Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks

被引:67
作者
Nabavi-Pelesaraei, Ashkan [1 ,2 ]
Rafiee, Shahin [1 ]
Hosseinzadeh-Bandbafha, Homa [3 ]
Shamshirband, Shahaboddin [4 ]
机构
[1] Univ Tehran, Fac Agr Engn & Technol, Dept Agr Machinery Engn, Karaj, Iran
[2] Tehran Municipal, Management Fruit & Vegetables Org, Control & Assessment, Tehran, Iran
[3] Bu Ali Sina Univ, Dept Biosyst Engn, Fac Agr, Hamadan, Iran
[4] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Technol, Kuala Lumpur 50603, Malaysia
关键词
Artificial neural network; Energy; Greenhouse gas emission; Kiwifruit production; NEW-ZEALAND; AGRICULTURAL PRODUCTION; SENSITIVITY-ANALYSIS; EXHAUST EMISSIONS; WHEAT PRODUCTION; APPLE PRODUCTION; CO2; EMISSIONS; INPUT-OUTPUT; IRAN; PROVINCE;
D O I
10.1016/j.jclepro.2016.05.188
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The purpose of this study was to apply artificial neural networks (ANNs) for forecasting and sensitivity analysis of energy inputs and GHG emissions of three groups of kiwifruit orchards of different sizes in Guilan Province, Iran. The initial data were collected from 80 kiwifruit producers in Langroud City, Guilan Province. The total energy input and output were estimated at 37.32 GJ ha(-1) and 43.44 GJ ha-1, respectively. The ANOVA (analysis of variance) results showed significant variance among the different orchard sizes from an energy input point of view. The results revealed that the highest share of energy input was that of nitrogen fertilizer use in kiwifruit production. The main reason for the overuse of nitrogen fertilizer is government subsidies provided for chemical fertilizers, followed by high levels of nitrogen leaching due to high rainfall. The average values of some energy indices, such as energy use efficiency, energy productivity, net energy and energy intensiveness, were calculated as 1.16, 0.61 x 10(-3) kg GJ(-1), 6.12 GJ ha(-1) and 3.27 x 10(-3) GJ $(-1), respectively. The average total GHG emissions were calculated as 1310 kg CO2eq. ha(-1). Nitrogen fertilizer had the highest share in GHG emissions for kiwifruit production, with 26.17% of total emissions. The 12-9-9-2 structure ANN model was the best topology for predicting yield and GHG (greenhouse gas) emissions of kiwifruit production in the studied area. The coefficients of determination (R-2) of the best topology calculated were 0.987 and 0.992 for yield and greenhouse gas emissions, respectively, indicating the high correlation in the model. The results of model sensitivity analysis indicated that diesel fuel and nitrogen fertilizer were the most sensitive inputs for kiwifruit yield and greenhouse gas emissions, reflecting the important role of nitrogen fertilizer in the excess energy consumption and greenhouse gas emissions of kiwifruit orchards. According to the current study, it is suggested for new policies to be adopted to reduce nitrogen fertilizer consumption. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:924 / 931
页数:8
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