Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data

Autores
Dottori, Carolina Andrea; Suarez, Franco; Córdoba, Mariano; Giannini-Kurina, Franca; De Breuil, Soledad
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Viral diseases significantly impact agricultural production and food security globally, with orthotospovirus species causing substantial damage to various crops. In peanut (Arachis hypogaea L.), outbreaks of Groundnut ringspot orthotospovirus (GRSV) are particularly detrimental, resulting in significant yield losses. This study introduces an innovative methodology that utilizes a logistic regression model to predict orthotospovirus occurrence based on publicly available monthly average biometeorological data. Data from 835 georeferenced peanut fields across 16 growing seasons in Argentina were analyzed. The results showed that wind speed, temperature, relative humidity, and precipitation from the winter months preceding peanut planting were key factors influencing GRSV presence. The model’s predictive capacity, validated with k-fold cross-validation (k = 10), demonstrated an accuracy of 79 %, a specificity of 87 %, and a sensitivity of 61 %. Moreover, this study underscores the importance of preseason climatic conditions in thrips population dynamics and highlights the need for further research on the role of wind in orthotospovirus-disease occurrence. Additionally, a phytopathological map was generated to delineate high and low-risk areas for GRSV within the main peanut-producing region of Argentina. This map categorizes regions based on the probability of GRSV occurrence and its variability across growing seasons, providing valuable insights for targeted disease management. Both tools, the predictive model and the phytopathological risk map of viral occurrence, constitute resources that could be easily adopted by stakeholders, facilitating the implementation of sustainable management practices for peanut crop protection.
Instituto de Patología Vegetal
Fil: Dottori, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Dottori, Carolina Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Suarez, Franco. Universidad Nacional de Córdoba (UNC). Facultad de Ciencias Agropecuarias (FCA). Cátedra de Estadística y Biometría; Argentina
Fil: Córdoba, Mariano. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina
Fil: Giannini-Kurina, Franca. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina
Fil: De Breuil, Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: De Breuil, Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fuente
European Journal of Agronomy 170 : 127742 (September 2025)
Materia
Disease Management
Groundnuts
Cacahuete
Gestión de la Enfermedad
Arachis hypogaea
Forecast Model
Phytopathological Map
Groundnut Ringspot Orthotospovirus
Peanut
Maní
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:20.500.12123/26195
Acceder
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oai_identifier_str oai:localhost:20.500.12123/26195
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Predicting and mapping orthotospovirus risk in peanut crops using biometeorological dataDottori, Carolina AndreaSuarez, FrancoCórdoba, MarianoGiannini-Kurina, FrancaDe Breuil, SoledadDisease ManagementGroundnutsCacahueteGestión de la EnfermedadArachis hypogaeaForecast ModelPhytopathological MapGroundnut Ringspot OrthotospovirusPeanutManíViral diseases significantly impact agricultural production and food security globally, with orthotospovirus species causing substantial damage to various crops. In peanut (Arachis hypogaea L.), outbreaks of Groundnut ringspot orthotospovirus (GRSV) are particularly detrimental, resulting in significant yield losses. This study introduces an innovative methodology that utilizes a logistic regression model to predict orthotospovirus occurrence based on publicly available monthly average biometeorological data. Data from 835 georeferenced peanut fields across 16 growing seasons in Argentina were analyzed. The results showed that wind speed, temperature, relative humidity, and precipitation from the winter months preceding peanut planting were key factors influencing GRSV presence. The model’s predictive capacity, validated with k-fold cross-validation (k = 10), demonstrated an accuracy of 79 %, a specificity of 87 %, and a sensitivity of 61 %. Moreover, this study underscores the importance of preseason climatic conditions in thrips population dynamics and highlights the need for further research on the role of wind in orthotospovirus-disease occurrence. Additionally, a phytopathological map was generated to delineate high and low-risk areas for GRSV within the main peanut-producing region of Argentina. This map categorizes regions based on the probability of GRSV occurrence and its variability across growing seasons, providing valuable insights for targeted disease management. Both tools, the predictive model and the phytopathological risk map of viral occurrence, constitute resources that could be easily adopted by stakeholders, facilitating the implementation of sustainable management practices for peanut crop protection.Instituto de Patología VegetalFil: Dottori, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Dottori, Carolina Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Suarez, Franco. Universidad Nacional de Córdoba (UNC). Facultad de Ciencias Agropecuarias (FCA). Cátedra de Estadística y Biometría; ArgentinaFil: Córdoba, Mariano. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; ArgentinaFil: Giannini-Kurina, Franca. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; ArgentinaFil: De Breuil, Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: De Breuil, Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaElsevier2026-05-14T11:08:47Z2026-05-14T11:08:47Z2025-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/26195https://www.sciencedirect.com/science/article/abs/pii/S11610301250023821161-0301https://doi.org/10.1016/j.eja.2025.127742European Journal of Agronomy 170 : 127742 (September 2025)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2023-PD-L01-I074, Bases ecológicas y epidemiológicas para el diseño de estrategias de manejo de plagas agrícolas y forestalesinfo:eu-repograntAgreement/INTA/2023-PD-L03-I084, Estreses bióticos y abióticos en plantas. Estudios fisiológicos y patológicos para el diseño de estrategias de mejoramiento y manejoinfo: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)2026-05-28T08:47:26Zoai:localhost:20.500.12123/26195instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-05-28 08:47:26.615INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data
title Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data
