Analysis of the field-scale spatial pattern of peanut smut in Argentina

Autores
Paredes, Juan Andrés; Cazon, Luis Ignacio; Conforto, Erica Cinthia; Monguillot, Joaquín Humberto; Asinari, Florencia; González, Noelia R.; Rago, Alejandro Mario; Pérez, Agustín; Camiletti, Boris Xavier
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Peanut smut, caused by the soilborne pathogen Thecaphora frezzii, poses a significant threat to Argentina’s peanut production. As a monocyclic disease, the infections are restricted to pegs and pods, with no direct plant-to-plant spread. Spore dissemination occurs exclusively during harvest when infected pods release spores, which can persist in the soil for many years. The lack of detailed knowledge about the spatial pattern of peanut smut in commercial fields limits the design of efficient and cost-effective experiments, accurately monitoring disease progression, and evaluating the effectiveness of management strategies. This study integrates field-scale experiments with statistical tools to investigate the spatial patterns of peanut smut across different scales, and their association with crop practices and host–pathogen interactions. Peanut smut incidence (percentage of smutted pods in a sample) was assessed at both small and large scales. Binary power law (BPL) analysis was used to analyze data from the surveyed field samples. Spatial analysis using heterogeneity, dispersion, autocorrelation, and SADIE statistics revealed that peanut smut tends to exhibit a random spatial pattern at medium-to-high disease incidence levels (> 20%), whereas localized clustering patterns occur at lower incidences (< 6%), as confirmed by the BPL. Higher disease incidences were often recorded near field entrances, likely influenced by harvesting practices and activities that promote spore concentration in specific areas. These findings highlight the importance of avoiding field edges or entrances during sampling to ensure unbiased data collection for disease monitoring. Understanding the spatial dynamics of peanut smut enhances the ability to design accurate experiments, improve sampling methods and contributes to developing better disease management strategies.
Instituto de Patología Vegetal
Fil: Paredes, Juan Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Paredes, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Cazon, Luis Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Cazon, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Conforto, Erica Cinthia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Conforto, Erica Cinthia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Monguillot, Joaquín Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Monguillot, Joaquín Humberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Asinari, Florencia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Asinari, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: González, Noelia R. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; Argentina
Fil: Rago, Alejandro Mario. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro de Investigaciones Agropecuarias (CIAP); Argentina
Fil: Rago, Alejandro Mario. Universidad Nacional de Rio Cuarto. Facultad de Agronomía y Veterinaria; Argentina
Fil: Pérez, Agustín. University of Illinois Urbana-Champaign. Department of Crop Sciences; Estados Unidos
Fil: Camiletti, Boris X. University of Illinois Urbana-Champaign. Department of Crop Sciences; Estados Unidos
Fuente
European Journal of Plant Pathology : 1-19 (Published: 20 August 2025 )
Materia
Spatial Distribution
Epidemiology
Groundnuts
Distribución Espacial
Epidemiología
Argentina
Arachis hypogaea
Cacahuete
Soilborne Pathogen
Peanut Diseases
Peanuts
Thecaphora frezzii
Maní
Nivel de accesibilidad
acceso abierto
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:20.500.12123/23802
Acceder
id INTADig_095df25cb221e291a55fe09d081ddd72
oai_identifier_str oai:localhost:20.500.12123/23802
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Analysis of the field-scale spatial pattern of peanut smut in ArgentinaParedes, Juan AndrésCazon, Luis IgnacioConforto, Erica CinthiaMonguillot, Joaquín HumbertoAsinari, FlorenciaGonzález, Noelia R.Rago, Alejandro MarioPérez, AgustínCamiletti, Boris XavierSpatial DistributionEpidemiologyGroundnutsDistribución EspacialEpidemiologíaArgentinaArachis hypogaeaCacahueteSoilborne PathogenPeanut DiseasesPeanutsThecaphora frezziiManíPeanut smut, caused by the soilborne pathogen Thecaphora frezzii, poses a significant threat to Argentina’s peanut production. As a monocyclic disease, the infections are restricted to pegs and pods, with no direct plant-to-plant spread. Spore dissemination occurs exclusively during harvest when infected pods release spores, which can persist in the soil for many years. The lack of detailed knowledge about the spatial pattern of peanut smut in commercial fields limits the design of efficient and cost-effective experiments, accurately