Sivapirunthep, Panneepa

Preferred name

Alternative Name

Sivapirunthep, P.

Main Affiliation

School of Industrial Education and Technology

panneepa.si@kmitl.ac.th

Now showing 1 - 9 of 9

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Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks
(2025-01-01)
Jainonthee, Chalita
;
Sanwisate, Phutsadee
;
Sivapirunthep, Panneepa
;
Chaosap, Chanporn
;
Mektrirat, Raktham
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.
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Preslaughter handling factors affecting dead on arrival, condemnations, and bruising in broiler chickens raised without an antibiotic program
(2023-08-01)
Pirompud, Pranee
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Sivapirunthep, Panneepa
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Punyapornwithaya, Veerasak
;
Chaosap, Chanporn
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.
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Impact of transitioning from antibiotic use to antibiotic-free practices on broiler dead-on-arrival rates: A bayesian structural time series approach
(2025-08-01)
Sivapirunthep, Panneepa
;
Pirompud, Pranee
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Punyapornwithaya, Veerasak
;
Srisawang, Supitchaya
;
Jainonthee, Chalita
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.
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Predictive modeling of bruising in broiler chickens using machine learning algorithms
(2025-11-01)
Pirompud, Pranee
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Sivapirunthep, Panneepa
;
Punyapornwithaya, Veerasak
;
Chaosap, Chanporn
Bruising chicken broiler is caused by physical stress and injury to the skin and underlying tissues is a major problem in poultry production, affecting both animal welfare and economic outcomes. The aim of this study was to classify the bruising class (low or high percentage of carcass showing bruise at slaughterhouse) per truckload comparing the predictive performance of six machine learning (ML) models– Least Absolute Shrinkage and Selection Operator (LASSO), Classification Tree (CT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB)– and using a data set including information about season, time of transport, sex of the flock, flock size, chicken age, chicken mean body weight, housing stocking density, on farm mortality and culling rate, and feed withdrawal time. The general objective was to offer tools for the early detection of flocks with a higher likelihood of bruising and to highlight how ML can support decision-making, strengthen welfare monitoring programs, and reduce economic losses in commercial broiler production. The dataset comprised 26,031 truckloads, each averaging 3,026 ± 245 broilers, and included 14 predictors spanning the rearing, catching, transportation, and slaughtering phases. Data were collected from a commercial broiler producer in Thailand between 2021 and 2022. Bruising percentages ranged from 0 to 4%, with values above 0.8% classified as high, accounting for 41.4% of all truckloads. Due to this distribution, model training was conducted without resampling. Among the evaluated models, the XGB algorithm outperformed the other five machine learning models in predicting bruising in broilers, although the overall predictive accuracy was moderate, likely due to the absence of certain key variables. The most important variables included mean body weight, transport duration, in housing and transport crate stocking density, transport distance, mortality and culling rate throughout the birds' entire life at the farm, feed withdrawal time, and duration in lairage. These findings demonstrate the utility of ML, especially XGB, for supporting early identification of broiler flocks prone to bruising. By applying targeted management interventions—such as maintaining bird health, in housing and transport crate stocking density, feed withdrawal, and lairage duration—producers can mitigate bruising, thereby enhancing both animal welfare and operational efficiency.
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Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand
(2025-04-01)
Jainonthee, Chalita
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Sivapirunthep, Panneepa
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Pirompud, Pranee
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Punyapornwithaya, Veerasak
;
Srisawang, Supitchaya
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.
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Changes in Carcass Condemnation During a Six-Year Transition from Antibiotic-Based to Antibiotic-Free Broiler Production in Thailand: A Bayesian Structural Time-Series Analysis
(2026-07-01)
Punyapornwithaya, Veerasak
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Siriyakhun, Supitchaya
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Jainonthee, Chalita
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Pichpol, Duangporn
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Pirompud, Pranee
The transition from antibiotic-based (AB) to antibiotic-free (ABF) broiler production represents a major shift in poultry management, with potential implications for flock health, welfare, and processing outcomes. This study evaluated its impact on condemnation percentage (%condemnation) using Bayesian structural time-series (BSTS) analysis. Data from a Thai integrator comprised 105,899 truckload-level records (2015–2020) across 260 contract farms. The AB period (2015–2017) served as the baseline, and the ABF period (2018–2020) was assessed using counterfactual projections. Time-series decomposition and change-point analysis revealed an increasing trend in %condemnation during the early phase of ABF implementation, followed by a decline in 2020, with five structural shifts detected. The BSTS model estimated an absolute effect of +1.10% (95% CI: −1.50 to 3.80; p = 0.207) and a relative effect of +95% (95% CI: −38% to 657%), indicating no statistically significant causal impact. The transient increase may reflect short-term adaptation challenges, whereas subsequent stabilization may be associated with adaptation to ABF production and other concurrent management changes. Overall, the transition from AB to ABF production did not significantly affect %condemnation. Adaptive management measures were implemented as a company-wide policy but were not directly evaluated within the BSTS framework.
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Application of machine learning algorithms to predict dead on arrival of broiler chickens raised without antibiotic program
(2024-04-01)
Pirompud, Pranee
;
Sivapirunthep, Panneepa
;
Punyapornwithaya, Veerasak
;
Chaosap, Chanporn
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.
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Machine learning predictive modeling for condemnation risk assessment in antibiotic-free raised broilers
(2024-12-01)
Pirompud, Pranee
;
Sivapirunthep, Panneepa
;
Punyapornwithaya, Veerasak
;
Chaosap, Chanporn
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.
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Predicting and explaining high dead-on-arrival outcomes in meat-type ducks using deep learning: A path to improved welfare management
(2025-09-01)
Jainonthee, Chalita
;
Sanwisate, Phutsadee
;
Sivapirunthep, Panneepa
;
Chaosap, Chanporn
;
Pichpol, Duangporn
Dead-on-arrival (DOA) rates are a critical welfare and economic concern in poultry production, reflecting the cumulative impact of handling, transport, and lairage conditions on bird mortality. Compared to broilers and layers, meat-type ducks have received less attention in DOA research, despite their distinct physiological responses to preslaughter stressors and increasing relevance in commercial poultry production. Although machine learning models have been widely applied for DOA prediction, their limited transparency can hinder practical application in real-world settings. This study analyzed 8220 truckload entries of meat-type ducks recorded between 2022 and 2023, with the objective of developing an explainable deep learning model to predict high DOA outcomes using preslaughter management and environmental data. Deep learning models, owing to their complex architecture, offer superior predictive capacity and can capture nonlinear interactions in high-dimensional datasets. To enhance model interpretability and support practical application, SHapley Additive exPlanations (SHAP) was applied to identify the most influential predictors of DOA classification. The final model demonstrated strong classification performance, with an accuracy of 80.29 %, precision of 79.25 %, recall of 80.29 %, F1-score of 79.66 %, and an AUC-ROC of 76.03 %. Key predictors of high DOA included duck head count, lairage temperature, duck age, and transport duration. Notably, a higher number of ducks per truckload was strongly associated with elevated DOA risk (i.e., truckloads classified in the high DOA group), along with lairage temperatures and duck ages below the respective medians. Additionally, shorter transport durations were linked to increased mortality, highlighting the complex interplay of preslaughter stressors. By leveraging SHAP analysis, this study provided both global and local interpretability, ensuring that model outputs were not only accurate but also explainable. These findings support precision-driven preslaughter interventions, enabling industry stakeholders to optimize handling, transport, and lairage practices to reduce mortality rates and enhance duck welfare.

Sivapirunthep, Panneepa

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