Better Informing Treatment Decisions
The researchers first searched for patterns in records from thousands of cattle groups across 13 feedyards. Funded by the International Consortium for Antimicrobial Stewardship in Agriculture, the team worked with Microsoft, 3Cloud and Databricks to include weather data, which can affect animal health, in the models.
The first analysis found that treating an entire group of cattle did not substantially improve animal health to outweigh the cost of treatment in 72% of the groups studied. They also learned that cattle age and severity of health challenges played important roles in the value of the treatment.
The researchers then used data from 16,368 cattle groups to build four predictive models. The models performed well in testing, and adding economic data improved their predictions.
Make Every Treatment Count
Repeated or unnecessary antibiotic use can favor antibiotic-resistant bacteria. In other words, resistant bacteria are more likely to survive and multiply, potentially leaving producers with fewer reliable treatments when BRD strikes.
This research shows that treating an entire group of cattle upon arrival works best in certain situations and that the health benefits of group treatment depend on each group’s characteristics.
With further testing in commercial feedyards, the models could give producers a stronger basis for deciding when group treatment makes sense, helping them protect cattle health and welfare, avoid unnecessary costs and preserve critical antibiotics for the future.