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Assessing the presence of Pithomyces chartarum in pastureland using IoT sensors and remote sensing: the case study of Terceira Island (Azores, Portugal)

dc.contributor.authorÁvila, Mariana
dc.contributor.authorPinelo, João
dc.contributor.authorCasas, Enrique
dc.contributor.authorCapinha, César
dc.contributor.authorPabst, Rebecca
dc.contributor.authorSzczesniak, Iga
dc.contributor.authorDomingues, Elizabeth
dc.contributor.authorPinto, Carlos
dc.contributor.authorSantos, Valentina
dc.contributor.authorGil, Artur
dc.contributor.authorArbelo, Manuel
dc.date.accessioned2024-07-16T10:44:37Z
dc.date.available2024-07-16T10:44:37Z
dc.date.issued2024
dc.description.abstract: Spores from the fungus Pithomyces chartarum are commonly found on Azorean pastures. When consumed by cattle along with the grass, these spores cause health issues in the cattle, resulting in animal suffering and financial losses. For approximately two years, we monitored meteorological parameters using weather stations and collected and analyzed grass samples in a laboratory to control for the presence of spores. The data confirmed a connection between meteorology and sporulation, enabling the prediction of sporulation risk. To detect the presence of spores in pastures rather than predict it, we employed field spectrometry and Sentinel-2 reflectance data to measure the spectral signatures of grass while controlling for spores. Our findings indicate that meteorological variables from the past 90 days can be used to predict sporulation, which can enhance the accuracy of a web-based alert system used by farmers to manage the risk. We did not detect significant differences in spectral signatures between grass with and without spores. These studies contribute to a deeper understanding of P. chartarum sporulation and provide actionable information for managing cattle, ultimately improving animal welfare and reducing financial losses.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationÁvila, M., Pinelo, J., Casas, E., Capinha, C., Pabst, R., Szczesniak, I., Domingues, E., Pinto, C., Santos, V., Gil, A., & Arbelo, M. (2024). Assessing the presence of Pithomyces chartarum in pastureland using IoT sensors and remote sensing: the case study of Terceira Island (Azores, Portugal). Sensors, 24(14), 4485. https://doi.org/10.3390/s24144485pt_PT
dc.identifier.doi10.3390/s24144485pt_PT
dc.identifier.eissn1424-8220
dc.identifier.urihttp://hdl.handle.net/10451/65287
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationACORES-01-0247- FEDER-000046pt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/24/14/4485pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectIoTpt_PT
dc.subjectClimatic variablespt_PT
dc.subjectSpectral signaturept_PT
dc.subjectRemote sensingpt_PT
dc.subjectPithomycotoxicosispt_PT
dc.subjectSpectroradiometrypt_PT
dc.subjectSentinel-2pt_PT
dc.titleAssessing the presence of Pithomyces chartarum in pastureland using IoT sensors and remote sensing: the case study of Terceira Island (Azores, Portugal)pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue14pt_PT
oaire.citation.startPage4485pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume24pt_PT
person.familyNameCapinha
person.givenNameCésar
person.identifier.ciencia-id7714-2A88-CDE3
person.identifier.orcid0000-0002-0666-9755
person.identifier.ridK-6439-2017
person.identifier.scopus-author-id32867555000
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication4c666e7e-4ba8-4a41-8064-d26b3b9fc0f8
relation.isAuthorOfPublication.latestForDiscovery4c666e7e-4ba8-4a41-8064-d26b3b9fc0f8

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