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An overview on energy inputs and environmental emissions of grape production in West Azerbayjan of Iran

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  • Mardani, Aref
  • Taghavifar, Hamid

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 218713MJha−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 858.621kgCO2eqha−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.

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  • Mardani, Aref & Taghavifar, Hamid, 2016. "An overview on energy inputs and environmental emissions of grape production in West Azerbayjan of Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 918-924.
    • Handle: RePEc:eee:rensus:v:54:y:2016:i:c:p:918-924
    DOI: 10.1016/j.rser.2015.10.073
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    References listed on IDEAS

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    Cited by:

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    2. Xu, Bin & Lin, Boqiang, 2017. "Factors affecting CO2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model," Energy Policy, Elsevier, vol. 104(C), pages 404-414.
    3. Elahi, Ehsan & Khalid, Zainab, 2022. "Estimating smart energy inputs packages using hybrid optimisation technique to mitigate environmental emissions of commercial fish farms," Applied Energy, Elsevier, vol. 326(C).
    4. Elahi, Ehsan & Weijun, Cui & Jha, Sunil Kumar & Zhang, Huiming, 2019. "Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: A step towards livestock sustainability," Energy, Elsevier, vol. 183(C), pages 191-204.
    5. Soltanali, Hamzeh & Nikkhah, Amin & Rohani, Abbas, 2017. "Energy audit of Iranian kiwifruit production using intelligent systems," Energy, Elsevier, vol. 139(C), pages 646-654.
    6. Elhami, Behzad & Ghasemi Nejad Raini, Mahmoud & Soheili-Fard, Farshad, 2019. "Energy and environmental indices through life cycle assessment of raisin production: A case study (Kohgiluyeh and Boyer-Ahmad Province, Iran)," Renewable Energy, Elsevier, vol. 141(C), pages 507-515.
    7. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
    8. Tian, Dong & Zhang, Min & Xiong, Chuqiao & Mu, Weisong & Feng, Jianying, 2019. "Measuring the energy consumption and energy efficiency in two-harvest-a-year grape cultivation," Energy, Elsevier, vol. 189(C).
    9. Hossein Jargan & Abbas Rohani & Armaghan Kosari-Moghaddam, 2022. "Application of modeling techniques for energy analysis of fruit production systems," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2616-2639, February.

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