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dc.contributor.authorSafa, Majeeden
dc.contributor.authorSamarasinghe, Sandhyaen
dc.date.accessioned2012-02-12T23:51:12Z
dc.date.issued2010en
dc.identifier.urihttps://hdl.handle.net/10182/4220
dc.description.abstractAn artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year.1 In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The final model can predict energy consumption by using farm condition (size of wheat area and number paddocks), farmers’ social properties (education), and energy inputs (N and P use, fungicide consumption, seed consumption, and irrigation frequency), it can also predict energy use in Canterbury wheat farms with error margin of ±7% (± 1600 MJ/ha).en
dc.format.extent966-970en
dc.language.isoenen
dc.publisherWorld Academy of Science, Engineering and Technologyen
dc.relationThe original publication is available from - World Academy of Science, Engineering and Technologyen
dc.rightsCopyright © World Academy of Science, Engineering and Technologyen
dc.subjectCanterburyen
dc.subjectartificial neural networksen
dc.subjectenergy consumptionen
dc.subjectwheat productionen
dc.subjectmodellingen
dc.titleModelling of energy consumption in wheat production using neural networks: case study in Canterbury province, New Zealanden
dc.typeJournal Article
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agribusiness and Commerceen
lu.contributor.unitDepartment of Land Management and Systemsen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitDepartment of Environmental Managementen
lu.contributor.uniten
lu.contributor.uniten
dc.relation.isPartOfWorld of Science, Engineering and Technologyen
pubs.organisational-group/LU
pubs.organisational-group/LU/Faculty of Agribusiness and Commerce
pubs.organisational-group/LU/Faculty of Agribusiness and Commerce/LAMS
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/DEM
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/2018 PBRF Staff group
pubs.publication-statusPublisheden
pubs.volume72en
lu.identifier.orcid0000-0002-8670-6156
lu.identifier.orcid0000-0003-2943-4331


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