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Development and Evaluation of Combined Adaptive Neuro-Fuzzy Inference System and Multi-Objective Genetic Algorithm in Energy, Economic and Environmental Life Cycle Assessments of Oilseed Production

Author

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  • Seyed Hashem Mousavi-Avval

    (Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj 77871-31587, Iran
    Department of Food, Agricultural and Biological Engineering, The Ohio State University, Wooster, OH 44691, USA)

  • Shahin Rafiee

    (Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj 77871-31587, Iran)

  • Ali Mohammadi

    (Department of Engineering and Chemical Sciences, Karlstad University, 65188 Karlstad, Sweden)

Abstract

Energy consumption, economics, and environmental impacts of canola production were assessed using a combined technique involving an adaptive neuro-fuzzy inference system (ANFIS) and a multi-objective genetic algorithm (MOGA). Data were collected from canola farming enterprises in the Mazandaran province of Iran and were used to test the application of the combined modeling algorithms. Life cycle assessment (LCA) for one ha functional unit of canola production from cradle to farm gate was conducted in order to evaluate the impacts of energy, materials used, and their environmental emissions. MOGA was applied to maximize the output energy and benefit-cost ratio, and to minimize environmental emissions. The combined ANFIS–MOGA technique resulted in a 6.2% increase in energy output, a 144% rise in the benefit-cost ratio, and a 19.8% reduction in environmental emissions from the current canola production system in the studied region. A comparison of ANFIS–MOGA with the data envelopment analysis approach was also conducted and the results established that the former is a better system than the latter because of its ability to generate optimum conditions that allow for the assessment of a combination of parameters such as energy, economic, and environmental impacts of agricultural production systems.

Suggested Citation

  • Seyed Hashem Mousavi-Avval & Shahin Rafiee & Ali Mohammadi, 2020. "Development and Evaluation of Combined Adaptive Neuro-Fuzzy Inference System and Multi-Objective Genetic Algorithm in Energy, Economic and Environmental Life Cycle Assessments of Oilseed Production," Sustainability, MDPI, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:290-:d:472590
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    References listed on IDEAS

    as
    1. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Jafari, Ali & Mohammadi, Ali, 2011. "Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach," Energy, Elsevier, vol. 36(5), pages 2765-2772.
    2. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Jafari, Ali & Mohammadi, Ali, 2011. "Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach," Applied Energy, Elsevier, vol. 88(11), pages 3765-3772.
    3. Rafiee, Shahin & Mousavi Avval, Seyed Hashem & Mohammadi, Ali, 2010. "Modeling and sensitivity analysis of energy inputs for apple production in Iran," Energy, Elsevier, vol. 35(8), pages 3301-3306.
    4. Mohammadi, Ali & Cowie, Annette L. & Cacho, Oscar & Kristiansen, Paul & Anh Mai, Thi Lan & Joseph, Stephen, 2017. "Biochar addition in rice farming systems: Economic and energy benefits," Energy, Elsevier, vol. 140(P1), pages 415-425.
    5. Alluvione, Francesco & Moretti, Barbara & Sacco, Dario & Grignani, Carlo, 2011. "EUE (energy use efficiency) of cropping systems for a sustainable agriculture," Energy, Elsevier, vol. 36(7), pages 4468-4481.
    6. Mohammadi, Ali & Rafiee, Shahin & Jafari, Ali & Keyhani, Alireza & Mousavi-Avval, Seyed Hashem & Nonhebel, Sanderine, 2014. "Energy use efficiency and greenhouse gas emissions of farming systems in north Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 724-733.
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