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dc.contributor.authorEllis, Jennifer L.
dc.contributor.authorAlaiz-Moretón, Héctor
dc.contributor.authorNavarro-Villa, Alberto
dc.contributor.authorMcGeough, Emma J.
dc.contributor.authorPurcell, Peter
dc.contributor.authorPowell, Christopher D.
dc.contributor.authorO’Kiely, Padraig
dc.contributor.authorFrance, James
dc.contributor.authorLópez, Secundino
dc.date.accessioned2020-07-28T10:55:49Z
dc.date.available2020-07-28T10:55:49Z
dc.date.issued2020-04-21
dc.identifier.citationEllis, J.L.; Alaiz-Moretón, H.; Navarro-Villa, A.; McGeough, E.J.; Purcell, P.; Powell, C.D.; O’Kiely, P.; France, J.; López, S. Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation. Animals 2020, 10, 720. https://doi.org/10.3390/ani10040720en_US
dc.identifier.issn2076-2615
dc.identifier.urihttp://hdl.handle.net/11019/2190
dc.descriptionpeer-revieweden_US
dc.description.abstractIn vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH4) production, nor do all publications report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro CH4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted in best predictability of CH4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH4 emissions in vitro.en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofseriesAnimals;10
dc.rightsAttribution-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/us/*
dc.subjectin vitro gas productionen_US
dc.subjectmethaneen_US
dc.subjectrumenen_US
dc.subjectfeeden_US
dc.subjectmeta-analysisen_US
dc.subjectmachine learningen_US
dc.subjectneural networken_US
dc.titleApplication of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentationen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3390/ani10040720
dc.contributor.sponsorDepartment of Agriculture, Food and the Marineen_US
dc.contributor.sponsorGrantNumberRSF 05 224en_US
dc.contributor.sponsorGrantNumberRSF 06 361en_US
dc.contributor.sponsorGrantNumberRSF 07 517en_US
dc.source.volume10
dc.source.issue4
dc.source.beginpage720
refterms.dateFOA2020-07-28T10:55:50Z


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