Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity

Autores
Casanoves, Fernando; Macchiavelli, R.; Balzarini, Monica Graciela
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Multienvironment Trials (MET) are used to make cultivar recommendations about genotypes in plant breeding programs. Because of the presence of genotype × environment interaction, METs are usually conducted in multiple environments using designs that involve several replications per environment. Blocking of plots within each trial enables one to account for between plot variation. To improve the comparison of genotype means, taking into account within-trial spatial correlation as well as between-trial residual variance heterogeneity, alternative mixed models can be used. The objective of this study was to compare several spatial models, including or excluding heterogeneity of residual variances for cultivar evaluation in a set of independent peanut (Arachis hypogaea L.) METs. The modeling impact was evaluated by comparing genotype means from each trial. A series of 18 METs from a peanut breeding program, as according to a randomized complete block design (RCBD) at each location, were simultaneously fitted by (i) a classic analysis of variance model for an RCBD with blocks random and (ii) mixed models incorporating spatial correlation through isotropic and anisotropic covariance structures for the error terms (power correlation function) and including homogenous and heterogeneous residual variances to take into account the different environments having different precision. Results suggest that the model with stationary anisotropic error structure AR1×AR1 within each environment and heterogeneous residual variances constitutes a good alternative analysis for METs, but it was not always better than the RCBD models for peanut. Differences were found between long- and short-cycle peanut cultivars with respect to the best model.
Fil: Casanoves, Fernando. Centro Agronómico Tropical de Investigación y Enseñanza de Tecnología Agropecuaria; Puerto Rico
Fil: Macchiavelli, R.. Universidad de Puerto Rico; Puerto Rico
Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Materia
Error variation in multienvironment peanut trials
Spatial correlation
Multienvironment Trials
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/241948

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network_name_str CONICET Digital (CONICET)
spelling Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial HeterogeneityCasanoves, FernandoMacchiavelli, R.Balzarini, Monica GracielaError variation in multienvironment peanut trialsSpatial correlationMultienvironment Trialshttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1Multienvironment Trials (MET) are used to make cultivar recommendations about genotypes in plant breeding programs. Because of the presence of genotype × environment interaction, METs are usually conducted in multiple environments using designs that involve several replications per environment. Blocking of plots within each trial enables one to account for between plot variation. To improve the comparison of genotype means, taking into account within-trial spatial correlation as well as between-trial residual variance heterogeneity, alternative mixed models can be used. The objective of this study was to compare several spatial models, including or excluding heterogeneity of residual variances for cultivar evaluation in a set of independent peanut (Arachis hypogaea L.) METs. The modeling impact was evaluated by comparing genotype means from each trial. A series of 18 METs from a peanut breeding program, as according to a randomized complete block design (RCBD) at each location, were simultaneously fitted by (i) a classic analysis of variance model for an RCBD with blocks random and (ii) mixed models incorporating spatial correlation through isotropic and anisotropic covariance structures for the error terms (power correlation function) and including homogenous and heterogeneous residual variances to take into account the different environments having different precision. Results suggest that the model with stationary anisotropic error structure AR1×AR1 within each environment and heterogeneous residual variances constitutes a good alternative analysis for METs, but it was not always better than the RCBD models for peanut. Differences were found between long- and short-cycle peanut cultivars with respect to the best model.Fil: Casanoves, Fernando. Centro Agronómico Tropical de Investigación y Enseñanza de Tecnología Agropecuaria; Puerto RicoFil: Macchiavelli, R.. Universidad de Puerto Rico; Puerto RicoFil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaCrop Science Society of America2005-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/241948Casanoves, Fernando; Macchiavelli, R.; Balzarini, Monica Graciela; Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity; Crop Science Society of America; Crop Science; 45; 5; 9-2005; 1927-19330011-183XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://acsess.onlinelibrary.wiley.com/doi/10.2135/cropsci2004.0547info:eu-repo/semantics/altIdentifier/doi/10.2135/cropsci2004.0547info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:40:41Zoai:ri.conicet.gov.ar:11336/241948instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:40:41.584CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity
title Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity
spellingShingle Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity
Casanoves, Fernando
Error variation in multienvironment peanut trials
Spatial correlation
Multienvironment Trials
title_short Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity
title_full Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity
title_fullStr Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity
title_full_unstemmed Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity
title_sort Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity
dc.creator.none.fl_str_mv Casanoves, Fernando
Macchiavelli, R.
