Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina

Autores
Gómez Montenegro, Brenda Emiliana; Suárez, Franco Marcelo; Balzarini, Mónica; Giolitti, Fabian; Martino, Julia Andrea; Bruno, Cecilia; Alemandri, Vanina Maria
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Wheat streak mosaic virus (WSMV) is the causal agent of one of the most destructive diseases in wheat cultivation, with yield losses of up to 100%. This virus belongs to the genus Tritimovirus in the family Potyviridae and is transmitted by the wheat curl mite (WCM). Virus-vector-crop interactions are modulated by climatic variables in each wheat-growing region of Argentina, determining both disease development and vector population dynamics. The aim of this study was to analyse the relationship between the presence of WSMV in wheat and different biometeorological variables, using 1206 records from agricultural plots in the wheat-growing region of Argentina sampled between 2006 and 2022, aggregated at the plot level, resulting in 286 plots used as the unit of analysis. For this purpose, climatic variables were downloaded for the months of January to November. A stepwise logistic regression was applied with the diagnostic criteria of p-value and Variance Inflation Factor to reduce multicollinearity among variables. Subsequently, the presence/absence of WSMV was modelled with the selected variables through the use of a Random Forest classification. The predictive model identified key variables such as dew point in January and May, wind speed in February and April, relative humidity in June and accumulated rainfall from January to May, achieving an accuracy of 65% and the area under the receiver operating characteristics (ROC) curve of 0.75. The results underline the relevance of climatic conditions in the spread of the virus and highlight the need for preventive measures, such as vector management, the use of resistant cultivars and the adoption of agricultural practices adapted to climate variability.
Instituto de Patología Vegetal
Fil: Gómez Montenegro, Brenda Emiliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Gómez Montenegro, Brenda Emiliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Suárez, Franco Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; Argentina
Fil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; Argentina
Fil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Giolitti, Fabian. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Giolitti, Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Martino, Julia Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Martino, Julia Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Bruno, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; Argentina
Fil:Bruno, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Alemandri, Vanina Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Alemandri, Vanina Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fuente
Journal of phytopathology 174 (3) : e70334 (May/June 2026)
Materia
Climate Variability
Plant Diseases
Wheat
Variabilidad del Clima
Enfermedades de las Plantas
Aceria tosichella
Argentina
Trigo
Biometeorological Variables
WCM (Wheat Curl Mite)
WSMV
Wheat Streak Mosaic Virus
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/26586
Acceder
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oai_identifier_str oai:localhost:20.500.12123/26586
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network_name_str INTA Digital (INTA)
spelling Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of ArgentinaGómez Montenegro, Brenda EmilianaSuárez, Franco MarceloBalzarini, MónicaGiolitti, FabianMartino, Julia AndreaBruno, CeciliaAlemandri, Vanina MariaClimate VariabilityPlant DiseasesWheatVariabilidad del ClimaEnfermedades de las PlantasAceria tosichellaArgentinaTrigoBiometeorological VariablesWCM (Wheat Curl Mite)WSMVWheat Streak Mosaic VirusWheat streak mosaic virus (WSMV) is the causal agent of one of the most destructive diseases in wheat cultivation, with yield losses of up to 100%. This virus belongs to the genus Tritimovirus in the family Potyviridae and is transmitted by the wheat curl mite (WCM). Virus-vector-crop interactions are modulated by climatic variables in each wheat-growing region of Argentina, determining both disease development and vector population dynamics. The aim of this study was to analyse the relationship between the presence of WSMV in wheat and different biometeorological variables, using 1206 records from agricultural plots in the wheat-growing region of Argentina sampled between 2006 and 2022, aggregated at the plot level, resulting in 286 plots used as the unit of analysis. For this purpose, climatic variables were downloaded for the months of January to November. A stepwise logistic regression was applied with the diagnostic criteria of p-value and Variance Inflation Factor to reduce multicollinearity among variables. Subsequently, the presence/absence of WSMV was modelled with the selected variables through the use of a Random Forest classification. The predictive model identified key variables such as dew point in January and May, wind speed in February and April, relative humidity in June and accumulated rainfall from January to May, achieving an accuracy of 65% and the area under the receiver operating characteristics (ROC) curve of 0.75. The results underline the relevance of climatic conditions in the spread of the virus and highlight the need for preventive measures, such as vector management, the use of resistant cultivars and the adoption of agricultural practices adapted to climate variability.Instituto de Patología VegetalFil: Gómez Montenegro, Brenda Emiliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Gómez Montenegro, Brenda Emiliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Suárez, Franco Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; ArgentinaFil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; ArgentinaFil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Giolitti, Fabian. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Giolitti, Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Martino, Julia Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Martino, Julia Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Bruno, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; ArgentinaFil:Bruno, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Alemandri, Vanina Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Alemandri, Vanina Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaWiley2026-06-11T10:42:31Z2026-06-11T10:42:31Z2026-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/26586https://onlinelibrary.wiley.com/doi/10.1111/jph.703341439-0434 (Online)https://doi.org/10.1111/jph.70334Journal of phytopathology 174 (3) : e70334 (May/June 2026)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-06-18T09:34:29Zoai:localhost:20.500.12123/26586instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-06-18 09:34:30.106INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina
title Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina
spellingShingle Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina
Gómez Montenegro, Brenda Emiliana
Climate Variability
Plant Diseases
Wheat
Variabilidad del Clima
Enfermedades de las Plantas
Aceria tosichella
Argentina
Trigo
Biometeorological Variables
WCM (Wheat Curl Mite)
WSMV
Wheat Streak Mosaic Virus
title_short Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina
title_full Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina
title_fullStr Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina
title_full_unstemmed Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina
title_sort Climate-Associated Risk Patterns of Wheat Streak Mosaic Virus (WSMV) in Wheat-Producing Regions of Argentina
dc.creator.none.fl_str_mv Gómez Montenegro, Brenda Emiliana
Suárez, Franco Marcelo
Balzarini, Mónica
Giolitti, Fabian
Martino, Julia Andrea
Bruno, Cecilia
Alemandri, Vanina Maria
author Gómez Montenegro, Brenda Emiliana
author_facet Gómez Montenegro, Brenda Emiliana
Suárez, Franco Marcelo
Balzarini, Mónica
Giolitti, Fabian
Martino, Julia Andrea
Bruno, Cecilia
Alemandri, Vanina Maria
author_role author
author2 Suárez, Franco Marcelo
Balzarini, Mónica
Giolitti, Fabian
Martino, Julia Andrea
Bruno, Cecilia
Alemandri, Vanina Maria
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Climate Variability
Plant Diseases
Wheat
Variabilidad del Clima
Enfermedades de las Plantas
Aceria tosichella
Argentina
Trigo
Biometeorological Variables
WCM (Wheat Curl Mite)
WSMV
Wheat Streak Mosaic Virus
topic Climate Variability
Plant Diseases
Wheat
Variabilidad del Clima
Enfermedades de las Plantas
Aceria tosichella
Argentina
Trigo
Biometeorological Variables
WCM (Wheat Curl Mite)
WSMV
Wheat Streak Mosaic Virus
dc.description.none.fl_txt_mv Wheat streak mosaic virus (WSMV) is the causal agent of one of the most destructive diseases in wheat cultivation, with yield losses of up to 100%. This virus belongs to the genus Tritimovirus in the family Potyviridae and is transmitted by the wheat curl mite (WCM). Virus-vector-crop interactions are modulated by climatic variables in each wheat-growing region of Argentina, determining both disease development and vector population dynamics. The aim of this study was to analyse the relationship between the presence of WSMV in wheat and different biometeorological variables, using 1206 records from agricultural plots in the wheat-growing region of Argentina sampled between 2006 and 2022, aggregated at the plot level, resulting in 286 plots used as the unit of analysis. For this purpose, climatic variables were downloaded for the months of January to November. A stepwise logistic regression was applied with the diagnostic criteria of p-value and Variance Inflation Factor to reduce multicollinearity among variables. Subsequently, the presence/absence of WSMV was modelled with the selected variables through the use of a Random Forest classification. The predictive model identified key variables such as dew point in January and May, wind speed in February and April, relative humidity in June and accumulated rainfall from January to May, achieving an accuracy of 65% and the area under the receiver operating characteristics (ROC) curve of 0.75. The results underline the relevance of climatic conditions in the spread of the virus and highlight the need for preventive measures, such as vector management, the use of resistant cultivars and the adoption of agricultural practices adapted to climate variability.
Instituto de Patología Vegetal
Fil: Gómez Montenegro, Brenda Emiliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Gómez Montenegro, Brenda Emiliana. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Suárez, Franco Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; Argentina
Fil: Balzarini, Mónica. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; Argentina
Fil: Balzarini, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Giolitti, Fabian. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Giolitti, Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Martino, Julia Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Martino, Julia Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Bruno, Cecilia. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Estadística y Biometría; Argentina
Fil:Bruno, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Alemandri, Vanina Maria. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Alemandri, Vanina Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
description Wheat streak mosaic virus (WSMV) is the causal agent of one of the most destructive diseases in wheat cultivation, with yield losses of up to 100%. This virus belongs to the genus Tritimovirus in the family Potyviridae and is transmitted by the wheat curl mite (WCM). Virus-vector-crop interactions are modulated by climatic variables in each wheat-growing region of Argentina, determining both disease development and vector population dynamics. The aim of this study was to analyse the relationship between the presence of WSMV in wheat and different biometeorological variables, using 1206 records from agricultural plots in the wheat-growing region of Argentina sampled between 2006 and 2022, aggregated at the plot level, resulting in 286 plots used as the unit of analysis. For this purpose, climatic variables were downloaded for the months of January to November. A stepwise logistic regression was applied with the diagnostic criteria of p-value and Variance Inflation Factor to reduce multicollinearity among variables. Subsequently, the presence/absence of WSMV was modelled with the selected variables through the use of a Random Forest classification. The predictive model identified key variables such as dew point in January and May, wind speed in February and April, relative humidity in June and accumulated rainfall from January to May, achieving an accuracy of 65% and the area under the receiver operating characteristics (ROC) curve of 0.75. The results underline the relevance of climatic conditions in the spread of the virus and highlight the need for preventive measures, such as vector management, the use of resistant cultivars and the adoption of agricultural practices adapted to climate variability.
publishDate 2026
dc.date.none.fl_str_mv 2026-06-11T10:42:31Z
2026-06-11T10:42:31Z
2026-06-01
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/20.500.12123/26586
https://onlinelibrary.wiley.com/doi/10.1111/jph.70334
1439-0434 (Online)
https://doi.org/10.1111/jph.70334
url http://hdl.handle.net/20.500.12123/26586
https://onlinelibrary.wiley.com/doi/10.1111/jph.70334
https://doi.org/10.1111/jph.70334
identifier_str_mv 1439-0434 (Online)
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv Journal of phytopathology 174 (3) : e70334 (May/June 2026)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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