The use of machine learning to predict somatic cell count status in dairy cows post-calving
Jake S. Thompson, Martin J. Green, Robert Hyde, Andrew J. Bradley, Luke O’Grady · 2023
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Description
Jake Thompson and four colleagues taught a computer model to flag which dairy cows are likely to have udder trouble after calving. The dry weeks before birth are the key window to cure old infections and block new ones. Farms already log rich data on each cow through milk recording. The team drew records from 108 UK herds and set the target: a high somatic cell count in the first month after calving, a sign of infection in the udder.
They trained a boosted decision-tree model on data from fifty-six farms, using only facts known at dry-off. The aim is risk-based care: treat or watch the cows the model flags, and spare the rest needless antibiotics. Less drug use, less pain, and lower losses are the stakes. The twenty twenty-three study shows farm records, already sitting in databases, can be turned into cow-level forecasts.
Catalog record
| CREATOR | Jake S. Thompson, Martin J. Green, Robert Hyde, Andrew J. Bradley, Luke O’Grady |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |