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dc.contributor.authorMurphy, Michael D.
dc.contributor.authorO’Mahony, Michéal J.
dc.contributor.authorShalloo, Laurence
dc.contributor.authorFrench, Padraig
dc.contributor.authorUpton, John
dc.date.accessioned2020-01-06T09:39:57Z
dc.date.available2020-01-06T09:39:57Z
dc.date.issued2014-04-13
dc.identifier.citationMurphy, M., O’Mahony, M., Shalloo, L., French, P. and Upton, J. (2014). Comparison of modelling techniques for milk-production forecasting. Journal of Dairy Science, 97(6), .3352-3363. doi: https://doi.org/10.3168/jds.2013-7451en_US
dc.identifier.urihttp://hdl.handle.net/11019/1845
dc.descriptionpeer-revieweden_US
dc.description.abstractThe objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%) = 8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%) = 12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%) = 10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions.en_US
dc.language.isoenen_US
dc.publisherElsevier for American Dairy Science Associationen_US
dc.relation.ispartofseriesJournal of Dairy Science;Vol. 97(6)
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectdairy productionen_US
dc.subjectmilk-production forecastingen_US
dc.subjectmodellingen_US
dc.titleComparison of modelling techniques for milk-production forecastingen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3168/jds.2013-7451
refterms.dateFOA2015-04-13T00:00:00Z


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  • Livestock Systems [259]
    Teagasc LIvestock Systems Department includes Dairy, Cattle and Sheep research.

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