Logistic model to selection of energy cane clones

Detalhes bibliográficos
Autor(a) principal: Borella,Juliane
Data de Publicação: 2020
Outros Autores: Trautenmüller,Jonathan William, Brasileiro,Bruno Portela, Oliveira,Ricardo Augusto de, Bespalhok Filho,João Carlos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000900201
Resumo: ABSTRACT: Logistic regression analysis is a technique that may aid genetic breeding programs in the selection of clones, especially in the early stages where experimental accuracy is low. This research aimed to identify the most important agronomic traits for energy cane clonal selection, and to verify the efficiency of the logistic model in predicting the genotypes to be selected. Evaluations were carried out on 220 clones in the first ratoon. The data were subjected to binary logistic regression analysis. Stalk number per meter was the most important trait in the selection of energy cane clones. In addition, plants with lower grade for smut incidence had a greater chance of being selected. The predictive capacities of the qualitative and quantitative models were 94% and 88%, respectively. The use of a qualitative model proved to be effective at predicting the number of energy cane genotypes to be selected and could be used as a selection strategy.
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spelling Logistic model to selection of energy cane clonesgenetic improvementbiomassSaccharum sppABSTRACT: Logistic regression analysis is a technique that may aid genetic breeding programs in the selection of clones, especially in the early stages where experimental accuracy is low. This research aimed to identify the most important agronomic traits for energy cane clonal selection, and to verify the efficiency of the logistic model in predicting the genotypes to be selected. Evaluations were carried out on 220 clones in the first ratoon. The data were subjected to binary logistic regression analysis. Stalk number per meter was the most important trait in the selection of energy cane clones. In addition, plants with lower grade for smut incidence had a greater chance of being selected. The predictive capacities of the qualitative and quantitative models were 94% and 88%, respectively. The use of a qualitative model proved to be effective at predicting the number of energy cane genotypes to be selected and could be used as a selection strategy.Universidade Federal de Santa Maria2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000900201Ciência Rural v.50 n.9 2020reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20190750info:eu-repo/semantics/openAccessBorella,JulianeTrautenmüller,Jonathan WilliamBrasileiro,Bruno PortelaOliveira,Ricardo Augusto deBespalhok Filho,João Carloseng2020-07-20T00:00:00ZRevista
dc.title.none.fl_str_mv Logistic model to selection of energy cane clones
title Logistic model to selection of energy cane clones
spellingShingle Logistic model to selection of energy cane clones
Borella,Juliane
genetic improvement
biomass
Saccharum spp
title_short Logistic model to selection of energy cane clones
title_full Logistic model to selection of energy cane clones
title_fullStr Logistic model to selection of energy cane clones
title_full_unstemmed Logistic model to selection of energy cane clones
title_sort Logistic model to selection of energy cane clones
author Borella,Juliane
author_facet Borella,Juliane
Trautenmüller,Jonathan William
Brasileiro,Bruno Portela
Oliveira,Ricardo Augusto de
Bespalhok Filho,João Carlos
author_role author
author2 Trautenmüller,Jonathan William
Brasileiro,Bruno Portela
Oliveira,Ricardo Augusto de
Bespalhok Filho,João Carlos
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Borella,Juliane
Trautenmüller,Jonathan William
Brasileiro,Bruno Portela
Oliveira,Ricardo Augusto de
Bespalhok Filho,João Carlos
dc.subject.por.fl_str_mv genetic improvement
biomass
Saccharum spp
topic genetic improvement
biomass
Saccharum spp
description ABSTRACT: Logistic regression analysis is a technique that may aid genetic breeding programs in the selection of clones, especially in the early stages where experimental accuracy is low. This research aimed to identify the most important agronomic traits for energy cane clonal selection, and to verify the efficiency of the logistic model in predicting the genotypes to be selected. Evaluations were carried out on 220 clones in the first ratoon. The data were subjected to binary logistic regression analysis. Stalk number per meter was the most important trait in the selection of energy cane clones. In addition, plants with lower grade for smut incidence had a greater chance of being selected. The predictive capacities of the qualitative and quantitative models were 94% and 88%, respectively. The use of a qualitative model proved to be effective at predicting the number of energy cane genotypes to be selected and could be used as a selection strategy.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
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=S0103-84782020000900201
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000900201
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20190750
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 de Santa Maria
publisher.none.fl_str_mv Universidade Federal de Santa Maria
dc.source.none.fl_str_mv Ciência Rural v.50 n.9 2020
reponame:Ciência Rural
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Ciência Rural
collection Ciência Rural
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1749140555128897536

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