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dc.contributor.authorBerry, Donagh P.
dc.contributor.authorWallen, Sini E.
dc.contributor.authorPrestløkken, E.
dc.contributor.authorMeuwissen, Theodorus H.E.
dc.contributor.authorMcParland, Sinead
dc.date.accessioned2019-08-12T13:58:01Z
dc.date.available2019-08-12T13:58:01Z
dc.date.issued2018-03-28
dc.identifier.citationWallén, S., Prestløkken, E., Meuwissen, T., McParland, S. and Berry, D. (2018). Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows. Journal of Dairy Science, [online] 101(7), pp.6232-6243. Available at: https://dx.doi.org/10.3168/jds.2017-13874.en_US
dc.identifier.urihttp://hdl.handle.net/11019/1713
dc.descriptionpeer-revieweden_US
dc.description.abstractMid-infrared (MIR) spectroscopy of milk was used to predict dry matter intake (DMI) and net energy intake (NEI) in 160 lactating Norwegian Red dairy cows. A total of 857 observations were used in leave-one-out cross-validation and external validation to develop and validate prediction equations using 5 different models. Predictions were performed using (multiple) linear regression, partial least squares (PLS) regression, or best linear unbiased prediction (BLUP) methods. Linear regression was implemented using just milk yield (MY) or fat, protein, and lactose concentration in milk (Mcont) or using MY together with body weight (BW) as predictors of intake. The PLS and BLUP methods were implemented using just the MIR spectral information or using the MIR together with Mcont, MY, BW, or NEI from concentrate (NEIconc). When using BLUP, the MIR spectral wavelengths were always treated as random effects, whereas Mcont, MY, BW, and NEIconc were considered to be fixed effects. Accuracy of prediction (R) was defined as the correlation between the predicted and observed feed intake test-day records. When using the linear regression method, the greatest R of predicting DMI (0.54) and NEI (0.60) in the external validation was achieved when the model included both MY and BW. When using PLS, the greatest R of predicting DMI (0.54) and NEI (0.65) in the external validation data set was achieved when using both BW and MY as predictors in combination with the MIR spectra. When using BLUP, the greatest R of predicting DMI (0.54) in the external validation was when using MY together with the MIR spectra. The greatest R of predicting NEI (0.65) in the external validation using BLUP was achieved when the model included both BW and MY in combination with the MIR spectra or when the model included both NEIconc and MY in combination with MIR spectra. However, although the linear regression coefficients of actual on predicted values for DMI and NEI were not different from unity when using PLS, they were less than unity for some of the models developed using BLUP. This study shows that MIR spectral data can be used to predict NEI as a measure of feed intake in Norwegian Red dairy cattle and that the accuracy is augmented if additional, often available data are also included in the prediction model.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesJournal of Dairy Science;Vol. 101 (7)
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectpredictionen_US
dc.subjectnet energy intakeen_US
dc.subjectmid-infrared spectroscopyen_US
dc.subjectdry matter intakeen_US
dc.titleMilk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cowsen_US
dc.typeArticleen_US
dc.embargo.terms2019-03-28en_US
dc.identifier.doihttps://dx.doi.org/10.3168/jds.2017-13874
dc.contributor.sponsorthe Norwegian Research Councilen_US
dc.contributor.sponsorTINEen_US
dc.contributor.sponsorGENOen_US
dc.contributor.sponsorGrantNumber225233/E40en_US
refterms.dateFOA2019-03-28T00:00:00Z


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