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Machine Learning Methodologies, Wages Paid and the Most Relevant Predictors

dc.contributor.authorMartinho, Vítor
dc.date.accessioned2024-11-12T14:07:22Z
dc.date.available2024-11-12T14:07:22Z
dc.date.issued2024
dc.description.abstractThe agricultural sector worldwide has an economic dimension related to the remuneration of the production factors applied in the sector, an environmental contribution associated with the sustainability of rural places and a social dimension related to the employment creation and the consequent level of remuneration of the labour. The question here is about the level of wages paid in the agricultural sector across the European Union countries and about the main factors that may be taken into account to predict the level of these wages paid to agricultural workers. This research intends to select the models with better precision to predict the wages paid in the European Union agriculture and to suggest important predictors from the enormous number of indicators that may be identified in the farms. The findings obtained may be considered relevant support for the design of social and agricultural policies in the European framework.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMartinho, V.J.P.D. (2024). Machine Learning Methodologies, Wages Paid and the Most Relevant Predictors. In: Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-54608-2_8pt_PT
dc.identifier.doi10.1007/978-3-031-54608-2_8pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.19/8634
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer, Champt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-031-54608-2_8pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectFarm accountancy data networkpt_PT
dc.subjectEuropean Unionpt_PT
dc.titleMachine Learning Methodologies, Wages Paid and the Most Relevant Predictorspt_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage110pt_PT
oaire.citation.startPage99pt_PT
person.familyNamePereira Domingues Martinho
person.givenNameVítor João
person.identifier.ciencia-idF510-903F-51FA
rcaap.rightsclosedAccesspt_PT
rcaap.typebookPartpt_PT
relation.isAuthorOfPublicationd99fa017-5c04-4606-b382-f069996da23f
relation.isAuthorOfPublication.latestForDiscoveryd99fa017-5c04-4606-b382-f069996da23f

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