An overview on energy inputs and environmental emissions of grape production in West Azerbayjan of Iran. (February 2016)

• Record Type: Journal Article
• Authors: Mardani, Aref; Taghavifar, Hamid
• Is Part Of: Renewable & sustainable energy reviews. Volume 54(2016:Feb.)
• Page Start: 918
• Page End: 924
• Publication Date: 2016-02
• Subjects: Artificial neural networks -- Energy -- Greenhouse gas emissions -- Grape production; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2015.10.073
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: The present investigation is aimed at the assessment of the energy consumption of grape production in West Azerbayjan along with the assessment of environmental indices during the cultivation. The studies were carried out in terms of energy input and output, yield, energy use efficiency, specific energy, energy productivity, and net energy gain where CO2 emission was investigated as the substantial emission. As well, the portions of different direct, indirect, renewable and nonrenewable energy sources were encompassed. A supervised Artificial Neural Network was employed to prognosticate the energy and environmental indices for grape production in the studied region. Energy inputs included human labor, machinery, diesel fuel, herbicide, insecticide, chemical fertilizers, manure, irrigation water and electricity. The results showed that the total energy input and output for grape production were at 39968.49 and 218713 MJ ha −1, respectively. Among the energy inputs, Nitrogen with 35.6% and irrigation water with 21.81% allocated the greatest shares. The value of total greenhouse gas emission was estimated at 858.621 kg CO2eq ha −1 for grape production with the greatest portions for chemical fertilizers and irrigation, respectively. Of diversified Artificial Neural Network approaches, Levenberg–Marqardt training algorithm with root mean square equal to 0.2171 was achieved at 14 neurons in the hidden layer whilst the coefficient of determination values of 0.9927 and 0.9935 were obtained for energy input and environmental emission prediction, respectively.