Potential of milk MIR spectra to develop new health phenotypes for dairy cows in the GplusE project
- Vanlierde, A. , Grelet, C. , Gengler, N. , Ferris, C. , Sorensen, M. . , Höglund, J. , Carter, F. , Santoro, A. , Hermans, K. , Hostens, M. , Dardenne, P. & Dehareng, F. (2016). Potential of milk MIR spectra to develop new health phenotypes for dairy cows in the GplusE project. Proceedings in: EAAP 2016, Belfast, 29 août - 3 septembre,
| Type | Conference Proceedings |
| Year of conference | 2016 |
| Title | Potential of milk MIR spectra to develop new health phenotypes for dairy cows in the GplusE project |
| Conference name | EAAP 2016 |
| Conference location | Belfast |
| conference Date | 29 août - 3 septembre |
| Project/Service ref | GplusE |
| Abstract | Animal production systems, including dairying, must become more efficient. This is addressed within the “Genotype plus Environment” project (G plus E), an objective of which is to record novel cow phenotypes and to develop predictions for these novel phenotypes using milk MIR spectra. MIR allows the ‘status’ of cows to be predicted using a rapid, cost effective, routine process. Data (liveweight, body condition score, uterine health, residual feed intake, lameness,…) and samples (milk, blood, liver, feed,…) were collected from 135 dairy cows on 3 European farms (AFBI-UK, UCD-IRL, AU-DK) from calving until day 49 post calving. Those data constitute a substantial database which permits to link those phenotypes of interest to potential biomarkers, and especially the mid infrared (MIR) spectra of milk. Classification models have been developed from milk MIR spectra with a PLSDA technique. For example a model developed from 60 observations allows us to distinguish animals with or without lameness with a good predicted classification of 68 and 71% respectively. Other regression models have been developed to predict molecules of interest from milk MIR spectra. Some of them can be used with a threshold (eg. level of milk NAGase which is associated to an inflammation status), while others have the potential to be predicted quantitatively (eg. IGF1 which is linked to uterine health). This database therefore allows developing tools to predict new health indicators from milk MIR spectra that can be easily implemented at a large scale. Those observations will be validated through new data collected with the same protocol from 3 other European farms. |
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| Authors | Vanlierde, A., Grelet, C., Gengler, N., Ferris, C., Sorensen, M. ., Höglund, J., Carter, F., Santoro, A., Hermans, K., Hostens, M., Dardenne, P., Dehareng, F. |
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