Feature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorEpagri-
Autor(es): dc.contributorFederal University of Santa Maria-
Autor(es): dc.contributorUniarp-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorLaval University-
Autor(es): dc.creatorHahn, Leandro-
Autor(es): dc.creatorKurtz, Claudinei-
Autor(es): dc.creatorde Paula, Betania Vahl-
Autor(es): dc.creatorFeltrim, Anderson Luiz-
Autor(es): dc.creatorHigashikawa, Fábio Satoshi-
Autor(es): dc.creatorMoreira, Camila-
Autor(es): dc.creatorRozane, Danilo Eduardo-
Autor(es): dc.creatorBrunetto, Gustavo-
Autor(es): dc.creatorParent, Léon-Étienne-
Data de aceite: dc.date.accessioned2025-08-21T18:00:09Z-
Data de disponibilização: dc.date.available2025-08-21T18:00:09Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-024-55647-9-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/297545-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/297545-
Descrição: dc.descriptionWhile onion cultivars, irrigation and soil and crop management have been given much attention in Brazil to boost onion yields, nutrient management at field scale is still challenging due to large dosage uncertainty. Our objective was to develop an accurate feature-based fertilization model for onion crops. We assembled climatic, edaphic, and managerial features as well as tissue tests into a database of 1182 observations from multi-environment fertilizer trials conducted during 13 years in southern Brazil. The complexity of onion cropping systems was captured by machine learning (ML) methods. The RReliefF ranking algorithm showed that the split-N dosage and soil tests for micronutrients and S were the most relevant features to predict bulb yield. The decision-tree random forest and extreme gradient boosting models were accurate to predict bulb yield from the relevant predictors (R2 > 90%). As shown by the gain ratio, foliar nutrient standards for nutritionally balanced and high-yielding specimens producing > 50 Mg bulb ha−1 set apart by the ML classification models differed among cultivars. Cultivar × environment interactions support documenting local nutrient diagnosis. The split-N dosage was the most relevant controllable feature to run future universality tests set to assess models’ ability to generalize to growers’ fields.-
Descrição: dc.descriptionCaçador Experimental Station Research and Rural Extension of Santa Catarina (Epagri) Epagri, Abílio Franco Street, 1500, Santa Catarina-
Descrição: dc.descriptionItuporanga Experimental Station Research and Rural Extension of Santa Catarina (Epagri) Epagri, Lageado Águas Negras General Road, Santa Catarina-
Descrição: dc.descriptionDepartment of Soil Federal University of Santa Maria, Ave. Roraima, 1000, Building 42, RS-
Descrição: dc.descriptionUniversity Alto Vale do Rio do Peixe Uniarp, Victor Baptista Adami Street, 800, Santa Catarina-
Descrição: dc.descriptionState University Paulista “Julio Mesquita Filho”, Campus Registro. Registro, Av. Nelson Brihi Badur, 430-
Descrição: dc.descriptionDepartment of Soils and Agrifood Engineering Laval University-
Descrição: dc.descriptionState University Paulista “Julio Mesquita Filho”, Campus Registro. Registro, Av. Nelson Brihi Badur, 430-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleFeature-specific nutrient management of onion (Allium cepa) using machine learning and compositional methods-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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