RESUMO
Ruminal tympany (bloat) has long been an issue for large and small livestock operations. Though improvements in feedlot management practices have reduced its occurrence, it is still highly prevalent and is known to detrimentally affect animal performance, welfare, and in many instances, lead to animal death. Current decision support systems and diet formulation software omit the inclusion of bloat prediction based on animal performance. Here, we aim to predict bloat incidence in implanted and non-implanted feedlot steers from performance data comparing linear (LDB) and non-linear decision boundaries. Eighteen crossbred Angus × Hereford steers: BW (491.13 ± 25.78 kg) and age (12 ± 1 mo) were randomly distributed into implanted and non-implanted treatments. All animals were randomly assigned to one of two pens fit with automated monitoring systems for BW, freshwater intake, and water intake behavior: water intake event visit, no water intake event visit (NWIE), and time spent drinking. DM intake (DMI) was individually recorded from all animals through the Calan Gate system for 135 d (30 d adaptation, 105 d experimental diet). Incidences of bloat were recorded as bloat instances regardless of severity to ensure that early onset detection of bloat was recorded and properly identified in predictive models. Logistic regression with a binomial distribution and a logit link function was utilized to predict the incidences of bloat through LDB. Feature selection and penalization of coefficients were explored through L1 (sum of absolute values) and L2 (sum of squares) penalization to avoid overfitting of models. Additional NLDB and a non-parametric LDB are examined for prediction. Accuracy, specificity, and sensitivity were high for the models reported. No significant differences were observed between LDB and NLDB, with the highest specificity (predicting bloat) value of 0.820 for stepwise feature selection algorithms, and a value of 0.832 for the artificial neural network. Highest accuracy was 0.829 for ridge regression, and 0.847 for the random forest with hyperparameter tuning. DM intake, BW, and NWIE were the three most important variables for the prediction of feedlot bloat showing clear drops in DMI and BW and increases in NWIE when animals bloated. The lack of difference in predictive performance between LDB and NLDB highlights the often-overlooked concept that machine learning algorithms are not always the only/best modeling technique. Additionally, the models reported herein carry acceptable predictive performance for inclusion into management decisions that reduce bloat incidences in feedlot cattle.
