Extensive vs. intensive sugar beet production: Energy and environmental performance in Hamadan, Iran (a fuzzy clustering approach)

被引:1
作者
Namdari, Majid [1 ]
Rafiee, Shahin [2 ]
Hosseinpour, Soleiman [2 ]
机构
[1] Univ Zanjan, Fac Agr, Dept Plant Prod & Genet, Zanjan 4537138791, Iran
[2] Univ Tehran, Coll Agr & Nat Resources, Fac Agr, Dept Agr Machinery Engn, Karaj, Iran
关键词
C -means fuzzy clustering; Environmental emissions; Energy consumption; Farm management; Sugar beet; Iran; LIFE-CYCLE ASSESSMENT; GREENHOUSE-GAS EMISSIONS; WHEAT PRODUCTION; CROPPING SYSTEMS; USE EFFICIENCY; CONSUMPTION; PROVINCE; IMPACTS;
D O I
10.1016/j.jenvman.2025.126281
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
A substantial body of research indicates that extensive farming systems can offer various benefits compared to intensive systems. However, it is important to note that extensive systems may also have certain environmental drawbacks that could potentially exceed those of intensive systems. The central question is which farming systems demonstrate superior energy and environmental performance. This study examines the environmental implications of extensive and intensive sugar beet production systems in Hamadan Province, Iran, with a focus on the role of energy intensity. A random sample of 88 sugar beet farms was selected for data collection. To differentiate between extensive and intensive sugar beet farms, a c-means fuzzy clustering model was employed to cluster farms based on their energy inputs. The extensive farming system (Cluster I) exhibited lower energy consumption, higher energy efficiency, and reduced environmental impacts compared to the intensive farming system (Cluster II). These results highlight the potential of adopting more sustainable practices, such as improved irrigation management and reduced reliance on chemical inputs, to enhance the environmental performance of sugar beet production. This study provides valuable insights into the energy and environmental implications of sugar beet cultivation in Iran. The findings underscore the need for a shift towards more sustainable farming practices to mitigate environmental impacts and ensure the long-term viability of the agricultural sector. This study successfully employed c-means fuzzy clustering to optimize energy consumption in sugar beet cultivation, thereby enhancing energy and environmental performance.
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页数:9
相关论文
共 49 条
[21]  
Khoshnevisan B, 2015, J AGR SCI TECH-IRAN, V17, P49
[22]   Developing a fuzzy clustering model for better energy use in farm management systems [J].
Khoshnevisan, Benyamin ;
Rafiee, Shahin ;
Omid, Mahmoud ;
Mousazadeh, Hossein ;
Shamshirband, Shahaboddin ;
Ab Hamid, Siti Hafizah .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 48 :27-34
[23]   A clustering model based on an evolutionary algorithm for better energy use in crop production [J].
Khoshnevisan, Benyamin ;
Bolandnazar, Elham ;
Barak, Sasan ;
Shamshirband, Shahaboddin ;
Maghsoudlou, Hamid ;
Altameem, Torki A. ;
Gani, Abdullah .
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2015, 29 (08) :1921-1935
[24]   Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production [J].
Khoshnevisan, Benyamin ;
Rafiee, Shahin ;
Omid, Mahmoud ;
Mousazadeh, Hossein .
ENERGY, 2013, 58 :588-593
[25]   Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks [J].
Khoshnevisan, Benyamin ;
Rafiee, Shahin ;
Omid, Mahmoud ;
Yousefi, Marziye ;
Movahedi, Mehran .
ENERGY, 2013, 52 :333-338
[26]   Latent but not absent: The 'long tail' nature of rural special education and its dynamic correction mechanism [J].
Li, Bowen ;
Li, Guangqin ;
Luo, Ji .
PLOS ONE, 2021, 16 (03)
[27]   Artificial intelligence for reducing the carbon emissions of 5G networks in China [J].
Li, Tong ;
Li, Yong .
NATURE SUSTAINABILITY, 2023, 6 (12) :1522-1523
[28]   The evolution of China's wind power industry innovation network from the perspective of multidimensional proximity [J].
Li, Yue ;
Qian, Keyan ;
Wang, Zhuo ;
Xu, Anfeng .
TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT, 2024,
[29]  
MAJ, 2024, Ministry of agriculture of Iran (MAJ)
[30]   Agricultural Greenhouses: Resource Management Technologies and Perspectives for Zero Greenhouse Gas Emissions [J].
Maraveas, Chrysanthos ;
Karavas, Christos-Spyridon ;
Loukatos, Dimitrios ;
Bartzanas, Thomas ;
Arvanitis, Konstantinos G. ;
Symeonaki, Eleni .
AGRICULTURE-BASEL, 2023, 13 (07)