Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Fernandes, Mariele Monique Honorato [UNESP]
Data de Publicação: 2020
Outros Autores: Coelho, Anderson Prates [UNESP], Silva, Matheus Flavio da [UNESP], Bertonha, Rafael Scabello [UNESP], de Queiroz, Renata Fernandes [UNESP], Furlani, Carlos Eduardo Angeli [UNESP], Fernandes, Carolina [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.catena.2020.104505
http://hdl.handle.net/11449/201518
Resumo: One of the most used evaluations to monitor soil compaction is based on soil penetration resistance (SPR). However, since SPR is influenced by soil moisture, this evaluation performed in the field may often lead to incorrect interpretations. This study aimed to evaluate the accuracy of models in the estimation of soil penetration resistance with standardized moisture (SPRlab) based on soil penetration resistance measured in the field (SPRfield) and on soil moisture (U) and indicate the best soil layer and best model for that. Samplings were carried out in the years 2016 (72 points – 24 in each layer) and 2017 (270 points – 90 in each layer) in three soil layers (0.00–0.10 m, 0.10–0.20 m and 0.20–0.30 m). Samples collected in 2017 were used to calibrate the models and samples collected in 2016 were used to validate them. The models used were obtained by multiple linear and nonlinear regressions and artificial neural networks (ANNs). Models were calibrated with all sampled layers and stratified per layer. In the latter case, the samples were separated into two parts, one with the surface layer (0.00–0.10 m) and another with subsurface layers (0.10–0.20 m and 0.20–0.30 m). SPRlab can be estimated with high accuracy from SPRfield and U measured in the field. We recommend the use of ANN models (MLP or RBF) and soil samples collected from the 0.10–0.30 m layer for the monitoring of soil penetration resistance.
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spelling Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networksLinear modelsModels accuracyNonlinear modelsSoil moistureOne of the most used evaluations to monitor soil compaction is based on soil penetration resistance (SPR). However, since SPR is influenced by soil moisture, this evaluation performed in the field may often lead to incorrect interpretations. This study aimed to evaluate the accuracy of models in the estimation of soil penetration resistance with standardized moisture (SPRlab) based on soil penetration resistance measured in the field (SPRfield) and on soil moisture (U) and indicate the best soil layer and best model for that. Samplings were carried out in the years 2016 (72 points – 24 in each layer) and 2017 (270 points – 90 in each layer) in three soil layers (0.00–0.10 m, 0.10–0.20 m and 0.20–0.30 m). Samples collected in 2017 were used to calibrate the models and samples collected in 2016 were used to validate them. The models used were obtained by multiple linear and nonlinear regressions and artificial neural networks (ANNs). Models were calibrated with all sampled layers and stratified per layer. In the latter case, the samples were separated into two parts, one with the surface layer (0.00–0.10 m) and another with subsurface layers (0.10–0.20 m and 0.20–0.30 m). SPRlab can be estimated with high accuracy from SPRfield and U measured in the field. We recommend the use of ANN models (MLP or RBF) and soil samples collected from the 0.10–0.30 m layer for the monitoring of soil penetration resistance.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)São Paulo State University (Unesp) School of Agricultural and Veterinarian Sciences, Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900 JaboticabalSão Paulo State University (Unesp) School of Agricultural and Veterinarian Sciences, Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900 JaboticabalUniversidade Estadual Paulista (Unesp)Fernandes, Mariele Monique Honorato [UNESP]Coelho, Anderson Prates [UNESP]Silva, Matheus Flavio da [UNESP]Bertonha, Rafael Scabello [UNESP]de Queiroz, Renata Fernandes [UNESP]Furlani, Carlos Eduardo Angeli [UNESP]Fernandes, Carolina [UNESP]2020-12-12T02:34:36Z2020-12-12T02:34:36Z2020-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.catena.2020.104505Catena, v. 189.0341-8162http://hdl.handle.net/11449/20151810.1016/j.catena.2020.1045052-s2.0-85078889419Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCatenainfo:eu-repo/semantics/openAccess2024-06-07T14:24:19Zoai:repositorio.unesp.br:11449/201518Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:41:30.409463Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
title Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
spellingShingle Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
Fernandes, Mariele Monique Honorato [UNESP]
Linear models
Models accuracy
Nonlinear models
Soil moisture
title_short Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
title_full Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
title_fullStr Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
title_full_unstemmed Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
title_sort Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
author Fernandes, Mariele Monique Honorato [UNESP]
author_facet Fernandes, Mariele Monique Honorato [UNESP]
Coelho, Anderson Prates [UNESP]
Silva, Matheus Flavio da [UNESP]
Bertonha, Rafael Scabello [UNESP]
de Queiroz, Renata Fernandes [UNESP]
Furlani, Carlos Eduardo Angeli [UNESP]
Fernandes, Carolina [UNESP]
author_role author
author2 Coelho, Anderson Prates [UNESP]
Silva, Matheus Flavio da [UNESP]
Bertonha, Rafael Scabello [UNESP]
de Queiroz, Renata Fernandes [UNESP]
Furlani, Carlos Eduardo Angeli [UNESP]
Fernandes, Carolina [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Fernandes, Mariele Monique Honorato [UNESP]
Coelho, Anderson Prates [UNESP]
Silva, Matheus Flavio da [UNESP]
Bertonha, Rafael Scabello [UNESP]
de Queiroz, Renata Fernandes [UNESP]
Furlani, Carlos Eduardo Angeli [UNESP]
Fernandes, Carolina [UNESP]
dc.subject.por.fl_str_mv Linear models
Models accuracy
Nonlinear models
Soil moisture
topic Linear models
Models accuracy
Nonlinear models
Soil moisture
description One of the most used evaluations to monitor soil compaction is based on soil penetration resistance (SPR). However, since SPR is influenced by soil moisture, this evaluation performed in the field may often lead to incorrect interpretations. This study aimed to evaluate the accuracy of models in the estimation of soil penetration resistance with standardized moisture (SPRlab) based on soil penetration resistance measured in the field (SPRfield) and on soil moisture (U) and indicate the best soil layer and best model for that. Samplings were carried out in the years 2016 (72 points – 24 in each layer) and 2017 (270 points – 90 in each layer) in three soil layers (0.00–0.10 m, 0.10–0.20 m and 0.20–0.30 m). Samples collected in 2017 were used to calibrate the models and samples collected in 2016 were used to validate them. The models used were obtained by multiple linear and nonlinear regressions and artificial neural networks (ANNs). Models were calibrated with all sampled layers and stratified per layer. In the latter case, the samples were separated into two parts, one with the surface layer (0.00–0.10 m) and another with subsurface layers (0.10–0.20 m and 0.20–0.30 m). SPRlab can be estimated with high accuracy from SPRfield and U measured in the field. We recommend the use of ANN models (MLP or RBF) and soil samples collected from the 0.10–0.30 m layer for the monitoring of soil penetration resistance.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-12T02:34:36Z
2020-12-12T02:34:36Z
2020-06-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.catena.2020.104505
Catena, v. 189.
0341-8162
http://hdl.handle.net/11449/201518
10.1016/j.catena.2020.104505
2-s2.0-85078889419
url http://dx.doi.org/10.1016/j.catena.2020.104505
http://hdl.handle.net/11449/201518
identifier_str_mv Catena, v. 189.
0341-8162
10.1016/j.catena.2020.104505
2-s2.0-85078889419
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Catena
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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