Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach
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 p...
| Autores principales: | , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2019
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| Materias: | |
| Acceso en línea: | 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 |
| _version_ | 1855483464209399808 |
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| author | 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_browse | Bandoni, Jose Alberto Blanco, Anibal Manuel Chantre Balacca, Guillermo Ruben Gigon, Ramon Lodovichi, Mariela Victoria Lopez, Ricardo Luis Sabbatini, Mario Ricardo Vigna, Mario Raul |
| 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_sort | Chantre Balacca, Guillermo Ruben |
| collection | INTA Digital |
| 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. |
| format | Artículo |
| id | INTA4603 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA46032019-03-14T12:27:11Z Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach Chantre Balacca, Guillermo Ruben Blanco, Anibal Manuel Lodovichi, Mariela Victoria Bandoni, Jose Alberto Sabbatini, Mario Ricardo Lopez, Ricardo Luis Vigna, Mario Raul Gigon, Ramon Avena Fatua Malezas Emergencia Plántulas Análisis de la Regresión Métodos Estadísticos Weeds Emergence Seedlings Regression Analysis Statistical Methods 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 2019-03-14T12:25:08Z 2019-03-14T12:25:08Z 2012-10 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng info:eu-repo/semantics/restrictedAccess application/pdf Elsevier Computers and Electronics in Agriculture 88 : 95-102 (October 2012) |
| spellingShingle | Avena Fatua Malezas Emergencia Plántulas Análisis de la Regresión Métodos Estadísticos Weeds Emergence Seedlings Regression Analysis Statistical Methods Chantre Balacca, Guillermo Ruben Blanco, Anibal Manuel Lodovichi, Mariela Victoria Bandoni, Jose Alberto Sabbatini, Mario Ricardo Lopez, Ricardo Luis Vigna, Mario Raul Gigon, Ramon Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach |
| title | 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_short | Modeling Avena fatua seedling emergence dynamics: an artificial neural network approach |
| title_sort | modeling avena fatua seedling emergence dynamics an artificial neural network approach |
| topic | Avena Fatua Malezas Emergencia Plántulas Análisis de la Regresión Métodos Estadísticos Weeds Emergence Seedlings Regression Analysis Statistical Methods |
| 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 |
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