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Modelling fuel consumption in wheat production using artificial neural networks

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  • Safa, Majeed
  • Samarasinghe, Sandhya

Abstract

An ANN (artificial neural network) approach was used to model the fuel consumption of wheat production. This study was conducted over 35,300 ha of irrigated and dry land wheat fields in Canterbury in the 2007–2008 harvest year. From an extensive data collection involving 40 farms, the total fuel consumption in wheat production was estimated at 65.3 l/ha. On average, fuel consumption in tillage and harvesting was more than in other operations, at 29.6 l/ha (45%) and 18 l/ha (28%), respectively. The ANN model developed was capable of predicting fuel consumption in wheat production under different conditions using technical and social factors. This will help farmers find the best practice to reduce their expenditure, with minimum income reduction. This study investigated the potential for using ANN to forecast fuel consumption, as compared to traditional regression models. After examining more than 140 different factors, 8 were selected as influential input into the model. The final neural network model can predict fuel consumption based on farm conditions (size of wheat area and number of sheep), farmers' social considerations (level of education), farm operation (number of passes of plough), machinery condition (age of sprayer) and farm inputs (P, herbicide and insecticide consumption) in arable farms in Canterbury with an error margin of ±8% (±5.6 l/ha).

Suggested Citation

  • Safa, Majeed & Samarasinghe, Sandhya, 2013. "Modelling fuel consumption in wheat production using artificial neural networks," Energy, Elsevier, vol. 49(C), pages 337-343.
    • Handle: RePEc:eee:energy:v:49:y:2013:i:c:p:337-343
    DOI: 10.1016/j.energy.2012.10.055
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    References listed on IDEAS

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    Cited by:

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    2. Šarauskis, Egidijus & Masilionytė, Laura & Juknevičius, Darius & Buragienė, Sidona & Kriaučiūnienė, Zita, 2019. "Energy use efficiency, GHG emissions, and cost-effectiveness of organic and sustainable fertilisation," Energy, Elsevier, vol. 172(C), pages 1151-1160.
    3. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    4. Šarauskis, Egidijus & Vaitauskienė, Kristina & Romaneckas, Kęstutis & Jasinskas, Algirdas & Butkus, Vidmantas & Kriaučiūnienė, Zita, 2017. "Fuel consumption and CO2 emission analysis in different strip tillage scenarios," Energy, Elsevier, vol. 118(C), pages 957-968.
    5. Wörz, Sascha & Bernhardt, Heinz, 2017. "A novel method for optimal fuel consumption estimation and planning for transportation systems," Energy, Elsevier, vol. 120(C), pages 565-572.
    6. 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.

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