Minimum number of measurements for evaluating soursop (Annona muricata L.) yield
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | LOCUS Repositório Institucional da UFV |
Texto Completo: | http://dx.doi.org/10.4238/gmr16029594 http://www.locus.ufv.br/handle/123456789/12905 |
Resumo: | Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages. |
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Minimum number of measurements for evaluating soursop (Annona muricata L.) yieldAnnona muricataBreedingRepeatabilityBiometricsRepeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages.Genetics and Molecular Research2017-11-08T15:35:33Z2017-11-08T15:35:33Z2017-05-31info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlepdfapplication/pdf16765680http://dx.doi.org/10.4238/gmr16029594http://www.locus.ufv.br/handle/123456789/12905engv. 16, n. 2, gmr16029594, May 2017Sánchez, C.F.B.Teodoro, P.E.Londoño, S.Silva, L.A.Peixoto, L.A.Bhering, L.L.info:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFV2024-07-12T07:11:40Zoai:locus.ufv.br:123456789/12905Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452024-07-12T07:11:40LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false |
dc.title.none.fl_str_mv |
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield |
title |
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield |
spellingShingle |
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield Sánchez, C.F.B. Annona muricata Breeding Repeatability Biometrics |
title_short |
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield |
title_full |
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield |
title_fullStr |
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield |
title_full_unstemmed |
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield |
title_sort |
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield |
author |
Sánchez, C.F.B. |
author_facet |
Sánchez, C.F.B. Teodoro, P.E. Londoño, S. Silva, L.A. Peixoto, L.A. Bhering, L.L. |
author_role |
author |
author2 |
Teodoro, P.E. Londoño, S. Silva, L.A. Peixoto, L.A. Bhering, L.L. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Sánchez, C.F.B. Teodoro, P.E. Londoño, S. Silva, L.A. Peixoto, L.A. Bhering, L.L. |
dc.subject.por.fl_str_mv |
Annona muricata Breeding Repeatability Biometrics |
topic |
Annona muricata Breeding Repeatability Biometrics |
description |
Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-11-08T15:35:33Z 2017-11-08T15:35:33Z 2017-05-31 |
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 |
16765680 http://dx.doi.org/10.4238/gmr16029594 http://www.locus.ufv.br/handle/123456789/12905 |
identifier_str_mv |
16765680 |
url |
http://dx.doi.org/10.4238/gmr16029594 http://www.locus.ufv.br/handle/123456789/12905 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
v. 16, n. 2, gmr16029594, May 2017 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
pdf application/pdf |
dc.publisher.none.fl_str_mv |
Genetics and Molecular Research |
publisher.none.fl_str_mv |
Genetics and Molecular Research |
dc.source.none.fl_str_mv |
reponame:LOCUS Repositório Institucional da UFV instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
UFV |
institution |
UFV |
reponame_str |
LOCUS Repositório Institucional da UFV |
collection |
LOCUS Repositório Institucional da UFV |
repository.name.fl_str_mv |
LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV) |
repository.mail.fl_str_mv |
fabiojreis@ufv.br |
_version_ |
1817559906925936640 |