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dc.contributor.authorUpton, John
dc.contributor.authorMurphy, Michael D.
dc.contributor.authorShalloo, Laurence
dc.contributor.authorGroot Koerkamp, Peter W.G.
dc.contributor.authorDe Boer, Imke J.M.
dc.date.accessioned2019-10-31T12:41:38Z
dc.date.available2019-10-31T12:41:38Z
dc.date.issued2014-06-07
dc.identifier.citationUpton, J., Murphy, M., Shalloo, L., Groot Koerkamp, P. and De Boer, I. A mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstration. Journal of Dairy Science, 2014, 97(8), 4973-4984. DOI: http://dx.doi.org/10.3168/jds.2014-8015en_US
dc.identifier.urihttp://hdl.handle.net/11019/1820
dc.descriptionpeer-revieweden_US
dc.description.abstractOur objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1 yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesJournal of Dairy Science;Vol. 97 (8)
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectenergyen_US
dc.subjectelectricityen_US
dc.subjectmilk productionen_US
dc.subjectmechanistic modelen_US
dc.titleA mechanistic model for electricity consumption on dairy farms: Definition, validation, and demonstrationen_US
dc.typeArticleen_US
dc.embargo.terms2015-06-07en_US
dc.identifier.doihttps://dx.doi.org/10.3168/jds.2014-8015
dc.contributor.sponsorINTERREG IVB North-West Europeen_US
refterms.dateFOA2015-06-07T00:00:00Z


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  • Livestock Systems [128]
    Teagasc LIvestock Systems Department includes Dairy, Cattle and Sheep research.

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Attribution-NonCommercial-ShareAlike 3.0 United States
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