Sivapirunthep, Panneepa

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Preferred name
Sivapirunthep, Panneepa
Alternative Name
Sivapirunthep, P.
Main Affiliation
Email
panneepa.si@kmitl.ac.th

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    Item type:Publication,
    Preslaughter handling factors affecting dead on arrival, condemnations, and bruising in broiler chickens raised without an antibiotic program
    (2023-08-01)
    Pirompud, Pranee
    ;
    Sivapirunthep, Panneepa
    ;
    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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    Item type:Publication,
    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.