Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes
Autor(a) principal: | |
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Data de Publicação: | 2020 |
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=S1984-70332020000300204 |
Resumo: | Abstract Forage plant breeders often use visual scores to assess agronomic traits because of the costs associated with in-depth phenotyping in the initial stages of breeding cycles. The aim of this study was to investigate the impact of the number of graders on the effectiveness of indirect selection of high-yielding genotypes and determine an optimal number of graders in the early-stage trials of Urochloa ruziziensis. For that purpose, five graders assessed 2.219 U. ruziziensis genotypes in an augmented block design. Biomass production and vigor scores were evaluated in two cuts and were analyzed using a linear mixed model approach. Vigor scores were analyzed considering each grader's score and the combinations of two, three, four, and five graders. Genetic variance was significant for both traits. Visual evaluation was effective in identifying productive genotypes based on the statistical criteria. The optimal number of graders for indirect selection of high-yielding U. ruziziensis genotypes is three. |
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Crop Breeding and Applied Biotechnology |
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Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypesBrachiaria ruziziensisvisual selectionaccuracyforage breedingAbstract Forage plant breeders often use visual scores to assess agronomic traits because of the costs associated with in-depth phenotyping in the initial stages of breeding cycles. The aim of this study was to investigate the impact of the number of graders on the effectiveness of indirect selection of high-yielding genotypes and determine an optimal number of graders in the early-stage trials of Urochloa ruziziensis. For that purpose, five graders assessed 2.219 U. ruziziensis genotypes in an augmented block design. Biomass production and vigor scores were evaluated in two cuts and were analyzed using a linear mixed model approach. Vigor scores were analyzed considering each grader's score and the combinations of two, three, four, and five graders. Genetic variance was significant for both traits. Visual evaluation was effective in identifying productive genotypes based on the statistical criteria. The optimal number of graders for indirect selection of high-yielding U. ruziziensis genotypes is three.Crop Breeding and Applied Biotechnology2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332020000300204Crop Breeding and Applied Biotechnology v.20 n.3 2020reponame:Crop Breeding and Applied Biotechnologyinstname:Sociedade Brasileira de Melhoramento de Plantasinstacron:CBAB10.1590/1984-70332020v20n3a48info:eu-repo/semantics/openAccessFonseca,Jales Mendes OliveiraNunes,José Airton RodriguesGonçalves,Flavia Maria AvelarSouza Sobrinho,Fausto deBenites,Flávio Rodrigo GandolfiLima Teixeira,Davi Henriqueeng2020-10-13T00:00:00Zoai:scielo:S1984-70332020000300204Revistahttps://cbab.sbmp.org.br/#ONGhttps://old.scielo.br/oai/scielo-oai.phpcbabjournal@gmail.com||cbab@ufv.br1984-70331518-7853opendoar:2020-10-13T00:00Crop Breeding and Applied Biotechnology - Sociedade Brasileira de Melhoramento de Plantasfalse |
dc.title.none.fl_str_mv |
Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes |
title |
Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes |
spellingShingle |
Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes Fonseca,Jales Mendes Oliveira Brachiaria ruziziensis visual selection accuracy forage breeding |
title_short |
Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes |
title_full |
Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes |
title_fullStr |
Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes |
title_full_unstemmed |
Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes |
title_sort |
Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes |
author |
Fonseca,Jales Mendes Oliveira |
author_facet |
Fonseca,Jales Mendes Oliveira Nunes,José Airton Rodrigues Gonçalves,Flavia Maria Avelar Souza Sobrinho,Fausto de Benites,Flávio Rodrigo Gandolfi Lima Teixeira,Davi Henrique |
author_role |
author |
author2 |
Nunes,José Airton Rodrigues Gonçalves,Flavia Maria Avelar Souza Sobrinho,Fausto de Benites,Flávio Rodrigo Gandolfi Lima Teixeira,Davi Henrique |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Fonseca,Jales Mendes Oliveira Nunes,José Airton Rodrigues Gonçalves,Flavia Maria Avelar Souza Sobrinho,Fausto de Benites,Flávio Rodrigo Gandolfi Lima Teixeira,Davi Henrique |
dc.subject.por.fl_str_mv |
Brachiaria ruziziensis visual selection accuracy forage breeding |
topic |
Brachiaria ruziziensis visual selection accuracy forage breeding |
description |
Abstract Forage plant breeders often use visual scores to assess agronomic traits because of the costs associated with in-depth phenotyping in the initial stages of breeding cycles. The aim of this study was to investigate the impact of the number of graders on the effectiveness of indirect selection of high-yielding genotypes and determine an optimal number of graders in the early-stage trials of Urochloa ruziziensis. For that purpose, five graders assessed 2.219 U. ruziziensis genotypes in an augmented block design. Biomass production and vigor scores were evaluated in two cuts and were analyzed using a linear mixed model approach. Vigor scores were analyzed considering each grader's score and the combinations of two, three, four, and five graders. Genetic variance was significant for both traits. Visual evaluation was effective in identifying productive genotypes based on the statistical criteria. The optimal number of graders for indirect selection of high-yielding U. ruziziensis genotypes is three. |
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=S1984-70332020000300204 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332020000300204 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1984-70332020v20n3a48 |
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.20 n.3 2020 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_ |
1754209188083400704 |