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Developing a fuzzy clustering model for better energy use in farm management systems

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  • Khoshnevisan, Benyamin
  • Rafiee, Shahin
  • Omid, Mahmoud
  • Mousazadeh, Hossein
  • Shamshirband, Shahaboddin
  • Hamid, Siti Hafizah Ab

Abstract

Wheat is considered as one of the most important strategic crops in Iran, and Iran agricultural ministry has some special plans to encourage farmers to cultivate this crop, so that farmers are willing to cultivate this crop through the country. The previous studies carried out by researchers in Iran showed that the energy consumption in cultivation of this crop is not efficient and there is a high degree of inefficiency in wheat cultivation in Iran. Also, wheat cultivation in Iran is responsible for a high amount of greenhouse gas (GHG) emissions. In order to differentiate between efficient and inefficient farms, a c-means fuzzy clustering model has been developed and the surveyed wheat farms have been clustered based on three features, i.e. GHG emission, energy ratio and benefit cost ratio. The results showed that the farms which were selected as cluster 2 had the best performance where the total input energy and total GHG were calculated as 38,826.9MJ per ha and 3185kgCO2,eq per tonne of crop. In other words, the farms in cluster 2 outperformed cluster 1 and 3 where they performed 34 and 19% better than the two other clusters in terms of energy input and 9 and 27% in CO2 emission per tonne of produced crop. The higher output energy and lower input energy in farms of cluster 2 have caused a better economic performance where the benefit cost ratio was calculated as 1.9. The results of this study demonstrate the successful application of fuzzy clustering approach for better use of energy in cropping systems which can lead to a better environmental and economic performance.

Suggested Citation

  • Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein & Shamshirband, Shahaboddin & Hamid, Siti Hafizah Ab, 2015. "Developing a fuzzy clustering model for better energy use in farm management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 27-34.
    • Handle: RePEc:eee:rensus:v:48:y:2015:i:c:p:27-34
    DOI: 10.1016/j.rser.2015.03.029
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    3. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Sharifi, Mohammad & Hosseinpour, Soleiman & Shah, Ajay, 2017. "Combined application of Life Cycle Assessment and Adaptive Neuro-Fuzzy Inference System for modeling energy and environmental emissions of oilseed production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 807-820.

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