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Modelling Energy Use and Fuel Consumption in Wheat Production Using Indirect Factors and Artificial Neural Networks

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Neural Information Processing (ICONIP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7664))

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Abstract

This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury, New Zealand in the 2007-2008 harvest year. The Artificial Neural Network models (ANNs), after examining more than 140 several direct and indirect parameters, can predict energy use and fuel consumption based on farm conditions, farmers’ social considerations, farm operation, machinery condition and farm inputs, arable farms in Canterbury with an error margin of ±12% (± 2900 MJ/ha) and ±8% (± 5.6 l/ha), respectively.

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Author information

Authors and Affiliations

  1. Department of Agricultural Management and Property Studies, Lincoln University, New Zealand

    Majeed Safa

  2. Department of Environmental Management, Lincoln University, New Zealand

    Sandhya Samarasinghe

Authors
  1. Majeed Safa
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  2. Sandhya Samarasinghe
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Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Education City, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, Hubei, China

    Zhigang Zeng

  3. College of Computer Science, Chongqing University, 174 Shazhengjie Street, 400044, Chongqing, China

    Chuandong Li

  4. Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Chi Sing Leung

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© 2012 Springer-Verlag Berlin Heidelberg

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Safa, M., Samarasinghe, S. (2012). Modelling Energy Use and Fuel Consumption in Wheat Production Using Indirect Factors and Artificial Neural Networks. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_4

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  • DOI: https://doi.org/10.1007/978-3-642-34481-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34480-0

  • Online ISBN: 978-3-642-34481-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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