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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/23802
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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)2025-09-29T13:47:31Zoai: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:l2025-09-29 13:47:32.094INTA 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 |
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INTA Digital (INTA) |
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Instituto Nacional de Tecnología Agropecuaria |
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INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
repository.mail.fl_str_mv |
tripaldi.nicolas@inta.gob.ar |
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