Predicting subacute ruminal acidosis from milk mid-infrared estimated fatty acids and machine learning on Canadian commercial dairy herds.

Our objective was to validate the possibility of detecting SARA from milk Fourier transform mid-infrared spectroscopy estimated fatty acids (FA) and machine learning. Subacute ruminal acidosis is a common condition in modern commercial dairy herds for which diagnosis remains challenging due to its symptoms often being subtle, nonexclusive, and not immediately apparent. This observational study aimed at evaluating the possibility of predicting SARA by developing machine learning models to be applied to farm data and to provide an estimated portrait of SARA prevalence in commercial dairy herds.

Relationship between farm management strategies, reticuloruminal pH variations, and risks of subacute ruminal acidosis.

Low reticuloruminal pH (rpH), often observed in subacute ruminal acidosis (SARA), may negatively affect rumen health and animal performance. To investigate the variability of rpH and the prevalence of SARA on commercial farms, we conducted an observational study on 110 early-lactation Holstein cows of different parities from 12 farms selected to cover a broad range of farm management strategies. The rpH of each cow was continuously monitored for 50 d using wireless boluses.