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dc.contributor.authorMunoz-Tamayo Ren
dc.contributor.authorRamirez Agudelo JFen
dc.contributor.authorDewhurst RJen
dc.contributor.authorMiller Gen
dc.contributor.authorVernon Ten
dc.contributor.authorKettle Hen
dc.date.accessioned2018-11-05T13:13:11Z
dc.date.available2018-11-05T13:13:11Z
dc.date.issued2018
dc.identifier.urihttps://doi.org/10.1017/S1751731118002550
dc.identifier.urihttp://hdl.handle.net/11262/11544
dc.description.abstractLarge efforts have been deployed in developing methods to estimate methane emissions from cattle. For large scale applications, accurate and inexpensive methane predictors are required. Within a livestock precision farming context, the objective of this work was to integrate real-time data on animal feeding behaviour with an in silico model for predicting the individual dynamic pattern of methane emission in cattle. The integration of real-time data with a mathematical model to predict variables that are not directly measured constitutes a software sensor. We developed a dynamic parsimonious grey-box model that uses as predictor variables either dry matter intake (DMI) or the intake time (IT). The model is described by ordinary differential equations. Model building was supported by experimental data of methane emissions from respiration chambers. The data set comes from a study with finishing beef steers (cross-bred Charolais and purebred Luing finishing). Dry matter intake and IT were recorded using feed bins. For research purposes, in this work, our software sensor operated off-line. That is, the predictor variables (DMI, IT) were extracted from the recorded data (rather than from an on-line sensor). A total of 37 individual dynamic patterns of methane production were analyzed. Model performance was assessed by concordance analysis between the predicted methane output and the methane measured in respiration chambers. The model predictors DMI and IT performed similarly with a Lin’s concordance correlation coefficient (CCC) of 0.78 on average. When predicting the daily methane production, the CCC was 0.99 for both DMI and IT predictors. Consequently, on the basis of concordance analysis, our model performs very well compared with reported literature results for methane proxies and predictive models. As IT measurements are easier to obtain than DMI measurements, this study suggests that a software sensor that integrates our in silico model with a real-time sensor providing accurate IT measurements is a viable solution for predicting methane output in a large scale context.en
dc.description.sponsorshipScottish Government RESAS Strategic Research Programme (RD2.2.9; SD1; SD3)en
dc.language.isoenen
dc.relation.isformatof14955en
dc.relation.ispartofAnimalen
dc.rightsCopyright © The Animal Consortium 2018 This is an Accepted Manuscript of an article published by Cambridge University Press in a revised form with their editorial input. The final published version is available online: https://doi.org/10.1017/S1751731118002550
dc.subjectGreenhouse gasen
dc.subjectMethaneen
dc.subjectModellingen
dc.subjectRuminanten
dc.subjectPrecision farmingen
dc.titleA parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattleen
dc.typeArticleen
dc.description.versionAccepted manuscript
rioxxterms.publicationdate2018-10-18
rioxxterms.typeJournal Article/Reviewen
dcterms.dateAccepted2018-09-07
refterms.accessExceptionNAen
refterms.dateDeposit2018-11-05
refterms.dateEmbargoEnd2019-04-18
refterms.dateFreeToDownload2019-04-18
refterms.dateFreeToRead2019-04-18
refterms.dateToSearch2019-04-18
refterms.depositExceptionNAen
refterms.panelUnspecifieden
refterms.technicalExceptionNAen
refterms.versionAMen


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