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Resource management in cropping systems using artificial intelligence techniques: a case study of orange orchards in north of Iran

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Abstract

Management of energy use and reduction of greenhouse gas emissions (GHG) in agricultural system is the important topic. For this purpose, many methods have been proposed in different researches for solution of these items in recent years. Obviously, the selection of appropriate method was a new concern for researchers. Accordingly, the energy inputs and GHG emissions of orange production in north of Iran were modeled and optimized by artificial neural networks (ANN) and multi-objective genetic algorithm (MOGA) in this study and the results obtained were compared with the results of data envelopment analysis (DEA) approach. Results showed that, on average, an amount of 25,582.50 MJ ha−1 was consumed in orange orchards in the region and the nitrogen fertilizer was accounted for 36.84 % of the total input energy. The outcomes of this study demonstrated that on average 803 kg carbon dioxide (kgCO2eq.) is emitted per ha and diesel fuel is responsible for 35.7 % of all emissions. The results of ANN signified that they were capable of modeling crop output and total GHG emissions where the model with a 13-4-2 topology had the highest accuracy in both training and testing steps. The optimization of energy consumption using MOGA revealed that the total energy consumption and GHG emissions of orange production can be reduced to the values of 13,519 MJ ha−1 and 261 kgCO2eq. ha−1, respectively. A comparison between MOGA and DEA clearly showed the better performance of MOGA due to simultaneous application of different objectives and the global optimum solutions produced by the last generation.

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Acknowledgments

The financial support provided by the University of Tabriz, Iran, is duly acknowledged.

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Authors and Affiliations

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

    Ashkan Nabavi-Pelesaraei, Shahin Rafiee & Majid Yousefinejad-Ostadkelayeh

  2. Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    Reza Abdi

  3. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband

Authors
  1. Ashkan Nabavi-Pelesaraei
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  2. Reza Abdi
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  3. Shahin Rafiee
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  4. Shahaboddin Shamshirband
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  5. Majid Yousefinejad-Ostadkelayeh
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Correspondence to Ashkan Nabavi-Pelesaraei or Shahaboddin Shamshirband.

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Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S. et al. Resource management in cropping systems using artificial intelligence techniques: a case study of orange orchards in north of Iran. Stoch Environ Res Risk Assess 30, 413–427 (2016). https://doi.org/10.1007/s00477-015-1152-z

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