S, Jing C, Chang H (2014) Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm. Energy Convers Manag 84:88–96

    Article  Google Scholar 

  • Fallah-Mehdipour E, Haddad OB, Mariño M (2009) MOPSO in multipurpose operation of single-reservoir system. In: World environmental and water resources congress 2009@ sGreat Rivers. ASCE, pp 5003–5011

  • Fazel Zarandi MH, Mosadegh H, Fattahi M (2013) Two-machine robotic cell scheduling problem with sequence-dependent setup times. Comput Oper Res 40:1420–1434. doi:10.1016/j.cor.2012.09.006

    Article  Google Scholar 

  • Joodavi A, Zare M, Mahootchi M (2015) Development and application of a stochastic optimization model for groundwater management: crop pattern and conjunctive use consideration. Stoch Environ Res Risk Assess 27:1–12

    Google Scholar 

  • Karimi N, Zandieh M, Karamooz HR (2010) Bi-objective group scheduling in hybrid flexible flowshop: a multi-phase approach. Expert Syst Appl 37:4024–4032. doi:10.1016/j.eswa.2009.09.005

    Article  Google Scholar 

  • Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H (2013a) Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production. Energy 58:588–593

    Article  Google Scholar 

  • Khoshnevisan B, Rafiee S, Omid M, Yousefi M, Movahedi M (2013b) Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks. Energy 52:333–338

    Article  Google Scholar 

  • Khoshnevisan B, Bolandnazar E, Barak S, Shamshirband S, Maghsoudlou H, Altameem TA, Gani A (2014a) A clustering model based on an evolutionary algorithm for better energy use in crop production. Stoch Environ Res Risk Assess. doi:10.1007/s00477-014-0972-6

    Google Scholar 

  • Khoshnevisan B, Rajaeifar MA, Clark S, Shamahirband S, Anuar NB, Mohd Shuib NL, Gani A (2014b) Evaluation of traditional and consolidated rice farms in Guilan Province Iran, using life cycle assessment and fuzzy modeling. Sci Total Environ 481:242–251

    Article  CAS  Google Scholar 

  • Khoshnevisan B, Shariati HM, Rafiee S, Mousazadeh H (2014c) Comparison of energy consumption and GHG emissions of open field and greenhouse strawberry production. Renew Sustain Energy Rev 29:316–324

    Article  CAS  Google Scholar 

  • Kitani O (1999) CIGR handbook of agricultural engineering Vol, V, energy and biomass engineering. ASAE publication, ST Joseph, pp 534–549

    Google Scholar 

  • Kizilaslan H (2009) Input–output energy analysis of cherries production in Tokat Province of Turkey. Appl Energy 86:1354–1358

    Article  Google Scholar 

  • Lim CP, Jain LC (2009) Advances in swarm intelligence. Springer, Berlin

    Book  Google Scholar 

  • Lin JG (1976) Multiple-objective problems: pareto-optimal solutions by method of proper equality constraints automatic control. IEEE Trans 21:641–650

    Google Scholar 

  • Malcolm G, Camargo G, Ishler V, Richard T, Karsten H (2015) Energy and greenhouse gas analysis of northeast US dairy cropping systems Agriculture. Ecosyst Environ 199:407–417

    Article  CAS  Google Scholar 

  • Mohammadi A, Omid M (2010) Economical analysis and relation between energy inputs and yield of greenhouse cucumber production in Iran. Appl Energy 87:191–196

    Article  CAS  Google Scholar 

  • Mohammadi A, Rafiee S, Mohtasebi SS, Rafiee H (2010) Energy inputs–yield relationship and cost analysis of kiwifruit production in Iran. Renew Energy 35:1071–1075

    Article  Google Scholar 

  • Mohammadshirazi A, Akram A, Rafiee S, Mousavi Avval SH, Bagheri Kalhor E (2012) An analysis of energy use and relation between energy inputs and yield in tangerine production. Renew Sustain Energy Rev 16:4515–4521

    Article  Google Scholar 

  • Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A (2011a) Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach. Energy 36:2765–2772

    Article  Google Scholar 

  • Mousavi-Avval SH, Rafiee S, Jafari A, Mohammadi A (2011b) Optimization of energy consumption for soybean production using data envelopment analysis (DEA) approach. Appl Energy 88:3765–3772

    Article  Google Scholar 

  • Nabavi-Pelesaraei A, Abdi R, Rafiee S (2014) Neural network modeling of energy use and greenhouse gas emissions of watermelon production systems. J Saudi Soc Agric Sci 5(19):2193–2202

    Google Scholar 

  • Najafi AA, Niaki STA, Shahsavar M (2009) A parameter-tuned genetic algorithm for the resource investment problem with discounted cash flows and generalized precedence relations. Comput Oper Res 36:2994–3001. doi:10.1016/j.cor.2009.01.016

    Article  Google Scholar 

  • Namdari M (2011) Energy use and cost analysis of watermelon production under different farming technologies in Iran. Int J Environ Sci 1:1144–1153

    Google Scholar 

  • Ozkan B, Kurklu A, Akcaoz H (2004) An input–output energy analysis in greenhouse vegetable production: a case study for Antalya region of Turkey. Biomass Bioenergy 26:89–95

    Article  Google Scholar 

  • Ozkan B, Ceylan RF, Kizilay H (2011) Energy inputs and crop yield relationships in greenhouse winter crop tomato production. Renew Energy 36:3217–3221

