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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/241948
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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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1844613287730216960 |
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13.070432 |