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

作者：F. Huot, S. Claveau, A. Bunel, D. Warner, D.E. Santschi, R. Gervais, Éric R. Paquet · 发表于：Journal of Dairy Science · 年份：2024 · DOI：10.3168/jds.2024-25034 · 被引用次数：6 · 研究领域：Ruminant Nutrition and Digestive Physiology、Genetic and phenotypic traits in livestock、Effects of Environmental Stressors on Livestock

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. A first dataset, composed of 488 milk samples from 67 cows (initial DIM = 8.5 ± 6.18; mean ± SD) from 7 commercial dairy farms and their corresponding SARA classification (SARA+ if rumen pH <6.0 for 300 min, otherwise SARA-) was used for the development of machine learning models. Three sets of predictive variables (milk major components [MMC], milk FA [MFA], and MMC combined with MFA [MMCFA]) were submitted to 3 different algorithms, namely elastic net (EN), extreme gradient boosting, and partial least squares, and evaluated using 3 different scenarios of cross-validation. The accuracy, sensitivity, and specificity of the resulting 27 models were analyzed using a linear mixed model. Model performance was not significantly affected by the choice of algorithm. Model performance was improved by including FA estimations (MFA and MMCFA as opposed to MMC alone). Based on these results, 1 mode...