Sensitivity analysis of energy inputs in crop production using artificial neural networks

Alireza Khoshroo, Ali Emrouznejad*, Ahmadreza Ghaffarizadeh, Mehdi Kasraei, Mahmoud Omid

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)992-998
Number of pages7
JournalJournal of Cleaner Production
Issue numberPart 1
Early online date4 Jun 2018
Publication statusPublished - 1 Oct 2018

Bibliographical note

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


  • Artificial neural networks
  • Energy efficiency
  • Grape production
  • Sensitivity analysis


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