Bayesian network: a simplified approach for environmental similarity studies on maize
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Crop Breeding and Applied Biotechnology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332019000100070 |
Resumo: | Abstract The current methodologies used to evaluate environmental similarities do not allow the simultaneous analysis and categorization of the environments. The objective of this study was to verify the possibility of using the Bayesian network (BN) to detect similarities between environments for plant height, lodging, and grain yield in maize. Thirteen experimental varieties were grown in six environments to measure the traits plant height, lodging, and grain yield. The BN was constructed for each trait, using the Hill-Climbing algorithm. Results were compared with the simple part of the genotypes x environments interaction, clustering by the Lin’s method and by simple correlation between environments. The Lin’s method clustered environments with predominance of complex interaction for all traits. The BN is efficient to analyze environmental similarity for plant height and grain yield since it detected the highest correlations. The BN revealed no connections among the environments that presented predominance of complex interaction. |
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Crop Breeding and Applied Biotechnology |
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Bayesian network: a simplified approach for environmental similarity studies on maizeZea maysprediction methodenvironmental correlationgenotype x environment interactionAbstract The current methodologies used to evaluate environmental similarities do not allow the simultaneous analysis and categorization of the environments. The objective of this study was to verify the possibility of using the Bayesian network (BN) to detect similarities between environments for plant height, lodging, and grain yield in maize. Thirteen experimental varieties were grown in six environments to measure the traits plant height, lodging, and grain yield. The BN was constructed for each trait, using the Hill-Climbing algorithm. Results were compared with the simple part of the genotypes x environments interaction, clustering by the Lin’s method and by simple correlation between environments. The Lin’s method clustered environments with predominance of complex interaction for all traits. The BN is efficient to analyze environmental similarity for plant height and grain yield since it detected the highest correlations. The BN revealed no connections among the environments that presented predominance of complex interaction.Crop Breeding and Applied Biotechnology2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332019000100070Crop Breeding and Applied Biotechnology v.19 n.1 2019reponame:Crop Breeding and Applied Biotechnologyinstname:Sociedade Brasileira de Melhoramento de Plantasinstacron:CBAB10.1590/1984-70332019v19n1a10info:eu-repo/semantics/openAccessAmaral,Camila Baptista doOliveira,Gustavo Hugo Ferreira deEghrari,KianBuzinaro,RodolfoMôro,Gustavo Vittieng2019-04-08T00:00:00Zoai:scielo:S1984-70332019000100070Revistahttps://cbab.sbmp.org.br/#ONGhttps://old.scielo.br/oai/scielo-oai.phpcbabjournal@gmail.com||cbab@ufv.br1984-70331518-7853opendoar:2019-04-08T00:00Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantasfalse |
dc.title.none.fl_str_mv |
Bayesian network: a simplified approach for environmental similarity studies on maize |
title |
Bayesian network: a simplified approach for environmental similarity studies on maize |
spellingShingle |
Bayesian network: a simplified approach for environmental similarity studies on maize Amaral,Camila Baptista do Zea mays prediction method environmental correlation genotype x environment interaction |
title_short |
Bayesian network: a simplified approach for environmental similarity studies on maize |
title_full |
Bayesian network: a simplified approach for environmental similarity studies on maize |
title_fullStr |
Bayesian network: a simplified approach for environmental similarity studies on maize |
title_full_unstemmed |
Bayesian network: a simplified approach for environmental similarity studies on maize |
title_sort |
Bayesian network: a simplified approach for environmental similarity studies on maize |
author |
Amaral,Camila Baptista do |
author_facet |
Amaral,Camila Baptista do Oliveira,Gustavo Hugo Ferreira de Eghrari,Kian Buzinaro,Rodolfo Môro,Gustavo Vitti |
author_role |
author |
author2 |
Oliveira,Gustavo Hugo Ferreira de Eghrari,Kian Buzinaro,Rodolfo Môro,Gustavo Vitti |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Amaral,Camila Baptista do Oliveira,Gustavo Hugo Ferreira de Eghrari,Kian Buzinaro,Rodolfo Môro,Gustavo Vitti |
dc.subject.por.fl_str_mv |
Zea mays prediction method environmental correlation genotype x environment interaction |
topic |
Zea mays prediction method environmental correlation genotype x environment interaction |
description |
Abstract The current methodologies used to evaluate environmental similarities do not allow the simultaneous analysis and categorization of the environments. The objective of this study was to verify the possibility of using the Bayesian network (BN) to detect similarities between environments for plant height, lodging, and grain yield in maize. Thirteen experimental varieties were grown in six environments to measure the traits plant height, lodging, and grain yield. The BN was constructed for each trait, using the Hill-Climbing algorithm. Results were compared with the simple part of the genotypes x environments interaction, clustering by the Lin’s method and by simple correlation between environments. The Lin’s method clustered environments with predominance of complex interaction for all traits. The BN is efficient to analyze environmental similarity for plant height and grain yield since it detected the highest correlations. The BN revealed no connections among the environments that presented predominance of complex interaction. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
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=S1984-70332019000100070 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332019000100070 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1984-70332019v19n1a10 |
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 |
Crop Breeding and Applied Biotechnology |
publisher.none.fl_str_mv |
Crop Breeding and Applied Biotechnology |
dc.source.none.fl_str_mv |
Crop Breeding and Applied Biotechnology v.19 n.1 2019 reponame:Crop Breeding and Applied Biotechnology instname:Sociedade Brasileira de Melhoramento de Plantas instacron:CBAB |
instname_str |
Sociedade Brasileira de Melhoramento de Plantas |
instacron_str |
CBAB |
institution |
CBAB |
reponame_str |
Crop Breeding and Applied Biotechnology |
collection |
Crop Breeding and Applied Biotechnology |
repository.name.fl_str_mv |
Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantas |
repository.mail.fl_str_mv |
cbabjournal@gmail.com||cbab@ufv.br |
_version_ |
1754209187639853056 |