Balzarini, Monica Graciela
author Casanoves, Fernando
author_facet Casanoves, Fernando
Macchiavelli, R.
Balzarini, Monica Graciela
author_role author
author2 Macchiavelli, R.
Balzarini, Monica Graciela
author2_role author
author
dc.subject.none.fl_str_mv Error variation in multienvironment peanut trials
Spatial correlation
Multienvironment Trials
topic Error variation in multienvironment peanut trials
Spatial correlation
Multienvironment Trials
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Multienvironment Trials (MET) are used to make cultivar recommendations about genotypes in plant breeding programs. Because of the presence of genotype × environment interaction, METs are usually conducted in multiple environments using designs that involve several replications per environment. Blocking of plots within each trial enables one to account for between plot variation. To improve the comparison of genotype means, taking into account within-trial spatial correlation as well as between-trial residual variance heterogeneity, alternative mixed models can be used. The objective of this study was to compare several spatial models, including or excluding heterogeneity of residual variances for cultivar evaluation in a set of independent peanut (Arachis hypogaea L.) METs. The modeling impact was evaluated by comparing genotype means from each trial. A series of 18 METs from a peanut breeding program, as according to a randomized complete block design (RCBD) at each location, were simultaneously fitted by (i) a classic analysis of variance model for an RCBD with blocks random and (ii) mixed models incorporating spatial correlation through isotropic and anisotropic covariance structures for the error terms (power correlation function) and including homogenous and heterogeneous residual variances to take into account the different environments having different precision. Results suggest that the model with stationary anisotropic error structure AR1×AR1 within each environment and heterogeneous residual variances constitutes a good alternative analysis for METs, but it was not always better than the RCBD models for peanut. Differences were found between long- and short-cycle peanut cultivars with respect to the best model.
Fil: Casanoves, Fernando. Centro Agronómico Tropical de Investigación y Enseñanza de Tecnología Agropecuaria; Puerto Rico
Fil: Macchiavelli, R.. Universidad de Puerto Rico; Puerto Rico
Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
description Multienvironment Trials (MET) are used to make cultivar recommendations about genotypes in plant breeding programs. Because of the presence of genotype × environment interaction, METs are usually conducted in multiple environments using designs that involve several replications per environment. Blocking of plots within each trial enables one to account for between plot variation. To improve the comparison of genotype means, taking into account within-trial spatial correlation as well as between-trial residual variance heterogeneity, alternative mixed models can be used. The objective of this study was to compare several spatial models, including or excluding heterogeneity of residual variances for cultivar evaluation in a set of independent peanut (Arachis hypogaea L.) METs. The modeling impact was evaluated by comparing genotype means from each trial. A series of 18 METs from a peanut breeding program, as according to a randomized complete block design (RCBD) at each location, were simultaneously fitted by (i) a classic analysis of variance model for an RCBD with blocks random and (ii) mixed models incorporating spatial correlation through isotropic and anisotropic covariance structures for the error terms (power correlation function) and including homogenous and heterogeneous residual variances to take into account the different environments having different precision. Results suggest that the model with stationary anisotropic error structure AR1×AR1 within each environment and heterogeneous residual variances constitutes a good alternative analysis for METs, but it was not always better than the RCBD models for peanut. Differences were found between long- and short-cycle peanut cultivars with respect to the best model.
publishDate 2005
dc.date.none.fl_str_mv 2005-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/241948
Casanoves, Fernando; Macchiavelli, R.; Balzarini, Monica Graciela; Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity; Crop Science Society of America; Crop Science; 45; 5; 9-2005; 1927-1933
0011-183X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/241948
identifier_str_mv Casanoves, Fernando; Macchiavelli, R.; Balzarini, Monica Graciela; Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity; Crop Science Society of America; Crop Science; 45; 5; 9-2005; 1927-1933
0011-183X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://acsess.onlinelibrary.wiley.com/doi/10.2135/cropsci2004.0547
info:eu-repo/semantics/altIdentifier/doi/10.2135/cropsci2004.0547
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Crop Science Society of America
publisher.none.fl_str_mv Crop Science Society of America
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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