Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorAgricultural Research and Rural Extension Agency of Santa Catarina (Epagri)-
Autor(es): dc.contributorLaval University-
Autor(es): dc.contributorFederal University of Santa Maria-
Autor(es): dc.contributorGovernment Administrative Center-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Alto Vale do Rio do Peixe-
Autor(es): dc.creatorHahn, Leandro-
Autor(es): dc.creatorParent, Léon-Étienne-
Autor(es): dc.creatorPaviani, Angela Cristina-
Autor(es): dc.creatorFeltrim, Anderson Luiz-
Autor(es): dc.creatorWamser, Anderson Fernando-
Autor(es): dc.creatorRozane, Danilo Eduardo-
Autor(es): dc.creatorEnder, Marcos Matos-
Autor(es): dc.creatorGrando, Douglas Luiz-
Autor(es): dc.creatorMoura-Bueno, Jean Michel-
Autor(es): dc.creatorBrunetto, Gustavo-
Data de aceite: dc.date.accessioned2025-08-21T23:32:16Z-
Data de disponibilização: dc.date.available2025-08-21T23:32:16Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1371/journal.pone.0268516-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240992-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240992-
Descrição: dc.descriptionBrazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers’ observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production.-
Descrição: dc.descriptionNatural Sciences and Engineering Research Council of Canada-
Descrição: dc.descriptionCaçador Experimental Station Agricultural Research and Rural Extension Agency of Santa Catarina (Epagri), Caçador-
Descrição: dc.descriptionDepartment of Soils and Agrifood Engineering Laval University-
Descrição: dc.descriptionDepartment of Soil Federal University of Santa Maria, Rio Grande do Sul-
Descrição: dc.descriptionExecutive Secretariat for the Environment Government Administrative Center, Santa Catarina-
Descrição: dc.descriptionState University of Paulista “Julio Mesquita Filho” Campus Registro-
Descrição: dc.descriptionUniversity of Alto Vale do Rio do Peixe, Caçador-
Descrição: dc.descriptionState University of Paulista “Julio Mesquita Filho” Campus Registro-
Descrição: dc.descriptionNatural Sciences and Engineering Research Council of Canada: NSERC-2254-
Idioma: dc.languageen-
Relação: dc.relationPLoS ONE-
???dc.source???: dc.sourceScopus-
Título: dc.titleGarlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.