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

• Autor(a) principal: Martinho, Vítor
• Data de Publicação: 2023
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.19/8090
• topic: Agriculture4.0; Artificial Neural Networks; Multilayer Perceptron

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.