IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v135y2017icp405-412.html
   My bibliography  Save this article

Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks

Author

Listed:
  • Taheri-Rad, Alireza
  • Khojastehpour, Mehdi
  • Rohani, Abbas
  • Khoramdel, Surur
  • Nikkhah, Amin

Abstract

The aim of this study was to model the energy flows for the production of different paddy rice cultivars in Golestan province of Iran using Cobb-Douglas (CD) model and artificial neural network (ANN). The studied energy inputs were human labor, agricultural machinery, diesel fuel, seed, biocide, chemical fertilizers, farmyard manure and electricity. The results indicated that the total energy consumption of paddy farms for high-yielding long grain (H-YLG) and high-grade long grain (H-GLG) paddy production were 108,392 and 89,835 MJha−1, respectively. Also, CD model and sensitivity analysis of energy inputs on paddy yield showed that the impacts of human labor, biocide and chemical fertilizers inputs on paddy yield were higher than the other inputs. The ANN results highlighted that the best model consisted of an input layer with eight inputs, one hidden layer with 25 neurons and an output layer (8–25–1 topology) for both H-YLG and H-GLG paddy cultivars. The results obtained from the comparative study of CD and ANN models for predicting the amount of H-GLG paddy production revealed that the ANN model with the determination coefficient (R2) of 0.99 and the root mean square error (RMSE) of 1.93 kgha1 estimated the outputs more accurately than the CD model. Besides, the ANN model estimated more accurate and precise outputs than CD model for predicting the production of H-YLG.