spellingShingle Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data
Dottori, Carolina Andrea
Disease Management
Groundnuts
Cacahuete
Gestión de la Enfermedad
Arachis hypogaea
Forecast Model
Phytopathological Map
Groundnut Ringspot Orthotospovirus
Peanut
Maní
title_short Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data
title_full Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data
title_fullStr Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data
title_full_unstemmed Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data
title_sort Predicting and mapping orthotospovirus risk in peanut crops using biometeorological data
dc.creator.none.fl_str_mv Dottori, Carolina Andrea
Suarez, Franco
Córdoba, Mariano
Giannini-Kurina, Franca
De Breuil, Soledad
author Dottori, Carolina Andrea
author_facet Dottori, Carolina Andrea
Suarez, Franco
Córdoba, Mariano
Giannini-Kurina, Franca
De Breuil, Soledad
author_role author
author2 Suarez, Franco
Córdoba, Mariano
Giannini-Kurina, Franca
De Breuil, Soledad
author2_role author
author
author
author
dc.subject.none.fl_str_mv Disease Management
Groundnuts
Cacahuete
Gestión de la Enfermedad
Arachis hypogaea
Forecast Model
Phytopathological Map
Groundnut Ringspot Orthotospovirus
Peanut
Maní
topic Disease Management
Groundnuts
Cacahuete
Gestión de la Enfermedad
Arachis hypogaea
Forecast Model
Phytopathological Map
Groundnut Ringspot Orthotospovirus
Peanut
Maní
dc.description.none.fl_txt_mv Viral diseases significantly impact agricultural production and food security globally, with orthotospovirus species causing substantial damage to various crops. In peanut (Arachis hypogaea L.), outbreaks of Groundnut ringspot orthotospovirus (GRSV) are particularly detrimental, resulting in significant yield losses. This study introduces an innovative methodology that utilizes a logistic regression model to predict orthotospovirus occurrence based on publicly available monthly average biometeorological data. Data from 835 georeferenced peanut fields across 16 growing seasons in Argentina were analyzed. The results showed that wind speed, temperature, relative humidity, and precipitation from the winter months preceding peanut planting were key factors influencing GRSV presence. The model’s predictive capacity, validated with k-fold cross-validation (k = 10), demonstrated an accuracy of 79 %, a specificity of 87 %, and a sensitivity of 61 %. Moreover, this study underscores the importance of preseason climatic conditions in thrips population dynamics and highlights the need for further research on the role of wind in orthotospovirus-disease occurrence. Additionally, a phytopathological map was generated to delineate high and low-risk areas for GRSV within the main peanut-producing region of Argentina. This map categorizes regions based on the probability of GRSV occurrence and its variability across growing seasons, providing valuable insights for targeted disease management. Both tools, the predictive model and the phytopathological risk map of viral occurrence, constitute resources that could be easily adopted by stakeholders, facilitating the implementation of sustainable management practices for peanut crop protection.
Instituto de Patología Vegetal
Fil: Dottori, Carolina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Dottori, Carolina Andrea. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Suarez, Franco. Universidad Nacional de Córdoba (UNC). Facultad de Ciencias Agropecuarias (FCA). Cátedra de Estadística y Biometría; Argentina
Fil: Córdoba, Mariano. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina
Fil: Giannini-Kurina, Franca. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias. Cátedra de Estadística y Biometría; Argentina
Fil: De Breuil, Soledad. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: De Breuil, Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
description Viral diseases significantly impact agricultural production and food security globally, with orthotospovirus species causing substantial damage to various crops. In peanut (Arachis hypogaea L.), outbreaks of Groundnut ringspot orthotospovirus (GRSV) are particularly detrimental, resulting in significant yield losses. This study introduces an innovative methodology that utilizes a logistic regression model to predict orthotospovirus occurrence based on publicly available monthly average biometeorological data. Data from 835 georeferenced peanut fields across 16 growing seasons in Argentina were analyzed. The results showed that wind speed, temperature, relative humidity, and precipitation from the winter months preceding peanut planting were key factors influencing GRSV presence. The model’s predictive capacity, validated with k-fold cross-validation (k = 10), demonstrated an accuracy of 79 %, a specificity of 87 %, and a sensitivity of 61 %. Moreover, this study underscores the importance of preseason climatic conditions in thrips population dynamics and highlights the need for further research on the role of wind in orthotospovirus-disease occurrence. Additionally, a phytopathological map was generated to delineate high and low-risk areas for GRSV within the main peanut-producing region of Argentina. This map categorizes regions based on the probability of GRSV occurrence and its variability across growing seasons, providing valuable insights for targeted disease management. Both tools, the predictive model and the phytopathological risk map of viral occurrence, constitute resources that could be easily adopted by stakeholders, facilitating the implementation of sustainable management practices for peanut crop protection.
publishDate 2025
dc.date.none.fl_str_mv 2025-09
2026-05-14T11:08:47Z
2026-05-14T11:08:47Z
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/20.500.12123/26195
https://www.sciencedirect.com/science/article/abs/pii/S1161030125002382
1161-0301
https://doi.org/10.1016/j.eja.2025.127742
url http://hdl.handle.net/20.500.12123/26195
https://www.sciencedirect.com/science/article/abs/pii/S1161030125002382
https://doi.org/10.1016/j.eja.2025.127742
identifier_str_mv 1161-0301
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2023-PD-L01-I074, Bases ecológicas y epidemiológicas para el diseño de estrategias de manejo de plagas agrícolas y forestales
info:eu-repograntAgreement/INTA/2023-PD-L03-I084, Estreses bióticos y abióticos en plantas. Estudios fisiológicos y patológicos para el diseño de estrategias de mejoramiento y manejo
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 Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv European Journal of Agronomy 170 : 127742 (September 2025)
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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