Document Detail


Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows.
MedLine Citation:
PMID:  24997658     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Interest is increasing in the feed intake complex of individual dairy cows, both for management and animal breeding. However, energy intake data on an individual-cow basis are not routinely available. The objective of the present study was to quantify the ability of routinely undertaken mid-infrared (MIR) spectroscopy analysis of individual cow milk samples to predict individual cow energy intake and efficiency. Feed efficiency in the present study was described by residual feed intake (RFI), which is the difference between actual energy intake and energy used (e.g., milk production, maintenance, and body tissue anabolism) or supplied from body tissue mobilization. A total of 1,535 records for energy intake, RFI, and milk MIR spectral data were available from an Irish research herd across 36 different test days from 535 lactations on 378 cows. Partial least squares regression analyses were used to relate the milk MIR spectral data to either energy intake or efficiency. The coefficient of correlation (REX) of models to predict RFI across lactation ranged from 0.48 to 0.60 in an external validation data set; the predictive ability was, however, strongest (REX = 0.65) in early lactation (<60 d in milk). The inclusion of milk yield as a predictor variable improved the accuracy of predicting energy intake across lactation (REX = 0.70). The correlation between measured RFI and measured energy balance across lactation was 0.85, whereas the correlation between RFI and energy balance, both predicted from the MIR spectrum, was 0.65. Milk MIR spectral data are routinely generated for individual cows throughout lactation and, therefore, the prediction equations developed in the present study can be immediately (and retrospectively where MIR spectral data have been stored) applied to predict energy intake and efficiency to aid in management and breeding decisions.
Authors:
S McParland; E Lewis; E Kennedy; S G Moore; B McCarthy; M O'Donovan; S T Butler; J E Pryce; D P Berry
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-7-2
Journal Detail:
Title:  Journal of dairy science     Volume:  -     ISSN:  1525-3198     ISO Abbreviation:  J. Dairy Sci.     Publication Date:  2014 Jul 
Date Detail:
Created Date:  2014-7-6     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  2985126R     Medline TA:  J Dairy Sci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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