Row–Col and Bayesian approach seeking to improve the predictive capacity and selection of passion fruit
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
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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oai:scielo:S0103-90162022000400501 |
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USP-18 |
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Scientia Agrícola (Online) |
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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 |
_version_ |
1748936466064474112 |