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

被引:26
作者
Khoshnevisan, Benyamin [1 ]
Rafiee, Shahin [1 ]
Omid, Mahmoud [1 ]
Mousazadeh, Hossein [1 ]
Shamshirband, Shahaboddin [2 ]
Ab Hamid, Siti Hafizah [3 ]
机构
[1] Univ Tehran, Fac Agr Engn & Technol, Dept Agr Machinery Engn, Tehran, Iran
[2] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Informat Technol, Kuala Lumpur 50603, Malaysia
[3] Univ Malaya, Fac Comp Sci Informat Technol, Dept Software Engn, Kuala Lumpur 50603, Malaysia
关键词
Energy use management; Farm management; Environmental impacts; Artificial intelligence; c-Means fuzzy clustering; LIFE-CYCLE ASSESSMENT; GREENHOUSE-GAS EMISSIONS; NORTH CENTRAL REGION; K-MEANS ALGORITHM; WHEAT PRODUCTION; SENSITIVITY-ANALYSIS; USE EFFICIENCY; INPUT-OUTPUT; USE PATTERN; ECONOMICAL ANALYSIS;
D O I
10.1016/j.rser.2015.03.029
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 38,826.9 MJ per ha and 3185 kgCO(2,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. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:27 / 34
页数:8
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