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How can a changing climate influence the productivity of traditional olive orchards? Regression analysis applied to a local case study in Portugal

2023Artigo científico Acesso aberto
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dc.contributor.authorSilveira, Carlos
dc.contributor.authorAlmeida, Arlindo
dc.contributor.authorRibeiro, A.C.
dc.date.accessioned2023-10-20T10:53:23Z
dc.date.available2023-10-20T10:53:23Z
dc.date.issued2023
dc.description.abstractNowadays, the climate is undoubtedly one of the main threats to the sustainability of olive orchards, especially in the case of rainfed traditional production systems. Local warming, droughts, and extreme weather events are some of the climatological factors responsible for environmental thresholds in relation to crops being exceeded. The main objective of this study was to investigate the influence of microclimatic variability on the productivity of traditional olive orchards in a municipality located in northeastern Portugal. For this purpose, official data on climate, expressed through agrobioclimatic indicators, and olive productivity for a 21-year historical period (2000–2020) were used to evaluate potential correlations. In addition, a comprehensive regression analysis involving the dataset and the following modeling scenarios was carried out to develop regression models and assess the resulting predictions: (a) Random Forest (RF) with selected features; (b) Ordinary Least- Squares (OLS) with selected features; (c) OLS with correlation features; and (d) OLS with all features. For the a and b scenarios, features were selected applying the Recursive Feature Elimination with Cross-Validation (RFECV) technique. The best statistical performance was achieved considering nonlinearity among variables (a scenario, R2 = 0.95); however, it was not possible to derive any model given the underlying methodology to this scenario. In linear regression applications, the best fit between model predictions and the real olive productivity was obtained when all the analyzed agro-bioclimatic indicators were included in the regression (d scenario, R2 = 0.85). When selecting only the most relevant indicators using RFECV and correlation techniques, moderate correlations for the b and c regression scenarios were obtained (R2 of 0.54 and 0.49, respectively). Based on the research findings, especially the regression models, their adaptability to other olive territories with similar agronomic and environmental characteristics is suggested for crop management and regulatory purposes.pt_PT
dc.description.sponsorshipThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CIMO (UIDB/00690/2020 and UIDP/00690/2020) and SusTEC (LA/P/0007/2020). This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSilveira, Carlos; Almeida, Arlindo; Ribeiro, A.C. (2023). How can a changing climate influence the productivity of traditional olive orchards? Regression analysis applied to a local case study in Portugal. Climate. eISSN 2225-1154. 11:6, p. 1-20pt_PT
dc.identifier.doi10.3390/cli11060123pt_PT
dc.identifier.eissn2225-1154
dc.identifier.urihttp://hdl.handle.net/10198/28804
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationLA/P/0007/2020pt_PT
dc.relationMountain Research Center
dc.relationMountain Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMicroclimatept_PT
dc.subjectRainfed orchardspt_PT
dc.subjectOlive productivitypt_PT
dc.subjectAgro-bioclimatic indicatorspt_PT
dc.subjectRegression modelspt_PT
dc.subjectOlive yield responsespt_PT
dc.subjectSustainabilitypt_PT
dc.titleHow can a changing climate influence the productivity of traditional olive orchards? Regression analysis applied to a local case study in Portugalpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleMountain Research Center
oaire.awardTitleMountain Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00690%2F2020/PT
oaire.citation.endPage20pt_PT
oaire.citation.issue6pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleClimatept_PT
oaire.citation.volume11pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSilveira
person.familyNameAlmeida
person.familyNameRibeiro
person.givenNameCarlos
person.givenNameArlindo
person.givenNameAntónio C.
person.identifier.ciencia-id351A-FA79-885E
person.identifier.ciencia-idF91B-D395-4C34
person.identifier.ciencia-id9D19-E833-BE97
person.identifier.orcid0000-0002-9424-174X
person.identifier.orcid0000-0003-2724-2504
person.identifier.orcid0000-0002-8280-9027
person.identifier.ridX-1640-2018
person.identifier.scopus-author-id55849743000
person.identifier.scopus-author-id14120668500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication1398fb36-ddf9-4a63-a4c1-d0c8602903fc
relation.isAuthorOfPublication219f1dcf-2196-48d0-bbce-fbd14deacf9c
relation.isAuthorOfPublicationb7ecf809-561e-40ce-86b0-603a60433710
relation.isAuthorOfPublication.latestForDiscovery219f1dcf-2196-48d0-bbce-fbd14deacf9c
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