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dc.contributor.authorZhang, Fan
dc.contributor.authorUpton, John
dc.contributor.authorShalloo, Laurence
dc.contributor.authorShine, Philip
dc.contributor.authorMurphy, Michael D.
dc.identifier.citationF. Zhang, J. Upton, L. Shalloo, P. Shine, M.D. Murphy, Effect of Introducing Weather Parameters on the Accuracy of Milk Production Forecast Models, Information Processing in Agriculture (2019), doi:
dc.description.abstractThe objective of this study was to analyze the effect of adding meteorological data to the training process of two milk production forecast models. The two models chosen were the nonlinear auto-regressive model with exogenous input (NARX) and the multiple linear regression (MLR) model. The accuracy of these models were assessed using seven different combinations of precipitation, sunshine hours and soil temperature as additional model training inputs. Lactation data (daily milk yield and days in milk) from 39 pasture-based Holstein-Friesian Irish dairy cows were selected to compare to the model outputs from a central database. The models were trained using historical milk production data from three lactation cycles and were employed to predict the total daily milk yield of a fourth lactation cycle for each individual cow over short (10-day), medium (30-day) and long-term (305-day) forecast horizons. The NARX model was found to provide a greater prediction accuracy when compared to the MLR model when predicting annual individual cow milk yield (kg), with R2 values greater than 0.7 for 95.5% and 14.7% of total predictions, respectively. The results showed that the introduction of sunshine hours, precipitation and soil temperature data improved the prediction accuracy of individual cow milk prediction for the NARX model in the short, medium and long-term forecast horizons. Sunshine hours was shown to have the largest impact on milk production with an improvement of forecast accuracy observed in 60% and 70% of all predictions (for all 39 test cows from both groups). However, the overall improvement in accuracy was small with a maximum forecast error reduction of 4.3%. Thus, the utilization of meteorological parameters in milk production forecasting did not have a substantial impact on forecast accuracy.en_US
dc.relation.ispartofseriesInformation Processing in Agriculture;
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.subjectMilk production forecastingen_US
dc.subjectDairy modellingen_US
dc.subjectModel optimizationen_US
dc.subjectMeteorological dataen_US
dc.titleEffect of introducing weather parameters on the accuracy of milk production forecast modelsen_US

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  • Livestock Systems [316]
    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