    Article  Google Scholar 

  • Pishgar-Komleh S, Ghahderijani M, Sefeedpari P (2012) Energy consumption and CO2 emissions analysis of potato production based on different farm size levels in Iran. J Clean Prod 33:183–191

    Article  Google Scholar 

  • Rajaeifar MA, Ghobadian B, Heidari MD, Fayyazi E (2013) Energy consumption and greenhouse gas emissions of biodiesel production from rapeseed in Iran. J Renew Sustain Energy 5(6):063134

  • Rajaeifar MA, Ghobadian B, Safa M, Heidari MD (2014a) Energy life-cycle assessment and CO2 emissions analysis of soybean based-biodiesel: A case study. J Clean Prod 66:233–241

  • Rajaeifar MA, Akram A, Ghobadian B, Rafiee S, Heidari MD (2014b) Energy-economic life cycle assessment (LCA) and greenhouse gas emissions analysis of olive oil production in Iran. Energy 66:139–149

  • Rajaeifar MA, Tabatabaei M, Ghanavati H (2015) Data supporting the comparative life cycle assessment of different municipal solid waste management scenarios. Data Brief 3:189–194

  • Royan M, Khojastehpour M, Emadi B, Mobtaker HG (2012) Investigation of energy inputs for peach production using sensitivity analysis in Iran. Energy Convers Manag 64:441–446

    Article  Google Scholar 

  • Sadeghi J, Sadeghi A, Saidi-Mehrabad M (2011) A parameter-tuned genetic algorithm for vendor managed inventory model for a case single-vendor single-retailer with multi-product and multi-constraint. J Optim Indus Eng 4:57–67

    Google Scholar 

  • Sadeghi J, Sadeghi S, Niaki STA (2014) A hybrid vendor managed inventory and redundancy allocation optimization problem in supply chain management: an NSGA-II with tuned parameters. Comput Oper Res 41:53–64. doi:10.1016/j.cor.2013.07.024

    Article  Google Scholar 

  • Samavatean N, Rafiee S, Mobli H, Mohammadi A (2011) An analysis of energy use and relation between energy inputs and yield, costs and income of garlic production in Iran. Renew Energy 36:1808–1813

    Article  Google Scholar 

  • Shamshirband S, Khoshnevisan B, Yousefi M, Bolandnazar E, Anuar NB, Wahab AWA, Khan SUR (2015) A multi-objective evolutionary algorithm for energy management of agricultural systems—a case study in Iran. Renew Sustain Energy Rev 44:457–465

    Article  Google Scholar 

  • Tabatabaie SMH, Rafiee S, Keyhani A, Heidari MD (2013) Energy use pattern and sensitivity analysis of energy inputs and input costs for pear production in Iran. Renew Energy 51:7–12

    Article  Google Scholar 

  • Taghavifar H, Mardani A (2015) Energy consumption analysis of wheat production in West Azarbayjan utilizing life cycle assessment (LCA). Renew Energy 74:208–213

    Article  Google Scholar 

  • Taguchi G, Chowdhury S, Wu Y (2005) Taguchi’s quality engineering handbook. Wiley, Hoboken

    Google Scholar 

  • Torki-Harchegani M, Ebrahimi R, Mahmoodi-Eshkaftaki M (2015) Almond production in Iran: an analysis of energy use efficiency (2008–2011). Renew Sustain Energy Rev 41:217–224

    Article  Google Scholar 

  • Vadiee A, Martin V (2014) Energy management strategies for commercial greenhouses. Appl Energy 114:880–888

    Article  Google Scholar 

  • Yousefi M, Omid M, Rafiee S, Ghaderi S (2013) Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN Journal homepage: www IJEE IEEFoundation org 4:1041–1052

  • Yousefi M, Khoshnevisan B, Shamshirband S, Motamedi S, Nasir MHNM, Arif M, Ahmad R (2015) Support vector regression methodology for prediction of output energy in rice production. Stoch Environ Res Risk Assess, pp 1–12

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Acknowledgments

The financial support provided by University of Tehran is acknowledged. Also, we want to express our deep appreciation of all Mr. Benyamin Khoshnevisan’s making effort to help us revise the study. The research of the first author (Sasan Barak) was supported by the Operational Programme Education for Competitiveness (Project No. CZ.1.07/2.3.00/20.0296).

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Authors and Affiliations

  1. Faculty of Economics, Technical University Ostrava, Ostrava, Czech Republic

    Sasan Barak

  2. Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

    Marziye Yousefi

  3. Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Gazvin, Iran

    Hamidreza Maghsoudlou

  4. Department of Existing Vessels, Iranian Classification Society, Tehran, Iran

    Sanaz Jahangiri

Authors
  1. Sasan Barak
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  2. Marziye Yousefi
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  3. Hamidreza Maghsoudlou
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  4. Sanaz Jahangiri
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Corresponding authors

Correspondence to Marziye Yousefi or Sanaz Jahangiri.

Appendices

Appendix 1

See Tables 9, 10, and 11.

Table 9 Energy coefficients and Life cycle inventory data as well as energy equivalents of different inputs used and outputs in watermelon production
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Table 10 Characterization factors of inputs employed in watermelon production
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Table 11 The parameters and coefficients of objective functions
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Appendix 2

See Figs. 9, 10, 11, 12, 13, and 14.

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Barak, S., Yousefi, M., Maghsoudlou, H. et al. Energy and GHG emissions management of agricultural systems using multi objective particle swarm optimization algorithm: a case study. Stoch Environ Res Risk Assess 30, 1167–1187 (2016). https://doi.org/10.1007/s00477-015-1098-1

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