Local Factors Impact Accuracy of Garlic Tissue Test Diagnosis

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MetadadosDescriçãoIdioma
Autor(es): dc.contributor(EPAGRI)-
Autor(es): dc.contributorUniversité Laval-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Alto Vale do Rio do Peixe (UNIARP)-
Autor(es): dc.contributorUniversidade Federal de Sergipe (UFS)-
Autor(es): dc.contributorUniversidade Federal do Paraná (UFPR)-
Autor(es): dc.creatorHahn, Leandro-
Autor(es): dc.creatorParent, Léon-Étienne-
Autor(es): dc.creatorFeltrim, Anderson Luiz-
Autor(es): dc.creatorRozane, Danilo Eduardo-
Autor(es): dc.creatorEnder, Marcos Matos-
Autor(es): dc.creatorTassinari, Adriele-
Autor(es): dc.creatorKrug, Amanda Veridiana-
Autor(es): dc.creatorBerghetti, Álvaro Luís Pasquetti-
Autor(es): dc.creatorBrunetto, Gustavo-
Data de aceite: dc.date.accessioned2025-08-21T17:10:59Z-
Data de disponibilização: dc.date.available2025-08-21T17:10:59Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agronomy12112714-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246299-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246299-
Descrição: dc.descriptionThe low productivity of garlic in Brazil requires more efficient nutritional management. For this, environmental and fertilization-related factors must be adjusted to a set of local conditions. Our objective was to provide an accurate diagnosis of the nutrient status of garlic crops in southern Brazil. The dataset comprised 1024 observations, 962 as field tests conducted during the 2015–2017 period to train the model, and 61 field observations collected during the 2018–2019 period to validate the model. Machine learning models (MLM) related garlic yield to managerial, edaphic, plant, and climatic features. Compositional data analysis (CoDa) methods allowed classification of nutrients in the order of limitation to yield where MLM detected nutrient imbalance. Tissue analysis alone returned an accuracy of 0.750 in regression and 0.891 in classification about the yield cutoff of 11 ton ha−1. Adding all features documented in the dataset, accuracy reached 0.855 in regression and 0.912 in classification. Local diagnosis based on MLM and CoDa and accounting for local features differed from regional diagnosis across features. Local nutrient diagnosis may differ from regional diagnosis because several yield-impacting factors are taken into account and benchmark compositions are representative of local conditions.-
Descrição: dc.descriptionCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada-
Descrição: dc.descriptionNatural Sciences and Engineering Research Council of Canada-
Descrição: dc.descriptionCaçador Experimental Station Santa Catarina State Agricultural Research and Rural Extension Agency (EPAGRI), SC-
Descrição: dc.descriptionDepartment of Soils and Agrifood Engineering Université Laval-
Descrição: dc.descriptionAgronomy Department São Paulo State University “Júlio Mesquita Filho”, SP-
Descrição: dc.descriptionAgronomy Department University of Alto Vale do Rio do Peixe (UNIARP), SC-
Descrição: dc.descriptionSoil Science Department Federal University of Santa Maria (UFSM), RS-
Descrição: dc.descriptionForest Science Department Federal University of Paraná (UFPR), RS-
Descrição: dc.descriptionAgronomy Department São Paulo State University “Júlio Mesquita Filho”, SP-
Descrição: dc.descriptionCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada: NSERC-2254-
Descrição: dc.descriptionNatural Sciences and Engineering Research Council of Canada: NSERC-2254-
Idioma: dc.languageen-
Relação: dc.relationAgronomy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAdaboost-
Palavras-chave: dc.subjectAllium sativum-
Palavras-chave: dc.subjectcompositional distance-
Palavras-chave: dc.subjectgrowth-limiting factors-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectperturbation vector-
Palavras-chave: dc.subjectrandom forest-
Título: dc.titleLocal Factors Impact Accuracy of Garlic Tissue Test Diagnosis-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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