Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/127655
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dc.contributor.authorChantre, Guillermo R.-
dc.contributor.authorBlanco, Antonio M.-
dc.contributor.authorForcella, Frank-
dc.contributor.authorAcker, Rene C. van-
dc.contributor.authorSabbatini, M. R.-
dc.contributor.authorGonzález-Andújar, José Luis-
dc.date.accessioned2016-01-15T11:34:40Z-
dc.date.available2016-01-15T11:34:40Z-
dc.date.issued2013-01-23-
dc.identifierdoi: 10.1017/S0021859612001098-
dc.identifierissn: 1469-5146-
dc.identifier.citationJournal of Agricultural Science 152(2): 254- 262 (2014)-
dc.identifier.urihttp://hdl.handle.net/10261/127655-
dc.description.abstractNon-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 conventional 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.description.sponsorshipThis research was partially supported by grants from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET PIP No 11220100100222) and Universidad Nacional del Sur (PGI 24/A157) to G.R.C., A.M.B. and M.R.S. and by grants from FEDER funds and the Spanish Ministry of Economy and Competitiveness (AGL2009-7883) to J.L.G.-A.-
dc.publisherCambridge University Press-
dc.relation.isversionofPostprint-
dc.rightsclosedAccess-
dc.titleA comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence-
dc.typeartículo-
dc.identifier.doi10.1017/S0021859612001098-
dc.relation.publisherversionhttp://dx.doi.org/10.1017/S0021859612001098-
dc.date.updated2016-01-15T11:34:40Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
dc.contributor.funderConsejo Nacional de Investigaciones Científicas y Técnicas (Argentina)-
dc.contributor.funderUniversidad Nacional del Sur-
dc.contributor.funderMinisterio de Economía y Competitividad (España)-
dc.contributor.funderEuropean Commission-
dc.relation.csic-
dc.identifier.funderhttp://dx.doi.org/10.13039/501100002923es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100005740es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
item.grantfulltextnone-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.openairetypeartículo-
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