Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials

Autores
Ostengo, Santiago; Cuenya, María Inés; Balzarini, Monica Graciela
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Comparative multienvironment trials (METs) of sugarcane genotypes are frequently conducted using a randomized complete-block design (RCBD) within environments. However, blocking does not always ensure spatial variation control because of differential competition for resources among neighboring genotypes. Heterogeneity within trials may also cause between-trial heterocedasticity. This work aims to evaluate different Linear Mixed Models (LMMs) that enable the analysis of spatial correlation and residual heterogeneity among trials for both tons of cane per hectare (TCH) and sucrose content (SC%) in three series of mutienvironmetal trials conducted to evaluate advanced sugarcane clones. A total of 16 sugarcane trials conducted at different sites and in different crop cycles (age) were analyzed. Individual (age×site combination) and multienvironment analyses were performed. For SC%, the classic RCBD analysis within trial was adequate. For TCH, the anisotropic autoregressive model of order 1 (AR1×AR1) was the best to compare genotype means in most trials, allowing gain in information equivalent, on average, to the addition of 1.6 replicates to the original design. In the case of multienvironment analysis, the AR1×AR1 within-trial with among-trial heteroscedasticity was the best model to compare variety means, both for TCH and SC%. The results showed how a more appropriate mixed model would help avoid commission of judgment errors in sugarcane variety recommendations.
Fil: Ostengo, Santiago. Gobierno de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial Obispo Colombres; Argentina
Fil: Cuenya, María Inés. Gobierno de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial Obispo Colombres; Argentina
Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Materia
Anisotropic-Autoregressive Spatial Models
Efficiency
Experimental Design
Linear Mixed Model
Yield 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:11336/44887
Acceder
id CONICETDig_cd81026beeefe5ca20cfd61c815d7f8a
oai_identifier_str oai:ri.conicet.gov.ar:11336/44887
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment TrialsOstengo, SantiagoCuenya, María InésBalzarini, Monica GracielaAnisotropic-Autoregressive Spatial ModelsEfficiencyExperimental DesignLinear Mixed ModelYield Trialshttps://purl.org/becyt/ford/4.4https://purl.org/becyt/ford/4Comparative multienvironment trials (METs) of sugarcane genotypes are frequently conducted using a randomized complete-block design (RCBD) within environments. However, blocking does not always ensure spatial variation control because of differential competition for resources among neighboring genotypes. Heterogeneity within trials may also cause between-trial heterocedasticity. This work aims to evaluate different Linear Mixed Models (LMMs) that enable the analysis of spatial correlation and residual heterogeneity among trials for both tons of cane per hectare (TCH) and sucrose content (SC%) in three series of mutienvironmetal trials conducted to evaluate advanced sugarcane clones. A total of 16 sugarcane trials conducted at different sites and in different crop cycles (age) were analyzed. Individual (age×site combination) and multienvironment analyses were performed. For SC%, the classic RCBD analysis within trial was adequate. For TCH, the anisotropic autoregressive model of order 1 (AR1×AR1) was the best to compare genotype means in most trials, allowing gain in information equivalent, on average, to the addition of 1.6 replicates to the original design. In the case of multienvironment analysis, the AR1×AR1 within-trial with among-trial heteroscedasticity was the best model to compare variety means, both for TCH and SC%. The results showed how a more appropriate mixed model would help avoid commission of judgment errors in sugarcane variety recommendations.Fil: Ostengo, Santiago. Gobierno de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial Obispo Colombres; ArgentinaFil: Cuenya, María Inés. Gobierno de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial Obispo Colombres; ArgentinaFil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaTaylor & Francis, Inc.2015-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/44887Ostengo, Santiago; Cuenya, María Inés; Balzarini, Monica Graciela; Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials; Taylor & Francis, Inc.; Journal of Crop Improvement; 29; 1; 1-2015; 53-641542-7536CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1080/15427528.2014.965861info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1080/15427528.2014.965861info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:05:16Zoai:ri.conicet.gov.ar:11336/44887instacron: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:34982025-10-22 12:05:16.907CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials
title Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials
spellingShingle Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials
Ostengo, Santiago
Anisotropic-Autoregressive Spatial Models
Efficiency
Experimental Design
Linear Mixed Model
Yield Trials
title_short Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials
