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Abstract(s)
Thereisanenormouspotentialtoproducebioenergy fromagriculture, forestryandother
landuseintheEuropeanUnion(EU)farms.TheagriculturalsectorintheEUmember-states
has conditionstoincreasethe contributions of renewableenergiesthrough better use ofthe
residuesandtheproductionofenergycrops.Nonetheless,theprofitabilityofthesealternative
agricultural outputs, in somecircumstances, and the need forland for food production, for
example, have been obstacles to effective positioning of the EU farms as sources of
bioenergy.Fromthisperspective,thisstudyintendstoassessthecurrentcontextoftheenergy
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)wereconsideredfortheyear2020.Thisstatisticalinformationwasanalysedthrough
machine learning approaches, namely those associated with multilayer perceptron (MLP)
algorithmsfromtheartificialneuralnetworks (ANN)methodologies.Theresultsfromthese
datashowthatenergycrops dohave not relevantimportanceintheEuropeanUnion farms.
Ontheotherhand,whenthesecropsappear,theyareproducedbylargerfarms,withgreater
competitivenessandwhichreceivemoresubsidies.
Description
Keywords
Agriculture4.0 Artificial Neural Networks Multilayer Perceptron
Pedagogical Context
Citation
Publisher
Hellenic Association of Regional Scientists