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

Impact of transitioning from antibiotic use to antibiotic-free practices on broiler dead-on-arrival rates: A bayesian structural time series approach

This study assessed the impact of transitioning from an antibiotic (AB) to an antibiotic-free (ABF) production system on dead-on-arrival (%DOA) rates during broiler transport. Data from 105,898 truckloads across 200 to 280 farms over six years were analyzed, comparing three years before (2015-2017) and after (2018-2020) the ABF transition. Decomposition analysis revealed a decline in %DOA from 2015 to 2017, followed by stability until 2019 and another decline into 2020. Seasonal fluctuations were observed, with %DOA peaking between February and April and reaching its lowest point in October. Changepoint analysis identified six significant shifts in %DOA, with the highest values occurring in 2015. Following the ABF transition, %DOA temporarily increased for about six months before stabilizing. Bayesian structural time series (BSTS) analysis showed that observed %DOA closely matched predicted values, indicating no significant effect from the ABF transition (p = 0.485; posterior probability = 51 %). These findings suggest that transport mortality can be effectively controlled without antibiotics by maintaining robust practices, such as improved sanitation, controlled rearing stocking density, optimized brooding, and enhanced pre-slaughter management.

Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand

Antibiotic-free (ABF) broiler production plays an important role in promoting sustainable and welfare-oriented poultry farming. However, this production system presents challenges, particularly an increased susceptibility to stress and mortality during transport. This study aimed to (i) analyze time series data on the monthly percentage of dead-on-arrival (%DOA) and (ii) compare the performance of various time series models. Data on %DOA from 127,578 broiler transport truckloads recorded between 2018 and 2024 were aggregated into monthly %DOA values. The data were then decomposed to identify trends and seasonal patterns. The time series models evaluated in this study included SARIMA, NNAR, TBATS, ETS, and XGBoost. These models were trained using data from January 2018 to December 2023, and their forecasting accuracy was evaluated on test data from January to December 2024. Model performance was assessed using multiple error metrics, including MAE, MAPE, MASE, and RMSE. The results revealed a distinct seasonal pattern in %DOA. Among the evaluated models, TBATS and ETS demonstrated the highest forecasting accuracy when applied to the test data, with MAPE values of 21.2% and 22.1%, respectively. These values were considerably lower than those of NNAR at 54.4% and XGBoost at 29.3%. Forecasts for %DOA in 2025 showed that SARIMA, TBATS, ETS, and XGBoost produced similar trends and patterns. This study demonstrated that time series forecasting can serve as a valuable decision-support tool in ABF broiler production. By facilitating proactive planning, these models can help reduce transport-related mortality, improve animal welfare, and enhance overall operational efficiency.

Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks

Dead on arrival (DOA) refers to animals, particularly poultry, that die during the pre-slaughter phase. Elevated rates of DOA frequently signify substandard welfare conditions and might stem from multiple causes, resulting in diminished productivity and economic losses. This study included 18,643 truckload entries from 45 farms, encompassing a total of 23,191,809 meat-type ducks sent to a single slaughterhouse in Eastern Thailand between January 2019 and December 2023. The objective of this study was twofold: first, to classify high DOA rates (≥ 0.15%) using several predictors, including season, period of the day, number of ducks per truckload, distance, duration of transportation, age, average body weight, lairage time, and temperature at the lairage area. This classification was performed using machine learning (ML) algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). Additionally, several data-sampling techniques, including oversampling, undersampling, Random Over-Sampling Examples (ROSE), and Synthetic Minority Over-sampling Technique (SMOTE), were utilized to address the issue of imbalanced data. Second, to analyze variable importance contributing to the predictive outcomes. The descriptive analysis revealed a mean DOA percentage of 0.14% (range: 0 to 22.46%, SD = 0.49). The results of the high DOA classification indicated that among all models, XGBoost-Up, XGBoost-Down, and RF-Down were the top three models, achieving the highest overall scores in evaluation metrics including Area Under the ROC Curve (AUC), sensitivity, precision, and F1-score. The primary factors contributing to the high predictive performance of the models were the number of ducks per truckload, temperature at the lairage area, and average body weight. Additionally, the duration and distance of transportation, as well as the period of transportation, were secondary factors contributing to the outcome. These factors should be further investigated to minimize losses during the pre-slaughter phase in meat-type ducks. Additionally, considering these factors when managing transportation can help create conditions that reduce duck deaths.

Study Rapid, Quantitative, and Simultaneous Detection of Drug Residues and Immunoassay in Chickens

Different levels of residual drugs can be monitored within a relatively safe range without causing harm to human health if the appropriate dosing methodology is considered and the drug withdrawal period is controlled during poultry and livestock raising. Antimicrobials are factors that can suppress the growth of microorganisms, and antibiotic residues in livestock farming have been considered as a potential cause of antimicrobial resistance in animals and humans. Antimicrobial drug resistance is associated with the capability of a microorganism to survive the inhibitory effects of the antimicrobial components. Antibiotic residue presence in chicken is a human health concern due to its negative effects on consumer health. Neglected aspects related to the application of veterinary drugs may threaten the safety of both humans and animals, as well as their environment. The detection of chemical contaminants is essential to ensure food quality. The most important antibiotic families used in veterinary medicines are β-lactams (penicillins and cephalosporins), tetracyclines, chloramphenicols, macrolides, spectinomycin, lincosamide, sulphonamides, nitrofuranes, nitroimidazoles, trimethoprim, polymyxins, quinolones, and macrocyclics (glycopeptides, ansamycins, and aminoglycosides). Antibiotic residue presence is the main contributor to the development of antibiotic resistance, which is considered a chief concern for both human and animal health worldwide. The incorrect application and misuse of antibiotics carry the risk of the presence of residues in the edible tissues of the chicken, which can cause allergies and toxicity in hypersensitive consumers. The enforcement of the regulation of food safety depends on efficacious monitoring of antimicrobial residues in the foodstuff. In this review, we have explored the rapid detection of drug residues in broilers.

Machine learning predictive modeling for condemnation risk assessment in antibiotic-free raised broilers

The condemnation of broiler carcasses in the poultry industry is a major challenge and leads to significant financial losses and food waste. This study addresses the critical issue of condemnation risk assessment in the discarding of antibiotic-free raised broilers using machine learning (ML) predictive modeling. In this study, ML approaches, specifically least absolute shrinkage and selection operator (LASSO), classification tree (CT), and random forests (RF), were used to evaluate and compare their effectiveness in predicting high condemnation rates. The dataset of 23,959 truckloads from 2021 to 2022 contained 14 independent variables covering the rearing, catching, transportation, and slaughtering phases. Condemnation rates between 0.26% and 25.99% were used as the dependent variable for the analysis, with the threshold for a high conviction rate set at 3.0%. As high condemnation rates were in the minority (8.05%), sampling methods such as random over sampling (ROS), random under sampling (RUS), both sampling (BOTH), and random over sampling example (ROSE) were used to account for imbalanced datasets. The results showed that RF with RUS performed better than the other models for balanced datasets. In this study, mean body weight, weight per crate, mortality and culling rates, and lairage time were identified as the 4 most important variables for predicting high condemnation rates. This study provides valuable insights into ML applications for predicting condemnation rates in antibiotic-free raised broilers and provides a framework to improve decision-making processes in establishing farm management practices to minimize economic losses in the poultry industry. The proposed methods are adaptable for different broiler producers, which increases their applicability in the industry.

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.