title_full Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials
title_fullStr Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials
title_full_unstemmed Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials
title_sort Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials
dc.creator.none.fl_str_mv Ostengo, Santiago
Cuenya, María Inés
Balzarini, Monica Graciela
author Ostengo, Santiago
author_facet Ostengo, Santiago
Cuenya, María Inés
Balzarini, Monica Graciela
author_role author
author2 Cuenya, María Inés
Balzarini, Monica Graciela
author2_role author
author
dc.subject.none.fl_str_mv Anisotropic-Autoregressive Spatial Models
Efficiency
Experimental Design
Linear Mixed Model
Yield Trials
topic Anisotropic-Autoregressive Spatial Models
Efficiency
Experimental Design
Linear Mixed Model
Yield Trials
purl_subject.fl_str_mv https://purl.org/becyt/ford/4.4
https://purl.org/becyt/ford/4
dc.description.none.fl_txt_mv Comparative multienvironment trials (METs) of sugarcane genotypes are frequently conducted using a randomized complete-block design (RCBD) within environments. However, blocking does not always ensure spatial variation control because of differential competition for resources among neighboring genotypes. Heterogeneity within trials may also cause between-trial heterocedasticity. This work aims to evaluate different Linear Mixed Models (LMMs) that enable the analysis of spatial correlation and residual heterogeneity among trials for both tons of cane per hectare (TCH) and sucrose content (SC%) in three series of mutienvironmetal trials conducted to evaluate advanced sugarcane clones. A total of 16 sugarcane trials conducted at different sites and in different crop cycles (age) were analyzed. Individual (age×site combination) and multienvironment analyses were performed. For SC%, the classic RCBD analysis within trial was adequate. For TCH, the anisotropic autoregressive model of order 1 (AR1×AR1) was the best to compare genotype means in most trials, allowing gain in information equivalent, on average, to the addition of 1.6 replicates to the original design. In the case of multienvironment analysis, the AR1×AR1 within-trial with among-trial heteroscedasticity was the best model to compare variety means, both for TCH and SC%. The results showed how a more appropriate mixed model would help avoid commission of judgment errors in sugarcane variety recommendations.
Fil: Ostengo, Santiago. Gobierno de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial Obispo Colombres; Argentina
Fil: Cuenya, María Inés. Gobierno de Tucumán. Ministerio de Desarrollo Productivo. Estación Experimental Agroindustrial Obispo Colombres; Argentina
Fil: Balzarini, Monica Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
description Comparative multienvironment trials (METs) of sugarcane genotypes are frequently conducted using a randomized complete-block design (RCBD) within environments. However, blocking does not always ensure spatial variation control because of differential competition for resources among neighboring genotypes. Heterogeneity within trials may also cause between-trial heterocedasticity. This work aims to evaluate different Linear Mixed Models (LMMs) that enable the analysis of spatial correlation and residual heterogeneity among trials for both tons of cane per hectare (TCH) and sucrose content (SC%) in three series of mutienvironmetal trials conducted to evaluate advanced sugarcane clones. A total of 16 sugarcane trials conducted at different sites and in different crop cycles (age) were analyzed. Individual (age×site combination) and multienvironment analyses were performed. For SC%, the classic RCBD analysis within trial was adequate. For TCH, the anisotropic autoregressive model of order 1 (AR1×AR1) was the best to compare genotype means in most trials, allowing gain in information equivalent, on average, to the addition of 1.6 replicates to the original design. In the case of multienvironment analysis, the AR1×AR1 within-trial with among-trial heteroscedasticity was the best model to compare variety means, both for TCH and SC%. The results showed how a more appropriate mixed model would help avoid commission of judgment errors in sugarcane variety recommendations.
publishDate 2015
dc.date.none.fl_str_mv 2015-01
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/11336/44887
Ostengo, Santiago; Cuenya, María Inés; Balzarini, Monica Graciela; Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials; Taylor & Francis, Inc.; Journal of Crop Improvement; 29; 1; 1-2015; 53-64
1542-7536
CONICET Digital
CONICET
url http://hdl.handle.net/11336/44887
identifier_str_mv Ostengo, Santiago; Cuenya, María Inés; Balzarini, Monica Graciela; Modeling Spatial Correlation Structure in Sugarcane (Saccharum spp.) Multienvironment Trials; Taylor & Francis, Inc.; Journal of Crop Improvement; 29; 1; 1-2015; 53-64
1542-7536
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1080/15427528.2014.965861
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1080/15427528.2014.965861
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 Taylor & Francis, Inc.
publisher.none.fl_str_mv Taylor & Francis, Inc.
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
_version_ 1846782405687377920
score 12.982451

Publicaciones similares