Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach

Autores
Chantre Balacca, Guillermo Ruben; Blanco, Anibal Manuel; Lodovichi, Mariela Victoria; Bandoni, Jose Alberto; Sabbatini, Mario Ricardo; Lopez, Ricardo Luis; Vigna, Mario Raul; Gigon, Ramon
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Avena fatua is an invasive weed of the semiarid region of Argentina. Seedling emergence patterns are very irregular along the season showing a great year-to-year variability mainly due to a highly unpredictable precipitation regime. Non-linear regression techniques are usually unable to accurately predict field emergence under such environmental conditions. Artificial Neural Networks (ANNs) are known for their capacity to describe highly non-linear relationships among variables thus showing a high potential applicability in ecological systems. The objectives of the present work were to develop different ANN models for A. fatua seedling emergence prediction and to compare their predictive capability against non-linear regression techniques. Classical hydrothermal-time indices were used as input variable for the development of univariate models, while thermal-time and hydro-time were used as independent input variables for developing bivariate models. The accumulated proportion of seedling emergence was the output variable in all cases. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Obtained results indicate a higher accuracy and generalization performance of the optimal ANN model in comparison to non-linear regression approaches. It is also demonstrated that the use of thermal-time and hydro-time as independent explanatory variables in ANN models yields better prediction than using combined hydrothermal-time indices in classical NLR models. The best obtained ANN model outperformed in 43.3% the best NLR model in terms of RMSE of the test set. Moreover, the best obtained ANN predicted accumulated emergence within the first 50% of total emergence 48.3% better in average than the best developed NLR model. These outcomes suggest the potential applicability of the proposed modeling approach in weed management decision support systems design.
EEA Bordenave
Fil: Chantre Balacca, Guillermo Ruben. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Lodovichi, Mariela Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: López, Ricardo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; Argentina
Fil: Vigna, Mario Raúl. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; Argentina
Fil: Gigón, Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; Argentina
Fuente
Computers and Electronics in Agriculture 88 : 95-102 (October 2012)
Materia
Avena Fatua
Malezas
Emergencia
Plántulas
Análisis de la Regresión
Métodos Estadísticos
Weeds
Emergence
Seedlings
Regression Analysis
Statistical Methods
Nivel de accesibilidad
acceso restringido
Condiciones de uso
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/4603
Acceder
id INTADig_f7c4dc8a393f3ff940ad7fbfee291dcd
oai_identifier_str oai:localhost:20.500.12123/4603
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Modeling Avena fatua seedling emergence dynamics: an artificial neural network approachChantre Balacca, Guillermo RubenBlanco, Anibal ManuelLodovichi, Mariela VictoriaBandoni, Jose AlbertoSabbatini, Mario RicardoLopez, Ricardo LuisVigna, Mario RaulGigon, RamonAvena FatuaMalezasEmergenciaPlántulasAnálisis de la RegresiónMétodos EstadísticosWeedsEmergenceSeedlingsRegression AnalysisStatistical MethodsAvena fatua is an invasive weed of the semiarid region of Argentina. Seedling emergence patterns are very irregular along the season showing a great year-to-year variability mainly due to a highly unpredictable precipitation regime. Non-linear regression techniques are usually unable to accurately predict field emergence under such environmental conditions. Artificial Neural Networks (ANNs) are known for their capacity to describe highly non-linear relationships among variables thus showing a high potential applicability in ecological systems. The objectives of the present work were to develop different ANN models for A. fatua seedling emergence prediction and to compare their predictive capability against non-linear regression techniques. Classical hydrothermal-time indices were used as input variable for the development of univariate models, while thermal-time and hydro-time were used as independent input variables for developing bivariate models. The accumulated proportion of seedling emergence was the output variable in all cases. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Obtained results indicate a higher accuracy and generalization performance of the optimal ANN model in comparison to non-linear regression approaches. It is also demonstrated that the use of thermal-time and hydro-time as independent explanatory variables in ANN models yields better prediction than using combined hydrothermal-time indices in classical NLR models. The best obtained ANN model outperformed in 43.3% the best NLR model in terms of RMSE of the test set. Moreover, the best obtained ANN predicted accumulated emergence within the first 50% of total emergence 48.3% better in average than the best developed NLR model. These outcomes suggest the potential applicability of the proposed modeling approach in weed management decision support systems design.EEA BordenaveFil: Chantre Balacca, Guillermo Ruben. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Lodovichi, Mariela Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; ArgentinaFil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; ArgentinaFil: López, Ricardo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; ArgentinaFil: Vigna, Mario Raúl. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; ArgentinaFil: Gigón, Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; ArgentinaElsevier2019-03-14T12:25:08Z2019-03-14T12:25:08Z2012-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://www.sciencedirect.com/science/article/pii/S0168169912001901http://hdl.handle.net/20.500.12123/46030168-1699https://doi.org/10.1016/j.compag.2012.07.005Computers and Electronics in Agriculture 88 : 95-102 (October 2012)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccess2025-10-16T09:29:27Zoai:localhost:20.500.12123/4603instacron: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 09:29:28.177INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
title Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
spellingShingle Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
Chantre Balacca, Guillermo Ruben
Avena Fatua
Malezas
Emergencia
Plántulas
Análisis de la Regresión
