Arid
DOI10.1016/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
ISSN0959-6526
EISSN1879-1786
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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