Type

Journal Article

Authors

D P Berry
J E Pryce
S T Butler
M O'Donovan
B McCarthy
S G Moore
E Kennedy
E Lewis
S McParland

Subjects

Agriculture & Food Science

Topics
biomarkers predictor energy efficiency milk production mid infrared spectrum milk yield energy balance mid infrared spectrometry partial least squares regression feed efficiency dairy cows energy intake

Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows. (2014)

Abstract 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.
Collections Ireland -> Teagasc -> PubMed
Ireland -> Teagasc -> Livestock Systems
Ireland -> Teagasc -> Animal & Bioscience
Ireland -> Teagasc -> Animal & Grassland Research & Innovation Programme

Full list of authors on original publication

D P Berry, J E Pryce, S T Butler, M O'Donovan, B McCarthy, S G Moore, E Kennedy, E Lewis, S McParland

Experts in our system

1
D P Berry
Teagasc
Total Publications: 243
 
2
S T Butler
Teagasc
Total Publications: 89
 
3
M O'Donovan
Teagasc
Total Publications: 62
 
4
B McCarthy
Teagasc
Total Publications: 9
 
5
S G Moore
Teagasc
Total Publications: 11
 
6
E Kennedy
Teagasc
Total Publications: 46
 
7
E Lewis
Teagasc
Total Publications: 30
 
8
S McParland
Teagasc
Total Publications: 44