Random forest modeling to identify key farm-to-fork factors influencing Campylobacter ecology in pastured poultry systems☆
Minho Kim, Walid G Al Hakeem, Michael J Rothrock · 2026
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Description
Minho Kim, Walid G Al Hakeem, and Michael J Rothrock used machine learning in a 2026 study of Campylobacter in pastured chickens. This gut bacterium is a food safety risk, yet what drives its spread from farm to final product is unclear.
Data came from 11 farms in the US southeast, gathered between 2014 and 2017, with 1,942 broiler samples of soil, feces, gut contents, and rinsed carcasses. Random forest models pointed to the farm itself as the strongest guide to whether the bacterium was present. Bird age and season also mattered, with summer peaks noted. Weather played a role too, as strong wind and rain on sampling day linked to lower levels in feces.
Catalog record
| CREATOR | Minho Kim, Walid G Al Hakeem, Michael J Rothrock |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 47053 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |