Site-Specific Nutrient Diagnosis of Orange Groves

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversité Laval-
Autor(es): dc.contributorFederal University of Ceará-
Autor(es): dc.contributorCentro de Citricultura Sylvio Moreira-
Autor(es): dc.contributorFederal University of Santa Maria-
Autor(es): dc.creatorYamane, Danilo Ricardo-
Autor(es): dc.creatorParent, Serge-Étienne-
Autor(es): dc.creatorNatale, William-
Autor(es): dc.creatorCecílio Filho, Arthur Bernardes-
Autor(es): dc.creatorRozane, Danilo Eduardo-
Autor(es): dc.creatorNowaki, Rodrigo Hiyoshi Dalmazzo-
Autor(es): dc.creatorMattos Junior, Dirceu de-
Autor(es): dc.creatorParent, Léon Etienne-
Data de aceite: dc.date.accessioned2025-08-21T21:49:49Z-
Data de disponibilização: dc.date.available2025-08-21T21:49:49Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/horticulturae8121126-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249512-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249512-
Descrição: dc.descriptionNutrient diagnosis of orange (Citrus sinensis) groves in Brazil relies on regional information from a limited number of studies transferred to other environments under the ceteris paribus assumption. Interpretation methods are based on crude nutrient compositions that are intrinsically biased by genetics X environment interactions. Our objective was to develop accurate and unbiased nutrient diagnosis of orange groves combining machine learning (ML) and compositional methods. Fruit yield and foliar nutrients were quantified in 551 rainfed 7–15-year-old orange groves of ‘Hamlin’, ‘Valência’, and ‘Pêra’ in the state of São Paulo, Brazil. The data set was further documented using soil classification, soil tests, and meteorological indices. Tissue compositions were log-ratio transformed to account for nutrient interactions. Ionomes differed among scions. Regression ML models showed evidence of overfitting. Binary ML classification models showed acceptable values of areas under the curve (>0.7). Regional standards delineating the multivariate elliptical hyperspace depended on the yield cutoff. A shapeless blob hyperspace was delineated using the k-nearest successful neighbors that showed comparable features and reported realistic yield goals. Regionally derived and site-specific reference compositions may lead to differential interpretation. Large-size and diversified data sets must be collected to inform ML models along the learning curve, tackle model overfitting, and evaluate the merit of blob-scale diagnosis.-
Descrição: dc.descriptionNatural Sciences and Engineering Research Council of Canada-
Descrição: dc.descriptionDepartment of Plant Production São Paulo State University (UNESP), SP-
Descrição: dc.descriptionDepartment of Soils and Agri-Food Engineering Université Laval-
Descrição: dc.descriptionDepartment of Plant Science Federal University of Ceará, CE-
Descrição: dc.descriptionDepartment of Agronomy São Paulo State University (UNESP), SP-
Descrição: dc.descriptionInstituto Agronômico de Campinas (IAC) Centro de Citricultura Sylvio Moreira, SP-
Descrição: dc.descriptionDepartment of Soils Federal University of Santa Maria, RS-
Descrição: dc.descriptionDepartment of Plant Production São Paulo State University (UNESP), SP-
Descrição: dc.descriptionDepartment of Agronomy São Paulo State University (UNESP), SP-
Descrição: dc.descriptionNatural Sciences and Engineering Research Council of Canada: #2254-
Idioma: dc.languageen-
Relação: dc.relationHorticulturae-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcentered log ratio-
Palavras-chave: dc.subjectlocal diagnosis-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectnutrient balance-
Título: dc.titleSite-Specific Nutrient Diagnosis of Orange Groves-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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