Estimating gypsum equirement under no-till based on machine learning technique
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista ciência agronômica (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902015000200250 |
Resumo: | Chemical stratification occurs under no-till systems, including pH, considering that higher levels are formed from the soil surface towards the deeper layers. The subsoil acidity is a limiting factor of the yield. Gypsum has been suggested when subsoil acidity limits the crops root growth, i.e., when the calcium (Ca) level is low and/or the aluminum (Al) level is toxic in the subsoil layers. However, there are doubts about the more efficient methods to estimate the gypsum requirement. This study was carried out to develop numerical models to estimate the gypsum requirement in soils under no-till system by the use of Machine Learning techniques. Computational analyses of the dataset were made applying the M5'Rules algorithm, based on regression models. The dataset comprised of soil chemical properties collected from experiments under no-till that received gypsum rates on the soil surface, throughout eight years after the application, in Southern Brazil. The results showed that the numerical models generated by rule induction M5'Rules algorithm were positively useful contributing for estimate the gypsum requirements under no-till. The models showed that Ca saturation in the effective cation exchange capacity (ECEC) was a more important attribute than Al saturation to estimate gypsum requirement in no-till soils. |
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Estimating gypsum equirement under no-till based on machine learning techniqueRule inductionCalciumAluminumSubsoil acidityPhosphogypsumChemical stratification occurs under no-till systems, including pH, considering that higher levels are formed from the soil surface towards the deeper layers. The subsoil acidity is a limiting factor of the yield. Gypsum has been suggested when subsoil acidity limits the crops root growth, i.e., when the calcium (Ca) level is low and/or the aluminum (Al) level is toxic in the subsoil layers. However, there are doubts about the more efficient methods to estimate the gypsum requirement. This study was carried out to develop numerical models to estimate the gypsum requirement in soils under no-till system by the use of Machine Learning techniques. Computational analyses of the dataset were made applying the M5'Rules algorithm, based on regression models. The dataset comprised of soil chemical properties collected from experiments under no-till that received gypsum rates on the soil surface, throughout eight years after the application, in Southern Brazil. The results showed that the numerical models generated by rule induction M5'Rules algorithm were positively useful contributing for estimate the gypsum requirements under no-till. The models showed that Ca saturation in the effective cation exchange capacity (ECEC) was a more important attribute than Al saturation to estimate gypsum requirement in no-till soils.Universidade Federal do Ceará2015-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902015000200250Revista Ciência Agronômica v.46 n.2 2015reponame:Revista ciência agronômica (Online)instname:Universidade Federal do Ceará (UFC)instacron:UFC10.5935/1806-6690.20150004info:eu-repo/semantics/openAccessGuimarães,Alaine MargareteCaires,Eduardo FáveroSilva,Karine Sato daRocha,José Carlos Ferreira daeng2015-10-09T00:00:00Zoai:scielo:S1806-66902015000200250Revistahttp://www.ccarevista.ufc.br/PUBhttps://old.scielo.br/oai/scielo-oai.php||alekdutra@ufc.br|| ccarev@ufc.br1806-66900045-6888opendoar:2015-10-09T00:00Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC)false |
dc.title.none.fl_str_mv |
Estimating gypsum equirement under no-till based on machine learning technique |
title |
Estimating gypsum equirement under no-till based on machine learning technique |
spellingShingle |
Estimating gypsum equirement under no-till based on machine learning technique Guimarães,Alaine Margarete Rule induction Calcium Aluminum Subsoil acidity Phosphogypsum |
title_short |
Estimating gypsum equirement under no-till based on machine learning technique |
title_full |
Estimating gypsum equirement under no-till based on machine learning technique |
title_fullStr |
Estimating gypsum equirement under no-till based on machine learning technique |
title_full_unstemmed |
Estimating gypsum equirement under no-till based on machine learning technique |
title_sort |
Estimating gypsum equirement under no-till based on machine learning technique |
author |
Guimarães,Alaine Margarete |
author_facet |
Guimarães,Alaine Margarete Caires,Eduardo Fávero Silva,Karine Sato da Rocha,José Carlos Ferreira da |
author_role |
author |
author2 |
Caires,Eduardo Fávero Silva,Karine Sato da Rocha,José Carlos Ferreira da |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Guimarães,Alaine Margarete Caires,Eduardo Fávero Silva,Karine Sato da Rocha,José Carlos Ferreira da |
dc.subject.por.fl_str_mv |
Rule induction Calcium Aluminum Subsoil acidity Phosphogypsum |
topic |
Rule induction Calcium Aluminum Subsoil acidity Phosphogypsum |
description |
Chemical stratification occurs under no-till systems, including pH, considering that higher levels are formed from the soil surface towards the deeper layers. The subsoil acidity is a limiting factor of the yield. Gypsum has been suggested when subsoil acidity limits the crops root growth, i.e., when the calcium (Ca) level is low and/or the aluminum (Al) level is toxic in the subsoil layers. However, there are doubts about the more efficient methods to estimate the gypsum requirement. This study was carried out to develop numerical models to estimate the gypsum requirement in soils under no-till system by the use of Machine Learning techniques. Computational analyses of the dataset were made applying the M5'Rules algorithm, based on regression models. The dataset comprised of soil chemical properties collected from experiments under no-till that received gypsum rates on the soil surface, throughout eight years after the application, in Southern Brazil. The results showed that the numerical models generated by rule induction M5'Rules algorithm were positively useful contributing for estimate the gypsum requirements under no-till. The models showed that Ca saturation in the effective cation exchange capacity (ECEC) was a more important attribute than Al saturation to estimate gypsum requirement in no-till soils. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-06-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902015000200250 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902015000200250 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5935/1806-6690.20150004 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Universidade Federal do Ceará |
publisher.none.fl_str_mv |
Universidade Federal do Ceará |
dc.source.none.fl_str_mv |
Revista Ciência Agronômica v.46 n.2 2015 reponame:Revista ciência agronômica (Online) instname:Universidade Federal do Ceará (UFC) instacron:UFC |
instname_str |
Universidade Federal do Ceará (UFC) |
instacron_str |
UFC |
institution |
UFC |
reponame_str |
Revista ciência agronômica (Online) |
collection |
Revista ciência agronômica (Online) |
repository.name.fl_str_mv |
Revista ciência agronômica (Online) - Universidade Federal do Ceará (UFC) |
repository.mail.fl_str_mv |
||alekdutra@ufc.br|| ccarev@ufc.br |
_version_ |
1750297487584788480 |