Prediction of soybean yield cultivated under subtropical conditions using artificial neural networks

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorMoreira, Adônis-
Autor(es): dc.creatorBonini Neto, Alfredo-
Autor(es): dc.creatorBonini, Carolina dos Santos Batista-
Autor(es): dc.creatorMoraes, Larissa A. C.-
Autor(es): dc.creatorHeinrichs, Reges-
Data de aceite: dc.date.accessioned2025-08-21T18:09:12Z-
Data de disponibilização: dc.date.available2025-08-21T18:09:12Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1002/agj2.21360-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249935-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249935-
Descrição: dc.descriptionMathematical models that incorporate biotic and abiotic attributes are important tools for improving fertilizer use efficiency and reducing production costs for soybean [Glycine max (L.) Merrill] crop. In this study, artificial neural networks (ANNs) were used to estimate soybean grain yield (GY) under subtropical conditions in Brazil from plant morphological and nutritional data collected from 16 cultivars in two growing seasons. The ANNs were adequately trained, with a mean squared error of approximately 10−5 between the outputs obtained (via ANN) and desired (via experimental field), equivalent to a mean percentage error of 70.1 kg ha−1 (1.6%), confirming their efficacy as a tool to estimate GY. Smaller plant height, higher foliar calcium, magnesium and chlorophyll concentrations, and greater numbers of grains per pod and branches per plant were associated with higher GY, whereas oil content, crude protein content, and foliar manganese and potassium concentrations had no predicted effects on GY.-
Descrição: dc.descriptionEmbrapa Soybean – Soil Science and Plant Nutrition-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University - mathematical modeling-
Descrição: dc.descriptionCollege of Agricultural and Technological Sciences São Paulo State University Júlio de Mesquita Filho – Crop Science-
Descrição: dc.descriptionEmbrapa Soybean – Plant Physiology-
Descrição: dc.descriptionSchool of Sciences and Engineering São Paulo State University - mathematical modeling-
Descrição: dc.descriptionCollege of Agricultural and Technological Sciences São Paulo State University Júlio de Mesquita Filho – Crop Science-
Idioma: dc.languageen-
Relação: dc.relationAgronomy Journal-
???dc.source???: dc.sourceScopus-
Título: dc.titlePrediction of soybean yield cultivated under subtropical conditions using artificial neural networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.