Repository logo
 
Publication

Predictive Machine Learning Approaches to Agricultural Output

2024Book part Restricted access
Simple item page
dc.contributor.authorMartinho, Vítor
dc.date.accessioned2024-11-12T12:14:10Z
dc.date.available2024-11-12T12:14:10Z
dc.date.issued2024
dc.description.abstractThe agricultural sector needs to increase agricultural production to guarantee food security worldwide, however, to achieve these objectives agriculture must improve the sustainability of its activities and processes, specifically improving the efficiency of the sector. In these frameworks, adjusted agricultural planning and management is crucial, where the availability of information plays a determinant role, as well as the consideration of new technologies and methodologies. In the context of the new approaches of analysis, digital methodologies may bring relevant added value, namely those associated with predictive machine learning technologies. From this perspective, this study intends to identify the most adjusted models to predict the European Union farming output, taking into account machine learning approaches and statistical information from the Farm Accountancy Data Network. The results obtained highlight the most important farming variables that must be taken into account to predict the total output in the European Union farms.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationMartinho, V.J.P.D. (2024). Predictive Machine Learning Approaches to Agricultural Output. 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_1pt_PT
dc.identifier.doi10.1007/978-3-031-54608-2_1pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.19/8627
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_1pt_PT
dc.subjectIBM SPSS modelerpt_PT
dc.subjectFarm accountancy data networkpt_PT
dc.subjectEuropean Union farmspt_PT
dc.titlePredictive Machine Learning Approaches to Agricultural Outputpt_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage17pt_PT
oaire.citation.startPage1pt_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

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
32569ec2-8502-4a1c-87e2-ae82b7138bb6 (Versão publicada).pdf
Size:
4.86 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.79 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

CERNAS - Capítulo em obra internacional, como autor
ESAV - DEAS - Capítulo em obra internacional, como autor