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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/44887
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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 Técnicas2025-09-29T10:32:48Zoai: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:34982025-09-29 10:32:48.488CONICET 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 |
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1844614342515884032 |
score |
13.070432 |