Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit

Detalhes bibliográficos
Autor(a) principal: Souza,André Oliveira
Data de Publicação: 2022
Outros Autores: Viana,Alexandre Pio, Silva,Fabyano Fonseca e, Azevedo,Camila Ferreira, Silva,Flavia Alves da, Silva,Fernando Higino Lima e
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000400501
Resumo: ABSTRACT Methods for genetic improvement of semi–perennial species, such as passion fruit, often involve large areas, unbalanced data, and lack of observations. Some strategies can be applied to solve these problems. In this work, different models and approaches were tested to improve the precision of estimates of genetic evaluation models for several characteristics of the passion fruit. A randomized block design (RBD) model was compared to a posteriori correction, adding two factors to the model (post–hoc blocking Row–Col). These models were also combined with the frequentist and Bayesian approaches to identify which combination yields the most accurate results. These approaches are part of a strategic plan in a perennial plant breeding program to select promising genitors of passion to compose the next selection cycle. For Bayesian, we tested two priors, defining different values for the distribution parameters of effect variances of the model. We also performed a cross–validation test to choose a priori values and compare the frequentist and Bayesian approaches using the root mean square error (RMSE) and the correlation between the predicted and observed values, called Predictive capacity of the model (PC). The model with the post–hoc blocking Row–Col design captured the spatial variability for productivity and number of fruits, directly affecting the experimental precision. Both approaches applied to the models showed a similar performance, with predictive capacity and selective efficiency leading to the selection of the same individuals.
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spelling Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruitREMLposterioriexperimental precisionABSTRACT Methods for genetic improvement of semi–perennial species, such as passion fruit, often involve large areas, unbalanced data, and lack of observations. Some strategies can be applied to solve these problems. In this work, different models and approaches were tested to improve the precision of estimates of genetic evaluation models for several characteristics of the passion fruit. A randomized block design (RBD) model was compared to a posteriori correction, adding two factors to the model (post–hoc blocking Row–Col). These models were also combined with the frequentist and Bayesian approaches to identify which combination yields the most accurate results. These approaches are part of a strategic plan in a perennial plant breeding program to select promising genitors of passion to compose the next selection cycle. For Bayesian, we tested two priors, defining different values for the distribution parameters of effect variances of the model. We also performed a cross–validation test to choose a priori values and compare the frequentist and Bayesian approaches using the root mean square error (RMSE) and the correlation between the predicted and observed values, called Predictive capacity of the model (PC). The model with the post–hoc blocking Row–Col design captured the spatial variability for productivity and number of fruits, directly affecting the experimental precision. Both approaches applied to the models showed a similar performance, with predictive capacity and selective efficiency leading to the selection of the same individuals.Escola Superior de Agricultura "Luiz de Queiroz"2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000400501Scientia Agricola v.79 n.4 2022reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USP10.1590/1678-992x-2020-0361info:eu-repo/semantics/openAccessSouza,André OliveiraViana,Alexandre PioSilva,Fabyano Fonseca eAzevedo,Camila FerreiraSilva,Flavia Alves daSilva,Fernando Higino Lima eeng2021-07-20T00:00:00Zoai:scielo:S0103-90162022000400501Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2021-07-20T00:00Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
title Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
spellingShingle Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
Souza,André Oliveira
REML
posteriori
experimental precision
title_short Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
title_full Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
title_fullStr Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
title_full_unstemmed Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
title_sort Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
author Souza,André Oliveira
author_facet Souza,André Oliveira
Viana,Alexandre Pio
Silva,Fabyano Fonseca e
Azevedo,Camila Ferreira
Silva,Flavia Alves da
Silva,Fernando Higino Lima e
author_role author
author2 Viana,Alexandre Pio
Silva,Fabyano Fonseca e
Azevedo,Camila Ferreira
Silva,Flavia Alves da
Silva,Fernando Higino Lima e
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Souza,André Oliveira
Viana,Alexandre Pio
Silva,Fabyano Fonseca e
Azevedo,Camila Ferreira
Silva,Flavia Alves da
Silva,Fernando Higino Lima e
dc.subject.por.fl_str_mv REML
posteriori
experimental precision
topic REML
posteriori
experimental precision
description ABSTRACT Methods for genetic improvement of semi–perennial species, such as passion fruit, often involve large areas, unbalanced data, and lack of observations. Some strategies can be applied to solve these problems. In this work, different models and approaches were tested to improve the precision of estimates of genetic evaluation models for several characteristics of the passion fruit. A randomized block design (RBD) model was compared to a posteriori correction, adding two factors to the model (post–hoc blocking Row–Col). These models were also combined with the frequentist and Bayesian approaches to identify which combination yields the most accurate results. These approaches are part of a strategic plan in a perennial plant breeding program to select promising genitors of passion to compose the next selection cycle. For Bayesian, we tested two priors, defining different values for the distribution parameters of effect variances of the model. We also performed a cross–validation test to choose a priori values and compare the frequentist and Bayesian approaches using the root mean square error (RMSE) and the correlation between the predicted and observed values, called Predictive capacity of the model (PC). The model with the post–hoc blocking Row–Col design captured the spatial variability for productivity and number of fruits, directly affecting the experimental precision. Both approaches applied to the models showed a similar performance, with predictive capacity and selective efficiency leading to the selection of the same individuals.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
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=S0103-90162022000400501
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000400501
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1678-992x-2020-0361
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 Escola Superior de Agricultura "Luiz de Queiroz"
publisher.none.fl_str_mv Escola Superior de Agricultura "Luiz de Queiroz"
dc.source.none.fl_str_mv Scientia Agricola v.79 n.4 2022
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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