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dc.contributor.authorShine, Philip
dc.contributor.authorUpton, John
dc.contributor.authorSefeedpari, Paria
dc.contributor.authorMurphy, Michael D.
dc.date.accessioned2020-07-28T10:44:38Z
dc.date.available2020-07-28T10:44:38Z
dc.date.issued2020-03-10
dc.identifier.citationShine, P.; Upton, J.; Sefeedpari, P.; Murphy, M.D. Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies 2020, 13, 1288. https://doi.org/10.3390/en13051288en_US
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/11019/2188
dc.descriptionpeer-revieweden_US
dc.description.abstractThe global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practicesen_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofseriesEnergies;13
dc.rightsAttribution-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/3.0/us/*
dc.subjectdairyen_US
dc.subjectenergyen_US
dc.subjectreviewen_US
dc.subjectmodellingen_US
dc.subjectefficiencyen_US
dc.subjectsustainable agricultureen_US
dc.subjectmachine-learningen_US
dc.titleEnergy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analysesen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3390/en13051288
dc.contributor.sponsorSustainable Energy Authority of Irelanden_US
dc.contributor.sponsorGrantNumber18/RDD/317en_US
dc.source.volume13
dc.source.issue5
dc.source.beginpage1288
refterms.dateFOA2020-07-28T10:44:38Z


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  • Livestock Systems [265]
    Teagasc LIvestock Systems Department includes Dairy, Cattle and Sheep research.

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Attribution-ShareAlike 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-ShareAlike 3.0 United States