Application of machine learning algorithms to predict dead on arrival of broiler chickens raised without antibiotic program.

Understanding the factors of dead-on-arrival (DOA) incidents during pre-slaughter handling is crucial for informed decision-making, improving broiler welfare, and optimizing farm profitability. In this study, 3 different machine learning (ML) algorithms - least absolute shrinkage and selection operator (LASSO), classification tree (CT), and random forest (RF) - were used together with 4 sampling techniques to optimize imbalanced data. The dataset comes from 22,115 broiler truckloads from a large producer in Thailand (2021-2022) and includes 14 independent variables covering the rearing, catching, and transportation stages. The study focuses on DOA% in the range of 0.10 to 1.20%, with a threshold for high DOA% above 0.3%, and records DOA% per truckload during pre-slaughter ante-mortem inspection. With a high DOA rate of 25.2%, the imbalanced dataset prompts the implementation of 4 methods to tune the imbalance parameters: random over sampling (ROS), random under sampling (RUS), both sampling (BOTH), and synthetic sampling or random over sampling example (ROSE). The aim is to improve the performance of the prediction model in classifying and predicting high DOA%. The comparative analysis of the different error metrics shows that RF outperforms the other models in a balanced dataset. In particular, RUS shows a significant improvement in prediction performance across all models compared to the original unbalanced dataset. The identification of the 4 most important variables for predicting high DOA percentages - mortality and culling rate, rearing stocking density, season, and mean body weight - emphasizes their importance for broiler production. This study provides valuable insights into the prediction of DOA status using an ML approach and contributes to the development of more effective strategies to mitigate high DOA percentages in commercial broiler production.

Preslaughter handling factors affecting dead on arrival, condemnations, and bruising in broiler chickens raised without an antibiotic program.

In Thailand, knowledge about the factors affecting broiler losses during the preslaughter process is very limited, especially for broilers raised without an antibiotic program. The objective of this study was to determine the preslaughter factors that influence the incidence of dead on arrival (DOA), condemnations, and bruising in broilers raised without antibiotics. Data from 13,581 truckloads of broilers raised without an antibiotic program in 95 contract farms of one of Thailand's largest broiler producers in 2021 were analyzed using a generalized linear mixed model that accounted for farm as a random effect. Results showed that the following risk factors were associated with the occurrence of DOA, condemnations, and bruising: season, time of transport, sex, age at slaughter, mortality and culling rate, and weight per crate. While mean body weight affected the incidence of condemnations and bruising, transport time and lairage time affected DOA and bruising. Feed withdrawal time affected DOA and condemnations. Rearing stocking density only affected condemnation rate. Reducing or eliminating the effects of these risk factors could reduce production losses due to DOA, condemnations, and bruising, thereby improving animal welfare and producer profitability. Reducing weight per crate could reduce DOA, condemnations, and bruising. Reducing lairage time could reduce DOA and bruising, while reducing feed withdrawal time could reduce DOA and condemnations. Raising broilers at a younger age with a lower slaughter weight could prevent the occurrence of DOA, condemnations, and bruising.