monitoring disease progression, and evaluating the effectiveness of management strategies. This study integrates field-scale experiments with statistical tools to investigate the spatial patterns of peanut smut across different scales, and their association with crop practices and host–pathogen interactions. Peanut smut incidence (percentage of smutted pods in a sample) was assessed at both small and large scales. Binary power law (BPL) analysis was used to analyze data from the surveyed field samples. Spatial analysis using heterogeneity, dispersion, autocorrelation, and SADIE statistics revealed that peanut smut tends to exhibit a random spatial pattern at medium-to-high disease incidence levels (> 20%), whereas localized clustering patterns occur at lower incidences (< 6%), as confirmed by the BPL. Higher disease incidences were often recorded near field entrances, likely influenced by harvesting practices and activities that promote spore concentration in specific areas. These findings highlight the importance of avoiding field edges or entrances during sampling to ensure unbiased data collection for disease monitoring. Understanding the spatial dynamics of peanut smut enhances the ability to design accurate experiments, improve sampling methods and contributes to developing better disease management strategies.Instituto de Patología VegetalFil: Paredes, Juan Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Paredes, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Cazon, Luis Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Cazon, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Conforto, Erica Cinthia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Conforto, Erica Cinthia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Monguillot, Joaquín Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: Monguillot, Joaquín Humberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Asinari, Florencia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Asinari, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); ArgentinaFil: González, Noelia R. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; ArgentinaFil: Rago, Alejandro Mario. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro de Investigaciones Agropecuarias (CIAP); ArgentinaFil: Rago, Alejandro Mario. Universidad Nacional de Rio Cuarto. Facultad de Agronomía y Veterinaria; ArgentinaFil: Pérez, Agustín. University of Illinois Urbana-Champaign. Department of Crop Sciences; Estados UnidosFil: Camiletti, Boris X. University of Illinois Urbana-Champaign. Department of Crop Sciences; Estados UnidosSpringer2025-09-15T10:06:17Z2025-09-15T10:06:17Z2025-08-20info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/23802https://link.springer.com/article/10.1007/s10658-025-03124-y0929-18731573-8469 (online)https://doi.org/10.1007/s10658-025-03124-yEuropean Journal of Plant Pathology : 1-19 (Published: 20 August 2025 )reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repograntAgreement/INTA/2019-PD-E4-I090-001, Análisis de patosistemas en cultivos agrícolas y especies forestales. Caracterización de sus componentesinfo:eu-repograntAgreement/INTA/2023-PD-L01-I074, Bases ecológicas y epidemiológicas para el diseño de estrategias de manejo de plagas agrícolas y forestalesinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-16T09:32:34Zoai:localhost:20.500.12123/23802instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-16 09:32:35.003INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Analysis of the field-scale spatial pattern of peanut smut in Argentina
title Analysis of the field-scale spatial pattern of peanut smut in Argentina
spellingShingle Analysis of the field-scale spatial pattern of peanut smut in Argentina
Paredes, Juan Andrés
Spatial Distribution
Epidemiology
Groundnuts
Distribución Espacial
Epidemiología
Argentina
Arachis hypogaea
Cacahuete
Soilborne Pathogen
Peanut Diseases
Peanuts
Thecaphora frezzii
Maní
title_short Analysis of the field-scale spatial pattern of peanut smut in Argentina
title_full Analysis of the field-scale spatial pattern of peanut smut in Argentina
title_fullStr Analysis of the field-scale spatial pattern of peanut smut in Argentina
title_full_unstemmed Analysis of the field-scale spatial pattern of peanut smut in Argentina
title_sort Analysis of the field-scale spatial pattern of peanut smut in Argentina
dc.creator.none.fl_str_mv Paredes, Juan Andrés
Cazon, Luis Ignacio
Conforto, Erica Cinthia
Monguillot, Joaquín Humberto
Asinari, Florencia
González, Noelia R.
Rago, Alejandro Mario
Pérez, Agustín
Camiletti, Boris Xavier
author Paredes, Juan Andrés
author_facet Paredes, Juan Andrés
Cazon, Luis Ignacio
Conforto, Erica Cinthia
Monguillot, Joaquín Humberto
Asinari, Florencia
González, Noelia R.
Rago, Alejandro Mario
Pérez, Agustín
Camiletti, Boris Xavier
author_role author
author2 Cazon, Luis Ignacio
Conforto, Erica Cinthia
Monguillot, Joaquín Humberto
Asinari, Florencia
González, Noelia R.