Métodos Estadísticos
Weeds
Emergence
Seedlings
Regression Analysis
Statistical Methods
title_short Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
title_full Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
title_fullStr Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
title_full_unstemmed Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
title_sort Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
dc.creator.none.fl_str_mv Chantre Balacca, Guillermo Ruben
Blanco, Anibal Manuel
Lodovichi, Mariela Victoria
Bandoni, Jose Alberto
Sabbatini, Mario Ricardo
Lopez, Ricardo Luis
Vigna, Mario Raul
Gigon, Ramon
author Chantre Balacca, Guillermo Ruben
author_facet Chantre Balacca, Guillermo Ruben
Blanco, Anibal Manuel
Lodovichi, Mariela Victoria
Bandoni, Jose Alberto
Sabbatini, Mario Ricardo
Lopez, Ricardo Luis
Vigna, Mario Raul
Gigon, Ramon
author_role author
author2 Blanco, Anibal Manuel
Lodovichi, Mariela Victoria
Bandoni, Jose Alberto
Sabbatini, Mario Ricardo
Lopez, Ricardo Luis
Vigna, Mario Raul
Gigon, Ramon
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Avena Fatua
Malezas
Emergencia
Plántulas
Análisis de la Regresión
Métodos Estadísticos
Weeds
Emergence
Seedlings
Regression Analysis
Statistical Methods
topic Avena Fatua
Malezas
Emergencia
Plántulas
Análisis de la Regresión
Métodos Estadísticos
Weeds
Emergence
Seedlings
Regression Analysis
Statistical Methods
dc.description.none.fl_txt_mv Avena fatua is an invasive weed of the semiarid region of Argentina. Seedling emergence patterns are very irregular along the season showing a great year-to-year variability mainly due to a highly unpredictable precipitation regime. Non-linear regression techniques are usually unable to accurately predict field emergence under such environmental conditions. Artificial Neural Networks (ANNs) are known for their capacity to describe highly non-linear relationships among variables thus showing a high potential applicability in ecological systems. The objectives of the present work were to develop different ANN models for A. fatua seedling emergence prediction and to compare their predictive capability against non-linear regression techniques. Classical hydrothermal-time indices were used as input variable for the development of univariate models, while thermal-time and hydro-time were used as independent input variables for developing bivariate models. The accumulated proportion of seedling emergence was the output variable in all cases. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Obtained results indicate a higher accuracy and generalization performance of the optimal ANN model in comparison to non-linear regression approaches. It is also demonstrated that the use of thermal-time and hydro-time as independent explanatory variables in ANN models yields better prediction than using combined hydrothermal-time indices in classical NLR models. The best obtained ANN model outperformed in 43.3% the best NLR model in terms of RMSE of the test set. Moreover, the best obtained ANN predicted accumulated emergence within the first 50% of total emergence 48.3% better in average than the best developed NLR model. These outcomes suggest the potential applicability of the proposed modeling approach in weed management decision support systems design.
EEA Bordenave
Fil: Chantre Balacca, Guillermo Ruben. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Lodovichi, Mariela Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: Bandoni, Jose Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Sabbatini, Mario Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina
Fil: López, Ricardo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; Argentina
Fil: Vigna, Mario Raúl. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; Argentina
Fil: Gigón, Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave; Argentina
description Avena fatua is an invasive weed of the semiarid region of Argentina. Seedling emergence patterns are very irregular along the season showing a great year-to-year variability mainly due to a highly unpredictable precipitation regime. Non-linear regression techniques are usually unable to accurately predict field emergence under such environmental conditions. Artificial Neural Networks (ANNs) are known for their capacity to describe highly non-linear relationships among variables thus showing a high potential applicability in ecological systems. The objectives of the present work were to develop different ANN models for A. fatua seedling emergence prediction and to compare their predictive capability against non-linear regression techniques. Classical hydrothermal-time indices were used as input variable for the development of univariate models, while thermal-time and hydro-time were used as independent input variables for developing bivariate models. The accumulated proportion of seedling emergence was the output variable in all cases. A total of 528 input/output data pairs corresponding to 11 years of data collection were used in this study. Obtained results indicate a higher accuracy and generalization performance of the optimal ANN model in comparison to non-linear regression approaches. It is also demonstrated that the use of thermal-time and hydro-time as independent explanatory variables in ANN models yields better prediction than using combined hydrothermal-time indices in classical NLR models. The best obtained ANN model outperformed in 43.3% the best NLR model in terms of RMSE of the test set. Moreover, the best obtained ANN predicted accumulated emergence within the first 50% of total emergence 48.3% better in average than the best developed NLR model. These outcomes suggest the potential applicability of the proposed modeling approach in weed management decision support systems design.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
2019-03-14T12:25:08Z
2019-03-14T12:25:08Z
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 https://www.sciencedirect.com/science/article/pii/S0168169912001901
http://hdl.handle.net/20.500.12123/4603
0168-1699
https://doi.org/10.1016/j.compag.2012.07.005
url https://www.sciencedirect.com/science/article/pii/S0168169912001901
http://hdl.handle.net/20.500.12123/4603
https://doi.org/10.1016/j.compag.2012.07.005
identifier_str_mv 0168-1699
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
eu_rights_str_mv restrictedAccess
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 Computers and Electronics in Agriculture 88 : 95-102 (October 2012)
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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score 12.712165

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