Jainonthee Chalita, Sivapirunthep Panneepa, Pirompud Pranee, Punyapornwithaya Veerasak, Srisawang Supitchaya, Chaosap Chanporn
Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand.
Veterinary Public Health and Food Safety Centre for Asia Pacific (VPHCAP), Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand.
Animals (Basel). 2025 Apr 20;15(8):1179. doi: 10.3390/ani15081179.
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.
无抗生素（ABF）肉鸡生产在促进可持续和注重福利的家禽养殖方面发挥着重要作用。然而，这种生产系统存在挑战，特别是在运输过程中对应激和死亡率的易感性增加。本研究旨在（i）分析每月到达时死亡百分比（%DOA）的时间序列数据，以及（ii）比较各种时间序列模型的性能。2018年至2024年期间记录的127,578辆肉鸡运输卡车的%DOA数据被汇总为每月的%DOA值。然后对数据进行分解以识别趋势和季节性模式。本研究评估的时间序列模型包括SARIMA、NNAR、TBATS、ETS和XGBoost。这些模型使用2018年1月至2023年12月的数据进行训练，并在2024年1月至12月的测试数据上评估其预测准确性。使用包括MAE、MAPE、MASE和RMSE在内的多个误差指标评估模型性能。结果显示%DOA存在明显的季节性模式。在评估的模型中，TBATS和ETS应用于测试数据时显示出最高的预测准确性，MAPE值分别为21.2%和22.1%。这些值明显低于NNAR的54.4%和XGBoost的29.3%。2025年%DOA的预测表明，SARIMA、TBATS、ETS和XGBoost产生了相似的趋势和模式。本研究表明，时间序列预测可以作为ABF肉鸡生产中有价值的决策支持工具。通过促进主动规划，这些模型可以帮助降低与运输相关的死亡率，改善动物福利，并提高整体运营效率。