Rago, Alejandro Mario
Pérez, Agustín
Camiletti, Boris Xavier
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Spatial Distribution
Epidemiology
Groundnuts
Distribución Espacial
Epidemiología
Argentina
Arachis hypogaea
Cacahuete
Soilborne Pathogen
Peanut Diseases
Peanuts
Thecaphora frezzii
Maní
topic Spatial Distribution
Epidemiology
Groundnuts
Distribución Espacial
Epidemiología
Argentina
Arachis hypogaea
Cacahuete
Soilborne Pathogen
Peanut Diseases
Peanuts
Thecaphora frezzii
Maní
dc.description.none.fl_txt_mv Peanut smut, caused by the soilborne pathogen Thecaphora frezzii, poses a significant threat to Argentina’s peanut production. As a monocyclic disease, the infections are restricted to pegs and pods, with no direct plant-to-plant spread. Spore dissemination occurs exclusively during harvest when infected pods release spores, which can persist in the soil for many years. The lack of detailed knowledge about the spatial pattern of peanut smut in commercial fields limits the design of efficient and cost-effective experiments, accurately monitoring disease progression, and evaluating the effectiveness of management strategies. This study integrates field-scale experiments with statistical tools to investigate the spatial patterns of peanut smut across different scales, and their association with crop practices and host–pathogen interactions. Peanut smut incidence (percentage of smutted pods in a sample) was assessed at both small and large scales. Binary power law (BPL) analysis was used to analyze data from the surveyed field samples. Spatial analysis using heterogeneity, dispersion, autocorrelation, and SADIE statistics revealed that peanut smut tends to exhibit a random spatial pattern at medium-to-high disease incidence levels (> 20%), whereas localized clustering patterns occur at lower incidences (< 6%), as confirmed by the BPL. Higher disease incidences were often recorded near field entrances, likely influenced by harvesting practices and activities that promote spore concentration in specific areas. These findings highlight the importance of avoiding field edges or entrances during sampling to ensure unbiased data collection for disease monitoring. Understanding the spatial dynamics of peanut smut enhances the ability to design accurate experiments, improve sampling methods and contributes to developing better disease management strategies.
Instituto de Patología Vegetal
Fil: Paredes, Juan Andrés. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Paredes, Juan Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Cazon, Luis Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Cazon, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Conforto, Erica Cinthia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Conforto, Erica Cinthia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Monguillot, Joaquín Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: Monguillot, Joaquín Humberto. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Asinari, Florencia. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; Argentina
Fil: Asinari, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFyMA); Argentina
Fil: González, Noelia R. Fundación ArgenINTA. Delegación IFFIVE. Córdoba; Argentina
Fil: Rago, Alejandro Mario. Instituto Nacional de Tecnología Agropecuaria (INTA). Centro de Investigaciones Agropecuarias (CIAP); Argentina
Fil: Rago, Alejandro Mario. Universidad Nacional de Rio Cuarto. Facultad de Agronomía y Veterinaria; Argentina
Fil: Pérez, Agustín. University of Illinois Urbana-Champaign. Department of Crop Sciences; Estados Unidos
Fil: Camiletti, Boris X. University of Illinois Urbana-Champaign. Department of Crop Sciences; Estados Unidos
description Peanut smut, caused by the soilborne pathogen Thecaphora frezzii, poses a significant threat to Argentina’s peanut production. As a monocyclic disease, the infections are restricted to pegs and pods, with no direct plant-to-plant spread. Spore dissemination occurs exclusively during harvest when infected pods release spores, which can persist in the soil for many years. The lack of detailed knowledge about the spatial pattern of peanut smut in commercial fields limits the design of efficient and cost-effective experiments, accurately monitoring disease progression, and evaluating the effectiveness of management strategies. This study integrates field-scale experiments with statistical tools to investigate the spatial patterns of peanut smut across different scales, and their association with crop practices and host–pathogen interactions. Peanut smut incidence (percentage of smutted pods in a sample) was assessed at both small and large scales. Binary power law (BPL) analysis was used to analyze data from the surveyed field samples. Spatial analysis using heterogeneity, dispersion, autocorrelation, and SADIE statistics revealed that peanut smut tends to exhibit a random spatial pattern at medium-to-high disease incidence levels (> 20%), whereas localized clustering patterns occur at lower incidences (< 6%), as confirmed by the BPL. Higher disease incidences were often recorded near field entrances, likely influenced by harvesting practices and activities that promote spore concentration in specific areas. These findings highlight the importance of avoiding field edges or entrances during sampling to ensure unbiased data collection for disease monitoring. Understanding the spatial dynamics of peanut smut enhances the ability to design accurate experiments, improve sampling methods and contributes to developing better disease management strategies.
publishDate 2025
dc.date.none.fl_str_mv 2025-09-15T10:06:17Z
2025-09-15T10:06:17Z
2025-08-20
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/20.500.12123/23802
https://link.springer.com/article/10.1007/s10658-025-03124-y
0929-1873
1573-8469 (online)
https://doi.org/10.1007/s10658-025-03124-y
url http://hdl.handle.net/20.500.12123/23802
https://link.springer.com/article/10.1007/s10658-025-03124-y
https://doi.org/10.1007/s10658-025-03124-y
identifier_str_mv 0929-1873
1573-8469 (online)
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2019-PD-E4-I090-001, Análisis de patosistemas en cultivos agrícolas y especies forestales. Caracterización de sus componentes
info:eu-repograntAgreement/INTA/2023-PD-L01-I074, Bases ecológicas y epidemiológicas para el diseño de estrategias de manejo de plagas agrícolas y forestales
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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 openAccess
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 Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv European Journal of Plant Pathology : 1-19 (Published: 20 August 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
_version_ 1846143594500456448
score 12.8982525

Publicaciones similares