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Control charts and neural networks for oestrus dectection in dairy cows

dc.contributor.authorKrieter, Joachim
dc.contributor.authorStamer, Eckhard
dc.contributor.authorJunge, Wolfgang
dc.contributor.editorWenkel, K.-O.
dc.contributor.editorWagner, P.
dc.contributor.editorMorgenstern, M.
dc.contributor.editorLuzi, K.
dc.contributor.editorEisermann, P.
dc.date.accessioned2019-08-26T09:34:33Z
dc.date.available2019-08-26T09:34:33Z
dc.date.issued2006
dc.description.abstractExponentially weighted moving average control charts and neural networks were used for oestrus detection in dairy cows. The analysis involved 373 cows, each with one verified oestrus event. Model inputs were the traits activity, measured by pedometer, and the period (days) since last oestrus. In total 10,386 records were available, which were partitioned into training and validation subsets to train and test the neural network (multifold cross-validation). When the trained neural network was applied to the validation sets, the averaged sensitivity, specificity and error rate were 77.5, 99.6 and 9.1%, respectively. Performance for the same data with the univariate control chart was less successful. Neural networks are useful tools to improve computerised oestrus detection in dairy cows.en
dc.identifier.isbn3-88579-172-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/24700
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofLand- und Ernährungswirtschaft im Wandel – Aufgaben und Herausforderungen für die Agrar und Umweltinformatik
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-78
dc.titleControl charts and neural networks for oestrus dectection in dairy cowsen
dc.typeText/Conference Paper
gi.citation.endPage136
gi.citation.publisherPlaceBonn
gi.citation.startPage133
gi.conference.date06.-08. März 2006
gi.conference.locationPotsdam
gi.conference.sessiontitleRegular Research Papers

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