Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling

Autores
Peralta, Nahuel; Ferreyra, Juan M.; Di Rienzo, Julio
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The comparative performance trials (CPT) of experimental and commercial cultivars in multi-environment trials, allow the determination of genotype (G) response to the different environment (E) and interaction (GxE). The final goal is to provide selection guidelines to identify the best genotypes to advance in the pipeline of the breeding program. Nowadays, the use of the digital information technologies to monitoring yield can be used in the CPTs to accurate measuring and recording of grain yield as well as the local environmental context. This research was carried out at 138 locations corresponding to commercial fields located in Argentina. The harvested was made with yield monitor and weighing machine wagon. To compare the grain yield of hybrids within each E and GXE interaction, the MLM of ANAVA with spatial correlation was adjusted for each location. Whereas, for CPT-without spatial data the ANAVA simple was applicate. The new analytic strategy showed greater power to: (a) find significant differences (p<0.05) in the GxE interaction, (b) increase 2.5 times the amount of information generated, i.e. than 1 CPT (1 location) analyzed with the new strategy. The new approach improves the data-driven decision making to support advancement processes from precommercial to commercial.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Digital tools
Data modeling
Hybrid competitiveness
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/115520
Acceder
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network_name_str SEDICI (UNLP)
spelling Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modelingPeralta, NahuelFerreyra, Juan M.Di Rienzo, JulioCiencias InformáticasDigital toolsData modelingHybrid competitivenessThe comparative performance trials (CPT) of experimental and commercial cultivars in multi-environment trials, allow the determination of genotype (G) response to the different environment (E) and interaction (GxE). The final goal is to provide selection guidelines to identify the best genotypes to advance in the pipeline of the breeding program. Nowadays, the use of the digital information technologies to monitoring yield can be used in the CPTs to accurate measuring and recording of grain yield as well as the local environmental context. This research was carried out at 138 locations corresponding to commercial fields located in Argentina. The harvested was made with yield monitor and weighing machine wagon. To compare the grain yield of hybrids within each E and GXE interaction, the MLM of ANAVA with spatial correlation was adjusted for each location. Whereas, for CPT-without spatial data the ANAVA simple was applicate. The new analytic strategy showed greater power to: (a) find significant differences (p&lt;0.05) in the GxE interaction, (b) increase 2.5 times the amount of information generated, i.e. than 1 CPT (1 location) analyzed with the new strategy. The new approach improves the data-driven decision making to support advancement processes from precommercial to commercial.Sociedad Argentina de Informática e Investigación Operativa2020-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf194-197http://sedici.unlp.edu.ar/handle/10915/115520enginfo:eu-repo/semantics/altIdentifier/url/http://49jaiio.sadio.org.ar/pdfs/cai/CAI_24.pdfinfo:eu-repo/semantics/altIdentifier/issn/2525-0949info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:26:54Zoai:sedici.unlp.edu.ar:10915/115520Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:26:54.817SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling
title Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling
spellingShingle Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling
Peralta, Nahuel
Ciencias Informáticas
Digital tools
Data modeling
Hybrid competitiveness
title_short Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling
title_full Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling
title_fullStr Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling
title_full_unstemmed Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling
title_sort Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling
dc.creator.none.fl_str_mv Peralta, Nahuel
Ferreyra, Juan M.
Di Rienzo, Julio
author Peralta, Nahuel
author_facet Peralta, Nahuel
Ferreyra, Juan M.
Di Rienzo, Julio
author_role author
author2 Ferreyra, Juan M.
Di Rienzo, Julio
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Digital tools
Data modeling
Hybrid competitiveness
topic Ciencias Informáticas
Digital tools
Data modeling
Hybrid competitiveness
dc.description.none.fl_txt_mv The comparative performance trials (CPT) of experimental and commercial cultivars in multi-environment trials, allow the determination of genotype (G) response to the different environment (E) and interaction (GxE). The final goal is to provide selection guidelines to identify the best genotypes to advance in the pipeline of the breeding program. Nowadays, the use of the digital information technologies to monitoring yield can be used in the CPTs to accurate measuring and recording of grain yield as well as the local environmental context. This research was carried out at 138 locations corresponding to commercial fields located in Argentina. The harvested was made with yield monitor and weighing machine wagon. To compare the grain yield of hybrids within each E and GXE interaction, the MLM of ANAVA with spatial correlation was adjusted for each location. Whereas, for CPT-without spatial data the ANAVA simple was applicate. The new analytic strategy showed greater power to: (a) find significant differences (p&lt;0.05) in the GxE interaction, (b) increase 2.5 times the amount of information generated, i.e. than 1 CPT (1 location) analyzed with the new strategy. The new approach improves the data-driven decision making to support advancement processes from precommercial to commercial.
Sociedad Argentina de Informática e Investigación Operativa
description The comparative performance trials (CPT) of experimental and commercial cultivars in multi-environment trials, allow the determination of genotype (G) response to the different environment (E) and interaction (GxE). The final goal is to provide selection guidelines to identify the best genotypes to advance in the pipeline of the breeding program. Nowadays, the use of the digital information technologies to monitoring yield can be used in the CPTs to accurate measuring and recording of grain yield as well as the local environmental context. This research was carried out at 138 locations corresponding to commercial fields located in Argentina. The harvested was made with yield monitor and weighing machine wagon. To compare the grain yield of hybrids within each E and GXE interaction, the MLM of ANAVA with spatial correlation was adjusted for each location. Whereas, for CPT-without spatial data the ANAVA simple was applicate. The new analytic strategy showed greater power to: (a) find significant differences (p&lt;0.05) in the GxE interaction, (b) increase 2.5 times the amount of information generated, i.e. than 1 CPT (1 location) analyzed with the new strategy. The new approach improves the data-driven decision making to support advancement processes from precommercial to commercial.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/115520
url http://sedici.unlp.edu.ar/handle/10915/115520
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/2525-0949
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
dc.format.none.fl_str_mv application/pdf
194-197
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.070432

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