Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.jclepro.2018.05.249 |
Sensitivity analysis of energy inputs in crop production using artificial neural networks | |
Khoshroo, Alireza1; Emrouznejad, Ali2; Ghaffarizadeh, Ahmadreza3; Kasraei, Mehdi4; Omid, Mahmoud5 | |
通讯作者 | Khoshroo, Alireza ; Emrouznejad, Ali |
来源期刊 | JOURNAL OF CLEANER PRODUCTION
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ISSN | 0959-6526 |
EISSN | 1879-1786 |
出版年 | 2018 |
卷号 | 197页码:992-998 |
英文摘要 | Sensitivity analysis establishes priorities for research and allows to identify and rank the most important factors which lead to great improvements in output factors. The aim of this study is to examine sensitivity analysis of inputs in grape production. We are proposing to perform sensitivity analysis using partial rank correlation coefficient (PRCC) which is the most reliable and efficient method, and we apply this for the first time in crop production. This research investigates the use of energy in the vineyard of a semi-arid zone of Iran. Energy use efficiency, energy productivity, specific energy and net energy were calculated. Various artificial neural network (ANN) models were developed to predict grape yield with respect to input energies. ANN models consist of a multilayer perceptron (MLP) with seven neurons in the input layer, one and two hidden layer(s) with different number of neurons, and an output layer with one neuron. Input energies were labor, machinery, chemicals, farmyard manure (FYM), diesel, electricity and water for irrigation. Sensitivity analysis was performed on over 100 samples of parameter space generated by Latin hypercube sampling method, which was then fed to the ANN model to predict the yield for each sample. The PRCC between the predicted yield and each parameter value (input) was used to calculate the sensitivity of the model to each input. Results of sensitivity analysis showed that machinery had the greatest impact on grape yield followed by diesel fuel and labor. (C) 2018 The Authors. Published by Elsevier Ltd. |
英文关键词 | Grape production Artificial neural networks Sensitivity analysis Energy efficiency |
类型 | Article |
语种 | 英语 |
国家 | Iran ; England ; USA |
收录类别 | SCI-E |
WOS记录号 | WOS:000441998400091 |
WOS关键词 | CONDENSATE GAS-RESERVOIRS ; GRAPE PRODUCTION ; USE EFFICIENCY ; ASPHALTENE PRECIPITATION ; FUTURE PROJECTION ; ECONOMIC-ANALYSIS ; WHEAT PRODUCTION ; APPLE PRODUCTION ; TEHRAN PROVINCE ; OUTPUT-ANALYSIS |
WOS类目 | Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences |
WOS研究方向 | Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/210754 |
作者单位 | 1.Univ Yasuj, Fac Agr, Dept Agr Engn, Yasuj, Iran; 2.Aston Univ, Aston Business Sch, Birmingham, W Midlands, England; 3.Univ Southern Calif, Ctr Appl Mol Med, Los Angeles, CA USA; 4.Shiraz Univ, Fac Agr, Dept Biosyst Engn, Shiraz, Iran; 5.Univ Tehran, Fac Agr Engn & Technol, Karaj, Iran |
推荐引用方式 GB/T 7714 | Khoshroo, Alireza,Emrouznejad, Ali,Ghaffarizadeh, Ahmadreza,et al. Sensitivity analysis of energy inputs in crop production using artificial neural networks[J],2018,197:992-998. |
APA | Khoshroo, Alireza,Emrouznejad, Ali,Ghaffarizadeh, Ahmadreza,Kasraei, Mehdi,&Omid, Mahmoud.(2018).Sensitivity analysis of energy inputs in crop production using artificial neural networks.JOURNAL OF CLEANER PRODUCTION,197,992-998. |
MLA | Khoshroo, Alireza,et al."Sensitivity analysis of energy inputs in crop production using artificial neural networks".JOURNAL OF CLEANER PRODUCTION 197(2018):992-998. |
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