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dc.contributor.authorShine, Philip
dc.contributor.authorScully, Ted
dc.contributor.authorUpton, John
dc.contributor.authorMurphy, Michael D.
dc.date.accessioned2020-06-17T15:54:19Z
dc.date.available2020-06-17T15:54:19Z
dc.date.issued2018-03-05
dc.identifier.citationShine, P., Scully, T., Upton, J. and Murphy, M. Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture, 2018, 148, 337-346. doi: https://dx.doi.org/10.1016/j.compag.2018.02.020en_US
dc.identifier.urihttp://hdl.handle.net/11019/2013
dc.descriptionpeer-revieweden_US
dc.description.abstractAn analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesComputers and Electronics in Agriculture;Vol. 148
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectdairy farmen_US
dc.subjectMLRen_US
dc.subjectmultiple linear regressionen_US
dc.subjectwater consumptionen_US
dc.subjectelectricity consumptionen_US
dc.subjectmodel accuracyen_US
dc.titleMultiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farmsen_US
dc.typeArticleen_US
dc.embargo.terms2019-03-05en_US
dc.identifier.doihttps://dx.doi.org/10.1016/j.compag.2018.02.020
dc.contributor.sponsorInstitutes of Technology Irelanden_US
dc.contributor.sponsorDepartment of Agriculture, Food and the Marineen_US
dc.contributor.sponsorSustainable Energy Authority of Irelanden_US
refterms.dateFOA2019-03-05T00:00:00Z


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  • Livestock Systems [200]
    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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