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 tr...
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| Format: | Article |
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Elsevier
2025-01-01
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| Series: | Poultry Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0032579124012264 |
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| author | Chalita Jainonthee Phutsadee Sanwisate Panneepa Sivapirunthep Chanporn Chaosap Raktham Mektrirat Sudarat Chadsuthi Veerasak Punyapornwithaya |
| author_facet | Chalita Jainonthee Phutsadee Sanwisate Panneepa Sivapirunthep Chanporn Chaosap Raktham Mektrirat Sudarat Chadsuthi Veerasak Punyapornwithaya |
| author_sort | Chalita Jainonthee |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9b73ea0e73e845afac56715f94387402 |
| institution | Kabale University |
| issn | 0032-5791 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Poultry Science |
| spelling | doaj-art-9b73ea0e73e845afac56715f943874022025-01-22T05:40:49ZengElsevierPoultry Science0032-57912025-01-011041104648Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducksChalita Jainonthee0Phutsadee Sanwisate1Panneepa Sivapirunthep2Chanporn Chaosap3Raktham Mektrirat4Sudarat Chadsuthi5Veerasak Punyapornwithaya6PhD 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, ThailandDepartment of Livestock Development, Bangkok 10400, ThailandDepartment of Agricultural Education, Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, ThailandDepartment of Agricultural Education, Faculty of Industrial Education and Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, ThailandResearch Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand; Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, ThailandDepartment of Physics, Faculty of Science, Naresuan University, Phitsanulok 65000, ThailandResearch Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand; Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S0032579124012264Dead on arrivalMachine learningMeat-type duckPre-slaughter mortalityWelfare |
| spellingShingle | Chalita Jainonthee Phutsadee Sanwisate Panneepa Sivapirunthep Chanporn Chaosap Raktham Mektrirat Sudarat Chadsuthi Veerasak Punyapornwithaya Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks Poultry Science Dead on arrival Machine learning Meat-type duck Pre-slaughter mortality Welfare |
| title | Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks |
| title_full | Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks |
| title_fullStr | Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks |
| title_full_unstemmed | Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks |
| title_short | Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks |
| title_sort | data driven insights into pre slaughter mortality machine learning for predicting high dead on arrival in meat type ducks |
| topic | Dead on arrival Machine learning Meat-type duck Pre-slaughter mortality Welfare |
| url | http://www.sciencedirect.com/science/article/pii/S0032579124012264 |
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