Suggested Citation

  • Taheri-Rad, Alireza & Khojastehpour, Mehdi & Rohani, Abbas & Khoramdel, Surur & Nikkhah, Amin, 2017. "Energy flow modeling and predicting the yield of Iranian paddy cultivars using artificial neural networks," Energy, Elsevier, vol. 135(C), pages 405-412.
    • Handle: RePEc:eee:energy:v:135:y:2017:i:c:p:405-412
    DOI: 10.1016/j.energy.2017.06.089
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217310836
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.06.089?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sayin, Cengiz & Nisa Mencet, M. & Ozkan, Burhan, 2005. "Assessing of energy policies based on Turkish agriculture:: current status and some implications," Energy Policy, Elsevier, vol. 33(18), pages 2361-2373, December.
    2. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Yousefi, Marziye & Movahedi, Mehran, 2013. "Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks," Energy, Elsevier, vol. 52(C), pages 333-338.
    3. Ozkan, Burhan & Akcaoz, Handan & Fert, Cemal, 2004. "Energy input–output analysis in Turkish agriculture," Renewable Energy, Elsevier, vol. 29(1), pages 39-51.
    4. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Jafari, Ali & Mohammadi, Ali, 2011. "Improving energy use efficiency of canola production using data envelopment analysis (DEA) approach," Energy, Elsevier, vol. 36(5), pages 2765-2772.
    5. Ramedani, Z. & Rafiee, S. & Heidari, M.D., 2011. "An investigation on energy consumption and sensitivity analysis of soybean production farms," Energy, Elsevier, vol. 36(11), pages 6340-6344.
    6. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Jafari, Ali & Mohammadi, Ali, 2011. "Optimization of energy consumption for soybean production using Data Envelopment Analysis (DEA) approach," Applied Energy, Elsevier, vol. 88(11), pages 3765-3772.
    7. Pishgar-Komleh, S.H. & Sefeedpari, P. & Rafiee, S., 2011. "Energy and economic analysis of rice production under different farm levels in Guilan province of Iran," Energy, Elsevier, vol. 36(10), pages 5824-5831.
    8. Canakci, M. & Akinci, I., 2006. "Energy use pattern analyses of greenhouse vegetable production," Energy, Elsevier, vol. 31(8), pages 1243-1256.
    9. Singh, Surendra & Singh, Sarjinder & Mittal, J.P. & Pannu, C.J.S. & Bhangoo, B.S., 1994. "Energy inputs and crop yield relationships for rice in Punjab," Energy, Elsevier, vol. 19(10), pages 1061-1065.
    10. Muazu, A. & Yahya, A. & Ishak, W.I.W. & Khairunniza-Bejo, S., 2015. "Energy audit for sustainable wetland paddy cultivation in Malaysia," Energy, Elsevier, vol. 87(C), pages 182-191.
    11. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Mohammadi, Ali, 2011. "Optimization of energy consumption and input costs for apple production in Iran using data envelopment analysis," Energy, Elsevier, vol. 36(2), pages 909-916.
    12. Ozkan, Burhan & Ceylan, R. Figen & Kizilay, Hatice, 2011. "Comparison of energy inputs in glasshouse double crop (fall and summer crops) tomato production," Renewable Energy, Elsevier, vol. 36(5), pages 1639-1644.
    13. Hatirli, Selim Adem & Ozkan, Burhan & Fert, Cemal, 2005. "An econometric analysis of energy input-output in Turkish agriculture," Renewable and Sustainable Energy Reviews, Elsevier, vol. 9(6), pages 608-623, December.
    14. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    15. Hosseinzadeh-Bandbafha, Homa & Safarzadeh, Dariush & Ahmadi, Ebrahim & Nabavi-Pelesaraei, Ashkan & Hosseinzadeh-Bandbafha, Ehssan, 2017. "Applying data envelopment analysis to evaluation of energy efficiency and decreasing of greenhouse gas emissions of fattening farms," Energy, Elsevier, vol. 120(C), pages 652-662.
    16. Safa, M. & Samarasinghe, S., 2011. "Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”," Energy, Elsevier, vol. 36(8), pages 5140-5147.
    17. Kuswardhani, Nita & Soni, Peeyush & Shivakoti, Ganesh P., 2013. "Comparative energy input–output and financial analyses of greenhouse and open field vegetables production in West Java, Indonesia," Energy, Elsevier, vol. 53(C), pages 83-92.
    18. Erdal, Gülistan & Esengün, Kemal & Erdal, Hilmi & Gündüz, Orhan, 2007. "Energy use and economical analysis of sugar beet production in Tokat province of Turkey," Energy, Elsevier, vol. 32(1), pages 35-41.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Morteza Zangeneh & Narges Banaeian & Sean Clark, 2021. "Meta-Analysis on Energy-Use Patterns of Cropping Systems in Iran," Sustainability, MDPI, vol. 13(7), pages 1-28, March.
    2. Pritpal Singh & Gurdeep Singh & G. P. S. Sodhi, 2022. "Data envelopment analysis based optimization for improving net ecosystem carbon and energy budget in cotton (Gossypium hirsutum L.) cultivation: methods and a case study of north-western India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2079-2119, February.
    3. Soltanali, Hamzeh & Nikkhah, Amin & Rohani, Abbas, 2017. "Energy audit of Iranian kiwifruit production using intelligent systems," Energy, Elsevier, vol. 139(C), pages 646-654.
    4. Kaur, Karman & Prasad, Narayan & Prasad, Narayan, 2021. "Modelling Input Energy Used in Wheat Production in India Using Artificial Neural Network," 2021 Conference, August 17-31, 2021, Virtual 315051, International Association of Agricultural Economists.
    5. Pradyot Ranjan Jena & Babita Majhi & Rajesh Kalli & Ritanjali Majhi, 2023. "Prediction of crop yield using climate variables in the south-western province of India: a functional artificial neural network modeling (FLANN) approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 11033-11056, October.
    6. 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).
