Jainonthee Chalita, Sanwisate Phutsadee, Sivapirunthep Panneepa, Chaosap Chanporn, Mektrirat Raktham, Chadsuthi Sudarat, Punyapornwithaya Veerasak
PhD Program in Veterinary Science (International Program), Faculty of Veterinary Medicine, Chiang Mai University, under the CMU Presidential Scholarship; Veterinary Public Health and Food Safety Centre for Asia Pacific (VPHCAP), Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand; Research Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand.
Department of Livestock Development, Bangkok 10400, Thailand.
Poult Sci. 2025 Jan;104(1):104648. doi: 10.1016/j.psj.2024.104648. Epub 2024 Dec 6.
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.
到达时已死亡（DOA）是指动物，尤其是家禽，在屠宰前阶段死亡。较高的到达时已死亡率通常意味着福利条件不达标准，可能由多种原因引起，导致生产力下降和经济损失。本研究纳入了来自45个农场的18643车次记录，涵盖2019年1月至2023年12月期间运往泰国东部一家屠宰场的总计23191809只肉鸭。本研究的目的有两个：第一，使用多个预测变量对高到达时已死亡率（≥0.15%）进行分类，这些预测变量包括季节、一天中的时间段、每车次鸭子数量、距离、运输时长、年龄、平均体重、圈养时间以及圈养区域的温度。这种分类使用了机器学习（ML）算法，如最小绝对收缩和选择算子（LASSO）、支持向量机（SVM）、决策树（DT）、随机森林（RF）和极端梯度提升（XGBoost）。此外，还采用了几种数据采样技术，包括过采样、欠采样、随机过采样示例（ROSE）和合成少数过采样技术（SMOTE）来解决数据不平衡问题。第二，分析对预测结果有贡献的变量重要性。描述性分析显示到达时已死亡的平均百分比为0.14%（范围：0至22.46%，标准差=0.49）。高到达时已死亡率分类的结果表明，在所有模型中，XGBoost-Up、XGBoost-Down和RF-Down是排名前三的模型，在包括ROC曲线下面积（AUC）、灵敏度、精度和F1分数在内的评估指标中获得了最高的总体分数。对模型高预测性能有贡献的主要因素是每车次鸭子数量、圈养区域的温度和平均体重。此外，运输时长和距离以及运输时间段是导致该结果的次要因素。应进一步研究这些因素，以尽量减少肉鸭屠宰前阶段的损失。此外，在管理运输时考虑这些因素有助于创造减少鸭子死亡的条件。
