Dairy management practices associated with multi-drug resistant fecal commensals and in cull cows: a machine learning approach.

BACKGROUND

Understanding the effects of herd management practices on the prevalence of multidrug-resistant pathogenic and commensals spp. in dairy cattle is key in reducing antibacterial resistant infections in humans originating from food animals. Our objective was to explore the herd and cow level features associated with the multi-drug resistant, and resistance phenotypes shared between , and spp. using machine learning algorithms.

METHODS

Randomly collected fecal samples from cull dairy cows from six dairy farms in central California were tested for multi-drug resistance phenotypes of and spp. Using data on herd management practices collected from a questionnaire, we built three machine learning algorithms (decision tree classifier, random forest, and gradient boosting decision trees) to predict the cows shedding multidrug-resistant and commensal bacteria.

RESULTS

The decision tree classifier identified rolling herd average milk production as an important feature for predicting fecal shedding of multi-drug resistance in or commensal bacteria. The number of culled animals, monthly culling frequency and percentage, herd size, and proportion of Holstein cows in the herd were found to be influential herd characteristics predicting fecal shedding of multidrug-resistant phenotypes based on random forest models for and commensal bacteria. Gradient boosting models showed that higher culling frequency and monthly culling percentages were associated with fecal shedding of multidrug resistant or commensal bacteria. In contrast, an overall increase in the number of culled animals on a culling day showed a negative trend with classifying a cow as shedding multidrug-resistant bacteria. Increasing rolling herd average milk production and spring season were positively associated with fecal shedding of multidrug- resistant . Only six individual cows were detected sharing tetracycline resistance phenotypes between and either of the commensal bacteria.

DISCUSSION

Percent culled and culling rate reflect the increase in culling over time adjusting for herd size and were associated with shedding multidrug resistant bacteria. In contrast, number culled was negatively associated with shedding multidrug resistant bacteria which may reflect producer decisions to prioritize the culling of otherwise healthy but low-producing cows based on milk or beef prices (with respect to dairy beef), amongst other factors. Using a data-driven suite of machine learning algorithms we identified generalizable and distant associations between antimicrobial resistance in and fecal commensal bacteria, that can help develop a producer-friendly and data-informed risk assessment tool to reduce shedding of multidrug-resistant bacteria in cull dairy cows.