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Energy Crops: Assessments In The European Union Agricultural Regions Through Machine Learning Approaches

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  • Vitor Joao Pereira Domingues MARTINHO

    (Coordinator Professor with Habilitation, Agricultural School (ESAV) and CERNAS-IPV Research Centre, Polytechnic Institute of Viseu (IPV), Portugal)

Abstract

There is an enormous potential to produce bioenergy from agriculture, forestry and other land use in the European Union (EU) farms. The agricultural sector in the EU member-states has conditions to increase the contributions of renewable energies through better use of the residues and the production of energy crops. Nonetheless, the profitability of these alternative agricultural outputs, in some circumstances, and the need for land for food production, for example, have been obstacles to effective positioning of the EU farms as sources of bioenergy. From this perspective, this study intends to assess the current context of the energy crops in the farms of the EU agricultural regions and identify a model that supports the prediction of these frameworks. For that, data from the Farm Accountancy Data Network (FADN) were considered for the year 2020. This statistical information was analysed through machine learning approaches, namely those associated with multilayer perceptron (MLP) algorithms from the artificial neural networks (ANN) methodologies. The results from these data show that energy crops do have not relevant importance in the European Union farms. On the other hand, when these crops appear, they are produced by larger farms, with greater competitiveness and which receive more subsidies.

Suggested Citation

  • Vitor Joao Pereira Domingues MARTINHO, 2023. "Energy Crops: Assessments In The European Union Agricultural Regions Through Machine Learning Approaches," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 29-42, June.
  • Handle: RePEc:hrs:journl:v:xv:y:2023:i:1:p:29-42
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    References listed on IDEAS

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    More about this item

    Keywords

    Agriculture 4.0; Artificial Neural Networks; Multilayer Perceptron;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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