Sensitivity analysis of energy inputs in crop production using artificial neural networks. (1st October 2018)

• Record Type: Journal Article
• Authors: Khoshroo, Alireza; Emrouznejad, Ali; Ghaffarizadeh, Ahmadreza; Kasraei, Mehdi; Omid, Mahmoud
• Is Part Of: Journal of cleaner production. Volume 197(2018)Part 1
• Page Start: 992
• Page End: 998
• Publication Date: 2018-10-01
• Subjects: Grape production -- Artificial neural networks -- Sensitivity analysis -- Energy efficiency; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.05.249
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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. Graphical abstract: Topology of a simple artificial neural network.