The Application of Machine Learning to Model the Impacts of Extreme Climatic Events on the Productivity of Dwarf Green Coconut Trees in the Eastern Amazon

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal Rural University of Amazonia-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorNery, Maryelle Kleyce M.-
Autor(es): dc.creatorFernandes, Gabriel S. T.-
Autor(es): dc.creatorPinto, João V. de N.-
Autor(es): dc.creatorRua, Matheus L.-
Autor(es): dc.creatorSantos, Miguel Gabriel M.-
Autor(es): dc.creatorRibeiro, Luis Roberto T.-
Autor(es): dc.creatorNavarro, Leandro M.-
Autor(es): dc.creatorde Souza, Paulo Jorge O. P.-
Autor(es): dc.creatorRolim, Glauco de S.-
Data de aceite: dc.date.accessioned2025-08-21T16:43:51Z-
Data de disponibilização: dc.date.available2025-08-21T16:43:51Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agriengineering7020033-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/305381-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/305381-
Descrição: dc.descriptionThe coconut crop (Cocos nucifera L.) is essential in humid tropical regions, contributing to the economy and food security. However, its perennial nature makes it sensitive to climate variability, particularly extreme events that affect productivity. This study evaluated the impacts of extreme climatic events on the productivity of dwarf green coconut in northeastern Pará, analyzing rainy (PC—December to July) and less rainy (PMC—August to November) periods between 2015 and 2023. Meteorological and experimental data were used, including extreme climate variables such as maximum temperature (HT) and precipitation (HEP), defined by the 90th percentiles, and low precipitation (LP, 10th percentile). Predictive models, such as Multiple Linear Regression (MLR) and Random Forest (RF), were developed. RF showed better performance, with an RMSE equivalent to 20% of the average productivity, while that of MLR exceeded 50%. However, RF struggled with generalization in the test set, likely due to overfitting. The inclusion of lagged productivity (productivity t-1) highlighted its significant influence. During the PC, extreme high precipitation (HEP) events and excessive water surplus (HE) occurring after the fifth month of inflorescence development contributed to increased productivity, whereas during the PMC, low-precipitation (LP) events led to productivity reductions. Notably, under certain circumstances, elevated precipitation can mitigate the negative impacts of low water availability. These findings underscore the need for adaptive management strategies to mitigate climatic impacts and promote stability in dwarf green coconut production.-
Descrição: dc.descriptionCollaborative Innovation Project of Colleges and Universities of Anhui Province-
Descrição: dc.descriptionSoil-Plant-Atmosphere Interaction in Amazonia Research Group Socio-Environmental and Water Resources Institute Belém Campus Federal Rural University of Amazonia, PA-
Descrição: dc.descriptionDepartment of Exact Sciences São Paulo State University Júlio de Mesquita Filho—UNESP, SP-
Descrição: dc.descriptionDepartment of Exact Sciences São Paulo State University Júlio de Mesquita Filho—UNESP, SP-
Descrição: dc.descriptionCollaborative Innovation Project of Colleges and Universities of Anhui Province: 403902/2021-5-
Idioma: dc.languageen-
Relação: dc.relationAgriEngineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectclimate change-
Palavras-chave: dc.subjectCocos nuciferaL-
Palavras-chave: dc.subjectPython 3.10.9-
Título: dc.titleThe Application of Machine Learning to Model the Impacts of Extreme Climatic Events on the Productivity of Dwarf Green Coconut Trees in the Eastern Amazon-
Tipo de arquivo: dc.typelivro digital-
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