    7. Di Lullo, G. & Oni, A.O. & Kumar, A., 2023. "The development of complex engineering models using artificial neural network-based proxy models for life cycle assessments of energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    8. 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).
    9. Chandra, Ankit & Heeren, Derek M. & Odhiambo, Lameck & Brozović, Nicholas, 2023. "Water-energy-food linkages in community smallholder irrigation schemes: Center pivot irrigation in Rwanda," Agricultural Water Management, Elsevier, vol. 289(C).
    10. Javad Zare Derakhshan & Saeed Firouzi & Armaghan Kosari-Moghaddam, 2022. "Energy audit of tobacco production agro-system based on different farm size levels in northern Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2715-2735, February.
    11. 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.
    12. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elsoragaby, Suha & Yahya, Azmi & Mahadi, Muhammad Razif & Nawi, Nazmi Mat & Mairghany, Modather, 2019. "Energy utilization in major crop cultivation," Energy, Elsevier, vol. 173(C), pages 1285-1303.
    2. Kazemi, Hossein & Kamkar, Behnam & Lakzaei, Somayeh & Badsar, Meysam & Shahbyki, Malihe, 2015. "Energy flow analysis for rice production in different geographical regions of Iran," Energy, Elsevier, vol. 84(C), pages 390-396.
    3. Yang, Zhiyuan & Zhu, Yuemei & Zhang, Jinyue & Li, Xuyi & Ma, Peng & Sun, Jiawei & Sun, Yongjian & Ma, Jun & Li, Na, 2022. "Comparison of energy use between fully mechanized and semi-mechanized rice production in Southwest China," Energy, Elsevier, vol. 245(C).
    4. Kazemi, Hossein & Bourkheili, Saeid Hassanpour & Kamkar, Behnam & Soltani, Afshin & Gharanjic, Kambiz & Nazari, Noor Mohammad, 2016. "Estimation of greenhouse gas (GHG) emission and energy use efficiency (EUE) analysis in rainfed canola production (case study: Golestan province, Iran)," Energy, Elsevier, vol. 116(P1), pages 694-700.
    5. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein & Rajaeifar, Mohammad Ali, 2014. "Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran," Agricultural Systems, Elsevier, vol. 123(C), pages 120-127.
    6. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein & Shamshirband, Shahaboddin & Hamid, Siti Hafizah Ab, 2015. "Developing a fuzzy clustering model for better energy use in farm management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 27-34.
    7. Asgharipour, Mohammad Reza & Mondani, Farzad & Riahinia, Shahram, 2012. "Energy use efficiency and economic analysis of sugar beet production system in Iran: A case study in Khorasan Razavi province," Energy, Elsevier, vol. 44(1), pages 1078-1084.
    8. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Yousefi, Marziye & Movahedi, Mehran, 2013. "Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks," Energy, Elsevier, vol. 52(C), pages 333-338.
    9. Hossein Kazemi Author- Department of Agronomy, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Iran, 2016. "Energy Balance in Modern Agroecosystems; Why and How?," Agricultural Research & Technology: Open Access Journal, Juniper Publishers Inc., vol. 1(5), pages 101-104, June.
    10. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein, 2013. "Applying data envelopment analysis approach to improve energy efficiency and reduce GHG (greenhouse gas) emission of wheat production," Energy, Elsevier, vol. 58(C), pages 588-593.
    11. Muhammad N. Ashraf & Muhammad H. Mahmood & Muhammad Sultan & Redmond R. Shamshiri & Sobhy M. Ibrahim, 2021. "Investigation of Energy Consumption and Associated CO 2 Emissions for Wheat–Rice Crop Rotation Farming," Energies, MDPI, vol. 14(16), pages 1-18, August.
    12. Tabatabaie, Seyed Mohammad Hossein & Rafiee, Shahin & Keyhani, Alireza, 2012. "Energy consumption flow and econometric models of two plum cultivars productions in Tehran province of Iran," Energy, Elsevier, vol. 44(1), pages 211-216.
    13. Houshyar, Ehsan & Azadi, Hossein & Almassi, Morteza & Sheikh Davoodi, Mohammad Javad & Witlox, Frank, 2012. "Sustainable and efficient energy consumption of corn production in Southwest Iran: Combination of multi-fuzzy and DEA modeling," Energy, Elsevier, vol. 44(1), pages 672-681.
    14. Yuan, Shen & Peng, Shaobing & Wang, Dong & Man, Jianguo, 2018. "Evaluation of the energy budget and energy use efficiency in wheat production under various crop management practices in China," Energy, Elsevier, vol. 160(C), pages 184-191.
    15. Muhammad N. Ashraf & Muhammad H. Mahmood & Muhammad Sultan & Narges Banaeian & Muhammad Usman & Sobhy M. Ibrahim & Muhammad U. B. U. Butt & Muhammad Waseem & Imran Ali & Aamir Shakoor & Zahid M. Khan, 2020. "Investigation of Input and Output Energy for Wheat Production: A Comprehensive Study for Tehsil Mailsi (Pakistan)," Sustainability, MDPI, vol. 12(17), pages 1-22, August.
    16. Barut, Zeliha Bereket & Ertekin, Can & Karaagac, Hasan Ali, 2011. "Tillage effects on energy use for corn silage in Mediterranean Coastal of Turkey," Energy, Elsevier, vol. 36(9), pages 5466-5475.
    17. Sara Ilahi & Yongchang Wu & Muhammad Ahsan Ali Raza & Wenshan Wei & Muhammad Imran & Lyankhua Bayasgalankhuu, 2019. "Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    18. Kizilaslan, Halil, 2009. "Input-output energy analysis of cherries production in Tokat Province of Turkey," Applied Energy, Elsevier, vol. 86(7-8), pages 1354-1358, July.
    19. Singh, Pritpal & Singh, Gurdeep & Sodhi, G.P.S., 2019. "Energy auditing and optimization approach for improving energy efficiency of rice cultivation in south-western Punjab, India," Energy, Elsevier, vol. 174(C), pages 269-279.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:135:y:2017:i:c:p:405-412. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.