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

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dc.contributor.authorMoreira, Adônis
dc.contributor.authorBonini Neto, Alfredo [UNESP]
dc.contributor.authorBonini, Carolina dos Santos Batista [UNESP]
dc.contributor.authorMoraes, Larissa A. C.
dc.contributor.authorHeinrichs, Reges [UNESP]
dc.contributor.institutionEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T16:13:16Z
dc.date.available2023-07-29T16:13:16Z
dc.date.issued2023-01-01
dc.description.abstractMathematical 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.en
dc.description.affiliationEmbrapa Soybean – Soil Science and Plant Nutrition
dc.description.affiliationSchool of Sciences and Engineering São Paulo State University - mathematical modeling
dc.description.affiliationCollege of Agricultural and Technological Sciences São Paulo State University Júlio de Mesquita Filho – Crop Science
dc.description.affiliationEmbrapa Soybean – Plant Physiology
dc.description.affiliationUnespSchool of Sciences and Engineering São Paulo State University - mathematical modeling
dc.description.affiliationUnespCollege of Agricultural and Technological Sciences São Paulo State University Júlio de Mesquita Filho – Crop Science
dc.identifierhttp://dx.doi.org/10.1002/agj2.21360
dc.identifier.citationAgronomy Journal.
dc.identifier.doi10.1002/agj2.21360
dc.identifier.issn1435-0645
dc.identifier.issn0002-1962
dc.identifier.scopus2-s2.0-85158826497
dc.identifier.urihttp://hdl.handle.net/11449/249935
dc.language.isoeng
dc.relation.ispartofAgronomy Journal
dc.sourceScopus
dc.titlePrediction of soybean yield cultivated under subtropical conditions using artificial neural networksen
dc.typeArtigo
unesp.author.orcid0000-0003-4023-5990[1]
unesp.departmentZootecnia - FCATpt

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Dracena - FCAT - Faculdade de Ciências Agrárias e Tecnológicas