Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL
Descripción del Articulo
The pathogen Fusarium oxysporum is the major limiting factor in the production of the cape gooseberry crop Physalis peruviana, due to the inexperience of growers to identify and manage it. The objective of this research was to identify the relationship between the chemical characteristics of the soi...
Autores: | , , , , , |
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Formato: | artículo |
Fecha de Publicación: | 2022 |
Institución: | Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua |
Repositorio: | Revistas - Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua |
Lenguaje: | español |
OAI Identifier: | oai:revista.unibagua.edu.pe:article/95 |
Enlace del recurso: | https://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95 |
Nivel de acceso: | acceso abierto |
Materia: | APC árbol de decisiones factores predisponentes modelos supervisados decision tree predisposing factors supervised models |
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Revistas - Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua |
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dc.title.none.fl_str_mv |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL Modelo de estimación para relacionar factores de suelo asociados con la presencia y ausencia de Fusarium oxysporum Schltdl en el cultivo de uchuva Physalis peruviana L |
title |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL |
spellingShingle |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL Cruz-Castiblanco, Ginna APC árbol de decisiones factores predisponentes modelos supervisados APC decision tree predisposing factors supervised models |
title_short |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL |
title_full |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL |
title_fullStr |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL |
title_full_unstemmed |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL |
title_sort |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaL |
dc.creator.none.fl_str_mv |
Cruz-Castiblanco, Ginna Pérez-Caro, Wilmer Martínez-Lemus, Erika Sandoval-Cáceres, Yuly Wilches-Ortiz, Wilmar Villa-Triana, Alba |
author |
Cruz-Castiblanco, Ginna |
author_facet |
Cruz-Castiblanco, Ginna Pérez-Caro, Wilmer Martínez-Lemus, Erika Sandoval-Cáceres, Yuly Wilches-Ortiz, Wilmar Villa-Triana, Alba |
author_role |
author |
author2 |
Pérez-Caro, Wilmer Martínez-Lemus, Erika Sandoval-Cáceres, Yuly Wilches-Ortiz, Wilmar Villa-Triana, Alba |
author2_role |
author author author author author |
dc.subject.none.fl_str_mv |
APC árbol de decisiones factores predisponentes modelos supervisados APC decision tree predisposing factors supervised models |
topic |
APC árbol de decisiones factores predisponentes modelos supervisados APC decision tree predisposing factors supervised models |
description |
The pathogen Fusarium oxysporum is the major limiting factor in the production of the cape gooseberry crop Physalis peruviana, due to the inexperience of growers to identify and manage it. The objective of this research was to identify the relationship between the chemical characteristics of the soil and the presence and absence of Fusarium oxysporum. Soil samples were collected from 100 cape gooseberry production units. This process was carried out in plots with healthy plants and plants affected by the pathogen Fusarium oxysporum Schltdl. Descriptive analysis, Pearson correlations, principal components (PCA) and Machine Learning models were used to analyze the information associated with the chemical elements of the soil of cape gooseberry farms. The Decision Tree Classifier model showed the best predictive performance with Accuracy metrics of 0.58, Recall of 0.57, and F1 Score of 0.51, allowing to establish that the presence of F. oxysporum is associated with elements such as: Ca, K, Effective Cation Exchange Capacity (ETC), pH and % Sand. Finally, the development of this work is intended to outline predictive models as a tool that can be included in management plans for this disease. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-28 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95 |
url |
https://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95 |
dc.language.none.fl_str_mv |
spa |
language |
spa |
dc.relation.none.fl_str_mv |
https://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95/110 https://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95/163 https://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95/233 |
dc.rights.none.fl_str_mv |
Derechos de autor 2022 Revista Científica Dékamu Agropec https://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Derechos de autor 2022 Revista Científica Dékamu Agropec https://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf text/html audio/mpeg |
dc.publisher.none.fl_str_mv |
Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua (UNIFSLB) |
publisher.none.fl_str_mv |
Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua (UNIFSLB) |
dc.source.none.fl_str_mv |
Revista Científica Dékamu Agropec; Vol. 3 No. 2 (2022): Revista Científica Dékamu Agropec; 14-25 Revista Científica Dékamu Agropec; Vol. 3 Núm. 2 (2022): Revista Científica Dékamu Agropec; 14-25 2709-3190 2709-3182 10.55996/dekamuagropec.v3i2 reponame:Revistas - Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua instname:Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua instacron:UNIBAGUA |
instname_str |
Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua |
instacron_str |
UNIBAGUA |
institution |
UNIBAGUA |
reponame_str |
Revistas - Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua |
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Revistas - Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua |
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1845891516047818752 |
spelling |
Estimation model to relate soil factors associated with the presence and absence of Fusarium oxysporumSchltdl in the cape gooseberry crop Physalis peruvianaLModelo de estimación para relacionar factores de suelo asociados con la presencia y ausencia de Fusarium oxysporum Schltdl en el cultivo de uchuva Physalis peruviana L Cruz-Castiblanco, GinnaPérez-Caro, Wilmer Martínez-Lemus, Erika Sandoval-Cáceres, Yuly Wilches-Ortiz, Wilmar Villa-Triana, AlbaAPCárbol de decisionesfactores predisponentesmodelos supervisadosAPCdecision treepredisposing factorssupervised modelsThe pathogen Fusarium oxysporum is the major limiting factor in the production of the cape gooseberry crop Physalis peruviana, due to the inexperience of growers to identify and manage it. The objective of this research was to identify the relationship between the chemical characteristics of the soil and the presence and absence of Fusarium oxysporum. Soil samples were collected from 100 cape gooseberry production units. This process was carried out in plots with healthy plants and plants affected by the pathogen Fusarium oxysporum Schltdl. Descriptive analysis, Pearson correlations, principal components (PCA) and Machine Learning models were used to analyze the information associated with the chemical elements of the soil of cape gooseberry farms. The Decision Tree Classifier model showed the best predictive performance with Accuracy metrics of 0.58, Recall of 0.57, and F1 Score of 0.51, allowing to establish that the presence of F. oxysporum is associated with elements such as: Ca, K, Effective Cation Exchange Capacity (ETC), pH and % Sand. Finally, the development of this work is intended to outline predictive models as a tool that can be included in management plans for this disease.El patógeno Fusarium oxysporum es la mayor limitante en la producción del cultivo de uchuva Physalis peruviana, a causa de la inexperiencia de los productores para identificarlo y manejarlo. En ese sentido el objetivo de esta investigación fue identificar la relación entre las características químicas del suelo, con la presencia y ausencia de Fusarium oxysporum. La recolección de muestras de suelo fue de 100 unidades productoras uchuva. Este proceso fue realizado en parcelas con plantas sanas y plantas afectadas por el patógeno Fusarium oxysporum Schltdl. Se empleó los análisis descriptivos, correlaciones de Pearson, componentes principales (APC) y modelos Machine Learning para analizar la información asociada a los elementos químicos del suelo, de fincas productoras de uchuva. El modelo Decision Tree Classifier mostró el mejor rendimiento predictivo con métricas de Accuracy de 0.58, Recall de 0.57, y el F1 Score de 0.51, permitiendo establecer que la presencia de F. oxysporum está asociada a elementos como: Ca, K, Capacidad de intercambio catiónico efectiva (CICE), pH y % Arena. Los hallazgos en esta investigación son de utilidad para perfilar modelos predictivos y que se podrían incluir en los planes de manejo de esta enfermedad.Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua (UNIFSLB)2022-12-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlaudio/mpeghttps://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95Revista Científica Dékamu Agropec; Vol. 3 No. 2 (2022): Revista Científica Dékamu Agropec; 14-25Revista Científica Dékamu Agropec; Vol. 3 Núm. 2 (2022): Revista Científica Dékamu Agropec; 14-252709-31902709-318210.55996/dekamuagropec.v3i2reponame:Revistas - Universidad Nacional Intercultural Fabiola Salazar Leguía de Baguainstname:Universidad Nacional Intercultural Fabiola Salazar Leguía de Baguainstacron:UNIBAGUAspahttps://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95/110https://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95/163https://revista.unibagua.edu.pe/index.php/dekamuagropec/article/view/95/233Derechos de autor 2022 Revista Científica Dékamu Agropechttps://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccessoai:revista.unibagua.edu.pe:article/952024-08-13T17:11:08Z |
score |
13.02468 |
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).