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dc.contributor.author
Chantre Balacca, Guillermo Ruben
dc.contributor.author
Blanco, Anibal Manuel
dc.contributor.author
Forcella, F.
dc.contributor.author
Van Acker, R. C.
dc.contributor.author
Sabbatini, Mario Ricardo
dc.contributor.author
González Andújar, J. L.
dc.date.available
2017-02-08T20:45:40Z
dc.date.issued
2013-01
dc.identifier.citation
Chantre Balacca, Guillermo Ruben; Blanco, Anibal Manuel; Forcella, F.; Van Acker, R. C.; Sabbatini, Mario Ricardo; et al.; A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence; Cambridge University Press; Journal Of Agricultural Science; 152; 2; 1-2013; 254-262
dc.identifier.issn
0021-8596
dc.identifier.uri
http://hdl.handle.net/11336/12718
dc.description.abstract
Non-linear regression (NLR) techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks (ANNs) present interesting and alternative features for such modelling purposes. In the present work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) ANN were developed to study wild oat emergence under non-moisture restriction conditions using data from different locations worldwide. Results indicated a higher accuracy of the neural network in comparison with the NLR approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. The bivariate ANN model outperformed the con- ventional Weibull approach, in terms of RMSE of the test set, by 70·8%. These outcomes suggest the potential applicability of the proposed modelling approach in the design of weed management decision support systems.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Cambridge University Press
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Weed Emergence Models
dc.subject
Hydrothermal-Time
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Hydro-Time
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Thermal-Time
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Weibull Model
dc.subject.classification
Agronomía, reproducción y protección de plantas
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Agricultura, Silvicultura y Pesca
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CIENCIAS AGRÍCOLAS
dc.title
A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-12-01T19:41:05Z
dc.journal.volume
152
dc.journal.number
2
dc.journal.pagination
254-262
dc.journal.pais
Reino Unido
dc.journal.ciudad
Cambridge
dc.description.fil
Fil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
dc.description.fil
Fil: Blanco, Anibal Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
dc.description.fil
Fil: Forcella, F.. United States Department Of Agriculture. Agricultural Research Service; Argentina
dc.description.fil
Fil: Van Acker, R. C.. University Of Guelph; Canadá
dc.description.fil
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(i); Argentina
dc.description.fil
Fil: González Andújar, J. L.. Consejo Superior de Investigaciones Cientificas. Instituto de Agricultura Sostenible; España
dc.journal.title
Journal Of Agricultural Science
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1017/S0021859612001098
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.cambridge.org/core/journals/journal-of-agricultural-science/article/div-classtitlea-comparative-study-between-non-linear-regression-and-artificial-neural-network-approaches-for-modelling-wild-oat-span-classitalicavena-fatuaspan-field-emergencediv/A3592A37A45503BEE582E8CFEFA78313


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